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Question: We present Line-Storm, an interactive computer system for creative performance. The context we investigated was writing on paper using Line-Storm. We used self-report questionnaires as part of research involving human participants, to evaluate Line-Storm. Line-Storm consisted of a writing stylus and writing pad, augmented with electronics. The writing pad was connected to a contact microphone, and the writing stylus had a small micro-controller board and peripherals attached to it. The signals from these electronic augmentations were fed into the audio-synthesis environment Max/MSP to produce an interactive soundscape. We attempted to discover whether Line-Storm enhanced a self-reported sense of being present and engaged during a writing task, and we compared Line-Storm to a non-interactive control condition. After performing statistical analysis in SPSS, we were unable to support our research hypothesis, that presence and engagement were enhanced by Line-Storm. Participants reported they were, on average, no more present and engaged during the experimental condition than during the control condition. As creativity is subtle, and varies with person, time, context, space and so many other factors, this was somewhat expected by us. A statistically significant of our study is that some participants responded to Line-Storm more positively than others. These Preservers of Line-Storm were a group, distinct from other participants, who reported greater presence and engagement and who wrote more words with Line-Storm and during the control condition. We discuss the of our research and place Line-Storm in an artistic-technological context, drawing upon writings by Martin Heidegger when considering the nature of Line-Storm. Future work includes modifying interactive components, improving aesthetics and using more miniaturized electronics, experimenting with a drawing task instead of a writing task, and collaborating with a composer of electronic music to make a more interesting, immersive, and engaging interactive soundscape for writing or drawing performance. Our philosophy is that people have become frugal regarding "joy"! How we all are becoming increasingly suspicious of all joy! The desire for joy already calls itself a "need to recuperate" and is beginning to be ashamed of itself. -Nietzsche Tod Machover has emphasized the need to augment existing, traditional musical instruments while ensuring these augmentations act as stimuli to the creative process, not simply as additional features. One focus of this paper is to find a way to enhance human creativity. Another is to observe the emergence of the work when the system is used. A third, is our attempt to make something that is fun to use. We have conceived, designed, constructed, evaluated, our system called Line-Storm 1, attempting to enhance a sense of both presence and engagement in the user. Only through performance with Line-Storm, does Line-Storm come into being. The method of experience sampling-interrupting a person as they go through their daily activities and asking questions about their experience-has been used to find that when peoples minds are wandering, they are less happy. "Be Here Now," a mantra popularized in the United States by, for example, Dr. Richard Alpert, who became Baba Ram Dass. This mantra now occurs in a leading business publication urging middle managers everywhere to "be present" to be a "great leader" and presumably to reap the rewards of "success." Even the LSD experimentation Dass describes in Be Here Now, carried out on a small, socially acceptable scale in Silicon Valley, where tech workers "microdose" themselves with LSD, to enhance their creativity and improve interpersonal interactions. Some esoteric practices leading to creative work may conjure images of the lone painter or poet, or of a sculptor in her studio. It is not only Silicon Valley technocrats, scrambling for millions and billions of dollars, who might benefit from enhancing human creativity. Even now one is ashamed of resting (equated to waste of time in our mind), and prolonged reflection almost gives people a bad conscience. One thinks with a watch in ones hand, while eating meals, and reading the latest news of the stock market; we live today not to miss out on anything. -Nietzsche Note that Nietzsche was writing well over 100 years before "FOMO," or "fear of missing out," became an expression related to early 21st-century smartphone users. Our point is that we recognize that there are different meanings to the phrase creative work. For example, billionaires and poets are not endorsing the same thing when both use the word "creative" or the word "work," though both may praise "creative work." Some decry the extreme measures taken by LSD trippers in the 1960s, and want to turn the drug into an effective money-making tool. An irony is that creative work translates into fortunes undreamt of by poets such as Robert Frost. There is a story in which Joseph Heller, author of the novel Catch-22, when told of an investment banker who had made more money last year than he might ever to be expected to make from the novel, replied that he had something the investment banker would never have: enough. So, we argue that it is possible that what was good for Heller, in the anecdote, would probably not have been good for the investment banker, even when the concept of creative work is broadened to include both their endeavors. Enhancing one type of creative work may not enhance the other. The ecstasy of the composer remarked upon by Csikszentmihalyi or of the novelist, may not be found in the same way the "A-ha!" of the software developer is found. Our work involving Line-Storm has been an attempt to provide a ludic system for use by the creative worker. Gaver defines a ludic system as one that is used for its own sake, and not for some other end. By attempting to increase a users sense of presence and engagement-their being here now-our hope is to provide an immersive environment in which to do creative work with a writing stylus such as the mechanical pencil we chose to use. Taskscape is a complex term from Ingold's "The Temporality of the Landscape", which we will refer to later, when speaking of the new possibilities of a task that Line-Storm exposes, as affordances in Gibson's sense of the term. One of our committee members, a professor of music, suggested that our work involves the taskscape of the creative worker, working with a writing stylus and paper. This taskscape includes the place, people, and objects surrounding the creative worker doing creative work. The taskscape is social. The experience of the user of our system, and of the research participants who gave of their time to be a part of this thesis, is a social experience, and the writing tasks they performed are tasks that fit into "an array of activities"-which include the writing of this sentence. We do not know-as above, because too little work has been done in this area-whether the taskscape of a user of Line-Storm is altered in ways more conducive to writing poetry than to the drafting of microprocessor plans, for example, or vice versa. Rather than devise a completely new tool, we have chosen to augment an otherwise ordinary mechanical pencil 2. Perhaps by looking 2 We could have similarly augmented a paintbrush or a pen, though the away from our goal, creative enhancement-as we must when looking at faint night-sky objects with the naked eye -and making the use of the system the primary activity, and the work done with it a secondary activity, we think we will find ourselves progressing in that direction, whereas a direct approach would not have succeeded. By giving a chance for play, we have hoped our system, Line-Storm, serves as stimulant and facilitator "to the creative process itself," as Machover advises. Line-Storm is not digital art as such. Its products are physical objects and phenomena. They are (analog) drawings or writings. It produces sounds, which are-though digitally mediated-analog sounds. The computer is, in Line-Storm, an intermediary and a facilitator, with a visual arts component and sound, satisfying criteria for Demers' second sub-genre of sound art. Line-Storm amplifies and augments the sonic aspects. The sounds made, while writing or drawing, are captured using a contact microphone and are played through headphones. Sounds of natural phenomena-the sounds of a thunderstorm-augment the writing or drawing experience. These sounds are recorded-analog yet are digitally mediated . Performance with Line-Storm can involve performance in art, in technology, and/or in play. A performer using Line-Storm may be using it for different reasons, including for the fun of using it, to write a letter to a friend, to write down a cooking recipe, to write poetry, to draw, because it is a curious thing one wants to understand, or for other reasons. A performance occurs "as action, interaction, and relation". Line-Storm is an interactive system, where the performer's actions cause sounds to occur, which may influence subsequent actions. The sounds can be controlled to some degree, by the performer. The drawing or writing produced during performance is one product of the performance. The sounds, which can be recorded and played back, are another product. The audience of the performance may be the performer alone, or a person or persons presented with one or more products of the performance, the written or drawn product or the sound produced. Line-Storm is a way of "honoring the ordinary" in Schechner's words. Schechner refers to "Nietzschean" play, making specific reference to a dice roll. This is contrasted with playing a game with rules that are agreed upon by everyone before play starts. Play is a way of introducing flow into ones life and has an organic quality. Deleuze has discussed Nietzsches approach to the dice roll, in Nietzsche and Philosophy. Sounds which we added to LineStorm included those of thunderstorms, which have organic qualities similar to movement of air through a room, pushed by a ceiling fan, or sounds from nearby birds. Thunderstorms followed by quiet rain can be medicative to some as well. We wanted to use analog (thunderstorm) sounds for our analog ludic system. Gamification is contrary to play and playfulness and renders "personal" relationships impersonal. Bohme has commented upon the superfluity of personal relationships to the normal functioning of society. The digital medium is one of permanence and impermanence. Privacy becomes a concern in the digital realm, in ways not found in the analog realm. Talk of a "right to be forgotten" has occurred surrounding the at-times oppressive permanence of the digital medium. Line-Storm as a medium provides the privacy familiar from the analog world. Letters can be (and have been) intercepted, shared unexpectedly, and so on, but personal letters written on paper are not automatically added to databases, compiled on users of internet services. One motivation of Line-Storm is that it could preserve the practice of the handwritten letter. No human tools are more beautifully designed for their purpose than traditional musical instruments, which no colpaintbrush would have required a different approach. We depend in part on the sounds made by the user's touching of the writing pad, and we cannot expect a paintbrush to make the same level of sound made by a pencil lead. lection of buttons, wires, and sensors can replace. That's why it is important that technology be used to augment, not replace, existing instruments. -Tod Machover Previous work, that investigated augmenting a writing stylus with electronic or computer systems, includes MusicGrip, a pressuresensor-controlled system in which a writing stylus was used to control analog synthesizers. Musc Grip used a one-to-one correspondence between sensor input and synthesizer output. Shichinohe et al. used a camera system to implement an augmented-reality system to aid in the instruction of calligraphic writing . Their system monitored brush position and body posture, providing both ambient (color) feedback and verbal feedback. Part of a performance-the Brain Opera-the Digital Baton was a wireless baton, augmented with sensors, used as a New Interface for Musical Expression (NIME). The baton carried an infrared LED at its tip, pressure-sensitive resistors that were controlled by the performer's fingers gripping the baton, and three +/-5g accelerometers. These inputs were mapped to musical parameters. The Digital Baton was a wired NIME, but the authors did discuss what could be done to make it wireless. Much work relevant to ours has been done by Tod Machover, whose research group at MIT's Media Lab developed the technology behind Guitar Hero. His work with hyperinstruments (electronic augmentations of traditional music instruments) ) and are very interesting. Machover's Hyperstring Trilogy was composed and performed using hyperinstruments-hypercello, hyperviolin, and hyperviola-which were traditional classical instruments augmented with sensors. The entire performance was itself augmented using computer programs that generated accompanying musical sounds. Machover's philosophy of augmenting, and not replacing, traditional tools, is one we have followed in our work. LiveScribe (http://www.livescribe.com), which has produced a wireless pen with handwriting recognition, no longer develops the electronic writing pen it once did, so we did not involve the company's work in our work. Work involving the augmentation of objects other than writing utensils or musical instruments includes the Sonic City system, in which the urban environment served as the interface. Sonic City equipped an urban pedestrian with a music-generating computer that was "aware" of the way the user traversed the streets and sidewalks of the city. The Bluetooth Radio Ball Interface (BRBI) augmented a sport ball with sensors, providing sound and music capabilities (mediated by a computer and a Bluetooth radio connection). The Urban Musical Game was another augmentation project involving a sport ball and sound/music generation based on the ball's motions; video of use of Urban Musical Game have been made available on Vimeo (https://vimeo.com/26413625) and (https://vimeo.com/22120867). Measurement of writing motions helps diagnose people suffering from obsessive-compulsive disorder . Handwriting and cell-phone texting have been compared as therapies for Brocas aphasia, with handwriting emerging as the more effective treatment. Embodied cognition models have been used to investigate neural relationships with character writing, copying, and recognition. Preschool children have taken part in fMRI experiments, which demonstrate the importance of "learningby-doing" approaches to literacy learning, with kinesthetic activity working in tandem with cognition. Existential phenomenology has informed thought regarding the teaching of personal writing, without any technological involvement. Recent work by Kiefer has found neuropsychological evidence for benefits from writing by hand, as opposed to writing using a computer keyboard, including improved learning of reading and writing skills in young children. Morphy and Graham argue students more generally, appear to write better when using word processors than when composing by hand, considering the composition tools (spell check, grammar check, etc.) available in modern word processing software. Al-Ghabra focused on the importance of handwriting for the development of composition skills in college students. Earlier work by Collier and Werier found no difference between high-level characteristics of textual production in proficient adult writers who composed either by hand or while using a word processor. An early impetus for our work was a desire to encourage handwritten letter writing, to provide a system that might encourage a person to increase or maintain their stamp-and-envelope transmission of handwritten letters. Thoreau decried some forms of letter writing, writing that, "The penny-post is, commonly, an institution through which you seriously offer a man that penny for his thoughts which is so often safely offered in jest"; from a different viewpoint, writing letters has been a way for families to stay connected through the generations and has functioned alongside newer media. Twentieth-century German philosopher Martin Heidegger commented, in his Parmenides, upon handwriting, declaring its superiority over use of a typewriter. Philosopher of technology Don Ihde faulted Heidegger for Heidegger's comparison. Philosopher Jacques Derrida also faulted Heidegger, for implying, while emphasizing the importance of "the hand" for humanity, that human beings only have one hand. A typewriter does not offer the affordance of being easily carried up a mountainalthough Nietzsche owned a portable typewriter. Likewise, the poem title of a friend, "Notebooks," would read differently if it had to do, not with notebooks, but with some digital note-taking contrivance such as Google's Keep app (http://keep.google.com). Ihde reminds us of the non-transparency of electronic and digital communications media such as the telephone, and here, with Nietzsche's typewriter and Gregory Lawless's poem, we see some effects of medium, in practice (typewriter) and in discourse (poem title). Ihde does not want to go with Heidegger, to declare there is not just a difference between media but a hierarchy of values. These dismissals, by Ihde and Derrida, of Heidegger's criticism of the typewriter and his preference for handwriting, seem to pretend to have the benefit of hindsight. Ihde and Derrida do not convince us, because they, like the king Thamus, do not look enough at what might support Heidegger's position, but only criticize it-their seeming invincibility coming from their objections to Heidegger being voiced decades after his death in 1976. Heidegger decries what he sees as hastiness in the face of a technologically facilitated information glut: "[N]owadays we take in everything in the quickest and cheapest way, only to forget it just as quickly, instantly." Both Heidegger in his "Memorial Address," and Jacques Ellul in The Technological Society, declare technology to have become "autonomous" (in Ellul's phrasing), saying its progression could not be stopped, even if human beings wanted to stop it. " These [technological] forces have moved long since beyond his will and have outgrown his capacity for decision," Heidegger says, regarding our relationship to technology and "calculative thinking". Our thinking here is that technology creates more options, including the option to not use it; non-users of a technology have been considered by Satchell and Dourish. These are core questions we consider as we discuss Line-Storm. Should we augment human capability, or should we replace it with a technological contrivance? As discussed above, we have followed Machover in choosing to augment human creative capability, using Line-Storm. For work done by Csikszentmihalyi, in Creativity: Flow and the Psychology of Discovery and Invention, ninety-one persons were interviewed who were deemed to have made significant contributions to their fields. Many others, who excluded themselves from his study, were skeptical of studying creativity or of participation in the study as being worthy of their time, and some insisted they were too busy being creative to stop and talk about it. A direct approach to enhancing creativity, Csikszentmihalyi writes, is less effective than are attempts to place the creative worker in a favorable environment; but beautiful surroundings are not what he means. The creative worker creates an environment conducive to creative thought and work, despite otherwise unfavorable surroundings; "they manage to give their surroundings a personal pattern". On the other hand, he denies there is proof that a person needs "delightful" surroundings to engage in creative work. There are emotional aspects as well-creativity is not only cognition, taken as reasoning-and attention is the finite resource that allows the creative person to find problems to solve, that have gone unrecognized until they have found them. Our work attempts to alter the state of the creative worker, short of accomplishing shamanistic technique or administering psychedelics. Csikszentmihalyi makes a similar claim for the creative worker. The creative worker has their attention focused in areas outside the "status quo" (Csikszentmihalyi, Motivation and Creativity: Towards a Synthesis of Structural and Energistic Approaches to). Creativity is lauded widely yet creativity works for good and bad. Cropley wrote that a computer hacker who circumvents security measures to steal money, has exhibited creativity no less than a symphonic composer imagining a new melodic line. Sternberg, writing of what is known about creativity, iterates two points: creativity is mostly "domain-specific," and it is partly independent of measured intelligence quotient (IQ). Much ink, including that of Thoreau, has been spilled comparing creativity to play. Play does not need the context of a game, to be play. Play may be contrasted with the world of production and work. In attempting to provide an immersive experience conducive to the presence and engagement of the creative worker, we recognize the worker-even a solitary worker writing alone in a micro-environment they have fashioned to their needs-as engaging in a performance. The act of sitting down to write a poem might be viewed by some poets as requiring a "smooth" execution; but others will likely view it as Schechner has described the "actual" and the roughness of the performance of writing the poem as "the genuine meeting between performer and problem"; having a sense of presence and engagement is a desirable state. Line-Storm is interactive. It augments an ordinary pencil and an ordinary pad of paper, adding new interaction possibilities, new affordances. It responds to the person engaged in using it-and it is immersive. The headphones may make it "easy to forget the outside world," allowing the user to "concentrate completely" on the writing task . Line-Storm is an attempt at providing creative workers with a new tool. Citing Edward Tenner, Runco cautions that tools do not have to be poorly made or poorly designed or have "an undesirable feature, to cause problems" involving either the creative worker or others. Combining technology with art and art works does not, in itself, enhance creativity. Heidegger inquired into whether the common conception of technology, in which technology is neutral and independent of its uses, was supportable, and found the essence of technology to be a view of the world and all that is in it, as resource to be put to use, or standing-reserve. Han, in In the Swarm and Psychopolitics, and Bohme, in Invasive Technification, question the place of technologies in our lives, and the role of the associated, technological, perspective in dominating other forms of life. Lucas observed of the world depicted in his 1971 film, THX 1138, that "nobody was having any fun, but no one was unhappy". We have made a new piece of technology that is based on fun. We tread softly when we attempt to bring new technologies into the practice of writing or drawing by hand with pencil and paper. Dan Ariely posed a thought experiment, in his 2010 book The Upside of Irrationality, in which the reader was asked to imagine how a large cash reward might motivate them to greater creativity. After he considered the experimental evidence, he wrote that money is a poor motivator to creative production. According to Ariely, it is not clear how much of our "mental activity" is under our "direct control," especially when we are working under pressure. Our system might prove more difficult to use than ordinary pencil and paper, and this is not in itself a problem for us because creativity is different than usability. In addition creativity may not be fully mechanizable. Creativity is not only randomness or sheer novelty; it requires filtering by an intelligence. Counterintuitive incentivisation may be called for when attempting to stimulate creativity. Making a task more difficult through the use of unusual tools, may stimulate creative production. Changing the affordances of a once-familiar taskscape, may be key to inducing creative thought, making one see a thing or activity in a new way. We present the details of our implementation of Line-Storm. We implemented the software interface and sound-synthesis engine of Line-Storm using Max/MSP, Version 7.2.3, 64-bit edition. Max/MSP is the mature, commercial successor to Miller Puckettes Pd (aka Pure Data) (https://puredata.info/downloads/pure-data), a free and opensource project. Like its predecessor, Max/MSP is a graphical programming environment. Objects in the Max/MSP GUI windows can be interconnected and otherwise manipulated inside patchers (graphical representations of program files in Max/MSP). A Max/MSP program or patcher appears generally as one or more objects connected by patch cords joining inputs and outputs. Inputs and outputs can be: symbolic, that is, textual; numeric; or signals running at an audio rate, typically 44.1 kHz. Max/MSP allows us to interface with a wider range of electronic devices, and we have elected to use the serial-port communication capabilities of Max/MSP to bring our sensor data into the Max/MSP application, as a control signal, through a serial port on our laptop computer. Max/MSP has further advantages over some other music-synthesis DAWs such as FM8; Max/MSP is programmable, and it is welldocumented. The sensor-fob, shown above, in Figure 17, comprises multiple PCB circuit-boards, powered by a lithium-polymer battery, and five solidcore, insulated copper wires soldered between two of the PCB circuitboards. The primary board is an Arduino Fio v3 microcontroller board. This type of Arduino board includes a socket into which an XBee radio transceiver module can be inserted (see Figure 21, below). The Fio v3 can control the inserted XBee radio transceiver module. We have a Digi International XBee radio transceiver module (type S1) inserted into the socket of our Fio v3. The Fio v3 has multiple GPIO/ADC (general purpose input/output or analog-to-digital converter) pins, three of which we have soldered wires to. These three wires are soldered at their ends, to an Adafruit ADXL335 3-axis accelerometer, to its three, analog output-signal pins. Two more pins, and two more wires, connect Vcc and GND on the Fio v3 and ADXL335. The wires are rigid; they both connect the boards and hold them in constant positions relative to each other, in a fixed orientation. See Figure 22, below, which shows the solder connections between the Fio v3 and the ADXL335. We chose to orient the boards parallel to each other, the top or front face of the Fio v3 facing the bottom or back face of the ADXL335. The axes of the ADXL335 3-axis accelerometer are oriented along the lengths, widths, and surface normals of both parallel boards. The x-axis of the sensor is oriented along the length of the Fio v3; the y-axis along the width, and the z-axis along the surface normal. The adhesive-tape connection of the sensor-fob to the stylus allows quick changes in the relative orientations of the stylus and sensor. When carrying out experiments involving human subjects, we maintained a constant relative orientation of sensor-fob to stylus, with the XBeeradio end of the sensor-fob pointing in the direction of the eraser on the pencil-stylus, as shown in Figure 16, above. We used an Adafruit ADXL335 3-axis accelerometer mounted to a breakout board (see Figure 20, which also shows solder points). The ADXL335 has an acceleration-measurement range of +/-3g, where g is the average acceleration due to gravity at Earths surface. While there was a similar model, available from Adafruit, which had a +/-5g range, we found that the ADXL335 +/-3g measurement range was sufficient for our system. Moving the sensor-fob, with its attached ADXL335 unit, in as violent a manner as we were able to do while holding it with a hand, we sometimes reached minimum and maximum sensor-output values, but not always; reaching these values was difficult. Lesser motions were well within the +/-3g range, giving sensor output values below the approximately 1000 maximum and above the approximately 0 (zero) minimum. Sensor output values, raw from the GPIO/ADC pins, range from 0 to approximately 1000, with a center value of approximately 500. This range is compatible with an 8-bit ADC, which the Fio v3 uses. Values below about 500 indicate negative accelerations relative to the corresponding sensor axis, while values above 500 indicate positive accelerations relative to the corresponding sensor axis. More programming and other details can be found in [FirstSecondAuthor]. We present the details of our experimental evaluation of Line-Storm, which involved research involving human participants. Our study involved participation by thirteen persons, but data for one of these participants was discarded, leaving twelve participants with valid data. The participant whose data we discarded had filled out the questionnaires by circling the value 3, in all cases but one, on both Line-Storm and control-condition questionnaires, strongly suggesting they did not follow directions or complete the forms in good faith. We had roughly half female and half male, including one who chose not to self-identify. Participant ages are ranged from 18 years to 34 years. We ran our experiments at two sites. We began advertising near the end of the Spring semester, and it seems likely many students were interested in obtaining extra credit at that time. In three days, we were able to run ten participants. We present and analyze the of our research study. We were unable to support our research hypothesis, that participants' sense of presence and engagement would be greater during the experimental, interactive condition than during the control, non-interactive condition. Because creativity and engagement is somewhat fleeing and vary from one person to another, this was not so unexpected . Using our abbreviation for control and experimental questionnaires' first item, measuring self-reported level of presence and engagement-PANDE for "present and engaged," a suffix of 0 (zero) referring to the control condition questionnaire item and a suffix of a 1 referring to the experimental condition questionnaire item-we found we could not reject the null hypothesis, PANDE0 = PANDE1. We compared mean self-reported level, of being "present and engaged" during the experimental and control conditions, using two statistical tests. We performed a paired-samples test using the Student's t distribution, suitable for small sample sizes from normal populations, and we performed a paired-samples test using the nonparametric, distribution-free Wilcoxon Signed Rank Test, suitable for symmetrical distributions of small samples. We performed these two statistical tests for all paired variables, that is, for every matched item on the control and experimental questionnaires. We did find a statistically significant difference between experimental and control conditions, for two questionnaire items, abbreviated NAT (for naturalness of interaction with the system) and ADJEXP (for adjustment to the system experience). Both these items were rated lower for the experimental condition than for the control condition, indicating participants found the experimental system interactions unnatural and had difficulty adjusting to using it, compared to the control condition. We performed Pearson correlations, and found several statistically significant correlations, discussed below. For example, those participants who reported they lost track of time during the experimental condition also tended to write more during the experimental condition. There was a non-significant correlation between losing track of time and word count during the control condition. One participant circled 3 for all questionnaire items on both the control and experimental questionnaires, except for one item for which this participant circled 3. We excluded this participant from our data analysis for this reason. They did not appear to follow the instructions for the experiment. When examining word counts (WC0 and WC1), we excluded two participants who drew pictures instead of writing, leaving a sample size of ten. We performed K-means clustering classification, discussed below. A group of participants responded differently to the experimental condition than did the rest of the participants. There was also a group who responded differently to the control condition than did the rest of the participants and we have termed these Preservers of Line-Storm. To perform our statistical analyses, used IBM's SPSS Statistics, Version 25 (https://www.ibm.com/products/spss-statistics), because it is an industry standard statistics processing application. Many tutorials are available online, in textual/graphical and video formats. SPSS also has links to user forums and documentation integrated into the application. • There were strong, significant (p<0.01) correlations between the initial, baseline level of a sense of presence and engagement and response items 4 (NAT) and 7 (ADJEXP), for both control and experimental conditions. • A sense of presence and engagement correlated strongly and significantly (p<0.01) with adjustment to the "control devices" (augmented stylus, augmented writing pad) (ADJCTL) for both control and experimental conditions. • There were strong, significant correlations between a sense of the naturalness of interactions with the system and baseline sense of presence and engagement, ease of adjustment to the system experience, and ease of adjustment to the control devices (ADJCTL), for both control and experimental conditions. • There was a group of participants who responded more favorably to the experimental condition than the rest of the participants (analysis performed using K-means clustering tests). This is significant for our experiments. • Those who wrote more in the control condition wrote more in the experimental condition. This is also significant for our experiments. • The more participants lost track of time, in the experimental condition, the more they wrote-or vice versa. This is significant for our experiments. • We found correlations between a sense of presence and engagement during the experimental condition (PANDE1), and the degree to which a participant lost track of time while using the system during the experimental condition. This is significant for our experiment, and we call these participants as the Preservers of Line-Storm. We discuss our experimental . We found that participants found their interactions with the system more natural during the control condition, and less natural during the experimental condition. Participants adjusted to the system experience more quickly during the control condition than they did during the experimental condition. Our findings indicate that there appears to have been a significant group of participants, roughly half the participants, the Preservers of LineStorm (see below, What Line-Storm Is: Equipment, Art Work, and Preservation), who became immersed during the experimental condition. These participants tended to write more during the control and experimental conditions, they tended to experience the sound components of the system (control and experimental) in a way that led to their reporting less prominence of the visual aspects of the system, and they tended to lose track of time during the experimental condition. It seems likely that attention fluctuates over time, and the mind naturally wanders and returns. Future work would include investigation of the ways such natural fluctuations in attention would be relevant to our work. Considering the ordering of questionnaire completion was nearly always the same (Demographic, Experimental, Control), natural fluctuations in attention (and presence and engagement) may help to explain our . Considering how we might have been wearing out our participants, by making demands upon their attentional resources, future work might be done that minimized attentional fatigue. When interpreting our , we are interpreting the interpretations of our study participants. Their interpretations played a role in the coming-into-being of Line-Storm. Martin Heidegger wrote that, "Just as a work cannot be without being created, but is essentially in need of creators, so what is created cannot itself come into being without those who preserve it". With reference to Heideggers essay, "The Origin of the Work of Art," we see two ways Line-Storm can be approached. First, as equipment, Line-Storm is a tool we have made for a purpose. As equipment, Line-Storm has a thingly character and an equipmental character. As a thing, Line-Storm exists as an object that can be encountered in the world, like a rock. As equipment, LineStorm is experienced as part of a matrix of all equipment. It exists in a context of equipment, purposes, and activities. As ready-to-hand, it withdraws, becomes transparent, and the person using it is engrossed in the work. As present-at-hand, Line-Storm sticks out as a thing, instead of becoming transparent and allowing the user to become engrossed in the work. As art work, Line-Storm is created, not only made. It has a thingly character and a workly character. Preservation "does not degrade it to the role of a stimulator of [mere lived experience]". It is not a tool in this case, where it would be "released beyond itself, to be used up in usefulness". As art work, Line-Storm is bringing forth of the work that there lies this offering that it be. These two modes of being that Line-Storm permits, refer to two ways of being with LineStorm: as technician or as performer. For a technician, Line-Storm is equipment. Equipment serves a purpose and is not an end but a means. As Schopenhauer declared, equipment and "all other human works," that are not art works, "exist only for the maintenance and relief of our existence"; art works "exist for their own sake". When we evaluated Line-Storm in terms of its capacity for leading to a possible increase in self-reported presence and engagement, we treated it as equipment. Yet some participants, while using Line-Storm, treated it not as equipment but as art work. Hence, we will refer to the group of participants who gave higher ratings to Line-Storm, and who wrote more while using it, as the Preservers of Line-Storm. The Preservers let Line-Storm be what it is. Without the Preservers, Line-Storm "cannot itself come into being". Preservers here simply means to us are those participants who seem to like Line-Storm and used it enough so that to be creative in their own mind. Line-Storm permits itself to be used in performance. A performance with Line-Storm could be understood to point out the overlapping of sensory or perceptual modes commonly thought of as separate. Seeing, hearing, moving, and proprioception involve cross-modal transfer. The sound and visual aspects overlap more strongly in Line-Storm than in ordinary writing or drawing, because of the amplification of what had been quiet sounds, i.e. the sound made by stylus on the paper which was amplified and merged with other sounds, such as thunderstorm. We perceive an object or art work as affordances, the tripartite interrelation of environment, organism, and activity. Because of cross-modal transfer, sound, visual, and tactile affordances play together in Line-Storm. When we look, we see what we can do-although, as with a wooden sculpture-puzzle, we may not immediately see all affordances. To solve the puzzle is to discover hidden affordances. Line-Storm makes affordances prominent, in the writing stylus and writing pad, that may not have been apparent: their sound-producing capabilities, which can be used in a performance. Preservers of LineStorm find its affordances. This is a kind of knowing; the work of preserving a work is know-how. A performance with Line-Storm, as preservation, is a knowing and a realization of the affordances of Line-Storm, without which it cannot be Line-Storm. Preservation is the fulfillment of the work that is Line-Storm. Both preservers and we, as creators of Line-Storm, belong essentially to its creation. In 2006, a living Japanese maple tree was augmented with nitinol wires and optical and audio sensors. The tree moved its branches, using the nitinol "muscle" wires, in response to the presence of people detected by its sensors. Coffin discussed this art work in the context of Heideggerian phenomenology, and discussed two different implementations of the tree totem, Breeze. In one, Breeze was constructed using a live Japanese maple. The flexible branches of the maple, and the bushy shape of the tree, hid the mechanical components from view, and attendees of the festival, where Breeze was exhibited, tended to interact freely with the tree and differently than during the second exhibition. At the second exhibition, a mountain laurel was used, whose stiffer limbs and more open shape put the mechanical components of the installation on display. When the mechanical components were hidden from view, during the first exhibit, interactions took place with people treating the tree as ready-to-hand. The mechanical components of Breeze withdrew, became transparent, and the people at the first exhibit interacted with Breeze as an interactive art work. When a tool is present-at-hand, the tool exists differently for the person. A broken hammer is present-at-hand as an object, not ready-to-hand as a useful tool. A broken hammer does not allow the person to engage in the work but is open for inspection. During the second exhibit, with the mechanical components poorly hidden by the tall, open shape of the mountain laurel, attendees at the exhibit tended to comment on the engineering of Breeze instead of interacting with it freely as had the attendees during the first exhibit. This is quite interesting for our implementation as well. Line-Storm appears to have existed differently for different research study participants. We propose that those who seemed to enjoy using Line-Storm engaged with it as ready-to-hand, while those who did not appear to enjoy using Line-Storm engaged with it as present-at-hand. For the former, Line-Storm became transparent and withdrew itself, allowing the participants to write or draw as well as they would have with an ordinary pencil and paper. For the latter, Line-Storm obtruded and interfered with the writing task. Those participants for whom Line-Storm was present-at-hand were not able to engage with the work but were distracted by the strange contraption, the mechanical and interactive aural properties of the device. We propose that Csikszentmihalyi's concept of flow implies engagement with tools and tasks in a ready-to-hand mode. A tool being present-at-hand is indicative of the absence of the flow state. The peasant woman wears her shoes in the field. Only here are they what they are. They are all the more genuinely so, the less the peasant woman thinks about the shoes while she is at work, or looks at them at all, or is even aware of them. She stands and walks in them. That is how shoes actually serve. It is in this process of the use of equipment that we must actually encounter the character of equipment. Line-Storm is equipment, when a participant is using it as equipment. Its equipmental character manifests when the user is engaged with the work and not distracted by the system's appearance or feel. Its "thingly" character is manifest for a participant who is not having fun using it, is not in a flow state, and is not present and engaged while using it. We suggest that these latter participants did not experience Line-Storm. One component of Line-Storm is its interactivity. A certain type of actions and intentions are required on the part of the participant for Line-Storm to be what it is. Coffin wrote of Breeze, and of interactive systems more generally, that interactions with them may be "effortless, unscripted, emergent, and engaged" if the mapping of responses is well done with respect to our "meaning-making sensibilities". Our goal that Line-Storm would provide for increased presence and engagement was not met for all participants. Still, some participants appeared to have had fun and play while using Line-Storm. Some of these participants likely experienced Line-Storm as art work, and so we would have found preservers for our work, who brought out its workly character, and who would belong to it just as we belong to it as its creators. This justifies our efforts. The participants' prior knowledge is relevant when considering their responses to Line-Storm. Line-Storm, as a tool, exists not by itself but among a constellation of related tools; those related tools, some of which a given participant may be familiar with, and some of which they may not be familiar with, allow Line-Storm "to be this equipment that it is". A participant's degree of familiarity with related tools, such as an envelope, stamp, mail-box, and pencil and paper, help to determine what Line-Storm is for that participant. We see a nearly significant (r = 0.620, p = 0.056) correlation of current writing or drawing (by hand) practice to number of words written while using Line-Storm. We think that, this correlation indicates participants who regularly wrote or drew by hand were better able to experience Line-Storm as it was intended, and see its' authenticity. We conceived our work, initially, as an entertainment system, to be used for one's own pleasure while writing in a journal. We followed that by hoping to jolt users out of complacent acquaintance with paper and pencil and present the writing tools and writing situation as if for the first time, to encourage the practice of writing and sending handwritten letters. We finished the work by attempting to enhance human creativity when working with a writing stylus and paper writing pad, by increasing participants' sense of presence and engagement. We found correlations and K-means clustering that did suggest there was a group of participants who responded favorably to Line-Storm. We expected that a direct approach to enhancing creativity may/would fail; we attempted to construct a system the use of which would be an end and not only a means, and hoped this might lead, indirectly, to enhancing creativity by encouraging play and playfulness. We provided a ludic environment for creative work, in which some users would focus on using the system, not expecting an outcome and will create their own play/outcome and accept what emerges or not-no quest, no winners, no points or gold to deliver outcome-based satisfaction. In a ludic system, therefore, the creative work (outcome is what it is) and the would be a secondary consideration and may emerge by itself, an indirect of the use of the system. We hoped participants in our experiments would find themselves "losing themselves," and a group of participants did tend to lose track of time while they used or performed with Line-Storm. We believe these participants became more absorbed while using the experimental system, exactly our intention. Losing oneself while using the system might open one up to creative energies, thoughts, feelings, and actions that would ordinarily not occur, as Nietzsche wrote. Future work would include the following items, listed as follows: (a) Clean up Max/MSP code and put functionality back in place, that would allow the triggering of multiple thunderstorm samples in quick succession. We would reinstate the capability of triggering multiple thunderstorm samples in rapid sequence. A committee member responded positively to a version of our experimental system that did trigger multiple thunderstorm samples in rapid sequence, and their gratifyingly positive response to our system was what we had hoped to bring to our research participants. (b) Make a cover for the electronic components on the sensor-fob. This would provide better aesthetic appeal and would minimize distractions from things like blinking lights and hanging wires. (c) Extension to a mobile platform -a mobile platform would use a smartphone or tablet and would not require a laptop computer, which limits the places our system could be used. (d) Investigate the use of more miniaturized RF components. We do not need the relatively large antennae of the XBee radios, which can operate over a larger distance than we envision for the use of our system. Bluetooth would provide the necessary range. (e) Investigate using more miniaturized micro-controller boards. The Arduino Fio v3 was the smallest board we found, when we began our work, with all the functionality we needed. A smaller board would make a less intrusive sensor-fob. (f) Experiment with different styli, including a paintbrush, a child's crayon, a marker, a piece of chalk, a paint roller, and so on. Attaching a contact microphone to the surfaces used with many of these would probably produce a suitable-strength vibration for use with our system. (g) Experiment with a baton-type stylus like the one used by Paradiso and Machover in the Brain Opera. (h) Investigate a wrist-worn appliance to augment or replace the motion-tracking capability of the stylus sensor-fob. (i) Gather more data involving a larger sample size. (j) Vary the type of music listened to during the control condition. (k) Consider ways to run experiments without wearing out participants by making excessive demands on their attention. (l) Experiment with a multi-user system. Users could be situated in the same place or could communicate via a computer network such as the internet. (m) Collaborate with a composer of music or a composer of electroacoustic music. We discarded our attempts at constructing an interactive generator of electroacoustic music. Collaborating with a person skilled in the creation of electronic music would be of great benefit in future as well. Specifically, such collaboration would improve the system by mapping accelerometer data to sonic parameters in an effective way, something we were unable to do to our satisfaction. More data points will also help our analysis; we would have liked to have run more experiments. We used statistical techniques that were designed for small samples, the Student's t Test and the Wilcoxon Signed-Rank Test. Our K-means clustering analysis would have been improved, we believe, by the addition of more data points. Finally, it has occurred to us that augmentation itself is innovative. Augmenting means a possibility that is completely different than the original. The Preservers of Line-Storm, in our experiments, showed that there is promise for our augmented interface-it may not enhance the sense of presence and engagement and lead to more creative writing, which was the hypothesis we had hoped for, but, as we discussed, creativity is difficult to capture anyway. Still, our work provided a completely different experience through augmented interaction to creative writing which enhanced the user experience, which simple creative writing, using ordinary pen and paper, or word-processing software cannot provide.

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Interactive stylus based sound incorporating writing system

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This paper explores using wearable sensory augmenting technology to facilitate first-hand perspective-taking of what it is like to have cat-like whiskers.


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Question: Language style transfer is the problem of migrating the content of a source sentence to a target style. In many applications, parallel training data are not available and source sentences to be transferred may have arbitrary and unknown styles. In this paper, we present an encoder-decoder framework under this problem setting. Each sentence is encoded into its content and style latent representations. By recombining the content with the target style, we can decode a sentence aligned in the target domain. To adequately constrain the encoding and decoding functions, we couple them with two loss functions. The first is a style discrepancy loss, enforcing that the style representation accurately encodes the style information guided by the discrepancy between the sentence style and the target style. The second is a cycle consistency loss, which ensures that the transferred sentence should preserve the content of the original sentence disentangled from its style. We validate the effectiveness of our proposed model on two tasks: sentiment modification of restaurant reviews, and dialog response revision with a romantic style. Style transfer is a long-standing research problem that aims at migrating the content of a sample from a source style to a target style. Recently, great progress has been achieved by applying deep neural networks to redraw an image in a particular style BID7 BID10 BID2 BID20 BID12. However, until now very few approaches have been proposed for style transfer of natural language sentences, i.e., changing the style or genre of a sentence while preserving its semantic content. For example, we would like a system that can convert a given text piece in the language of Shakespeare BID14; or rewrite product reviews with a favored sentiment BID17.One important issue on language style transfer is that parallel data are unavailable. For instance, considering the task of rewriting a negative review of a product to its counterpart with a positive sentiment, we can hardly find paired data that describe the same content. Yet, many text generation frameworks require parallel data, such as the popular sequence-to-sequence model in machine translation and document summarization BID16, and thus are not applicable under this scenario. A few recent approaches have been proposed for style transfer with non-parallel data BID4 BID17. Their key idea is to learn a latent representation of the content disentangled from the source style, and then recombine it with the target style to generate the corresponding sentence. All the above approaches assume that data have only two styles, and their task is to transfer sentences from one style to the other. However, in many practical settings, we may deal with sentences in more than two styles. Taking the review sentiment modification as an example again, some reviews may be neither positive nor negative, but in a neutral style. Moreover, even reviews considered negative can be categorized into more fine-grained sentiments, such as anger, sadness, boredom and other negative styles. It may be beneficial if such styles are treated differently. As another example, consider a chatbot with a coherent persona, which has a consistent language behavior and interaction style BID9. A simple framework for this task is to first use human dialog data to train a chatbot system, such as a retrieval-based dialog model BID11, and then transfer the output responses with a language style transfer model so that multi-round responses always have a consistent style. Note that the human dialog sentences are collected from different users, and users' expressions of the content and tones may be in different personalized characteristics. Thus the output responses retrieved from the dialog model may have the language style of any user. Simply treating the responses with a single style and employing the existing style transfer models would lead to unsatisfactory . Hence, in this paper, we study the setting of language style transfer in which the source data to be transferred can have various (and possibly unknown) styles. Another challenging problem in language style transfer is that the transferred sentence should preserve the content of the original sentence disentangled from its style. To tackle this problem, BID17 assumed the source domain and the target domain share the same latent content space, and trained their model by aligning these two latent spaces. BID4 constrained that the latent content representation of the original sentence could be inferred from the transferred sentence. However, these attempts considered content modification in the latent content space but not the sentence space. In this work, we develop an encoder-decoder framework that can transfer a sentence from a source domain to its counterpart in a target domain. The training data in the two domains are non-parallel, and sentences in the source domain can have arbitrary language styles but those in the target domain are with a consensus style. We encode each sentence into two latent representations, one for the content disentangled from the style, and the other for the style. Intuitively, if a source sentence is considered having the target style with a high probability, its style representation should be close to the target style representation. To make use of this idea, we enforce that the discrepancy between an arbitrary style representation and the target style representation should be consistent with the closeness of its sentence style to the target style. A cycle consistency loss is further introduced to avoid content change by directly considering the transferred sentence. Its idea is that the generated sentence, when put back into the encoder and recombined with its original style representation, can recover the original sentence. We evaluate the performance of our proposed model on two tasks. The first is the sentiment modification task with its source domain containing more than one sentiments, and the second is to transfer general dialog responses to a romantic style. Most style transfer approaches in the literatures focus on vision data, and some of them are also designed for the non-parallel data setting. BID7 proposed to disentangle the content representations from image attributes, and control the image generation by manipulating the graphics code that encodes the attribute information. BID2 used the Convolutional Neural Networks (CNNs) to learn separated representations of the image content and style, and then created the new image from their combination. Some approaches have been proposed to align the two data domains with the idea of the generative adversarial networks (GAN) BID3. BID10 proposed the coupled GAN framework to learn a joint distribution of multidomain data by the weight-sharing constraint. BID20 introduced a cycle consistency loss, which minimizes the gap between the transferred images and the original ones. However, due to the discreteness of the natural language, this loss function cannot be directly applied on text data. In our work, we show how the idea of cycle consistency can be used on text data. Only a small number of approaches have been proposed for language style transfer. To handle the non-parallel data problem, BID14 revised the latent representation of a sentence in a certain direction guided by a classifier, so that the decoded sentence imitates those favored by the classifier. BID1 encoded textual property values with embedding vectors, and adopted a conditioned language model to generate sentences satisfying the specified content and style properties. BID4 used the variational auto-encoder (VAE) to encode the sentence into a latent content representation disentangled from the source style, and then recombine it with the target style to generate its counterpart, An additional distribution is added to enforce that the generated sentence and the original sentence share the same latent content representation. BID17 considered transferring between two styles simultaneously. Specifically, they utilized adversarial training in the Professor-Forcing framework BID8, to align the generated sentences from one style to the data domain of the other style. We also adopt similar adversarial training in our model. However, since we assume the source domain contains data with various and possibly unknown styles, we cannot align data from the target domain to the source domain as in BID17. We now formally present our problem formulation. Suppose there are two data domains, one source domain X 1 in which each sentence may have its own language style, and one target domain X 2 consisting of data with the same language style. During training, we observe n samples from X 1 and m samples from X 2, denoted as X 1 = {x DISPLAYFORM0 Note that we can hardly find a sentence pair (x DISPLAYFORM1 2) that describes the same content. Our task is to design a model to learn from these non-parallel training data such that for an unseen testing sentence x ∈ X 1, we can transfer it into its counterpartx ∈ X 2, wherex should preserve the content of x but with the language style in X 2. Similar to BID17; BID4, we assume each sentence x can be decomposed into two representations: one is the style representation y ∈ Y, and the other is the content representation z ∈ Z, which is disentangled from its style. Each sentence x (i) 1 ∈ X 1 has its individual style y DISPLAYFORM0 1, while all the sentences x (i) 2 ∈ X 2 share the same style, denoted as y *. Our model is built upon the encoder-decoder framework. In the encoding module, we assume that z and y of a sentence x can be obtained through two encoding functions E z (x) and E y (x) respectively: DISPLAYFORM1 DISPLAYFORM2 where E y (x) = 1 {x∈X1} · g(x) + 1 {x∈X2} · y, and 1 {·} is an indicator function. When a sentencex comes from source domain, we use a function g(x) to encode its style representation. For x from target domain, a shared style representation y is used. Both y * and parameters in g(x) are learnt jointly together with other parameters in our model. For the decoding module, we first employ a reconstruction loss to encourage that the sentence from the decoding function given z and y of a sentence x can well reconstruct x itself. Here, we use a probabilistic generator G as the decoding function and the reconstruction loss is: DISPLAYFORM3 where θ denotes the parameter of the corresponding module. To enable style transfer using non-parallel training data, we enforce that for a sample x 1 ∈ X 1, its decoded sequence using G given its content representation z and the target style y * should be in the target domain X 2. We use the idea of GAN BID3 ) and introduce an adversarial loss to be minimized in decoding. The goal of the discriminator D is to distinguish between G(z 1, y *) and G(z 2, y *), while the generator tries to bewilder the discriminator: DISPLAYFORM4 As discussed in Section 2, since our source domain X 1 contains sentences with various unknown language styles but not a consistent style, it is impossible for us to apply a discriminator to determine whether a sentence transferred from X 2 is aligned in the domain X 1 as in BID17.During optimization, we adopt the continuous approximation in BID4 for gradients propagation in adversarial training over discrete sentences. That is, instead of feeding a single word as the input to the generator, we use the approximation averaging word embeddings by a multinomial distribution. This distribution is computed as softmax(o t /γ), where o t is the logit vector output by the generator at time step t, γ > 0 is a temperature parameter. Next, we follow the framework of Professor-Forcing BID8, which matches two sequences of output words using a discriminator D. Specifically, we have one kind of sequences G(z 2, y *) teacher-forced by the ground-truth sample x 2 ∈ X 2, and the other one G(z 1, y *) with z 1 obtained from samples in X 1, in which the input at each time step is self-generated by the previous continuous approximation. However, the above encoder-decoder framework is under-constrained. First, for a sample x 1 ∈ X 1, y 1 can have an arbitrary value that minimizes the above losses in Equation 3 and 4, which may not DISPLAYFORM5 Figure 1: Basic model with the style discrepancy loss. Solid lines: encode and decode the sample itself; dash lines: transfer DISPLAYFORM6 Figure 2: Proposed cycle consistency loss (can be applied for samples in X 2 similarly).necessarily capture the sentence style. This will affect the other decomposed part z, making it not fully represent the content which should be invariant with the style. Second, the discriminator can only encourage the generated sentence to be aligned with the target domain X 2, but cannot guarantee to keep the content of the source sentence intact. To address the first problem, we propose a style discrepancy loss, to constrain that the learnt y should have its distance from y * guided by another discriminator which evaluates the closeness of the sentence style to the target style. For the second problem, we get inspired by the idea in BID20 and introduce a cycle consistency loss applicable to word sequence, which requires that the generated sentencex can be transferred back to the original sentence x. By using a portion of the training data, we can first train a discriminator D s to predict whether a given sentence x has the target language style with an output probability, denoted as p Ds (x ∈ X 2). When learning the decomposed style representation y 1 for a sample x 1 ∈ X 1, we enforce that the discrepancy between this style representation and the target style representation y *, should be consistent with the output probability from D s. Specifically, since the styles are represented with embedding vectors, we measure the style discrepancy using the 2 norm: DISPLAYFORM0 Intuitively, if a sentence has a larger probability to be considered having the target style, its style representation should be closer to the target style representation y *. Thus, we would like to have d(y 1, y *) positively correlated with 1 − p Ds (x 1 ∈ X 2). To incorporate this idea in our model, we use a probability density function q(y 1, y *), and define the style discrepancy loss as: DISPLAYFORM1 where f (·) is a valid probability density function. p Ds (x 1 ∈ X 2) is pre-trained and then fixed. If a sentence x 1 has a large p Ds (x 1 ∈ X 2), incorporating the above loss into the encoder-decoder framework will encourage a large q(y 1, y *) and hence a small d(y 1, y *), which means y 1 would be close to y *. In our experiment, we instantiate q(y 1, y *) with the standard normal distribution for simplicity: DISPLAYFORM2 However, better probability density functions can be used if we have some prior knowledge of the style distribution. With Equation 8, the style discrepancy loss can be equivalently minimized by: DISPLAYFORM3 3.4 CYCLE CONSISTENCY LOSS Inspired by BID20, we require that a sentence transferred by the generator G should preserve the content of its original sentence, and thus it should have the capacity to recover the original sentence in a cyclic manner. For a sample x 1 ∈ X 1 with its transferred sentencex 1 having the target style y *, we encodex 1 and combine its contentz 1 with its original style y 1 for decoding. We should expect that with a high probability, the original sentence x 1 is generated. For a sample x 2 ∈ X 2, though we do not aim to change its language style in our task, we can still compute its cycle consistency loss for the purpose of additional regularization. We first choose an arbitrary style y 1 obtained from a sentence in X 1, and transfer x 2 into this y 1 style. Next, we put this generated sentence into the encoder-decoder model with the style y *, and the original sentence x 2 should be generated. Formally, the cycle consistency is: DISPLAYFORM4 3.5 FULL OBJECTIVE An illustration of our basic model with the style discrepancy loss is shown in Figure 1 and the full model is combined with the cycle consistency loss shown in Figure 2. To summarize, the full loss function of our model is: DISPLAYFORM5 where λ 1, λ 2, λ 3 are parameters balancing the relative importance of the different loss parts. The overall training objective is a minmax game played among the encoder E z, E y, generator G and discriminator D: DISPLAYFORM6 We implement the encoder E z using an RNN with the last hidden state as the content representation, and the style encoder g(x) using a CNN with the output representation of the last layer as the style representation. The generator G is an RNN that takes the concatenation of the content and style representation as the initial hidden state. The discriminator D and the pre-trained discriminator D s used in the style discrepancy loss are CNNs with the similar network structure in E y followed by a sigmoid output layer. Yelp: Raw data are from the Yelp Dataset Challenge Round 10, which are restaurant reviews on Yelp. Generally, reviews rated with 4 or 5 stars are considered positive, 1 or 2 stars are negative, and 3 stars are neutral. For positive and negative reviews, we use the processed data released by BID17. For neutral reviews, we follow similar steps in BID17 to process and select the data. We first filter out neutral reviews (rated with 3 stars and categorized with the keyword 'restaurant') with the length exceeding 15 or less than 3. Then, data selection in is used to ensure a large enough vocabulary overlap between neutral data and data in BID17. Afterwards, we sample 500k sentences from the ing dataset as the neutral data. We use the positive data as the target style domain. Based on the three classes of data, we construct two datasets with multiple styles:• Positive+Negative (Pos+Neg): we add different numbers of positive data (50k, 100k, 150k) into the negative data, so that the source domain contains data with two sentiments.• Neutral+Negative (Neu+Neg): we combine neutral (50k, 100k, 150k) and negative data together. We consider these datasets are harder to learn from. For the Pos+Neg dataset, we can make use of a pre-trained classifier to possibly filter out some positive data so that most of the source data have the same style and the model in BID17 can work. However, the neutral data cannot be removed in this way. Also, most of the real data may be in the neutral sentiment, and we want to see if such sentences can be transferred well. Details about the data statistics can be found in TAB6 in the Appendix. Chat: We use sentences from a real Chinese dialog dataset as the source domain. Users can chat with various personalized language styles, which are not easy to be classified into one of the three sentiments as in Yelp. Romantic sentences are collected from several online novel websites and filtered by human annotators. Our task is to transfer the dialog sentences with a romantic style, characterized by the selected romantic sentences. TAB7 in the Appendix shows detailed statistics about this dataset. We implement our model using Tensorflow BID0. We use GRU as the encoder and generation cells in our encoder-decoder framework. Dropout BID18 ) is applied in GRUs and the dropout probability is set to 0.5. Throughout our experiments, we set the dimension of the word embedding, content representation and style representation as 200, 1000 and 500 respectively. For the style encoder g(x), we follow the CNN architecture in BID5, and use filter sizes of 200 × {1, 2, 3, 4, 5} with 100 feature maps each, so that the ing output layer is of size 500, i.e., the dimension of the style representation. The pre-trained discriminator D s is implemented similar to g(x) but using filter sizes 200 × {2, 3, 4, 5} with 250 feature maps each. Statistics of data used to pre-train D s are shown in TAB8 and TAB0 in the Appendix. The testing accuracy of the pre-trained D s is 95.82% for Yelp and 87.72% for Chat respectively. We further set the balancing parameters λ 1 = λ 2 = 1, λ 3 = 5, and train the model using the Adam optimizer BID6 with the learning rate 10 −4. All input sentences are padded so that they have the same length 20 for Yelp and 35 for Chat. Furthermore, we use the pre-trained word embeddings Glove for Yelp and the Chinese word embeddings trained on a large amount of Chinese news data for Chat when training the classifier. We compare our method with BID17 which is the state-of-the-art language style transfer model with non-parallel data, and we name as Style Transfer Baseline (STB). As described in Section 2 and 3, STB is built upon an auto-encoder framework. It focuses on transferring sentences from one style to the other. The text styles are represented by two embedding vectors. It assumes source domain and target domain share a content space, and relies on adversarial training methods to align content spaces of two domains. We keep the configurations of the modules in STB, such as the encoder, decoder and discriminator, the same as ours for a fair comparison. Following BID17, we use a model-based evaluation metric. Specifically, we use a pretrained evaluation classifier to classify whether the transferred sentence has the correct style. The classifier is implemented same as the discriminator D s. Statistics of the data used for the evaluation classifier are shown in TAB0 in the Appendix. The testing accuracy of evaluation classifiers is 95.36% for Yelp and 87.05% for Chat. We repeat the training three times for each experiment setting and report the mean accuracy on the testing data with their standard deviation. We first perform experiments on the source data containing both positive and negative reviews. In this setting, we specifically compare two versions of both STB and our model, one with the cycle consistency loss and one without, to validate the effectiveness of the cycle consistency loss 1. Results are shown in TAB0. It can be seen that incorporating the cycle consistency loss improves the performance for both STB and our proposed model consistently. We further manually examine the generated sentences for a detailed study of the various methods. TAB1 shows a few samples for the above setting with 150k positive samples used. Overall, our full model can generate grammatically correct positive reviews without changing the original content in more cases than the other methods. For some simple sentences such as the first example, all models perform well. For the second example in which the input sentence is more complex, both versions of STB and our basic model without the cycle consistency loss cannot generate fluent sentences, but our full model still succeeds. However, our model also suffers some mistakes as shown in the third example. Though it successfully makes the sentence positive, some additional information about the food is added, which is not discussed in the original sentence. Original Sentence service has gotten worse and worse at this location.STB service is great for the family and family. STB (with Cyc) service has always great and at this location. Ours (without Cyc) service has been better than the best experience. Ours service was super friendly and food was great.Next, we compare the of STB and our proposed method in TAB0. As the number of positive sentences in the source data increases, the average performance of both versions of STB decreases drastically. This is reasonable because STB introduces a discriminator to align the sentences from the target domain back to the source domain, and when the source domain contains more positive samples, it is hard to find a good alignment to the source domain. Meanwhile the performance of our model, even the basic one without the cycle consistency loss, does not fluctuate much with the increase of the number of positive samples, showing that our model is not that sensitive to the source data containing more than one sentiments. Overall, our model with the cycle consistency loss performs the best. The above setting is not challenging enough, because we can use a pre-trained discriminator similar to D s in our model, to remove those samples classified as positive with high probabilities, so that only sentences with a less positive sentiment remain in the source domain. Thus, we test our second dataset which combines neutral reviews and negative reviews as the source domain. In this setting, in case that some positive sentences exist in those neutral reviews, when STB is trained, we use the same pre-trained discriminator in our model to filter out samples classified as positive with probabilities larger than 0.9. In comparison, our model utilizes all the data, since it naturally allows for those data with styles similar to the target style. In the following, we report and analyze both STB and our model with the cycle consistency loss added. The experimental in TAB2 show that STB (with Cyc) suffers a large performance drop with 150k neutral data mixed in the source domain, while our model remains stable. In real applications, there may be only a small amount of data in the target domain. To simulate this scenario, we limit the amount of the target data (randomly sampled from the positive data) used for training, and evaluate the robustness of the compared methods. TAB3 shows the experimental . It is surprising to see that both methods obtain relatively steady accuracies with different numbers of target samples. Yet, our model surpasses STB (with Cyc) in all the cases. As in the Yelp experiment, we vary the number of target sentences to test the robustness of the compared methods. The experimental are shown in TAB4. As can be seen, STB (with Cyc) obtains a relatively low performance with only 10k target samples, and as more target samples are used, its performance increases. However, the accuracy of our model is relatively high even with 10k target samples used, and remains stable in all the cases. Thus, our model achieves better performance as well as stronger robustness on Chat. A few examples are shown in Table 6. We can see that our model generally successfully transfers the sentence into a romantic style with some romantic phrases used. Table 6: Example sentences on Chat transferred into a romantic style. English translations are provided (* denotes that the sentence has grammar mistakes in Chinese).Original Sentence 回眸一笑 就 好 It is enough to look back and smile STB (with Cyc) 回眸一笑 就 好 了 It would be just fine to look back and smile Ours 回眸一笑, 勿念 。 Look back and smile, please do not miss me. Original Sentence 得过且过 吧! Just live with it! STB (with Cyc) 想不开 吧, 我 的 吧 。 I just take things too hard. * Ours 爱到深处, 随遇而安 。 Love to the depths, enjoy myself wherever I am. Original Sentence 自己 的 幸福 给 别人 了 Give up your happiness to others STB (with Cyc) 自己 的 幸福 给 别人, 你 的 。 Give up your happiness to others. * Ours 自己 的 幸福 是 自己, 自己 的 。 Leave some happiness to yourself, yourself. In this paper, we present an encoder-decoder framework for language style transfer, which allows for the use of non-parallel data and source data with various unknown language styles. Each sentence is encoded into two latent representations, one corresponding to its content disentangled from the style and and the other representing the style only. By recombining the content with the target style, we can decode a sentence aligned in the target domain. Specifically, we propose two loss functions, i.e., the style discrepancy loss and the cycle consistency loss, to adequately constrain the encoding and decoding functions. The style discrepancy loss is to enforce a properly encoded style representation while the cycle consistency loss is used to ensure that the style-transferred sentences can be transferred back to their original sentences. Experimental on two tasks demonstrate that our proposed method outperforms the state-of-the-art style transfer method BID17 We randomly select 200 test samples from Yelp and perform human evaluations on four aspects of the : content: estimates if the content of an input sentence is preserved in a transferred sentence; content rating has 0 (changed), 1 (synonym substitution or partially changed), and 2 (unchanged); sentiment: estimates if the sentiment of a transferred sentence is consistent with the target sentiment; sentiment rating has 0 (unchanged and wrong), 1 (changed but wrong), 2 (correct); fluency: estimates the fluency of transferred sentences; fluency is rated from 1 (unreadable) to 4 (perfect); overall: estimates the overall quality of transferred sentences; overall rating ranges from 1 (failed) to 4 (perfect).We hired five annotators and averaged their evaluations. TAB0 shows on Yelp when the source domain contains not only negative sentences but also 150k positive sentences (row 3 in TAB0), and TAB0 shows on Yelp when the target domain contains only 100k positive sentences (row 1 in TAB3). As can be seen, our model is better in terms of sentiment accuracy and overall quality, which is consistent with the automatic evaluation . We experiment on revising modern text in the language of Shakespeare at the sentence-level as in BID14. Following their experimental setup, we collect 29388 sentences authored by Shakespeare and 54800 sentences from non-Shakespeare-authored works. The length of all the sentences ranges from 3 to 15. Statistics of data for training and evaluating the style transfer model are shown in TAB0, 14, and 15 in Section 6.1. Since the dataset is small, we train the discriminator D s using a subset of the data for training the style transfer model. The testing accuracy of D s is 87.6%. The evaluation classifier has a testing accuracy 88.7%.Our model achieves a classification accuracy of 95.1% and STB with cycle consistency loss achieves 94.1%. Following are some examples. Compared with STB, our model can generate sentences which are more fluent and have a higher probability to have a correct target style. However, we find that both STB and our model tend to generate short sentences and change the content of source sentences in more cases in this set of experiment than in the Yelp and Chat datasets. We conjecture this is caused by the scarcity of training data. Sentences in the Shakespeare's style form a vocabulary of 8559 words, but almost 60% of them appear less than 10 times. On the other hand, the source domain contains 19962 words, but there are only 5211 common words in these two vocabularies. Thus aligned words/phrases may not exist in the dataset.

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We present an encoder-decoder framework for language style transfer, which allows for the use of non-parallel data and source data with various unknown language styles.

This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate  the concept). We show that CAV can be learned given a relatively small set of examples. Hypothesis testing with CAV can answer whether a particular concept (e.g., gender) is more important in predicting a given class (e.g., doctor) than other sets of concepts. Interpreting networks with CAV does not require any retraining or modification of the network.