Reviewerly/exHarmony huggingface dataset:
Intermediaries Do Matter: Voluntary Standards and the Right to Data Portability

This paper enlightens an understudied aspect of the application of the General Data Protection Regulation (GDPR) Right to Data Portability (RtDP), introducing a framework to analyse empirically the voluntary data portability standards adopted by various data controllers. The first section explains how the RtDP wording creates some “grey areas” that allow data controllers a broad interpretation of the right. Secondly, the paper shows why the regulatory initiatives affecting the interpretation of these “grey areas” can be framed as “regulatory standard-setting (RSS) schemes”, which are voluntary standards of behaviour settled either by private, public, or non-governmental actors. The empirical section reveals that in the EU, between 2000 and 2020, the number of such schemes increased every year and most of them were governed by private actors. Finally, the historical analysis highlights that the RtDP was introduced when many private-run RSS schemes were already operating, and no evidence suggests that the GDPR impacted significantly on their spread.
Static Analysis for Ada, C/C++ and Python

Spazio IT has been working on the Independent Software Verification and Validation of several codebases, some written in Ada, others in C/C++ and more recently also in Python; in all cases Spazio IT has used static analysis techniques and tools facilitating code inspection. Static analysis has always proven to be beneficial, but depending on the programming language, its advantages have emerged in different areas and namely: metrics and structural analysis for Ada, bugs finding at execution/semantic level for C/C++ and errors finding at compilation/syntactical level for Python. The paper presents first some general considerations on static analysis, then it concentrates on static analysis specifically applied to Ada, C/C++ and Python codebases. Finally, the paper describes Spazio IT future activities in the area of static analysis.
ISSUES OF STUDYING PHILOSOPHY AT UNIVERSITY IN THE CONDITIONS OF DISTANCE LEARNING

The key issue of this article is the process of teaching and studying philosophy at university in modern conditions. The authors touch upon the topic of intensive information technology development, as well as the need to transform philosophical knowledge as the basis of natural intelligence, without which it is impossible to harmoniously develop both the educational process at university and the formation of personality in particular. The authors analyse the strengths and weaknesses of teaching philosophy in a distance learning environment; characterise the prospects for teaching philosophy at Russian university of the 21st century.
Performance of Water Level Forecasting Based on Chaos Approach Using Data Splitting

Forecasting accuracy should be prioritised in flood plain areas.This research focuses on data split of water level time series datasets in producing excellent forecasts as measured by coefficient correlation (CC).The datasets involved 6000 hours chosen from a recent research location at Sungai Dungun water level, which the data was split into different ratio datasets (50:50, 60:40, 70:30, 80:20, 90:10).A recent study has proved that the chaotic dynamic existed in time series data when running the data using the Cao method.The dataset used was divided into training and testing data to evaluate the performance based on the local linear approximation method.Those sets of data required a combination of parameters for prediction.In this study, the data split of water level time series data gave impacts to the combination of parameters for prediction.The result obtained was in the range of strong forecast using chaos approach with over 95% accuracy in every dataset.In addition, the dataset with a 50:50 ratio showed the highest CC obtained, and its values decreased in ascending order of 60:40, 70:30, 80:20, and 90:10.It showed that the splitting data of training and testing had an impact on prediction results.The higher number of training data ran, the lower number of CC was obtained.However, the chaos method still gives excellent prediction results, even when forecasting using different ratios of data set.
The impact of SMEs on Kuwait's economic sustainable economic growth: what do Pearson correlation coefficients confirm

Several studies confirm that Small and Medium-Sized Enterprises (SMEs) play an important role in developing the countries' economies in terms of increasing the Gross Domestic Product (GDP), creating new jobs and decreasing unemployment, and increasing the goods' export and reducing the poverty. This paper provides a picture of Kuwaiti SMEs and their impact on sustainable economic development. In terms of methodology, secondary data are used for the purposes of this study. These data are taken from the State Statistical Office of Kuwait for the period 2012-2019. The study involves data for macroeconomic indicators and SMEs indicators. SPSS software is used as a statistical tool for data analysis. Findings show that the development of SME has no significant impact on Kuwait's economic growth for the analysed period, which opens many questions and opportunities for the Kuwaiti Government with regards to their policies about SMEs support and development.
Decentralized and Secured Voting System with Blockchain Technology

Blockchain-based e-voting is a secured web-based voting software system. There are many voting systems that are in practice over the years like the ballot paper voting system, postal ballots and EVM system. All the mentioned systems do not ensure the safety of the votes and the trust that the voters have to put in. A Blockchain is a cryptographically connected peer-to-peer network of computers called nodes. Blockchain is the decentralized distributed technology used to create a secure voting system. All the data is secured in the public ledger by cryptographic hashing and is validated by a consensus algorithm. The Truffle framework is used for implementation, testing and deployment. Smart contract is built using Ether as security tokens. Smart contract deploys blockchain incorporating system logic written in a solidity programming language. Ganache is used as a virtual Ethereum client.
Traffic Classification Based on CNN-LSTM Hybrid Network

Network traffic classification is a significant method of network anomaly detection and plays a critical role in cyberspace security. Accompanied by the rapid development of network traffic diversity, traditional detection methods are not any longer adapt to the complex network environments. In the article, we use the method of deep learning and present a traffic classification method, which directly operates on raw traffic data. A hybrid neural network combining 1D CNN and LSTM network is used for learning the spatial and temporal characteristics of the stream in the meantime, which is verified on the CICIDS2017 dataset. Finally, we applied the model verified on the public data set to real network traffic, and achieved good results. It can be concluded from the experimental results that our arithmetic has higher accuracy on public dataset as well as can be applied to real network environments to solve real-world problems.
m-adic residue codes over F_q[v]/(v^s-v) and their applications to quantum codes

Due to their rich algebraic structure, cyclic codes have a great deal of significance amongst linear codes. Duadic codes are the generalization of the quadratic residue codes, a special case of cyclic codes. The $m$-adic residue codes are the generalization of the duadic codes. The aim of this paper is to study the structure of the $m$-adic residue codes over the quotient ring $\frac{{{\mathbb{F}_q}\left[ v \right]}}{{\left\langle {{v^s} - v} \right\rangle }}$. We determine the idempotent generators of the $m$-adic residue codes over $\frac{{{\mathbb{F}_q}\left[ v \right]}}{{\left\langle {{v^s} - v} \right\rangle }}$. We obtain some parameters of optimal $m$-adic residue codes over $\frac{{{\mathbb{F}_q}\left[ v \right]}}{{\left\langle {{v^s} - v} \right\rangle }}$ with respect to Griesmer bound for rings. Furthermore, we derive a condition for $m$-adic residue codes over $\frac{{{\mathbb{F}_q}\left[ v \right]}}{{\left\langle {{v^s} - v} \right\rangle }}$ to contain their dual. By making use of a preserving-orthogonality Gray map, we construct a family of quantum error correcting codes from the Gray images of dual-containing $m$-adic residue codes over $\frac{{{\mathbb{F}_q}\left[ v \right]}}{{\left\langle {{v^s} - v} \right\rangle }}$ and give some examples to illustrate our findings.
On Glocal Explainability of Graph Neural Networks

Graph Neural Networks (GNNs) derive outstanding performance in many graph-based tasks, as the model becomes more and more popular, explanation techniques are desired to tackle its black-box nature. While the mainstream of existing methods studies instance-level explanations, we propose Glocal-Explainer to generate model-level explanations, which consumes local information of substructures in the input graph to pursue global explainability. Specifically, we investigate faithfulness and generality of each explanation candidate. In the literature, fidelity and infidelity are widely considered to measure faithfulness, yet the two metrics may not align with each other, and have not yet been incorporated together in any explanation technique. On the contrary, generality, which measures how many instances share the same explanation structure, is not yet explored due to the computational cost in frequent subgraph mining. We introduce adapted subgraph mining technique to measure generality as well as faithfulness during explanation candidate generation. Furthermore, we formally define the glocal explanation generation problem and map it to the classic weighted set cover problem. A greedy algorithm is employed to find the solution. Experiments on both synthetic and real-world datasets show that our method produces meaningful and trustworthy explanations with decent quantitative evaluation results.
Blockchain-based architecture centred patient for decentralised storage and secure sharing health data

The healthcare sector has seen data-sharing systems emerge between patients and different healthcare providers. These systems have provided an excellent service to this sector and have positively contributed to scientific research and medical diagnosis. On the other hand, data security issues have arisen. Besides, traditional centralised storage methods pose a significant threat to this information's survival, which continues to increase with the emergence of portable devices. This work proposes an architecture that combines revolutionary technologies: Blockchain and the interplanetary file system (IPFS) to secure sharing and decentralised healthcare data storage.
Dataset for Bitcoin arbitrage in different cryptocurrency exchanges

Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using dplyr, reshape2, plyr packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 to 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 to 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities (Bruzgė and Šapkauskienė [1]). However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question.
A Self-adaptive Differential Evolution Algorithm for Solving Optimization Problems

This research proposes a novel self-adaptive differential evolution algorithm for solving continuous optimization problems. This paper focuses on redesiging the self-adaptive strategy for the mutation parameters. The new mutation parameters adjust themselves to the current situation of the algorithm. When the search is stagnant, the first mutation parameter that scales the difference between the best vector and the target vector will be increased. In contrast, the second mutation parameter that scales the difference between two random target vectors will be decreased. On the other hand, when the search progresses well towards the global optimum, the algorithm will enhance the search of the surrounding space by doing the opposite of the above actions. The performance of the proposed self-adaptive differential evolution algorithm was evaluated and compared with the classic differential evolution algorithm on 7 benchmark functions. The experimental results showed that the proposed algorithm converged much faster than the classic differential evolution algorithm on all benchmark functions.
FedPeer: A Peer-to-Peer Learning Framework Using Federated Learning

The computing power of personal devices has increased in the recent past, and the large amount of data is being generated by these devices. Owning to security concerns, research is currently done on training machine learning models locally on these devices. In order to train a global model, two different approaches have been proposed in the recent past. One, in which the models trained on the devices are aggregated at a central location, and the aggregated model is transmitted back to the devices and the other, where devices communicate the models among themselves without the need of a central server. Both these approaches have their own share of advantages and short comings. In this paper, we propose FedPeer a new decentralized machine learning approach where the nodes are clustered based on confidence level of predicting the data. The higher confident nodes form a federated cluster, whereas the remaining nodes participate in a peer-to-peer decentralized learning along with a representative from the federated cluster. Our experiments show that this approach is proved to have a faster convergence rate and lower communication overhead when compared to the either a federated approach or a complete peer-to-peer method.
FalCon: A Faithful Contrastive Framework for Response Generation in TableQA Systems

In a practical TableQA system, response generation is a critical module to generate a natural language description of the SQL and the execution result. Due to the complex syntax of SQL and matching issues with table content, this task is prone to produce factual errors. In this paper, we propose FalCon, a Faithful Contrastive generation framework to improve the factual correctness of generated responses. FalCon forces the generation model to identify examples with factual errors in the latent space during training and takes contrastive examples into consideration during inference. We also propose two new automatic metrics to further evaluate faithfulness specialized to this task. Experimental results show FalCon brings a favorable performance improvement on both automatic and human evaluation amongst various baseline methods (The code of FalCon is released at https://github.com/whuFSN/FalCon ).
SAD: Website Fingerprinting Defense Based on Adversarial Examples

Website Fingerprinting (WF) attacks can infer website names from encrypted network traffic when the victim is browsing the website. Inherent defenses of anonymous communication systems such as The Onion Router (Tor) cannot compete with current WF attacks. The state-of-the-art attack based on deep learning can gain over 98% accuracy in Tor. However, the existing defense will bring high bandwidth overhead, affect the user’s network experience or cannot be used in actual scenarios. Some researchers found that deep learning models are vulnerable to adversarial examples. In this paper, based on adversarial examples we propose Segmented Adversary Defense (SAD) for deep learning-based WF attacks. Network traffic is divided into multiple segments. Then, the adversarial examples for each segment of traffic can be generated by SAD. Finally, dummy packets from adversarial examples are inserted after each segment traffic. Experimentally, our results show that SAD can effectively reduce the accuracy of WF attacks. The technique drops the accuracy of the state-of-the-art attack hardened from 96% to 3% while incurring only 40% bandwidth overhead. Compared with the existing proposed defense named Deep Fingerprinting Defender (DFD), the defense effect of SAD is better under the same bandwidth overhead.
Classification of Blast Cells in Leukemia Using Digital Image Processing and Machine Learning

Leukemia is a fast-developing type of blood cancer that gets worse quickly in the children and adults and needs prompt treatment. Thus, this work displays an attempt that has been made to design a fast and cost-effective computer-aided system for classification of blast cells in leukemia using digital image processing and machine learning. The white blood cells in the microscopic images of blood smears are initially extracted by the Otsu’s method, and a cell separation algorithm is applied to break up the overlapped cells. Subsequently, several features are extracted from the whole cell, nucleus, and cytoplasm. The resulted overall accuracy of 90.32% for identification of leukemia affected cells is achieved. Therefore, the current system yielded very promising results in terms of classification accuracy of leukemia-affected cells that can be distinguished from the unaffected ones. However, future research is still needed to develop the diagnostic accuracy.
GeoGebra Augmented Reality: Ideas for Teaching and Learning Math

The methodologies of teaching mathematics in engineering must be active, and directed to the area of courses, so that freshman students are motivated in their learning and thus be able to overcome their difficulties. Augmented reality (AR) is an immersing experience that can be introduced into learning content such as integral calculus and solids of revolution. This paper presents ideas for teaching and learning math using GeoGebra AR. The results presented and visualized show that students engage in this virtual environment integrated into their real world and that the contents explored in a creative way are learned by students. Learning math is done within augmented reality!
SMOTE-LMKNN: A Synthetic Minority Oversampling Technique Based on Local Means-Based k-Nearest Neighbor

Traditional classifiers are trapped by the class-imbalanced problem due to the fact that they are biased toward the majority class. Oversampling methods can improve imbalanced classification by creating synthetic minority class samples. Noise generation has been a great challenge in oversampling methods. Filtering-based and direction-change methods are proposed against noise generation. Yet, the adopted noise filters in filtering-based methods are biased to the majority class. Besides, the [Formula: see text]-nearest neighbor (KNN)-based interpolation in filtering-based and direction-change methods is susceptible to abnormal samples (e.g. outliers, noise or unsafe borderline samples). To overcome noise generation while solving the above shortcomings of filtering-based and direction-change methods, this work presents a new synthetic minority oversampling technique based on local means-based KNN (SMOTE-LMKNN). In SMOTE-LMKNN, the local mean-based KNN (LMKNN) is first introduced to describe the local characteristic of imbalanced data. Second, a new LMKNN-based noise filter is proposed to remove noise and unsafe borderline samples. Third, the interpolation between a base sample and its LMKNN is proposed to create synthetic minority class samples. Empirical results of extensive experiments with 18 data sets show that SMOTE-LMKNN is competent compared with seven popular oversampling methods in training KNN classifier and classification and regression tree (CART).
Introduction to Continuous Variable Quantum Key Distribution

In this talk, we will review the present status of continuous-variable quantum key distribution, including optical configuration and security analysis, and would like to discuss future prospects for integration with coherent optical communications.
Color Vision Deficiency and Live Recoloring

Color information is an essential part of our daily life. Color Vision Deficiency (CVD), commonly known as color blindness, is a significant cause of concern for a fairly large number of people in the world. In general, such people lack the ability to differentiate and recognize certain colors. CVD is classified into several different categories such as monochromacy, dichromacy and anomalous trichromacy and have been sub-categorized further. This paper provides a novel scheme to address CVD by employing variations of colors during pixel plotting to capture color disparities and resolve through color compensation. The proposed scheme is capable of identifying type of CVD and then based on that specific type of CVD, provides color contrast variation. The proposed scheme is capable of handling both stored and live transmission of visual information (both static and continuous). The type of CVD can be determined by identifying the particular color deficiency associated with it. The proposed scheme involves color transformation from RGB (Red, Green, and Blue) scheme to LMS (Long, Medium, and Short) scheme. This transformation not only helps in identifying the specific color deficiency but also in adjustments of color contrasts. The efficient processing and rendering of recolored video and/or images, allows the affected CVD patients to observe affected shades in the recolored frames of video or images.
Case Base Maintenance: Clustering Informative, Representative and Divers Cases (C IRD)

Maintenance is generally defined in the field of software and knowledge engineering as an activity that takes place after the development of the system is complete, and the application has already been deployed in operation. The success factor for case-based reasoning (CBR) systems is the quality of their case bases, in order to guarantee this quality, a maintenance process must be planned, and this is how the field of case base maintenance (CBM) emerged. The goal of this paper is to propose a case base maintenance strategy that delivers a small case base size, removes irrelevant cases from the case base, and targets only valuable cases to be retained to increase classification accuracy. We propose a case base maintenance approach C_IRD that focuses on balancing the efficiency of case retrieval and the competence of a case base by employing a soft clustering technique: FCM. The method delivers interesting abilities and is able to maintain case bases with satisfactory accuracy by reducing its size, which leads to a reduction in retrieval time.