Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Reviews of E-commerce Sites Using Machine Learning Algorithms

Customers of e-commerce platforms exchange their thoughts with such kinds of languages. In the age of the present competitive business world, sentiment analysis is widely used in the e-commerce industry to improve efficiency and better understand to make business decisions. Earlier research on sentiment analysis was in English but there is no such significant work in Bangla language and Romanized Bangla language reviews. Therefore, we have developed a machine learning model where reviews on three different languages (Bangla, English, and Romanized Bangla) are used and applied six machine learning algorithms. We have demonstrated a comparative analysis with existing work and have discussed the detailed accuracy, precision, recall, F1 scores, and ROC area. We have prepared three datasets and labeled all the reviews data as Negative, Positive, Neutral, Slightly Negative, and Slightly Positive sentiment. To perform the analysis, the preprocessed datasets were trained using machine learning techniques, and the model performances is evaluated. For the Bangla dataset, Support Vector Machine(SVM) algorithm performed best by achieving 94% accuracy and for the English and Romanized Bangla dataset, Random Forest algorithm performed best by achieving 93% and 94% accuracy respectively.
Quantifying the Performance of Adversarial Training on Language Models with Distribution Shifts

Adversarial training has recently emerged as an important defense mechanism to robustify machine learning models in the presence adversarial examples. Although adversarial training can boost the robustness of machine learning algorithms by a margin, research has not been conducted to determine if adversarial training is effective in the long-term. As deployments of machine learning algorithms are characterized by dynamics, change of the underlying model is inevitable. The dynamics are a result of model's evolution over time by introducing new training data and drifting the model by changing its parameters. In this paper, we examine the limitations of adversarial training due to the temporal changes of machine learning models. Using a natural language task, we conduct various experiments using a variety of datasets to measure the impact of concept drift on the efficacy of adversarial training. In particular, our analysis shows that certain adversarially-trained models are even more prone to the drift than others. In particular, WordCNN and LSTM-based models are shown more susceptible to the temporal changes than others such as BERT. We validate our findings using multiple real-world datasets on different network architectures. Our work calls for further research into the temporal aspects of adversarial training.
Multilayer graphs

In this short position paper, we argue that there is a need for a unifying data model that can support popular graph formats such as RDF, RDF* and property graphs, while at the same time being powerful enough to naturally store information from complex knowledge graphs, such as Wikidata, without the need for a complex reification scheme. Our proposal, called the multilayer graph model, presents a simple and flexible data model for graphs that can naturally support all of the above, and more. We also observe that the idea of multilayer graphs has appeared in existing graph systems from different vendors and research groups, illustrating its versatility.
Minun

Entity Matching (EM) is an important problem in data integration and cleaning. More recently, deep learning techniques, especially pre-trained language models, have been integrated into EM applications and achieved promising results. Unfortunately, the significant performance gain comes with the loss of explainability and transparency, deterring EM from the requirement of responsible data management. To address this issue, recent studies extended explainable AI techniques to explain black-box EM models. However, these solutions have the major drawbacks that (i) their explanations do not capture the unique semantics characteristics of the EM problem; and (ii) they fail to provide an objective method to quantitatively evaluate the provided explanations. In this paper, we propose Minun, a model-agnostic method to generate explanations for EM solutions. We utilize counterfactual examples generated from an EM customized search space as the explanations and develop two search algorithms to efficiently find such results. We also come up with a novel evaluation framework based on a student-teacher paradigm. The framework enables the evaluation of explanations of diverse formats by capturing the performance gain of a "student" model at simulating the target "teacher" model when explanations are given as side input. We conduct an extensive set of experiments on explaining state-of-the-art deep EM models on popular EM benchmark datasets. The results demonstrate that Minun significantly outperforms popular explainable AI methods such as LIME and SHAP on both explanation quality and scalability.
Positive-and-Negative Learning for Single Image Dehazing

Single image dehazing has been a challenging problem for following high-level computer vision task due to the terrible information distortion. We noticed that most of existing dehazing methods are tend to pay more attention to positive sample and ignore crucial effect of negative sample. In this paper, we proposed a single image dehazing method that learns both positive sample (i.e. clean image) and negative sample (i.e. hazy image) to restore a hazy image, named Positive-and-negative Learning Dehazing Network (PNLDN). Firstly, we design a Positive Learning Dehazing Network by adopting Teacher Network based on knowledge distillation technology. Positive knowledge is absorbed by Dehazing Network. Secondly, to generate a better dehazing image, we proposed a Negative Contrast Optimization (NCO) module to push the dehazing output away from hazy image. Thirdly, we improved the common downsampling and upsampling method by using a deformable RoI (region of interest) pooling layer, named Dynamic Enhanced Sampling (DES). DES assists CNN to receive more spatial information of image and process irregular edge of objects. Experimental results have demonstrated that PNLDN surpasses state-of-the-art single image dehazing methods.
PacJam

Real-world software is usually built on top of other software provided as packages that are managed by package managers. Package managers facilitate code reusability and programmer productivity but incur significant software bloat by installing excessive dependent packages. This dependency hell increases potential security issues and hampers rapid response to newly discovered vulnerabilities. We propose a package-oriented debloating framework, PacJam, for adaptive and security-aware management of an application's dependent packages. PacJam improves upon existing debloating techniques by providing a configurable fallback mechanism via post-deployment policies. It also elides the need to completely specify the application's usage scenarios and does not require runtime support. Moreover, PacJam enables to rapidly mitigate newly discovered vulnerabilities with minimal impact on the application's functionality. We evaluate PacJam on 10 popular and diverse Linux applications comprising 575K-39M SLOC each. Compared to a state-of-the-art approach, piecewise debloating, PacJam debloats 66% of the packages per application on average, reducing the attack surface by removing 46% of CVEs and 69% (versus 66%) of gadgets, with significantly less runtime overhead and without the need to install a custom loader.
Research and Implementation of Parallel Optimization of SM3 Algorithm Based on Multithread

The national secret SM3 cryptographic hash algorithm is to generate a fixed hash value after iterative compression and expansion of a group of messages, which is mainly suitable for the generation of digital signatures and random verification codes. The algorithm is based on the hash calculation, which is a computationally intensive task with high computational intensity. In order to improve the operating efficiency of the algorithm, make it achieve optimized operating effects on a multi-core system. This article uses multi-threaded parallel to rewrite the algorithm in parallel: through the privatization of variable attributes to avoid memory read and write conflicts in multi-threaded parallel, through dynamic task scheduling to achieve multi-threaded load balancing, so as to realize the parallel version of SM3 algorithm. Experimental results show that the parallel algorithm has a good linear acceleration when the number of threads increases, and the speedup ratio is increased by 2-6 times compared with the serial algorithm, which can provide a reference for the performance improvement of the algorithm on multi-core computing components.
SnapSolve — A Novel Mathematics Equation Solver using Deep Learning

Handwritten recognition has achieved tremendous success in the real world. It has various applications such as equation solver, automation, etc. Researchers have proven how recognition of handwritten language characters is done by using a three-step procedure using machine learning algorithms. Moreover, the OCR method allows converting data from physical to digital. Machine learning algorithms like SVM, K-means, Naive Bayes, Decision Trees are used for text recognition. Recognizing handwritten text is a big challenge as the handwriting style of the person can vary from time to time. Poor resolution of the image will lead to inaccurate text recognition. With the increase in advancements, researchers found deep learning models are providing better efficiency than machine learning models. Deep Learning is providing better accuracy for image segmentation, object detection, speech recognition, and image classification as well. Handwritten mathematical equation solving has been implemented using the CNN model. This system is solving offline handwritten polynomial equations using Deep Learning CNN model.
Required competencies for e-learning among science and mathematics supervisors: post-pandemic features of education

Purpose The aim of this study was to measure the readiness of science and mathematics supervisors to utilize technology and online learning platforms for teachers' plans and professional development, during and after the period of the COVID-19 pandemic. Design/methodology/approach To achieve this aim, the researchers developed a questionnaire comprising of 55 items based on the instruments used in pertinent studies. A mixed-methods research design was employed, whereby a quantitative online survey was supplemented by focus group discussions with selected supervisors. Survey data were subjected to one-way analysis of variance and t -test, while information obtained via focus groups was coded to identify common themes related to the obstacles and challenges supervisors face. Findings When completing the survey, the supervisors approached proficiency using technology; however, focus group discussions revealed misconceptions related to e-leaning and limitations in their abilities to use technology in schools, as well as obstacles imposed by the structure and management of the educational system. T Practical implications These findings indicate that supervisors need support in acquiring the competencies required for integrating technology in education, and that their support to teacher community needs to be grounded in clear and systematic approaches and best educational practices. Originality/value These findings indicate that supervisors need support in acquiring the competencies required for integrating technology in education, and that their support to teacher community needs to be grounded in clear and systematic approaches and best educational practices.
A Survey on the Applications of Image Classification Based on Convolution Neural Network

Convolutional neural network has an obvious advantage to deal with image classification, compared to other deep learning algorithms. In this paper, we review current methods for classifying images and introduce classical image classification models based on CNN. CNN is a traditional and useful network and widely used in various fields of artificial intelligence. We conclude the feature extraction methods of image classification, including linear feature extraction and non-linear feature extraction. Additionally, the applications of CNN and the related models from 2012 to 2021 have been discussed in chronological order in this paper.
Trajectory Obfuscation and Detection in Internet-of-vehicles

In Internet-of-vehicles, vehicles cooperate with each other by transmitting Internet-of-vehicles and location-based service (LBS) providers optimize services by analyzing trajectory data collected from drivers. Nevertheless, illegal trajectories generated by attackers or malicious drivers can obfuscate the process of analysis and breach the quality of service. Some mechanisms protect drivers’ location privacy by using obfuscation-based schemes. Obfuscation-based mechanisms report LBS with obfuscated trajectories data rather than actual trajectories, which increases difficulties to detect illegal trajectories accurately. This paper focuses on detecting illegal trajectories when all drivers employ obfuscation-based mechanisms to protect location privacy. In this paper, we propose a dynamic obfuscation mechanism in road networks based on Geo-indistinguishability to dynamically protect drivers’ location privacy. Considering personalization in road networks, we also propose a classification mechanism to detect illegal trajectories in road networks. Illegal trajectories are generated based on real trajectories to simulate actions of malicious drivers and attackers. Experiment results in real road networks show that the classifier can detect illegal obfuscated trajectories with at least 94% Area Under the Curve (AUC) score, which outperforms than existing works in road networks.
Accuracy Estimation for Autonomous Navigation of a Quadruped Robot in Construction Progress Monitoring

Construction progress monitoring is a critical inspection activity. It is mostly a manual process performed through in-person visual inspection, which is time-consuming and prone to human error. The key in progress monitoring is accurate, timely, and regular data collection and analysis of the construction process. Autonomous data collection with mobile robots can potentially improve the accuracy of data acquisition for construction progress monitoring. This study uses a four-legged robot to automate data collection in construction progress monitoring. The goal is to study the navigation accuracy of the legged robot in the on-site data acquisition process of progress monitoring. The research studies human operator positioning in the data collection process and undertakes a set of experimental investigations with the legged robot to retrace the path recorded on its controller and analyze the accuracy of the robot's autonomous navigation in multiple missions. Findings reveal an Autowalk position error of 0.283 m to 0.633 m from the recorded path.
DyGraph

Dynamic graph processing, execution on vertex-edge graphs that change over time, is quickly becoming a key computing need of the twenty-first century. Dynamic graph algorithms unlock real-time optimization solutions and a wide range of data-mining applications in logistics, finance, marketing, healthcare, and social media, among many others. However, graph algorithms are extremely memory-bound (i.e., their performance is limited by the bandwidth of memory accesses on the underlying hardware platform, rather than the compute capacity). Moreover, dynamic graph algorithms are being applied to increasingly-large datasets, further straining the memory systems and reducing performance. As a result, additional research is needed to leverage new memory technologies for faster, more efficient, dynamic graph-based processing. Such research is difficult without access to hitherto unavailable industrial-scale dynamic graph datasets to evaluate solutions.
Detection of Traffic Sign Based on Improved YOLOv4

To improve safe driving and reduce the mental fatigue of drivers, accurate and fast access to traffic sign guidance information in road scenes is needed. This work proposes a traffic sign detection method based on improved YOLOv4. First, the MLP architecture is combined with a convolutional neural network to enhance the network's ability to detect objects. Second, the same-level connection is added within PANet to enhance the feature information extracted by the network. Experiments show that the improved YOLOv4 algorithm can improve the detection accuracy of 416&#x00D7;416 sample images from 72.95% to 78.84%, and still meet the real-time detection requirements. After comparing our method with similar algorithms, the experimental results show that our algorithm has certain advantages.
Markovian imprecise jump processes: Extension to measurable variables, convergence theorems and algorithms

The existing framework of Markovian imprecise jump processes, also known as imprecise continuous-time Markov chains, is limited to bounded real variables that depend on the state of the system at a finite number of (future) time points. This is an issue in many applications, because typically the variables of interest depend on the state of the system at all time points in some – possibly unbounded – (future) interval, and they can be unbounded or even extended real valued; examples of such variables are temporal averages, the number of (selected) jumps in some interval and hitting times. To eliminate this shortcoming, we assume that the sample paths are càdlàg and use measure theory to extend the domain of Markovian imprecise jump processes to extended real-valued variables that may depend on the state of the system at all (future) time points – that is, the extended real variables that are bounded below or above and are measurable with respect to the σ-algebra generated by the cylinder events. We investigate the continuity properties of the extended lower and upper expectations with respect to point-wise convergent sequences, and this yields generalisations of the Monotone Convergence Theorem and Lebesgue's Dominated Convergence Theorem. For two particular classes of variables, we strengthen these convergence theorems and present an iterative scheme to approximate their lower and upper expectations. The first class is the number of selected jumps in some interval, and the second class are real variables that take the form of a Riemann integral over some interval; this second class includes temporal averages and occupancy times.
A pixel-level coarse-to-fine image segmentation labelling algorithm

Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.
ASAP

The security of digital communication relies on few cryptographic protocols that are used to protect internet traffic, from web sessions to instant messaging. These protocols and the cryptographic primitives they rely on have been extensively studied and are considered secure. Yet, sophisticated attackers are often able to bypass rather than break security mechanisms. Kleptography or algorithm substitution attacks (ASA) describe techniques to place backdoors right into cryptographic primitives. While highly relevant as a building block, we show that the real danger of ASAs is their use in cryptographic protocols. In fact, we show that highly desirable security properties of these protocols - forward secrecy and post-compromise security - imply the applicability of ASAs. We then analyze the application of ASAs in three widely used protocols: TLS, WireGuard, and Signal. We show that these protocols can be easily subverted by carefully placing ASAs. Our analysis shows that careful design of ASAs makes detection unlikely while leaking long-term secrets within a few messages in the case of TLS and WireGuard, allowing impersonation attacks. In contrast, Signal's double-ratchet protocol shows higher immunity to ASAs, as the leakage requires much more messages.
Mathematical Methods in Biomedical Studies: The Need for New Approaches to Study of Follicular Thyrocytes

The current stage of medical science development requires updating the methodological and procedural base of research, which leads to the expansion of the mathematical methods' scope for medical purposes, including medical diagnosis. Most frequently, its problems are solved by Bayesian, correlation and regression analysis, phase interval method and the methodology of the logical conclusion (logical programming), which operate on quantitative information and are not designed to use qualitative and binary data. The methodology of the fuzzy-set logic, which permits to transform qualitative information into mathematical dependencies, is not widely used yet in the study of biological objects, as it assumes a rigid dependence of some phenomena on others, which is not typical of living biological systems. This limits the use of mathematical technologies to study the characteristics of changes that occur in cells of the body under the influence of various factors. To effectively solve a number of multidisciplinary medical, medico-social and social problems, namely polyetiologically caused thyroid pathology, it is necessary to develop modern informative approaches to study the activity of the thyroid gland in normal and pathology based on mathematical methods.
Representation and Extraction of Physics Knowledge Based on Knowledge Graph and Embedding-Combined Text Classification for Cooperative Learning

Physical knowledge is the foundation of most engineering fields in particular such as product design, analysis, and operation and maintenance. However, due to the complexity of physical concepts, laws, and calculations, students can be easily overwhelmed by the conceptual ideas in the process of learning physics. This paper proposes a new way for helping students grasp the logical relation between the physics knowledge points based on neural networks and knowledge graph technology. Specifically, we use Python scripts to collect the articles about physics knowledge on the Internet as the raw data. After removing the special characters and other irrelevant text, the rest of the data is passed to several neural networks based on BERT and ERNIE for their effective and efficient classification into seven kinds of physics knowledge. The experimental results show that using ERNIE-BERT for embedding and using RCNN for the downstream model achieve the best performance. Knowledge graph is used to build a tree structure of physics knowledge, holding the physics knowledge picked out by the neural networks under corresponding nodes.