Reviewerly/exHarmony huggingface dataset:
A Metadata and Z Score-based Load-Shedding Technique in IoT-based Data Collection Systems

The Internet-of-Things (IoT) has emerged as an alternative to communicate different pieces of technology to foster the distributed data collection. The measurement projects and the Real-time data processing are articulated to take advantage of this environment, fostering a sustainable data-driven decision making. The Data Stream Processing Strategy (DSPS) is a Stream Processing Engine focused on measurement projects, where each concept is previously agreed through a measurement framework. The Measurement Adapter (MA) is a component whose responsibility is to pair each metric’s definition from the measurement project with data sensors to transmit data (i.e., measures) with metadata (i.e., tags indicating the data meaning) together. The Gathering Function (GF) receives and derivates data for its processing from each MA, while it implements load-shedding (LS) techniques based on Metadata to avoid a processing collapse when all MAs informs jointly and frequently. Here, a Metadata and Z-score based load-shedding technique implemented locally in the MA is proposed. Thus, the load-shedding is located at the same data source to avoid data transmission and saving resources. Also, an incremental estimation of average, deviations, covariance, and correlations are implemented and employed to calculate the Z-scores and to data retain/discard selectively. Four simulations discrete were designed and performed to analyze the proposal. Results indicate that the local LS required only 24% of the original data transmissions, a minimum of 18.61 ms as the data lifespan, while it consumes 890.26 KB. As future work, other kinds of dependencies analysis will be analyzed to provide local alternatives to LS.
A Blockchain-Based Traceability System for Waste Management in Smart Cities

Abstract Waste tracking is becoming an important concern for developed countries as well as developing regions, where municipalities aim to assure proper waste management considering environmental and economic objectives. Waste tracking is important not only for a transparent reporting system compatible with environmental regulations but also for economically viable waste collection and recovery solutions. In this paper, a waste tracking system based on the blockchain technology is introduced where different entities involved in the system will be able to retrieve required data from the platform and decide on their level of contributions. The conventional technologies do not provide a sufficient level of transparency and coordination among different entities. With the introduction of blockchain as a tamper-proof technology, municipalities can enhance the efficiency of their waste management efforts. The proposed blockchain technology can connect proper stakeholders towards collaboration and sharing information. The concept of a smart contract for waste management is discussed and further, a decision-making framework is developed to guide users of the system select proper services available to them, depending on the level of data sharing, cost, reliability, and the security level that they expect from the system.
SMOT: Single-Shot Multi Object Tracking

We present single-shot multi-object tracker (SMOT), a new tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from errors made by the object detectors, SMOT adopts the recently proposed scheme of tracking by re-detection. We combine this scheme with SSD detectors by proposing a novel tracking anchor assignment module. With this design SMOT is able to generate tracklets with a constant per-frame runtime. A light-weighted linkage algorithm is then used for online tracklet linking. On three benchmarks of object tracking: Hannah, Music Videos, and MOT17, the proposed SMOT achieves state-of-the-art performance.
Strategic Dimensions of Information Security Risk Management

Information security is thus a big threat to the survival of enterprises. In all context and forms, it is an imperative to provide adequate safeguards and measures to management the risk arising from flow of information and data. The business models of organizations are highly dependent on flow of information during the business processes. The management of information security has several perspectives. In this paper, the legal, quality and human resource perspectives have been discussed. The Information Security Risk Management Model must balance these perspectives to optimize for best value derived out of it.
A novel and secure authentication scheme for the Internet of Things over algebraic structure

The Internet of Things (IoT) is an evolving world-view in which the devices around us are interconnected to provide smooth, fast and efficient communication and contextual amenities. In the IoT, due to the limitless number of devices, each device cannot be authenticated in a short time. Similarly, receiving their authentication request at the same moment is hard. Therefore it is necessary to have a safe and efficient authentication system that authenticates devices in the context of resource-restricted IoT at once. This paper present a novel and efficient cryptography based authentication over seminearring root extraction problem for the IoT which verifies the authenticity of the devices taking part in the communication.
The Impact of Stratified Teaching on the Academic Performance of Chinese Middle School Students: A Meta-Analysis

Stratified teaching is an effective method and means to implement teaching students per their aptitude. Domestic and foreign scholars have carried out many experimental and quasi-experimental studies to observe the impact of stratified teaching on students’ academic performance, but the results are quite different. So, can stratified teaching effectively help Chinese students improve their academic performance? How big is its impact? Which model is more suitable for Chinese students? To answer these questions, this article uses meta-analysis to quantitatively analyze 22 Chinese studies on the impact of Stratified teaching on student academic performance. We found that (i) stratified teaching has a positive effect on students’ academic performance, and the overall combined effect size is 0.53; (ii) among the seven subjects of mathematics, English, physics, chemistry, biology, geography, and information technology, stratified teaching has had a positive effect on their learning. Stratified teaching’s order of effect on different subjects was English &gt; Physics &gt; Geography &gt; Information Technology &gt; Mathematics &gt; Biology &gt; Chemistry; (iii) stratified teaching is suitable for students of different sizes of classrooms. However, the smaller the number of students in the classroom, the better the learning effect, and (iv) stratified teaching is more suitable for improving their learning in the mobile learning system.
Application of Blockchain Technology in the Field of Network and Information Security

Abstract In essence, blockchain refers to a database with core removed. It can support multi-party cooperation in different fields through special electronic technology and data management mode. Blockchain technology can be considered as the security field of encryption. It can solve the related problems of network and information security. However, China’s blockchain technology is not mature enough in many aspects. Many technologies are still in the process of research. This paper analyzes the application of blockchain technology in network and information security.
Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.
Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

Abstract This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper. We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.
High-Security Image Steganography Technique using XNOR Operation and Fibonacci Algorithm

Since the number of internet users is increasing and sensitive information is exchanging continuously, data security has become a problem.Image steganography is one of the ways to exchange secret data securely using images.However, several issues need to be mitigated, especially in the imperceptibility (security) aspect, which is the process of embedding secret data in the images that can be vulnerable to attacks.This paper focuses on developing a secure method for hiding secret messages in an image, based on the standard Least Significant Bit (LSB).Before proceeding with the embedding stage, the secret message's size is reduced by compression using the Huffman algorithm, followed by two operations, which are the Boolean operation Exclusive-NOR (XNOR) operation and the Fibonacci algorithm when selecting pixels to embed the secret message.As a result of these processes, a stego-image is created with two secret keys.We obtained promising results against standard images with higher Peak Signal-to-Noise Ratio (PSNR) values of 66.6170, 65.8928, and 65.9386 dB for Lena.bmp,Baboon.bmp, and Pepper.bmp,respectively, as compared to other state-of-the-art schemes.The evaluation stage proves the increasing level of security as well as imperceptibility.
Actuating wearables for motor skill learning: a constructive design research perspective

The integration of actuators into wearables to support haptic output and shape change provides an alternative to overused modalities like audio and video that allows a tighter coupling of feedback to body parts. Adopting a research-through-design approach, we report on six design explorations on how actuated clothing can support users to learn a movement skill, e.g. for sports or for the purposes of rehabilitation. Our exploration of actuation in wearables focuses on the aesthetics of form and of interaction, especially in relation to expressiveness and supporting how the user relates to other individuals. In this article, we discuss challenges and design potentials related to user experience and aesthetics of actuating wearables in this context.
Common information, matroid representation, and secret sharing for matroid ports

Linear information and rank inequalities as, for instance, Ingleton inequality, are useful tools in information theory and matroid theory. Even though many such inequalities have been found, it seems that most of them remain undiscovered. Improved results have been obtained in recent works by using the properties from which they are derived instead of the inequalities themselves. We apply here this strategy to the classification of matroids according to their representations and to the search for bounds on secret sharing for matroid ports.
Helping People with Language Learning Disabilities Using Native Mobile Voice Recognition — Exploring Its Limits and Advantages

This article presents an intelligent system for people with language learning disabilities called MarLuc. MarLuc aims to improve people’s skills in their native language rather than a second language. The system was born as a tiny Computer Assisted Language Learning (CALL) web-based application targeted to improve pronunciation. Later it became a Mobile Assisted Language Learning (MALL) application and has therefore incorporated some powerful native resources such as voice recognition present on mobile devices. Working with personal mobile devices has brought new exciting possibilities, including but not limited to easy access to native voice recognition and the utilization of bots. In this study, we will show the advantages and some limitations of using native Siri or Android based voice recognition.
Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers

Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation and biomarker analysis are performed independently. We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. To this end, we evaluate four Bayesian neural networks to sample from the posterior distribution and estimate the uncertainty. We then assign confidence measures to the biomarker and propose statistical models for its integration in group analysis and disease classification. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference.
Perfomance analysis of relational databases MySQL, PostgreSQL, MariaDB and H2

This article will compare the performance of relational databases MySQL, PostgreSQL, MariaDB and H2. The speed of executing will be tested on simple and more complex SQL queries. The tests results will show which database is more efficient.
Mining Review Unit Model for Online Review Analysis

An increasing number of people are choosing to shop online; hence, online reviews are an increasingly influential factor in consumer purchasing decisions. However, extracting useful information from online reviews is a challenge in the analysis of consumer sentiment. In this paper, we focus on the automatic discovery of the features evaluated in online reviews and the expression of sentiment. We propose a novel fine-grained topic model called the “review unit topic model” (RUTM) to extract semantic meanings and polarities. In this model, a review unit rather than a review sentence is treated as the representational model, and prior knowledge of sentiment is further exploited to identify aspect-aware sentiment polarities. We evaluate RUTM extensively using real-world review data. Experimental results demonstrate that the proposed model outperforms well-established baseline models in sentiment analysis tasks.
Two decades of distributed global radiation time series across a mountainous semiarid area (Sierra Nevada, Spain)

Abstract. The main drawback of the reconstruction of high-resolution distributed global radiation (Rg) time series in mountainous semiarid environments is the common lack of station-based solar radiation registers. This work presents 19 years (2000–2018) of high-spatial-resolution (30 m) daily, monthly, and annual global radiation maps derived using the GIS-based model proposed by Aguilar et al. (2010) in a mountainous area in southern Europe: Sierra Nevada (SN) mountain range (Spain). The model was driven by in situ daily global radiation measurements, from 16 weather stations with historical records in the area; a 30 m digital elevation model; and 240 cloud-free Landsat images. The applicability of the modeling scheme was validated against daily global radiation records at the weather stations. Mean RMSE values of 2.63 MJ m−2 d−1 and best estimations on clear-sky days were obtained. Daily Rg at weather stations revealed greater variations in the maximum values but no clear trends with altitude in any of the statistics. However, at the monthly and annual scales, there is an increase in the high extreme statistics with the altitude of the weather station, especially above 1500 m a.s.l. Monthly Rg maps showed significant spatial differences of up to 200 MJ m−2 per month that clearly followed the terrain configuration. July and December were clearly the months with the highest and lowest values of Rg received, and the highest scatter in the monthly Rg values was found in the spring and fall months. The monthly Rg distribution was highly variable along the study period (2000–2018). Such variability, especially in the wet season (October–May), determined the interannual differences of up to 800 MJ m−2 yr−1 in the incoming global radiation in SN. The time series of the surface global radiation datasets here provided can be used to analyze interannual and seasonal variation characteristics of the global radiation received in SN with high spatial detail (30 m). They can also be used as cross-validation reference data for other global radiation distributed datasets generated in SN with different spatiotemporal interpolation techniques. Daily, monthly, and annual datasets in this study are available at https://doi.org/10.1594/PANGAEA.921012 (Aguilar et al., 2021).
Algorithms for CRT-variant of Approximate Greatest Common Divisor Problem

Abstract The approximate greatest common divisor problem (ACD) and its variants have been used to construct many cryptographic primitives. In particular, the variants of the ACD problem based on Chinese remainder theorem (CRT) are being used in the constructions of a batch fully homomorphic encryption to encrypt multiple messages in one ciphertext. Despite the utility of the CRT-variant scheme, the algorithms that secures its security foundation have not been probed well enough. In this paper, we propose two algorithms and the results of experiments in which the proposed algorithms were used to solve the variant problem. Both algorithms take the same time complexity $\begin{array}{} \displaystyle 2^{\tilde{O}(\frac{\gamma}{(\eta-\rho)^2})} \end{array}$ up to a polynomial factor to solve the variant problem for the bit size of samples γ , secret primes η , and error bound ρ . Our algorithm gives the first parameter condition related to η and γ size. From the results of the experiments, it has been proved that the proposed algorithms work well both in theoretical and experimental terms.
Computing Neural Networks with Homomorphic Encryption and Verifiable Computing

The widespread use of machine learning and in particular of Artificial Neural Networks (ANN) raises multiple security and data privacy issues. Recent works propose to preserve data confidentiality during the inference process, available as an outsourced service, using Homomorphic Encryption techniques. However, their setting is based on an honest-but-curious service provider and none of them addresses the problem of result integrity. In this paper, we propose a practical framework for privacy-preserving predictions with Homomorphic Encryption (HE) and Verifiable Computing (VC). We propose here a partially encrypted Neural Network in which the first layer consists of a quadratic function and its homomorphic evaluation is checked for integrity using a VC scheme which is slight adaption of the one of Fiore et al. []. Inspired by the neural network model proposed by Ryffel et al. [] which combines adversarial training and functional encryption for partially encrypted machine learning, our solution can be deployed in different application contexts and provides additional security guarantees.We validate our work on the MNIST handwritten recognition dataset for which we achieve high accuracy (97.54%) and decent latency for a practical deployment (on average 3.8 s for both homomorphic evaluation and integrity proof preparation and 0.021 s for the verification).
Immersive Analytics of Anomalies in Multivariate Time Series Data with Proxy Interaction

In industry and science, sensor data play a vital role in research, optimisation, monitoring, testing and many other use cases. When performing tests with repeated cycles of similar behaviour, e.g., durability tests, it is often important to find anomalous sensor behaviour that deviates from regular patterns in the data. We here explore the design space of VRbased immersive analytics for time series data, for use e.g., in engineering contexts where an underlying application is also given in VR. The use of 3D visualisation for time series exploration is a much-discussed topic and careful consideration for its use must be taken. With the rise of immersive environments, we re-visit the classic problem of 3D time series visualisation and introduce an immersive walk-up usable interaction proxy that supports efficient navigation of otherwise possibly occluded time series views. The proxy indicates anomalies in the data for easy access and provides efficient zooming and filtering controls, among other effective interaction possibilities. This approach is combined with suitable data analysis techniques, providing an environment for effective and efficient immersive anomaly detection and comparative data analysis that we call WaveCharts. We demonstrate the applicability of our approach by two real-world use cases, and we discuss the necessary tools it provides to aid the analysis process of large sensor data.