Reviewerly/exHarmony huggingface dataset:
NBA: defensive distillation for backdoor removal via neural behavior alignment

Abstract Recently, deep neural networks have been shown to be vulnerable to backdoor attacks. A backdoor is inserted into neural networks via this attack paradigm, thus compromising the integrity of the network. As soon as an attacker presents a trigger during the testing phase, the backdoor in the model is activated, allowing the network to make specific wrong predictions. It is extremely important to defend against backdoor attacks since they are very stealthy and dangerous. In this paper, we propose a novel defense mechanism, Neural Behavioral Alignment (NBA), for backdoor removal. NBA optimizes the distillation process in terms of knowledge form and distillation samples to improve defense performance according to the characteristics of backdoor defense. NBA builds high-level representations of neural behavior within networks in order to facilitate the transfer of knowledge. Additionally, NBA crafts pseudo samples to induce student models exhibit backdoor neural behavior. By aligning the backdoor neural behavior from the student network with the benign neural behavior from the teacher network, NBA enables the proactive removal of backdoors. Extensive experiments show that NBA can effectively defend against six different backdoor attacks and outperform five state-of-the-art defenses.
ECOLOGY OF PROFESSIONAL SELF-AWARENESS FORMATION OF FUTURE PSYCHOLOGISTS

The article elucidates the peculiarities of the professional self-awareness formation of future psychologists in the professional training process in a higher education institution (hereinafter, HEI). On the basis of the conducted empirical research, it was clarified such peculiarities of the formation of professional self-awareness structural components of future psychologists as cognitive, emotional, motivational, and operational during training in HEI. The materials present the main results of mathematical and statistical analysis and interpretation of research results. The revealed trends indicate that the normative process of professional development in HEI is characterized by qualitative and gradual changes in the professional self-awareness of higher education students. In particular, motivational and empathic components are identified as the main components of the studied phenomenon.&#x0D; Moreover, to the presented results of empirical research, in the context of the ecological approach, the problem of social and psychological determinants of the formation of future psychologists' professional self-awareness is highlighted. The identification and inclusion of such determinants in the educational process organization will contribute to the actualization of ecological and psychological mechanisms of students' professional and personal formation. Among the general recommendations for the environmentalization of the professional educational environment in the HEI, the article proposed the practice of mentoring junior students by senior students on the basis of the "equal to equal" principle, as well as providing intervision and supervisory support for future psychologists at the stage of higher professional education.
Security and performance implications of BGP rerouting-resistant guard selection algorithms for Tor

Tor is the most popular anonymization network with millions of daily users. This makes it an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods preferring Tor nodes with higher resilience to active BGP attacks. In this work, we analyze the implications of such proposals and demonstrate that two state-of-the-art path selection methods, namely Counter-RAPTOR and DPSelect, are not as secure as thought before. First, we show that DPSelect achieves only one third of its originally claimed resilience and, thus, is not as resilient as widely accepted. Second, we reveal that the resilience to active BGP attacks on the way back, i.e., from the first anonymization node to a given Tor user, provided by both methods is significantly lower than on the forward path. Beside their lower resilience in specific cases, we show that for particular users the usage of Counter-RAPTOR and DPSelect also leads to leakage of user’s location. Furthermore, we uncover the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious Tor middle node can fingerprint the user’s location and the confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.
Application of Remote Sensing Data to Assess Tidal Inundation and Its Potential for Food Crop Cultivation

One of the causes of the global food crisis is climate change. To overcome the global food crisis, it is done by opening up new lands for the cultivation of food crops. The diversity of locations for food crop cultivation is very necessary. One of the locations that has not been widely developed for food crop cultivation is tidal land. One of the obstacles to the development of tidal land is the inundation of tidal land. Periodic monitoring of tidal inundation is able to determine the temporal pattern of inundation that can be used for the preparation of types of food crops that can be cultivated. Monitoring locations in South Kalimantan and Central Kalimantan. Monitoring the height of tidal inundation is carried out using remote sensing technology. Data source from the ERA5-Land Satellite (Land Monthly Averaged by Hour of Day) ECMWF. Periodic data 1981–2020. Analysis of tidal inundation heights was carried out during the periods December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON) at three depths (1, 2 and 3 m). The results show that in a year, there are two patterns of inundation which is the DJF and MAM pattern and JJA and SON one. DJF and MAM pattern has more (flooded) than stagnant water conditions in the JJA and SON months’ groups. Flooded conditions affect the type of rice variety to be planted. Characteristics of rice plants related to waterlogging or tidal land are plant height. In the DJF and MAM month groups, rice varieties with higher plant height can be planted than those planted in the JJA and SON month groups (variety paddy Seratus Malam and Kartuna).
Contribution to methods and techniques of scientific research: Structure of the scientific research report - 5. Conclusions

Drawing conclusions from the research is probably the most important section of the research report. Inference and conclusions, as a product of this process, are based on research findings, which form a factual basis for drawing conclusions. They highlight the specific importance of the work and allow for a certain cognition of science and practice, and are tasked with identifying key research findings and linking them with what was said in the introduction.
CASE STUDY ON SPEECH TO SCRIPT TRANSLATION FROM ENGLISH TO BEARY

Humans communicate with one another using language. Nowadays, most people face difficulties when conversing with others who do not know their language or other language. To mitigate this problem there has been a rise in interest in speech to script translation have been increasing rapidly. Speech to Script translation play a significant role in facilitating communication between speakers of various languages. Attempt was made automatically identify the speech in English language and translate script into Beary language. The proposed paper has two phases: During first phase the system converts the speech which is given as a source into Kannada text which is then transliterated to English language and in the second phase the converted text is transliterated to Beary language. In this study, the system uses independently created audio for both English and Kannada, resulting in a fair and balanced text pronunciation in both languages and also it uses the corpus of Kannada language that acts as a mediator between English and Beary language. The approaches used are rule-based and example-based, and the future work will focus on direct translation of speech to Beary language using sequence methods.
SISTEM INFORMASI PENDATAAN ANAK PANTI ASUHAN “RIANG” BERBASIS WEBSITE

&#x0D; &#x0D; &#x0D; &#x0D; Abstract&#x0D; Childcare Social Institution is a social welfare business institution that has the responsibility to provide social welfare services to neglected children by carrying out assistance and alleviation of neglected children, providing replacement services for parents / guardians of children in fulfilling physical, mental and social needs to foster children so as to obtain broad, appropriate and adequate opportunity for the development of his personality as expected as part of the future generation of the nation's ideals and as people who will actively participate in the field of national development.&#x0D; Data collection on orphanage children that are not properly implemented is one of the obstacles in checking the number of orphanage children who have not been in homes and who are still in the institution, with education levels, origin, how long they are in the orphanage and various information related to children House.&#x0D; The problem is how to design and build a system for collecting orphanage children. With the aim of building a website-based orphanage data collection information system. Which is useful for facilitating access to orphanage data, archiving data on orphanages that are more efficient, facilitating the processing of orphanage data.&#x0D; The results of this study are an information system for data collection of website-based "Riang" orphanage.&#x0D; &#x0D; Keywords: Orphanages, information systems, data collection, foster children, archives&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Neural Model-Applying Network (Neuman): A New Basis for Computational Cognition

NeuMAN represents a new model for computational cognition synthesizing important results across AI, psychology, and neuroscience. NeuMAN is based on three important ideas: (1) neural mechanisms perform all requirements for intelligence without symbolic reasoning on finite sets, thus avoiding exponential matching algorithms; (2) the network reinforces hierarchical abstraction and composition for sensing and acting; and (3) the network uses learned sequences within contextual frames to make predictions, minimize reactions to expected events, and increase responsiveness to high-value information. These systems exhibit both automatic and deliberate processes. NeuMAN accords with a wide variety of findings in neural and cognitive science and will supersede symbolic reasoning as a foundation for AI and as a model of human intelligence. It will likely become the principal mechanism for engineering intelligent systems.
A close look at a systematic method for analyzing sets of security advice

Abstract We carry out a detailed analysis of the security advice coding method (SAcoding) of Barrera et al., which is designed to analyze security advice in the sense of measuring actionability and categorizing advice items as practices, policies, principles, or outcomes. The main part of our analysis explores the extent to which a second coder’s assignment of codes to advice items agrees with that of a first, for a dataset of 1013 security advice items nominally addressing Internet of Things devices. More broadly, we seek a deeper understanding of the soundness and utility of the SAcoding method, and the degree to which it meets the design goal of reducing subjectivity in assigning codes to security advice items. Our analysis results in suggestions for modifications to the coding tree methodology, and some recommendations. We believe the coding tree approach may be of interest for analysis of qualitative data beyond security advice datasets alone.
Development of the Main Library of the Republic of Mari El: Unique Publication and Source Finds

The analysis is presented of T.A. Petrova’s book “The Main Library of the Republic: the Time of Formation. 1918—1936” (2022). The book is a study of the almost unexplored period of the history of the National Library named after S.G. Chavain of the Mari El Republic. 1918—1936 is the period of radical changes in society and approval of new principles of library organization. On the wide material of documentary sources from state, departmental and personal archives the basic marks of the library’s development are shown: the features of library work organization, formation of book stock, methods and forms of work with readers. The history of the library is illustrated with the facts and data about the people working there at that time. The book is announced as a historical and archival research, well thought out and appropriate to the content structure, attracts with its logic, clarity and simplicity of narration. Each of three main parts is a separate historical, biographical and librarian sketch of different stages of library’s development, chronologically almost entirely coinciding with the tenure of M.A. Chernova, I.E. Pavlov, A.R. Kavush as heads of the library. The detailed work by T.A. Petrova gives a full and complete picture of the development of the main library of the republic during the first years of Mari Autonomous Region existence. The book contains new and earlier unknown to science materials.
Hierarchical confusion matrix for classification performance evaluation

Abstract This study proposes the novel concept of hierarchical confusion matrix, opening the door for popular confusion-matrix-based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. The concept is developed to a generalised form and proven its applicability to all types of hierarchical classification problems including directed acyclic graphs, multi-path labelling, and non-mandatory leaf-node prediction. Finally, measures based on the novel confusion matrix are used for three real-world hierarchical classification applications and compared to established evaluation measures. The results, the conformity with important attributes of hierarchical classification schemes and its broad applicability justify its recommendation.
The Effect of Yoga Practice on Cervical Tactile Acuity and Body Awareness

Body-mind-based holistic methods of relaxation and improved well-being, such as yoga and meditation, improve body awareness and have often been used to enhance quality of life and the ability to cope with pain. We aimed to compare tactile sensory acuity and body awareness in healthy sedentary individuals who practiced yoga regularly and in control participants who had not practiced yoga. Participants were 60 individuals, aged between 18 and 35 years who were divided into two groups according to whether they had previously practiced yoga. We used the two-point discrimination (TPD) test to determine participants’ tactile acuity, as measured with a digital calliper at the C7, C5, C3, C1 and T1 spinal segments and with the Body Awareness Questionnaire (BAQ). The TPD measurements of individuals who practiced yoga and meditation had a lower discriminatory threshold compared to those who had not practiced yoga ( p &lt; .001), and the self-reported BAQ score of yoga practitioners was higher than that of the controls ( p &lt; .001). We found a positive correlation between the length of the prior duration of yoga experience and self-reported body awareness ( r = .567, p &lt; .001). There was a significant negative correlation (r = −.379, p = .015) between the C5 segment and the TPD measurements, but not for the other cervical spinal segments ( p &gt; .05). There was a negative correlation between the length of prior yoga practice and the TPD measurements in all cervical segments ( p &lt; .001). The most negative correlation was found at the C7 segment ( r = −.844, p &lt; .001) and the least negative correlation was found at the C3 segment ( r = −.669, p &lt; .001). These data suggest that yoga and meditation practices may improve well-being and diminish pain by increasing body awareness and tactile sensory acuity in the cervical region.
DOES THE EMPATHY MACHINE MAKE JOURNALISM STUDENTS COMPASSIONATE? AN EMPIRICAL STUDY

In 360-degree virtual reality, news stories put the user (who consumes news through a head-mounted display) inside the story. The present study tries to find out whether select 360-degree video documentaries make the users empathize with the story, situation, and people involved in the story. On the other hand, in the news media, journalists and documentary filmmakers work hard to get the news out to the public. People who read the news have to understand how journalists present the news. In 360-degree documentaries, there is no such boundary. This is why the current study is significant in determining how the users feel about the documentaries they watch using virtual reality headsets (HMDs). The current study also tried to find out if there was a difference in the level of compassion between the control group and the experimental group after watching a 360-degree VR documentary. It also looked at how the empathy machine affected the level of compassion of both male and female journalism students. This study used a quantitative approach and a questionnaire that the participants filled out on their own. The respondents who belong to the experimental group and who filled out the pre-test questionnaire were asked to use VR headsets (Oculus Quest 2) to watch a 360-degree documentary. The control group respondents were asked to watch the content using mobile phones.
Socio-cognitive caste-based optimization

Metaheuristics are universal optimization algorithms that are used to solve difficult problems, which are unsolvable by classic approaches. In this paper, we aim to construct a novel class of socio-cognitive metaheuristics based on the caste metaphor. We focus on classic evolutionary and agent-based metaheuristics, adding a sociologically inspired structure of the population and cognitively inspired variation operators. In addition to giving the background and details of the proposed algorithms, we apply them to the optimization of a variety of difficult benchmark problems.
Hearing the voice of experts: Unveiling Stack Exchange communities’ knowledge of test smells

Refactorings are transformations to improve the code design without changing overall functionality and observable behavior. During the refactoring process of smelly test code, practitioners may struggle to identify refactoring candidates and define and apply corrective strategies. This paper reports on an empirical study aimed at understanding how test smells and test refactorings are discussed on the Stack Exchange network. Developers commonly count on Stack Exchange to pick the brains of the wise, i.e., to ‘look up’ how others are completing similar tasks. Therefore, in light of data from the Stack Exchange discussion topics, we could examine how developers understand and perceive test smells, the corrective actions they take to handle them, and the challenges they face when refactoring test code aiming to fix test smells. We observed that developers are interested in others’ perceptions and hands-on experience handling test code issues. Besides, there is a clear indication that developers often ask whether test smells or anti-patterns are either good or bad testing practices than code-based refactoring recommendations.
Construction and management of industrial system failure mode knowledge graph

In the process of industrial production and to ensure the production order, it is necessary to monitor the process in real time, detect errors and take action in advance to reduce losses. Failure Mode and Effects Analysis (FMEA) is a systematic activity to analyze product modules, parts and various operations in the production process to identify potential failure modes and analyze their possible consequences. This leads to necessary actions being taken in advance to improve product quality and reliability. Efficient management of FMEA data is beneficial for controlling the production process and improving production quality. Based on the failure mode and effects analysis (FMEA) data of industrial systems, this paper builds a knowledge graph of failure modes and designs, and develops the corresponding modules for the management functions, including knowledge graph creation, knowledge graph storage, and knowledge graph retrieval. First, the ontology structure of the failure mode is designed in terms of the failure mode of industrial systems. Second, the facts are extracted from the unstructured data in FMEA, the structured data is cleaned, the abnormal data is eliminated, and the missing data is recovered. Third, according to the correlation between the pattern level ontology, the knowledge graph triplet is created and the FMEA knowledge graph is constructed; then the storage function of the FMEA knowledge graph is designed and implemented based on the graph database neo4j; finally, the KNN algorithm for the similarity search in the FMEA knowledge graph is proposed.
Carrier Aircraft Landing Control Technology Based on Deep Reinforcement Learning

In this paper, a pitching control method based on Deep Deterministic Policy Gradient (DDPG) algorithm for carrier aircraft landing and descending stage is studied. DDPG controller takes pitch angle rate error, pitch angle error and altitude error as input, and output as elevator deflection, realizing the rapid pitch angle response of carrier-aircraft under different landing states. Compared with traditional PID controller, network training of Actor-Critic for DDPG attitude controller greatly improves the calculation efficiency of control quantity, and reduces the difficulty of parameter optimization. The simulation experiment in this paper was based on the F/A-18 aircraft aerodynamics model constructed in Matlab/Simulink, and the intensive learning and training environment built on PyCharm platform was used to realize the data interaction between the two platforms through UDP communication. The simulation results show that the attitude controller based on reinforcement learning designed in this paper has the characteristics of fast response speed and small dynamic error, and meets the control accuracy requirements in the experiment.
Hybrid Discrete Particle Swarm Optimization for Energy-Aware Scheduling on Test and Control Systems

In this paper, a hybrid discrete particle swarm optimization algorithm (HDPSO-VNS) is proposed for energy-constrained performance optimization in test and control systems (TCSs). Task scheduling among multiple processors needs to balance energy consumption and scheduling quality, which exhibits the characteristics of NP-hard. Considering the periodicity and dependency of test tasks, a directed acyclic graph is utilized to depict the priority relationship between real-time tasks. The proposed hybrid metaheuristic algorithm initiates with particle swarm optimization to obtain an approximate optimal processor allocation strategy. A variable neighborhood search strategy is applied to expand search space through three different neighborhood actions, refining local optimization ability of HDPSOVNS based on the current optimal solution. The algorithm is evaluated in a heterogeneous processor environment of CloudSim, and comparative experimental results confirm the feasibility and effectiveness.