query: Digital image forensics: a booklet for beginners

positive: Can we trust digital image forensics? Compared to the prominent role digital images play in nowadays multimedia society, research in the field of image authenticity is still in its infancy. Only recently, research on digital image forensics has gained attention by addressing tamper detection and image source identification. However, most publications in this emerging field still lack rigorous discussions of robustness against strategic counterfeiters, who anticipate the existence of forensic techniques. As a result, the question of trustworthiness of digital image forensics arises. This work will take a closer look at two state-of-the-art forensic methods and proposes two counter-techniques; one to perform resampling operations undetectably and another one to forge traces of image origin. Implications for future image forensic systems will be discussed.

positive: On Advances in Statistical Modeling of Natural Images Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered.

positive: Tamper Hiding: Defeating Image Forensics This paper introduces novel hiding techniques to counter the detection of image manipulations through forensic analyses. The presented techniques allow to resize and rotate (parts of) bitmap images without leaving a periodic pattern in the local linear predictor coefficients, which has been exploited by prior art to detect traces of manipulation. A quantitative evaluation on a batch of test images proves the proposed method’s efficacy, while controlling for key parameters and for the retained image quality compared to conventional linear interpolation.

positive: SHEEP, GOATS, LAMBS and WOLVES: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation Performance variabilit y in speech and speaker recognition systems can be attributed to many factors. One major factor, which is often acknowledged but seldom analyzed, is inherent differences in the recognizabilit y of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers, including sheep, goats, lambs and wolves, depending on their behavior with respect to automatic recognition systems. In this paper we propose statistical tests for the existence of these animals and apply these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation.

positive: Statistical Tools for Digital Forensics A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.

negative: Validation of Document Clustering based on Purity and Entropy measures Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluster, but fuzzy c-means allow data belong to different clusters. Fuzzy clustering is a powerful unsupervised method for the analysis of data. Cluster validity measure is useful in estimating the optimal number of clusters. Purity and Entropy are the validity measures used in this

negative: Revised and abbreviated forms of the Genderism and Transphobia Scale: tools for assessing anti-trans prejudice. Many studies of anti-trans prejudice have measured such attitudes using the Genderism and Transphobia Scale (GTS; Hill & Willoughby, 2005). The GTS is unique in assessing negative attitudes and propensity for violence toward trans people. The present research addressed previously observed limitations in the psychometric properties of data produced by the GTS, including inconsistencies in factor structure and subscale scoring across studies. Results across the present 2 studies (Ns = 314 and 250) yielded 2 refined versions of the GTS: the 22-item GTS-Revised (GTS-R) and a more abbreviated 13-item GTS-R-Short Form (GTS-R-SF), each of which produced stable 2-factor structures corresponding with the intended negative attitudes and propensity for violence dimensions of the GTS. The 2 versions differ in that the GTS-R-SF Genderism/Transphobia subscale focuses on more severe expressions of prejudicial attitudes, whereas the longer GTS-R Genderism/Transphobia subscale assesses subtle expressions of prejudice as well. The Gender-Bashing subscale is identical across the 2 versions. Thus, researchers and practitioners may choose the GTS-R or GTS-R-SF depending on the breadth of prejudicial attitudes they wish to assess. Reliability estimates for GTS-R and GTS-R-SF scale and subscale item responses were acceptable and stable across the 2 studies, and validity evidence was garnered in Study 2. These findings can inform use of the GTS-R and GTS-R-SF in research and practice settings, where psychometric precision and efficiency are both critical.

negative: Development and Implementation of a Chat Bot in a Social Network This document describes how to implement a Chat Bot on the Twitter social network for entertainment and viral advertising using a database and a simple algorithm. Having as a main theme a successfully implementation of a Chat Bot preventing people classify it as SPAM, as a result of this a Twitter account (@DonPlaticador) that works without the intervention of a person and every day earns more followers was obtained.

negative: A comprehensive security architecture for SDN SDN enables the administrators to configure network resources very quickly and to adjust network-wide traffic flow to meet changing needs dynamically. However, there are some challenges for implementing a full-scale carrier SDN. One of the most important challenges is SDN security, which is beginning to receive attention. With new SDN architecture, some security threats are common to traditional networking, but the profile of these threats (including their likelihood and impact and hence their overall risk level) changes. Moreover, there are some new security challenges such as bypassing predefined mandatory policies by overwriting flow entries and data eavesdropping by inserting fraudulent flow entries. This paper is to design open-flow specific security solutions and propose a comprehensive security architecture to provide security services such as enforcing mandatory network policy correctly and receiving network policy securely for SDN in order to solve these common security issues and new security challenges. It can also help the developers to implement security functions to provide security services when developing the SDN controller.

negative: Adversarial synthesis learning enables segmentation without target modality ground truth A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT). Previously, two stage methods have been proposed to use cycle generative adversarial networks (CycleGAN) to synthesize training images for a target modality. Then, these efforts trained a segmentation network independently using synthetic images. However, these two independent stages did not use the complementary information between synthesis and segmentation. Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT. The end-to-end EssNet achieved significantly higher median Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and even higher than canonical multi-atlas segmentation (0.9125) and ResNet method (0.9107), which used the CT manual labels.

negative: Competing for Attention in Social Media under Information Overload Conditions Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.

negative: SVM based forest fire detection using static and dynamic features A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

negative: Optimal Beaconing Control for Epidemic Routing in Delay-Tolerant Networks Owing to the uncertainty of transmission opportunities between mobile nodes, the routing in delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Energy-efficient algorithms and policies for DTN are crucial to maximizing the message delivery probability while reducing the delivery cost. In this contribution, we investigate the problem of energy-efficient optimal beaconing control in a DTN. We model the message dissemination under variable beaconing rate with a continuous-time Markov model. Based on this model, we then formulate the optimization problem of the optimal beaconing control for epidemic routing and obtain the optimal threshold policy from the solution of this optimization problem. Furthermore, through extensive numerical results, we demonstrate that the proposed optimal threshold policy significantly outperforms the static policy with constant beaconing rate in terms of system energy consumption savings.

negative: SEAME: a Mandarin-English code-switching speech corpus in south-east asia In Singapore and Malaysia, people often speak a mixture of Mandarin and English within a single sentence. We call such sentences intra-sentential code-switch sentences. In this paper, we report on the development of a Mandarin-English codeswitching spontaneous speech corpus: SEAME. The corpus is developed as part of a multilingual speech recognition project and will be used to examine how Mandarin-English codeswitch speech occurs in the spoken language in South-East Asia. Additionally, it can provide insights into the development of large vocabulary continuous speech recognition (LVCSR) for code-switching speech. The corpus collected consists of intra-sentential code-switching utterances that are recorded under both interview and conversational settings. This paper describes the corpus design and the analysis of collected corpus.

negative: Bicycle-Sharing System Analysis and Trip Prediction Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.

negative: FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.

negative: Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime) In today's era, data has been increasing in volume, velocity and variety. Due to large and complex collection of datasets, it is difficult to process on traditional data processing application. So, this leads to emerging of new technology called data analytics. Data analytics is a science of exploring raw data and elicitation the useful information and hidden pattern. The main aim of data analytics is to use advance analytics techniques for huge and different datasets. Datasets may vary in sizes from terabytes to zettabytes and can be structured or unstructured. The paper gives the comprehensive and theoretical analysis of four open source data analytics tools which are RapidMiner, Weka, R Tool and KNIME. The study describes the technical specification, features, and specialization for each selected tool. By employing the study the choice and selection of tools can be made easy. The tools are evaluated on basis of various parameters like volume of data used, response time, ease of use, price tag, analysis algorithm and handling.

negative: Pattern classification with missing data: a review Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

negative: Digital Clock and Data Recovery Circuits for Optical Links Clock and Data Recovery (CDR) circuits perform the function of recovering clock and re-timing received data in optical links. These CDRs must be capable of tolerating large input jitter (high JTOL), filter input jitter (low JTRAN with no jitter peaking) and in burst-mode applications be capable of phase locking in a very short time. In this paper, we elucidate these design tradeoffs and present various CDR architectures that can overcome them. Specifically, D/PLL CDR architecture that achieves high JTOL, low JTRAN, and no jitter peaking is described. A new burst-mode CDR that can lock instantaneously while filtering input jitter is also discussed.

negative: A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy Nowadays, a prosperity of electronic commerce (E-commerce) not only gives more convenience to consumers but brings more new opportunities in online advertising and marketing. Online advertisers can understand more about consumer preferences based on their daily online shopping and browsing behaviors. The development of big data and cloud computing techniques further empower advertisers and marketers to have a data-driven and consumer-specific preference recommendation based on the online browsing histories. In this research, a decision support system is proposed to predict a consumer purchase intention during browsing sessions. The proposed decision support system categorizes online browsing activities into purchase-oriented and general sessions using extreme boosting machines. With the browsing content entropy features, the proposed method achieves 41.81% recall and 34.35% F score. It further shows its strong predictive capability compared to other benchmark algorithms including logistic regression and traditional ensemble models. The proposed method can be implemented in real-time bidding algorithms for online advertising strategies. Ad deliveries on browsing session with potential purchase intention not only improve the effectiveness of advertisements but significantly increase last-touch attributions for campaign performance.

negative: Cluster Ensembles for High Dimensional Clustering : An Empirical Study This paper studies cluster ensembles for high dimensional data clustering. We examine three different approaches to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on two popular dimension reduction techniques, random projection and principal component analysis (PCA). We present evidence showing that ensembles generated by random projection perform better than those by PCA and further that this can be attributed to the capability of random projection to produce diverse base clusterings. We also examine four different consensus functions for combining the clusterings of the ensemble. We compare their performance using two types of ensembles, each with different properties. In both cases, we show that a recent consensus function based on bipartite graph partitioning achieves the best performance.

negative: Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.

negative: Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.

negative: ResearchGate versus Google Scholar: Which finds more early citations? ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors may upload preprints to ResearchGate, it may use these to provide early impact evidence for new papers. This article assesses the whether the number of citations found for recent articles is comparable to other citation indexes using 2675 recently-published library and information science articles. The results show that in March 2017, ResearchGate found less citations than did Google Scholar but more than both Web of Science and Scopus. This held true for the dataset overall and for the six largest journals in it. ResearchGate correlated most strongly with Google Scholar citations, suggesting that ResearchGate is not predominantly tapping a fundamentally different source of data than Google Scholar. Nevertheless, preprint sharing in ResearchGate is substantial enough for authors to take seriously.

negative: Boosting item keyword search with spreading activation Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.

negative: RFID in robot-assisted indoor navigation for the visually impaired We describe how radio frequency identification (RFID) can be used in robot-assisted indoor navigation for the visually impaired. We present a robotic guide for the visually impaired that was deployed and tested both with and without visually unpaired participants in two indoor environments. We describe how we modified the standard potential fields algorithms to achieve navigation at moderate walking speeds and to avoid oscillation in narrow spaces. The experiments illustrate that passive RFID tags deployed in the environment can act as reliable stimuli that trigger local navigation behaviors to achieve global navigation objectives.

negative: A Social Network-Based Recommender System (SNRS) Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, an item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most

negative: Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.

negative: A static hand gesture recognition algorithm using k-mean based radial basis function neural network The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.

negative: Generative Event Schema Induction with Entity Disambiguation This paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement.

negative: Computer Forensics Field Triage Process Model With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, the system(s)/storage media be transported back to a lab environment for a more thorough examination and analysis. The CFFTPM has been successfully used in various real world cases, Journal of Digital Forensics, Security and Law, Vol. 1(2) 20 and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model’s forensic soundness, investigative support capabilities and practical considerations.

negative: Cloud forensics: A research perspective Cloud computing and digital forensics are both developing topics and researching these topics requires an understanding of the main aspects of both cloud computing and digital forensics. In cloud computing it is necessary not only to understand its characteristics and the different services and deployment models but also to survey the underpinning elements of cloud computing such as virtualization and the distributed computing which are important to identify its impact on current digital forensics guidelines and procedures. Unlike papers discussing the challenges and opportunities presented by cloud computing in relation to digital forensics, in this paper, we will discuss the underpinning cloud computing elements which are required to provide forensics friendly cloud services. Furthermore, we suggest a set of questions that will aid in the process of cloud forensics analysis.

negative: Evaluating Digital Forensic Options for the Apple iPad The iPod Touch, iPhone and iPad from Apple are among the most popular mobile computing platforms in use today. These devices are of forensic interest because of their high adoption rate and potential for containing digital evidence. The uniformity in their design and underlying operating system (iOS) also allows forensic tools and methods to be shared across product types. This paper analyzes the tools and methods available for conducting forensic examinations of the Apple iPad. These include commercial software products, updated methodologies based on existing jailbreaking processes and the analysis of the device backup contents provided by iTunes. While many of the available commercial tools offer promise, the results of our analysis indicate that most comprehensive examination of the iPad requires jailbreaking to perform forensic duplication and manual analysis of its media content.

negative: Defining Digital Forensic Examination and Analysis Tool Using Abstraction Layers This paper uses the theory of abstraction layers to describe the purpose and goals of digital forensic analysis tools. Using abstraction layers, we identify where tools can introduce errors and provide requirements that the tools must follow. Categories of forensic analysis types are also defined based on the abstraction layers. Abstraction layers are not a new concept, but their usage in digital forensic analysis is not well documented.

negative: A Proposed Digital Forensics Business Model to Support Cybercrime Investigation in Indonesia Digital forensics will always include at least human as the one who performs activities, digital evidence as the main object, and process as a reference for the activities followed. The existing framework has not provided a description of the interaction between human, interaction between human and digital evidence, as well as interaction between human and the process itself. A business model approach can be done to provide the idea regarding the interaction in question. In this case, what has been generated by the author in the previous study through a business model of the digital chain of custody becomes the first step in constructing a business model of a digital forensics. In principle, the proposed business model already accommodates major components of digital forensics (human, digital evidence, process) and also considers the interactions among the components. The business model suggested has contained several basic principles as described in The Regulation of Chief of Indonesian National Police (Perkap) No 10/2010. This will give support to law enforcement to deal with cybercrime cases that are more frequent and more sophisticated, and can be a reference for each institution and organization to implement digital forensics activities.

negative: Network forensic frameworks: Survey and research challenges Network forensics is the science that deals with capture, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper makes an exhaustive survey of various network forensic frameworks proposed till date. A generic process model for network forensics is proposed which is built on various existing models of digital forensics. Definition, categorization and motivation for network forensics are clearly stated. The functionality of various Network Forensic Analysis Tools (NFATs) and network security monitoring tools, available for forensics examiners is discussed. The specific research gaps existing in implementation frameworks, process models and analysis tools are identified and major challenges are highlighted. The significance of this work is that it presents an overview on network forensics covering tools, process models and framework implementations, which will be very much useful for security practitioners and researchers in exploring this upcoming and young discipline. a 2010 Elsevier Ltd. All rights reserved.

negative: A second generation computer forensic analysis system The architecture of existing – first generation – computer forensic tools, including the widely used EnCase and FTK products, is rapidly becoming outdated. Tools are not keeping pace with increased complexity and data volumes of modern investigations. This paper discuses the limitations of first generation computer forensic tools. Several metrics for measuring the efficacy and performance of computer forensic tools are introduced. A set of requirements for second generation tools are proposed. A high-level design for a (work in progress) second generation computer forensic analysis system is presented. a 2009 Digital Forensic Research workshop. Published by Elsevier Ltd. All rights reserved.


query: Device to device interaction analysis in IoT based Smart Traffic Management System: An experimental approach

positive: Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users' devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.

positive: Device-to-device communication as an underlay to LTE-advanced networks In this article device-to-device (D2D) communication underlaying a 3GPP LTE-Advanced cellular network is studied as an enabler of local services with limited interference impact on the primary cellular network. The approach of the study is a tight integration of D2D communication into an LTE-Advanced network. In particular, we propose mechanisms for D2D communication session setup and management involving procedures in the LTE System Architecture Evolution. Moreover, we present numerical results based on system simulations in an interference limited local area scenario. Our results show that D2D communication can increase the total throughput observed in the cell area.

positive: Design aspects of network assisted device-to-device communications Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design.

negative: Augmenting geospatial data provenance through metadata tracking in geospatial service chaining In a service-oriented environment, heterogeneous data from distributed data archiving centers and various geo-processing services are chained together dynamically to generate on-demand data products. Creating an executable service chain requires detailed specification of metadata for data sets and service instances. Using metadata tracking, semantics-enabled metadata are generated and propagated through a service chain. This metadata can be employed to validate a service chain, e.g. whether metadata preconditions on the input data of services can be satisfied. This paper explores how this metadata can be further exploited to augment geospatial data provenance, i.e., how a geospatial data product is derived. Provenance information is automatically captured during the metadata tracking process. Semantic Web technologies, including OWL and SPARQL, are used for representation and query of this provenance information. The approach can not only contribute to the automatic recording of geospatial data provenance, but also provide a more informed understanding of provenance information using Semantic Web technologies. & 2009 Elsevier Ltd. All rights reserved.

negative: Trust management for the internet of things and its application to service composition The Internet of Things (IoT) integrates a large amount of everyday life devices from heterogeneous network environments, bringing a great challenge into security and reliability management. Recognizing that the smart objects in IoT are most likely human-carried or human-operated devices, we propose a scalable trust management protocol for IoT, with the emphasis on social relationships. We consider multiple trust properties including honesty, cooperativeness, and community-interest to account for social interaction. Each node performs trust evaluation towards a limited set of devices of its interest only. The trust management protocol is event-driven upon the occurrence of a social encounter or interaction event, and trust is aggregated using both direct observations and indirect recommendations. We analyze the effect of trust parameters on trust assessment accuracy and trust convergence time. Our results show that there exists a trade-off between trust assessment accuracy vs. trust convergence time in the presence of false recommendations attacks performed by malicious nodes. We demonstrate the effectiveness of the proposed trust management protocol with a trust-based service composition application. Our results indicate that trust-based service composition significantly outperforms non-trust-based (random) service composition and its performance approaches the maximum achievable performance with global knowledge.

negative: Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices —The pedestrian inertial navigation systems are generally based on Pedestrian Dead Reckoning (PDR) algorithm. Considering the physiological characteristics of pedestrian movement, we use the cyclical characteristics and statistics of acceleration waveform and features which are associated with the walking speed to estimate the stride length. Due to the randomness of the pedestrian hand-held habit, the step events cannot always be detected by using the periods of zero velocity updates (ZUPTs). Furthermore, the signal patterns of the sensor could differ significantly depending on the carrying modes and the user’s hand motion. Hence, the step detection and the associated adaptive step length model using a handheld device equipped with accelerometer is required to obtain high-accurate measurements. To achieve this goal, a compositional algorithm of empirical formula and Back-propagation neural network by using handheld devices is proposed to estimate the step length, as well as achieve the accuracy of step detection higher than 98%. Furthermore, the proposed joint step detection and adaptive step length estimation algorithm can help much in the development of Pedestrian Navigation Devices (PNDs) based on the handheld inertial sensors.

negative: Toward the Minimal Universal Petri Net A universal Petri net with 14 places, 42 transitions, and 218 arcs was built in the class of deterministic inhibitor Petri nets (DIPNs); it is based on the minimal Turing machine (TM) of Woods and Neary with 6 states, 4 symbols, and 23 instructions, directly simulated by a Petri net. Several techniques were developed, including bi-tag system (BTS) construction on a DIPN, special encoding of TM tape by two stacks, and concise subnets that implement arithmetic encoding operations. The simulation using the BTS has cubic time and linear space complexity, while the resulting universal net runs in exponential time and quadratic space with respect to the target net transitions' firing sequence length. The technique is applicable for simulating any TM by the Petri net.

negative: Transference of kettlebell training to strength, power, and endurance. Kettlebells are a popular implement in many strength and conditioning programs, and their benefits are touted in popular literature, books, and videos. However, clinical data on their efficacy are limited. The purpose of this study was to examine whether kettlebell training transfers strength and power to weightlifting and powerlifting exercises and improves muscular endurance. Thirty-seven subjects were assigned to an experimental (EXP, n = 23; mean age = 40.9 ± 12.9 years) or a control group (CON; n = 14; mean age = 39.6 ± 15.8 years), range 18-72 years. The participants were required to perform assessments including a barbell clean and jerk, barbell bench press, maximal vertical jump, and 45° back extensions to volitional fatigue before and after a 10-week kettlebell training program. Training was structured in a group setting for 2 d·wk(-1) for 10 weeks. A repeated measures analysis of variance was conducted to determine group × time interactions and main effects. Post hoc pairwise comparisons were conducted when appropriate. Bench press revealed a time × group interaction and a main effect (p < 0.05). Clean and jerk and back extension demonstrated a trend toward a time × group interaction, but it did not reach significance (p = 0.053). However, clean and jerk did reveal a main effect for time (p < 0.05). No significant findings were reported for maximal vertical jump. The results demonstrate a transfer of power and strength in response to 10 weeks of training with kettlebells. Traditional training methods may not be convenient or accessible for strength and conditioning specialists, athletes, coaches, and recreational exercisers. The current data suggest that kettlebells may be an effective alternative tool to improve performance in weightlifting and powerlifting.

negative: Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.

negative: Skew detection of scanned document images —Skewing of the scanned image is an inevitable process and its detection is an important issue for document recognition systems. The skew of the scanned document image specifies the deviation of the text lines from the horizontal or vertical axis. This paper surveys methods to detect this skew in

negative: Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

negative: Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.

negative: Continuous and fine-grained breathing volume monitoring from afar using wireless signals In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a person's breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the person's body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the person's body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor user's breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.

negative:
Polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population. BACKGROUND
Genome-wide association studies in adults have identified numerous genetic variants related to psychiatric disorders and related traits, such as schizophrenia and educational attainment. However, the effects of these genetic variants on behaviour in the general population remain to be fully understood, particularly in younger populations. We investigated whether polygenic scores of five psychiatric disorders and educational attainment are related to emotional and behaviour problems during early childhood.

METHODS
From the Generation R Study, we included participants with available genotype data and behavioural problems measured with the Child Behavior Checklist (CBCL) at the age of 3 (n = 1,902), 6 (n = 2,202) and 10 years old (n = 1,843). Polygenic scores were calculated for five psychiatric disorders and educational attainment. These polygenic scores were tested for an association with the broadband internalizing and externalizing problem scales and the specific CBCL syndrome scale scores.

RESULTS
Analysis of the CBCL broadband scales showed that the schizophrenia polygenic score was associated with significantly higher internalizing scores at 3, 6 and 10 years and higher externalizing scores at age 3 and 6. The educational attainment polygenic score was associated with lower externalizing scores at all time points and lower internalizing scores at age 3. No associations were observed for the polygenic scores of bipolar disorder, major depressive disorder and autism spectrum disorder. Secondary analyses of specific syndrome scores showed that the schizophrenia polygenic score was strongly related to the Thought Problems scores. A negative association was observed between the educational attainment polygenic score and Attention Problems scores across all age groups.

CONCLUSIONS
Polygenic scores for adult psychiatric disorders and educational attainment are associated with variation in emotional and behavioural problems already at a very early age.

negative: A decision cognizant Kullback-Leibler divergence In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback-Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback-Leibler divergence (DC-KL), to reduce the contribution of the minority classes, which obscure the true degree of classifier incongruence. We investigate the properties of the novel divergence measure analytically and by simulation studies. The proposed measure is demonstrated to be more robust to minority class clutter. Its sensitivity to estimation noise is also shown to be considerably lower than that of the classical KL divergence. These properties render the DC-KL divergence a much better statistic for discriminating between classifier congruence and incongruence in pattern recognition systems.

negative: Approaches , methods , metrics , measures , and subjectivity in ontology evaluation : A survey Ontology evaluation is concerned with ascertain two important aspects of ontologies: quality and correctness. The distinction between the two is attempted in this survey as a way to better approach ontology evaluation. The role that ontologies play on the semantic web at large has been has been argued to have catalyzed the proliferation of ontologies in existence. This has also presented the challenge of deciding one the suitability of an given ontology to one’s purposes as compared with another ontology in a similar domain. This survey intends to analyze the state of the art in ontology evaluate spanning such topics as the approaches to ontology evaluation, the metrics and measures used. Particular interest is given to Data-driven ontology evaluation with special emphasis on the notion of bias and it’s relevance to evaluation results. Chief among the outputs of this survey is the gap analysis on the topic of ontology evaluation.

negative: Radar interferometry offers new insights into threats to the Angkor site The conservation of World Heritage is critical to the cultural and social sustainability of regions and nations. Risk monitoring and preventive diagnosis of threats to heritage sites in any given ecosystem are a complex and challenging task. Taking advantage of the performance of Earth Observation technologies, we measured the impacts of hitherto imperceptible and poorly understood factors of groundwater and temperature variations on the monuments in the Angkor World Heritage site (400 km2). We developed a two-scale synthetic aperture radar interferometry (InSAR) approach. We describe spatial-temporal displacements (at millimeter-level accuracy), as measured by high-resolution TerraSAR/TanDEM-X satellite images, to provide a new solution to resolve the current controversy surrounding the potential structural collapse of monuments in Angkor. Multidisciplinary analysis in conjunction with a deterioration kinetics model offers new insights into the causes that trigger the potential decline of Angkor monuments. Our results show that pumping groundwater for residential and touristic establishments did not threaten the sustainability of monuments during 2011 to 2013; however, seasonal variations of the groundwater table and the thermodynamics of stone materials are factors that could trigger and/or aggravate the deterioration of monuments. These factors amplify known impacts of chemical weathering and biological alteration of temple materials. The InSAR solution reported in this study could have implications for monitoring and sustainable conservation of monuments in World Heritage sites elsewhere.

negative: Crafting Integrated Multichannel Retailing Strategies Multichannel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. Multichannel retailers dominate today's retail landscape. While there are many benefits of operating multiple channels, these retailers also face many challenges. In this article, we discuss the key issues concerning multichannel retailing, including the motivations and constraints of going multichannel, the challenges of crafting multichannel retailing strategies and opportunities for creating synergies across channels, key retail mix decisions facing multichannel retailers, and the dynamics of multichannel retailing. We synthesize current knowledge drawn from the academic literature and industry practice, and discuss potential directions for future research. © 2010 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

negative: A fast and efficient sift detector using the mobile GPU Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.

negative: Home, habits, and energy: examining domestic interactions and energy consumption This paper presents findings from a qualitative study of people's everyday interactions with energy-consuming products and systems in the home. Initial results from a large online survey are also considered. This research focuses not only on "conservation behavior" but importantly investigates interactions with technology that may be characterized as "normal consumption" or "over-consumption." A novel vocabulary for analyzing and designing energy-conserving interactions is proposed based on our findings, including: cutting, trimming, switching, upgrading, and shifting. Using the proposed vocabulary, and informed by theoretical developments from various literatures, this paper demonstrates ways in which everyday interactions with technology in the home are performed without conscious consideration of energy consumption but rather are unconscious, habitual, and irrational. Implications for the design of energy-conserving interactions with technology and broader challenges for HCI research are proposed.

negative: What affects web credibility perception - an analysis of textual justifications In this paper, we present the findings of a qualitative analysis of 15,750 comments left by 2,041 participants in a Reconcile web credibility evaluation study. While assessing the credibility of the presented pages, respondents of the Reconcile studies were also asked to justify their ratings in writing. This work attempts to give an insight into the factors that affected the credibility assessment. To the best of our knowledge, the presented study is the most-recent large-scale study of its kind carried out since 2003, when the Fogg et al. śHow do users evaluate the credibility of Web sites? A study with over 2,500 participants’ paper was published. The performed analysis shows that the findings made a decade ago are still mostly valid today despite the passage of time and the advancement of Internet technologies. However we report a weaker impact of webpage appearance. A much bigger dataset (as compared to Fogg’s studies) allowed respondents to reveal additional features, which influenced the credibility evaluations.

negative: Coupled depth learning In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map has been computed. We express a global (coarse) depth map of an image as a linear combination of a depth basis learned from training examples. The depth basis captures spatial and statistical regularities and reduces the problem of global depth estimation to the task of predicting the input-specific coefficients in the linear combination. This is formulated as a regression problem from a holistic representation of the image. Crucially, the depth basis and the regression function are coupled and jointly optimized by our learning scheme. We demonstrate that this results in a significant improvement in accuracy compared to direct regression of depth pixel values or approaches learning the depth basis disjointly from the regression function. The global depth estimate is then used as a guidance by a local refinement method that introduces depth details that were not captured at the global level. Experiments on the NYUv2 and KITTI datasets show that our method outperforms the existing state-of-the-art at a considerably lower computational cost for both training and testing.

negative: CzEngVallex: a Bilingual Czech-English Valency Lexicon This paper introduces a new bilingual Czech-English verbal valency lexicon (called CzEngVallex) representing a relatively large empirical database. It includes 20,835 aligned valency frame pairs (i.e., verb senses which are translations of each other) and their aligned arguments. This new lexicon uses data from the Prague Czech-English Dependency Treebank and also takes advantage of the existing valency lexicons for both languages: the PDT-Vallex for Czech and the EngVallex for English. The CzEngVallex is available for browsing as well as for download in the LINDAT/CLARIN repository. The CzEngVallex is meant to be used not only by traditional linguists, lexicographers, translators but also by computational linguists both for the purposes of enriching theoretical linguistic accounts of verbal valency from a cross-linguistic perspective and for an innovative use in various NLP tasks.

negative: Semantics and implementation of continuous sliding window queries over data streams In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.

negative: Atrial Fibrillation Detection Based on Poincaré plot of RR Intervals Atrial fibrillation (AF) is one of the most common types of arrhythmia which significantly increases the risk factor of stroke – especially in elderly population. In this paper an algorithm is presented which is suitable for the effective detection of AF, by using merely heart rate data as input. The method is based on the processing of Poincaré plots constructed from the set of 30 RR intervals. During the analysis of Poincaré plots the dispersion of points around the diagonal line is calculated and the number of clusters is determined by a self-developed cluster analyzer. The decision criterion of AF relies on these two parameters. On the one hand, the algorithm was tested on 10 AF and 10 normal rhythm ECG signals of the PhysioNet Database, achieving the average sensitivity (Se) of 98.69% and the average specificity (Sp) of 99.59%. On the other hand, 10 AF and 10 normal clinically confirmed records of a heart rate meter were also processed, resulting the average Se and Sp of 96.89% and 99.00%, respectively.

negative: Optical Character Recognition from Text Image Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact character using feature matching between the extracted character and the template of all characters as a measure of similarity.

negative: FEBEI-Face Expression Based Emoticon Identification CSB 657 Computer Vision The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.

negative: Analysis and Application of Discrete Halbach Magnet Array With Unequal Arc Lengths and Unequally Changed Magnetization Directions This paper presents a general process to analyze and optimize the performance for an arbitrary discrete Halbach magnet array which is “unnormal” on magnetization direction and arc length of each segment. Besides the original type, a new arrangement of Halbach array is proposed, which does not have main PM segments. Two-dimensional field analytical models for predicting the air-gap field distribution of both arrangements are offered and validated by an FEA method. Then, a multi-objective optimizer was applied for different requirements, and comparison between normal and optimized 3-segment Halbach array is given to verify the effect of optimization. At last, some potential applications and follow-up study are discussed.

negative: ANANAS - A Framework for Analyzing Android Applications Android is an open software platform for mobile devices with a large market share in the smart phone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS is an expandable and modular framework for analyzing Android applications. It takes care of common needs for dynamic malware analysis and provides an interface for the development of plugins. Adaptability and expandability have been main design goals during the development process. An abstraction layer for simple user interaction and phone event simulation is also part of the framework. It allows an analyst to script the required user simulation or phone events on demand or adjust the simulation to his needs. Six plugins have been developed for ANANAS. They represent well known techniques for malware analysis, such as system call hooking and network traffic analysis. The focus clearly lies on dynamic analysis, as five of the six plugins are dynamic analysis methods.

negative: UltraGesture: Fine-Grained Gesture Sensing and Recognition With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound based human gesture recognition systems have been proposed. They mostly rely on low resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 97% for 12 gestures including finger click and rotation.

negative: BLE-Based Accurate Indoor Location Tracking for Home and Office Nowadays the use of smart mobile devices and the accompanying needs for emerging services relying on indoor location-based services (LBS) for mobile devices are rapidly increasing. For more accurate location tracking using Bluetooth Low Energy (BLE), this paper proposes a novel trilateration-based algorithm and presents experimental results that demonstrate its effectiveness.

negative: Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting Intelligent buildings are quickly becoming cohesive and integral inhabitants of cyberphysical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever-increasing data sources, such as ubiquitous smart devices and sensors, while mimicking various approaches previously known in software, hardware, and bioinspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. It discusses everything from the prospects of U.S. and world energy consumption to insights into the future of intelligent buildings based on the latest technological advancements in U.S. industry and government.

negative: Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management While cloud computing has brought paradigm shifts to computing services, researchers and developers have also found some problems inherent to its nature such as bandwidth bottleneck, communication overhead, and location blindness. The concept of fog/edge computing is therefore coined to extend the services from the core in cloud data centers to the edge of the network. In recent years, many systems are proposed to better serve ubiquitous smart devices closer to the user. This article provides a complete and up-to-date review of edge-oriented computing systems by encapsulating relevant proposals on their architecture features, management approaches, and design objectives.

negative: Mobile Multimedia Recommendation in Smart Communities: A Survey Due to the rapid growth of Internet broadband access and proliferation of modern mobile devices, various types of multimedia (e.g., text, images, audios, and videos) have become ubiquitously available anytime. Mobile device users usually store and use multimedia contents based on their personal interests and preferences. Mobile device challenges such as storage limitation have, however, introduced the problem of mobile multimedia overload to users. To tackle this problem, researchers have developed various techniques that recommend multimedia for mobile users. In this paper, we examine the importance of mobile multimedia recommendation systems from the perspective of three smart communities, namely mobile social learning, mobile event guide, and context-aware services. A cautious analysis of existing research reveals that the implementation of proactive, sensor-based and hybrid recommender systems can improve mobile multimedia recommendations. Nevertheless, there are still challenges and open issues such as the incorporation of context and social properties, which need to be tackled to generate accurate and trustworthy mobile multimedia recommendations.