Reviewerly/exHarmony huggingface dataset:
Exploration and Assessment of Interaction in an Immersive Analytics Module: A Software-Based Comparison

The focus of computer systems in the field of visual analytics is to make the results clear and understandable. However, enhancing human-computer interaction (HCI) in the field is less investigated. Data visualization and visual analytics (VA) are usually performed using traditional desktop settings and mouse interaction. These methods are based on the window, icon, menu, and pointer (WIMP) interface, which often results in information clutter and is difficult to analyze and understand, especially by novice users. Researchers believe that introducing adequate, natural interaction techniques to the field is necessary for building effective and enjoyable visual analytics systems. This work introduces a novel virtual reality (VR) module to perform basic visual analytics tasks and aims to explore new interaction techniques in the field. A pilot study was conducted to measure the time it takes students to perform basic tasks for analytics using the developed VR module and compares it to the time it takes them to perform the same tasks using a traditional desktop to assess the effectiveness of the VR module in enhancing student’s performance. The results show that novice users (Participants with less programming experience) took about 50% less time to complete tasks using the developed VR module as a comrade to a programming language, notably R. Experts (Participants with advanced programming experience) took about the same time to complete tasks under both conditions (R and VR).
The main stages and mechanisms for the formation of professionally important qualities of students in the process of studying at the university

The article deals with the main stages and mechanisms for the formation of professionally important qualities of students in the process of studying at the university. The success of becoming a specialist depends mainly on how successful the student is in all stages of study, acquiring important qualities and skills for the future profession. Also in the article we turn to dynamics of development of personal and individual qualities of students, necessary for successful professional activity.
HISFCOS: Half-Inverted Stage Block for Efficient Object Detection Based on Deep Learning

Recent advances in object detection play a key role in various industrial applications. However, a fully convolutional one-stage detector (FCOS), a conventional object detection method, has low detection accuracy given the calculation cost. Thus, in this study, we propose a half-inverted stage FCOS (HISFCOS) with improved detection accuracy at a computational cost comparable to FCOS based on the proposed half inverted stage (HIS) block. First, FCOS has low detection accuracy owing to low-level information loss. Therefore, an HIS block that minimizes feature loss by extracting spatial and channel information in parallel is proposed. Second, detection accuracy was improved by reconstructing the feature pyramid on the basis of the proposed block and improving the low-level information. Lastly, the improved detection head structure reduced the computational cost and amount compared to the conventional method. Through experiments, the proposed method defined the optimal HISFCOS parameters and evaluated several datasets for fair comparison. The HISFCOS was trained and evaluated using the PASCAL VOC and MSCOCO2017 datasets. Additionally, the average precision (AP) was used as an evaluation index to quantitatively evaluate detection performance. As a result of the experiment, the parameters were increased by 0.5 M compared to the conventional method, but the detection accuracy was improved by 3.0 AP and 1.5 AP in the PASCAL VOC and MSCOCO datasets, respectively. in addition, an ablation study was conducted, and the results for the proposed block and detection head were analyzed.
A Fine-tuned Gradient Boosting Method to Improve Sentiment Classification of Lockdown Extension Tweets

Social media is one of the sources that handles huge amount of data on an extraordinary scale. People sharing their own ideas, thoughts, views related to the current topic or trending topic using the biggest platform in social media like Twitter, Facebook, etc. As unforeseen as the event of Covid infection 2019 (COVID-19) was, it has been fundamentally influencing individuals everywhere on the world, there is a demand to examine and analyze the individuals on the endemic COVID-19. This paper concentrates on the sentiment analysis of COVID-19 data which was extracted from twitter using python language and analyzed by machine learning algorithms and to predict people’s reaction towards lockdown extension, what the careful steps they have to take are and whether individuals are following government's rules and so on. Tweets have been collected from Twitter using Tweepy API. After extracted, the text was classified with the help of Vader, and it was trained and tested with 6 machine learning algorithms to find the accuracy and prediction about the public’s view. After the comparison of those 6 algorithms and found the best and fine-tuned some features of Gradient Boosting algorithm to enhance the prediction. This study concludes that majority of Indian peoples supported the Government for taking such a decision to take care of themselves.
Masked Face Detection System Using Convolutional Neural Networks

The global health epidemic caused by the breakout of a coronavirus disease in 2019 (COVID-19) has had a significant impact on the way we view our environment and live our daily lives. People should wear masks and maintain social distance as a precaution. As a result, detecting face masks has become a critical responsibility in assisting the global community. This report describes a simplified method for accomplishing this goal by utilising TensorFlow, Keras, OpenCV, Convolutional Neural Networks, and some basic Machine Learning packages. If a person is discovered without a face mask, an alert warning is issued, and the person's face is captured. In addition, the value of mask and unmask faces are saved in the cloud for future use. Using this deep learning enables the system to be faster and more precise to detect the faces, and as a result, the accuracy of mask and unmask faces detection is higher than 90%. As all the facilities and premises open and the number of COVID-19 cases continues to rise across the country, everyone must adhere to the safety precautions until the outbreak is over. As a result, this module assists in recognising people wearing masks when entering premises.
Motion Capture of Music Performances

Abstract Motion capture (mocap)—the recording of three-dimensional movement using high-accuracy systems—has become a standard research tool in the analysis of music performances in the last two decades. A variety of systems is currently available, ranging from optical, multi-camera (passive and/or active) infra-red systems and inertial systems (using orientation sensors) to electromagnetic trackers providing six degrees-of-freedom (DoF) measurement per marker/sensor. Recent advances in technology have made many of these systems more affordable, allowing access to a large research community. Music-related mocap applications include the tracking movements of solo or group, beginner, or expert performers and instruments for teaching performance skills, comparing movement strategies across performers, generating movement synthesis parameters in animation, and use in real-time music interaction. This chapter introduces the basic concepts behind motion capture, reviews the most common mocap technologies used in the study of music performance, and presents several examples of research, pedagogy, and artistic uses. Mocap of single acoustic instrument performances is reviewed, including violin, cello, piano, clarinet, timpani, and acoustic guitar, as well as examples of mocap of multiple instruments. Finally, we discuss the limitations of mocap and possible solutions to overcome them.
Pricing on Use of Cloud Computing and Internet of Things for Optimized Agricultural Practices

This paper endeavours to provide a better future for the agro industry in middle-and low-income countries such as India. The project incorporates various technical domains such as the Internet of Things (IoT) and Cloud - Computing to optimise agricultural practices. The focus here is directed primarily towards farms located in the much-isolated areas far from the reaches of contemporary technology. Only 186 million people utilise the internet in rural India, which has a population of 918 million people according to the 2011 census. As more and more farms and cottage industries are centred in and around rural zones, connectivity and access to the web is virtually absent. The situation above doesn’t bode well for farmers and cultivators who hope to obtain the very best out of every yield. The system thus proposed uses a combination of Wireless Sensor Networks, GSM modules (or even fixed landline connections) along with the cloud to effectively retrieve accurate real time data from the fields and transmit it through a medium such as a desktop / mainframe to the cloud where it is stored and analysed using tested algorithms that then provide an optimal guide to the farmer located at the field.
Development of Anomalous Video Detection System using Hybrid-Features Analysis of Actions and Scene-Backgrounds Information

Detecting an anomalous event in video clips captured from a surveillance camera is an important task, especially for security system purposes. However, as the probability of such occurrence is very low, automatic detection of the anomalous event is then necessary to replace the human labor-intensive works. Researchers have developed various methods to solve this problem, however, most of the proposed methods limit their definitions of anomalous events that might be perceived as having a different meaning when it occurs in other scene backgrounds. We have developed an automatic anomalous video detection system by extracting the individual action from the video clips, followed by extracting also various scene-background characteristics related with the respective action, and represented as a Video Graph to be classified as an anomaly through a Graph Convolutional Networks. We also constructed a new database as the available databases could not be used in this experiment. Results of experiments show that the various anomalous actions videos have been successfully detected with higher recognition capability compared with that of the conventional methods.
MLIM: A CTR prediction model describing evolution law of user interest

With the advent of the digital economy era, business systems such as web advertising and recommendation system have put forward the demand for predicting the click through rate (CTR) of items. However, the current CTR prediction research is not enough to mine user behavior, resulting in the lack of accuracy of user interest representation. In this paper, we propose a CTR prediction model, called MLIM, which can deep mine the evolution law of user interest. Specifically, we first use BiGRU to obtain the low-level user interest representation in the interest extraction layer, and then continue to use attention mechanism, BiGRU and sliding time window multi-components collaborative modeling in the interest evolution layer to obtain multi-level user interest representation with richer information, which can improve the accuracy of CTR prediction to a certain extent. Comprehensive experiments on two real datasets show that the proposed model achieves better performance than the mainstream baselines integrating user behavior analysis.
Handmade: A Scientist’s Search for Meaning through Making, Anna Ploszajski (2021)

Review of: Handmade: A Scientist’s Search for Meaning through Making , Anna Ploszajski (2021) London: Bloomsbury Publishing (Sigma), 320 pp., 288 pp., 288 pp., ISBN 978-1-47297-107-4, h/bk, £16.19 ISBN 978-1-47297-106-7, e-book (Epub and Mobi), £12.59 ISBN 978-1-47297-106-7, e-book (PDF), £12.59
MSLS: An Intelligent Face Expression Identification System using Modified Sparse Learning Scheme

Now-a-days, image processing needs and related fields are growing in drastic manner as well as the technology enhance itself periodically. In this Face expression identification is a crucial need and attain more importance. For example, interpersonal communication, computerized education, video and pictures search, intelligent surroundings and safety detection systems might all benefit from the ability to recognize natural feelings in real human faces. Face-recognition software has always been assessed on experimentally controlled data, in which it is not reflective of the real-world environment. Facial expressions may be recognized using a novel interactive technique. Face emotions identification system is a long-term organization registration challenge for adaptive face expression detection. The following are the major advantages of this procedure: In the first place, a relevant constructs growth model describes the movement patterns of the unique face characteristics of different manifestations in terms of the disciplines; in the second place, a sparsity multi-group object recognition technique builds a pertinent spatial face expression map for each interpretation, in which it is able to explain the massive changes in the facial features of the entire population and repress the partiality due to massive inter-subject facial variability's; in the third place, a feature extraction approach uses both temporal and spatial image exposure information to help identification of a sparsely represented object.
Doubly Pessimistic Algorithms for Strictly Safe Off-Policy Optimization

We study offline reinforcement learning (RL) in the presence of safety requirements: from a dataset collected a priori and without direct access to the true environment, learn an optimal policy that is guaranteed to respect the safety constraints. We address this problem by modeling the safety requirement as an unknown cost function of states and actions, whose expected value with respect to the policy must fall below a certain threshold. We then present an algorithm in the context of finite-horizon Markov decision processes (MDPs), termed Safe-DPVI that performs in a doubly pessimistic manner when 1) it constructs a conservative set of safe policies; and 2) when it selects a good policy from that conservative set. Without assuming the sufficient coverage of the dataset or any structure for the underlying MDPs, we establish a data-dependent upper bound on the suboptimality gap of the safe policy Safe-DPVI returns. We then specialize our results to linear MDPs with appropriate assumptions on dataset being well-explored. Both data-dependent and specialized bounds nearly match that of state-of-the-art unsafe offline RL algorithms, with an additional multiplicative factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\frac{\Sigma_{h=1}^{H}\alpha_{h}}{H}$</tex> , where α <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> characterizes the safety constraint at time-step <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$h$</tex> . We further present numerical simulations that corroborate our theoretical findings. A full version referred to as technical report of this paper is accessible at: https://offline-rl-neurips.github.io/2021/pdf/21.pdf
Efficient Breast Cancer Prediction using Hybrid Deep Learning in mammographic images

Because of Breast cancer, there are 684,996 passing are for the most part happened around the world. This made the main source of cancer. Early detection and treatment of breast cancer save the women life and avoid death. Recently, image processing based approach achieves better success rate in the filed of cancer detection. In this work, the Hybrid DL strategies like MobileNet and Long Short Term Memory (LSTM) are introduced for classification. The Hybrid Mobile Net and LSTM (HML) is proposed and assessed in terms of Dice coefficient, Accuracy, sensitivity, F1 score and specificity. This proposed HML is compared and the past DL classification methods
Captcha Recognition Based on Attention Mechanism

Captcha recognition is a worthful work to study, since it does help Internet security and also promotes the field of pattern recognition. In this work, we concentrate on the attention mechanism to this classification task for ameliorating the function of our baseline network. In our experiment, we arbitrarily combine the two modules in CBAM and the coordinate attention module that is considered to be efficient and novel. Then we add this combined attention to our baseline network. From the test results, we see that the better attention for this task is CBAM (spatial first, then channel attention), which improves the recognition accuracy to about 0.8% based on the mini-dataset generated by ourselves.
Crowd counting and localization based on codec structure

Crowd counting algorithm began to develop towards the combination of counting and localization. However, the existing methods pay little attention to the localization task, so we propose a crowd counting and localization network based on codec structure, named CALNet. At the end of encoding, the network efficiently fuses different scale information through Adaptive Feature Fusion (AFF) module, to weaken the influence of change of head scale and camera perspective. In the decoding part, we designed the Attention Enhancement Upsampling (AEU) module to obtain high-quality regression map, which uses high-level semantics to guide the model to fuse shallow features. In addition, due to the problem that the existing label maps can not give good consideration to the counting and localization tasks, we proposed a new label map generation method. The new label map not only ensures the simple counting mode, but also has excellent localization performance, and it can be easily migrated to the existing counting methods. The experimental results show that our approach can achieve good results in counting accuracy and localization accuracy.
Differentially Private Federated Learning with Drift Control

In this paper, we consider the problem of differentially private federated learning with statistical data heterogeneity. More specifically, users collaborate with the parameter server (PS) to jointly train a machine learning model using their local datasets that are non-i.i.d. across users. The PS is assumed to be honest-but-curious so that the data at users need to be kept private from the PS. More specifically, interactions between the PS and users must satisfy differential privacy (DP) for each user. In this work, we propose a differentially private mechanism that simultaneously deals with user-drift caused by non-i.i.d. data and the randomized user participation in the training process. Specifically, we study SCAFFOLD, a popular federated learning algorithm, that has shown better performance on dealing with non-i.i.d. data than previous federated averaging algorithms. We study the convergence rate of SCAFFOLD under differential privacy constraint. Our convergence results take into account time-varying perturbation noises used by the users, and data and user sampling. We propose two time-varying noise allocation schemes in order to achieve better convergence rate and satisfy a total DP privacy budget. We also conduct experiments to confirm our theoretical findings on real world dataset.
Counterfeit Drug Detection System with Multi-Layered Check and SCM using Blockchain

10% of drugs in the world are counterfeits and 200 billion dollars are wasted on counterfeits. These counterfeit drugs not only contain toxic doses but also cause poisoning that leads to death. Using these drugs, not only reduces the treatment of contagious health problems but also increases disease progression that leads to fatal. This project produces accurate results about counterfeit drugs and the usage of drugs if it is not counterfeit. Using Optical Character Recognition (OCR) check of medicine, the check requires the minute details and then determines whether the medicine sold is genuine or not. So, they can get the percentage of how genuine the drugs are. By doing this project, one could help people by making them aware of a counterfeit drug that they have purchased.
A single maneuvering target tracking algorithm based on STUKF for automotive radar

Aiming at the hidden danger caused by the extensive application of automotive radar, an improved single maneuvering target tracking algorithm of automotive radar is proposed in this paper, and its research and simulation are carried out. In automotive radar applications, the movement of a single model does not accurately describe the motion of target relative to the radar, in order to improve the accuracy of the description of target motion state, this article put forward the Interactive Multiple Model Strong Tracking Unscented Kalman Filter (IMM - STUKF) algorithm, this algorithm not only solves the problem of imprecise description of the actual motion state of the target, it also makes up for the poor robustness of traditional UKF algorithm and ensures the tracking accuracy of single maneuvering target. Experimental simulation results show that the proposed algorithm can reduce tracking error more effectively than the traditional IMM-UKF algorithm.
Towards Porting Operating Systems with Program Synthesis

The end of Moore's Law has ushered in a diversity of hardware not seen in decades. Operating system (and system software) portability is accordingly becoming increasingly critical. Simultaneously, there has been tremendous progress in program synthesis. We set out to explore the feasibility of using modern program synthesis to generate the machine-dependent parts of an operating system. Our ultimate goal is to generate new ports automatically from descriptions of new machines. One of the issues involved is writing specifications, both for machine-dependent operating system functionality and for instruction set architectures. We designed two domain-specific languages: Alewife for machine-independent specifications of machine-dependent operating system functionality and Cassiopea for describing instruction set architecture semantics. Automated porting also requires an implementation. We developed a toolchain that, given an Alewife specification and a Cassiopea machine description, specializes the machine-independent specification to the target instruction set architecture and synthesizes an implementation in assembly language with a customized symbolic execution engine. Using this approach, we demonstrate successful synthesis of a total of 140 OS components from two pre-existing OSes for four real hardware platforms. We also developed several optimization methods for OS-related assembly synthesis to improve scalability. The effectiveness of our languages and ability to synthesize code for all 140 specifications is evidence of the feasibility of program synthesis for machine-dependent OS code. However, many research challenges remain; we also discuss the benefits and limitations of our synthesis-based approach to automated OS porting.