Reviewerly/exHarmony huggingface dataset:
A Supervised Approach For Extractive Text Summarization Using Minimal Robust Features

Over the past decade or so the amount of data on the Internet has increased exponentially. Thus arises the need for a system that processes this immense amount of data into useful information that can be easily understood by the human brain. Text summarization is one such popular method under research that opens the door to dealing with voluminous data. It works by generating a compressed version of the given document by preserving the important details. Text summarization can be classified into Abstractive and Extractive summarization. Extractive summarization methods reduce the burden of summarization by selecting a subset of sentences that are important from the actual document. Although there are many suggested approaches to this methodology, researchers in the field of natural language processing are especially attracted to extractive summarization. The model that is introduced in this paper implements extractive text summarization using a supervised approach with the minimalistic features extracted. The results reported by the proposed model are quite satisfactory with an average ROUGE-1 score of 0.51 across 5 reported domains on the BBC news article dataset.
Design of Kids-specific URL Classifier using Recurrent Convolutional Neural Network

The use of digital devices has increased exponentially in the last decade. Especially, young children spend most of their time surfing for various reasons such as homework. assignments and projects etc. Parental Control is highly important for monitoring the browsing behavior of children. Though several content-based web page classification approaches are available, it requires the entire web page contents for classification purpose. This leads to wastage of bandwidth due to unnecessary downloads. The exponential growth of internet demands URL based classifiers to adapt to the dynamic web, and to make swift decisions on the fly. To address this problem, a deep learning based approach has been proposed in this research that can extract the features only from the URL of a web page. To learn the patterns for Kids-specific web sites automatically, Convolutional Neural Network (CNN) is combined with Bidirectional Gated Recurrent Unit (BGRU) to extract rich context aware features as well as to preserve the sequence information in the URL. By conducting various experiments on the benchmark collection Open Directory Project (ODP), it is shown that an accuracy of 82.04% can be achieved.
Users’ Knowledge and Motivation on Using Cryptocurrency

Cryptocurrency is a decentralized digital currency stored in an online form that does not fall under governments and banks control. This study aimed at understanding users’ knowledge and their motivation on using cryptocurrency through structured interviews. Data was gathered through in- person interviews with three participants (n = 3). The analysis of the interviews indicated that (1) most participants have a clear understanding of cryptocurrency nature, albite some misconceptions, (2) most participants were able to explain technical aspects of cryptocurrency such as mining and market cap, (3) main uses of cryptocurrency according to participants were investment and currency, (4) perceived benefits of cryptocurrency are decentralization, security, anonymity, ease of use, and low fees, (5) perceived disadvantages were cryptocurrency not being recognized everywhere. Finally, (6) all participants planned to continue using cryptocurrency in the future. Although the number of participants was small, this research can be considered as a pilot study for future research aiming at understanding cryptocurrency users’ behaviour.
Extracting Aspect Terms using CRF and Bi-LSTM Models

Sentiment Analysis deals with analysing the reviews stated by its consumers for any product. If such analysis is performed at a deeper level, it enables us to identify the consumer's sentiment towards each feature of the product. The sentiment expressed may not be same towards each feature. The analysis of this sort is called Aspect Based Sentiment Analysis (ABSA) and it has been sub-divided into four subtasks. In this paper, the detailed study of the approaches used for the first subtask of ABSA, i.e. Aspect Term Extraction (ATE) is presented. This paper discusses how ATE can be performed for the reviews in a rich morphological language, like Hindi. The models proposed for ATE of Hindi Reviews are Conditional Random Field (CRF) and Bidirectional Long-Short-Term-Memory (Bi-LSTM) models with novel architecture. The CRF based approach with novel feature, 'Cluster-id' improved F-measure from 41.07% to 42.71%. However, with 5-fold cross-validation, the CRF model attained an F-measure of 44.54%. By using our proposed Bi-LSTM based model with PoS vector, the F-measure obtained is 44.49%.
s-SBIR: Style Augmented Sketch based Image Retrieval

Sketch-based image retrieval (SBIR) is gaining increasing popularity because of its flexibility to search natural images using unrestricted hand-drawn sketch query. Here, we address a related, but relatively unexplored problem, where the users can also specify their preferred styles of the images they want to retrieve, e.g., color, shape, etc., as keywords, whose information is not present in the sketch. The contribution of this work is three-fold. First, we propose a deep network for the problem of style-augmented SBIR (or s-SBIR) having three main components - category module, style module and mixer module, which are trained in an end-to-end manner. Second, we propose a quintuplet loss, which takes into consideration both the category and style, while giving appropriate importance to the two components. Third, we propose a normalized composite evaluation metric or ncMAP which can quantitatively evaluate s-SBIR approaches. Extensive experiments on subsets of two benchmark image-sketch datasets, Sketchy and TU-Berlin show the effectiveness of the proposed approach.
Hybrid Feature Learning and Engineering Based Approach for Face Shape Classification

Face shape classification is a vital process to choose an appropriate eyelashes, hairstyle and facial makeup, and selection of a suitable glasses' frames according to the guidelines from experts. Measuring face characteristics by beauty experts manually costs time and efforts. Therefore, developing automated face shape identification system could alleviate the need for additional time and efforts made by experts. Many automatic face shape classification methods have been proposed in the literature; however, the existing methods tackle many challenges due to the complexity of face geometry and variation in its characteristics. This paper presents a deep convolutional neural network method (CNN) approach for classifying face shape into five types. The proposed method which is based on merging the features learnt by CNN with hand crafted features represented by histogram of oriented gradients (HOG) and facial landmarks has proven to be efficient in identification of facial shape. The obtained results demonstrate that the proposed method is promising in identifying the shape of face achieving accuracy of 81.1%.
Indexical structures to enable knowledge mining tasks

In this paper, it is shown that semantic modeling technologies rely on data indexing systems. The semantic information theory known today is based on the method of using the principle of index expressions, using the division into real, possible and virtual individuals/data objects. Organization of a search on the Web for the concept of conceptual modeling of information processes based on variable domains that is being developed in the work is based on the natural requirements imposed on data organization. The model structure implements the separation of individuals, which manifests itself in the interaction of data organization, search and indexing. The identity of the data and the information processes designated by them is achieved in this model structure. A skeleton view of the organization of the search is being developed, leading to the maintenance of a network of variable domains and the possibility of deploying on them a conceptual modeling of interacting information processes.
Interactive Visual Exploration of Human Mobility Correlation Based on Smart Card Data

Public transportation agencies call for an intuitive, interactive, and reusable visualization tool to detect patterns of crime (i.e. pickpockets and gangs) or missing commuters on public transportation systems. Few existing visualization techniques have visually explored mobility correlations of targets and their companions, who are characterized in diverse mobility types, by using discrete travel hints extracted from a massive amount of data. To fill this gap, a visual analytical system is provided to conduct a group-based and individual-based exploration of mobility correlations of passengers of interest, based on an auto integration of multiple queries. How passengers differ from or correlate with each other are further examined based on their spatiotemporal distributions in trajectories and ODs. Real-world case studies, as well as user feedback made by 30 participants, demonstrate the effectiveness of the system in detecting specific targets and their companions featured in diverse mobility types, or in characterizing their spatiotemporal aggregation patterns for a further tracking on public transportation systems.
Video super-resolution via dense non-local spatial-temporal convolutional network

In this paper, we present a novel end-to-end deep neural network for the problem of video super-resolution. In contrast to most previous methods where frames need to wrap for temporal alignment based on the estimated optical flow, we propose short-temporal and bidirectional long-temporal blocks to exploit the spatial-temporal dependencies existing in inter-frames. It can effectively model the sudden and smooth varying motions of videos and overcome the limitations of explicit motion estimation. In addition, by introducing dense feature concatenation, it provides an effective way to combine the low-level and high-level features for boosting the reconstruction of mid/high-frequency information as shown in our analysis and experiment. Furthermore, we present a region-level non-local feature enhancing structure, which captures the spatial-temporal correlations of any two positions and makes use of long-distance relevant information. Extensive evaluations and comparisons with the current state-of-the-art approaches demonstrate the effectiveness of the proposed framework.
Research on CLPKC-IDPKC cross-domain identity authentication for IoT environment

According to the distribution of IoT nodes and the characteristics of information transmission, the traditional cross-domain signature scheme cannot meet the requirements of real-time information confidentiality in the IoT. Based on this, this paper proposes a cross-domain based on CLPKC and IDPKC. Signcryption authentication mechanism, which is based on the computational Diffie–Hellman problem (CDHP, Computational Diffie–Hellman problem) and the modification of Diffie–Hellman (mICDH, modification Inverse Computational Diffie–Hellman) under the random oracle model Under the assumption of the problem, the scheme satisfies confidentiality, is not styling and anonymity. The scheme has a fast computing speed, realizes fast identity authentication under the premise of satisfying data confidentiality, and supports different encryption system environments in which the communication parties are located, which is more suitable for the actual communication characteristics of the IoT environment.
Performance Analysis on Student Feedback using Machine Learning Algorithms

It investigates opinion mining by means of supervised learning techniques to search out the emotion of the student input bolstered characterized choices of instructing and learning. The examination led includes the apparatus of a blend of AI and common language preparing systems on understudy input data accumulated from module investigation overview consequences of VR Siddhartha Engineering College, Vijayawada. Additionally, to offer a grade by grade clarification of the technique of accomplishment of opinion mining on or after scholar remarks using the open source tool Python, the work additionally offers a comparative overall performance take a look remarks supported, extracted alternatives like examination, teaching and so on. The consequences are as compared to be trying to find out higher overall performance with relevance several evaluation standards designed for the various techniques.
Brines of the Siberian platform (Russia): Geochemistry and processing prospects

Complex researches have focused on chloride brines in the large artesian basins of the Siberian Platform. The highly concentrated ground brines are unique natural solutions that are fraught with unsolved mystery associated with their origin and the formation of their chemical composition. Most scientists believe that the most important process controlling the formation of these brines was interaction between palaeoseawaters and host rocks. The newest geochemical and stable isotope data (18О, 2H, 37Cl, 81Br, 87Sr/86Sr) showed that chloride sodium ground brines of the Siberian Platform originated as the result of halogenous rock leaching, whereas chloride calcium brines derive from buried bittern connate waters. The main evolutionary process was a long interaction in the water–rock system. This paper also provides information on the vast resource of multicomponent brines, so-called "liquid ores", which are widespread over a large area of the Siberian Platform. It is an unconventional hydromineral raw material, whose use and exploitation has an extremely high potential as it may constitute a serious alternative to solid mineral deposits. The innovative methodology and pioneering methods for the processing and extraction of valuable components (especially Li) from brine are suggested.
Virtual Learning Environment in Lexical Skills Development for Active Vocabulary Expansion in Non-Language Students Who Learn English

The paper examines problems related to development of lexical skills in non-language students when learning foreign languages exemplified by the English language. The authors analyse common aspects of active vocabulary expansion techniques, specify and clarify the necessity of Moodle virtual learning environment implementation as a tool for self-directed vocabulary learning. Based on the study findings, the authors conclude that lexical skills development can be improved, particularly active vocabulary expansion, transfer of words from the passive to the active vocabulary and overcoming a fear of producing monologues and participating in dialogues due to an active vocabulary deficit.
Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification

Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.
Integrating GitLab metrics into coursework consultation sessions in a software engineering course

Software developers use version control systems for collaborative coding. These systems are integrated into several software development platforms (including GitLab and GitHub) which support additional software engineering functionalities. Using these platforms in an educational context allows students to gain skills relevant to industry, whilst providing a means of keeping track of their activities. In this paper, we investigate the effect of presenting teams of students with GitLab metrics about their performance at coursework consultation sessions (checkpoint sessions), with a particular focus on the number of issues assigned and completed, and the number of commits made to the repository. A comparative analysis of project marks in two consecutive academic years indicates that these checkpoint sessions may lead to better student outcomes. An interview study with students and teaching assistants identified viewing the GitLab metrics in the checkpoints as an opportunity to see the relative contributions of team members and address resulting issues, and as a catalyst for improving engagement with the team project. The study also identified drawbacks of using the metrics too simplistically, and suggested that it was important to consider the quality and amount of written code, as well as the number of times someone committed to the repository.
Application of an Efficient Graph-Based Partitioning Algorithm for Extended Target Tracking Using GM-PHD Filter

One of the main tasks involving the extended target tracking is how to partition the measurement set accurately and efficiently. In this article, an efficient graph-based partitioning algorithm is introduced for extended target tracking. To reduce the computational load and the interference of clutter on the measurement set partition, a measurement set preprocessing method based on density-based clustering algorithm is presented. An intuitive directed k-nearest neighbor (kNN) graph model based on graph theory is established to represent the relationship between different measurements in the measurement set that needs to be segmented. In the framework of directed kNN graph, a novel similarity metric based on shared nearest neighbor (SNN) is used, and a pairwise similarity that integrates the number of elements in the set of SNN and the closeness of data points is constructed. The spectral clustering algorithm is used to process the multiway cut in the directed kNN graph. The graph-based partitioning algorithm is applied to the extended target Gaussian mixture probability hypothesis density filter. Simulation results illustrate the advantages of our proposed graph-based partitioning algorithm in performance and computational efficiency.
Improving the performance of histogram-based data hiding method in the video environment

The rapid development of information technology has brought security to be a crucial factor. Information being transferred between different parties must be well protected to prevent the file from illegal access. Data hiding, as one of the possible securing methods, has been introduced; nevertheless, the quality of the stego file and the capacity of the secret are still challenging. In this research, we work on these problems by exploring the histogram developed in the frame and the prediction error generated between two consecutive frames of a video file. In the generated stego file, these updated prediction errors do not change the composition and the total number of frames. The experimental results show that this proposed method has achieved a better result than the compared algorithms, which is shown by the Peak Signal to Noise Ratio (PSNR) value for various capacities. Equivalently, the number of bits that can be held is higher than others for the same stego quality. Besides, this method is reversible, in which both the cover and the secret can be fully extracted from the stego file. It means this scheme is appropriate to implement in a system, which requires the existence of those both data.
Object and background disentanglement for unsupervised cross-domain person re-identification

Person re-identification (re-ID) is an important task in many application fields. While most previous works have conducted feature embedding under the supervision of a prior information, it is also true that data collection and annotation in real-world scenarios are very expensive. Moreover, although researchers have borrowed transferring knowledge to alleviate the dependence on labeling, the bias among various domains remains an open problem. Accordingly, motivated by the observation that reducing the style and background differences between domains can promote the generalization capability of the learning model, this paper proposes an unsupervised model, namely, person component decomposition and synthesis generative adversarial network (PCDS-GAN), to minimize the distribution gap among multiple person re-ID datasets. More specifically, we first disentangle the pedestrian image into foreground, background and style features, then use these features to synthesize person images with various backgrounds from the target domain. Finally, the synthesized images are used to train person re-ID models. Comprehensive experiments demonstrate that our model can effectively reduce the domain gap, and also outperforms state-of-the art methods on the Market-1501 and CUHK03 benchmarks.
Transformer with sparse self‐attention mechanism for image captioning

Recently, transformer has been applied to the image caption model, in which the convolutional neural network and the transformer encoder act as the image encoder of the model, and the transformer decoder acts as the decoder of the model. However, transformer may suffer from the interference of non-critical objects of a scene and meet with difficulty to fully capture image information due to its self-attention mechanism's dense characteristics. In this Letter, in order to address this issue, the authors propose a novel transformer model with decreasing attention gates and attention fusion module. Specifically, they firstly use attention gate to force transformer to overcome the interference of non-critical objects and capture objects information more efficiently via truncating all the attention weights that smaller than gate threshold. Secondly, through inheriting attentional matrix from the previous layer of each network layer, the attention fusion module enables each network layer to consider other objects without losing the most critical ones. Their method is evaluated using the benchmark Microsoft COCO dataset and achieves better performance compared to the state-of-the-art methods.