Reviewerly/exHarmony huggingface dataset:
User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning in Data Center

In general, multiple domain cyberspace security assessments are very important for data center security and can be implemented by reasoning user's permissions. However, while existing methods include some information from the physical and social domains, they do not provide a comprehensive representation of cyberspace. Existing reasoning methods are also based on expert given rules, resulting in inefficiency and a low degree of intelligence. To address this challenge, we create a Knowledge Graph (KG) of multiple domain cyberspace in order to provide a standard semantic description of the multiple domain cyberspace. Following that, we proposed a user's permissions reasoning method based on reinforcement learning. All permissions in cyberspace are represented as nodes, and an agent is trained to find all permissions that user can have according to user's initial permissions and cyberspace KG. We set 10 reward setting rules based on the features of cyberspace KG in the reinforcement learning of reward information setting, so that the agent can better locate user's all permissions and avoid blindly finding user's permissions. The results of the experiments showed that the proposed method can successfully reason about user's permissions and increase the intelligence level of the user's permissions reasoning method. At the same time, the F1 value of the proposed method is 6% greater than that of the Translating Embedding (TransE) method.
HATE SPEECH IN AMERICAN AND ARMENIAN POLITICAL DISCOURSE/ԱՏԵԼՈՒԹՅԱՆ ԽՈՍՔԸ ԱՄԵՐԻԿՅԱՆ ԵՎ ՀԱՅԱՍՏԱՆՅԱՆ ՔԱՂԱՔԱԿԱՆ ԴԻՍԿՈՒՐՍՈՒՄ/ЯЗЫК ВРАЖДЫ В АМЕРИКАНСКОМ И АРМЯНСКОМ ПОЛИТИЧЕСКОМ ДИСКУРСЕ

Hate speech is one of the basic concepts of linguistics and political rhetoric. In political speech, language expressions of hate speech are often decisive for the acceptance or rejection of the ideology and main theses of a politician’s. Since politics is mainly implemented through language: speeches, debates, addresses, etc., the linguistic manifestations of hate speech have a great deal of content and influence in them. The purpose of this study is to identify the main linguistic means of hate speech in American and Armenian political speech.
Artificial neural network applied on sintered BaTiO3-ceramic density

It is very important to determine microstructure parameters of consolidated ceramic samples, because it opens new frontiers for further microelectronics miniaturization and integrations. Therefore, controlling, predicting and designing the ceramic materials? properties are the objectives in ceramic materials consolidating process, within the science of sintering. In order to calculate the precise values of desired microstructure parameter at the level of the grains? coating layers based on the measurements on the bulk samples, we applied the artificial neural networks, as a powerful mathematical tool for mapping input-output data. Input signals are propagated forward, as well as the adjustable coefficients that contribute the calculated output signal, denoted as error, which is propagated backwards and replaced by examined parameter. In our previous research, we used neural networks to calculate different electrophysical parameters at the nano level of the grain boundary, like relative capacitance, breakdown voltage or tangent loss, and now we extend the research on sintered material?s density calculation. Errors on the network output were substituted by different consolidated samples density values measured on the bulk, thus enabling the calculation of precise material?s density values between the layers. We performed the neural network theoretical experiments for different number of neurons in hidden layers, according to experimental ceramics material?s density of ?=5.4x103[kg/m3], but it opens the possibility for neural networks application within other density values, as well.
FUNDAMENTALS OF QUALITY MANAGEMENT OF SENSORY-COGNITIVE DEVELOPMENT OF CHILDREN IN THE FOURTH YEAR OF LIFE IN A PRESCHOOL EDUCATION INSTITUTION

The article is devoted to the problem of managing the quality of sensory and cognitive development of children in the fourth year of life in the educational process of a preschool education institution. In particular, on the basis of the analysis of scientific intelligence on the research problem, it was determined that the sensory development of the child plays an invaluable role in the formation of his cognitive, research activity, is the basis of his mental development. Knowledge begins with the perception of objects and phenomena of the surrounding world. All other forms of cognition — memorization, thinking, imagination — are built on the basis of images of perception, are the result of their processing. Sensory education tasks are defined for each age, which correspond to the level of development of perception and at the same time contribute to the transition to higher levels. In this context, the fourth year of life is very important, because at this stage there are significant changes in the course of the mental processes of a growing personality, there is an expansion of ways and means of orientation in the environment, enrichment of children’s ideas about objects and phenomena of the surrounding world, and the development of cognitive activity is improved in interaction with an adult. Getting to know the properties of objects and the phenomena of the surrounding reality gives preschoolers the opportunity to apply the acquired knowledge in practical activities, to master various ways of learning about the surrounding reality. Ensuringthe effectiveness of sensory and cognitive development of children in a preschool education institution requires quality management of this process, deepening and concretization of pedagogical work.
PROFESSIONAL TEACHERS` COMMUNITIES: WHERE TO START AND WHERE TO MOVE

The article highlights the ways of teachers` cohesion in the professional communities with the developed horizontal connections. In the process of scientific searching the authors have analyzed the scientific literature on the issues of the study and the legislative documents, which regulate the activities of the professional communities of educators in Ukraine. It was installed that centers of teachers` professional development play an important role in the process of creating and coordination of the activities of professional communities. It is emphasized that professional communities in Ukraine are at the stage of development, and the centers of teachers` professional development often act through the method “sorting the options” and need detailed information regarding possible actions. The factors, with influence on teachers` cohesion in the teachers` communities are identified: the willingness to get the new information; possibility of joint searching of the ways of professional difficulties` overcoming; the need for support from the colleagues and exchange of experience with others. The arguments are provided in favor of teachers` participation in the professional communities, which can be used by the centers of teachers` professional development in the process of informing educators. Characteristics, which influence on the effectiveness of teachers` professional communities are identified: teachers` engagement in the discussions; observations of teachers` lessons of each other; providing and receiving the feedback to improve professional practices; exchange of experience and interactions based on “equal to equal” principle; sharing school`s mission and values; team approach to planning the joint work in the developing students` competencies; discussing the moments, which require additional attention; group responsibility for achieving by students of their educational results.
DILECTINGS AS A PROJECT OF THE CONTEMPORARY TOOL FOR TEACHER’S DIGITAL LITERATURE EDUCATIONAL COMPETENCES

Statement of the problem. The article is devoted to highlighting the main provisions regarding the imple- mentation of the DILECTINGS project as a set of measures for all participants in the educational process digital literacy development Research method. There were used digital reading methods, remote work methods using DLL and LDR Hub, the use of eTwinning/School Education Gateway platforms to perform digital reading tasks, the use of CANVAS technologies. Data. Teachers developed the skills of pedagogical activities using digital reading technologies, working in Moodle and eTwinning/School Education Gateway. Students developed competencies in digital reading, use of technology to create video files. Conclusions. The implementation of DILECTINGS project had provide formation of narrow and specialized competences in the use of technical means in educational activities
The presence of computers in Yugoslav popular music of the second half of the 1980s

This article is the result of my research of Yugoslav pop music discography in the second half of the 1980s, focused on the processes of implementation of digital musical instruments and general-purpose computers into music production - as original sound sources or replacements for acoustic instruments, as well as poetic metaphors and elements of graphic design of record covers. Based on the data available from recording staff listings, interviews with musicians/producers and the listening experience, the paper covers the most prominent examples of aforementioned practices.
Digital and Communication Competences of Foreign Language Teachers in Poland

Abstract Language education is directly related to the phenomenon of communication competence. This is important since the knowledge of foreign languages is intended to develop communication activities that are implemented in their nature. Communication competence is, however, a much broader concept, and in the context of language education, it not only refers to recipients (pupils) but also to broadcasters (teachers). This article presents the relationship between teachers’ activity in the field of mass media, their communication competence and social media. The purpose of this study is also to indicate the correlations between the opinions of teachers surveyed and the features of communication competence of students, as well as to assess the level of teachers’ competences. The methods used in this study included a questionnaire and interviews. A set of closed, open and mixed questions, as well as an in-depth interview based on a traditional questionnaire is included. It is difficult to prove the correlation between teachers’ communication and media competences and the effects of their work. Nevertheless, the high self-assessment of teachers’ competences seems to be related to the growing trend of English language skills in Polish society.
DAG-$$\Sigma $$: A DAG-Based Sigma Protocol for Relations in CNF

At CRYPTO 1994, Cramer, Damgård and Schoenmakers proposed a general method to construct proofs of knowledge (PoKs), especially for k-out-of-n partial knowledge, of which relations can be expressed in disjunctive normal form (DNF). Since then, proofs of k-out-of-n partial knowledge have attracted much attention and some efficient constructions have been proposed. However, many practical scenarios require efficient PoK protocols for partial knowledge in other forms. In this paper, we mainly focus on PoK protocols for k-conjunctive normal form (k-CNF) relations, which have n statements and can be expressed as follows: (i) k statements constitute a clause via “OR” operations, and (ii) the relation consists of multiple clauses via “AND” operations. We propose an alternative Sigma protocol (called DAG- $${\Sigma }$$ protocol) for k-CNF relations (in the discrete logarithm setting), by converting these relations to directed acyclic graphs (DAGs). Our DAG- $${\Sigma }$$ protocol achieves less communication cost and smaller computational overhead compared with Cramer et al.’s general method.
Code development of regulatory technology for legitimacy of cybersecurity adjudication

Artificial Intelligence (AI) on cybersecurity is designed to control financial transactions and agreements worldwide.It is an innovative regulatory tool architecturally networked to combat cyber threats and attacks in various degrees of war and crime perpetration from various banking industries and its corresponding government authority functions with implementing arms for execution of monitoring, reporting, and compliance.Regulatory Technology (RegTech) is an AI tool used by treasury departments and institutions, both local and private, for tracking malicious threats possible for money laundering and terrorism financing concealed in various settlements around the world.However, cybersecurity decisions need conformity in regulations and proceedings that it aims to engineer code development involving attacks under Regulatory Technology usage for administrative functions in favor of the financial intelligence authorities for combating and resolving issues on cyberwar.Therefore, political adjudication is a developing means of resolving gaps and optimizing judicial process principles within the policy function of financial intelligence.Hence, international laws and its accompanied policies must be globally harmonized in terms of federal and transnational tracking of financial flows of assets.
Scatter plot analysis of malleable variables in the post death era of George Floyd

died.It brought deep sorrow to mothers, accelerated anguish to young African American men, profound agony to fathers their sons, unfathomable grief to local and global communities, and abstruse anger to worldwide protesters.For nearly nine minutes (8:46), we all witnessed this hurtful recondite.It was, indeed, it as one of the absolute saddest days in the four lives of these authors who have and always will be advocates of human justice.The current climate in many cities to shift funding for police departments to social and human services is a movement worthy of merit in the times we live in, but only if an analysis is completed that identifies how these new funded areas will proactively prevent communities from becoming victims from the remaining funded law enforcement entities.Key here is to match the variables of the incidents with the needs and demands of preventative law enforcement.This is the main premise of our commentary -vetting with analysis and being proactive for prevention.Since the tragic death of George Floyd, the following killing of an African American has been reported as well as others: "Police in Vallejo, Calif., fatally shoot man with a hammer kneeling outside a Walgreens" Sean Monterrosa, 22, was outside a Walgreens that police say was being looted when he ran, then stopped in a half-kneeling position, police said.
Federated Offline Reinforcement Learning

Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this paper, we propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings.
Research on College English MOOC System Based on Artificial Intelligence Algorithm

With the progress of science and technology, the artificial intelligence with big data has developed so fast.Human life has experienced profound changes.In the process of teaching college English, the artificial intelligence technology, especially the development and application of college English MOOC system, is a new attempt, an opportunity and challenge faced by students in language learning.From the angle of artificial intelligence algorithm, the paper tries to enhance the effect of MOOC from the instructing and learning aspects of college English MOOC.
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution

In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions. Nevertheless, deploying these solutions on mobile devices still remains an active challenge as SR models are excessively demanding with respect to workload and memory footprint. Despite recent progress on on-device SR frameworks, existing systems either penalize visual quality, lead to excessive energy consumption or make inefficient use of the available resources. This work presents NAWQ-SR, a novel framework for the efficient on-device execution of SR models. Through a novel hybrid-precision quantization technique and a runtime neural image codec, NAWQ-SR exploits the multi-precision capabilities of modern mobile NPUs in order to minimize latency, while meeting user-specified quality constraints. Moreover, NAWQ-SR selectively adapts the arithmetic precision at run time to equip the SR DNN's layers with wider representational power, improving visual quality beyond what was previously possible on NPUs. Altogether, NAWQ-SR achieves an average speedup of 7.9x, 3x and 1.91x over the state-of-the-art on-device SR systems that use heterogeneous processors (MobiSR), CPU (SplitSR) and NPU (XLSR), respectively. Furthermore, NAWQ-SR delivers an average of 3.2x speedup and 0.39 dB higher PSNR over status-quo INT8 NPU designs, but most importantly mitigates the negative effects of quantization on visual quality, setting a new state-of-the-art in the attainable quality of NPU-based SR.
Polynomial computational complexity of matrix elements of finite-rank-generated single-particle operators in products of finite bosonic states

It is known that computing the permanent of the matrix $1+A$, where $A$ is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: an expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.
An Expressive Ansatz for Low-Depth Quantum Optimisation

The quantum approximate optimisation algorithm (QAOA) is a hybrid quantum-classical algorithm used to approximately solve combinatorial optimisation problems. It involves multiple iterations of a parameterised ansatz comprising a problem and mixer Hamiltonian, with the parameters being classically optimised. While QAOA can be implemented on NISQ devices, physical limitations can limit circuit depth and decrease performance. To address these limitations, this work introduces the eXpressive QAOA (XQAOA), an overparameterised variant of QAOA that assigns more classical parameters to the ansatz to improve its performance at low depths. XQAOA also introduces an additional Pauli-Y component in the mixer Hamiltonian, allowing the mixer to implement arbitrary unitary transformations on each qubit. To benchmark the performance of XQAOA at unit depth, we derive its closed-form expression for the MaxCut problem and compare it to QAOA, Multi-Angle QAOA (MA-QAOA), a classical-relaxed algorithm, and the state-of-the-art Goemans-Williamson algorithm on a set of unweighted regular graphs with 128 and 256 nodes for degrees ranging from 3 to 10. Our results indicate that at unit depth, XQAOA has benign loss landscapes, allowing it to consistently outperform QAOA, MA-QAOA, and the classical-relaxed algorithm on all graph instances and the Goemans-Williamson algorithm on graph instances with degrees greater than 4. Small-scale simulations also reveal that unit-depth XQAOA surpasses both QAOA and MA-QAOA on all tested depths up to five. Additionally, we find an infinite family of graphs for which XQAOA solves MaxCut exactly and analytically show that for some graphs in this family, XQAOA achieves a much larger approximation ratio than QAOA. Overall, XQAOA is a more viable choice for variational quantum optimisation on NISQ devices, offering competitive performance at low depths.
Reversible Debugging in Logic Programming

Reversible debugging is becoming increasingly popular for locating the source of errors. This technique proposes a more natural approach to debugging, where one can explore a computation from the observable misbehaviour backwards to the source of the error. In this work, we propose a reversible debugging scheme for logic programs. For this purpose, we define an appropriate instrumented semantics (a so-called Landauer embedding) that makes SLD resolution reversible. An implementation of a reversible debugger for Prolog, rever, has been developed and is publicly available.
Multi-fairness under class-imbalance

Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the often underrepresented protected group (e.g. female, non-white, etc.) in the critical minority class. Still, existing methods focus only on the overall error-discrimination trade-off, ignoring the imbalance problem, thus amplifying the prevalent bias in the minority classes. Therefore, solutions are needed to solve the combined problem of multi-discrimination and class-imbalance. To this end, we introduce a new fairness measure, Multi-Max Mistreatment (MMM), which considers both (multi-attribute) protected group and class membership of instances to measure discrimination. To solve the combined problem, we propose a boosting approach that incorporates MMM-costs in the distribution update and post-training selects the optimal trade-off among accurate, balanced, and fair solutions. The experimental results show the superiority of our approach against state-of-the-art methods in producing the best balanced performance across groups and classes and the best accuracy for the protected groups in the minority class.
Guiding Safe Exploration with Weakest Preconditions

In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic weakest preconditions to achieve a more precise safety analysis than existing tools without unduly impacting the training process. We evaluate the approach on a suite of continuous control benchmarks and show that it can achieve comparable performance to existing safe learning techniques while incurring fewer safety violations. Additionally, we present theoretical results showing that SPICE converges to the optimal safe policy under reasonable assumptions.