Reviewerly/exHarmony huggingface dataset:
Systematic Development of Machine for Abnormal Muscle Activity Detection

Anomaly detection algorithms have vast applications, from fraud detection in business transactions to rare pattern detection in a production line to help prevent machinery failures. The availability of quantitative clinical data makes a compelling case for using anomaly detection algorithms in clinical settings, for instance, to help prevent diagnosis errors. This work evaluates the feasibility of using Isolation Forest algorithm for detection of spikes in surface electromyography (sEMG) of biceps and muscle resistive force in upper limb spasticity datasets. Results show that the anomaly detection in sEMG data is a good predictor for the occurrence of catch. It could be deployed in rehabilitation robotic systems for injury prevention by linking the anomaly detection to the actuation module exerting force in the system.
Radar Working State Recognition Based on the Unsupervised and Incremental Method

Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm is used to cluster unlabeled samples to obtain the best initial clustering results. The clustering results are input as training samples into the BP neural network to train the recognition model, which realizes the unsupervised recognition. Secondly, the incremental AP (IAP) algorithm based on the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -nearest neighbor (KNN) idea is used to divide the incremental samples by calculating the closeness between samples. The incremental samples are added to the BP recognition model as a new known state to complete the model update, which realizes incremental recognition. The simulation experiments on three types of radar data sets show that the recognition accuracy of the proposed model can reach more than 83%, which verifies the feasibility and effectiveness of the method. In addition, compared with the AP algorithm and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>K</mi> </math> -means algorithm, the improved AP method improves 59.4%, 17.6%, and 53.5% in purity, rand index (RI), and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>F</mi> </math> -measure indexes, respectively, and the running time is at least 34.8% shorter than the AP algorithm. The time of processing incremental data is greatly reduced, and the clustering efficiency is improved. Experimental results show that this method can quickly and accurately identify radar working state and play an important role in giving full play to the adaptability and timeliness of the cognitive electronic countermeasures.
Virtual Leader based Trajectory Generation of UAV Formation for Visual Area Coverage

This paper proposes a trajectory generation strategy of quadcopter formation for area surveillance and inspection using multiple cameras. The proposed technique exploits a minimal virtual-leader based network topology to generate the executable trajectories where generating trajectories for each agent is no longer necessary. A coverage path planning algorithm is employed to generate a set of planar waypoints, which are then optimised to yield a minimum-jerk time-parametrised trajectory with dynamical constraints of the virtual leader to follow. In order to establish a formation pattern, the desired distance among the agents is then computed based on the operation mode, size of the area to cover, and the number of deployed UAVs. Numerical simulations demonstrate the effectiveness of the proposed method.
Optimization of VQE-UCC Algorithm Based on Spin State Symmetry

The accurate calculation of molecular energy spectra, a very complicated work, is of importance in many applied fields. Relying on the VQE-UCC algorithm, it is very possible to calculate the molecular energy spectrum on a noisy intermediate scale quantum computer. However, due to the limitation of the number of qubits and coherent time in quantum computers, the complexity of VQE-UCC algorithm still needs to be reduced in the simulation of macromolecules. We develop a new VQE-UCC method to calculate the ground state of the molecule according to the symmetry of the system, the complexity of which is reduced. Using this method we get the ground and excite state of four kinds of molecules. The method and the results are of great significance for the promotion of quantum chemical simulations.
Robustness via Cross-Domain Ensembles

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong prediction. The premise of the idea is that predictions made via different cues respond differently to a distribution shift, hence one should be able to merge them into one robust final prediction. We perform the merging in a straightforward but principled manner based on the uncertainty associated with each prediction. The evaluations are performed using multiple tasks and datasets (Taskonomy, Replica, ImageNet, CIFAR) under a wide range of adversarial and non-adversarial distribution shifts which demonstrate the proposed method is considerably more robust than its standard learning counterpart, conventional deep ensembles, and several other baselines.
Evolving Search Space for Neural Architecture Search.

The automation of neural architecture design has been a coveted alternative to human experts. Recent works have small search space, which is easier to optimize but has a limited upper bound of the optimal solution. Extra human design is needed for those methods to propose a more suitable space with respect to the specific task and algorithm capacity. To further enhance the degree of automation for neural architecture search, we present a Neural Search-space Evolution (NSE) scheme that iteratively amplifies the results from the previous effort by maintaining an optimized search space subset. This design minimizes the necessity of a well-designed search space. We further extend the flexibility of obtainable architectures by introducing a learnable multi-branch setting. By employing the proposed method, a consistent performance gain is achieved during a progressive search over upcoming search spaces. We achieve 77.3% top-1 retrain accuracy on ImageNet with 333M FLOPs, which yielded a state-of-the-art performance among previous auto-generated architectures that do not involve knowledge distillation or weight pruning. When the latency constraint is adopted, our result also performs better than the previous best-performing mobile models with a 77.9% Top-1 retrain accuracy.
Cloud Computing Resource Scheduling Based on Improved Particle Swarm Optimization Algorithm

Abstract With the limited resources of Cloud computing (hereinafter referred to as CC), we must improve the quality of scheduling and cost optimization. Therefore, we must organically integrate the resource pool of servers and computers, which will distribute resources dynamically according to the needs of users. Through CC Resource scheduling (hereinafter referred to as RS), we can improve resource utilization, which will greatly reduce the use cost. Through the CC sharing architecture model, we can implement QoS according to different needs of users, which can achieve flexible resource allocation. Therefore, we need to reasonably design the CC RS and allocation model, which will improve the resource utilization. At the same time, this algorithm can share information among individuals. However, the general PSO (hereinafter referred to as PSO) algorithm has the problem of discretization. This paper proposes an Improved PSO (hereinafter referred to as FPSO) algorithm, which can better solve the RS problem. Firstly, the goal of task scheduling is proposed. Then, an FPSO algorithm is proposed. Finally, some suggestions are put forward.
Aspect oriented Sentiment classification of COVID-19 twitter data; an enhanced LDA based text analytic approach

Social media has become one of the most important sources of information dissemination during crisis and pandemics. The unknown nature of these disasters makes it hard to analyze the comprehensive situational awareness through different aspects and sentiments to support authorities. Current aspect detection and sentiment analysis system largely relies on labelled data and also categorize the aspects manually. So, in this research, we proposed a hybrid text analytical framework to do aspect level public sentiments analysis. Our approach consists of three layers, first we extracted and clustered the aspects from the data by utilizing the widely used Latent dirichlet allocation (LDA) topic modelling, then we extracted the sentiments and label the dataset by using the linguistic inquiry and word count (LIWC) lexicon, then in third layer of our framework we mapped the aspects into sentiments and sentiments are then classified with well-known machine learning classifiers. Experiments with real dataset gives us promising results as compared to existing aspect oriented sentiment analysis approaches and our method with different variant of classifiers outperforms existing methods with highest F1 scores of 91 %.
Condensing a Sequence to One Informative Frame for Video Recognition

Video is complex due to large variations in motion and rich content in fine-grained visual details. Abstracting useful information from such information-intensive media requires exhaustive computing resources. This paper studies a two-step alternative that first condenses the video sequence to an informative "frame" and then exploits off-the-shelf image recognition system on the synthetic frame. A valid question is how to define "useful information" and then distill it from a video sequence down to one synthetic frame. This paper presents a novel Informative Frame Synthesis (IFS) architecture that incorporates three objective tasks, i.e., appearance reconstruction, video categorization, motion estimation, and two regularizers, i.e., adversarial learning, color consistency. Each task equips the synthetic frame with one ability, while each regularizer enhances its visual quality. With these, by jointly learning the frame synthesis in an end-to-end manner, the generated frame is expected to encapsulate the required spatio-temporal information useful for video analysis. Extensive experiments are conducted on the large-scale Kinetics dataset. When comparing to baseline methods that map video sequence to a single image, IFS shows superior performance. More remarkably, IFS consistently demonstrates evident improvements on image-based 2D networks and clip-based 3D networks, and achieves comparable performance with the state-of-the-art methods with less computational cost.
The Effect of Ballot Characteristics on the Likelihood of Voting Errors

Abstract In the United States, people are asked to vote on a myriad of candidates, offices, and ballot questions. The result is lengthy ballots that are time intensive and complicated to fill out. In this paper, we utilize a new analytical technique harnessing ballot scanner data from a statewide midterm election to estimate the effects of ballot complexity on voting errors. We find that increases in ballot length, increases in the number of local ballot questions, and increases in the number of candidates listed for single offices significantly increase the odds of encountering ballot marking and scanning errors. Our findings indicate that ballots’ characteristics can help election administrators make Election Day planning and resource allocation decisions that decrease ballot errors and associated wait times to vote while increasing the reliability of election results and voter confidence in the electoral process.
An Intelligent Math E-Tutoring System for Students with Specific Learning Disabilities

Students with specific learning disabilities (SLDs) often experience negative emotions when solving math problems, which they have difficulty managing. This is one reason that current math e-learning tools, which elicit these negative emotions, are not effective for these students. We designed an intelligent math e-tutoring system that aims to reduce students’ negative emotional behaviors. The system automatically detects possible negative emotional behaviors by analyzing gaze, inputs on the touchscreen, and response time. It then uses one of four intervention methods (e.g., hints or brain breaks) to prevent students from being upset. To form this design, we conducted a formative study with five teachers for students with SLDs. The teachers thought that the design of four intervention methods would help students with SLDs. Among the four intervention methods, providing brain breaks is new and particularly useful for the students. The teachers also suggested that the system should personalize the detection of negative emotional behaviors to help students who have more severe learning disabilities.
The Effectiveness of Dynamically Processed Incremental Descriptions in Human Robot Interaction

We explore the effectiveness of a dynamically processed incremental referring description system using under-specified ambiguous descriptions that are then built upon using linguistic repair statements, which we refer to as a dynamic system. We build a dynamically processed incremental referring description generation system that is able to provide contextual navigational statements to describe an object in a potential real-world situation of nuclear waste sorting and maintenance. In a study of 31 participants, we test the dynamic system in a case where a user is remote operating a robot to sort nuclear waste, with the robot assisting them in identifying the correct barrels to be removed. We compare these against a static non-ambiguous description given in the same scenario. As well as looking at efficiency with time and distance measurements, we also look at user preference. Results show that our dynamic system was a much more efficient method—taking only 62% of the time on average—for finding the correct barrel. Participants also favoured our dynamic system.
Time to be happy

Mobile Instant Messaging (MIM), and particularly WhatsApp Messenger, has become a prime communication channel within the extended family in Israel. It allows family members to communicate easily. This chapter focuses on the implications of using MIM among family members, alongside the pros and cons of semantic network analysis in the field of mediated communication. The chapter includes two case studies: (1) analysis of the actual chat messages in WhatsApp groups of two extended families, and (2) analysis of the perceptions and uses of MIM based on in-depth interviews with three generations of women in Israeli families. The findings show that WhatsApp is perceived as a useful and often preferable channel due to its variety of communication forms, including synchronous and asynchronous communication, and a major tool to coordinate family gatherings. Alongside this, when looking at the actual discourse among members, WhatsApp is mainly used as a phatic communication channel to project availability and presence while being absent. The conversations focus primarily on reproducing positive images of the family members with an emphasis on photo sharing. mobile instant messaging, WhatsApp, phatic communication, family communication
Kerogen Type Classification in Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning

Kerogen type in source rocks is directly related to its hydrocarbon generation potential. Its determination is often carried out with destructive methods. This study presents a non-destructive technique as an alternative to determine kerogen type using hyperspectral data and machine learning techniques. To present the technique, models were training using Support Vector Machines, K Nearest Neighbors, and Random Forest classifiers on spectral data collected in rock samples acquired from Taubaté Basin, Brazil, of an outcrop with high hydrocarbon generation potential. The models were trained and evaluated using spectral signatures measured with a spectroradiometer and the results were also tested on hyperspectral images of the samples. The experiments described here achieved accuracy above 0.8 with precision and recall above 0.62 and 0.8, respectively, for every kerogen type, indicating the soundness of the classification.
Echolocation as theory of digital sociality

Public attention on disconnection and digital detox focuses on the health and wellbeing associated with disconnecting without much attention on what happens to selfhood or identity when abruptly disconnected. In an age of ubiquitous internet and “always on” use practices, what does disconnection do? Focusing on what happens when we disconnect, at the micro level, reveals interesting echolocative communication patterns otherwise not noticed. Abruptly stopping the continuous call and response pattern of interaction among youth produces deep anxieties and feelings of existential vulnerability that are commonly brushed aside. The work in this article is part of a larger project related to echolocation as a theory of communication. In an era of constant connectivity and “always on” or more importantly, “always available” internet, the seemingly seamless and steady state of connectivity is, at the more granular level, a process of continual echolocation, in the way we might think of sonar, whereby certain animals like bats determine the shape and location of objects in space by sending steady streams of signals and attending closely to the quality of the echo. Echolocation challenges researchers and theorists to reconsider the core elements and processes in an era of continuous, machinic as well as human interaction in multiple and massive networks of information flow. This does not mean we no longer experience dyadic (two person) or intra interactions, of course, but echolocation, the process of moving, navigating, and positioning through radar-like call and response provides a promising model to apply to how humans make sense of who they are in the complexities of continuous and tangled data flows.
Faulty Point Unit: ABI Poisoning Attacks on Trusted Execution Environments

This article analyzes a previously overlooked attack surface that allows unprivileged adversaries to impact floating-point computations in enclaves through the Application Binary Interface (ABI). In a comprehensive study across 7 industry-standard and research enclave shielding runtimes for Intel Software Guard Extensions (SGX), we show that control and state registers of the x87 Floating-Point Unit (FPU) and Intel Streaming SIMD Extensions are not always properly sanitized on enclave entry. We furthermore show that this attack goes beyond the x86 architecture and can also affect RISC-V enclaves. Focusing on SGX, we abuse the adversary’s control over precision and rounding modes as an ABI fault injection primitive to corrupt enclaved floating-point operations. Our analysis reveals that this is especially relevant for applications that use the older x87 FPU, which is still under certain conditions used by modern compilers. We exemplify the potential impact of ABI quality-degradation attacks for enclaved machine learning and for the SPEC benchmarks. We then explore the impact on confidentiality, showing that control over exception masks can be abused as a controlled channel to recover enclaved multiplication operands. Our findings, affecting 5 of 7 studied SGX runtimes and one RISC-V runtime, demonstrate the challenges of implementing high-assurance trusted execution across computing architectures.
CLIPort: What and Where Pathways for Robotic Manipulation

How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding necessary for fine-grained manipulation. To this end, we propose a framework that combines the best of both worlds: a two-stream architecture with semantic and spatial pathways for vision-based manipulation. Specifically, we present CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2]. Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulated and real-world settings show that our approach is data efficient in few-shot settings and generalizes effectively to seen and unseen semantic concepts. We even learn one multi-task policy for 10 simulated and 9 real-world tasks that is better or comparable to single-task policies.
Specifics of the socio-cultural competency development of students in the framework of foreign language education

The purpose of the research is to study the specifics of socio-cultural competency development in the framework of foreign language education. To achieve the goal, the experiment was carried out aimed at studying the influence of foreign language teaching on the development of the socio-cultural competency. The research methodology and its algorithm were selected due to the purpose of the study. Experimental work included a complex psychological and pedagogical experiment, diagnosing the stated parameters and their dynamics. The experimental group included 31 students, the sampling is valid for gender and other significant characteristics. The results of the study confirm that the project method within the framework of foreign language education is effective for the development of socio-cultural competency of students.
Eccentric Gaze as a Possible Cause of "Zoom Fatigue".

The Covid-19 pandemic has led to a marked increase in the use of videoconferencing for social interaction. Many people report discomfort and disaffection with this modality, which has been labeled "Zoom Fatigue." Common videoconferencing hardware setups necessitate that if a user looks at the image of the person with whom they are in videoconference, they will not be looking directly at the camera and will appear to not be making direct eye contact. This study determined the minimum threshold of eccentric gaze in a videoconferencing setup above which subjects are perceived as not making direct eye contact by the majority of untrained observers.Image captures were made of four subjects successively fixating at small increments eccentric to a video camera, both vertically and horizontally ranging from 0.9 degrees to 19 degrees of eccentricity. The images were embedded in separate Powerpoint files for each subject. Each file was assessed by seven graders who indicated whether or not they felt the subject was looking directly at them in each slide.The threshold for which 75% of the graders could detect that the subject was not looking at them ranged from only 2.7 degrees for horizontal eccentricity to 5.4 degrees for vertical eccentricity.The hardware setups commonly used for videoconferencing result in persistent eccentric gaze of the participating individuals if they look at the image of the other participants. In theory, this could be a contributing cause of Zoom Fatigue.