Reviewerly/exHarmony huggingface dataset:
GLCM Feature-Based Texture Image Classification Using Support Vector Machine

Texture is one of the important characteristics of an image, and its features can be used to identify specific region of interest from the image. Texture features can be extracted for any kind of images like RGB, monochrome, aerial, satellite images. This chapter describes an approach for texture image classification based on Gray-level co-occurrence matrix (GLCM) features and machine learning algorithm like support vector machine. Ten different GLCM features were extracted and fed as input to support vector machine for classification. The proposed method is trained and tested with dataset collected from center for image analysis, Swedish University. In recent days, machine learning (ML) methods are highly used to mimic the complex mathematical expressions of texture features. The main objective of this chapter is to demonstrate the state of the art of ML models in texture image prediction and to give insight into the most suitable models.
HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Pattern Mining

Sequential pattern mining (SPM) under the nonoverlapping condition (or nonoverlapping SPM) is a type of data mining used to extract frequent gapped subsequences (known as patterns) from sequences, which is more valuable and versatile than other related methods. In nonoverlapping SPM, two occurrences cannot reuse the same sequence letter in the exact location as the occurrences. This method evaluates the frequency of the patterns in the sequence, and ignores the impact of external utility (item price or profit). Therefore, some low-frequency and essential patterns are overlooked. To address this issue, this paper introduces High Utility Self-adaptive Nonoverlapping Pattern (HSNP) mining and proposes HSNP-Miner, which includes two steps: support calculation and candi-date pattern generation. To calculate the support, we propose the NoSup algorithm, which can effectively calculate support while avoiding the creation of redundant nodes. An advanced upper bound method is employed to generate the candidate patterns more efficiently. Compared to other competitive methods, the experimental results demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence pat-tarns.
A Novel Approach for Data Scraping and Sentiment Analysis from Twitter using Machine Learning Techniques

In this paper we propose a method to analyze the sentiments of a user on a particular topic on the basis of their tweets. This method breaks up the texts into words, remove all the unnecessary stop words and then using a dictionary determines the sentiments by grouping certain words together. Using Tokenization it first splits those tweets into smaller units and using lemmatization it reduces the infected words to meaningfulwords before comparing those with the dictionary elements. Our purpose is to establish perceptive links between tweets and public sentiment for a better understanding of the public opinion. In this paper we present two applications of this method: recognizing depression in tweets and recognizing mass opinion. In the first application we use certain keywords to search for tweets that tend towards depression and our program analyzes the exact sentiments of the tweet to confirm the existence of depression. In the second application, tweets regarding a certain topic are scraped and they are analyzed together to give a result that represents the public opinion.
Parallelization of Non-Maximum Suppression

Non-maximum suppression (NMS) is an unavoidable post-processing step in the object detection pipeline. NMS selects the bounding boxes with a locally maximum confidence score and eliminates its neighboring candidates which have a large overlap with the selected boxes. Because this procedure is a sequential and iterative algorithm of O(N2) complexity, NMS running time is too slow to be applied to real-time object detection on the image which has many objects. To consider this issue, we propose a parallel computation method using GPU multi-cores to compute faster than the previous NMS. Our parallel NMS replicates the candidate boxes and performs both IoU calculation and comparison in parallel. We drastically reduced the complexity from O(N2) to O(N) and the time consumption of NMS to be applied to real-time detection with negligible degradation of detection performance and very slight additional memory consumption. Furthermore, when there is a small number of overlapped objects, our parallel NMS achieved an improvement in precision.
A trust module for the interaction with virtual characters

This paper describes the architecture of a trust module for the interaction between human users and virtual characters. The module is part of a bigger project whose goal is that of creating virtual and truly realistic characters capable of interacting with human users. An example of such a virtual character is a computer rendered museum clerk that interacts with visitors. The role of the security module in the envisioned scenarios is that of providing trust, mainly regarding the identity of the human user, but also about the “identity” of the virtual character. In this paper we design (and describe the implementation) of an architecture in which human users identities are checked through face recognition and handled with verifiable credentials managed through a public blockchain. The project has been funded by the European Union and is under realization.
Sentiment classification on Tamil and Telugu text using RNNs and Transformers

People use social media to express their opinion, thoughts, and views about a target. Sentiment analysis is found to be a common application that prevails currently and is adopted by all business and social domains. Through this, the business could be progressed and the profit could be raised marginally. Yet, people have the habit of expressing their views in the form of figurative language in which the precise meaning of the word should not be considered and as an alternative, the contrary analysis of that view is considered. This is found to be a difficult task since it might not be accounted for either positive or negative. The performance of the sentiment analysis tasks could be improved by considering these kinds of sarcastic sentences. Several works have been done on forming a model and training the model to detect sarcasm in several languages like Hindi, English, Telugu, etc. Keeping these considerations, a model has been developed which takes the input in the form of bilingual text and does the training to detect the presence of sentiment in the text. The model that has been developed works for the bilingual text and it identifies the existence of sentiment Experimental results show that the best model has an overall accuracy of 81 % on the test set.
Nanoparticles visualization and extraction for diagnosis and therapy in nanomedicine

Nanomedicine is a rapidly developing field of science that has the potential to treat a wide range of complicated ailments. This paper uses a mouse with an inflamed calf and iron oxide nanoparticles (IO-NPs) attached to the therapeutic medicine and put into the mouse’s eye to investigate drug delivery efficiency. The idea is to track and quantify drug delivered to the inflamed calf of the mouse. A high-resolution MRI approach was used to capture images of the inflammatory calf area. Knowing that iron oxide has a high magnetic strength in MRI, image processing techniques were used to calculate the position and number of IO-NPs linked to the administered medication. This paper proposes an image processing approach for detecting and extracting IO-NPs. The images go through pre-processing steps that includes noise filtering and background removal. IO-NPs are isolated from the surrounding tissues using Otsu’s method. The number of IO-NPs grouped in the region, as well as the quantity of medications supplied to the region of interest, can be estimated using IO-NPs extraction. The findings on nanoparticle detection and extraction appear to be a potential method for estimating the amount of medicine targeting a specific location.
Energy Data Catalog Item Extraction Method Based on Semi Supervised Feature Selection

With the development of energy technology, the variety and amount of energy data increase, which brings challenges to the construction of energy data catalog system and the use of energy data. In order to extract the energy data catalog items and reduce the redundancy of the energy catalog, this paper proposes an energy data catalog item extraction method based on the semi supervised feature selection algorithm of joint mutual information. Firstly, aiming at the problem of incomplete label information of energy catalog, semi supervised learning algorithm is used to transform semi supervised data set into supervised data set. Secondly, based on the joint mutual information, the importance of all items in the energy catalog is ranked. Then, according to the different feature subsets, the classification accuracy is calculated. Finally, the final energy data catalog item is obtained through comparative analysis. The results show that the proposed method can keep a high classification accuracy even if the label information of the data sample is incomplete, which verifies the rationality and effectiveness of the method.
OPBFT: Optimized Practical Byzantine Fault Tolerant Consensus Mechanism Model

Blockchain is a kind of decentralized distributed ledger technology. Consensus algorithm is one of the main technologies of blockchain. Its efficiency and security directly affect the overall performance of the blockchain systems. Nowadays, Practical Byzantine Fault Tolerance (PBFT) algorithm is widely applied in the consortium blockchain systems. However, this consensus algorithm cannot identify and remove Byzantine nodes in time. In order to address these problems, this paper proposes an Optimized Practical Byzantine Fault Tolerant (OPBFT) consensus algorithm. It contains an improved reputation model that evaluates the node’s credibility for different behaviors of the nodes during the consensus process, and integrates byzantine detection and degradation mechanisms to reduce the probability of malicious nodes becoming consensus nodes and solve the problem of increased transaction delay caused by it; Simultaneously, it adopts an optimized consistency protocol to reduce communication overhead and improve consensus efficiency. Finally, the argumentation and analysis are carried out from the aspects of communication overhead, throughput, delay and security.
Efficient Construction of Public-Key Matrices in Lattice-Based Cryptography: Chaos Strikes Again

In order to solve the problem of excessive communication overhead in the public-key encryption scheme based on the Learning With Error (LWE) problem, this paper proposes to construct the public-key matrices based on chaotic systems. Specifically, by taking advantages of Logistic mapping, a long pseudo-random sequence is generated from the initial state and the bifurcation parameter. The non-uniform chaotic sequence is further tweaked towards a pseudo-random sequence admitting uniform distributions. The public-key matrix of LWE is therefore constructed efficiently. Simulations confirm the correctness and efficiency of our scheme.
A new strategy of smooth variable structure filter to estimate unmeasured states in target tracking

In the field of target tracking, the Kalman filter is one of the most popular filters, but its robustness will decrease when target maneuvers. The novel smooth variable structure filter (SVSF) has attracted much attention for its robustness against disturbances and uncertainties. In linear systems, the existing methods of SVSF can estimate unmeasured states, which refer to the states that cannot be measured by sensors, but the estimation is with low accuracy. And there is little research on the estimation for unmeasured states by SVSF in nonlinear systems. Therefore, this paper has put forward a novel strategy to improve the estimation accuracy of unmeasured states in both linear and nonlinear systems. The proposed strategy uses the correlation of error covariance among state elements to calculate the value of unmeasured states. On the basis of the strategy deduced from linear system, one algorithm in linear system and two new extended algorithms in nonlinear systems are proposed. In simulations, different filter models are used by all filters to process Gaussian and non-Gaussian noise in linear and nonlinear systems. The comprehensive results prove that, the proposed linear algorithm has better performance than the existing methods of SVSF in linear systems. Two nonlinear algorithms also show good performance in the estimation for unmeasured states in nonlinear and non-Gaussian systems.
Qasar: Self-Supervised Learning Framework for Extractive Question Answering

Question Answering (QA) has become a foundational research area in Natural Language Understanding (NLU) with widespread applications in search, personal digital assistance, and conversational systems. Despite the success in open-domain question answering, existing extractive question answering models pre-trained using Wikipedia articles (e.g., SQuAD data) perform rather poorly in closed-domain and industrial scenarios. Further, a major limitation in adapting question answering systems to such contexts is the poor availability and the expensive annotation of domain-specific data. Thus, wide applicability of QA models are severely hampered in enterprise systems.In this paper, we aim to address the above challenges by introducing a novel QA framework, Qasar, using self-supervised learning for efficient domain adaptation. We show, for the first time, the advantage of fine-tuning pre-trained QA models for closed-domains by synthetically generated domain-specific questions and answers (from relevant documents) from large language models like T5. Further, we also propose a novel context retrieval component based on question-context semantic relatedness to further boost the accuracy of the Qasar QA framework. Experimental results show significant performance improvements on both open-and closed-domain QA datasets, while requiring no labelling efforts, which we believe will contribute to the ease of deployment of such systems in enterprise settings. The different modules of our framework (synthetic data generation, context retrieval, and question answering) can be fully reproduced by fine-tuning publicly available language models and QA models on SQuAD dataset as discussed in the paper.
Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques

Abnormal behavior detection from on-line/off-line videos is an emerging field in the area of computer vision. This plays a vital role in video surveillance-based applications to provide safety for humans at public places such as traffic signals, shopping malls, railway stations, etc. Surveillance cameras are meant to act as digital eyes (i.e., watching over activities at public places) and provide security. There are a number of cameras deployed at various public places to provide video surveillance, but in reality, they are used only after some incident has happened. Moreover, a human watch is needed in order to detect the person/cause of the incident. This makes surveillance cameras passive. Thus, there is a huge demand to develop an intelligent video surveillance system that can detect the abnormality/incident dynamically and accordingly raise an alarm to the nearest police stations or hospitals as per requirement. If AI-supported CCTV systems are deployed at commercial and traffic areas, then we can easily detect the incidents/crimes, and they can be traced in minimal time.
Deep Motion Flow Aided Face Video De-identification

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.
Q&amp;A participation and behavioral patterns on academic social networking sites: a comparative study of academic, corporate and government institutions on ResearchGate

Purpose This paper aims to explore question and answer (Q&amp;A) participation and behavioral patterns on academic social networking sites (ASNSs) from the perspective of multiple subjects such as academic, corporate and government institutions. Design/methodology/approach Focused on the Q&amp;A service of ASNSs, this study chooses ResearchGate (RG) as the target ASNS and collects a large-scale data set from it, involving a sample of users and a Q&amp;A sample about academic, corporate and government institutions. First, it studies the law of Q&amp;A participation and the distribution of the type of user according to the sample of users. Second, it compares question-asking behavior and question-answering behavior stimulated by questions among the three types of institutions based on the Q&amp;A sample. Finally, it discusses the Q&amp;A participation and behavioral patterns of the three types of institutions in academic Q&amp;A exchanges with full consideration of institutional attributes, and provides some suggestions for institutions and ASNSs. Findings The results show that these three types of institutions generally have a low level of participation in the Q&amp;A service of RG, and the numbers of questions and answers proposed by institutional users conform to the power-law distribution. There are differences in Q&amp;A participation and Q&amp;A behavioral patterns among academic, corporate and government institutions. Government and academic institutions have more users participating in the Q&amp;A service and their users are more willing to ask questions, while corporate institutions have fewer users who participate in the Q&amp;A service and their users are inclined to provide answers. Questions from corporate institutions attract much more attention than those from the other two types of institutions. Originality/value This study reveals and compares the Q&amp;A participation and the behavioral patterns of the three types of institutions in academic Q&amp;A, thus deepening the understanding of the attributes of institutions in the academic information exchange context. In practice, the results can help guide different institutions to use the Q&amp;A service of ASNSs more effectively and help ASNSs to better optimize their Q&amp;A service.
Exploring The Role of Law in The Governance of Cryptocurrency Systems and Why Limited Liability DAOs might be a Bad Idea

Cryptocurrencies are based on digitally signed updates (transactions) to a ledger maintained in a decentralized manner according to consensus mechanisms in compliance with consented protocols. Cryptocurrency systems are subject to several levels of governance. Self-regulation is crucial for maintaining the integrity of the systems and their attractiveness to the users. Market forces contribute to the survival and prosperity of the cryptocurrency systems that excel in self-regulation. However, self-regulation and market forces are imperfect mechanisms insufficient to align cryptocurrency systems governance with public policies. This paper explores the role of law in aligning cryptocurrency system governance with public policies, both as an innovation enabler and risk mitigator. Furthermore, this paper provides arguments against limited liability DAOs.
A GAN-based Background Noise Removal Method on Infrared Image of Gas-Insulated Transmission Line

Results of infrared inspection on 1100kV pipe gallery gas-insulated transmission line (GIL) project are seriously interfered by background noise such as the LED lights and induced heating on steel structures. In this paper, an image background noise removal method based on generative adversarial network (GAN) is proposed. Firstly, convolution neural network (CNN) is used to classify the different parts of GIL. Secondly, the method of threshold and graying are used to mark the classified parts. Finally, the general adverse network is used to repair marked interference parts of noise. In which the generator of GAN is used to repair the marked region to generate new infrared images without noise, and the discriminator is used to discriminate whether the new image output by the generator is successfully repaired. The results show that the proposed method can achieve better background removal effect on infrared images, and the texture feature of the image can preserve well when using GAN to remove the image noise compared with the image de-noising method of VAE. The application results on site show that it takes 0.26 seconds to classify each infrared image using CNN and 4 seconds to remove noise using GAN.
A timeline model for clinical events: empowering data

Data visualization is key in the Big Data context, enabling different cognitive perspectives over large datasets. These visual perspectives can prompt relevant advantages with regard to healthcare records, because they may contribute to a faster, more understandable, and adequate way to capture patients’ health history and overall condition, thus improving healthcare quality. Timelines are well-known visual artifacts that help healthcare professionals to visualize patients’ electronic health records (EHR) over a time period. As data stored in EHR tend to quickly grow with each interaction between patient and healthcare system both number and size, traditional linear timelines are can increasingly become more difficult to manage visually, as they often span over different screens. Considering that a holistic analysis is desirable to provide proper and quality health services, data visualization should enable a seamless understanding of patients’ health history and overall condition. When dealing with critical episodes - such as an emergency - where time is an important factor, this is even more decisive. Furthermore, traditional timelines do not support multidimensional data representation. This paper presents a new visual model of time-dependent EHR based on radial models. It is capable of simultaneously displaying several data categories that characterize patients’ medical history, enabling medical professionals to be aware of different data categories over time in a single display, without the need to scroll between screens.