Reviewerly/exHarmony huggingface dataset:
Implementation of a Case Investigation and Contact Tracing Program at an Academic Medical Center in New York City

Problem In the spring of 2020, New York City was an epicenter of COVID-19 infections, hospitalizations, and mortality. One of the hardest hit areas in New York City was central Brooklyn, where State University of New York (SUNY) Downstate is located. As the severity of the initial wave of the COVID-19 pandemic waned in New York City, SUNY Downstate Health Sciences University planned to institute COVID-19 surveillance on campus to limit its spread among employees and students. Approach In collaboration with university leadership and students across schools, the SUNY Downstate Contact Tracing Corps was established in July 2020. Using guidance from the Centers for Disease Control and Prevention and the New York State Department of Health, protocols for case and contact investigations, including scripts, were developed. Students were recruited and trained in remote and confidential case and contact investigations. Outcomes From August 1, 2020, to March 23, 2021, there were 185 COVID-19 cases referred to the contact tracing program; of these, 180 were successfully investigated and isolated (97%). Case investigations identified 111 Downstate close contacts; 105 were investigated and quarantined (94.5%). There were 122 exposure-only investigations referred to the program, and 121 were successfully investigated and quarantined (99%). Next Steps The SUNY Downstate Contact Tracing Corps transitioned to a core group of tracers in January 2021 and has been maintained at 4 to 6 tracers since that time. With the emergence of COVID-19 variants, the threat of new variants, and variable COVID-19 vaccination rates across Brooklyn, Downstate employees and students remain vulnerable to COVID-19 transmission. Given this, the SUNY Downstate Contact Tracing Corps will continue to conduct case and contact investigations.
Analysis of reputation-based mining paradigm under dishonest mining attacks

Since the introduction of Bitcoin, numerous studies on Bitcoin mining attacks have been conducted, and as a result, many countermeasures to these attacks have been proposed. The reputation-based mining paradigm is a comprehensive countermeasure solution to this problem with the goal of regulating the mining process and preventing mining attacks. This is accomplished by incentivizing miners to avoid dishonest mining strategies using reward and punishment mechanisms. This model was validated solely based on game theoretical analyses, and the real-world implications of this model are not known due to the lack of empirical data. To shed light on this issue, we designed a simulated mining platform to examine the effectiveness of the reputation-based mining paradigm through data analysis. We implemented block withholding attacks in our simulation and ran the following three scenarios: Reputation mode, non-reputation mode, and no attack mode. By comparing the results from these three scenarios, interestingly, we found that the reputation-based mining paradigm decreases the number of block withholding attacks, and as a result, the actual revenue of individual miners becomes closer to their theoretical expected revenue. In addition, we observed that the confidence interval test can effectively detect block withholding attacks; however, the test also results in a small number of false positive cases. Since the effectiveness of the reputation-based model relies on attack detection, further research is needed to investigate the effect of this model on other dishonest mining strategies.
Mems Reservoir Computing Using Frequency Modulated Accelerometer

We developed an intelligent MEMS sensor with data processing ability for data acquisition and conversion at low power consumption. It enables data processing at the sensing device for efficient distributed processing. We employed a MEMS reservoir which integrates sensing and computing functions in single device structure. In this research we designed a frequency modulated capacitive accelerometer and characterized its frequency response. The measured response was used for simulation of time series prediction and the machine learning task was successfully demonstrated.
Face-recognition System Design and Manufacture

The theory of face recognition is mainly from the idea of feature vectors. The face image is converted into a series of numbers to form a feature vector. For a feature vector of a face image, its content includes various features, such as: face height, face width, average face color, lips width, and nose height. The face-recognition operation is to compare the input of the feature vector of a face image with a large number of feature vectors in a dataset to identify the personal identity.The face recognition system in this paper is mainly implemented in the Python environment. Face generation is achieved by selfie of face. The image is cut to retain the part of the face, and stored in the database. Comparing the face image input with those saved in the dataset, if the similarity value passes the threshold value of true. The program will show the face image identification. Otherwise, the system will display a false message. The face recognition system is completed and the experimental results show the correct face-recognition. Finally the laptop Webcam is used to take the face image, and the result of comparison is also correct.
Data Visualization Using Google Data Studio

Executives seldom have the time to pore through information pages to figure out how their company is doing. As a result, sales data visualization is required to describe sales success for executives swiftly. Google Data Studio is a data visualization application that is an easy-to-use tool for presenting complicated data sets engagingly and understandably. You can view your data using Google Data Studio without knowing how to write. The service is cloud-based, accessible from anywhere, is free, and allows you to share reports with anybody you choose.
Methodology of continuity of results of various levels of professional pedagog-ical education

The relevance of the research subject is determined by the need for application of the principles of consistency and continuity in the training of pedagogical resource personnel. The system of continuous pedagogical education cannot function effectively without value orientations being defined for each stage of teacher education with respect to the professionally significant qualities, meanings and values student teachers possess. The aim of the article is to substantiate the methodology of continuing pedagogical education. Methods of analysis of professional and educational standards are applied. The result of the analysis is a list of value orientations of professional pedagogical activity. The empirical study was conducted using the questionnaire and MAST-test methods. The results state a common system of values and the difference in emphasis at different levels of teacher training (teacher training classes, teacher training colleges, universities, postgraduate education). As a result of the analysis, the universal pedagogical competencies necessary for the effective development of pedagogical activity are identified. These competencies allow the teacher to consciously perform labor functions, carry out training, education and development of the student, design the educational environment, interact with all subjects of the educational process. The article describes the indicators of the formation of universal pedagogical competencies for different levels of teacher training.
Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

The recognition technology of the radar signal modulation mode plays a critical role in electronic warfare, and the algorithm based on deep learning has significantly improved the recognition accuracy of radar signals. However, the convolutional neural networks became increasingly sophisticated with the progress of deep learning, making them unsuitable for platforms with limited computing resources. ResXNet, a novel multiscale lightweight attention model, is proposed in this paper. The proposed ResXNet model has a larger receptive field and a novel grouped residual structure to improve the feature representation capacity of the model. In addition, the convolution block attention module (CBAM) is utilized to effectively aggregate channel and spatial information, enabling the convolutional neural network model to extract features more effectively. The input time-frequency image size of the proposed model is increased to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>600</mn> <mo>×</mo> <mn>600</mn> </math> , which effectively reduces the information loss of the input data. The average recognition accuracy of the proposed model achieves 91.1% at -8 dB. Furthermore, the proposed model performs better in terms of unsupervised object localization with the class activation map (CAM). The classification information and localization information of the radar signal can be fused for subsequent analysis.
A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns

Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users’ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users’ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm.
Building Multi-turn Query Interpreters for E-commercial Chatbots with Sparse-to-dense Attentive Modeling

Predicting query intents is crucial for understanding user demands in chatbots. In real-world applications, accurate query intent classification can be highly challenging as human-machine interactions are often conducted in multiple turns, which requires the models to capture related information from the entire contexts. In addition, query intents tend to be fine-grained (up to hundreds of classes), containing lots of casual chats without clear intents. Hence, it is difficult for standard transformer-based models to capture complicated language characteristics of dialogues to support these applications. In this demo, we present AliMeTerp, a multi-turn query interpretation system, which can be seamlessly integrated into e-commercial chatbots in order to generate appropriate responses. Specifically, in AliMeTerp, we introduce SAM-BERT, a pre-trained language model for fine-grained query intent understanding, based on Sparse-to-dense Attentive Modeling. For model pre-training, a stack of Sparse-to-dense Attentive Encoders are employed to model the complicated dialogue structures from different levels. We further design Hierarchical Multi-grained Classification tasks for model fine-tuning. Experiments show SAM-BERT consistently outperforms strong baselines over multiple multi-turn chatbot datasets. We further show how AliMeTerp is deployed in real-world e-commercial chatbots to support real-time customer service.
MLI. Mouse: a new computer vision-based cursor control software

The personal computer opens tremendous opportunities for people, whether it is entertainment or work. However, people with disabilities cannot successfully use usual methods of interacting with a computer like a mouse and a keyboard. There are analogs that allow individuals who had disabilities to control the computer using computer vision. They can use their limbs, head, eyes, color markers as reference point, that is used to move a cursor on their computer screen. One of the most popular cursor control software is The Camera Mouse, in which the user selects the area that will serve as a reference point. However, people may find it difficult to adjust the sensitivity of the reference point movement. For example, if the sensitivity is low, some area around the edges of the screen may become inaccessible. At high sensitivity, not all the pointer movement potential is realized. And the high sensitivity, the small movements, noise, errors affect the swing of the reference pointer more. In this study, the MLI. Mouse proposed. It allows user to control the computer cursor using computer vision techniques. To control the cursor, a person needs a webcam, from which the current position of the reference point will be read. A hand or a QR code can be used as the reference point. A useful feature of this program is a convenient way to visualize and set the active area of the cursor movements. This is the area where the reference pointer movement is broadcast on the computer screen. With this, the program can be configured faster and more conveniently. The left mouse button clicking is implemented using Dwell click.
Towards Addressing Noise and Static Variations of Analog Computations Using Efficient Retraining

One of the most promising technologies to solve the energy efficiency problem for artificial neural networks on embedded systems is analog computing, which, however, is fraught with noise due to summations of unwanted or disturbing energy, and static variations related to manufacturing. While these inaccuracies can have a negative effect on the accuracy, in particular for naively deployed networks, the robustness of the networks can be significantly enhanced by a retraining procedure that considers the particular hardware instance. However, this hardware-in-the-loop retraining is very slow and thus often the bottleneck hindering the development of larger networks. Furthermore, it is hardware-instance-specific and requires access to the instance in question.Therefore, we propose a representation of a hardware instance in software, based on simple, parallelization-friendly software structures, which could replace the hardware for the major fraction of retraining. The representation is based on lookup tables, splines as interpolated functions and additive Gaussian noise to cover static variations together with electrical noise of the multiplier array and column-wise integrators. The combined approach using the proposed representation together with some final epochs of hardware-in-the-loop retraining reduces the overall training time from over 10 h to less than 2 h compared to a full hardware-in-the-loop retraining, while notably increasing accuracy. This work highlights that including device-specific static variations and noise in the training process is essential for a time-efficient hardware-aware network training for analog computations, and that major parts can be extracted from the hardware instance and represented with simple and efficient software structures. This work is the first step towards hardware-specific but hardware-inaccessible training, addressing speed and accuracy.
Noise reduction in dental CT images based on generative adversarial network

CT image technology has been widely used in recent research on dental imaging. High-quality CT images can be accessed by adopting a high-dose radiation dose that unfortunately exerts a detrimental influence on the human body. Reducing the radiation dose is a solution to eliminate the negative effect, while it will put noise into the CT images. In this paper, a dental CT image denoising method is proposed based on the generative adversarial network. A total of 6144 pairs in which each contained a noise dental CT image and a corresponding original dental CT image were trained through the model of the generative adversarial network. The test was carried out on the original untrained image. Comparing with the image without denoising, the test results indicate a significant improvement on the results. For example, the PSNR is increased by 21.52% and the SSIM is increased by 52.95%. In the comparison with the professional artificial vision performance, the result has also been significantly improved, which proves the effectiveness of the method in this paper. Moreover, the method proposed in this paper merely needs little training data. It is foreseeable that with the increasing of the training data, this method will have a better performance in the aspect of noise reduction in dental CT images.
NCSC warns UK universities of cyberthreats

The UK's National Cyber Security Centre (NCSC) has issued an assessment of the cyberthreats facing the country's universities.
Real-Time FEA-based breast deformation simulation using artificial neural network

Treatment of breast cancer involves two stages: diagnosis and treatment. It is difficult to correlate the imaging results at the two stages because as the patient’s position changes during treatment, the images captured during diagnosis do not represent the tumor location during the treatment. In the absence of real-time imaging during treatment, the visualization of tumor location is challenging for surgeons. In this paper, we combined finite element analysis (FEA) and neural network (NN) to accurately simulation real-time breast deformation for different patient’s positions. This research focuses on investigating visualization of breast deformation for different patients positions. We collected magnetic resonance imaging (MRI) images of a patient during diagnosis. This data was preprocessed to form a 3D reconstructed model that was used to run a FEA simulation. FEA simulates the deformation of breast tissues for different constraints, such as glandular ratio and gravity angle. However, it typically takes FEA to simulation such deformation from as fast as a few minutes to around 40 minutes when 8 cores were used. To obtain real-time visualization, we constructed a NN model that takes a breast gravity angle and glandular / fat ratio (breast material) as input to estimate breast deformation for different positions offline. The NN predicts the deformation and helps to visualize breast deformation in real-time (5 ms prediction time). Our results show that NN has the ability to map the gravity direction and breast material structure to the breast shape and tumor location accurately, while, keeping run time to a minimum. The average nodal displacement validation error of breast surface was 0.013 mm, and the tumor center location error was 0.05 mm. We took a patient’s diagnostic images as input, and used FEA to simulate breast deformation. Then we implemented two NNs, one to take gravity direction and material composition as input to predict breast surface nodal displacement; the other one used the same input, but output tumor center position. Finally, the model was used to provide a real-time breast deformation prediction during treatment (5ms). Our approach can be used to create tools that can be used inside an operating room for accurate tumor prediction.
Evolutionary Optimization Based Set Joint Integrated Probabilistic Data Association Filter

The joint integrated probabilistic data association (JIPDA) algorithm is widely used for the automatic tracking of multiple targets, but it has the well-known problem of track coalescence. By optimizing the posterior density, the accuracy of the target state estimation can be improved. Motivated by this idea, we developed a novel evolutionary optimization based joint integrated probabilistic data association (EOJIPDA) filter to overcome the coalescence problem of the JIPDA filter. The trace for the covariance matrix of the posterior density is used as the objective function for the above optimization problem. It is shown that the accuracy of the target state estimation can be improved by reducing the trace. Evolutionary optimization was employed to minimize the trace and optimize the posterior density. More specifically, we enumerated all the possible permutations of the targets and assign a unique index to each permutation. The resulting indices were randomly assigned to all possible association hypothesis events. Each assignment indicated one possible gene in the evolutionary algorithm. This process was repeated several times to arrive at the initial population. An illustrative example shows that the EOJIPDA filter can effectively improve the accuracy of state estimation. Numerical studies are presented for two challenging multi-target tracking scenarios with clutter and missed detections. The experimental results demonstrate that the EOJIPDA filter provides better tracking accuracy than traditional coalescence-avoiding methods.
Pearson Correlation Based Outlier Detection in Spatial-Temporal Data of IoT Networks

Outliers are values that change abruptly, either as a result of harsh climate (event) (or) as a result of sensor failure (error) [1]. Since sensor nodes are exposed to outside environment, they are more likely to generate defective data as a result of harse climate or sensor failure. Because more sensors are battery-powered, they may produce erroneous data if the batteries run out (error). Outlier detection techniques that have been used in the past have failed to distinguish between errors and events. This paper used spatial and temporal correlations to detect outliers in IoT sensors. Analysis of spatio-temporal data using Entropy-based and pearson correlation approaches is proposed in this paper.
An Iterative Montgomery Modular Multiplication Algorithm With Low Area-Time Product

This paper presents a highly efficient iterative Montgomery modular multiplication algorithm, wherein the computations of quotient and intermediate result in each iteration are done in parallel. This parallelism breaks the data dependency and thus reduces the computation latency. Moreover, this paper replaces required multiplications and additions in each iteration with compressions and encoding, thereby achieving a computation latency of order <inline-formula><tex-math notation="LaTeX">$d+6$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$d=\left\lceil N/m \right\rceil +2$</tex-math></inline-formula> is the number of iterations, <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> denotes the bitwidth of modulus <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of bits of the multiplier that are processed in each iteration of the algorithm. Hardware realization of the proposed Montgomery modular multiplication on a Xilinx Virtex-7 FPGA device shows <inline-formula><tex-math notation="LaTeX">$&gt; 41\%$</tex-math></inline-formula> computation latency saving and <inline-formula><tex-math notation="LaTeX">$&gt;31\%$</tex-math></inline-formula> area saving when <inline-formula><tex-math notation="LaTeX">$N=1,024$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$m=8$</tex-math></inline-formula> , compared with the best of previous state-of-art references. These savings amount to more than 63% reduction in terms of the area-latency product metric.