Reviewerly/exHarmony huggingface dataset:
Complete symmetric polynomials over finite fields have many rational zeros

Counting zeros of polynomials over finite fields is one of the most important topics in arithmetic algebraic geometry. In this paper, we consider the problem for complete symmetric polynomials. The homogeneous complete symmetric polynomial of degree $m$ in the $k$-variables $\\{x_1,x_2,\\ldots,x_k\\}$is defined as <disp-formula><tex-math><![CDATA[$$h_m(x_1,x_2,\\ldots,x_k):=\\sum_{1\\leq i_1\\leq i_2\\leq \\cdots \\leq i_m\\leq k}x_{i_1}x_{i_2}\\cdots x_{i_m}.$$]]></tex-math></disp-formula> A complete symmetric polynomial of degree $m$ over $\\f{q}$ in the $k$-variables $\\{x_1,x_2,\\ldots,x_k\\}$ is defined as <disp-formula><tex-math><![CDATA[$$h(x_1,\\ldots, x_k):=\\sum_{e=0}^m a_eh_e(x_1,x_2,\\ldots,x_k),$$]]></tex-math></disp-formula> where $a_e\\in~\\f{q}$ and $a_m\\not=0$. We are interested in counting the number of zeros and the number of zeros with pairwise distinct coordinates of a complete symmetric polynomial, respectively. Let <disp-formula><tex-math><![CDATA[$$N_q(h):= \\#\\{(a_1,\\ldots, a_k)\\in \\f{q}^k\\mid h(a_1,\\ldots, a_k)=0\\}$$]]></tex-math></disp-formula> denote the number of $\\f{q}$-rational points on theaffine hypersurface defined by $h(x_1,\\ldots,~x_k)=0$. Let <disp-formula><tex-math><![CDATA[$$N_q^*(h):=\\# \\{(a_1,\\ldots, a_k)\\in \\f{q}^k\\mid h(a_1,\\ldots, a_k)=0 \\mbox{and}\\, a_i\\not= a_j, \\forall\\, i\\not=j\\}$$]]></tex-math></disp-formula> denote the number of $\\f{q}$-rational points on theaffine hypersurface defined by $h(x_1,\\ldots,~x_k)=0$with the additional condition that the coordinates are distinct. In the paper Zhang and Wan (2020), the authors showed the lower bound $N_q(h)\\geq~6q^{k-3}$ if $q$ is odd, $k\\geq~3$ and $1\\leq~m\\leq~k-3$ and conjectured $N_q(h)\\geq~24q^{k-4}$ if $q$ is even, $k\\geq~4$ and $1\\leq~m\\leq~k-4.$ The key ingredient in the proof of the lower bound is to prove $N_q^*(h(x_1,x_2,x_3))\\geq~6$ for odd $q$, $k=3$ and $1\\leq~m\\leq~q-3$ which does not hold for even $q$ in general. In this paper, we deal with the even characteristic case. The main new results are the followings (suppose $\\f{q}$ is a finite field with characteristic $2$). (1) Let $h(x_1,x_2,~x_3):=\\sum_{e=0}^m~a_eh_e(x_1,x_2,x_3)\\in~\\f{q}[x_1,x_2,x_3]$ be a complete symmetric polynomials of degree $m$ with $1\\leq~m\\leq~q-3$. If $a_{e_0}\\neq~0$ for some odd $e_0$, then $N_q^*(h)\\geq~6.$ (2) Let $h(x_1,\\ldots,~x_k)$ be a complete symmetric polynomials in $k\\geq~3$ variables over $\\f{q}$ of any degree $m$ with $1\\leq~m\\leq~q-3$. If $m$ is odd, then $N_q(h)\\geq~6q^{k-3}.$ (3) Let $h(x_1,\\ldots,~x_k)$be a complete symmetric polynomial in $k\\geq~4$ variables over $\\f{q}$ ofdegree $m$ with $1\\leq~m\\leq~q-3$. Then $N_q(h)\\geq~6(q-1)q^{k-4}.$ (4) As a consequence, Conjecture 1.7 in the paper [Zhang~J,~Wan~D.~Rational~points~on~complete~symmetric~hypersurfaces~over~finite~fields.~Discrete~Math,~2020,~343:~112072] is true. That is, for any complete symmetric polynomial $h(x_1,\\ldots,~x_k)$ in $k\\geq~4$ variables over $\\f{q}$ ofdegree $m$ with $1\\leq~m\\leq~q-4$, we have$N_q(h)\\geq~24q^{k-4}.$
Interactive Range Queries for Healthcare Data under Differential Privacy

Analyses of fine-grained healthcare data by medical researchers can have many societal benefits, including helping to track the spread of COVID-19 and treatment successes. As healthcare data includes personally identifying information (PII), privacy loss needs to be prevented. Differential privacy permits data analysis without loss of individual privacy via a curator who guards the data and determines its appropriate release. An $\in$ parameter measures the noise applied to the query results to control exposure of sensitive data: a low $\in$ value corresponds to more privacy protection, while a higher $\in$ value releases more accurate results with less privacy. Range queries, which count the number of values in a dataset within a user-defined range, pose privacy challenges that are especially concerning in healthcare applications. For example, an adversary can make sequential and overlapping range queries over a sensitive attribute, resulting in isolating information about a specific individual. This work addresses range query privacy concerns in the healthcare domain by proposing an $\epsilon$-private Multi-Attribute DisAssembly Mechanism (MADAM). MADAM supports both single-attribute and multiattribute range queries involving sensitive attributes. The paper also presents BiMADAM, an extension that reduces the error to be polylogarithmic in the sensitivity degree of the queries.
Symbolic Regression Methods for Reinforcement Learning

Reinforcement learning algorithms can be used to optimally solve dynamic decision-making and control problems.With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings.Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require significant trial and error tuning of their meta-parameters.In this paper, we propose a new approach to constructing smooth value functions in the form of analytic expressions by means of symbolic regression.We introduce three off-line methods for finding value functions based on a state transition model: symbolic value iteration, symbolic policy iteration, and a direct solution of the Bellman equation.The methods are illustrated on four nonlinear control problems: velocity control under friction, one-link and two-link pendulum swing-up, and magnetic manipulation.The results show that the value functions not only yield well-performing policies, but also are compact, mathematically tractable and easy to plug into other algorithms.This makes them potentially suitable for further analysis of the closed-loop system.A comparison with an alternative approach using neural networks shows that our method outperforms the neural network-based one.
Digital competence of teachers and students in Yakutia: problems and prospects of e-learning during the pandemic

Under self-isolation and quarantine measures during the COVID-19 pandemic in 2020, distance learning is urgent at every educational level. These events affected the digital competence of teachers and students of pedagogical specialties who teach students and schoolchildren. The article analyzes the digital competencies required for a modern teacher to successfully implement students’ educational needs when using distance educational technologies and e-learning. The research objective is to increase the level of digital competence by assessing this level and identifying problems in the e-learning usage. The study was carried out through a questionnaire developed by the National Agency for Financial Research (NAFR) analytical center Russian Teachers’ Digital Literacy, allowing identification of the main competencies required for a modern teacher, such as work in distance learning environments, knowledge of the basics of cloud technologies, and the ability to organize online and offline interaction, using various services. According to the calculation of Kendall’s concordance coefficient, a ranking of the plus and minus sides identified in the organization of remote interaction among teachers and students of the Republic of Sakha (Yakutia) during the pandemic was made. The study outcomes allowed increasing the efficiency of work with students and teachers and organizing refresher courses and support in the e-learning implementation
Deep Learning Approach to Generate a Synthetic Cognitive Psychology Behavioral Dataset

Synthetic data generation is critical in machine and deep learning research to overcome the shortage of samples or dataset sizes.Various algorithms, including the generative adversarial network and autoencoder models, have been applied to generate artificial datasets in previous studies.In this study, we propose a synthetic data generation framework for a tabular dataset collected from cognitive psychology behavioral experiments based on deep learning algorithms.Tabular datasets for the Stroop task were used to develop our framework.On account of the relatively small sample size (N=102) of the dataset used in our study, we used a pre-trained generative adversarial network model to complement the size of the dataset.Furthermore, we proposed and applied five evaluation methods with statistical tests (overlapped sample test, constraint reflection test, correlation reflection test, distribution distance test, and feature distance test) to validate generation performance based on internal levels of table structure (instance level, feature level, and whole-set level evaluations).The proposed framework with a fine-tuned generative adversarial network algorithm was compared with a random generation method to verify generation performance, including the representation of the statistical characteristics of the original datasets.We found that the generated datasets from the proposed framework exhibited more similar statistical characteristics with the original dataset than the randomly generated datasets based on five evaluation methods.The results of this study provide not only generation algorithms for cognitive psychological datasets with tabular type but also a solution to the sample size issue for researchers.
Anomaly Pattern Detection in Streaming Data Based on the Transformation to Multiple Binary-Valued Data Streams

Abstract Anomaly pattern detection in a data stream aims to detect a time point where outliers begin to occur abnormally. Recently, a method for anomaly pattern detection has been proposed based on binary classification for outliers and statistical tests in the data stream of binary labels of normal or an outlier. It showed that an anomaly pattern can be detected accurately even when outlier detection performance is relatively low. However, since the anomaly pattern detection method is based on the binary classification for outliers, most well-known outlier detection methods, with the output of real-valued outlier scores, can not be used directly. In this paper, we propose an anomaly pattern detection method in a data stream using the transformation to multiple binary-valued data streams from real-valued outlier scores. By using three outlier detection methods, Isolation Forest(IF), Autoencoder-based outlier detection, and Local outlier factor(LOF), the proposed anomaly pattern detection method is tested using artificial and real data sets. The experimental results show that anomaly pattern detection using Isolation Forest gives the best performance.
A multi-level collaborative framework for elastic stream computing systems

An elastic stream computing system is expected to process dynamic and volatile data streams with low latency and high throughput in timely manner. Effective management of stream application is considered one of the keys to achieve elastic computing by scaling in/out the workload of each computing node properly during runtime. Many existing work tried to build an elastic stream computing system from one perspective or at one level, which limited to some extent the system performance improvement. To address the problems brought by single level management, in this paper, we propose and implement a multi-level collaborative framework (called Mc-Stream) for elastic stream computing systems. This paper introduces our solution from the following aspects: (1) Extensive experiments show that system performance is affected by multiple factors locating at different levels. A multi-level collaborative optimization strategy can coordinate those factors and optimize the performance to a greater extent. (2) A system model is constructed to explain the multi-level collaborative framework, with the creation of topology model, data model and grouping model. The process of multi-level collaborative framework is formalized, including optimizing instances number, determining data stream load ratio among instances and deploying instances. (3) The system performance is optimized at multiple levels (user level, instance level, scheduling level, and resource level). It is further improved by the components of lightweight instances management, available resource-aware data stream redirection, fast and effective scheduling management, and asynchronous runtime redeployment without state loss. (4) Mc-Stream is implemented on top of Apache Storm platform. Metrics are evaluated with real-world stream applications, such as the fulfillment of system latency, throughput and resources utilization. Experimental results show the significant improvements made by Mc-Stream: reducing average system latency by 32%, increasing average system throughput by 26% and average resources utilization by 34%, compared with existing state-of-the-art scheduling strategies.
Privacy and uniqueness of neighborhoods in social networks

The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser. Sharing such data, however, can lead to serious privacy issues, because individuals could be re-identified, not only based on possible nodes' attributes, but also from the structure of the network around them. The risk associated with re-identification can be measured and it is more serious in some networks than in others. While various optimization algorithms have been proposed to anonymize networks, there is still only a limited theoretical understanding of which network features are important for the privacy problem. Using network models and real data, we show that the average degree of networks is a crucial parameter for the severity of re-identification risk from nodes' neighborhoods. Dense networks are more at risk, and, apart from a small band of average degree values, either almost all nodes are uniquely re-identifiable or they are all safe. Our results allow researchers to assess the privacy risk based on a small number of network statistics which are available even before the data is collected. As a rule-of-thumb, the privacy risks are high if the average degree is above 10. Guided by these results, we explore sampling of edges as a strategy to mitigate the re-identification risk of nodes. This approach can be implemented during the data collection phase, and its effect on various network measures can be estimated and corrected using sampling theory. The new understanding of the uniqueness of neighborhoods in networks presented in this work can support the development of privacy-aware ways of designing network data collection procedures, anonymization methods, and sharing network data.
An analysis of top author citations in software engineering and a comparison with other fields

Abstract Ioannidis et al. provided a science-wide database of author citations. The data offers an opportunity to researchers in a field to compare the citation behavior of their field with others. In this paper, we conduct a systematic analysis of citations describing the situation in software engineering and compare it with the fields included in the data provided by Ioannidis et al. For comparison, we take the measures used by Ioannidis into consideration. We also report the top-scientists and investigate software engineering researchers’ activities in other fields. The data was obtained and provided by Ioannidis et al. based on the Scopus database. Our method for analysis focuses on descriptive statistics. We compared software engineering with other fields and reported demographic information for the top authors. The analysis was done without any modifications to the ranking. In the later analysis, we observed that 37% of researchers listed as software engineers were not in the software engineering field. On the other hand, the database included a large portion of top authors (ca. 60% to 80%) identified in other software engineering rankings. Other fields using the database are advised to review the author lists for their fields. Our research’s main risk was that researchers are listed that do not belong to our studied field.
Unembodiment on the Internet. Part 1: Theoretical Basis and Construct

Disembodiment of digital media users, known as the loss of their physical bodies in cyberspace is among the least-studied psychological consequences of the use of information technologies. In the digital era such kind of disembodiment might limit the development of a person’s individuality. That’s why in present study we aim to create its theoretical construct. The construct is based on the clinical conception of unembodiment of schizoids by the British psychiatrist R. Laing. There is an evidence to suggest that constant split between self and body, that digital media users demonstrate, creates conditions for disembodiment similar to the one of schizoids. This existential position is associated with feelings of loss of reality, self-substantiality and elusiveness of being. In the same way that the disembodiment of schizoids and inherent to it sense of self and view of the world are determined by their ‘shutupness’ in the inner world, the disembodiment of media users is determined by the extent to which their life is limited to the artificial computer environment. The theoretical construct was measured according to this hypothesis. Three measurements, which unites factors that stand against the disembodiment are presented in the article: Disembodiment as Virtualization, Preference of the Internet and their alternative Vitality of the Embodied Self.
Integrated Time-Fractional Diffusion Processes for Fractional-Order Chaos-Based Image Encryption

The purpose of this paper is to explore a novel image encryption algorithm that is developed by combining the fractional-order Chua’s system and the 1D time-fractional diffusion system of order α∈(0,1]. To this end, we first discuss basic properties of the fractional-order Chua’s system and the 1D time-fractional diffusion system. After these, a new spatiotemporal chaos-based cryptosystem is proposed by designing the chaotic sequence of the fractional-order Chua’s system as the initial condition and the boundary conditions of the studied time-fractional diffusion system. It is shown that the proposed image encryption algorithm can gain excellent encryption performance with the properties of larger secret key space, higher sensitivity to initial-boundary conditions, better random-like sequence and faster encryption speed. Efficiency and reliability of the given encryption algorithm are finally illustrated by a computer experiment with detailed security analysis.
An Efficient Electronic Cash System Based on Certificateless Group Signcryption Scheme Using Conformable Chaotic Maps

Signcryption schemes leveraging chaotic constructions have garnered significant research interest in recent years. These schemes have proffered practical solutions towards addressing the vast security vulnerabilities in Electronic Cash Systems (ECS). The schemes can seamlessly perform message confidentiality and authentication simultaneously. Still, their applications in emerging electronic cash platforms require a higher degree of complexity in design and robustness, especially as billions of online transactions are conducted globally. Consequently, several security issues arise from using open wireless channels for online business transactions. In order to guarantee the security of user information over these safety-limited channels, sophisticated security schemes are solely desired. However, the existing signcryption schemes cannot provide the required confidentiality and authentication for user information on these online platforms. Therefore, the need for certificateless group signcryption schemes (CGSS) becomes imperative. This paper presents an efficient electronic cash system based on CGSS using conformable chaotic maps (CCM). In our design, any group signcrypter would encrypt information/data with the group manager (GM) and send it to the verifier, who confirms the authenticity of the signcrypted information/data using the public criteria of the group. Additionally, the traceability, unforgeability, unlinkability, and robust security of the proposed CGSS-CCM ECS scheme have been built leveraging computationally difficult problems. Performance evaluation of the proposed CGSS-CCM ECS scheme shows that it is secure from the Indistinguishably Chosen Ciphertext Attack. Finally, the security analysis of the proposed technique shows high efficiency in security-vulnerable applications. Overall, the scheme gave superior security features compared to the existing methods in the preliminaries.
Rumour prevention in social networks with layer 2 blockchains

Social bots can cause social, political, and economical disruptions by spreading rumours. The state-of-the-art methods to prevent social bots from spreading rumours are centralised and such solutions may not be accepted by users who may not trust a centralised solution being biased. In this paper, we developed a decentralised method to prevent social bots. In this solution, the users of a social network create a secure and privacy-preserving decentralised social network and may accept social media content if it is sent by its neighbour in the decentralised social network. As users only choose their trustworthy neighbours from the social network to be part of its neighbourhood in the decentralised social network, it prevents the social bots to influence a user to accept and share a rumour. We prove that the proposed solution can significantly reduce the number of users who are share rumour.
The Impact of Faults on DNNs: A Case Study

Deep neural networks (DNNs) are showing superior advantages in different domains and are opening their path into critical applications where reliability is the main concern. DNNs can be executed in different hardware platforms, including general-purpose processors which usually operate under floating-point (FP) numbering systems. Considering the small range of weights in DNNs stored in the FP format, some bits remain constant as 0 or 1 for all weights. On the other hand, a single event upset may flip a bit, increasing or decreasing the value of a weight. In this paper, we analyze the effect of bit flips in a sample network of LeNetS, and show the sensitivity of convolution layers to faults and the vulnerability of D NN s to a single fault in a specific bit position. This is while the network is inherently robust against bit flips in the other bit positions. We then show that the choice of activation functions and pooling techniques could alleviate the negative effects of faults to a large extend.
The Correlation Between Structure and Reading Comprehension Achievement of the Students

This study was intended to find out the correlation between structure and reading comprehension achievement of the third year students of SMP Swasta Budi Insani Medan. There were 30 students out of 115 randomly selected as the sample. The data was collected by giving multiple choce test that consisting 50 items for structure test and 30 items for reading comprehension test. The data were analyzed by applying Pearson’s Product Moment Formula. The finding shows that r was 0,738. It means that, there is the possitive effect between structure and reading comprehension. This implies the more students know about the knowledge of structure, the better understanding the students will be added the reading compreheension. These proved to be greater than the critical value of r at the level of 0,05 which is 0,361. Based on the data which have been found in this study, it can be concluded that there is a significant correlation between the students structure and reading comprehension achievement.