Reviewerly/exHarmony huggingface dataset:
Профессиональное самоопределение как ведущий фактор развития конкурентоориентированности и конкурентоспособности старшеклассников

Introduction. The article discusses the relationship between career self-guidance of high school students and their competitiveness. The authors highlight the indicators of career self-guidance and analyze their influence on the actualization of socially significant qualities, promoting the development of adolescent competitiveness. The purpose of this article is to consider the specifics of career self-guidance of contemporary senior pupils and to analyze its influence on the competitive orientation of pupils.Materials and Methods. A questionnaire survey of high school students was selected for its reliability and validity in studying competitive skills. IBM SPSS Statistics 23 software was used to process and analyze the obtained data (N = 1 067 respondents).Results. The results reveal that the career self-guidance of high school students is a key factor in the process of formation of their competitive orientation. The main motives of future professional activity of senior students and the criteria of educational institution choice were defined. In addition, the key factors that influence the process of making decision of education continuing and their connection with competitive oriented pupils were considered. The indicators of career self-guidance process were revealed.Discussion and Conclusion. The research results may be useful for teachers and psychologists at high schools, for representatives who study competitive relations in educational sphere and for educators at vocational institutions and universities to develop career guidance strategies. These findings provide the following insights for future research: a comparative analysis of senior pupils’ career guidance measures` efficiency, conducted by different structures, the study of Internet technologies impact on career self-quidance and the study of atlases and new occupations list and their position in a future labor market.
INNOVATIVE APPROACHES OF ICT TEACHING IN VOCATIONAL EDUCATION OF PEOPLE WITH SPECIFIC LEARNING DISORDERS – PEDAGOGICAL PROJECT IN FIVE EUROPEAN COUNTRIES

INNOVATIVE APPROACHES OF ICT TEACHING IN VOCATIONAL EDUCATION OF PEOPLE WITH SPECIFIC LEARNING DISORDERS – PEDAGOGICAL PROJECT IN FIVE EUROPEAN COUNTRIES
Surveillance, social punishment, and the viability of Homo Virtualis

As emerging encryption technology results in widespread freedom from detection of online activity, Homo Virtualis might usefully be considered as Homo sapiens without a gun to its head—a highly evolved species experiencing, for the first time on a massive scale, a home environment in which the individual who engages in acts that harm others is not identifiable. This renders the punishment of antisocial behavior in this context impossible, clearing the warning signals for retribution and crippling this ancient and brutal method for limiting the occurrence of antisocial or unwanted behavior. If Homo Virtualis is to retain this newfound freedom from threat and invigilation, prosocial behavior must continue to prevail in the absence of credible threat of punishment for the antisocial. The prospects are far from certain, and an attendant spike in the frequency of misdeed would be sure to evoke a crackdown on encryption tech. On the bright side, a widespread failure of punishment to inhibit malice and malfeasance online might finally evoke serious consideration of how H. sapiens might ensure a low frequency of antisocial behavior absent the threat of dire consequence for transgression, via the positive reinforcement of prosocial conduct, rendering interpersonal and community sensitivity and service pleasurable rather than obligatory. Encryption technologies can provide valuable support for such a comprehensively humanistic effort to reduce malfeasance by eliminating threats of public censure for current and budding offenders’ own efforts to address their problematic proclivities.
AppNet: A Large-Scale Multi-layer Heterogeneous Complex App Network for Intelligent Program Search

The resources of mobile application in the app stores contains a vast amount of code and knowledge, which is of great significance to intelligent program search technology, but how to organize and utilize these multi-source heterogeneous data efficiently and integrate semantic information is still a key problem. In this paper, WordNet based AppNet, a multi-layer heterogeneous complex app network model was proposed, it completely describes the hierarchical structure between app-related tags, attributes, and code and in which aims to explore its application in the intelligent program search. Firstly, we expound the construction mechanism of AppNet and describe how does it realize mapping with WordNet, and then two simple real application scenarios were conducted based on AppNet in which to verify its validity and feasibility. We believe that the proposed AppNet model will provide researchers with more efficient ideas in the field of intelligent software development and search.
کانه زایی مس پورفیری، دگرسانی، سن سنجی زیرکن به روش U/Pb و پتروژنز سنگ های منطقه دلفارد (شمال باختر جیرفت)

Delfard area is located in the northwestern of Jiroft, formed the northwestern part of the Jabālbārez granitoid complex, in Uromiyeh- Dokhtar magmatic belt. The Jabālbārez granitoid complex has a wide range of rocks inclusive from diorite to alkali granite, which were formed by magmatic differentiation process over the several course of consecutive pulses. Last pulses are masses of porphyry that showed evidence of copper mineralization. One of these intrusive bodies is Delfard. Copper mineralization in Delfard area​​ is more important than other parts of Jabālbārez. Consequently, in the area, three porphyry masses showing the evidence of porphyry copper mineralization. Chalcopyrite, Pyrite, Malachite, Azurite and Magnetite are indicative minerals. Alteration zones in Delfard area are silicic, potassic, arjelic, phyllic, and propylitic that can be correlated with Lovell and Gilbert model. According to studies, Delfard has high potential for exploration and drilling. The average copper content in the ore is about 1800 ppm. Petrogenesis review of the mafic rocks in Delfard area shows that these rocks originated from a richer source than NMORB. In addition, the rocks of Delfard area are Flux melting pointer that happened in the subduction of oceanic crust under the adjacent crust. In other words, the sequence of calc-alkaline rocks of Delfard area attributed to compressional tectonic regime that intrusived and erupted along the Uromiyeh- Dokhtar zone with development and high volume in the form of magmatic horizons. Finally, petrogenesis of studied granite series is similar to the calc-alkaline granitoids, which was originated from basaltic magma mantle metasomatised by fluids from the subducted slab. Based on zircon U-Pb geochronology,the age of Delfard granitoid is 15/83±1/3 Ma. One can imagine that, magmatic differentiation process was completed in a short time and intrusive body was sequentially penetrated and placed in the earth's crust in a short time.
Enabling SSH Protocol Visibility in Flow Monitoring

The network flow monitoring has evolved to collect information beyond the network and transport layers, most importantly the application layer information. This information is used to improve network security and performance by enabling more precise performance analysis and intrusion detection. In this paper, we contribute to this effort by extending flow monitoring with information from the SSH protocol. Firstly, we analyze the SSH protocol to determine which information can be obtained from the connection establishment phase. Based on the analysis, we create an extension to our flow monitoring infrastructure that allows obtaining the selected information. Lastly, we analyze the SSH connections observed in the university campus network and discuss the benefits of performing the detailed SSH protocol analysis. We argue that with a precise recognition of login attempt results it is possible to improve the detection of successful brute-force password attacks. Moreover, we publish an anonymized version of our dataset including the SSH specific information.
Consumers’ Perception toward Brand Preferences while Purchasing Personal Care Products: A Study on Rajshahi City

This study conducted to focus the consumers’ perception of international brand over local brand while purchasing personal care products. Consumers’ perception highlighted on customer’s psychology that determines their viewing attitude towards using the local or international brand. Globalization makes the foreign trade easier than before and the availability of international brands confused the consumers’ ethnocentrism towards local brand but it is also true that using too much foreign products can destroy the employment opportunity of local people and also the country may face of security issues from foreigners. To examine the topic a convenient sampling method was applied to collect response from 280 customers from which 268 found free from errors; those respondents were used for the final study. The findings highlighted some factors that influence customers’ perception toward choosing brand while purchasing personal care products. Data were collected from local customer of new market and Shaheb Bazar area of Rajshahi city. Study reveals that according to the perception of the customer, product quality of local brands, price consideration, country of origin, availability of foreign brands and promotional actions have a significant role toward customer mind while purchasing products. Moreover the findings suggest that, for the wellbeing of the society, local products need foremost priority to expand their business. as it is known that more demand of local products lead more production that  create more job opportunity in the market and to build the country as self-sufficient, a customer focused local market should be enhanced. The major contribution of the current study is that it focuses consumer’s observation and suggestion to create more opportunities for local brand in market place.
TVV: Real-Time Visual Identity and Tracking with Edge Computing

Video surveillance today has become pervasive, making visual identification and tracking technology attractive to a broad class of applications like traffic counting, crime tracking, and Blockchain. However, visual tracking is also a victim of the ubiquity of surveillance camera: a huge amount of data that generated by the cameras leads to severe congestion problem, which decreases the frame rate and in turn affects the tracking accuracy. In this paper, we present TVV, a real-time visual tracking system that leverages edge computing to support accurate and continuous tracking in large scale areas. The design of TVV is based on a insight that almost 80% of frames in a video stream exhibit high quality, and such frames can be processed on the edge nodes using a lightweight filtering method named KCF. Based on this insight, TVV adaptively load the visual tacking task on the edge or the server, based on the quality of the currently generated frame. In this way, the traffic load is largely decreased, without sacrificing the tracking accuracy. Our experimental result show that the average frame rate of TVV achieves 45.75 fps, outperforming most state-of-the-art visual tracking approaches.
Audio Watermarking Based on FWHT and LUD

This chapter introduces an audio watermarking algorithm based on FWHT and LU decomposition (LUD). To the best of our knowledge, this is the first audio watermarking method based on FWHT, LUD, and quantization jointly. Initially, we preprocess the watermark data to enhance the security of the proposed algorithm. Then, the original audio is segmented into nonoverlapping frames and FWHT is applied to each frame. LUD is applied to the FWHT coefficients represented in a matrix form. Watermark data are embedded into the largest element of the upper triangular matrix obtained from the FWHT coefficients of each frame. Experimental results indicate that proposed algorithm is considerably robust and reliable against various attacks without degrading the quality of the watermarked audio. Moreover, it shows more excellent results than the state-of-the-art methods in terms of imperceptibility, robustness, and data payload. The main limitation of the existing audio watermarking techniques is the difficulty to obtain a favorable trade-off among imperceptibility, robustness, and data payload. To overcome this limitation, in this chapter, we propose a blind audio watermarking algorithm based on fast Walsh-Hadamard transform (FWHT) and LU decomposition (LUD). The main features of the proposed scheme are: (i) it utilizes the FWHT, LUD, and quantization jointly; (ii) it uses a tent map that contains the chaotic characteristic to enhance the confidentiality of the proposed algorithm; (iii) watermark is embedded into the largest element of the upper triangular matrix obtained from the FWHT coefficients of each frame by quantization; (iv) watermark extraction process is blind; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Experimental results indicate that the proposed watermarking algorithm shows high robustness against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms state-of-the-art methods [9–10, 15–16, 20, 22–23, 26, 28] in terms of imperceptibility, robustness, and data payload.
A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images.

We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI). Multiple instance learning (MIL) approaches, typically used for the analysis of WSIs, divide the image (bag) into patches (instances), which are used to predict a single bag-level label. These approaches perform poorly in cytopathology slides due to a unique bag structure: sparsely located informative instances with varying characteristics of abnormality. We address these challenges by considering multiple types of labels: bag-level malignancy and ordered diagnostic scores, as well as instance-level informativeness and abnormality labels. We study their contribution beyond the MIL setting by proposing a maximum likelihood estimation (MLE) framework, from which we derive a two-stage deep-learning-based algorithm. The algorithm identifies informative instances and assigns them local malignancy scores that are incorporated into a global malignancy prediction. We derive a lower bound of the MLE, leading to an improved training strategy based on weak supervision, that we motivate through statistical analysis. The lower bound further allows us to extend the proposed algorithm to simultaneously predict multiple bag and instance-level labels from a single output of a neural network. Experimental results demonstrate that the proposed algorithm provides competitive performance compared to several competing methods, achieves (expert) human-level performance, and allows augmentation of human decisions.
Audio Watermarking Based on LWT and QRD

This chapter presents a blind audio watermarking method based on LWT and QR decomposition (QRD) for audio copyright protection [31]. In our proposed method, initially the original audio is segmented into nonoverlapping frames. Watermark information is embedded into the largest element of the upper triangular matrix obtained from the low-frequency LWT coefficients of each frame. A blind watermark detection technique is introduced to identify the embedded watermark under various attacks. Simulation results indicate that the proposed watermarking method is highly robust against different attacks. In addition, it has high data payload and provides good imperceptible watermarked sounds. Moreover, it shows better result than the state-of-the-art methods in terms of imperceptibility and robustness. In this chapter, a blind audio watermarking method based on LWT and QR decomposition (QRD) is proposed. The main features of the proposed method are: (i) it utilizes the LWT and QRD jointly; (ii) it uses Bernoulli map, containing the chaotic characteristic to enhance the confidentiality of the proposed method; (iii) watermark extraction process is blind; (iv) subjective and objective evaluations reveal that the proposed method maintains a high audio quality; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Simulation results demonstrate that the proposed watermarking method shows high robustness against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms the state-of-the-art methods [9–10, 15–16, 20, 23, 24, 26, 29] in terms of imperceptibility, robustness, and data payload. The data payload of the proposed method is 172.39 bps, which is relatively higher than that of the state-of-the-art methods.
Flexible deployment of component-based distributed applications on the Cloud and beyond

In an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud.These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components.The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts.This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes.We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time.AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system.This reduces the manual application deployment process that is monotonous and prone to errors.Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected.We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.
Informational masking of speech analogues by intelligible and non-intelligible but acoustically similar interferers

Informational masking of target speech is generally greater when the interfering speech is intelligible than when it is not (e.g., speech from an unfamiliar language), but the relative contributions of acoustic-phonetic and linguistic interference are often difficult to assess owing to acoustic differences between interferers (e.g., different talkers). This study used three-formant analogues (F1 + F2 + F3) of natural sentences as targets and interferers. Target formants were presented monaurally (F0 = 120.3 Hz) either alone or accompanied with the contralateral ear by interfering formants from another sentence (F0 = 151.5 Hz); a target-to-masker ratio (TMR) between ears of 0, 6, or 12 dB was used. Interferers were either intelligible or rendered non-intelligible by delaying F2 and advancing F3 by 150 ms relative to F1, a manipulation designed to minimize spectro-temporal differences between corresponding interferers. Target-sentence intelligibility (keywords correct) was 67% when presented alone but fell considerably when a non-intelligible interferer was present (49%) and significantly further when the interferer was intelligible (41%). The changes in TMR produced neither a significant main effect nor an interaction with interferer type. The results suggest that although linguistic factors contribute to informational masking, interference with acoustic-phonetic processing of the target can explain much of the impact on intelligibility. [Work supported by ESRC.]
Speech privacy comparison of metrics

How do different speech privacy metrics compare, what are the correlations and differences, and what might we conclude from them? This paper will start with the noise reduction results from several common architectural assemblies. From those results, we will calculate each of the popular speech privacy metrics. Finally, we will explore the correlations and differences of the results.
Learning writing description paragraph based on mind mapping

This study was aimed to describe (1) instruction in writing a descriptive paragraph using mind mapping model to the seventh-grade students of SMP Negeri 2 Singaraja and (2) the ability of the seventh-grade students of SMP Negeri 2 Singaraja in writing a descriptive paragraph using mind mapping. This study used descriptive qualitative design. The subjects of this study were teachers and the seventh-grade students of SMP Negeri 2 Singaraja. The object was descriptive paragraph instruction using mind mapping. This study used an observation method and test to collect the data. The data gathered were analyzed using qualitative descriptive and quantitative descriptive analysis methods. The results showed that (1) the implementation of the instruction in writing a descriptive paragraph using mind mapping model by the teachers has closely followed the existing theory. (2) the ability of the seventh-grade students of SMP Negeri 2 Singaraja in writing a descriptive paragraph using mind mapping fell into good category. The score obtained was 76.41. Although the score obtained has fallen into category good, it seems that the teachers’ performance needs to be improved by giving more intensive practices, both in writing mind mapping and in developing it into a paragraph to produce a maximum result.
LibGuides: About the Library: Reserves

Library policies, services, and collections What kinds of materials may be put on reserve; how to put items on reserve; loan periods; more
Towards a Theory of Software Development Expertise.

Software development includes diverse tasks such as implementing new features, analyzing requirements, and fixing bugs. Being an expert in those tasks requires a certain set of skills, knowledge, and experience. Several studies investigated individual aspects of software development expertise, but what is missing is a comprehensive theory. We present a first conceptual theory of software development expertise that is grounded in data from a mixed-methods survey with 335 software developers and in literature on expertise and expert performance. Our theory currently focuses on programming, but already provides valuable insights for researchers, developers, and employers. The theory describes important properties of software development expertise and which factors foster or hinder its formation, including how developers' performance may decline over time. Moreover, our quantitative results show that developers' expertise self-assessments are context-dependent and that experience is not necessarily related to expertise.