query: Improve Chinese Word Embeddings by Exploiting Internal Structure

positive: A Convolutional Neural Network for Modelling Sentences The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

positive: Learning representations by back-propagating errors We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.

positive: Multilingual Models for Compositional Distributed Semantics We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages. We extend our approach to learn semantic representations at the document level, too. We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art. Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.

positive: Modeling Documents with Deep Boltzmann Machines We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.

negative: Sentiment Classification Using Machine Learning Techniques with Syntax Features Sentiment classification has adopted machine learning techniques to improve its precision and efficiency. However, the features are always produced by basic words-bag methods without much consideration for words' syntactic properties, which could play an important role in the judgment of sentiment meanings. To remedy this, we firstly generate syntax trees of the sentences, with the analysis of syntactic features of the sentences. Then we introduce multiple sentiment features into the basic words-bag features. Such features were trained on movie reviews as data, with machine learning methods (Naive Bayes and support vector machines). The features and factors introduced by syntax tree were examined to generate a more accurate solution for sentiment classification.

negative: Energy-Brushes: Interactive Tools for Illustrating Stylized Elemental Dynamics Dynamic effects such as waves, splashes, fire, smoke, and explosions are an integral part of stylized animations. However, such dynamics are challenging to produce, as manually sketching key-frames requires significant effort and artistic expertise while physical simulation tools lack sufficient expressiveness and user control. We present an interactive interface for designing these elemental dynamics for animated illustrations. Users draw with coarse-scale energy brushes which serve as control gestures to drive detailed flow particles which represent local velocity fields. These fields can convey both realistic and artistic effects based on user specification. This painting metaphor for creating elemental dynamics simplifies the process, providing artistic control, and preserves the fluidity of sketching. Our system is fast, stable, and intuitive. An initial user evaluation shows that even novice users with no prior animation experience can create intriguing dynamics using our system.

negative: DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.

negative: SEM-PPA: A semantical pattern and preference-aware service mining method for personalized point of interest recommendation Point-of-Interest (POI) recommendation has received increasing attention in Location-based Social Networks (LBSNs). It involves user behavior analysis, movement pattern model and trajectory sequence prediction, in order to recommend personalized services to target user. Existing POI recommendation methods are confronted with three problems: (1) they only consider the location information of users' check-ins, which causes data sparsity; (2) they fail to consider the order of users' visited locations, which is valuable to reflect the interest or preference of users; (3) users cannot be recommended the suitable services when they move into the new place. To address the above issues, we propose a semantical pattern and preference-aware service mining method called SEM-PPA to make full use of the semantic information of locations for personalized POI recommendation. In SEM-PPA, we firstly propose a novel algorithm to classify the locations into different types for location identification; then we construct the user model for each user from four aspects, which are location trajectory, semantic trajectory, location popularity and user familiarity; in addition, a potential friends discovery algorithm based on movement pattern is proposed. Finally, we conduct extensive experiments to evaluate the recommendation accuracy and recommendation effectiveness on two real-life datasets from GeoLife and Beijing POI. Experimental results show that SEM-PPA can achieve better recommendation performance in particular for sparse data and recommendation accuracy in comparison with other methods.

negative: Depixelizing pixel art We describe a novel algorithm for extracting a resolution-independent vector representation from pixel art images, which enables magnifying the results by an arbitrary amount without image degradation. Our algorithm resolves pixel-scale features in the input and converts them into regions with smoothly varying shading that are crisply separated by piecewise-smooth contour curves. In the original image, pixels are represented on a square pixel lattice, where diagonal neighbors are only connected through a single point. This causes thin features to become visually disconnected under magnification by conventional means, and creates ambiguities in the connectedness and separation of diagonal neighbors. The key to our algorithm is in resolving these ambiguities. This enables us to reshape the pixel cells so that neighboring pixels belonging to the same feature are connected through edges, thereby preserving the feature connectivity under magnification. We reduce pixel aliasing artifacts and improve smoothness by fitting spline curves to contours in the image and optimizing their control points.

negative: Morphological, molecular and statistical tools to identify Castanea species and their hybrids The aim of this study was to investigate, in relation to reference samples, the genetic structure of several natural Castanea populations of unknown origin and of hybrids from the clone collection of the Lourizán Forest Research Center. A total of 115 individuals sampled from four Castanea sativa stands located in the northwest of Spain, 61 Castanea crenata individuals and 27 Castanea mollissima individuals were classified on the basis of four morphological traits, and genotyped with 11 microsatellite loci to define a set of reference samples. The data analyzed with the program STRUCTURE detected four clusters: the two Asiatic species and two clusters in C. sativa. From these reference samples, pure individuals and hybrids of known genealogy were simulated to determine the efficiency with which STRUCTURE and NEWHYBRIDS assigned them to pure or hybrid groups and to a specific genealogical class, respectively. As expected, the discrimination and assignment of the simulated individuals improved with increasing F st value. The two clusters identified within C. sativa may correspond to gene pools with different adaptive characteristics previously identified in provenance tests; pure and admixed populations of both C. sativa gene pools were identified.

negative: Compact slow-wave millimeter-wave bandpass filter (BPF) using open-loop resonator A compact slow-wave millimeter-wave bandpass filter for wireless communication systems is developed and proposed in this paper. The filter is based on an open-loop resonator that is composed of a microstrip line with both ends loaded with folded open stubs. The folded arms of the open stubs are utilized to increase the loading capacitance to ground. In addition, the folded arms of the open stubs are also used to introduce cross couplings. The cross couplings can create transmission zeros at the edges of the desired passband. As a result, the filter can exhibit a passband with sharp rejection skirts. The filter is designed to have a fractional bandwidth of about 3.3% at a center frequency of 30.0 GHz. The filter design is realized using Rogers RT6002 substrate with a dielectric constant of 2.94 and a thickness of 0.127 mm. The filter design is successfully fabricated using the printed circuit board (PCB) technology. The fabricated filter is measured and an excellent agreement between the simulated and measured results is achieved. The fabricated filter has the advantages of compact size and excellent performance.

negative: Context-aware application scheduling in mobile systems: what will users do and not do next? Usage patterns of mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness can be the key to make mobile systems to become personalized and situation dependent in managing their resources. We first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find context-dependency in application usage patterns, for which we classify contexts in a personalized manner with unsupervised learning methods. Using the knowledge acquired, we develop a novel context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications in a timely manner. Our trace-driven simulations with 96 user traces demonstrate the benefits of CAS over existing algorithms. We also verify the practicality of CAS by implementing it on the Android platform.

negative: HUmanoid Robotic Leg via pneumatic muscle actuators : implementation and control In this article, a HUmanoid Robotic Leg (HURL) via the utilization of pneumatic muscle actuators (PMAs) is presented. PMAs are a pneumatic form of actuation possessing crucial attributes for the implementation of a design that mimics the motion characteristics of a human ankle. HURL acts as a feasibility study in the conceptual goal of developing a 10 degree-of-freedom (DoF) lower-limb humanoid for compliance and postural control, while serving as a knowledge basis for its future alternative use in prosthetic robotics. HURL’s design properties are described in detail, while its 2-DoF motion capabilities (dorsiflexion–plantar flexion, eversion–inversion) are experimentally evaluated via an advanced nonlinear PID-based control algorithm.

negative: Design Challenges/Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research Crisis informatics investigates how society's pervasive access to technology is transforming how it responds to mass emergency events. To study this transformation, researchers require access to large sets of data that because of their volume and heterogeneous nature are difficult to collect and analyze. To address this concern, we have designed and implemented an environment - EPIC Analyze - that supports researchers with the collection and analysis of social media data. Our research has identified the types of components - such as NoSQL, MapReduce, caching, and search - needed to ensure that these services are reliable, scalable, extensible, and efficient. We describe the design challenges encountered - such as data modeling, time vs. Space tradeoffs, and the need for a useful and usable system - when building EPIC Analyze and discuss its scalability, performance, and functionality.

negative: Evasion and Hardening of Tree Ensemble Classifiers Classifier evasion consists in finding for a given instance x the “nearest” instance x′ such that the classifier predictions of x and x′ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixed Integer Linear Program solver and finds the optimal evading instance under an expressive set of constraints. Our second algorithm trades off optimality for speed by using symbolic prediction, a novel algorithm for fast finite differences on tree ensembles. On a digit recognition task, we demonstrate that both gradient boosted trees and random forests are extremely susceptible to evasions. Finally, we harden a boosted tree model without loss of predictive accuracy by augmenting the training set of each boosting round with evading instances, a technique we call adversarial boosting.

negative: Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. Delay-period activity of prefrontal cortical cells, the neural hallmark of working memory, is generally assumed to be sustained by reverberating synaptic excitation in the prefrontal cortical circuit. Previous model studies of working memory emphasized the high efficacy of recurrent synapses, but did not investigate the role of temporal synaptic dynamics. In this theoretical work, I show that biophysical properties of cortical synaptic transmission are important to the generation and stabilization of a network persistent state. This is especially the case when negative feedback mechanisms (such as spike-frequency adaptation, feedback shunting inhibition, and short-term depression of recurrent excitatory synapses) are included so that the neural firing rates are controlled within a physiological range (10-50 Hz), in spite of the exuberant recurrent excitation. Moreover, it is found that, to achieve a stable persistent state, recurrent excitatory synapses must be dominated by a slow component. If neuronal firings are asynchronous, the synaptic decay time constant needs to be comparable to that of the negative feedback; whereas in the case of partially synchronous dynamics, it needs to be comparable to a typical interspike interval (or oscillation period). Slow synaptic current kinetics also leads to the saturation of synaptic drive at high firing frequencies that contributes to rate control in a persistent state. For these reasons the slow NMDA receptor-mediated synaptic transmission is likely required for sustaining persistent network activity at low firing rates. This result suggests a critical role of the NMDA receptor channels in normal working memory function of the prefrontal cortex.

negative: A survey of multicore processors General-purpose multicore processors are being accepted in all segments of the industry, including signal processing and embedded space, as the need for more performance and general-purpose programmability has grown. Parallel processing increases performance by adding more parallel resources while maintaining manageable power characteristics. The implementations of multicore processors are numerous and diverse. Designs range from conventional multiprocessor machines to designs that consist of a "sea" of programmable arithmetic logic units (ALUs). In this article, we cover some of the attributes common to all multicore processor implementations and illustrate these attributes with current and future commercial multicore designs. The characteristics we focus on are application domain, power/performance, processing elements, memory system, and accelerators/integrated peripherals.

negative: Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM Now-a-days, diabetes disease is considered one of the key reasons of death among the people in the world. The availability of extensive medical information leads to the search for proper tools to support physicians to diagnose diabetes disease accurately. This research aimed at improving the diagnostic accuracy and reducing diagnostic missclassification based on the extracted significant diabetes features. Feature selection is critical to the superiority of classifiers founded through knowledge discovery approaches, thereby solving the classification problems relating to diabetes patients. This study proposed an integration approach between the SVM technique and K-means clustering algorithms to diagnose diabetes disease. Experimental results achieved high accuracy for differentiating the hidden patterns of the Diabetic and Nondiabetic patients compared with the modern diagnosis methods in term of the performance measure. The T-test statistical method obtained significant improvement results based on KSVM technique when tested on the UCI Pima Indian standard dataset. Keywords—K-means Clustering; Diabetes Patients; SVM;

negative: Detecting Code Smells in Python Programs As a traditional dynamic language, Python is increasingly used in various software engineering tasks. However, due to its flexibility and dynamism, Python is a particularly challenging language to write code in and maintain. Consequently, Python programs contain code smells which indicate potential comprehension and maintenance problems. With the aim of supporting refactoring strategies to enhance maintainability, this paper describes how to detect code smells in Python programs. We introduce 11 Python smells and describe the detection strategy. We also implement a smell detection tool named Pysmell and use it to identify code smells in five real world Python systems. The results show that Pysmell can detect 285 code smell instances in total with the average precision of 97.7%. It reveals that Large Class and Large Method are most prevalent. Our experiment also implies Python programs may be suffering code smells further.

negative: End-to-end text recognition with convolutional neural networks Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003.

negative: A Comparative Study of Demographic Attribute Inference in Twitter Social media platforms have become a major gateway to receive and analyze public opinions. Understanding users can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes, such as ethnicity, gender, age, among others, have been extensively applied to characterize social media users. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles. Previous work has demonstrated the effectiveness of different user signals such as users’ posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users’ posts and train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals: self-descriptions and posts aggregated from users’ friends and followers, users’ profile images, and users’ names. We provide a comparative study of these signals side-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age. We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training models and evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self-descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation.

negative: Imaging cellular network dynamics in three dimensions using fast 3D laser scanning Spatiotemporal activity patterns in three-dimensionally organized cellular networks are fundamental to the function of the nervous system. Despite advances in functional imaging of cell populations, a method to resolve local network activity in three dimensions has been lacking. Here we introduce a three-dimensional (3D) line-scan technology for two-photon microscopy that permits fast fluorescence measurements from several hundred cells distributed in 3D space. We combined sinusoidal vibration of the microscope objective at 10 Hz with 'smart' movements of galvanometric x-y scanners to repeatedly scan the laser focus along a closed 3D trajectory. More than 90% of cell somata were sampled by the scan line within volumes of 250 μm side length. Using bulk-loading of calcium indicator, we applied this method to reveal spatiotemporal activity patterns in neuronal and astrocytic networks in the rat neocortex in vivo. Two-photon population imaging using 3D scanning opens the field for comprehensive studies of local network dynamics in intact tissue.

negative: Autoencoder node saliency: Selecting relevant latent representations The autoencoder is an artificial neural network that learns hidden representations of unlabeled data. With a linear transfer function it is similar to the principal component analysis (PCA). While both methods use weight vectors for linear transformations, the autoencoder does not come with any indication similar to the eigenvalues in PCA that are paired with eigenvectors. We propose a novel supervised node saliency (SNS) method that ranks the hidden nodes, which contain weight vectors for transformations. SNS is able to indicate the nodes specialized in a learning task. The latent representations of a hidden node can be described using a one-dimensional histogram. We apply normalized entropy difference (NED) to measure the ”interestingness” of the histograms, and conclude a property for NED values to identify a good classifying node. By applying our methods to real datasets, we demonstrate their ability to find valuable nodes and explain the learned tasks in autoencoders.

negative: Syntax Annotation for the GENIA Corpus Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.

negative: Algorithms for time series knowledge mining Temporal patterns composed of symbolic intervals are commonly formulated with Allen's interval relations originating in temporal reasoning. This representation has severe disadvantages for knowledge discovery. The Time Series Knowledge Representation (TSKR) is a new hierarchical language for interval patterns expressing the temporal concepts of coincidence and partial order. We present effective and efficient mining algorithms for such patterns based on itemset techniques. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. On a real data set a concise set of TSKR patterns can explain the underlying temporal phenomena, whereas the patterns found with Allen's relations are far more numerous yet only explain fragments of the data.

negative: Kodu Game Lab: a programming environment Kodu Game Lab is a tile-based visual programming tool that enables users to learn programming concepts through making and playing computer games. Kodu is a relatively new programming language designed specifically for young children to learn through independent exploration. It is integrated in a real-time isometric 3D gaming environment that is designed to compete with modern console games in terms of intuitive user interface and graphical production values.

negative: KairosVM: Deterministic introspection for real-time virtual machine hierarchical scheduling Consolidation and isolation are key technologies that promoted the undisputed popularity of virtualization in most of the computer industry. This popularity has recently led to a growing interest in real-time virtualization, making this technology enter the real-time system market. However, it has several issues due to the strict timing guarantees contracted. Moreover supporting legacy software stacks adds another level of complexity when the software is a black box. We present KairosVM, a latency-bounded, real-time extension to Linux's KVM module. It aims to bridge the lack of communication of the real-time requirements between the guest scheduler and the host scheduler, exploiting virtual machine introspection. The hypervisor captures the real-time requirements of the guest by catching previously added undefined instructions, without the need to do any modification to the guests. Our evaluations show that KairosVM's overhead is negligible when compared to existing introspection solutions thus can be used in real-time.

negative:
Music Feature Maps with Convolutional Neural Networks for Music Genre Classification Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure.
 But, facing the criticism relating to the difficulty in understanding the underlying relationships that neural networks learn in presence of a spectrogram, we propose to use, as entries of a CNN, a small set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality. With CNNs trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are more efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types reaches 91% accuracy on the GTZAN database.

negative: A study of the documentation essential to software maintenance Software engineering has been striving for years to improve the practice of software development and maintenance. Documentation has long been prominent on the list of recommended practices to improve development and help maintenance. Recently however, agile methods started to shake this view, arguing that the goal of the game is to produce software and that documentation is only useful as long as it helps to reach this goal.On the other hand, in the re-engineering field, people wish they could re-document useful legacy software so that they may continue maintain them or migrate them to new platform.In these two case, a crucial question arises: "How much documentation is enough?" In this article, we present the results of a survey of software maintainers to try to establish what documentation artifacts are the most useful to them.

negative: Efficient Edge Detection on Low-Cost FPGAs Improving the efficiency of edge detection in embedded applications, such as UAV control, is critical for reducing system cost and power dissipation. Field programmable gate arrays (FPGA) are a good platform for making improvements because of their specialised internal structure. However, current FPGA edge detectors do not exploit this structure well. A new edge detection architecture is proposed that is better optimised for FPGAs. The basis of the architecture is the Sobel edge kernels that are shown to be the most suitable because of their separability and absence of multiplications. Edge intensities are calculated with a new 4:2 compressor that consists of two custom-designed 3:2 compressors. Addition speed is increased by breaking carry propagation chains with look-ahead logic. Testing of the design showed it gives a 28% increase in speed and 4.4% reduction in area over previous equivalent designs, which demonstrated that it will lower the cost of edge detection systems, dissipate less power and still maintain highspeed control. Keywords—edge detection; FPGA; compressor; low-cost; UAV

negative: Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/ coling2018-xling_argument_mining.

negative: Greenhouse Temperature and Humidity Intelligent Control System The article based on embedded database of greenhouse temperature and humidity control system intelligent. Put forward by embedded database system set up in an ideal environment for data greenhouse temperature and humidity control, greenhouse crops in the process of growth under control. To address the growth of greenhouse crops in the process of temperature and humidity controlled environment is not ideal, at the same time improve the efficiency of control and cost-effective system. The article focuses on the structure of the control system, hardware, software design and system control strategy. The control system has a simple hardware structure, cost-effective, easy to use and maintenance, temperature and humidity data, and other advantages of good stability. Key-Words: Embedded database, Temperature and Humidity, Control ; data filtering, Greenhouse,

negative: Data-Driven Modeling for Chinese Ancient Architecture The existing 3D modeling studies of Chinese ancient architecture are mostly procedure driven and rely on fixed construction rules. Therefore, these methods have limited applications in virtual reality (VR) engineering. We propose a data-driven approach to synthesize 3D models from existing 3D data that provides more flexibility and fills the gap between academic studies and VR engineering. First, 3D architecture models were preprocessed and decomposed into components, and the components were clustered by their geometric features. Second, a Bayesian network was generated by learning from the dataset to represent the internal relationships between the architectural components. Third, the inference results of the trained network were utilized to generate a reasonable relationship matching to support the synthesis of the structural components. The proposed method can be used in 3D content creation for VR development and directly supports VR applications in practice.

negative: Cultural difference and adaptation of communication styles in computer-mediated group brainstorming Supporting creativity via collaborative group brainstorming is a prevalent practice in organizations. Today's technology makes it easy for international and intercultural group members to brainstorm together remotely, but surprisingly little is known about how culture and medium shape the underlying brainstorming process. In a laboratory study, we examined the influences of individual cultural background (American versus Chinese), group cultural composition (same- versus mixed-culture groups), and communication medium (text-only versus video-enabled chatrooms) on group brainstorming conversations. Cultural differences and adaptation in conversational talkativeness and responsiveness were identified. The text-only medium reduced cultural differences in talkativeness. Working in a mixed-culture group led to cultural adaptation in the communication style of Chinese but not American participants. We discuss implications for international group brainstorming.

negative:
Relationship between smartphone addiction and physical activity in Chinese international students in Korea BACKGROUND AND AIMS
Excessive usage of smartphones may induce social problems, such as depression and impairment of social and emotional functioning. Moreover, its usage can impede physical activity, but the relationship between smartphone addiction and physical activity is obscure. Therefore, we examined the relationship and the impact of excessive smartphone use on physical activity.

METHODS
This study collected data through the structured questionnaire consisting of general characteristics, the number and hours of smartphone usage, and the Smartphone Addiction Proneness Scale (SAPS) from 110 Chinese international students in Korea. The body composition and physical activity, such as the total daily number of steps and consumed calories, were measured.

RESULTS
In this study, high-risk smartphone users showed less physical activity, such as the total number of steps taken and the average consumed calories per day. Moreover, their body composition, such as muscle mass and fat mass, was significantly different. Among these factors, the hours of smartphone use revealed the proportional relationship with smartphone addiction (β = 0.209, p = 0.026), while the average number of walking steps per day showed a significant reverse proportional tendency in participants with smartphone addiction (β = -0.883, p < 0.001).

CONCLUSIONS
Participants with smartphone addiction were less likely to walk for each day. Namely, smartphone addiction may negatively influence physical health by reducing the amount of physical activity, such as walking, resulting in an increase of fat mass and a decrease of muscle mass associated with adverse health consequences.

negative:
Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study BACKGROUND
Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention.

OBJECTIVE
The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media.

METHODS
A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors.

RESULTS
A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC.

CONCLUSIONS
SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life.


query: Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

positive: Large-Scale Named Entity Disambiguation Based on Wikipedia Data This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.

positive: DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

positive: Using the WordNet Ontology in the GeoCLEF Geographical Information Retrieval Task This paper describes how we managed to use the WordNet ontology for the GeoCLEF 2005 English monolingual task. Both a query expansion method, based on the expansion of geographical terms by means of WordNet synonyms and meronyms, and a method based on the expansion of

positive: RelFinder: Revealing Relationships in RDF Knowledge Bases The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

positive: DBpedia - A crystallization point for the Web of Data The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. The resulting DBpedia knowledge base currently describes over 2.6 million entities. For each of these entities, DBpedia defines a globally unique identifier that can be dereferenced over the Web into a rich RDF description of the entity, including human-readable definitions in 30 languages, relationships to other resources, classifications in four concept hierarchies, various facts as well as data-level links to other Web data sources describing the entity. Over the last year, an increasing number of data publishers have begun to set data-level links to DBpedia resources, making DBpedia a central interlinking hub for the emerging Web of data. Currently, the Web of interlinked data sources around DBpedia provides approximately 4.7 billion pieces of information and covers domains such as geographic information, people, companies, films, music, genes, drugs, books, and scientific publications. This article describes the extraction of the DBpedia knowledge base, the current status of interlinking DBpedia with other data sources on the Web, and gives an overview of applications that facilitate the Web of Data around DBpedia.

negative: Hole-filling Based on Disparity Map and Inpainting for Depth- Image-Based Rendering Due to the changes in viewpoint, holes may appear in the novel view synthesized by depth-image-based rendering (DIBR). A hole-filling method combining disparity-mapbased hole-filling and inpainting is proposed. The method first eliminates matching errors in destination image according to the disparity map. Then, holes are classified into different types according to their sizes. Finally, a disparity-map-based approach and an improved exemplar-based inpainting algorithm are used to fill different types of holes according to the type of hole. Experimental results show that the artifacts at the edge of foreground objects can be reduced in synthesized view since the matching errors are eliminated before hole-filling. In addition, the proposed method can achieve more natural and satisfactory results in filled areas in comparison with the disparity-mapbased approach and Gautier’s inpainting algorithm, though it may result in higher time complexity. Keyword: Depth-image-based Rendering, Hole-filling, Holes, Disparity Map, Inpainting

negative: A Case for Standard Non-blocking Collective Operations In this paper we make the case for adding standard nonblocking collective operations to the MPI standard. The non-blocking point-to-point and blocking collective operations currently defined by MPI provide important performance and abstraction benefits. To allow these benefits to be simultaneously realized, we present an application programming interface for non-blocking collective operations in MPI. Microbenchmark and application-based performance results demonstrate that non-blocking collective operations offer not only improved convenience, but improved performance as well, when compared to manual use of threads with blocking collectives.

negative:
No data left behind: real-time insights from a complex data ecosystem The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query processing, state-of-the-art data analysis approaches solely operate on top of the local, historical data in the data lake, and ignore the fresh tail end of data that resides in the original remote sources. However, as many business operations depend on real-time analytics, this approach is no longer viable. The alternative is hand-crafting the analysis task to explicitly consider the characteristics of the various data sources and identify optimization opportunities, rendering the overall analysis non-declarative and convoluted.
 Based on our experiences operating in data lake environments, we design System-PV, a real-time analytics system that masks the complexity of dealing with multiple data sources while offering minimal response times. System-PV extends Spark with a sophisticated data virtualization module that supports multiple applications - from SQL queries to machine learning. The module features a location-aware compiler that considers source complexity, and a two-phase optimizer that produces and refines the query plans, not only for SQL queries but for all other types of analysis as well. The experiments show that System-PV is often faster than Spark by more than an order of magnitude. In addition, the experiments show that the approach of accessing both the historical and the remote fresh data is viable, as it performs comparably to solely operating on top of the local, historical data.

negative: Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) The simple, but general formal theory of fun and intrinsic motivation and creativity (1990-2010) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditional field of active learning, and is related to old, but less formal ideas in aesthetics theory and developmental psychology. It has been argued that the theory explains many essential aspects of intelligence including autonomous development, science, art, music, and humor. This overview first describes theoretically optimal (but not necessarily practical) ways of implementing the basic computational principles on exploratory, intrinsically motivated agents or robots, encouraging them to provoke event sequences exhibiting previously unknown, but learnable algorithmic regularities. Emphasis is put on the importance of limited computational resources for online prediction and compression. Discrete and continuous time formulations are given. Previous practical, but nonoptimal implementations (1991, 1995, and 1997-2002) are reviewed, as well as several recent variants by others (2005-2010). A simplified typology addresses current confusion concerning the precise nature of intrinsic motivation.

negative: $K$ -Band CMOS Differential and Quadrature Voltage-Controlled Oscillators for Low Phase-Noise and Low-Power Applications In this paper, modified circuit topologies of a differential voltage-controlled oscillator (VCO) and a quadrature VCO (QVCO) in a standard bulk 90-nm CMOS process are presented for low dc power and low phase-noise applications. By utilizing current-reuse and transformer-feedback techniques, the proposed VCO and QVCO can be operated at reduced dc power consumption while maintaining extraordinary circuit performance in terms of low phase-noise and low amplitude/phase errors. The VCO circuit topology is investigated to obtain the design procedure. The VCO is further applied to the QVCO design with a bottom-series coupling technique. The coupling network between two differential VCOs and device size are properly designed based on our proposed design methodology to achieve low amplitude and phase errors. Moreover, the VCO and the QVCO are fully characterized with amplitude and phase errors via a four-port vector network analyzer. With a dc power of 3 mW, the VCO exhibits a frequency tuning range from 20.3 to 21.3 GHz, a phase noise of - 116.4 dBc/Hz at 1-MHz offset, a figure-of-merit (FOM) of -198 dBc/Hz, a phase error of 3.8° , and an amplitude error of 0.9 dB. With a dc power of 6 mW, the QVCO demonstrates a phase noise of -117.4 dBc/Hz, a FOM of -195.6 dBc/Hz, a phase error of 4° , and an amplitude error of 0.6 dB. The proposed VCO and QVCO can be compared with the previously reported state-of-the-art VCOs and QVCOs in silicon-based technologies.

negative: Designing for collaborative creative problem solving Collaborative creativity is traditionally supported by formal techniques, such as brainstorming. These techniques improve the idea-generation process by creating group synergies, but also suffer from a number of negative effects. Current electronic tools to support collaborative creativity overcome some of these problems, but introduce new ones, by either losing the benefits of face-to-face communication or the immediacy of simultaneous contribution. Using an interactive environment as a test bed, we are investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. What are the design-factors influencing such a system? We have designed a brainstorming application that uses an interactive table and a large wall display, and compared the results of using it to traditional paper-based brainstorming in a user study with 30 participants. From the considerations that went into the design and the observations during the study we derive a number of design guidelines for collaborative systems in interactive environments.

negative: Decision feedback equalization As real world communication channels are stressed with higher data rates, intersymbol interference (ISI) becomes a dominant limiting factor. One way to combat this effect that has recently received considerable attention is the use of a decision feedback equalizer (DFE) in the receiver. The action of the DFE is to feed back a weighted sum of past decision to cancel the ISI they cause in the present signaling interval. This paper summarizes the work in this area beginning with the linear equalizer. Three performance criteria have been used to derive optimum systems; 1) minimize the noise variance under a "zero forcing" (ZF) constraint i.e., insist that all intersymbol interference is cancelled, 2) minimize the mean-square error (MMSE) between the true sample and the observed signal just prior to the decision threshold, and 3) minimize the probability of error (Min Pe). The transmitter can be fixed and the receiver optimized or one can obtain the joint optimum transmitter and receiver. The number of past decisions used in the feedback equalization can be finite or infinite. The infinite case is easier to handle analytically. In addition to reviewing the work done in the area, we show that the linear equalizer is in fact a portion of the DFE receiver and that the processing done by the DFE is exactly equivalent to the general problem of linear prediction. Other similarities in the various system structures are also shown. The effect of error propagation due to incorrect decisions is discussed, and the coaxial cable channel is used as an example to demonstrate the improvement available using DFE.

negative: Web evolution and Web Science This paper examines the evolution of the World Wide Web as a network of networks and discusses the emergence of Web Science as an interdisciplinary area that can provide us with insights on how the Web developed, and how it has affected and is affected by society. Through its different stages of evolution, the Web has gradually changed from a technological network of documents to a network where documents, data, people and organisations are interlinked in various and often unexpected ways. It has developed from a technological artefact separate from people to an integral part of human activity that is having an increasingly significant impact on the world. This paper outlines the lessons from this retrospective examination of the evolution of the Web, presents the main outcomes of Web Science activities and discusses directions along which future developments could be anticipated.

negative: Self-taught clustering This paper focuses on a new clustering task, called self-taught clustering. Self-taught clustering is an instance of unsupervised transfer learning, which aims at clustering a small collection of target unlabeled data with the help of a large amount of auxiliary unlabeled data. The target and auxiliary data can be different in topic distribution. We show that even when the target data are not sufficient to allow effective learning of a high quality feature representation, it is possible to learn the useful features with the help of the auxiliary data on which the target data can be clustered effectively. We propose a co-clustering based self-taught clustering algorithm to tackle this problem, by clustering the target and auxiliary data simultaneously to allow the feature representation from the auxiliary data to influence the target data through a common set of features. Under the new data representation, clustering on the target data can be improved. Our experiments on image clustering show that our algorithm can greatly outperform several state-of-the-art clustering methods when utilizing irrelevant unlabeled auxiliary data.

negative: Towards a questionnaire for measuring affective benefits and costs of communication technologies As CSCW creates and investigates technologies for social communication, it is important to understand the emotional benefits and costs of these systems. We propose the Affec-tive Benefits and Costs of Communication Technologies (ABCCT) questionnaire to supplement traditional qualita-tive methods of understanding communication media. We discuss the pilots of this survey with 45 children and 110 adults measuring the inter-item reliability of this instru-ment. We present the results of interviews with 14 children and 14 adults, which help confirm that the ABCCT measures the same constructs that may emerge through interview investigations. Finally, we demonstrate that the ABCCT is sensitive enough to discriminate between differ-ent communication technologies and has shown promise in some of its early adoption. Though the ABCCT is not with-out limitations, it may provide a way to compare technolo-gies in field deployments, draw findings across investiga-tions, and quantify the impact of specific design decisions.

negative: Segmenting Two-Sided Markets Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. Motivated by this, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller’s characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop efficient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions. We also test the validity of our assumptions on demand curves inferred from NYC taxicab log-data, as well as show the performance of our algorithms on synthetic experiments.

negative: A Hardware Platform for Evaluating Low-Energy Multiprocessor Embedded Systems Based on COTS Devices Embedded systems are usually energy constrained. Moreover, in these systems, increased productivity and reduced time to market are essential for product success. To design complex embedded systems while reducing the development time and cost, there is a great tendency to use commercial off-the-shelf (“COTS”) devices. At system level, dynamic voltage and frequency scaling (DVFS) is one of the most effective techniques for energy reduction. Nonetheless, many widely used COTS processors either do not have DVFS or apply DVFS only to processor cores. In this paper, an easy-to-implement COTS-based evaluation platform for low-energy embedded systems is presented. To achieve energy saving, DVFS is provided for the whole microcontroller (including core, phase-locked loop, memory, and I/O). In addition, facilities are provided for experimenting with fault-tolerance techniques. The platform is equipped with energy measurement and debugging equipment. Physical experiments show that applying DVFS on the whole microcontroller provides up to 47% and 12% energy saving compared with the sole use of dynamic power management and applying DVFS only on the core, respectively. Although the platform is designed for ARM-based embedded systems, our approach is general and can be applied to other types of systems.

negative: The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming This study investigates a prototyping of integrated system of Internet of Things based Wetting front detector (IOT-WFD) which focuses on how to enhance the IOT based Wetting front detector design for smart irrigation system. The empirical study was conducted with 2 sensors type to detect the wetting fronts which are the Frequency Domain Reflectrometry sensor (FDR) and Resistor-based sensor (RB) integrated and design with low-cost WFD. The results of this study point toward the IOT-WFD as an appropriated technology providing real time wetting front information in soil positively for application in terms of agricultural water management, with precision agriculture and efficient irrigation domain with a related decision knowledge that matches with the technology trend and smart farmer requirements. Evidence of positive results of this prototyping summary has been provided.

negative: Design & comparison of a conventional and permanent magnet based claw-pole machine for automotive application This paper presents the design and performance comparison of a conventional claw-pole machine with a permanent magnet based claw-pole machine for automotive application. The magnets are placed in the inter-claw region to decrease the leakage flux and to supply increased magnetic flux in the machine. It has been observed that with the addition of permanent magnets weighing only a few grams, there is a significant increment of more than 22% in output power of the machine. The geometric dimensions of magnets were also varied to verify their effects on performance and it has been observed that with the increase in magnet weight there is a non-linear increment in torque of the machine.

negative: Strategic Surge Pricing and Forecast Communication on On-Demand Service Platforms On-demand service platforms (e.g., Uber, Lyft) match consumers with independent workers nearby at short notice. To manage fluctuating supply and demand conditions across market locations (zones), many on-demand platforms provide market forecasts to workers and practice surge pricing, wherein the price in a particular zone is temporarily raised above the regular price. We jointly analyze the strategic role of surge pricing and forecast communication explicitly accounting for workers’ incentives to move between zones and the platform’s incentive to share forecasts truthfully. Conventional wisdom suggests that surge pricing is useful in zones where demand for workers exceeds their supply. However, we show that when the platform relies on independent workers to serve consumers across different zones, surge pricing is also useful in zones where supply exceeds demand. Because individual workers do not internalize the competitive externality that they impose on other workers, too few workers may move from a zone with excess supply to an adjacent zone requiring additional workers. Moreover, the platform may have an incentive to misreport market forecasts to exaggerate the need for workers to move. We show how and why distorting the price in a zone with excess supply through surge pricing can increase total platform profit across zones, by incentivizing more workers to move and by making forecast communication credible. Our analysis offers insights for effectively managing on-demand platforms through surge pricing and forecast sharing, and the resulting implications for consumers and workers.

negative: Biometric cryptosystems based fuzzy commitment scheme: a security evaluation Biometric systems are developed in order to replace traditional authentication. However, protecting the stored templates is considered as one of the critical steps in designing a secure biometric system. When biometric data is compromised, unlike passwords, it can’t be revoked. One methodology for biometric template protection is ‘Biometric Cryptosystem’. Biometric cryptosystems benefit from both fields of cryptography and biometrics where the biometrics exclude the need to remember passwords and the cryptography provides high security levels for data. In order to, develop these systems, Fuzzy Commitment Scheme (FCS) is considered as well known approach proposed in the literature to protect the user’s data and has been used in several applications. However, these biometric cryptosystems are hampered by the lack of formal security analysis to prove their security strength and effectiveness. Hence, in this paper we present several metrics to analyze the security and evaluate the weaknesses of biometric cryptosystems based on fuzzy commitment scheme.

negative: Leakage current mechanisms and leakage reduction techniques in deep-submicrometer CMOS circuits High leakage current in deep-submicrometer regimes is becoming a significant contributor to power dissipation of CMOS circuits as threshold voltage, channel length, and gate oxide thickness are reduced. Consequently, the identification and modeling of different leakage components is very important for estimation and reduction of leakage power, especially for low-power applications. This paper reviews various transistor intrinsic leakage mechanisms, including weak inversion, drain-induced barrier lowering, gate-induced drain leakage, and gate oxide tunneling. Channel engineering techniques including retrograde well and halo doping are explained as means to manage short-channel effects for continuous scaling of CMOS devices. Finally, the paper explores different circuit techniques to reduce the leakage power consumption.

negative: The path to personalized medicine. n engl j med 363;4 nejm.org july 22, 2010 301 to human illness, identified genetic variability in patients’ responses to dozens of treatments, and begun to target the molecular causes of some diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients’ responses to targeted therapy. The challenge is to deliver the benefits of this work to patients. As the leaders of the National Institutes of Health (NIH) and the Food and Drug Administration (FDA), we have a shared vision of personalized medicine and the scientific and regulatory structure needed to support its growth. Together, we have been focusing on the best ways to develop new therapies and optimize prescribing by steering patients to the right drug at the right dose at the right time. We recognize that myriad obstacles must be overcome to achieve these goals. These include scientific challenges, such as determining which genetic markers have the most clinical significance, limiting the off-target effects of gene-based therapies, and conducting clinical studies to identify genetic variants that are correlated with a drug response. There are also policy challenges, such as finding a level of regulation for genetic tests that both protects patients and encourages innovation. To make progress, the NIH and the FDA will invest in advancing translational and regulatory science, better define regulatory pathways for coordinated approval of codeveloped diagnostics and therapeutics, develop risk-based approaches for appropriate review of diagnostics to more accurately assess their validity and clinical utility, and make information about tests readily available. Moving from concept to clinical use requires basic, translational, and regulatory science. On the basic-science front, studies are identifying many genetic variations underlying the risks of both rare and common diseases. These newly discovered genes, proteins, and pathways can represent powerful new drug targets, but currently there is insufficient evidence of a downstream market to entice the private sector to explore most of them. To fill that void, the NIH and the FDA will The Path to Personalized Medicine

negative: On Real-Time Transactions Next generation real-time systems will require greater flexibility and predictability than is commonly found in today's systems. These future systems include the space station, integrated vision/robotics/AI systems, collections of humans/robots coordinating to achieve common objectives (usually in hazardous environments such as undersea exploration or chemical plants), and various command and control applications. The complexity of such systems due to timing constraints, concurrency, and distribution is high. It is accepted that the synchronization, failure atomicity, and permanence properties of transactions aid in the development of distributed systems. However, little work has been done in exploiting transactions in a real-time context. We have been attempting to categorize real-time data into classes depending on their time, synchronization, atomicity, and permanence properties. Then, using the semantics of the data and the applications, we are developing special, tailored, real-time transactions that only supply the minimal properties necessary for that class. This reduces the system overhead in supporting access to various types of data. The eventual goal is to verify that timing requirements can be met.

negative: The theory of multidimensional persistence Persistent homology captures the topology of a filtration - a one-parameter family of increasing spaces - in terms of a complete discrete invariant. This invariant is a multiset of intervals that denote the lifetimes of the topological entities within the filtration. In many applications of topology, we need to study a multifiltration: a family of spaces parameterized along multiple geometric dimensions. In this paper, we show that no similar complete discrete invariant exists for multidimensional persistence. Instead, we propose the rank invariant, a discrete invariant for the robust estimation of Betti numbers in a multifiltration, and prove its completeness in one dimension.

negative: Active ESD protection for input transistors in a 40-nm CMOS process This work presents a novel design for input ESD protection. By replacing the protection resistor with an active switch that isolates the input transistors from the pad under ESD stress, the ESD robustness can be greatly improved. The proposed designs were designed and verified in a 40-nm CMOS process using only thin oxide devices, which can successfully pass the typical industry ESD-protection specifications of 2-kV HBM and 200-V MM ESD tests.

negative: Intrinsic Evaluations of Word Embeddings: What Can We Do Better? This paper presents an analysis of existing methods for the intrinsic evaluation of word embeddings. We show that the main methodological premise of such evaluations is “interpretability” of word embeddings: a “good” embedding produces results that make sense in terms of traditional linguistic categories. This approach is not only of limited practical use, but also fails to do justice to the strengths of distributional meaning representations. We argue for a shift from abstract ratings of word embedding “quality” to exploration of their strengths and weaknesses.

negative: Significant Triples : Adjective + Noun + Verb Combinations We investigate the identification and, to some extent, the classification of collocational word groups that consist of an adjectival modifier (A), an accusative object noun (N), and a verb (V) by means of parsing a newspaper corpus with a lexicalized probabilistic grammar.1 Data triples are extracted from the resulting Viterbi parses and subsequently evaluated by means of a statistical association measure. The extraction results are then compared to predefined descriptive classes of ANV-triples. We also use a decision tree algorithm to classify part of the data obtained, on the basis of a small set of manually classified examples. Much of the candidate data is lexicographically relevant: the triples include idiomatic combinations (e.g. (sich) eine goldene Nase verdienen, ‘get rich’, lit. ‘earn oneself a golden nose’), combinations of N+V and N+A collocations (e.g. (eine) klare Absage erteilen, ‘refuse resolutely’, lit. ‘give a clear refusal’), next to cases where N+V or N+A collocations are found, in combination with other (not necessarily collocational) context partners. To extract such data from text corpora, a grammar is needed that captures verb+object relations: simple pattern matching on part-of-speech shapes is not sufficient. Statistical tools then allow to order the data in a way useful for subsequent manual selection by lexicographers. 1This work has been carried out in the context of the Transferbereich 32: Automatische Exzerption, a DFG-funded project aiming at the creation of support tools for the corpus-based updating of printed dictionaries in lexicography, carried out in cooperation with the publishers Langenscheidt KG and Duden BIFAB AG.

negative: Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobilityimpaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-degree-of-freedom system expands upon conventional control methods and combines the 7-DoF robotic arm control with the 2-degree-of-freedom power wheelchair control. The 3- degrees of redundancy are optimized to effectively perform activities of daily living and overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, which offers expandability for future research. A P300 Brain Computer Interface (BCI), the BCI2000, was implemented to control the WMRA system. The control is done by recording and analysing the brain activity through an electrode cap while providing visual stimulation to the user via a visual matrix. The visual matrix contains a symbolic or an alphabetic array corresponding to the motion of the WMRA. By recognizing online and in real-time, which element in the matrix elicited a P300, the BCI system can identify which element the user chose to communicate. The chosen element is then communicated to the controller of the WMRA system. The speed and accuracy of the BCI system was tested. This paper gives details of the WMRA's integration with the BCI2000 and documents the experimental results of the BCI and the WMRA in simulation.

negative: Dual-wideband log-periodic antennas Two log-periodic (LP) antenna configurations able to suppress a specific frequency band are introduced in this paper. LP antennas belong to the class of frequency independent antennas and are able to achieve multi-decade wide bandwidths. We start with a parametric study of a traditional planar curved tooth LP antenna aimed in determining the means for achieving specific bandwidth. Two techniques for accomplishing consistent near/far-field dual-band performance are introduced next. The first is a LP antenna integrated with a dual-wideband feed line, and the second one is a LP with removed resonant teeth. Antennas and feeds are designed on a microstrip substrate and method of moments modeling within FEKO is used to demonstrate their performance. Required dual-band operation is 1.8-5 GHz and 7-11 GHz.

negative: An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data : Background , Applications , and Ongoing Challenges Since the last two decades, XML has gained momentum as the standard for Web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, Web and Mobile Services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.

negative: Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems One of the main obstacles to highperformance Word Sense Disambiguation (WSD) is the knowledge acquisition bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of semantic relations from an encyclopedic resource, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, simple knowledge-lean disambiguation algorithms compete with state-of-the-art supervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets.

negative: Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which 1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; 2) automatically identifies the number of clusters; 3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.

negative: High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm a high-throughput, lightweight, language-agnostic EDL system that extracts and correctly disambiguates 75% more entities than state-of-the-art EDL systems and is significantly faster than them.

negative: Social media driven image retrieval People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.

negative: Query Segmentation and Resource Disambiguation Leveraging Background Knowledge Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for computing the parameter estimations. In order to maximize accuracy and usability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is obtained leveraging the underlying knowledge base; then the final correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identification in keyword queries reveal very promising results.

negative: Entity Extraction, Linking, Classification, and Tagging for Social Media: A Wikipedia-Based Approach Many applications that process social data, such as tweets, must extract entities from tweets (e.g., “Obama” and “Hawaii” in “Obama went to Hawaii”), link them to entities in a knowledge base (e.g., Wikipedia), classify tweets into a set of predefined topics, and assign descriptive tags to tweets. Few solutions exist today to solve these problems for social data, and they are limited in important ways. Further, even though several industrial systems such as OpenCalais have been deployed to solve these problems for text data, little if any has been published about them, and it is unclear if any of the systems has been tailored for social media. In this paper we describe in depth an end-to-end industrial system that solves these problems for social data. The system has been developed and used heavily in the past three years, first at Kosmix, a startup, and later at WalmartLabs. We show how our system uses a Wikipedia-based global “real-time” knowledge base that is well suited for social data, how we interleave the tasks in a synergistic fashion, how we generate and use contexts and social signals to improve task accuracy, and how we scale the system to the entire Twitter firehose. We describe experiments that show that our system outperforms current approaches. Finally we describe applications of the system at Kosmix and WalmartLabs, and lessons learned.