Reviewerly/exHarmony huggingface dataset:
Integrated Fuzzy based Decision Support System for the Management of Human Disease

To eliminate some of the inaccuracies in the diagnosis of human diseases, decision support systems based on algorithms and technologies such as Artificial Neural Network, Fuzzy Logic etc. have been used.The results of such diagnosis are used for treatment and management purposes.Inaccurate and imprecise diagnosis may lead to wrong treatment methods which in turn may result in death or complications.Although treatments are widely carried out using drugs, there exist other treatment methods such as alternative medicine, complimentary medicine which could be used for treatment.We propose an Integrated Fuzzy Based Decision Support System which focuses on the integration of both alternative and pure medicine for the management of malaria.The results obtained showed that integrating these two treatment and management methods will eliminate the limitations of the individual methods therefore bridging the gap between alternative and pure medicine in the treatment and management of human diseases.The system is implemented in C#.
Lorentz invariant quantum concurrence for $$\textit{SU}(2) \otimes \textit{SU}(2)$$ spin–parity states

The quantum concurrence of $$\textit{SU}(2) \otimes \textit{SU}(2)$$ spin–parity states is shown to be invariant under $$\textit{SO}(1,3)$$ Lorentz boosts and O(3) rotations when the density matrices are constructed in consonance with the covariant probabilistic distribution of Dirac massive particles. Similar invariance properties are obtained for the quantum purity and for the trace of unipotent density matrix operators. The reported invariance features—obtained in the scope of the $$\textit{SU}(2) \otimes \textit{SU}(2)$$ corresponding to just one of the inequivalent representations enclosed by the $$\textit{SL}(2,{\mathbb {C}})\otimes \textit{SL}(2,{\mathbb {C}})$$ symmetry—set a more universal and kinematical-independent meaning for the quantum entanglement encoded in systems containing not only information about spin polarization but also the correlated information about intrinsic parity. Such a covariant framework is used for computing the Lorentz invariant spin–parity entanglement of spinorial particles coupled to a magnetic field, through which the extensions to more general Poincaré classes of spinor interactions are straightforwardly depicted.
Sentiment Analysis of Twitter Use on Policy Institution Services using Naïve Bayes Classifier Method

Twitter is one of the social media used to respond to various services of public service institutions, including the police. The research aims to determine the community's assessment of the services and performance of police institutions delivered via Twitter. This study uses the Naïve Bayes Classifier algorithm to classify topics and public sentiment towards tweets from police agencies. The results obtained were 181 positive tweets, 322 negative tweets, and 33 neutral tweets. Sentiment analysis showed 55% responded positively to police activities, 19.1% responded positively to public comments, and 91.8% responded positively to social services. It can be concluded that most people support police activities and services, but most people are still dissatisfied with police performance.
Measures of Organizational Training in the Capability Maturity Model Integration (CMMI)

Training has a major impact on organizational commitment.Organizational objectives can be met by executing several training strategies and programs to enhance training.Organizational training aimed at developing employees' knowledge and skills.It shall enable employees to carry out their duties efficiently and effectively.Two goals and seven practices of the organizational training process area in the capability maturity model integration (CMMI) framework are analyzed through this study.That's done to set common measures for organizational training.CMMI is a framework for assessing and improving software systems.The researcher implemented the Goal Questions Metrics (GQM) model on two objectives and seven specific practices of the organizational training process area in CMMI.That's done to set measures.The researcher confirmed that the defined measures are the true measure for each one of the seven specific practices.
THE PROSPECTS OF USING CRYPTOCURRENCY BITCOIN AS A FINANCIAL INNOVATION IN THE INFORMATION ECONOMY OF UKRAINE

We have established that a relatively new market of cryptocurrency is the subject of great interest for research under the current conditions of financial innovations in payment services. Our findings have revealed objective prerequisites for creating cryptocurrency as an innovative means of information-based payment. Cryptocurrency poses a challenge to the financial system of Ukraine since both the central bank and other state regulators can not control its emission and circulation. They show the growth dynamics in cryptocurrency market capitalization where Bitcoin predominates. Although cryptocurrency is not included in the National Register of payment systems and it is officially prohibited for circulation in Ukraine, our country ranks among the world’s top five countries by number of users of different bitcoin-wallets.&#x0D; We have examined the strengths of Bitcoin that have made this cryptocurrency a convenient means of international payment with a high investment potential. The results show disadvantages to using cryptocurrency for both the whole country in general and ordinary consumers in particular. We have defined main threats that national governments can face in case of an unregulated spread of cryptocurrency. Also this study, based on the works of leading foreign scientists and financial market experts, systematizes factors that influence the price of Bitcoin. It substantiates opportunities and prospects for the cryptocurrency market development in Ukraine.&#x0D; The study has shown that cryptocurrencies have their own niche in payment systems; they are quite competitive and reliable financial instruments. The growth dynamics of cryptocurrency market capitalization in the world makes Bitcoin the most successful example of the use of virtual currency in the information economy. As the modern society is being inevitably virtualized, our country’s economy should follow the path of innovations by finding solutions to a number of technical, economic and legal issues on the cryptocurrency market development in Ukraine through involving the experience of the leading European countries.
Effective forewarning requires central route processing: Theoretical improvements on the counterargumentation hypothesis and practical implications for scam prevention

Financial scams have caused tremendous financial damage globally. In Japan, the police forewarn people by equipping them with scam-prevention techniques or providing awareness regarding examples of previous scams; however, this does not appear to effectively prevent the damage, as many scam victims do not remember these warnings when faced with actual scam encounters. Considering that scammers often use appeal to emotion techniques, peripheral processing during scam attempts might disturb people's abilities to recall the warnings on scammers' modus operandi, thus leading to failed counter-arguing efforts. We verified this hypothesis in an experimental setting by asking 162 participants to remember given forewarnings and resist deceptive advertisements. The results showed that participants gave the advertisers' manipulative intent a higher rating only when they processed the advertisement through a central route, in addition to being forewarned. This means that forewarning had no effect when participants processed the advertisement through a peripheral route. Moreover, forewarning recollection levels mediated the effect of processing route on this rating, which suggests that remembering forewarnings is necessary to generate counterarguments. This result expands the theory on forewarning effects and explains why people are susceptible to scam victimization. Furthermore, it provides implications for scam prevention.
ERRORS FOUND ON THE ENGLISH TRANSLATION SENTENCES OF THE THIRD-YEAR STUDENTS OF ENGLISH AT IKIP BUDI UTOMO

This study aimed to describe and classify the grammatical errors found on English translation sentences in terms of syntax and morphology. The analysis procedure applied was based on the surface strategy taxonomy proposed by Dulay et al. in which errors were categorized into errors of: omission, addition, misformation, and misordering. The result of this study indicated that the highest frequency is errors of misformation which covers 58 errors (46%), followed by omission which covers 47 errors (27%), addition with 12 errors (10%), and the lowest is misordering, 9 errors (7%). In terms of syntax and morphology, the researcher found 17 errors (18%) in articles, 7 errors (7%) in pronouns, 21 errors (22%) in prepositions, 15 errors (16%) in inflectional endings, 26 errors (28%) in tenses, and 8 errors (9%) in derivational endings. Thus, errors in tenses are the highest frequency of errors, and, errors in derivational morphemes are the lowest.
Data Visualization Analysis of Big Data Recruitment Positions in Hangzhou Based on Python

In the DT era, data will become the main source of energy.The job of "big data" is so popular that it is in short supply for a long time.The purpose of this study was to obtain the big data recruitment information in the ocean of Web, Use Python web crawler technology as a tool to crawl at the recruitment website of https://www.lagou.com,working place as "Hangzhou", the search criteria for the "big data", using the Pandas + Matplotlib to implement data cleansing, data analysis and data visualization in terms of the job seekers' concerns.The results obtained in this study include company size, education, working years, salary, skill requirements, development prospects, position and job benefits, etc.The results of data visualization provide reference for interested practitioners, but also provide colleges and universities with talent training direction.
Hyperspectral Face Recognition Using Block based Convolution Neural Network and AdaBoost Band Selection

Hyperspectral imaging, adding discrimination information along spectral dimension, offers a new chance for robust face recognition. To improve the effectiveness of facial feature represented by hyperspectral face data, we proposed a block-based hyperspectral face recognition method using bands selection and convolution neural network (CNN). Firstly, a small convolution neural network is trained to capture discriminative visual information for different blocks in face images. Secondly, an improved AdaBoost algorithm (AdaBoost.MS) is introduced to choose different optimal bands for different blocks. Then, each block label can be determined by the ensemble learning classification. Finally, the recognition result can be gotten by the majority voting principle. The experiment results based on PolyU-HSFD database show that block-level based bands selection can capture the more discriminative spectral features than the method based on image level. The proposed method outperforms the existing state-of-the-art methods.
Speaker specific feature based clustering and its applications in language independent forensic speaker recognition

Forensic speaker recognition (FSR) is the process of determining whether the source of a questioned voice recording (trace) is a specific individual (suspected speaker). The role of the forensic expert is to testify by using, if possible, a quantitative measure of this value to the value of the voice evidence. Using this information as an aid in their judgments and decisions are up to the judge and/or the jury. Most existing methods measure inter-utterance similarities directly based on spectrum-based characteristics, the resulting clusters may not be well related to speaker’s, but rather to different acoustic classes. This research addresses this deficiency by projecting language-independent utterances into a reference space equipped to cover the standard voice features underlying the entire utterance set. The resulting projection vectors naturally represent the language-independent voice-like relationships among all the utterances and are therefore more robust against non-speaker interference. Then a clustering approach is proposed based on the peak approximation in order to maximize the similarities between language-independent utterances within all clusters. This method uses a K-medoid, Fuzzy C-means, Gustafson and Kessel and Gath-Geva algorithm to evaluate the cluster to which each utterance should be allocated, overcoming the disadvantage of traditional hierarchical clustering that the ultimate outcome can only hit the optimum recognition efficiency. The recognition efficiency of K-medoid, Fuzzy C-means, Gustafson and Kessel and Gath-Geva clustering algorithms are 95.2%, 97.3%, 98.5% and 99.7% and EER are 3.62%, 2.91 %, 2.82%, and 2.61% respectively. The EER improvement of the Gath-Geva technique based FSRsystem compared with Gustafson and Kessel and Fuzzy C-means is 8.04% and 11.49% respectively
You do not have to be quick to be information literate!

When comparing how younger and older internet users search for information, young people often impress by operating Web applications quickly and effortlessly. However, information literacy is not only a question of speed; it is highly dependent on cognitive abilities such as monitoring and regulating the search process. To avoid a general deficiency perspective on older Web users, this study goes beyond the results of Web searching to focus on individual approaches to information problem solving. We conducted ten guided interviews based on two different search tasks the participants (aged 16-77) worked on while thinking aloud. Applying a qualitative content analysis approach, we find that younger participants (aged 16-26) use more productive strategies to collect information, but observed no age-related differences in completing a complex task. The strong task dependence of our results underlines the necessity of teaching regulative search techniques that are suitable for solving unstructured everyday problems in order to ensure that all users can make the most of today’s rich but unstructured information environment.
DNN-Based Simultaneous Screen-to-Camera and Screen-to-Eye Communications

Simultaneous screen-to-camera and screen-to-eye communications, i.e., watermarking, have been proposed in visible light communications. The main purpose of such communications is to provide many data bits for camera devices and visual information for human eyes by using a common displayed image. To this end, the existing studies leverage the capability discrepancy and distinctive features between the human vision system and camera devices. However, the existing techniques mainly require high refresh rates in both screen and camera devices to achieve better throughput while keeping high visual quality. In this paper, we propose a novel transmission scheme for efficient simultaneous screen-to- camera and screento- eye communications without a need of high refresh rates. Specifically, we use deep convolutional neural networks (DCNN)- based watermark encoder and decoder to embed many bits into high-quality images, and then to maximize throughput from the bit-embedded image. With end-to-end adversarial learning, the encoder networks learn a mapping function to embed digital data into an original image based on a perceptual loss function while the decoder networks also learn a mapping function from the bitembedded image to the data bits based on a cross-entropy loss function. From the evaluations, we show that the proposed watermark encoding and decoding networks yield high throughput from the bit-embedded images compared with a simple DCNNbased watermarking. In addition, the bit-embedded images on the screen achieve high quality for human perception.
Rogue AP Detection using Similarity of Backbone Delay Fluctuation Histogram

Nowadays, wireless LAN service has been taken for granted by everyone. However, there is an increase in the occurrence of cyber threats to wireless LAN. For example, one attack called Evil-Twin Attack places a rogue access point (AP) with the same SSID as the legitimate one so clients connect to it unknowingly. Once attacked, all of the traffic moving across the network is sniffed by attackers. In this paper, we propose a method to detect the rogue AP by comparing different delay fluctuations in the backbone network. We define a delay in the backbone network as a subtraction result of the ICMP round trip time from client to first gateway or router and from client to the Internet server. To reduce deviation, we gathered 100 delay samples and created histogram-based vectors for a trial. We compared delay histogram vectors with cosine distances among six different wireless networks, which have different backbone networks. We confirmed that there is a large difference in cosine distance values between the same and different SSIDs on different days. We propose a method to detect the rogue AP, which compares a current delay histogram and a delay histogram from past days then compares the result with the threshold. The results showed that the proposed method can detect rogue APs with 86.67 percent accuracy under 6.25 percent false positive rate.
Significant obstacle location with ultra-wide FOV LWIR stereo vision system

Intelligent driving is an active area of research in both industry and academia. In order to overcome the shortcomings of traditional machine vision such as visibility is easily affected by illumination conditions, the blind area of infrared small field of view (FOV) is too large and could not provide depth information, this paper proposes a method for detecting significant obstacles based on ultra-wide FOV long-wave infrared (LWIR) stereo vision system. The stereo vision positioning location with ultra-wide FOV is established by the generalized fisheye camera model. On the basis of analyzing obstacle imaging scale and the structure characteristics of the proposed stereo vision system, a multi-scale salient region detection method based on composite pattern is proposed, and its implementation process is described in detail. Experiment shows that the proposed ultra-wide FOV LWIR stereo vision system is able to detect and locate significant obstacles in ultra-wide FOV and the detection rate of pedestrians and vehicles in real complex street scenes is over 92.6%. At the same time, the relative error of pedestrian positioning with a distance of 5 m to 30 m near the central FOV is between 1.6%-10.3%, and its spatial location ability and advantages are verified. The proposed stereo vision system effectively overcomes the shortcomings of existing vision systems, expands the scope of machine vision, and can be used in the field of assistant driving and intelligent driving.
Towards a system for predicting the category of educational and vocational guidance questions using bidirectional encoder representations of transformers (BERT)

Educational and vocational guidance is a particularly important issue today, as it strongly determines the chances of successful professional integration into the increasingly difficult labor market.Families have understood this well since they have been interested, in the educational orientation of their child.In this sense, we have set up a system for classifying questions of educational and professional orientation in based on Holland's test using the BERT method.Text classification, particularly the classification of questions is a basic task in natural language processing which is a very profound concept in the field of artificial intelligence.As the most abundant data in the world today is in the form of texts, having a powerful word processing system is essential and is more than just a necessity.Recently, Transformers models such as Bidirectional encoder representations of transformers or BERT are a very popular NLP models known for producing remarkable results compared to other methods in a wide variety of NLP tasks.In this article, we demonstrate how to implement a multi-class classification using BERT.In particular, we explain the classification of questions concerning the field of educational and vocational guidance following the RIASEC typology of Holland.Our model allows us to obtain the category of each input question.In our case, we define four classes (Activity, Occupations, Abilities, and Personality) for the set of questions, which constitute our data set.The results of this approach demonstrate that our model achieves competitive performance.
Query-by-Example Spoken Term Detection using Attentive Pooling Networks

Query-by-example spoken term detection (QbE-STD) is attractive because its a key technology for retrieving and browsing spoken content without transcribing them into text. Several end-to-end models based on encoder architecture have been proposed for QbE-STD, in which the input pair, spoken query and audio segment, are first projected into fixed-length vector representations by feature extraction module and then similarity measure module is used to output detection score based on the representations. Attention mechanism has been applied into the feature extractor; however, traditional approach calculates attention vector for audio segment only, which makes it a one-way attention mechanism. In this paper, we present a novel feature extraction module based on two-way attention mechanism, called attentive pooling networks, for end-to-end QbE-STD. The main idea is to learn a similarity measure over the projected input pair and extract information in a way that two input items can directly influence the computation of each other's representation. Evaluations on the LibriSpeech corpus and cross-linguistic audio archive confirm the effectiveness of our proposed approach compared to the traditional ones.
Establishing Modern Bachelor-level Studies in Computer Science based on Higher Education Standard in Ukraine

The work considers the Computer Science standard for Bachelor's Degree Programs in Ukraine in the context of harmonizing its content with world practice.The CS standard defines the subject area description, which includes the object of activity, goals and objectives of training, the theoretical content of the domain, teaching methods and technologies.The competency-based model for the bachelors training in computer science is described, This model includes generic and subject specific competencies, learning outcomes.Relation generic competencies of CS educational standard and descriptors of Ukrainian NQF is shown.The methodology for developing the educational standard of the Bachelor of Computer Science is based on the systematization of the IT industry requirements to the competencies of alumnus.CS standard is approved by the Ministries of Education and Science of Ukraine and is mandatory for implementation in the educational practice of all universities.
Founders' Education and SME Performance in Kosovo

This research deals with the professional preparation of the founders of the SME sector in Kosovo, and also aims to present the state of abilities and skills of the founders of SMEs. Also of interest in this research is the analysis of the skills of managers, founders of SMEs, what is their perception regarding the reflection of education in the SME business, namely what is their performance which should be reflected in the success and development of SMEs. The paper therefore aims to analyze the education and reflection of this education on the success of SMEs. Economic and social development in Kosovo, and elsewhere, depends on the success of SME owners and managers. This management structure will be able to move SMEs forward if they have an important factor such as education and experience in the sector where the SME operates. This paper explores the professional background of the founders, the size of the business structure, the experience of the founders in the field where the SME was founded, the age at the time of its establishment, the impact of technology.