Reviewerly/exHarmony huggingface dataset:
Continuous-time generative graph neural network for attributed dynamic graphs

The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques1. In this area, graph representation learning (GRL) using graph neural networks (GNNs) has emerged during the early 90s2 and developed into an impactful approach for modeling graph-structured real-world data, such as social or biological networks. There are a variety of successful applications of GRL in computational neuro-science, chemistry, mathematics, and so on. While temporal changes (dynamics) play an essential role in many real-world applications, most of the literature on GNNs and GRL [8, 29], deals with static graphs. Since the few GNN models on dynamic graphs only consider exceptional cases of dynamics (i.e., attribute-dynamic graphs in discrete-time representation or structure-dynamic graphs in continuous-time representation), we aim to present a novel GNN model that can handle attribute-dynamic graphs in continuous time. This model learns any kind of graph information embedding, performs node/edge attribute forecasts, and allows for attribute-dynamic graph generation in both discrete and continuous-time.
Adversarial defense by restricting in-variance and co-variance of representations

Despite high accuracies achieved by deep neural networks (DNNs) in image classification, DNNs have been shown to be highly vulnerable to structured and unstructured perturbations to the input images. Robustness of many existing defense methods for these models suffers greatly when an attacker has full knowledge of the model and can iterate over the model to craft stronger attacks, which is known as white box attacks. We conduct empirical analysis on the representation of DNN under state-of-the-art attacks to find this causes instances to move closer to a false class in representation space when such perturbation is added to the input. This causes the model to make incorrect decisions even when the adversary and clean images are indistinguishable to human perception. Motivated by this observation, we propose a class-wise disentanglement on intermediate representations of DNN. Specifically, we force DNNs to learn same-class representations to be closer and different-class representations to be maximally farther apart. Moreover, we force the representations of clean and noisy data to be closer if it comes from the same class by restricting its variance in representation. In this approach, a DNN is forced to learn decision boundaries that are distinct for each class with clear separation. We observe that this constraint on representations enhance the robustness of learned models even against strongest white-box attacks. Further we evaluate extensively on both white-box and black-box settings and show significant gains in comparison to state-of-the art defenses. (Implementation:https://github.com/Jeevi10/AICR)
Comparison of modulate and demodulate methodologies for quantum communication

Quantum communication is a theory based on the idea that quantum states can be transferred from one place to another. This theory has several advanced features. Specifically, it has high security, low time cost and low power overhead. Quantum information can make the execution of tasks much less efficient. However, much research has been done to achieve quality control by proposing utterly new security algorithms. The modulation and demodulation tasks of QC have not been sufficiently studied. Therefore, the modulation and demodulation methods are systematically summarised and analysed according to the two types of "signal type" and "modulation method type", respectively. In summary, PSK modulation outperforms OOK modulation in SRM-based multi-user detection schemes. The serious shortcomings of generalised secrecy modulation in terms of information-theoretic security mean that the security of GCM is not good. Moreover, there are many practical problems still required to be addressed. For example, the computational complexity characterisation and optimisation with correlation type receivers. Especially for ultra-high SE improvement have a large N parameter. Switching the slits at a reliable and efficient pace is a challenge. The high SE reduce the importance of speed. The complexity of the ML decoder combing with the joint modulation/coding should be explicitly modelled. Finally, the design of optical set-up in combination with amplifier structures recovering the loss due to MPD design.
Challenges and Ethical Issues in Data Privacy

This paper strived to find out the challenges and ethical issues in the stream of data privacy in the present era of the twenty-first century. The present study focuses on empirical studies that were published during the period 2008-2020. During the research, efforts have been made to identify the challenges and ethical issues that the researchers have to face during the research process. In the study, it has been noticed that such kind of matters gained popularity after the year 2011. In the research, it has been found that there are many challenges to data privacy. It becomes challenging to follow ethics to maintain data confidentiality and security in this technology-driven era. In the end, based on available data, it has been concluded that there is an excellent need for stiff punishments for that person who misuse the data for their own sake and demoralize those people who make hard efforts to carry out the research and generate new concepts and ideas. So the government should take necessary measures toward data privacy areas for security and safety purposes.
A new family of 3D watermarks

We construct a new family of 3D periodic arrays suitable for water-marking video. Each member of the family is composed of columns of cyclic shifts of a Sidelnikov sequence in which the shifts are given by an m x n "grid" that is determined by applying a logarithmic quadratic map to the elements of a direct product Zm| x Zn. These arrays have peak auto-correlation on the order of p4 and non-peak auto- and cross-correlation on the order of p2 and unlike other constructions have unequal dimensions.
Evgeny Sashko: a person, an artist, a teacher

The subject of the study is the biography, scientific and pedagogical activity of the Khabarovsk teacher of drawing Evgeny Vyacheslavovich Sashko, the peculiarities of his personality as a person and a teacher. The stages of formation, the main types of creative activity of a teacher in the historical context of the formation of architectural education and the development of methods and technologies of artistic training of architects and designers on the basis of the Pacific National University are considered. For the first time, an attempt was made to recreate an integral creative portrait of E. V. Sashko, whose sphere of interests included decorative and applied art, as well as pedagogical work. He could fruitfully combine artistic creativity and the ability to teach, combining various fields of activity. The methodology of the research is based on a theoretical approach using the method of analysis of scientific works, generalization of pedagogical observations of educational and creative artistic activities, personal memories of authors, conversations with relatives, questionnaires and surveys of colleagues and students. Scientific novelty is associated with the involvement in scientific circulation of biographical sources and educational works of E. V. Sashko (in particular, in the field of architectural drawing techniques), which is mainly of importance for the training and education of young specialists at Pacific State University and other universities of the relevant profile, their professional growth and development. The results of the study are conclusions reflecting the specific contribution of E.V. Sashko in the development of artistic and graphic training of specialists. The method of architectural drawing, one of the main developers of which was E.V. Sashko, to this day serves as the basis for all programs of artistic training of students at the Pacific National University.
Transforming orthogonal inductive definition sets into confluent term rewrite systems

In this paper, we transform an orthogonal inductive definition set, which is a set of productions for inductive predicates, into a confluent term rewrite system such that a quantifier-free sequent is valid w.r.t. the inductive definition set if and only if an equation representing the sequent is an inductive theorem of the term rewrite system. To this end, we first propose a transformation of an orthogonal inductive definition set into a confluent term rewrite system that is equivalent to the inductive definition set in the sense of evaluating ground formulas. Then, we show that termination of the inductive definition set is proved by the generalized subterm criterion if and only if termination of the transformed term rewrite system is so. Finally, we show that the transformed term rewrite system with some rewrite rules for sequents has the expected property. In addition, we show a termination criterion for the union of term rewrite systems whose termination is proved by the generalized subterm criterion.
A study of unsupervised image super-resolution based on deep network prior

This research focuses on the topic of image super-resolution based on a deep network prior. This research uses a U-Net-like network structure and modifies the network's hyperparameters, such as the number of downsamples, to find a model with better performance using a control variable approach. Then this best model was applied to different classes of images, and the performance of the network model was investigated by measuring the effect of super-resolution on these classes of images. This research hopes to understand the network structure's role on image super-resolution through such exploratory experiments.
A Contrastive Corpus-Based Approach to the Expression of Adversarialness across English and Persian: A Case Study of Political Press Conferences

With the point of departure in discourse analysis (DA), this corpus comparative study concentrated on sequences with adversarial questions in political press conferences to investigate the differences in the use of adversarial questioning between two contexts, namely, English and Persian political press conferences, and particularly highlight the strategies which were used by journalists in such sequences of talk journalists and politicians so as to show the importance of improving linguistic proficiency in EFL context. For this reason, this study analyzed a corpus of data from some political press conferences with American and Iranian Government which is randomly selected from appropriate websites between 2007 and 2021. The study applied Clayman, Heritage, and McDonald (2006) for quantifying adversarial questions as exhibited by the press to the questions addressed the Presidents of Iran and the United States. Questions from selected conferences were coded according to adversarialness. Frequency and chi-square tests were used for analysis. The results showed significant difference across the 2 corpora in some journalists’ questions. In terms of question design, accountability questions in Iranian journalists’ questions as Persian corpus indicated higher frequency. In addition, in referencing frames, higher frequency was observed in declarative questions in Persian corpus. However, English corpus displayed higher frequency in other-referencing frames. Finally, in content adversarialness, global adversarialness indicated higher frequency in Persian corpus. Consequently, the results showed that adversarial questions were used in Persian corpus more significantly than English corpus. The results of the study not only will help the students gain linguistic competence, but it will also improve their overall language proficiency.
How Emotional is Your C-Suite?

Members of the C-suite – just like everyone else – are motivated by biases, fear, hope, ignorance, confusion, denial, and stubbornness, among other emotions that can exert more influence than due diligence data when their companies write huge checks to their favorite vendors, promote the most loyal members of their teams, or develop strategies based solely on their judgment and instinct – even when there's tons of empirical evidence suggesting the investments make no sense. Evidence-based best practices should be leveraged to reduce the number, nature, and depth of feelings about technology, but that requires C-suite willingness to weigh data over feelings, which can be a real challenge to executives who, let's face it, like to do what they want to do whenever they want to do it.
Synchronization operator for communication privacy

There exist situations in which the communication via public channels should not reveal the identity of the participants. We define a process calculus able to control such a communication by using a specific synchronization operator working over a given set of channels. This operator is inspired by existing protocols in which one process has to collect and combine the information received from several processes. To control the interaction, we define a typing system and show that it fulfills the type preservation under equivalence and reduction.
ElastiQuant

In training distributed machine learning, communicating model updates among workers has always been a bottleneck. The magnitude of impact on the quality of resultant models is higher when distributed training on low hardware specification devices and in uncertain real-world IoT networks where congestion, latency, bandwidth issues are common. In this scenario, gradient quantization plus encoding is an effective way to reduce cost when communicating model updates. Other approaches can be to limit the client-server communication frequency, adaptive compression by varying the spacing between quantization levels, reusing outdated gradients, deep compression to reduce transmission packet size, and adaptive tuning of the number of bits transmitted per round. The optimization levels provided by such and other non-comprehensive approaches do not suffice for high-dimensional NN models with large size model updates.
Semantic recommendation through Reinforcement Learning and weighted meta-paths

Explainable recommendation systems, which can produce high-accuracy recommendations and help users make quick decisions, have become a hotspot in research field. Most of existing research algorithms committed to improving the accuracy of recommendation, but ignored the interpretability of recommendation. We propose a semantic recommendation algorithm through Reinforcement Learning and weighted meta-paths which analyzes and selects the meta-path, and uses Reinforcement Learning network to train the weight of meta-path. This method can effectively solve data sparsity, improve the accuracy of recommendation, and increase interpretability. Compared with the baseline method, indicators of the recommended methods for integrating Reinforcement Learning with Heterogeneous Information Networks have been greatly improved, as verified and compared on a real MovieLens 1M dataset. Experimental results prove that the algorithm can effectively improve the accuracy and interpretability of the recommendation.
Design patterns mining using neural sub-graph matching

Design Patterns detection in Object-Oriented software systems is essential for effectively supporting program comprehension and re-engineering tasks. It helps to recover, from source code, the developers' design decisions and trade-offs that could be not up-to-date or even not documented. Several approaches to mine design patterns from source code have been defined in the last twelve years and they are all based on the analysis of object-oriented systems components, their relationships, and behaviors to identify the roles played in the patterns. Both static and dynamic approaches need to perform matching between data captured from the system with the design patterns specification that encodes the structure and the behavior of the micro-architectural solution. The matching process, in principle, can be formulated as a sub-graph matching problem that is NP-complete. This problem has been addressed in the literature using heuristics designed to produce good solutions in an acceptable time, but the task is still expensive with a significant trade-off between accuracy and performance. This work proposes the adoption of a neural-based approach that exploits graph neural networks to perform detection using a more efficient sub-graph matching step outperforming existing heuristics proposed for this task. The pattern detection approach has been assessed on several open-source systems widely used to perform design pattern detection obtaining very good results for both detection performances and efficiency.
Efficient Cloudlet Allocation to Virtual Machine to Impact Cloud System Performance

Performance is an essential characteristic of any cloud computing system. It can be enhance through parallel computing, scheduling and load balancing. This work evaluates the connection between the response time (RT) and virtual machine’s (VM) CPU utilization when cloudlets are allocated from the datacenter broker to VM. To accentuate the RT and VM’s CPU utilization, a set of 100 and 500 heterogeneous cloudlets are analyzed under hybridized provisioning, scheduling and allocation algorithm using CloudSim simulator. These includes space shared (SS) and time shared (TS) provisioning policy, shortest job first (SJF), first come first search (FCFS), round robin (RR) and a novel length-wise allocation (LwA) algorithm. The experimental analysis shows that the RT is the least when SJF is combined with RR allocation at 40.665 secs and VM’s CPU utilization is the least when SJF is combined with LwA policy at 12.48 in all combinations of SS and TS provisioning policy.
A similarity-based deep feature extraction method using divide and conquer for image classification

In the computer vision field, there are many problems to be solved such as consideration of connectivity between problems. These concerns are still indispensable. Existing approaches usually proceed with transfer learning of all problems through one space. This can be very complex and inefficient for one model to solve, and if the data does not have a constant distribution, it causes difficulties in extracting explicit features in multi-classification. In this paper, we propose a method for independent divided learning motivated by the divide and conquer algorithm. Our approach split the multi-classification problem into multiple small units so that all problems can be solved with multiple learners rather than all at once. In addition, it is not randomly dividing the data, but based on the similarity matrix recognized by hierarchical clustering, so that each divided learner can extract explicit features. As a result, the classifier brings a more balanced and improved performance than general transfer learning.
Reactive Answer Set Programming

Abstract Logic Production System (LPS) is a logic-based framework for modelling reactive behaviour. Based on abductive logic programming, it combines reactive rules with logic programs, a database and a causal theory that specifies transitions between the states of the database. This paper proposes a systematic mapping of the Kernel of this framework (called KELPS) into an answer set program (ASP). For this purpose a new variant of KELPS with finite models, called n -distance KELPS, is introduced. A formal definition of the mapping from this n -distance KELPS to ASP is given and proven sound and complete. The Answer Set Programming paradigm allows to capture additional behaviours to the basic reactivity of KELPS, in particular proactive, pre-emptive and prospective behaviours. These are all discussed and illustrated with examples. Then a hybrid framework is proposed that integrates KELPS and ASP, allowing to combine the strengths of both paradigms.
Update models for lying and misleading announcements

Lying and misleading announcements are a significant part of multi-agent communications. Providing a formal account of such announcements is important for representing and reasoning about effects of actions and epistemic planning in multi-agent domains. This paper defines update models for lying and misleading announcements in the high-level action language mA*. Applying our update models on a pointed Kripke structure---representing the current state of knowledge and beliefs of agents---results in a pointed Kripke structure in which lying (or misleading) announcements achieve the intended effects of deceiving other agents who are uncertain about the announced formula to believe in the wrong information.
Monocular Depth Matching With Hybrid Sampling and Depth Label Propagation

This paper proposes a monocular depth label propagation model, which describes monocular images into depth label distribution for the target classification matching. 1) Depth label propagation by hybrid sampling and salient region sifting, improve the discrimination of detection feature categories. 2) Depth label mapping and spectrum clustering to classify target, define the depth of the sorting rules. The experimental results of motion recognition and 3D point cloud processing, show that this method can approximately reach the performance of all previous monocular depth estimation methods. The neural network model black box training learning module is not used, which improves the interpretability of the proposed model.
Distributed energy centralized transaction method and system based on blockchain

As the shortage of energy resources and the deterioration of the ecological environment continue to intensify, distributed energy is an indispensable part of the future energy system. Distributed energy sources such as solar energy, wind energy, photovoltaics, etc., which are in line with the "dual carbon" goal, have low pollution, high efficiency, and high operational reliability, and will initially become an important source of power supply. However, there are many problems with distributed energy transactions in the traditional electricity market. With the continuous expansion of distributed energy transactions and the continuous increase of information and data, the clearing process has become very cumbersome. For the electricity supplier and consumer, there are problems in the electricity market transaction that the transaction volume caused by power loss does not match the actual received volume, and users do not obtain information in a timely manner. At present, most distributed transaction processes lack a central supervision mechanism. How to confirm and verify various information of both parties to the transaction to ensure transaction security is an urgent problem that needs to be resolved. This article proposes a centralized transaction method based on blockchain technology. Both parties of electricity purchase and sale write data to the blockchain through the blockchain client. After the block link receives the quotation submitted by the user, it will match and settle the two parties through continuous bilateral transactions to realize the centralized transaction clearing of distributed energy. The method proposed in this paper improves the efficiency of distributed energy transactions, and at the same time ensures that the information is authentic and cannot be tampered with.