Reviewerly/exHarmony huggingface dataset:
Hybrid Feature Vector Space based Ensemble Machine Learning Approach for Sentiment Analysis on Amazon Product Reviews

In recent era, people are getting more attracted to micro blogs and social media to share their daily activities and express feelings and opinions. Machine learning based sentiment analysis becomes immensely popular to judge the feelings about a particular content on how positive or negative their feelings and opinions are before taking important decisions. In this paper, we propose an effective and combined machine learning approach with an enhanced hybrid feature vector space of latent concepts and external information features. The latent concepts are prepared by a supervised machine learning approach, and the external information features, estimating the quality of the information shared in the documents, are classified by the unsupervised rule-based learning approach. A Random Forest ensemble method has been utilized to build a classifier model, and some standard performance measures such as accuracy, precision, recall, f1-score and Cohen’s Kappa value have been taken into account to analyze the performance. The novelty of this paper lies in the hybridization of feature vector space of latent concepts and external information features along with the Random Forest ensemble classifier. Based on the analyses, the proposed approach outperforms its counterparts as well as provides better outcomes against other solo latent concept-oriented approaches.
GPDP: Game-Enhanced Personalized Differentially Private Smart Community

Smart community, as a key component of Internet of Things (IoT), is experiencing fast booming, which significantly enhances residents' quality of life. Using fog computing to establish a smart community is an effective way to further develop smart and sustainable cities due to the reduction of bandwidth consumption and latency. However, privacy issues are emerging because the continuous attacks are putting sensitive information under great threats. Motivated by this, we propose a differentially private smart community model with personalized privacy protection. Differential privacy is deployed in a personalized way while a logarithmic function is leveraged to map data sensitivity to privacy protection level. Extensive evaluation results show the advantages of the proposed model from the perspective of the optimized trade-off between personalized privacy protection and data utility.
An Evaluation of the UIT-VSFC Dataset Using Modern Machine Learning Techniques and Word Embeddings

Feedback from students during the course and upon course completion has become a powerful resource to improve teaching quality and enhance student's learning experience. However, the available data for free use is limited, especially for a low-resource language like Vietnamese. Currently, there is only one dataset in the education domain, called the Vietnamese Students’ Feedback Corpus (UIT-VSFC), that has been published for free use. This study therefore aims at evaluating the available corpus to use as a benchmarking dataset for conducting future researches as well as developing real-world applications. In this paper, deep neural network (DNN) and recurrent neural network (RNN) models are developed employing a word embedding method for two different tasks, i.e., topic and polarity classification. The experimental results show that DNN models outperform RNN models with 85.22% (>84.30%) and 88.56% (>86.32%) of accuracy for topic and polarity classification, respectively. Error analysis is conducted to explore the confusion of labeling and imbalance of data in the dataset. Workarounds for solving the problems are presented together with their results.
Evaluation of interpretability methods for adversarial robustness on real-world datasets

Adversarial training is considered the most powerful approach for robustness against attacks on deep neural networks involving adversarial examples. However, recent works have shown that the similar robustness level can be achieved by other means, namely interpretability-based regularization. We evaluate these interpretability-based approaches on real-world ResNet models trained on CIFAR-10 and ImageNet datasets. Our results show that interpretability can marginally improve robustness when combined with adversarial training, however, they bring additional computational complexity making these approaches questionable for such models and datasets.
Extraction in Digital Forensic Investigation based on Video Enhancement and Machine Learning

Major shifts in the information technology landscape over the past two decades have made the collection and analysis of digital evidence an increasingly important tool for solving crimes and preparing court cases, as in day to day life, crime is becoming a big issue so to avoid and to protect from crime computer forensics is needed. As technology has become more portable and powerful, greater amounts of information are created, stored, and accessed. Modern devices can serve as huge repositories of personal information yet be carried in a pocket and accessed with a single hand or even voice command. There is a clear benefit to having ample information to obtain convictions, but law enforcement and other criminal justice partners need to balance the recovery and admissibility of digital evidence with privacy concerns. This gives rise to the need of its security Digital forensics is a branch of forensic science dealing with investigating, collecting, analyzing and reporting on some digital information. It is used in the detection and prevention of crime and in any dispute where evidence is stored digitally.
P-Grabcut for Accurate Pedestrian Segmentation

This paper proposes a pedestrian segmentation method using Part Affinity Fields(PAFs) and Grab-cut algorithm to separate pedestrian from background pixels in still image without human interaction. The proposed technique utilizes PAFs to detect the skeleton of pedestrian, automatically construct a 4-layer mask before employing Grab-cut which originally yields relatively poor segmentation result without human interaction. Experiment result shows the proposed method separates the pedestrian and background better than other method.
Simple Build Tool

The command line is great because if you want to test something, it’s not necessary to open an IDE or create a project. When you need to create something more complex, the REPL is the worst alternative, so there are other tools that you can use to compile and run your application.
Signal Recognition of Polyphase-Coded Radar Signals Based on Multifeature Fusion

For improving recognition accuracy of polyphase-coded radar signals under low signal to noise ratio (SNR), a recognition method based on multifeature and feature selection was proposed. First, feature parameters of polyphase-coded radar signals in time domain, frequency domain, and pseudo-Zernike moments of Choi-Williams distribution (CWD) were extracted, respectively. And then, redundancy features and small-correlation features were removed based on mutual information with greedy idea. Finally, recognition of polyphase-coded radar signals was implemented using support vector machines (SVM). The experimental results showed that the average recognition accuracy of proposed method was over 90% when SNR is 0 dB, and the recognition performance was superior to the existing methods.
Research on Recommendation System Using Big Data Based on Deep Learning

According to the recommendation algorithm based on deep learning, this article will verify the validity of the recommendation model through Item2vec and DeepFM models. To analyze the relationship between historical user behavior data and the users of the product are interested in, the Spark SQL scripts can be run according to statistical analysis methods, and generate the training set and test set, which is needed by train the model, and build predictive models, and get the collection of products that each user is interested in and improve the prediction effect through model optimization. Experimental verification and analysis show that the recommendation system can handle massive amounts of user and product data. It can also extract features from these data for training automatically, which is conducive to improving the quality and efficiency of users' shopping decisions. we first complete feature extraction based on the processed data set. At present, the most used feature extraction method is feature extraction based on artificial experience and statistical methods. However, because the actual application scenarios of the recommendation system are often more complicated, we used Spark SQL scripts to extract features in experiments. First, extract the experimental data from the distributed database, and use the workflow scheduling script to generate the data set required for model training and testing.
Towards Automatic Green Claim Detection

Companies frequently make claims about positive impacts on the environment, but these claims are not always valid. In this sense, "greenwashing" is used when a company states a false claim about its products and practices being environmentally friendly. It is an important issue and has attracted the attention of policymakers to strengthen consumer law and new companies with the mission of helping brands and consumers to become more eco-friendly. However, manual screening of websites and social networks for sustainable claims (green claims) is time-consuming. Automatic detection of green claims is an underexplored problem from a computer science perspective, and thus, we present the design, training and evaluation of different approaches in this study. Our experiments reveal that although pre-trained models present high performance, they also show sensibility to adversarial attacks, such as character-swap-based methods, which are common in social networks. In order to understand the applicability in a real-world scenario, we also evaluated its generalization performance, which showed a notable performance across different domains.
Estimating the Generation Timing of Responsive Utterances by Active Listeners of Spoken Narratives

Narrating is a fundamental human desire. In order to satisfy the desire, it is more effective to have multiple listeners listen to narratives than to have one listener does. Conversational agents, e.g., communication robots and smart speakers, are expected to listen to narratives, instead of human listeners. For such agents to be recognized as narrative listeners, it is necessary to generate responsive utterances at appropriate timings. This paper proposes a method to estimate the generation timings of responsive utterances by active listeners of spoken narratives. The proposed method estimates them by encoding acoustic and linguistic information extracted from narratives with a transformer-based approach. To evaluate the performance of the proposed method, an experiment was conducted using 131,616 responsive utterances to narratives. The experimental results demonstrated that the proposed method was effective for the estimation and both the acoustic and linguistic information was useful.
Link game recognition system based on image recognition

To realize the needs of players to complete Link games quickly and get high scores, a link game recognition system under the condition of real shooting is designed under the advantages of a computer's faster image processing speed than a human response. This paper mainly introduces the design process of the system based on the basic principle of template matching, analysis, and use of Hough transform, thinning, corner detection, path link, and other image processing methods, and finally the same can be eliminated two square images marked out. The practice shows that the system simplifies the operation, ensures accuracy, and improves recognition efficiency.
Online Collision Avoidance Algorithm for Lightweight Web3D Robot Based on M-BVH

With the rapid development of robot technology, robot simulation starts from C/S to B/S. However, since online collision avoidance is a major concern, there are only a few Web3D robot simulation platforms that can operate online on the Web in the industry. In order to effectively realize fast and high precision online collision avoidance on the Web, this paper proposes a set of online collision avoidance algorithms for lightweight Web3D robots based on M-BVH. M-BVH is a binary tree which use Morton code to divide and organize all of the triangles of the robot model. In our algorithm, we constructs 2 M-BVH trees to speed up the detection process, and aids lightweight optimization methods like the inner body elimination algorithm to reduce the computational burden of high precision collision avoidance in Web3D scenes. The experimental results show that the method of this paper has accurate collision avoidance detection accuracy in the Web3D scene of complex robots, and can ensure it has a fast detection speed.
The Utilization of Electronic Archives as a Support for Study Employee Performance at The Customs and Excise Office Type A Semarang Customs

Technological developments make it easier for employees to carry out work, including archive management employees. Archives undergo changes and developments in electronic form. The development of supporting technology for electronic archive management. This study aims to analyze the use of electronic records to support the effectiveness of employee performance and the factors that support employee performance. This qualitative research uses a descriptive approach. The technique used to collect data is interviews. The instrument used to collect data is a questionnaire. This study uses three stages of data analysis, namely data reduction, data presentation, and concluding. The research subject is the selected resource person, an employee with the position of Customs and Excise Inspector. The technique used to analyze the data is descriptive qualitative analysis. The results of this study indicate that the use of electronic archives supports employee performance. The benefits of using electronic archives include easy and fast use, safe, and time-saving. There are four factors supporting employee performance: the work environment, ability, motivation, and adequate facilities. Factors supporting employee performance in using electronic records are leaders who always encourage employees to learn and develop themselves and a comfortable work environment for employees, human resource capabilities, and adequate facilities and equipment provided by the institution.
Enhancing the Deep Learning-Based Breast Tumor Classification Using Multiple Imaging Modalities: A Conceptual Model

Breast tumors’ preliminary and unambiguous prognosis is critical for early detection and diagnosis. A specific study has established automated techniques that use only science imaging modalities to speculate on breast tumor development. Several types of research, however, have suggested rephrasing the current literature's breast tumor classifications. This study reviewed various imaging modalities for breast tumors and discussed breast tumor segmentation and classification using preprocessing, machine learning, and deep neural network techniques. This research aims to classify malignant and benign breast tumors using appropriate medical image modalities and advanced neural network techniques. It is critical to improving strategic decision analysis on various fronts, including imaging modalities, datasets, preprocessing techniques, deep neural network techniques, and performance metrics for classification. They used preprocessing techniques such as augmentation, scaling, and image normalization in the respective investigation to minimize the irregularities associated with medical imaging modalities. In addition, we discussed various architectures for deep neural networks. A convolutional neural network is frequently used to classify breast tumors based on medical images to create an efficient classification paradigm. It could be an existing network or one that has been developed from scratch. The accuracy, area-under-the-curves, precision, and F-measures metrics of the developed classification paradigm will be used to evaluate its performance.
Exploring Customer comments using Latent Dirichlet Allocation

With the advent of social media, a large number of customers share their experience on social media about the products & services that they consume. Detecting issues reported by customers is significant for providing solutions to their problems. As a result, there is an emerging sub-field of social media analytics to identify and comprehend customer feedback and understand topics hidden in it. Topic modeling algorithms, a branch of machine learning, extract relevant insights from customers' posts by identifying hidden words and patterns. This pilot study has been conducted to ascertain the most frequently discussed issues by users on social media microblogging site Twitter. We used Tf-idf to re-allocate the weights to feature words and Latent Dirichlet Allocation (LDA) for topic modeling. Additionally, we carried out a comparative study of LDA against GSDMM topic model.
tvopt: A Python Framework for Time-Varying Optimization

This paper introduces tvopt, a Python framework for prototyping and benchmarking time-varying (or online) optimization algorithms. The paper first describes the theoretical approach that informed the development of tvopt. Then it discusses the different components of the framework and their use for modeling and solving time-varying optimization problems. In particular, tvopt provides functionalities for defining both centralized and distributed online problems, and a collection of built-in algorithms to solve them, for example gradient-based methods, ADMM and other splitting methods. Moreover, the framework implements prediction strategies to improve the accuracy of the online solvers. The paper then proposes some numerical results on a benchmark problem and discusses their implementation using tvopt. The code for tvopt is available at [1].
West Nile Virus Prediction Based on Data Mining

This paper performed some exploratory data visualization on this data set. The nature and representation of input data was found out and the preliminary feature selection was conducted in this step. And this paper performed data preprocessing and feature engineering on this data set, which had critical importance of the accuracy of prediction results. The paper built multiple regression models on the missing values prediction in the testing set. The paper implemented various data mining algorithms to build predictive models, including Gaussian Naive Bayes classifier, K-Nearest Neighbors (K-NN) algorithm, Multi-layer Perceptron (MLP), Logistic regression, random forest and XGBoost. After the experiments, XGBoost classifier could give the best result among all the models.
Architecture and Performance Comparison of Permissioned Blockchains Platforms for Smart Contracts

Blockchain and Smart Contracts ensure security and automation in trustless scenarios, leading to innovative solutions in various industry branches. The Hyperledger open-source project adopts these technologies in the corporate business, providing platforms for developing distributed applications. This paper analyses and compares two widely used platforms to develop applications based on permissioned blockchains: Hyper-ledger Sawtooth and Hyperledger Fabric. We implement two prototypes based on the same smart contract to evaluate the performance of each tool. The results show that: i) Sawtooth parallel transaction execution performs up to 30&#x0025; better than serial execution only if the number of conflicting transactions remains low, and ii) Fabric has a much faster consensus protocol, but presents a low performance if the transactions are conflicting.
Quantum version of self-balanced binary search tree with strings as keys and applications

In this paper, we present the data structure that implements the Self-Balanced Binary Search Tree with strings as keys and quantum comparing procedure. We cannot use the standard Self-Balanced Binary Search because of an error probability for the quantum comparing procedure. We can solve the issue using the standard success probability boosting technique. The presented data structure is more effective (in terms of running time) than using boosting technique. We apply the data structure for the Most Frequently String problem. So, we obtain a quantum algorithm for the problem that is faster than the existing quantum algorithm, and the best classical algorithm in the case of a significant part of the input strings (we mean <i>O</i>(<i>n</i>)) has a length that is at least <i>ω</i>((log <i>n</i>)<sup>2</sup>). Here <i>n</i> means the number of strings in a collection.
Imitation Learning From Inconcurrent Multi-Agent Interactions

Multi-agent imitation learning (MA-IL) aims to inversely learn policies for all agents using demonstrations collected from an expert group. However, this problem has only been studied in the setting of Markov games (MGs) allowing participants for concurrent actions, and do not work for general MGs, with agents inconcurrently making decisions in different turns. In this work, we propose iMA-IL, a novel multi-agent imitation learning framework for general (inconcurrent) Markov games. The learned policies are proven to guarantee subgame perfect equilibrium (SPE), a stronger equilibrium than Nash equilibrium (NE). The experiment results demonstrate that compared to state-of-the-art baselines, our iMA-IL model can better infer the policy of each expert agent using their demonstration data collected from inconcurrent decision-making scenarios.
Analysis of Influencing Factors in Commercial Forest Redemption Based on LR-BP Neural Network

Logistic regression model combined with BP neural network model is proposed to empirically analyze the factors affecting the mechanism, especially in forest farmers' perception of redemption policies. Through the screening of logistic regression, the advantages of the BP neural network are used to reveal the hidden principle management. The key influencing factors provide a scientific basis for perfecting the pre-treatment work and the feedback system.
Advanced TensorFlow

After introducing the basic tensor operations, let's further explore the advanced operations, such as tensor merging and segmentation, norm statistics, tensor filling, and clipping. We will also use the MNIST dataset again to enhance our understanding of tensor operations in TensorFlow.
Two-stage Infrared Images Photovoltaic Panel Extraction Based on Deep Semantic Segmentation

Abstract: In order to improve the efficiency of photovoltaic (PV) power maintenance, it is necessary to regularly monitor the panels by infrared image defect detection within the plants. However, the complexity of background in infrared image is significant effect the accuracy and precision of defect detection. Thus, PV string segmentation and panel extraction are necessary and time-consuming before defect detection. In this paper, we propose an automatic PV panel area extraction algorithm for infrared images. First, image preprocessing is performed for transforming original infrared image into gray-version and denoising. Then, we apply deep semantic segmentation to segment PV string automatically, followed by PV panel extraction based on an edge extraction with vertical and horizontal direction, and morphological processing. The accuracy of the results is evaluated by fusion image visually and quantitatively by a mean completeness of 98.60%, a mean correctness of 91.53%, and a mean quality of 90.40%, are obtained with the proposed method.