Reviewerly/exHarmony huggingface dataset:
Dual-Line-Systolic Array for High Performance CNN Accelerator

Systolic array has been the crucial architecture for accelerating convolutional neural networks (CNN) since the success of Google’s TPU (Tensor Processing Unit). In this work, we propose high throughput and low delay dual-line-systolic array for accelerating the convolutional neural networks. With the line-by-line vector-style systolic dataflow, the peripheral circuit can be well simplified and the loading/offloading delay can be greatly reduced. Besides, to fully take advantage of the DSP (Digital signal processor) INT8 computation in FPGA, dual-line-systolic array is developed, by which the computation throughput can be doubled. Finally, the proposed accelerator is deployed on PYNQ-Z2 for practically accelerating VGG16 neural network, peek throughput of the convolution layer can reach as high as 107.21 GOPS, which has exceeded all of the previous works on the same hardware platform.
The U.S. Cyber Threat Landscape

Cybersecurity is concerned with protecting information, hardware, and software on the internet from unauthorized use, intrusions, sabotage, and natural disasters. It is the body of technologies, processes and practices designed to protect networks, computers, programs and data from attack, damage, or unauthorized access. The numerous ways in which computer systems and data can be compromised and the dramatic increase in cybercrimes have made cybersecurity a growing field. One of the most problematic elements of cybersecurity is the quick and constant evolving nature of security risks in critical infrastructure and major businesses all around the world. In this paper, we sketch a general frame for the cyber threat landscape in the United States of America by focusing on five major categories: ransomware, social engineering, third party software, deep fakes, and insider threats. We elaborate on each of these pillars by providing case studies from the past decade, as well as discussing ways to move forward.
Mobile health technology as a solution to self-control problems: The behavioral impact of COVID-19 contact tracing apps in Japan

Mobile technology has been widely utilized as an effective healthcare tool during the COVID-19 pandemic. Notably, over 50 countries have released contact-tracing apps to trace and contain infection chains. While earlier studies have examined obstacles to app uptake and usage, whether and how this uptake affects users' behavioral patterns is not well understood. This is crucial because uptake can theoretically increase or decrease behavior that carries infection risks.The goal of this study is to evaluate the impact of app uptake on the time spent out of home in Japan. It tests four potential underlying mechanisms that drive the uptake effect: compliance with stay-at-home requirements, learning about infection risk, reminders, and commitment device.We use unique nationwide survey data collected from 4,379 individuals aged between 20 and 69 in December 2020 and February 2021 in Japan. Japan has features suitable for this exercise. The Japanese government released a contact tracing app in June 2020, which sends a warning message to users who have been in close contact with an infected person. We conduct a difference-in-differences estimation strategy combined with the entropy balancing method.App uptake reduces the time spent out of home. Sensitivity analysis shows that it cannot be explained by unobserved confounders. Importantly, the impact is large even among users who have not received a warning message from the app, and even larger for those with poor self-control ability. Furthermore, individuals' self-control ability is negatively associated with the uptake decision, supporting our hypothesis that the apps serve as a commitment device.It may be beneficial to encourage citizens to uptake contact tracing apps and other forms of commitment devices. This study also contributes to the literature on mobile health (mHealth) by demonstrating its efficacy as a commitment device.
Selection of Precise Long Short Term Memory (LSTM) Hyperparameters based on Particle Swarm Optimization

Long Short-Term Memory (LSTM) neural networks must consider parameter settings for more accurate prediction results. This study aims to improve the LSTM performance using Particle Swarm Optimization (PSO) for hyperparameters selection. PSO may optimize the weight of the LSTM neural network, which results in minimal error prediction due to its feature selection and data normalization abilities. This study applies the LSTM architecture, consisting of 3 layers (input, hidden, and output) with several 7, 3, and 1. Root mean squared error (RMSE) and processing time evaluate the results. The results show that PSO tuning obtained six hyperparameters values, namely RMSprop, ReLU, MAE, batch size (128), Neuron (78), and Epoch (62). The RMSE of the LSTM with PSO tuning has a minor error value of 21.923 and the fastest processing time of 156 seconds. The results overcome other LSTM models with random parameters. The highest difference of RMSE between the proposed approach and the baseline model is 7.621. In other words, the PSO hyperparameters tuning efficiently reduces the LSTM errors. These findings could be useful as a recommendation for reducing the time-consuming efforts of hyperparameters selection.
The Impact of a Cyberattack at a Radiation Oncology Department: Immediate Response and Future Preparedness

Cyberattacks are increasing year after year and many organizations, including hospitals, are becoming targets. Radiation oncology is especially vulnerable because of the reliance on computer and network capabilities to transfer relevant patient information for safe and effective patient treatment. In early 2019, our institution was hit by a ransomware attack that brought down our oncology information system (OIS). Although we were not fully prepared for such an attack, a total of 69 treatment fractions occurred without our OIS thanks to the quick development of a contingency plan and the ability to restore the patients' records. The OIS was recovered by the manufacturer 4 days after the attack. We also have developed a contingency plan and outline important considerations for institutions trying to prepare for unexpected downtime such as a cyberattack.
Stimulus data and experimental design for a self-paced reading study on emoji-word substitutions

This data paper presents the experimental design and stimuli from an online self-paced reading study on the processing of emojis substituting lexically ambiguous nouns. We recorded reading times for the target ambiguous nouns and for emojis depicting either the intended target referent or a contextually inappropriate homophonous noun. Furthermore, we recorded comprehension accuracy, demographics and a self-assessment of the participants' emoji usage frequency. The data includes all stimuli used, the raw data, the full JavaScript code for the online experiment, as well as Python and R code for the data analysis. We believe that our dataset may give important insights related to the comprehension mechanisms involved in the cognitive processing of emojis. For interpretation and discussion of the experiment, please see the original article entitled "The processing of emoji-word substitutions: A self-paced-reading study".
The mediating effect of communication skills on the relationship between collaborative self-regulation, collaborative self-efficacy, team efficacy, and problem solving skills in university students learning community activities

Objectives This study examines the mediating effect of communication skills on the relationship between learners' collaborative self-regulation, collaborative self-efficacy, team efficacy, and problem solving skills in order to promote learning outcomes and find efficient support measures in university students' learning community activities.&#x0D; Methods To this end, data were collected and analyzed from 509 university students who participated in the learning community program operated in the 2021-1 semester at the A University Center for Teaching and Learning in Gyeonggi-do. Mediated regression analysis was conducted to find out whether communication skills mediates the relationship between collaborative self-regulation, collaborative self-efficacy, team efficacy, and problem solving skills. In addition, Sobel test was used to test the significance of the mediation effect.&#x0D; Results As a result of the study, it was found that communication skills had a partial mediating effect in the relationship between collaborative self-regulation, collaborative self-efficacy, and team efficacy. In addition, it was found that communication skills had a partial mediating effect in the relationship between collaborative self-regulation, collaborative self-efficacy, and problem solving skills.&#x0D; Conclusions Base on the research results, it was identified that the collaborative competencies of learners in the learning community activities of university students are effective in enhancing learning outcomes and developing core competencies. In addition, it was confirmed that communication skills is an important factor in learning community activities. It emphasizes the need for support to increase the level of learners' interaction, collaborative competency, and communication skills in learning community activities.&#x0D;
Unwillingness to cooperate with COVID-19 contact tracing in Japan

Contact tracing for COVID-19 relies heavily on the cooperation of individuals with authorities to provide information of contact persons. However, few studies have clarified willingness to cooperate and motivation to provide information for contact tracing. This study sought to describe willingness to cooperate and motivation to report contact persons for COVID-19 contact tracing among citizens in Japan, and to assess any associated sociodemographic factors. Cross-sectional study. This was an online-based survey using quota sampling. Participants were asked about their willingness to cooperate in reporting contacts for COVID-19 contact tracing if they tested positive. Participants also responded to questions regarding their reasons for cooperating or not cooperating and provided sociodemographic data. Multiple logistic regression analysis was performed to clarify associations between sociodemographic factors and willingness to cooperate. This study included 2844 participants. The proportion of participants who were not willing to cooperate in reporting contacts was 27.6%, with their main reasons being concerns about causing trouble for the other person and being criticised for revealing their names. Willingness to cooperate was lower among men, young adults and those with an educational level less than a university degree. To improve the effectiveness of contact tracing, educational campaigns, such as reducing the fear and stigma associated with COVID-19, may be important. Furthermore, it is essential to understand that individuals may have contacts whom they do not wish to disclose to others and to be considerate when handling such situations.
OLAP Mining with Educational Data Mart to Predict Students’ Performance

Academic institutions always try to use a solid platform for supporting their short-to-long term decisions related to academic performance. These platforms utilize the historical data and turn them into strategic decisions. The hidden patterns in the data need tools and approaches to be discovered. This paper aims to present a short roadmap for implementing educational data mart based on a data set form Alexandria Private Elementary School, located in Basrah province of Iraq in the 2017-2018 academic year. The educational data mart is implemented, then the cube is constructed to perform OLAP operations and present OLAP reports. Next, OLAP mining is performed on the educational cube using nine algorithms, namely: decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (both)), Logistic, Naïve Bayes, Neural Network, clustering with expectation maximization, clustering with K-means clustering, and Association rules mining. According to comparison of all algorithms, clustering with expectation maximization proved the highest accuracy with 96.76% for predicting the students’ performance and 96.12% for predicting students grades amongst all other algorithms
Anomaly based Incident Detection in Large Scale Smart Transportation Systems

Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.
Importance of Universal Human Values for Life: A Study

The human values are beliefs that have a deep-rooted worth in usefulness or importance to the holder of these qualities. These values are necessity in today’s society and education system. These values provide a meaning to the life of a human being. Usually, people become lazy and dull after being successful in this materialistic world, but if they have values, they would always have a purpose and guide in their life. Values are needed to teach to the children from the early age at school. This will help them to lead a peaceful and harmonious life which is free from evils. Hence, the aim of this paper is to highlight the important of universal human value in our life and how the theories on values are given by different researchers.
Philosophical Aspects of Determining the Main Components of the Formation of Professional Competence for Students

The most characteristic feature of modern society is changing in all spheres of life, which today drive science, economy, industry, and culture and affect the development of the information and educational space. The focus of the education system on the assimilation of knowledge, which was traditional and justified before, no longer corresponds to the current social order; it is determined by the objective need of society for a new person capable of independent, responsible, creative actions, an intellectual person, with critical thinking, liberated from dogmas, living in a complex. The fulfilment of these tasks requires a significant improvement in the quality of professional training of future specialists, based on the application of the philosophical aspects of human existence and development in the context of the formation of their professional competence and involves the development of personal qualities and creative abilities, the ability to independently acquire new knowledge and solve problems, and navigate in the life of society. Taking this into account, the article discusses in detail the main components of professional competence and provides the most effective ways to improve it.
A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion

Typically, singing voice conversion (SVC) depends on an embedding vector, extracted from either a speaker lookup table (LUT) or a speaker recognition network (SRN), to model speaker identity. However, singing contains more expressive speaker characteristics than conversational speech. It is suspected that a single embedding vector may only capture averaged and coarse-grained speaker characteristics, which is insufficient for the SVC task. To this end, this work proposes a novel hierarchical speaker representation framework for SVC, which can capture fine-grained speaker characteristics at different granularity. It consists of an up-sampling stream and three down-sampling streams. The up-sampling stream transforms the linguistic features into audio samples, while one down-sampling stream of the three operates in the reverse direction. It is expected that the temporal statistics of each down-sampling block can represent speaker characteristics at different granularity, which will be engaged in the up-sampling blocks to enhance the speaker modeling. Experiment results verify that the proposed method outperforms both the LUT and SRN based SVC systems. Moreover, the proposed system supports the one-shot SVC with only a few seconds of reference audio.
Whole slide image data utilization informed by digital diagnosis patterns

Despite the benefits of digital pathology, data storage and management of digital whole slide images introduces new logistical and infrastructure challenges to traditionally analog pathology labs.Our goal was to analyze pathologist slide diagnosis patterns to determine the minimum number of pixels required during the diagnosis.We developed a method of using pathologist viewing patterns to vary digital image resolution across virtual slides, which we call variable resolution images. An additional pathologist reviewed the variable resolution images to determine if diagnoses could still be rendered.Across all slides, the pathologists rarely zoomed in to the full resolution level. As a result, the variable resolution images are significantly smaller than the original whole slide images. Despite the reduction in image sizes, the final pathologist reviewer could still proide diagnoses on the variable resolution slide images.Future studies will be conducted to understand variability in resolution requirements between and within pathologists. These findings have the potential to dramatically reduce the data storage requirements of high-resolution whole slide images.
A comprehensive method of e-government transition for viable development in Iraq

Now more than ever, the government needs to find out that how well the system of Iraq can be transformed to efficiently offer public services to the citizens for consistent development growth. For many reasons, the answer lies in the development, innovation, and application of the latest technologies in Iraq. The use of electronic technology will offer fast and convenient access to public services and will allow the people of Iraq to participate in decision-making and designing and delivery of the services. These technologies assist in government openness, increasing accountability, and boosting public trust. A comprehensive approach that is based on the 4 key pillars to support the e-government transition of Iraq for consistent development growth is presented in this paper. This approach provides guidelines to the government of Iraq that encourage the application of technology for the betterment of people regardless of their age, sex, religious and political beliefs, financial status, disabilities, and area. It highlights the need of e government transition and provides a plan to focus on the key pillars to develop abilities for viable development growth in Iraq.
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning over Text

Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However to achieve this, contemporary Neural Module Networks models obtain strong supervision in form of specialized program annotation from the QA pairs through various heuristic parsing and exhaustive computation of all possible discrete operations on discrete arguments. Consequently they fail to generalize to more open-ended settings without such supervision. Hence, we propose Weakly Supervised Neuro-Symbolic Module Network (WNSMN) trained with answers as the sole supervision for numerical reasoning based MRC. WNSMN learns to execute a noisy heuristic program obtained from the dependency parse of the query, as discrete actions over both neural and symbolic reasoning modules and trains it end-to-end in a reinforcement learning framework with discrete reward from answer matching. On the subset of DROP having numerical answers, WNSMN outperforms NMN by 32% and the reasoning-free generative language model GenBERT by 8% in exact match accuracy under comparable weakly supervised settings. This showcases the effectiveness of modular networks that can handle explicit discrete reasoning over noisy programs in an end-to-end manner.
GoTube: Scalable Statistical Verification of Continuous-Depth Models

We introduce a new statistical verification algorithm that formally quantifies the behavioral robustness of any time-continuous process formulated as a continuous-depth model. Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states. We call our algorithm GoTube. Through its construction, GoTube ensures that the bounding tube is conservative up to a desired probability and up to a desired tightness. GoTube is implemented in JAX and optimized to scale to complex continuous-depth neural network models. Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube does not accumulate overapproximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques. We show that GoTube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability on a large set of experiments. GoTube is stable and sets the state-of-the-art in terms of its ability to scale to time horizons well beyond what has been previously possible.
Learning to Detect 3D Facial Landmarks via Heatmap Regression with Graph Convolutional Network

3D facial landmark detection is extensively used in many research fields such as face registration, facial shape analysis, and face recognition. Most existing methods involve traditional features and 3D face models for the detection of landmarks, and their performances are limited by the hand-crafted intermediate process. In this paper, we propose a novel 3D facial landmark detection method, which directly locates the coordinates of landmarks from 3D point cloud with a well-customized graph convolutional network. The graph convolutional network learns geometric features adaptively for 3D facial landmark detection with the assistance of constructed 3D heatmaps, which are Gaussian functions of distances to each landmark on a 3D face. On this basis, we further develop a local surface unfolding and registration module to predict 3D landmarks from the heatmaps. The proposed method forms the first baseline of deep point cloud learning method for 3D facial landmark detection. We demonstrate experimentally that the proposed method exceeds the existing approaches by a clear margin on BU-3DFE and FRGC datasets for landmark localization accuracy and stability, and also achieves high-precision results on a recent large-scale dataset.