Reviewerly/exHarmony huggingface dataset:
Intelligent Data Management to Facilitate Decision-Making in Healthcare

The advancements in digitization are transforming the healthcare industry, one of the prominent industries producing critical data through patient care. The management of structured and processed data is becoming a challenge. Collecting, storing, and analyzing the data by efficiently reducing the complexity of data management makes the healthcare industry one of the most valuable industries. Creating meaningful and accurate disease predictions is critical in the healthcare sector. A study was conducted using VOSviewer software, which led to four clusters of keywords from different domains based on occurrences and relevance taken from 1500 documents from 1995 to 2021 from Web of Science. These keywords were mapped to the fields impacting the data management in Healthcare to explore the potential problems based on several types of research to establish a framework with an exploratory analysis. The methodology applied in this analysis describes the progress in data management in Healthcare and can let researchers, scholars, and healthcare professionals gain insights for facilitating the healthcare decision-makers.
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.
Boosting Breast Cancer Classification from Microscopic Images Using Attention Mechanism

Attention mechanism is one of the foremost later and effective deep learning methods which have shown tremendous performance in detection and classification tasks, including medical imaging. Breast cancer is a primary cause of women's cancer-related morbidity and mortality globally; thus, early identification of this malignancy will help to reduce the number of fatalities. The accurate classification of a mild and deadly cancer in microscopic breast images can give an efficient and relatively low-cost technique for breast cancer early detection. This paper proposes a deep learning model based on an attention mechanism. The proposed attention mechanism derives its input features from pre-trained models and passes its output through a Multilinear perceptron and SoftMax for classification. We trained all models on the ICIAR2018 Grand challenge Breast Cancer dataset and compared them with the pre-trained and ensemble models. For quantitative analysis, validation tests were conducted using the performance metrics for each approach. The suggested approach is proven successful, with classification results improving by +1-6%, potentially reducing human errors in the diagnosis process. Furthermore, the proposed method outperforms the state-of-the-art accuracy, with a +3-8% improvement in performance results.
Keeping Students Connected and Engaged in a Wet-Lab Research Experience during a Time of Social Distancing via Mobile Devices and Video Conferencing Software

Two major COVID-19 pandemic challenges presented for in-person instruction included adhering to social distancing guidelines and accommodating remote learners who were temporarily isolated or permanently participating from afar. At Binghamton University, our First-year Research Immersion (FRI) program was challenged with providing students with a wet lab course-based undergraduate research experience (CURE), an intense hands-on experience that emphasized student teamwork, lab protocol development, iteration, troubleshooting, and other elements of the scientific process that could not be replicated in a fully remote environment. We developed an innovative technology approach to maximize all students' connection to the lab research experience, utilizing dedicated mobile devices (iPod Touch) and video conferencing software (Zoom) to synchronously connect remote learners to in-person learners, peer mentors, and instructors in our FRI research labs. In this way, despite limited lab capacities and fluctuating remote learning populations, we were able to connect remote learners to their peers and mentors in real-time and give them responsibilities that allowed them to be engaged and feel like meaningful participants in the research process. Although our students reported a preference for in-person labs, they noted that this hybrid model was better than other traditionally employed remote-learning lab options. We believe that the lessons learned here can be applied to improve access to research in all situations and allow us to be prepared for other catastrophic disruptions to the educational system.
Using Kernel SHAP XAI Method to Optimize the Network Anomaly Detection Model

Anomaly detection and its explanation is important in many research areas such as intrusion detection, fraud detection, unknown attack detection in network traffic and logs. It is challenging to identify the cause or explanation of &#x201C;why one instance is an anomaly?&#x201D; and the other is not due to its unbounded and lack of supervisory nature. The answer to this question is possible with the emerging technique of explainable artificial intelligence (XAI). XAI provides tools and techniques to interpret and explain the output and working of complex models such as Deep Learning (DL). This paper aims to detect and explain network anomalies with XAI, kernelSHAP method. The same approach is used to improve the network anomaly detection model in terms of accuracy, recall, precision and f-score. The experiment is conduced with the latest CICIDS2017 dataset. Two models are created (Model_1 and OPT_Model) and compared. The overall accuracy and F-score of OPT_Model (when trained in unsupervised way) are 0.90 and 0.76, respectively.
Parameter-Free Style Projection for Arbitrary Image Style Transfer

Arbitrary image style transfer is a challenging task which aims to stylize a content image conditioned on arbitrary style images. In this task the feature-level content-style transformation plays a vital role for proper fusion of features. Existing feature transformation algorithms often suffer from loss of content or style details, non-natural stroke patterns, and unstable training. To mitigate these issues, this paper proposes a new feature-level style transformation technique, named Style Projection, for parameter-free, fast, and effective content-style transformation. This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs. Extensive qualitative analysis, quantitative evaluation, and user study have demonstrated the effectiveness and efficiency of the proposed methods.
Secure Electronic Voting Machine using Biometric Authentication

The security issues like impersonation, counterfeiting, tampering are the major cause of concern in the current voting system. With the advancement in the technology, we can improve the security using biometric information for verification and authentication purpose. In this paper, the design of an Electronic Voting Machine (EVM) is proposed that will be able to mitigate the problems of tampering, fake voting, and security issues to strengthen the trust of the citizens in the electoral process. In this scheme biometric data (like Aadhaar) will be linked with an electronic voting machine in order to authenticate and validate the eligibility of voters so that only eligible and authorized persons can cast their vote. The proposed prototype Electronic Voting Machine consists of a Control Unit, a Ballot Unit and a Liquid Crystal Display (LCD) monitor. The LCD is used to display the casted vote to the voters. The Ballot Unit is provided to voters and all operations are performed in the Control Unit. The Control Unit has a microcontroller to perform operations and execute algorithms. The system was tested and found to be cost-effective, reliable, secure, and tamper proof.
Image-Text Alignment and Retrieval Using Light-Weight Transformer

With the increasing demand for multi-media data retrieval in different modalities, cross-modal retrieval algorithms based on deep learning are constantly updated. However, most of them have trouble with large model parameters and insufficient intrinsic nature between different modalities. We proposed a Light-weight Transformer Alignment Network (LTAN), which adopts the current mainstream visual and textual feature extraction methods. With convolutional neural network combined with light-weight transformer architecture and fully connected neural network, LTAN improves the generalization ability of the model while maintaining high performance. In order to extract visual features that lay emphasis on global details, enhancement paths are constructed to fuse precise location signals stored in low-level features with semantic information extracted from high-level to improve the model retrieval accuracy. It obtains the state-of-the-art results on image and sentence retrieval on MS-COCO and Flickr30k datasets. On the MS-COCO 1K test set, our model obtains an improvement of 3.9% and 2.5% respectively for the image and sentence retrieval tasks on the Recall@1 metric. The size of our model is 15% smaller than models using standard transformer as backbone.
Cybersecurity Awareness Modeling Associated with Influential Factors Using AHP Technique: A Case of Railway Organizations in Thailand

This study aims to create a method for identifying and evaluating the factors that impact Cybersecurity Awareness (CSA) of Internet users. Focusing on the employees in the railway organizations in Thailand, seven individual different factors, including attitude, cognitive, experience (in cyber-attacks), education and gender are used to determine and measure the factors associated with CSA of users. After conducting a decision-making technique called the Analytic Hierarchy Process (AHP), for deriving the weight of each factor, the study shows that the most important factor is cognitive, its weight is 26.23 %. While the weights of experience, degree (of education), behavior, finance and gender are 22.43 %, 16.41 %, 15.92 %, 10.13 %, 4.99 %, 3.90 % respectively. Therefore, the CSA model associated with those factors is introduced eventually.
Two-Tiered Consensus Mechanism Based on Proof of Work and Proof of Stake

The key features that distinguish blockchain as one of the most secure technologies are - decentralization, privacy, and immutability. It has converted a low-trust centralized ledger held by a single third party into a high-trust decentralized ledger held by multiple entities. The main contribution of blockchain is the consensus protocol, which ensures the stable operation of the blockchain system. Consensus occurs when all nodes in a blockchain network agree on a common state of the ledger. Thus, a consensus must prohibit malicious actors from controlling the network while also granting equal rights to all nodes and mandating their participation in the consensus process. Existing algorithms such as proof of work and proof of stake are vulnerable to future attacks such as 51&#x0025; attack. The proposed algorithm solves issues such as unfair miner selection, 51&#x0025;-attack, forking, double spending problem with minimal computational resources. It provides adequate protection regardless of the attacker&#x0027;s hashing ability or currency holdings and also resolves the enormous waiting time issue for transaction confirmation.
Early Stage Diabetes Prediction via Extreme Learning Machine

Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world&#x2019;s adult population has diabetes. Forty-six percent of people with diabetes have not been diagnosed. Diabetes can develop several other severe diseases that can lead to patient death. Developing and rural areas suffer the most due to the limited medical providers and financial situations. This paper proposed a novel approach based on an extreme learning machine for diabetes prediction based on a data questionnaire that can early alert the users to seek medical assistance and prevent late diagnoses and severe illness development.
Computing with Invertible Logic: Combinatorial Optimization with Probabilistic Bits

Special purpose accelerators for solving combinato-rial optimization problems have been receiving increasing attention. One contender in this approach is based on probabilistic computing with probabilistic or p-bits. P-bits constitute the fundamental computational unit between bits and q-bits, fluc-tuating between logic 0 and 1. Modified magnetoresistive RAM technology opens up the possibility of building highly integrated probabilistic computers with millions of p-bits. For medium-scale p-bit based accelerators conventional CMOS technology using FPGAs and ASICs offer a competitive alternative. Here, we present some of the latest results using p-bits, in sparse, modular, and scalable p-circuits. Results are presented for combinatorial optimization problems such as integer factorization and Boolean satisfiability (SAT), using the principles of invertible logic for generating hardware-aware graph representations. Invertible logic allows the design of Boolean circuits that can be operated in reverse: for example, a digital multiplier built out of elemental logic gates (AND, OR, NOT), can be used to factor integers. Similarly, SAT problems can simply be solved by constructing the corresponding logical circuit in hardware and running it in re-verse. Using powerful probabilistic algorithms such as simulated annealing and parallel tempering, we show how interconnected p-bits in invertible probabilistic circuits can solve computationally hard optimization problems such as Boolean SAT and integer factorization up to 25-bit semiprimes (in the tens of millions) showing competitive performance amongst all Ising Machines.
Self-Calibrated Multi-Sensor Wearable for Hand Tracking and Modeling

We present a multi-sensor system for consistent 3D hand pose tracking and modeling that leverages the advantages of both wearable and optical sensors. Specifically, we employ a stretch-sensing soft glove and three IMUs in combination with an RGB-D camera. Different sensor modalities are fused based on the availability and confidence estimation, enabling seamless hand tracking in challenging environments with partial or even complete occlusion. To maximize the accuracy while maintaining high ease-of-use, we propose an automated user calibration that uses the RGB-D camera data to refine both the glove mapping model and the multi-IMU system parameters. Extensive experiments show that our setup outperforms the wearable-only approaches when the hand is in the field-of-view and outplays the camera-only methods when the hand is occluded.
Current applications and challenges of digital pathology in cytopathology

Cytopathology stands to gain a multitude of benefits from the movement toward digitization. The increased demand for rapid on-site evaluation (ROSE), primary diagnosis, education, and biomarker quantification can be met by the use of telecytology, whole slide imaging (WSI), and digital image analysis solutions. In this review, we will focus on currently available clinical applications as well as challenges in digital cytopathology.
Category Alignment Adversarial Learning for Cross-modal Retrieval

Cross-modal retrieval aims to retrieve one semantically similar media from multiple media types based on queries entered by another type of media. An intuitive idea is to map different media data into a common space and then directly measure content similarity between different types of data. In this paper, we present a novel method, called Category Alignment Adversarial Learning (CAAL) for cross-modal retrieval. It aims to find a common representation space supervised by category information, in which the samples from different modalities can be compared directly. Specifically, CAAL firstly employs two parallel encoders to generate common representations for image and text features respectively. Furthermore, we employ two parallel GANs with category information to generate fake image and text features which next will be utilized with already generated embedding to reconstruct the common representation. At last, two joint discriminators are utilized to reduce the gap between the mapping of the first stage and the embedding of the second stage. Comprehensive experimental results on four widely-used benchmark datasets demonstrate the superior performance of our proposed method compared with the state-of-the-art approaches.
A Robust and Healthy Against PVT Variations TRNG Based on Frequency Collapse

True Random Number Generator (TRNG) is used in many applications, generally for generating random cryptography keys. In this way, the trust of the cryptography system depends on the quality of the random numbers generated. However, the entropy fluctuations produced by external perturbations generate some false positives in the random sequence. These false positives can generate a disastrous scenario, depending on the application. This work presents the results of different tests to demonstrate the robustness and health of the TRNG based on frequency collapse. The TRNG passed all entropy tests provided for NIST SP800-90B and AIS31. The entropy test denotes a 0.9789 minimum normalized entropy and 7.998 Shannon entropy. In addition, the TRNG passes the health tests provided for NIST SP800-90B. The health test shows a number of identical values <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{v}=0\%$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{v-1} &lt; 0.004\%$ </tex-math></inline-formula> and a maximum cutoff value <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MC_{v}=10$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LMC_{v}=13$ </tex-math></inline-formula> in the repetition count and adaptive proportion tests, respectively. The implementation passed all the statistical tests provided for NIST SP800-22 and AIS20. Besides, the implementation passes the different tests with Process, Voltage, and Temperature (PVT) variations. The TRNG is implemented in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18~\mu m$ </tex-math></inline-formula> General Purpose (GP) CMOS technology, occupying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25600~\mu m^{2}$ </tex-math></inline-formula> with four entropy sources. Finally, the implementation presents a 7.3 until 9.2-Mb/s of bit rate, 0.56 until 1.88-mW of power consumption, and 77.2 until 204.3-pJ/bit of energy per bit using an entropy source with 16 and 2 delay stages, respectively.
Image Deraining and Denoising Convolutional Neural Network For Autonomous Driving

Perception plays an important role in reliable decision-making for autonomous vehicles. Over the last ten years, huge advances have been made in the field of perception. However, perception in extreme weather conditions is still a difficult problem, especially in rainy weather conditions. In order to improve the detection effect of road objects in rainy environments, we analyze the physical characteristics of the rain layer and propose a deraining and denoising convolutional neural network structure. Based on this network structure, we design ablation experiments and experiment results show that our method can effectively improve the accuracy of object detection in rainy conditions.
Using Augmented Reality to Improve Learning Efficacy in a Mechanical Assembly Course

Mechanical assembly courses are widely set up for mechanical and vehicle engineering majors. Teaching in these classes is traditionally presented in the form of 2-D lectures, which are ineffective for students to understand complex 3-D information. The study presented in this article aimed to investigate whether augmented reality (AR) could improve students’ learning efficacy in a mechanical assembly course. An AR-based assembly instruction app on mobile devices was developed, and a comparative research approach was adopted. A total of 104 junior students from four classes on automotive engineering at Nanjing Forestry University participated in the experiment during their professional skill training course. The control group was provided with handouts as usual, and the experimental group was required to use AR instructions during the assembly processes. Several evaluation indicators, such as assembly quality and speed, were recorded and analyzed at the same time. The results showed that such indicators were significantly improved with the utilization of AR. In addition, a questionnaire was specifically designed and distributed to all the students at the end of the course. It was found that several learning factors, such as learning interest and academic achievements in the experimental group, showed a positive correlation.
Posets with interfaces as a model for concurrency

We introduce posets with interfaces (iposets) and generalise their standard serial composition to a new gluing composition. In the partial order semantics of concurrency, interfaces and gluing allow modelling events that extend in time and across components. Alternatively, taking a decompositional view, interfaces allow cutting through events, while serial composition may only cut through edges of a poset. We show that iposets under gluing composition form a category, which generalises the monoid of posets under serial composition up to isomorphism. They form a 2-category when a subsumption order and a lax tensor in the form of a non-commutative parallel composition are added, which generalises the interchange monoids used for modelling series-parallel posets. We also study the gluing-parallel hierarchy of iposets, which generalises the standard series-parallel one. The class of gluing-parallel iposets contains that of series-parallel posets and the class of interval orders, which are well studied in concurrency theory, too. We also show that it is strictly contained in the class of all iposets by identifying several forbidden substructures.