Reviewerly/exHarmony huggingface dataset:
Using Bayesian Networks to Characterize Student Performance across Multiple Assessments of Individual Standards

This paper explores how student performance across the full set of multiple modular assessments of individual standards, which we refer to as mini-assessments, from a large scale, operational program of interim assessment can be summarized using Bayesian networks. We follow a completely data-driven approach in which no constraints are imposed to best reflect the empirical relationships between these assessments, and a learning trajectory approach in which constraints are imposed to mirror the stages of a mathematic learning trajectory to provide insight into student learning. Under both approaches, we aim to draw a holistic picture of performance across all of the mini-assessments that provides additional information for students, educators, and administrators. In particular, the graphical structure of the network and the conditional probabilities of mastery provide information above and beyond an overall score on a single mini-assessment. Uses and implications of our work are discussed.
Blockchain

In this chapter, we'll learn what a blockchain is and its various elements and see the blockchain through the lens of distributed computing. Also, we will present formal definitions and properties of the blockchain. In addition, we will also introduce Bitcoin and Ethereum. Finally, I will introduce some blockchain use cases.
Cryptography

This chapter will cover cryptography and its two main types, symmetric cryptography and public key cryptography. After exploring some fundamental ideas, we will dive deeper into symmetric key primitives and then public key primitives.
Social media analysis by innovative hybrid algorithms with label propagation

• Semantic values of terms improve topic based Tweet classification performance. • Label Propagation Algorithm can be enriched by semantic weights on edges. • Combination of semantic classifiers superior to either type. • Preprocessing is important for short text classification. • Label Propagation Algorithm with semantics is efficient in sentiment polarity. Due to the huge size of the data accumulated on microblogging sites, recently, two fundamental questions have become very popular: 1) What percentage of this accumulated data has positive or negative sentiment polarity? 2) How is the distribution of this accumulated data on different topics? Inspired by these motivated necessities, this paper presents several different algorithms which are based on the Label Propagation Algorithm (LPA) in order to handle previously mentioned two fundamentals tasks: sentiment polarity detection task and topic-based text classification task. These algorithms are the Label Propagated- Relevance Frequency Classifier (LP-RFC) and LP-Abstract Frequency Classifier (LP-AFC). These algorithms can be defined as new semantic smoothing classifiers, which take advantage of the semantic connections among terms in the label propagation phase of the LPA. Additionally, another classifier, namely LP-Com RFC+AFC , was built. LP-Com RFC+AFC is actually a weighted summation classifier of the individual LP-RFC and LP-AFC. Furthermore, considering the shortage of labeled data in real-world scenarios, a semi-supervised version of LP-RFC and LP-AFC, namely “Merging Unlabeled and Labeled Instances with Semantic Values of Terms” (MULIS), was designed and implemented. For the experiments of the sentiment polarity detection task, three different datasets were use and for the experiments of topic-based text classification task, a self-collected tweet dataset was use. According to the experimental results, the suggested algorithms, and their composite form, LP-Com RFC+AFC , generated higher F1 scores than all of the baseline algorithms at nearly all of the training splits on the datasets.
Integrated Beam Steering Using a 2D Focusing Grating Coupler for Scalable Trapped Ion Quantum Computing

A scalable beam steering device is proposed for control of ion trap quantum computers. A combination of a waveguide array and a two-dimensional focusing grating coupler is used to generate 8xN beam spots at 729 nm wavelength in free space.
Predicting Mortality Rate based on Comprehensive Features of Intensive Care Unit Patients

Predictive analytics is gaining momentum in health-care since the adoption of electronic health record (EHR) system in hospitals. In particular, machine learning models are built using the critical care EHR data and the information provided during the ICU admissions to predict the mortality of patients admitted in ICU. As per the MIMIC-IV dataset, the survival rate of patients admitted in ICU is found to be 89.76%. This paper proposes a hybrid prediction technique that uses Random Forest and XGBoost for predicting the mortality rate. The proposed techniques performed well in predicting mortality rate despite the class imbalance problem of the dataset. The experiments conducted on MIMIC-IV dataset yields prediction accuracy of 89.72%.
Performance Estimation of Photonic Neural Network Accelerator with Magneto-optical Switch Array

We estimated performance of photonic neural network accelerator with magneto-optical switch array which provides nonvolatile weighting in convolution layer. Power consumption and operation time assuming AlexNet are calculated including modulators and photodetectors.
Improve the Response Time with Content Mining based Web Page Ranking

The existing information retrieval systems use page ranking algorithms that rely solely on analysis of linked information and examination of text content of the linked sites resulting in retrieval of a huge collection of web pages by the user for a given query. Whereas it is required that the information retrieval system shall re-rank search results according to their relevancy with respect to the user query. In this paper we propose a mechanism to improve the response time using a novel content based web page ranking mechanism deployed at a proxy server that not only measures the quality of the webpages as per the relevance of the user query but also attempts to reduce the delay in response (perceived latency).
A New 3d Face Recognition Method Based on Combined Category Information

In view of the shortcomings of existing face recognition that there are changes in face pose or changes in light intensity and angle, it is impossible to make full use of the training sample category information for training, resulting in recognition errors. The image feature space decomposition and extraction in this paper adopts the combined category information. The non-negative matrix factorization method can make full use of the category information of training samples and improve the recognition performance. The experimental results show that compared with the traditional non-negative matrix factorization method, the method adopted in this paper has better improvement and promotion in the effectiveness of feature extraction and face recognition rate.
The Implementation of Movies and TV Plays Analysis System Combined with Knowledge Graph and Data Visualization

More and more movies and television plays have been produced in recent years, but a few have succeeded in the market. Therefore, the analysis and speculation of the success factors of movies and television plays are very important for the producers and investors. The existing analysis platform only analyzes the benefits generated by movies and television dramas in a certain period and lacks the ability of prediction and reasoning. To analyze the key factors affecting the success of movies and television dramas and provide a reference for producers and investors, we design and implement a movies and television plays analysis system combined with a knowledge graph and data visualization technology. First of all, we crawl the information of movies and television plays and user comments on the Douban website; Then, the entities and relationships are extracted by OpenUE toolkit, and Neo4j is used to construct and store the knowledge graph in movies and television plays. On this basis, we utilize the improved TransR algorithm for knowledge completion and reasoning. Finally, combined with the knowledge graph, we analyze the success factors of popular movies and TV plays and visualize the analysis results in various chart types.
Multiple-Instance Learning with Efficient Transformer for Breast Tumor Image Classification in Bright Challenge

A computational workflow based on multiple-instance learning (MIL) and an efficient transformer was established to classify breast cancer subtypes from hematoxylin-and-eosin images. A tissue segmentation model was trained to identify only ductal epithelium patches for feature embedding and classification. Two feature extractors using self-supervised learning and fully supervised pretraining were tested together with a method based on MIL and a transformer on the dataset provided in the BRIGHT Challenge. The best approach achieved average F1 scores of 0.750 and 0.503 on the validation set for the two tasks, although the performance degraded to 0.643 and 0.388, respectively, on the final test set.
Transformer-Based Malicious Traffic Detection for Internet of Things

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to defend against malicious traffic from IoT devices. In this paper, a novel malicious traffic detection method is proposed based on the deep learning method. Specifically, a Transformer-based encoder is designed to automatically select key features of IoT traffic for the detection task, which avoids the cumbersome feature screening process that has been widely used in traditional machine learning methods. To address the complexity of the feature space and improve the efficiency of model training, we exploit the correlation between the characteristics of malicious traffic and the device type of IoT bots to further improve the detection accuracy by introducing a device classification auxiliary loss in the training phase. Experimental results show that our method outperforms the state-of-the-art machine learning-based methods in terms of accuracy, precision, recall and f1-score on real IoT traffic traces. In addition, the benefit of device type information on detection efficiency is verified.
EOSIOAnalyzer: An Effective Static Analysis Vulnerability Detection Framework for EOSIO Smart Contracts

EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.
Content-aware bitrate-based video quality assessment metric

No-reference (NR) Video Quality Assessment (VQA) methods often use bitrate as one of the most important parameters. This usually does not take into account that the quality of the video sequence at a given bitrate depends not only on the encoder used but also on the complexity of the video content as well as its resolution. In this paper, we present a novel low complexity Content-Aware Bitrate-Based (CABB) VQA metric that takes into account the spatial resolution, bitrate, spatial and temporal information of video sequences and can be used to evaluate video quality in video streaming. The metric was developed based on the Structural Similarity Index Measure (SSIM) computed for 11 training sequences with different complexity and for 5 spatial resolutions. The metric itself does not use SSIM scoring to assess video quality but estimates the SSIM of a test video sequence. The accuracy of the proposed metric was tested on three video sequences that were not used for the metric modeling, and the Pearson Linear Correlation Coefficient (PLCC) values were calculated for the actual SSIM values and the values obtained by CABB are above 0.9. PLCC values for CABB results for 85 test video sequences and corresponding subjective Difference Mean Opinion Score values were also calculated. The PLCC results showed that the proposed metric achieved a higher correlation with the subjective quality scores compared to the published bitrate-based metric.
PISTIS: Composing AWS Spot Instances with Guarantees

AWS offers transient instances as a way to sell unused capacity in their data centers. Although these transient instances come with a steep discount at their prices, up to 90% of discount compared to on-demand instances, they do not come with availability guarantees, thus, they are not reliable for regular usage on the Internet. This paper presents PISTIS, a framework that uses reliability engineering models that provides reliable computing resources over transient instances by proactively replacing components that are expected to fail. We evaluated our model and the results show that we can achieve the same reliability of regular on-demand instances but with a price reduction of up to 81% in the best cases compared to on-demand instances.
Chinese Medical Named Entity Recognition Based on Multi-word Segmentation and Multi-layer BILSTM

To solve the problem that a single Bidirectional Long Short-Term Memory (BILSTM) model can only learn the features of a specific dimension, and the local dependence between characters is easily lost when character-level segmentation is used to extract semantic features, this paper proposes a Chinese medical named entity recognition method based on multi-segmentation and multi-layer BILSTM. The purpose of multi-word segmentation is to enrich the semantic features that can be used in the model learning process, so as to further improve the entity recognition ability of the model. Multi-layer bidirectional long short-term memory can obtain feature information of different dimensions by setting hidden layers of different sizes, and use the attention mechanism to capture global information. Experimental results show that this method can significantly improve the performance of Chinese medical named entity recognition, and obtain 78.69% F value in real electronic medical record dataset.
Automobile Insurance Fraud Detection: An Overview

Frauds are committed in a highly professional way, due to which companies sometimes fail to identify that any fraud has occurred. Unprofessional frauds are also taking place, but they can be identified easily by the companies. Detecting a fraud traditionally is carried out using manual techniques, but by using data mining and algorithms from machine leaning or deep learning the detection process is automated and the frauds can be detected in a more efficient and structured way with more accuracy. The processing speed of information is gradually increased by using various algorithms of machine learning. The highlight of this survey paper is to compare different methods based on their performance. The algorithms help by detecting crucial patterns found in historical data and recognizing them if found in input data. The survey provides proper understanding of different methodologies used for automobile fraud detection. Sometimes machine learning cannot be used to detect multiple frauds involving behavior changes. We propose a vehicle insurance fraud detection mechanism using LSTM RNN networks. LSTM is commonly used in deep learning to design time series information.
Exploiting Syntactic Information to Boost the Fine-tuning of Pre-trained Models

Recently, pre-trained language models have achieved great success in the field of natural language processing (NLP). NLP downstream tasks have also seen remarkable improvement with the help of pre-trained models. However, syntactic information, such as dependency trees, is ignored in the process of fine-tuning, even though this prior information naturally exists in sentences. In this paper, we propose a model to exploit syntactic information to boost the fine-tuning of pre-trained models on downstream tasks. Our model encodes the syntactic information using a graph convolutional network (GCN) and then integrates it into text representations explicitly. In particular, we build a dependency graph from the word connections in the dependency tree obtained from an off-the-shelf dependency parser, aiming to preserve the dependency and syntactic structure in a sentence. We conduct extensive experiments on four types of downstream tasks. The empirical results show that our method significantly outperforms the strong pre-trained baseline models. Of note, our base-scale models outperform the large-scale baselines, with ob-vious performance gains, and an approximately 3-fold difference in the size of the model parameters is observed.
Cross-Domain Unseen Activity Recognition Using Transfer Learning

Activity Recognition (AR) models perform well with a large number of available training instances.However, in the presence of sensor heterogeneity, sensing biasness and variability of human behaviors and activities and unseen activity classes pose key challenges to adopting and scaling these pre-trained activity recognition models in the new environment.These challenging unseen activities recognition problems are addressed by applying transfer learning techniques that leverage a limited number of annotated samples and utilize the inherent structural patterns among activities within and across the source and target domains.This work proposes a novel AR framework that uses the pretrained deep autoencoder model and generates features from source and target activity samples.Furthermore, this AR framework establishes correlations among activities between the source and target domain by exploiting intra-and inter-class knowledge transfer to mitigate the number of labeled samples and recognize unseen activities in the target domain.We validated the efficacy and effectiveness of our AR framework with three real-world data traces (Daily and Sports, Opportunistic, and Wisdm) that contain 41 users and 26 activities in total.Our AR framework achieves performance gains ≈ 5-6% with 111, 18, and 70 activity samples (20% annotated samples) for Das, Opp, and Wisdm datasets.In addition, our proposed AR framework requires 56, 8, and 35 fewer activity samples (10% fewer annotated examples) for Das, Opp, and Wisdm, respectively, compared to the state-of-the-art Untran model.
LeG for Enhancing Student Communication and Learning Experience

This paper presents an experimental mobile app called Learning eGroup (LeG) for enhancing student communication and learning experience. In particular, it facilitates the formation of mobile learning groups, enhancing student/teacher interaction and sharing open educational resources (OERs). A basic chatbot function is also provided. The LeG app has been tested by students and student survey results are presented in the paper, providing insights into the development of mobile learning groups.
Hardware Development for Joint Sparse Decentralized Heterogeneous Data Fusion for Target Estimation

For surveillance applications, users are typically interested in detecting and tracking targets of interest. In many surveillance systems, heterogeneous sensor data are collected by sensors of varying sensing modalities with different dimensionalities. However, individual sensing system collects only one kind of signal, leading to a lack of accuracy to characterize the target. One of the most popular approaches to overcome single sensor assessment is data fusion. In our recent work, a new joint-sparse data-level fusion (JSDLF) approach to integrate heterogeneous sensor data for target discovery is developed in the matter of using a decentralized architecture algorithm. Several decentralized implementations of the data-level fusion approach based on the JSDLF approach were developed and investigated in the software aspect. In this paper, the hardware development of this algorithm will be displayed. Several unmanned aerial vehicles (UAVs) were utilized as nodes for image or RF SIGINT data processing. Meanwhile, a computer with RF SIGINT data processing communicated with these nodes as decentralized architecture as the hardware development of this algorithm. The performance of the decentralized hardware development is represented in this paper and the results are comparable with only software development in the algorithm.
WDM-Enabled Photonic Edge Computing

We experimentally realize photonic edge computing over an 86-km fiber link with 3 THz optical bandwidth and demonstrate DNN inference at 98.8% accuracy with optical energy consumption below 40 aJ/MAC.
Cybersecurity and Data Breach Harms: Theory and Reality

This paper challenges the view among some privacy scholars that private law should routinely recognize dignitary, emotional distress, or potential future harms in commercial data breach cases. Such harms might be cognizable in specific and relatively rare circumstances, but they are not empirically or doctrinally viable in the mine run of cases. A realistic account of how commercial cybercrime works and how cybercriminals make money demonstrates that a reasonable person should not become excessively anxious upon receipt of a data breach notification. At this point in the history of cyberspace, commercial cybercrime is a systemic problem more than an individual one. Systemic solutions focused on strengthening data security provisions in comprehensive privacy laws, enhancing payment card security, and reforming aspects of the credit reporting and U.S. Social Security numbering systems should play a more important role than private litigation. A focus on anxiety-based harms in data breach cases, in contrast, would yield few cybersecurity benefits while distorting longstanding tort doctrines and transferring rents to class action lawyers.
Abnormal behavior detection of stationary objects in surveillance videos with visualization and classification

SUMMARY Anomaly detection in video systems has been popular over several years. It is still challenging to detect anomalies in a static object. To manage this objective, we focus on changes in the position of a stationary object in videos. In a normal scenario, the pixel values of the static object are fixed while in abnormal motion the fixed values change. We introduce a new concept to determine anomalies based on manual annotations in each video frame, only over a part of a static object in a frame such that it can be taken as a reference for the whole. Through color channel splitting we determine mask image, from which handcrafted features such as scratch area, perimeter, equivalent diameter and density are calculated. In the next step, we analyze frame‐wise changes in feature values using a linear regression model, feature values are constant when the object remains stationary while there is a rise or fall in values when an object changes location. We classify feature values through anomaly scores and thresholds. In this model, we are evaluating our proposed framework on 12 real‐time video datasets. Results are compared with existing techniques which are outperforming in terms of accuracy, mean square error and area under the curve.