Reviewerly/exHarmony huggingface dataset:
An Enhanced Video Inpainting Technique with Grey Wolf Optimization for Object Removal Application

Video inpainting is the most trending research topic from the last decade. Video inpainting is the process of restoring the damaged parts of the vintage video or the filling of the regions by removing the unwanted objects with sophisticated techniques. The video inpainting is achieved by dividing the video into frames and the motion of the moving objects in the frames are tracked by applying the motion tracking method. The existing inpainting method proposed by the Criminisi, neglected the local similarities in the images so it suffered from dropping effect in the priority computation. This paper proposed a new priority computation method by introducing gradient operation with the addition of curvature in the data term and local structure measurement function with structure tensor theory as an additional term. Later, the patch matching is achieved with the Sum of Absolute Difference (SAD) distance method. Further, the optimal patch is selected by applying the Grey Wolf Optimization (GWO) algorithm. The efficiency of the proposed video inpainting technique is evaluated with the performance metrics, viz., Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Edge Similarity (ESIM) executed in MATLAB. The PSNR and SSIM of the proposed method for Fontaine_chatelet video is improved by 18.9% and 4.19% than existing method. The proposed method is compared with other existing methods also and it outperformed the existing methods.
Differentially Private String Sanitization for Frequency-Based Mining Tasks

Strings are used to model genomic, natural language, and web activity data, and are thus often shared broadly. However, string data sharing has raised privacy concerns stemming from the fact that knowledge of length-k substrings of a string and their frequencies (multiplicities) may be sufficient to uniquely reconstruct the string; and from that the inference of such substrings may leak confidential information. We thus introduce the problem of protecting length-k substrings of a single string S by applying Differential Privacy (DP) while maximizing data utility for frequency-based mining tasks. Our theoretical and empirical evidence suggests that classic DP mechanisms are not suitable to address the problem. In response, we employ the order-k de Bruijn graph G of S and propose a sampling-based mechanism for enforcing DP on G. We consider the task of enforcing DP on G using our mechanism while preserving the normalized edge multiplicities in G. We define an optimization problem on integer edge weights that is central to this task and develop an algorithm based on dynamic programming to solve it exactly. We also consider two variants of this problem with real edge weights. By relaxing the constraint of integer edge weights, we are able to develop linear-time exact algorithms for these variants, which we use as stepping stones towards effective heuristics. An extensive experimental evaluation using real-world large-scale strings (in the order of billions of letters) shows that our heuristics are efficient and produce near-optimal solutions which preserve data utility for frequency-based mining tasks.
The Landscape of ML

Each of these three components involves design choices for the representation of data, their features and labels, the model and loss function. This chapter details the high-level design choices used by some of the most popular ML methods. Figure 3.1 depicts these ML methods in a two-dimensional plane whose horizontal axis represents different hypothesis spaces and the vertical axis represents different loss functions.
Improving the Performance of Blockchain Sharding Protocols with Collaborative Transaction Verification

Sharding is a promising approach to scalable blockchains. While sharding eliminates duplicate work between shards, it does not remove intra-shard duplication, i.e., every peer has to verify all transactions in its shard, thus becoming a newly exposed bottleneck. Aiming to improve the performance of individual shards, we propose Collaborative Transaction Verification (CTV), which allows peers to verify fewer transactions by sharing verification results. Equipped with dependency awareness, CTV guarantees that peers reach the same system state despite different transaction verification and execution orders. We implemented an OmniLedger-like sharding protocol based on Bitcoin Core and integrated CTV into the protocol. The evaluation results show that CTV can improve the performance of a shard by 2.6x.
Engineering Student Attitudes to Digital Reading Teaching Technology in the post-coronavirus era

Purpose: The purpose of this paper is to evaluate engineering student attitudes to using a digital reader device for reading digital texts in a post-coronavirus academic teaching environment. The results of this study could help to inform pedagogies for online teaching and student transitions into university technology-enabled educational innovation for engineering. Design/methodology/approach: An online survey was distributed to engineering student participants and a student focus group was conducted. The survey contained 14 student experience questions related to the use of the digital reader; most questions were quantitative in nature. The focus group concentrated on 4 areas: the new contexts, pandemic and post-pandemic; the digital reader; digital reading behaviours; and, the impact on current and future student learning. Findings: Student digital reading of engineering academic texts cannot be separated from distinct research investigation processes, such as finding information and writing. Engineering students rarely just read. They read, write, and find information in interconnected academic processes of inquiry. Research limitations/implications: This study was limited to engineering students using the Kindle digital reading device and specific engineering academic texts. Practical implications: The study makes recommendation to inform the development of digital reading in online teaching environments and student transitions to higher education. Originality/value: The study builds on existing research with students describing interconnected digital reading, writing, and finding activities in post-pandemic contexts.
Accessible and engaging voices: Teaching English to adult second-language learners with visual impairment using WhatsApp voice notes in India

This case study explored the possibility of using WhatsApp voice notes to teach English to adult learners with visual impairment. The study has documented in detail the challenges and advantages of learning and teaching English through WhatsApp. In this case study, both quantitative and qualitative data were collected and integrated and analysed to understand feasibility of using WhatsApp to teach English to learners with visual impairment. Even though the case study was primarily qualitative, some quantitative data were also collected and analysed numerically. The numerical data were analysed using descriptive statistics and reported using numbers and in the form of tables and figures. Furthermore, non-numerical data were examined inductively and reported thematically. The advantage of using WhatsApp voice notes was that it provided a platform for the learners to converse in English and an opportunity to reflect on their own learning process and progress critically. It provided a collaborative learning experience independent of the conventional learning experience of a classroom. The study revealed that the learners with visual impairment were more engaged in the learning process and in improving their speaking skill when WhatsApp voice notes were used. The coding of messages and detailed analysis of message categories clearly revealed the social-emotional skills of each participant in the study. The message categories were adapted from Community of Inquiry Framework and message categories suggested by Naaman et al.
Money Transaction Fraud Detection Using Harris Grey Wolf-Based Deep Stacked Auto Encoder

Due to the intrinsic properties of transactional data, like concept drift, noise, data imbalance, and borderline entities, the fraud detection poses a challenging issue in bank transaction. A number of solutions are developed for detecting the fraud, but these solutions reveal ineffective performance. Therefore, an effective fraud detection framework named Harris Grey Wolf (HGW)-based Deep stacked auto encoder is proposed to perform the fraud detection mechanism in bank transaction by solving the data imbalance issues. The HGW-based deep stacked auto encoder is developed using the characteristic features of the standard Harris Hawks Optimizer (HHO), and Grey Wolf Optimizer (GWO). The proposed HGW-based Deep stacked auto encoder provides an effective and optimal solution in detecting the frauds using the fitness function, which considers the minimal error value and evaluate the best solution based on the iterations. The useful and the appropriate features are effectively selected from the transactional data, as these features enhanced the accuracy of detection rate.
News Insider: Innovating News Understanding to Improve the Quality of Reading Experience

Currently, with the advent of myriad news applications, which have increased the volume of information, users are faced with the challenge of discovering this information effectively and efficiently. In this paper, we present one web-based news platform, NewsInsider, providing an array of novel features for exploring and reading news in Singapore. NewsInsider integrates multiple news crawlers to extract news from online sources, employs news analytics and Artificial Intelligence to automatically process the news (e.g., categorizing the news, extracting trending topics, detecting the named entities, and tagging the geographic location to the news). Based on all the extracted information, NewsInsider provides the readers with novel search functionalities to find news stories using the location, categories, topics, entities, and postcodes. Additionally, NewsInsider employs visual graphs and a master-detailed interface to present the information related to news stories in a meaningful and intuitive way.
Precise Bayes Classifier: Summary of Results

The Bayes Classifier is shown to have the minimal classification error, in addition to interpretable predictions. However, it requires the knowledge of underlying distributions of the predictors to be usable. This requirement is almost never satisfied. Naive Bayes classifiers and variants estimate this classifier by assuming the independence among predictors. This restrictive assumption hinders both the accuracy of these classifiers and their interpretability, as the calculated probabilities become less reliable. Moreover, it is argued in the literature that interpretability comes at the expense of accuracy and vice versa. In this paper, we are motivated by the accurate and interpretable nature of the Bayes Classifier. We propose Precise Bayes, which is a computationally efficient estimation of the Bayes Classifier based on a new formulation. Our method makes no assumptions, neither on independence nor on underlying distributions. We devise a new theoretical minimal error rate for our formulation and show that the error rate of Precise Bayes approaches this limit with increasing number of samples learned. Moreover, the calculated posterior probabilities, are actual empirical probabilities calculated by counting the observations and outcomes. This makes the predictions made by Precise Bayes fully explainable. Our evaluations on generated datasets and real datasets validate our theoretical claims on prediction error rate and computational efficiency.
Satellite Image Based Spatio-Temporal Variation Assessment in Captive Limestone Mines for Long-Term Viability

Land use and Land cover mapping are crucial applications for planning and disaster mitigation as well as prevention. Such mapping is possible via satellite imagery. Every pixel in the training set must be labelled in order to employ machine learning techniques combined with image analysis for such land cover mapping. This paper mainly concentrates on the mining sector for assessing sustainable land environmental management plans. Google Earth mapping is one of the most efficient, economical, and reliable methods for better understanding of spatial and temporal changes of limestone mining area with less human efforts. High-resolution Google Earth images are used in assessing the temporal changes of the Limestone mine area in Kadapa district from 2011 to 2020. Google Earth Pro open-source software was used to extract images of captive limestone mines, which were then clipped using a polygon tool to aid in the creation of a land-use region occupied by the active mining area. It was found that the active mining area variation rate is in the Dalmia Cement Limestone mine is 308.49% which is the highest compared to Bharathi Cement Limestone Mine mining area variation rate (240%), which is second, and the lowest mining area variation rate is found in the Niduzuvvi Limestone Block-1 area, which is 11.67%. Based on the overall impact of limestone mining in the study area, it is recommended that all stakeholders especially mine and cement plant owners; pay close attention to the area’s land environmental issues. The proposed technique is used for quality assessment in terms of prediction accuracy. The system so developed is observed to outperform existing systems implemented with the benchmarks counterparts.
Question Answer Re-Ranking using Syntactic Relationship

With the arrival of the World Wide Web, the tremendous increase in textual data has encouraged the development of such platforms where a user can answer a question or ask a question in natural language. Community Question Answering (CQA) based websites play a significant role in the rise of the Social Web. These systems are designed to answer complex user queries effectively. In this study, a system has been proposed to solve the problem of re-ranking relevant answers to community questions by considering the syntactic structures between them using Tree Kernels i.e. Partial Tree Kernels (PTK), SubTree Kernels (STK), and SubSet Tree Kernels (SSTK). For this purpose, various experiments were conducted to achieve maximum accuracy and mean average precision score. The results were compared with an already existing state-of-art system and with a system using standard information retrieval similarity measures including cosine similarity, BM25, Levenshtein distance, and Jaccard coefficient. Results show the superior performance of tree kernels over compared baseline similarity measures.
Integrating of Promising Computer Network Technology with Intelligent Supervised Machine Learning for Better Performance

The Software defined network (SDN) controller has such networks universal sight and allows for centralized management and control for the networks. The algorithms of Machine learning used alone or combined with the SDN controller's northbound applications in order to make intelligent SDN. SDN is such potential networking design that blends network's programmability with central administration. The control and the data planes are separated in SDN, and the network with central management point is called SDN controller, which may be programmed and utilized as a brain of the network. Lately, the community of researchers have shown a greater willingness to take advantage of current advances in artificial intelligence to give the SDN best decision making and learning skills. Our research found that combining SDN with Intelligent Supervised Machine Learning (ISML) is very important for performance improvement. ISML is the development of algorithms that can generate broad patterns and assumptions from external source instances in order to portend the predestination of future instances. The ISML algorithms of classification goal is to categorize data based on past information. In data science problems, classification is used rather frequently. To solve such problems, a number of successful approaches were already presented, including rule-based techniques, instance-based techniques, logic-based techniques, and stochastic techniques. This study examined the ISML algorithms' efficiency by checking the precision, accuracy, and with or without SDN recall.
Fuzzy Intuitionist Approach for Resistance to Change Analysis in a Digital Transformation Process

The change management process of a digital transformation project requires a rigorous approach. If resistance is an essential step in the mourning process inherent in any change, overcoming the reluctance of employees is a major issue for every company. The change management process must integrate dimensions related to strategy, organization, communication and human resources. Digital transformation must identify the obstacles to its deployment and respond to the various raised queries. In this paper, we develop a fuzzy intuitionistic method of resistance to change’ scoring. The originality of our proposal lies in taking charge of the positive and negative assessment of the factors of resistance to change. This approach is flexible and scalable, it will allow the actors of each organizational unit to measure the resistance to change of their teams and by aggregation to assess this measure at the level of the department and the entire organization.
Research on Canny Edge Feature Detection Technology of Color Image Based on Vector Properties

Canny edge detection based on optimization algorithm has the advantages of high accuracy and high signal-to-noise ratio. However, because the Canny algorithm was based on gray image, some edge information will be lost during the process of converting color image to gray image. Aiming at this problem, the edge detection method of color image based on vector properties was proposed in this paper. According to the properties of vectors with direction and length, the image gradient calculation of Canny algorithm was improved from the angle and length of vector, in order to realize edge feature detection of color image directly. Compared with the results detected by the conventional Canny algorithm, this method can get clearer and more accurate edges. When combined with specific color markers in specific application scenarios, this method can obtain ideal detection results that only contains the edge of the marker.
ESDL: Entity Summarization with Deep Learning

Entity summarization is the task of generating a general-purpose abridged description of Knowledge Graph entities. It became a valuable task and attracted a lot of attention due to the explosion in the size of semantic datasets. While most existing methods are unsupervised and do not use deep learning, in this short research paper we present ESDL, a simple yet effective supervised deep learning model. In this model, we exploit textual semantics for encoding RDF triples. As a supervised model, it uses frequency scores programmatically extracted from human-generated summaries as labels for training. Experimental results on a public benchmark dataset show that our model achieved comparable F1 scores to the state-of-the-art model DeepLENS [1] and outperformed all other models with statistical significance. Furthermore, our model has a straightforward structure and less computational complexity, resulting in ESDL comparing favorably in terms of efficiency. Experiments show that ESDL is 40% faster than DeepLENS.
Hybrid Pruning Method Based on Convolutional Neural Network Sensitivity and Statistical Threshold

Abstract The hybrid pruning algorithm can not only ensure the precision of the network, but also achieve a good balance between pruning ratio and computation. However, traditional pruning algorithms use coarse-grained or fine-grained pruning networks, which have the tradeoff problem between pruning rate and computation amount. To this end, this paper presents. A hybrid pruning method of sensitivity and statistical threshold. Firstly, coarse-grained pruning is carried out on the network, and a fast sensitivity test is conducted on the convolutional layer of the network to determine the channels that need pruning within the tolerance range of network precision decline. Then, fine-grained pruning is performed on the network. Count the weights of the pruned network, calculate the thresholds of the weights of each layer, and delete the weights less than the thresholds so as to further reduce the size of the network and the amount of calculation. Hybrid pruning performs very well in AlexNet and Resnet networks. In particular, the method proposed in this paper is used in CIFAR-10 dataset, and the compression of FLOPs is 60%, while the compression of parameter number is nearly 80%. Compared with single pruning method, Hybrid pruning is better.
Lossy Compression of Quality Values in Sequencing Data

The dropping cost of sequencing human DNA has allowed for fast development of several projects around the world generating huge amounts of DNA sequencing data. This deluge of data has run up against limited storage space, a problem that researchers are trying to solve through compression techniques. In this study we address the compression of SAM files, the standard output files for DNA alignment. We specifically study lossy compression techniques used for quality values reported in the SAM file and analyze the impact of such lossy techniques on the CRAM format. We present a series of experiments using a data set corresponding to individual NA12878 with three different fold coverages. We introduce a new lossy model, dynamic binning, and compare its performance to other lossy techniques, namely Illumina binning, LEON and QVZ. We analyze the compression ratio when using CRAM and also study the impact of the lossy techniques on SNP calling. Our results show that lossy techniques allow a better CRAM compression ratio. Furthermore, we show that SNP calling performance is not negatively affected and may even be boosted.
An Investigation of Compound Variable Names Toward Automated Detection of Confusing Variable Pairs

A successful naming of variables is key to making the source code readable. Programmers may use a compound variable name by concatenating two or more words to make it easier to understand and more informative. While each compound variable name itself may be easy-to-understand, a collection of such variables sometimes makes a “confusing” variable pair if their names are highly similar, e.g., “shippingHeight,” vs. “shippingWeight.” A confusing variable pair would adversely affect the code readability because it may cause a misreading or a mix-up of variables. Toward automated support for enhancing the code readability, this paper conducts a large-scale investigation of compound variable names in Java programs to find quantitative criteria of the confusing variable pairs. The investigation collects 31,806,749 pairs of compound-named variables from 684 open-source Java projects and analyzes them from two different perspectives of name similarity: the string similarity and the semantic similarity.
RC-Struct: Reservoir Computing Meets Knowledge of Structure in MIMO-OFDM

This paper introduces a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier is built to perform the multi-class detection by leveraging the repetitive constellation structure in the communication system. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier efficiently utilizes the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. The experiment demonstrates the effectiveness of RC-Struct in the MIMO-OFDM system with the dynamically adapted link. The results shed light on combining communication domain knowledge and learning-based receive processing for 5G and 5G Beyond.