Miracle-dz/UniFAR-FTUs huggingface dataset:
PushToSki - An Indoor Ski Training System Using Haptic Feedback

Haptic feedback is an intuitive way of improving required postures in sports without having the trainee change their head-pose towards visual cues and therefore possibly worsening their overall body-pose. However, this feedback is not possible in a dynamic sport like alpine skiing which is why we propose a virtual reality ski training system that uses vibration as a haptic feedback method. Our system uses a commercially available indoor ski simulator and several trackers to capture the user’s motion together with a set of vibration motors which will provide direct, haptic feedback to the user. Our system therefore allows giving haptic feedback even while the trainee is moving on the simulator.
Toward an approach using graph-theoretic for IoT botnet detection

IoT devices are contributing significantly to increasing utility in life, increasing efficiency, increasing labor productivity as well as reducing costs. However, the fact that many manufacturers aim to speed up production and reduce production costs, most IoT devices have weak security features, easily infected by botnet malware. Static analysis of IoT botnet malware samples is a viable approach to fully understand the behavior of IoT botnet from there to mitigate and prevent. However, current analysis methods based on operation code or function call graphs generally do not work well with diversity in central processing unit (CPU) architecture and are often resource-intensive. In this paper, we propose a solution that uses graph-theoretic to present features that are effective in detecting botnet IoT. The main contribution of the paper is extracting PSI graph from executable files and exploiting a representative set of features from PSI-graph, which has a negligible number of attributes but achieves positive performance for IoT botnet malware detection (94.84% accuracy)
A Pipeline Retrospective on USD & Conduit

Over the past three years, Blue Sky Studios built a USD-centric layer on top of its next generation pipeline framework, Conduit. This transition involved mapping the legacy Blue Sky workflows into USD constructs. In addition, direct artist feedback during the delivery of six short films provided insights that informed the evolution of the Conduit backend to support these modernized workflows.
Research on UAV Obstacle Detection based on Data Fusion of Millimeter Wave Radar and Monocular Camera

Abstract—Obstacle avoidance detection of small UAVs has been a challenging problem because of size and weight constraints. In this paper, a fusion of MMW and monocular camera data is proposed for small UAVs obstacle detection systems. A MMW sensor is used to detect distance and angle and the image of obtacles capturing by the camera. Next, the target point information detected by MMW is calibrated into the image to complete the data fusion. Then, the optimized edge detection algorithm and image grayscale frequency saliency map are used to segment the obstacle area in images. The proposed method was evaluated by experiments in a real flying environment which consist of obstacles with textures and shadows. In the experiments, we successfully detect the shape of obstacles for complex situations. Obstacles with complex textures and shadows can be effectively detected, which shows that the method has good robustness.
aSIT: An Interface-oriented Distributed Automated Test System

Modern Internet services are often complex, large-scale distributed systems that consist of multiple software modules which may be developed by different teams using different programming languages. This multi-team collaboration and multiple development languages have brought new challenges to distributed program testing tools. Traditional manual testing cannot meet the testing requirements of large-scale distributed systems, and automated testing tools are usually used to complete testing tasks. This paper believes that enterprises with separate development and testing teams are more suitable to apply interface testing to realize automatic testing, and have designed and implemented a set of interface-oriented distributed automated testing systems—aSIT. Existing automated testing tools cannot well support scenarios such as internal heterogeneous service integration, multi-team collaboration, environmental network isolation, and massive test case execution. aSIT combined with actual engineering practice has carried out some specific designs to solve the above problems, so that it can be applied in the enterprise and improve the efficiency of the test team. After nearly 10 months of continuous using within the enterprise, testers can focus on use case design, the defect verification time was reduced to 1% of manual testing, the number of regression test cases increased by 30 times, and the average requirement delivery time was reduced by 63%.
The Profound Heterogeneity of Substance Use Disorders: Implications for Treatment Development

A single treatment approach will never be sufficient to address the diversity of individuals with substance use disorders (SUDs). SUDs have historically defied definition through simple characterizations or models, and no single characterization has led to the development of broadly effective interventions. The range of dimensions of heterogeneity among individuals with SUDs, including severity of the disorder, type of substance, and issues that frequently co-occur, underscores the need for highly tailored approaches. Personalized medicine for individuals with SUDs will require two major developments. First, given the diversity of individuals with SUDs, multivariate phenotyping is needed to identify the particular features driving addictive processes in a given individual. Second, a wider range of interventions that directly target core mechanisms of addiction and the problems that co-occur with them is needed. As clinicians cannot be expected to master the full range of interventions that may target these core processes, developing interventions that can be delivered easily, flexibly, and systematically via technology will facilitate the ability to truly tailor them to this highly complex and challenging population. One such technology-delivered intervention, Computer-Based Training for Cognitive Behavioral Therapy (CBT4CBT), is used as an example to illustrate a vision for the future of highly tailored interventions for individuals with SUDs.
Hybrid Spoken Language Translation Using Sentence Splitting Based on Syntax Structure

In this paper, we propose a hybrid spoken language translation method utilizing sentence segmentation. By portioning the sentence us-ing the result of syntax analysis, we can utilize rule-based control of the integration of sub-translations translated by a suitable method for each segment. We also report a preliminary experiment on translation quality of our prototype Japanese-to-English translation system. We conﬁrmed that our method achieved a 13.4% advantage in NIST score for the individual RBMT method, and a 6.0% advantage for the individual EBMT method.
Up close and personal with a Translator – How Translators Really Work

This tutorial is intended for researchers and developers who are working on Computer Assisted Technology (CAT), and want to know more about how translators do their work, and what they need from such technology. It will teach them how to acquire such knowledge using a simple Human Computer Interaction Technique (HCI) called Contextual Inquiry. Contextual Inquiry is a well-known, mature technique (Beyer and Holtzblatt, 1998), where researchers observe and interview potential endusers while they are involved in their normal day to day work. One advantage of this technique, compared to other requirement elicitation methods, is that it is generative. Instead of just asking endusers what they think they need (which often turns out to be different from what they actually do need), researchers aim for a deep understanding of important details of the end-user's world. This intimate knowledge can then be leveraged to design new and possibly disruptive technologies and processes, which are nevertheless well grounded in actual end-user needs and context, and have therefore a greater chance of being adopted. The presenters of this tutorial have been using this technique in the context of several Computer Assisted Translation technology projects, and have found it to be invaluable (Désilets et al, 2008). The tutorial will be highly interactive and allows attendees to get hands on experience with the technique, by observing an actual translator doing her normal work. Attendees also get to ask probing questions, and collectively analyze what they saw and heard during the interview, in terms of what it might mean for CAT development. For many developers and researchers in the audience, this may be their very first exposure to the work of actual end-users of their technology, and this in itself, has been shown to produce long-lasting and valuable changes in developers attitudes towards end users (Beyer and Holtzblatt, 1998).
Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching

Abstract We present a new method, Soloist,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i)Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.
Neural Networks Trained via Reinforcement Learning Stabilize Walking of a Three-Dimensional Biped Model With Exoskeleton Applications

Our group is developing a cyber-physical walking system (CPWS) for people paralyzed by spinal cord injuries (SCI). The current CPWS consists of a functional neuromuscular stimulation (FNS) system and a powered lower-limb exoskeleton for walking with leg movements in the sagittal plane. We are developing neural control systems that learn to assist the user of this CPWS to walk with stability. In a previous publication (Liu et al., Biomimetics, 2019, 4, 28), we showed a neural controller that stabilized a simulated biped in the sagittal plane. We are considering adding degrees of freedom to the CPWS to allow more natural walking movements and improved stability. Thus, in this paper, we present a new neural network enhanced control system that stabilizes a three-dimensional simulated biped model of a human wearing an exoskeleton. Results show that it stabilizes human/exoskeleton models and is robust to impact disturbances. The simulated biped walks at a steady pace in a range of typical human ambulatory speeds from 0.7 to 1.3 m/s, follows waypoints at a precision of 0.3 m, remains stable, and continues walking forward despite impact disturbances and adapts its speed to compensate for persistent external disturbances. Furthermore, the neural network controller stabilizes human models of different statures from 1.4 to 2.2 m tall without any changes to the control parameters. Please see videos at the following link: 3D biped walking control.
Cognition-Mortality Associations Are More Pronounced When Estimated Jointly in Longitudinal and Time-to-Event Models

With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as applied to data from a large-sample, repeated-measures study of older adults (N = 5,954; ages 50–87 years at assessment; 4,453 deceased at last census). Cognitive trajectories indicated worse performance in decedents and when estimated jointly with mortality risk, but this was attenuated after adjustment for health-related covariates. Better cognitive performance predicted lower mortality risk, and, importantly, cognition-mortality associations were more pronounced when estimated in joint models. Associations between mortality risk and crystallized abilities only emerged under joint estimation. This may have important implications for cognitive reserve, which posits that knowledge and skills considered well-preserved in later life (i.e., crystallized abilities) may compensate for declines in abilities more prone to neurodegeneration, such as recall memory and problem solving. Joint longitudinal-survival models thus appear to be important (and currently underutilized) for research in cognitive epidemiology.
An electrically switchable wideband metamaterial absorber based on graphene at P band

Abstract Graphene has the capability of dynamically tuning its conductivity through gate voltage. Based on this fact, an electrically switchable wideband metamaterial absorber at low frequencies is presented and investigated in this paper. Our calculated results show that its absorption is over 90% from 400 to 1,000 MHz with the Fermi level of graphene being at 0 eV and the absorption band can be switched by adjusting the Fermi level of graphene without changing its physical structure. Moreover, the surface current distribution enables us to reveal the switchable wideband absorption characteristics of our designed metamaterial absorber. At last, we prove that its absorption property is polarization-insensitive due to the rotational symmetry of the structural unit. This work may provide a further step in the development of switchable sensors and absorbers at low frequencies.
New and interesting seaweed records from the Hakai area of the central coast of British Columbia, Canada: Chlorophyta

Abstract Since 2011 we have been documenting seaweed diversity and abundance along a poorly studied area of the central coast of British Columbia, Canada. This first installment focuses on the Chlorophyta. To date, 42 species have been recorded, and we have obtained DNA sequences for most. Although most of these species reportedly have wide distributions along the west coast of North America, others appear to represent new northern or southern records or possible introductions, and a number have yet to be described. New southern limits are recorded for Acrosiphonia sonderi, Acrosiphonia sp. , Protomonostroma undulatum, and Ulva pouliotii sp. nov., and new northern records are documented for Ulva expansa, U. stenophylla, and another undescribed species of Ulva. Among species of Cladophoraceae, we obtained a sequence only for Cladophora opaca, a Japanese species not previously recorded from North America, and an undetermined species of Rhizoclonium. We sequenced three species of Derbesia, none of which is D. marina, the currently recognized species for this area; all three require taxonomic treatment. A sequence for a shell-boring species, which is tentatively identified as Eugomontia sacculata, was also obtained. These findings extend our understanding of the diversity and biogeography of green macroalgae in the northeast Pacific.
Bühler’s organon model of communication: a semiotic analysis of advertising slogans

Abstract This article investigates the member units of sign systems used in contemporary Iranian advertising slogans according to the Organon Model introduced by Karl Bühler. In dealing with this subject, the writer introduces Bühler’s Organon Model and provides a short definition of the term advertising slogan. The next part of this article shows the three types of conative functions with regard to such slogans. The corpus of this study is the slogan of a hundred contemporary Iranian advertising messages used in Iranian markets which have not been studied from a semiotic perspective. The result of this research shows the tendencies of Iranian advertising messages to lean towards the descriptive type of conative function.
Beyond the Block: A Novel Blockchain-Based Technical Model for Long-Term Care Insurance

ABSTRACT The insurance business is characterized by complicated transactional interrelationships among various stakeholders involved in insurance-related activities. Given this unique nature, the century-old challenge in the insurance industry is to effectively reduce transaction costs among the stakeholders while maintaining business privacy and trust. Although blockchain is a promising technology to mitigate this challenge, two technical issues, namely (1) inefficiency in data auditing and (2) difficulty in verifying encrypted data, are of strategic importance when applying blockchain to the insurance industry. To address these technical challenges, we propose an innovative blockchain-based technical model, InsurModel, in the context of newly initiated long-term care insurance in China. Specifically, we utilize cryptographical methods including “zero-knowledge-proof” to 1) represent business interdependence and 2) verify confidential business information without disclosure of specifics. We demonstrate the scalability and applicability of InsurModel and explore its strategic implications in constraining adverse behaviors of the stakeholders.
Work values of Filipino college students

ABSTRACT Work values refer to the business ethics and personal work preferences of individuals. Using the Filipino Work Values Scale, we characterised 10 work values of 141 full-time Filipino college students. Interpersonal, intellectual-achievement and occupational work values emerged as the most salient work values among the students. The rest appeared as either “weak” (environmental, material, managerial, religious, organisational and family-centred) or “very weak” (variety). Reflective of family influence over career orientation, some of the work values correlated with parents’ educational and professional backgrounds. As student work values are considered to influence long-term career choices and employability, comprehensively characterising work values may be useful in tailoring career counselling interventions that can help improve career pathing and job placements of students.
The Use Of Blockchain As A Resource For Combating Corruption In Global Shipping: An Interpretive Case Study

ABSTRACT Corruption is one of the most troubling societal challenges facing businesses today. Businesses have been combating corruption in fragmented ways, sometimes by creating anti-corruption policies applicable to certain stakeholders and, at other times, by harnessing digital technologies. Recently, the power of blockchain, with its capacity to provide full transactional disclosure and thereby reduce uncertainty, insecurity, and ambiguity in transactions, has been touted as being a game changer in the fight against corruption. Based on a study of the global shipping industry, we find that blockchain mitigates both process and document-related corruption. Based on these findings, we develop an understanding of how corruption may be combated using both social and digital/informational resources, including blockchain technology. Our model, drawing on past work on corruption, shows the complex interplay between identity, institutional actors, technical and other resources, and practices, and we develop conditions that could be effective in fighting corruption by using technologies such as blockchain.
The Experiences of Licensed Clinicians Counseling Lesbian, Gay, Bisexual, and Queer Survivors of Intimate Partner Violence

Abstract The purpose of this generic qualitative study was to explore the experiences of 10 licensed clinicians who have provided services to lesbian, gay, bisexual, and queer populations (LGBQ) who are survivors of intimate partner violence (IPV). After the data was collected, transcribed, and coded, five main themes emerged from this study: (1) past experiences impacting rendering services, (2) confirmation of IPV within LGBQ populations, (3) challenges of rendering services, (4) training considerations, and (5) a call for change in the mental health profession. Implications, including training considerations, and directions for future research are considered.
Attribution of Responsibility after Failures within Platform Ecosystems

ABSTRACT As digital platform ecosystems grow in prominence, their interconnectedness and complexity also grow, making operational failure likely. How failures in such systems affect user perceptions of separate ecosystem components, however, is not well understood. This research investigates attribution of responsibility and discontinuance recommend ations for ecosystem components after failures of ambiguous origin. Building on attribution theory, platform ecosystems literature, and research on digital borders, we conducted two scenario-based experiments investigating negative consequences of failure for ecosystem components. We also explored contingent effects from design elements (border strength) and contextual factors (disruption severity). Results demonstrated that when failures occur, negative consequences diffuse to all ecosystem components, with apps receiving the strongest discontinuance recommendations. Greater disruption severity increased discontinuance recommendations for the app. Furthermore, border strength between ecosystem components shifted negative consequences for failure toward the platform (e.g., operating system [OS] and device). Perceptions of locus and controllability were the primary mechanisms driving attributions of responsibility for failure. However, contrary to attribution theory, lack of failure stability increased blame for the app instead of reducing it. Despite higher coordination costs, our results indicate the importance of better-integrated ecosystems that experience fewer faults and that app developers bear the greatest burden in delivering this experience. Furthermore, attribution for failure can be shaped by clearly delineated borders. Thus, design decisions affecting border strength should be actively managed by ecosystem participants, and app developers may be incentivized to elevate border strength.