Reviewerly/exHarmony huggingface dataset:
Data-independent low-complexity KLT approximations for image and video coding

The Karhunen–Loève transform (KLT) is often used for data decorrelation and dimensionality reduction. The KLT is able to optimally retain the signal energy in only few transform components, being mathematically suitable for image and video compression. However, in practice, because of its high computational cost and dependence on the input signal, its application in real-time scenarios is precluded. This work proposes low-computational cost approximations for the KLT. We focus on the blocklengths N∈{4,8,16,32} because they are widely employed in image and video coding standards such as JPEG and high efficiency video coding (HEVC). Extensive computational experiments demonstrate the suitability of the proposed low-complexity transforms for image and video compression.
A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.
Real‐time multi‐GPU‐based 8KVR stitching and streaming on 5G MEC/Cloud environments

In this study, we propose a multi-GPU-based 8KVR stitching system that operates in real time on both local and cloud machine environments. The proposed system first obtains multiple 4 K video inputs, decodes them, and generates a stitched 8KVR video stream in real time. The generated 8KVR video stream can be downloaded and rendered omnidirectionally in player apps on smartphones, tablets, and head-mounted displays. To speed up processing, we adopt group-of-pictures-based distributed decoding/encoding and buffering with the NV12 format, along with multi-GPU-based parallel processing. Furthermore, we develop several algorithms such as equirectangular projection-based color correction, real-time CG overlay, and object motion-based seam estimation and correction, to improve the stitching quality. From experiments in both local and cloud machine environments, we confirm the feasibility of the proposed 8KVR stitching system with stitching speed of up to 83.7 fps for six-channel and 62.7 fps for eight-channel inputs. In addition, in an 8KVR live streaming test on the 5G MEC/cloud, the proposed system achieves stable performances with 8 K@30 fps in both indoor and outdoor environments, even during motion.
A Novel Method of Identifying Optimal Interval Regression Model Using Structural Risk Minimization and Approximation Error Minimization

Uncertain measurements derived from many practical applications tend to be constructed as interval regression model (IRM), consisted of upper regression model (URM) and lower regression model (LRM). Motivated by interval regression analysis, a novel method of identifying IRM is proposed in this paper by combining the principle of structural risk minimization with approximation error minimization. Taken the superiorities of model sparse representation and computational efficiency of linear programming support vector regression (LP-SVR) and some ideas from ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm minimization on approximation error into consideration, the proposed method not only possesses the characteristics of adjusting a flexible interval spread, but also independently constructs URM and LRM, instead of adopting the traditional estimated center model and estimated radius of IRM which is the incapability of dealing with asymmetrical interval. More importantly, model complexity for IRM is under control by our approach. First, ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norms minimization on approximation error for URM and LRM are constructed, and the both optimization problems subject to respective constraints are integrated into LP-SVR to form new upper and lower optimization problems, respectively. Following that, optimization problems corresponding to URM and LRM are solved by linear programming and IRM is thus constructed. Finally, several simulations are provided to show the validity and applicability of the proposed method.
Behaviors of Unwarranted Password Identification via Shoulder-Surfing during Mobile Authentication

Password-based mobile user authentication is vulnerable to shoulder-surfing. Despite the increasing research on user password entry behavior and mobile security, there is limited understanding of how an adversary identifies a password through shoulder-surfing during mobile authentication. This study empirically examines the behaviors and strategies of password identification through shoulder-surfing with multiple observation attempts and from different observation distances. The results of analyzing data collected from a user study reveal the strategies and dynamics of password identification behaviors. The findings have implications for enhancing users’ password security and improving the design of mobile authentication methods.
Small-scale Image Semantic Segmentation Method Based on Multilevel Superposition and Enhancement Fusion

Aiming at the problem of small-scale target loss and boundary discontinuity in image semantic segmentation, this paper proposes an image semantic segmentation method based on deep learning multi-level superposition and enhancement fusion. This method uses DeepLabV3+ network structure and constructs multi-level feature extraction. The method of multi-level superposition and jump connection improves the ability to acquire local features, so that while segmenting large targets, local feature information can also be obtained, and the segmentation accuracy of local target boundaries is improved. The network is trained, validated and tested on the public data set PASCAL VOC2012. Experiments show that the visual segmentation effect of this method on small-scale images is significantly better than most semantic segmentation methods that have appeared. At the same time, in the overall accuracy of all categories and the accuracy of several typical categories, the method in this paper is also superior to many previous methods.
Requirements prioritization based on multiple criteria using Artificial Intelligence techniques

Traditional methods for requirements prioritization (RP) are currently limited by scalability and lack of automation issues. In recent years, there has been an exponential growth in the use of Artificial Intelligence (AI) techniques in different areas of software engineering (e.g., requirements analysis, testing, maintenance). In particular, I have found thirteen RP methods applying AI techniques such as machine learning, or genetic algorithms. 38% of these approaches seek to improve the scalability problem, whereas only 15% of them aim to improve the automation aspect along the RP process. Moreover, all these studies have carried out their evaluations with a number of requirements no greater than 100.In order to address the issues of scalability and lack of automation in RP, the present research project aims to propose a semi-automatic multiple-criteria prioritization method for functional and non-functional requirements of software projects developed within the Software Product-Lines paradigm. The proposed RP method will be based on the combination of Natural Language Processing techniques and Machine Learning algorithms, and for its validation, empirical studies will be carried out with real web-based geographic information systems (GIS). This paper describes the problem and technical challenges to be addressed, the related works, as well as the main contributions of the proposed solution.
Detection of Hate Speech by Employing Support Vector Machine with Word2Vec Model

Social media can be seen as one prominent assimilation of technology into human life interaction. Its presence is now likely inseparable to us and its usage, whether individually or communally, evidently has been impactful by means of news spread-both positive and negative ones. As a highlight, Indonesia records more than 3,640 hate speech cases from 2018 to this day. This issue has been the main drive of our research. We aim to produce a model for hate speech detection posted on a social media platform. The data was obtained from github-a hosting provider, consisting of tweets. Word2vec was employed as the method for feature extraction while support vector machine (SVM) with RBF as kernel function was used for data classification. The model was built and tested with a 70:30 ratio of data training and testing, in which we achieved the highest accuracy level of 85% with the settings of gamma =0.1 and C-value =10. The accuracy dropped to 69.7% when the model was tested with different datasets. With the development of hate speech detection models, we are optimistic towards a better society where social media users are less prone to negatively-intended information spread.
Twitter Sentiment Analysis using Machine Learning Algorithms for COVID-19 Outbreak in New Zealand

The use of data obtained from social media for data mining has benefited the process of analysing public opinions. Sentiment analysis, also referred to as opinion mining, helps public health officials and other governmental agencies to understand the public's concerns, panics, emotions and interactions to provide effective services and information. This research focuses on the sentiment analysis of the COVID-19 pandemic outbreak in New Zealand using Twitter data. The analyses derived from several machine learning classification techniques, in particular Naive Bayes, K-Nearest Neighbour (KNN), Convolutional Neural Network (CNN), and Support Vector Machine (SVM). In addition, the researchers implemented these algorithms in two different data mining platforms, namely Python and RapidMiner, then compared the metrics obtained from these techniques and platforms to identify the best algorithm suited for sentiment analysis. Finally, the researchers illustrate the experimental results that show the performance of Naïve Bayes and SVM, which indicates a longer computational time and led to an improved Twitter sentiment analysis result that outperforms the other models. After that, validate these models' effectiveness by comparing the obtained results for the same models in Python and RapidMiner.
Level of Password Vulnerability

Nowadays password vulnerability is very dangerous for accounts on the internet. The need to create an account is very important as it can properly store personal data. However, with a password, an account can maintain the integrity or authenticity of the account owner. Several things are very influential so that they make passwords vulnerable, such as several criteria in making passwords, namely the length of the password, the elements used in the password, reuse of passwords, frequently changing passwords, and other things that will be discussed in this research. To determine the password vulnerability of current users. From the questionnaire data, the most vulnerable key in password security is password reusability and frequently changed password.
Improving Maximum Likelihood Estimation Using Marginalization and Black-Box Variational Inference

Based upon Black Box Variational Inference, a new set of classification algorithms has recently emerged. The goals of this set of algorithms are twofold: 1) increasing generalization power; 2) decreasing computational and implementation complexity. To this end, we assume a set of latent variables during the generation of data points. We subsequently marginalize the conventional classification likelihood objective function w.r.t this set of latent variables and then apply black-box variational inference to estimate the marginalized likelihood. We evaluate the performance of the proposed method by comparing the results obtained from the application of our method to real-world datasets with those obtained using several classification algorithms. The experimental results prove that our proposed method is competitive.
Forward and Backward Secure Searchable Encryption Scheme Supporting Conjunctive Queries Over Bipartite Graphs

Dynamic searchable encryption, which allows clients to outsource their encrypted data to cloud servers and retain the ability to query and update data, has received wide attention. In the setting, it is essential to ensure that a server infers as little as possible about the content of the outsourced data and the queries it processes. In this article, we propose a forward and backward secure searchable encryption scheme on bipartite graphs (FBSSE-BG) that offers the strongest level of backward security. In particular, we introduce the notion of update counter to construct a new bi-directional index structure, which realizes conjunctive queries over bipartite graphs and supports flexible updates of outsourced data. Besides, to minimize the information revealed to servers, we propose a new oblivious data structure to store the bi-directional index and use a semantically-secure encryption scheme to encrypt node information, such that servers can only observe a series of ORAM locations and encrypted paths. Finally, we prove the security of FBSSE-BG by using the real-world versus ideal-world formalization and provide experimental efficiency evaluations for its implementations.
Document Search in Information Retrieval System Using Vector Space Model

The current pandemic has spread everywhere. Various effects of pressure in the economic, educational, and social sectors are forced to adjust. So that people really need information about efforts to prevent the spread is very necessary. Search Engine is a program that is used as a tool to find more information on the internet. Search Engine is one of the discussions in the field of Information Retrieval. This system is a document search of unstructured properties. Thus, being able to provide the information needs of a large set of documents (on a local computer server or the internet). The vector space model is one of the many models in Information Retrieval that is used to get the distance and direction between keywords and documents by representing them into vectors. Then the results of ranking using cosine similarity with a dataset of 90 articles about covid19 along with 4 keywords will be tested with precision, recall, and accuracy calculations. The results of the precision calculation get a value of 60% - 73%, recall gets a value for each of the keywords 81%-100% and gets an accuracy value of 85% -89%. The results of these experiments indicate that information retrieval with vector space model is effective with good and stable performance used for information retrieval.
IoT based health monitoring and record management using distributed ledger

Abstract Frequent visit of doctors during pandemicis not recommended due to COVID-19 scenario. Internet of Things (IoT) based sensors can be used to measure andto do certain tests at home. The blockchain-based strategy promises to aid in the delivery of health-care services while maintaining data privacy. To maintain transparency in the health record, medications, claim history, etc., up-to-dated distributed ledger is required. Doctors have sometimes necessitate patients to undergo lab tests or buy drugs from those organisations merely in order to generate personal profit, even if the medical shop or pathology labs have a bad reputation. Therefore, blockchain technology provides trust among multiple parties so that any party could not be misguided. At times, the patient visits the hospital in critical condition and they may not be in situation to explain about medical history. In such scenario, the information stored in distributed leger may be helpful to the doctors for further treatment. The medical emergencies such as cardiac arrest and breathing issues, the patient requires immediate treatment or medicine at his/her location, the intelligent drone is useful for faster delivery of medicines and needed medical accessories.
Structure-inducing pre-training

Abstract Language model pre-training and the derived general-purpose methods have reshaped machine learning research. However, there remains considerable uncertainty regarding why pre-training improves the performance of downstream tasks. This challenge is pronounced when using language model pre-training in domains outside of natural language. Here we investigate this problem by analysing how pre-training methods impose relational structure in induced per-sample latent spaces—that is, what constraints do pre-training methods impose on the distance or geometry between the pre-trained embeddings of samples. A comprehensive review of pre-training methods reveals that this question remains open, despite theoretical analyses showing the importance of understanding this form of induced structure. Based on this review, we introduce a pre-training framework that enables a granular and comprehensive understanding of how relational structure can be induced. We present a theoretical analysis of the framework from the first principles and establish a connection between the relational inductive bias of pre-training and fine-tuning performance. Empirical studies spanning three data modalities and ten fine-tuning tasks confirm theoretical analyses, inform the design of novel pre-training methods and establish consistent improvements over a compelling suite of methods.
A Hybrid Approach to News Recommendation Based on Knowledge Graph and Long Short-Term User Preferences

Currently, most news recommendation methods mainly rely on the representation of single information, and only a few methods consider various information. The rich knowledge entities and common sense of news are often ignored. To do this, a hybrid news recommendation method based on a knowledge graph and long short-term user preferences is proposed. First, the attention network is introduced to extract important words, features, and entities from a variety of news information and linked entity information to represent news. Then, using long-term user representations to initialize the hidden states of the gate recurrent unit network, built for short-term user representations, to learn the final representation of users. We conducted a comprehensive experimental study by comparing several baseline methods. The results show that our proposed model can achieve higher recommendation accuracy.
A Percentile Firefly Algorithm an Application to the Set Covering Problem

The application of continuous metaheuristic algorithms to combinatorial problems is an area of interest at the engineering level. This interest is related to the fact that many of the problems that are addressed at the industrial level are of the combinatorial type and a not lesser subset of these are of the NP-hard type. In this article, we propose a binarization mechanism for continuous metaheuristics that uses the concept of percentile. This percentile concept is applied to the firefly optimization algorithm to solve the set covering problem (SCP). To determine the importance of the percentile concept, experiments were designed to identify its contribution. Additionally, to see that the proposal is adequate, the efficiency of the algorithm is compared using reference instances. The results indicate that the binary percentile firefly algorithm (BPFA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.
Service Bursting Based on Binary PSO in Hybrid Cloud Environment

Given the competition and rapidly-evolving market associated challenges, companies need to be innovative and agile, particularly regarding the customers dedicated web applications. Nowadays, hybrid cloud stands as an attractive solution for organizations that tend to use a combination of private and public cloud implementations, following their appropriate needs to apply the available resources and speed up execution in the most profitable ways. In this regard, deploying the new applications would certainly entail placing and devoting some components to the private cloud option, while reserving others to the public cloud option. For this purpose, a Binary Particle Swarm Optimization (BPSO) based approach is proposed, useful for maintaining an effective service bursting optimization within the hybrid cloud framework. Using a real IBM based benchmark, the experimental results appear to reveal that our advanced approach scored results turn out to outperform those documented in the experiment works in terms of cost.
Multiple Instance Triplet Loss for Weakly Supervised Multi-Label Action Localisation of Interacting Persons

With the abundance of videos and the high cost of data annotation, weakly supervised action localisation has gained more attention. However, most of the works on weakly supervised action localisation focus on single action and single person action localisation. Recently, new approaches have been proposed to extend the weakly supervised action localisation task towards multi-label scenarios where multiple persons can interact with each other and perform multiple actions at the same time. For longer videos, these methods subdivide the training videos into very short clips and discard the temporal consistency of actions across these short clips. In this work, we address this issue and propose the Multiple Instance Triplet Loss (MITL) where consistent instances that are temporally close should be more similar than distant and inconsistent instances. It is an extension of the triplet loss to bags, where a bag comprises all person detections at a keyframe. We evaluate our proposed approach on the challenging AVA dataset, where it achieves state-of-the-art results when the weakly labelled training videos are longer than 1 second.
Impeccable Circuits III

As a recent fault-injection attack, SIFA defeats most of the known countermeasures. Although error-correcting codes have been shown effective against SIFA, they mainly require a large redundancy to correct a few bits. In this work, we propose a hybrid construction with the ability to detect and correct injected faults at the same time. We provide a general implementation methodology which guarantees the correction of up to t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> -bit faults and the detection of at most t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> faulty bits. Exhaustive evaluation of our constructions, by the open-source fault diagnostic tool VerFI, indicate the success of our designs in achieving the desired goals.