Reviewerly/exHarmony huggingface dataset:
Facial Expression Electric Wheelchair Control Instruction Using Image Processing

Tetraplegia is a type of paralysis that affects upper and lower limbs due to damage of spinal cord or brain. This condition causes difficulty to move and most of the time caretaker is needed to help the patients. This project proposed the design and implementation of an image processing technique in capturing and categorizing different facial gesture and use it as control instructions for electric wheelchair. The aim was to reduce the dependency to caretaker especially for mobility oftetraplegia patient. The deep learning Haar Cascade Classifier identify the expression of a face through image processing in livevideo capture. The Open Computer Vision (OpenCV) in Python was used to detect, recognize, and analyze the facial expression. Convolution Neural Network (CNN), a deep learning operation will act as trainer that analyze an open-source data to create a model as reference for the facial expression recognition. In orderto make the system operated as a standalone system, the Raspberry Pi module that connects with Pi Camera was used as the platform to capture the live video, perform processing, and produce the output control that give instructions to move such as forward, right, left and stop. Based on the analysis of the system performance, the system was capable to produce high accuracyof detection and correctly produce the electric wheelchair controlinstruction according to the facial expressions.
Physical Hijacking Attacks against Object Trackers

Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector's accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object's bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average.
BEM: Bit-level Sparsity-aware Deep Learning Accelerator with Efficient Booth Encoding and Weight Multiplexing

The floating-point weights of multiple trained deep neural networks (DNN) models reveal abundant bit-level sparsity and continuity in the mantissa. To better accommodate these features and hence speed up DNN inference. We Proposed BEM, a hardware runtime-acceleration technique that focuses on bit-level sparsity and bit-continuity. It employs efficient booth-encoding and weight multiplexing to reduce trivial computations during DNN inference and power loss significantly. The fundamental idea is to categorize encoded bits based on their specialized actions, eliminating the need for repetitive operation judgments, and altering calculating methods for different encoded bits. We investigated three standard image recognition models, Resnet18, Densenet121, and Resnext101, and discovered the following results: (1) no accuracy loss (0%), (2) 2.08x inference speedup over the original model, and (3) at least 2.76x efficiency boost on DNN inference over standard booth encoding.
Semantic enhanced for out-of-distribution detection

While improving the performance on the out-of-distribution (OOD) benchmark dataset, the existing approach misses a portion of the valid discriminative information such that it reduces the performance on the same manifold OOD (SMOOD) data. The key to addressing this problem is to prompt the model to learn effective and comprehensive in-distribution (ID) semantic features. In this paper, two strategies are proposed to improve the generalization ability of the model to OOD data. Firstly, the original samples are replaced by features extracted from multiple "semantic perspectives" to obtain a comprehensive semantics of the samples; Second, the mean and variance of the batch samples are perturbed in the inference stage to improve the sensitivity of the model to the OOD data. The method we propose does not employ OOD samples, uses no pre-trained models in training, and does not require pre-processing of samples during inference. Experimental results show that our method enhances the semantic representation of ID data, surpasses state-of-the-art detection results on the OOD benchmark dataset, and significantly improves the performance of the model in detecting the SMOOD data.
Disengaged response behavior when the response button is blocked: Evaluation of a micro-intervention

In large-scale assessments, disengaged participants might rapidly guess on items or skip items, which can affect the score interpretation's validity. This study analyzes data from a linear computer-based assessment to evaluate a micro-intervention that blocked the possibility to respond for 2 s. The blocked response was implemented to prevent participants from accidental navigation and as a naive attempt to prevent rapid guesses and rapid omissions. The response process was analyzed by interpreting log event sequences within a finite-state machine approach. Responses were assigned to different response classes based on the event sequence. Additionally, post hoc methods for detecting rapid responses based on response time thresholds were applied to validate the classification. Rapid guesses and rapid omissions could be distinguished from accidental clicks by the log events following the micro-intervention. Results showed that the blocked response interfered with rapid responses but hardly led to behavioral changes. However, the blocked response could improve the post hoc detection of rapid responding by identifying responses that narrowly exceed time-bound thresholds. In an assessment context, it is desirable to prevent participants from accidentally skipping items, which in itself may lead to an increasing popularity of initially blocking responses. If, however, data from those assessments is analyzed for rapid responses, additional log data information should be considered.
The blind spot of online creative idea generation studies: A perspective of media materiality

Online creative idea generation is often considered an extension of traditional creative idea activities on the Internet platform, in which digital technology plays an important role. Consistent with the studies on traditional creative idea activities, the studies on online creative idea generation take the creativity of mass psychology as the core, and believe that digital technology can stimulate people's creative output. This study challenges the past research paradigm from the perspective of media materiality, redefines the processes and activities of online creative idea generation, and further suggests that it may affect people's psychology by reducing their creativity without real awareness. The study is innovative in both theory and practice. We also discuss the limitations and future directions of the study.
The main requirements and risks of digital transformation of education

In the context of the Fourth Industrial Revolution, the economy is in need of professionals with completely new competencies. So, the ability to make decisions based on big data, act in a situation of uncertainty, analyze and critically comprehend incoming information, etc. comes first. A modern employee is required to assimilate a new way of thinking, ethics and attitudes, the formation and development of digital culture and digital literacy. The training of such specialists becomes the most important task of education. However, the analysis of modern research shows that teachers themselves also need tools that would allow them to effectively perform their professional duties in the new economic reality. Based on the review of the requirements and the main groups of risks caused by the digital transformation of education, we conclude that the presence of a well-developed standardized model of digital competencies of teachers can become a reliable support for teachers and teachers. Also, the introduction of such a model in the educational programs of universities and professional development programs will bring the professional training of teachers in line with the requirements of digital transformation.
Effective and Efficient Video Compression by the Deep Learning Techniques

Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing highquality video content.In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN).The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM).Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression.Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors.Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video.Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate.On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video.
Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes.Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability.On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system.In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations.In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment.The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction.To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures.At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption.The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model.The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.
Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

Privacy preservation (PP) in Digital forensics (DF) is a conflicted and non-trivial issue.Existing solutions use the searchable encryption concept and, as a result, are not efficient and support only a keyword search.Moreover, the collected forensic data cannot be analyzed using existing well-known digital tools.This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB) privacy guidelines.To have an efficient investigation process and meet the increased volume of data, the presented framework is designed based on the selective imaging concept and advanced encryption standard (AES).The proposed framework has two main modules, namely Selective Imaging Module (SIM) and Selective Analysis Module (SAM).The SIM and SAM modules are implemented based on advanced forensic format 4 (AFF4) and SleuthKit open source forensics frameworks, respectively, and, accordingly, the proposed framework is evaluated in a forensically sound manner.The evaluation result is compared with other relevant works and, as a result, the proposed solution provides a privacy-preserving, efficient forensic imaging and analysis process while having also sufficient methods.Moreover, the AFF4 forensic image, produced by the SIM module, can be analyzed not only by SAM, but also by other well-known analysis tools available on the market.
Realization of Intellectual Potential: from Shatr to Alphazero Interactive Deep Neural Networks

One of the priorities of the system of ethno-cultural training is the development of inclinations and abilities, the realization of the intellectual potential of the individ-ual. The intellectual game of chess is a resource for the mental development of a person. In the article, the author offers a brief overview of the history of Kalmyk chess, as well as shows the effectiveness of modern Kalmyk chess players, an analy-sis of the development of logical thinking, speed and accuracy of mental opera-tions, from the origin to the use of artificial intelligence. The work also touched up-on various modern ways of playing a game in chess, and the effectiveness of their results in the context of this study. The materials and methods of this work are the theoretical method (analysis and generalization of scientific and methodological literature on the research topic). The results of the study and their discussion: the results of the tournaments were analyzed in the work, within the framework of which the study of the participants was conducted. The results of this experiment are presented. On the basis of the study, it can be concluded that the chess game is used to develop such human qualities as ingenuity and discipline, as well as a number of other important human qualities.
Structure and Analysis of Properties of Basic Comb Filters for Automatic Systems

The paper considers the issues of using comb filters (CF), known in radio engineering, as an alternative to continuous corrective filters, when they are implemented in controllers in automatic control systems. The convenience of their implementation in controllers is associated with the organization of a fixed number of sample passes in the processed signal. In this paper, the properties of two types of comb filters of the first order are highlighted and studied in detail: a high-frequency filter and a proportional (forming) filter. The characteristics of these filters are taken as a basis for the implementation of more complex characteristics of corrective devices in combination with numerical integration algorithms. The analysis of the properties of CF was carried out in the frequency domain. It is shown that the initial sections of the frequency characteristics of the CF at certain filter parameters coincide well with the continuous analogues of such devices. It is these areas of frequency characteristics, limited by the cutoff frequency of the open system, that determine the coincidence of static and dynamic properties of the synthesized system. It is shown that CF reduces on 30 % the level of the standard deviation (RMS) of uniformly distributed white noise at the filter output. The decrease in the level of RMS occurs due to the presence of periodically recurring dips in the amplitude-frequency response (frequency response) of filters forming a so-called comb. Due to the fact that the filters in question "cut out" harmonics in the output signal spectrum that are multiples of the inverse of the total duration of the transmitted reference intervals, then a special choice of this duration can not only reduce the level of the random signal at the CF output, but at the same time significantly reduce the level of regular interference, if it is present. Such filters can be simultaneously used as amplitude modulated signal demodulators.
Bridging Actionability Gap in Online How-To Instruction-Following Through Human-Chatbot Interaction

People frequently come across unfamiliar tasks to complete in daily and professional life. A common strategy to accomplish such tasks is to refer to online How-To tutorials (e.g., wikiHow pages) and follow the instructions step-by-step. However, such archived tutorials are not always actionable, since the sequence of actions is pre-defined and could result in a discrepancy between what was shared and what people need in operational situations. Differing from regular conversational instruction-giving, where people are able to communicate instructions adaptively based on the ongoing working status, archived procedures lack such flexibility. As such, people may face actionability issues in which they may have difficulties navigating the instruction, making decisions on what to do, and solving problems. My dissertation explores system designs using chatbots to accompany novice users to improve the situational actionability and social acceptability of online tutorials for non-expert users. I present ActionaBot, an experimental platform for exploring human-chatbot collaborative instruction giving to augment online tutorials. By participating in the Doctoral Consortium, I am eager to receive feedback from experienced CSCW researchers to inform my research direction. In the long run, I aim to contribute a better understanding of how people and the chatbot may collaborate to enable actionable knowledge sharing and transfer at a large scale.
An Online Memorial for Coping with Mass Shooting Tragedy by Combining Participatory Memory with Participatory Design of AI Use Cases

Mass shootings occur in America at an alarming rate. There is opportunity to intervene in this problem by designing technologies that support affected communities in processing gun violence tragedy. In this paper we report on the design of an online memorial for a mass shooting that affected our university’s local community. We demonstrate an alternative approach to online memorials that blends participatory memory with participatory design through remembrance artifacts that represent ideas for gun violence prevention technologies that could have prevented the tragedy and that may prevent future tragedies. We demonstrate participatory memory + design with our memorial called the OUrchive that supports retrospective and prospective reflection on the Oxford High School shooting through designing new use cases for AI to prevent mass shootings. Early community involvement suggests that the OUrchive supports personal reconciliation with tragedy by channeling trauma towards public discourse about potential solutions to gun violence.
Titan

The recent breakthrough of foundation model (FM) research raises a new trend to acquire efficient DL models by fine-tuning FMs with low-resource datasets. Current GPU clusters are mainly established to develop DL models by training from scratch. How to tailor a GPU cluster scheduler for FM fine-tuning workloads is still not explored.
HIPPA: Smart Wearables for Enriching the Gameplay of a Traditional Tag Game

Nowadays children are more and more focused on playing social games with their peers through technological devices staying most of the time immobile indoors. Pervasive games and location-based mobile games, such as Pokémon Go, have a stand against this negative phenomenon as gameplay takes place outdoors with the city as the game arena. However, there are still many possibilities to encourage children to actually play together outdoors and simultaneously challenge themselves to fitness gain via physical exercise as they used to do in the traditional social outdoor games, such as tag game. In this work in progress paper, we aim on adding challenge to the gameplay as well as enriching the haptic player and social experience of the tag game by design, crafting and implementation of a two-piece social gaming wearable, HIPPA (figure 1 a-c). HIPPA is aimed to offer a more competitive and fun tag player experience.
The Co-Creation Space: Supporting Asynchronous Artistic Co-creation Dynamics

Artistic co-creation empowers communities to shape their narratives, however HCI research does not support this multifaceted discussion and reflection process. In the context of community opera, we consider how to support co-creation through the design, implementation, and initial evaluation of the Co-Creation Space (CCS) to help community artists 1) generate raw artistic ideas, and 2) discuss and reflect on the shared meaning of those ideas. This work describes our user-centered process to gather requirements and design the tool, and validates its’ usability with 6 community opera participants. Our findings support the value of our tool for group discussion and personal reflection during the creative process.
Accelerating large-scale distributed neural network training with SPMD parallelism

Deep neural networks (DNNs) with trillions of parameters have emerged, e.g., Mixture-of-Experts (MoE) models. Training models of this scale requires sophisticated parallelization strategies like the newly proposed SPMD parallelism, that shards each tensor along different dimensions. A common problem using SPMD is that computation stalls during communication due to data dependencies, resulting in low GPU utilization and long training time. We present a general technique to accelerate SPMD-based DNN training by maximizing computation-communication overlap and automatic SPMD strategy search. The key idea is to duplicate the DNN model into two copies that have no dependency, and interleave their execution such that computation of one copy overlaps with communication of the other. We propose a dynamic programming algorithm to automatically identify optimized sharding strategies that minimize model training time by maximally enabling computation-communication overlap. Experiments show that our designs achieve up to 61% training speed-up as compared to existing frameworks.
Exhibiting Evidence in Remote Courtrooms: Design and Usability Study

Remote court proceedings, an intensely collaborative process, are a reality in the pandemic but struggle to support evidence review. Remote proceedings are likely to continue after the pandemic, making it imperative to strengthen evidence viewing and analysis for jurors. Our project addressed this problem by prototyping an assistive application that offered multiple points of view and evaluating it in a small user study. Five participants used the application to review case-specific exhibits in a hypothetical court session while thinking aloud. Participants were satisfied with the application, with almost no facilitation. We also identified the need to make a display of the setting in which the evidence was found more interactive and navigable.