Reviewerly/exHarmony huggingface dataset:
Anonymizing Prescription Data Against Individual Privacy Breach in Healthcare Database

Prescription data is a subset of the health-related data which can be collected by drug store during the patient's medication period. In general, prescription data consists of a set of transaction records which contains patients name or patient’s identification number and their prescribed medicine name. Analyzing such data using data mining techniques brings various advantages for drug stores. However, performing data mining task is not trivial for the drug stores and possibly the drug store dispatches the prescription data to another party for data analysis. While it can solve the data analysis problem, unfortunately, such activity may result in privacy breach since sensitive information i.e., types of patients' disease due to the data miner has background knowledge to infer certain medicine to the disease type. To guarantee individual privacy protection while at the same time preserving database utility a method called data anonymization should be employed prior to handling the prescription data to another party for data mining purpose. A data anonymization which is based on swapping technique can be a solution to address the problem. Experimental results show that the swapping method successfully protects individual privacy with respect to reduce the number of item lost and maintain data utility of the anonymized database.
Research on modeling of government debt risk comprehensive evaluation based on multidimensional data mining

In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation, and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional data mining. The MAFIA algorithm is used for multidimensional mining of government debt risk data, and K-means clustering algorithm is used for clustering processing of mined data. The KMV model is built based on the clustering findings, and the uncertainty factor is utilized to alter the model in order to provide a complete assessment of government debt risk using the modified KMV model. The experimental results show that the accuracy rate of government debt risk data mining is always above 91%, the relative error rate of evaluation is always below 3.4%, and the average evaluation time is 0.71 s, the practical application effect is good.
Estimation of Photovoltaic System Location and Orientation from Power Signals

We present a methodology and associated software implementation for estimating the latitude, longitude, tilt, and azimuth of a fixed-tilt photovoltaic (PV) array from a measured power signal. Our major contribution to this line of research is developing a robust estimation procedure for the local sunrise and sunset time from the power data and creating reusable, open-source code implementations. We validated the proposed algorithms on a labeled data set comprised of a fleet of more than 4400 inverter power measurements at 400 unique sites.
Immersive Virtual Experience Design Research of Jiaoshan Forest of Steles Calligraphy Art

Virtual reality technology is playing a more and more important role in the protection and inheritance of cultural heritage, but there are still many open topics on how to combine the characteristics of different cultural heritage and adopt VR technology to solve the immersion problem of experience. This paper discusses the elements of immersion creation from the three levels of appearance, behavior and reflection, and designs the virtual museum of Jiaoshan Forest of Steles, and creates the immersive virtual experience of Jiaoshan Forest of Steles calligraphy art from three aspects of space, interaction and emotion. The completed Forest of Steles virtual museum can make the experiencers experience the environment of the Forest of Steles immersively, and interpret the calligraphy art in an all-round and in-depth way, which is of great significance to the dissemination of the Forest of Steles calligraphy art and the inheritance of intangible cultural heritage.
Implementation of Ball Tracking Systems in Image Processing Using the Find Contour Method

One technology that is experiencing rapid development is digital image processing. Digital image processing is a scientific discipline that studies image processing techniques. The use of image management is increasing and is used in various fields, one of them in the field of robotics can be applied as an object detector. Image management is chosen in terms of detection because it is able to recognize objects and colors quickly. In the field of robotics these advantages are used to control robots to navigate, one example is in soccer robots. Soccer robots must be able to detect the presence of the ball to find and carry the ball and kick it towards the goal. The ball in the match system has been determined with a definite color, in this case it is orange. Therefore, the robot must be able to detect the orange ball to detect it. In detecting the ball, the parameters produced in this system are the distance of the robot to the ball, the angle of direction of the ball and also the ball according to the color or not. To solve this problem in this study using a method that is processing the RGB image produced by the camera into HSV after which color filtering is done and then finished using the find contour method. The results of this study noted the accuracy of the system in tracking ball objects by 92.87 percent at the distance of the ball to the camera and 88.19 percent at the angle of direction of the ball to the ball. The level of accuracy is due to the position of the camera when shooting.
Electronic resources of the national libraries of the Russian Federation constituents to promote cultural heritage of titular ethnic groups

Information activities of national regional libraries in promoting original traditional cultures, including that of Russian native minorities, are discussed. Cultural resources as presented at the websites of national libraries of RF constituents were examined, and 70 resources were identified, including 12 in national languages. The National Library of Chuvash Republic holds the largest number of independent digital resources. Digital publications, websites and portals are among the most popular library resources. The national regional libraries have been also developing and maintaining their databases, however their rate is rather insignificant. Many library Internet projects are characterized by their multilayer structure, which enables users to access various information resources in culture and arts via single access point. Ill-conceived navigation, lack of distinctstructural hierarchy, information mix-up when some resources, including those in regional studies are present in several website sections, are on the downside of representing resources on the libraries’ websites. According to the data obtained, virtual exhibitions make the largest share of individual digital resources. The libraries of Volga Federal District appear to be the most centered on designing and promoting resources on the culture of titular ethnic groups.
Eliminating Models During Model Elimination

We investigate the integration of SAT technology into clausal connection-tableau systems for classical first-order logic. Clauses present in tableaux during backtracking search are heuristically grounded and added to an incremental SAT solver. If the solver reports an unsatisfiable set of ground clauses at any point, search may be halted and a proof reported. This technique alone is surprisingly effective, but also supports further refinements “for free”. In particular we further investigate depth control of randomised search based on grounded clauses, and a kind of ground lemmata rule derived from the partial SAT model.
Science as a Tool for Social Development

Defining social efficiency and social technology is the core element of assessment methods and models, taking into account a wide range of objective and subjective factors. The assessment methods of social programmes (projects) efficiency are related to the social system’s orientation towards social protection and social services of the population and the use of the process approach and the transition to programme-target methods, outlining the main issues and mechanisms for social services and tasks for assessment, determining the basic requirements for efficiency assessment of social programming and the various stages. Considering all these matters, the research offers social programming efficiency assessment model based on “organizational efficiency” approach that covers a number of certain elements: system for resources acquisition, choice of goals, assessment of the impact on the external environment, choice of strategy, following the “what if” principle and the priorities of social activity, arising from the dynamic changes in social environment. Special emphasis is put on the differentiated effect on higher education institutions, depending on the field in which universities carry out teaching and research activities, as well as on the peculiarities of the university business model in the changing environment for development of the higher education institutions.
Topophilie und topophobie in stig sæterbakkens roman Gjennom natten

This article focuses on the emotional perception of space in Stig Sæterbakkenʼs novel Gjennom natten (2012). Topoanalysis is used to examine the various emotions that the protagonist Karl feels about specific spaces – be it a house, an apartment, a part of an apartment, or a city. It turns out that space serves as a projection surface for Karlʼs feelings. Therefore, his descriptions of the respective spaces usually coincide with the narratorʼs mood, and in this sense, topoanalysis allows us to predict and better understand the motivations behind the protagonistʼs actions. Based on Gaston Bachelardʼs space concept and Yi-Fu Thuanʼs terminology, Karl's feelings are classified as topophilia (love of place) and topophobia (fear of place). However, the analysis has shown that the binary opposition topophilia-topophobia is not sufficient for determining the emotions presented and, therefore, the terms “topoaversion” and “topoanimosity” are introduced.
The THFC Scheme for compactly committing AEAD

In order to overcome some disadvantages of the Encryptment scheme, that is, its hidden compression function should be a secure pseudo-random function to resist relevant key attacks: for key K, the adversary can arbitrarily select X and inquire K⊕X. Therefore, the new scheme of adding tweakable block cipher (TBC) in the paper makes it more resistant to related key attack. Because the attacker cannot directly select the value of the tweak in the base TBC. This method is improved on the HFC Encryptment scheme, and a new scheme is formed by adding the TBC into the compression function, which is called tweak-hash-function-chaining (THFC). It follows the HFC scheme and uses the Merkle-Damgård hash function. It then uses commit encryption and PRF (CEP) to transform the Encryptment scheme to compactly committing authenticated encryption with associated data (ccAEAD) to make it more robust against related key attacks. In addition, DM-AES is used to instantiate the compression function in ccAEAD scheme. While HFCs instantiated using DM-AES will perform very well on systems using AES-NI, they are not as fast as AES-GCM or OCB due to the need to re-key each block. Therefore, Deoxys-BC was selected to instantiate TBC in this paper to verify the ccAEAD scheme using THFC. Compared with Deoxys-I and Deoxys-II under the same conditions, the experimental data show that the performance of ccAEAD-128-128 is faster than that of the optimal Deoxys-I-128-128 after 512 bytes.
How Does Chinese Segmentation Strategy Effect on Sentiment Analysis of Short Text?

In term of Chinese natural language processing, it exits one particular problem that how to choose the strategy of word segmentation, which commonly includes char-based and word-based. Targeted at sentiment analysis of short text comparing with long text, the word-based segmentation faces the other problem that there are the more ambiguous or unregistered words in context of short text. The feature extraction done by the different Chinese Word Segmentation impact the statistic distribution of features, and further the accuracy of sentiment analysis. This paper evaluates five Chinese segmentation strategy effect on Sentiment Analysis of Short Text. We chose two word-based Chinese Word Segmentation (CWS), and three char-based n-gram, then transformed Bag-of-Word (BOW) to Vector Space Model (VSM) which finally was fed into several classifiers to predict sentiment polarity of short text. To reduce the impact of corpora, the study is based a collection of five public corpora.
Parameter-Adaptive NLM Denoising Algorithm Based on Hessian Matrix

The Non-Local Means (NLM) algorithm has gained wide applications in image denoising. However, the effect in practical application is limited by parameter setting and the pre-defined filtering parameter has to manually set in different noise levels. To solve the problem, we analyze the effect of the filtering parameter on images with different texture complexity and noise levels. Then, a new NLM algorithm is proposed based on the eigenvalues of the pixel to adjust the filtering parameter adaptively. Specifically, the linear scale space theory is used to obtain the Hessian matrix of each pixel in the image and calculate the associated eigenvalues, based on which a formula is developed to determine the filtering parameter. In order to achieve edge-preserving performance, the filtering parameter is selected differently. Experimental results show that filtering parameter adaptively determined by image noise levels as well as image texture, and accordingly achieve excellent denoising performance.
Emotion Classification on Indonesian Twitter Using Convolutional Neural Network (CNN)

Humans are inseparable from emotions, emotions fill human life at all times. Emotions have an impact on social relationships, memory, and decision-making. In the era of this research, humans tended to express emotions through social media such as Twitter in the form of videos, images and text. Over time, Social media has to turn out to be a critical part of most people’s lives. Human emotion is a research area that is widely researched, especially in the field of linguistics. In this study, we classified emotions with Convolutional Neural Network. In addition, we compared the performance with three different word embedding methods, Glove, word2vec, and fastText in classifying the given dataset. The dataset that we used were 4403 tweets which will be classified into 5 classes, namely: love, joy, anger, sadness, and fear. F1-score is employed as an evaluation metric. The results of our experiments show that the combination of CNN and word2vec can achieve 72.06% of F1-score, which increases the baseline model by 63.71%.
Decoding of (Interleaved) Generalized Goppa Codes

Generalized Goppa codes are defined by a code locator set <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> of polynomials and a Goppa polynomial G(x). When the degree of all code locator polynomials in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> is one, generalized Goppa codes are classical Goppa codes. In this work, binary generalized Goppa codes are investigated. First, a parity-check matrix for these codes with code locators of any degree is derived. A careful selection of the code locators leads to a lower bound on the minimum Hamming distance of generalized Goppa codes which improves upon previously known bounds. A quadratic-time decoding algorithm is presented which can decode errors up to half of the minimum distance. Interleaved generalized Goppa codes are introduced and a joint decoding algorithm is presented which can decode errors beyond half the minimum distance with high probability. Finally, some code parameters and how they apply to the Classic McEliece post-quantum cryptosystem are shown.
Ensemble UNet++ for Locating the Exponential Growth Virus Samples

Being able to detect virus exponential growth is of paramount importance in infectious diseases. We can use this to predict an epidemic, or check a virus adaptation to the drug pressure, or check the immune escape within a person. To detect the exponential growth subgroups in the virus sequencing data, we propose in this paper an ensemble method with UNet++. Two UNet++ networks are built in this study, one is designed for the large exponential block samples, the other is for the small exponential block samples. The population changes would lead to differences in the pairwise difference matrices. By transferring the pairwise difference matrices of sequencing data into heat maps, the ensemble UNet++ can precisely detect the exponential growth subgroups virus with sizes range from 5 to 125. The ensemble UNet++ achieves a respectable 98.55% accuracy (mean IoU).
USB Flash Drives Forensic Analysis to Detect Crown Jewel Data Breach in PT. XYZ (Coffee Shop Retail - Case Study)

USB flash drives are used widely to store or transfer data among the employees in the company. There was greater concern about leaks of information especially company crown jewel or intellectual property data inside the USB flash drives because of theft, loss, negligence or fraud. This study is a real case in XYZ company which aims to find remaining the company’s crown jewel or intellectual property data inside the USB flash drives that belong to the employees. The research result showed that sensitive information (such as user credentials, product recipes and customer credit card data) could be recovered from the employees’ USB flash drives. It could obtain a high-risk impact on the company as reputational damage and sabotage product from the competitor. This result will help many companies to increase security awareness in protecting their crown jewel by having proper access control and to enrich knowledge regarding digital forensic for investigation in the company or enterprise.
Artificial Neural Networks—Selected Models

There is no doubt that artificial neural networks have been attracting the attention of a large number of scientists, experts and the uninitiated from all over the world for many years
Foreign language learning experiences from future ESL language teachers: How online language learning affects peer interaction and cooperative learning

Education has suffered from drastic changes due to the recent COVID-19 pandemic. This investigation is a self-research study conducted by three foreign language students. The purpose of this research is to investigate how the new online learning modality affected language learning. This research focuses on cooperative learning and peer interaction in an online setting. This self-study research project will present the results of the longitudinal study carried out over eight months by students in a BA in English language teaching at the University of Guanajuato. The students were enrolled in foreign language learning classes, and data were collected in a Sway portfolio, using reflective journal entries and visual evidence. The data analysis presents qualitative results showing that online learning has negative effects on peer interaction, such as the feeling of loneliness and isolation. The results allowed us to obtain a major awareness about online learning and its challenges, as well as to reflect about the importance of peer interaction in language learning.
ELM Network Intrusion Detection Model Based on SLPP Feature Extraction

To improve the safety precaution level of network system, a combined network intrusion detection method is proposed based on Supervised Locality Preserving Projections (SLPP) feature extraction and Extreme Learning Machine (ELM). In this method, the feature extraction capability of SLPP is first used to reduce the dimensionality of the original network connection and system audit data, and get a feature set, then, based on this, the advantages of ELM in pattern recognition is adopted to build a network intrusion detection model for detecting and determining intrusion behavior. Simulation results show that, under the same experiment conditions, compared with traditional neural networks and support vector machines, the proposed method has more advantages in training efficiency and generalization performance.
Role of Artificial Intelligence in Autonomous Vehicle

Autonomous vehicles have a very technically new-age type of ring to it, and it promises to deliver on the promises it is making as of now. They insure to make our driving experience safer and to provide a better class of road safety. This idea being recently emerged, promotes the idea of vehicles that can sense their surroundings and navigate through without any human intervention. Even though the idea of autonomous vehicles sounds fascinating, it has yet to prove its credibility and to be examined before they can be widely and more commonly adopted by people on the roads. Since the idea of autonomous vehicles is based on artificial technology unlike the traditional network security as the AV depends heavily on sensor-based technology. In our paper we focus on the sensor-based technology and the algorithms that help with the functioning of the autonomous vehicles all the while including and throwing light on the topics like the conceptual framing of autonomous vehicles as well the market dynamics. Market dynamics basically cover the people’s view on the artificial intelligence fused with vehicles. The technical aspects of the same are presented in the paper to provide a synopsis of what exactly autonomous vehicles are and what all concepts and techniques are involved to make it all work like the prediction and mission planning as such. Since it is not possible to show and explain the approach of autonomous vehicles without its shortcomings, we have focused on them as well and ended on the note of the future scope of the idea behind autonomous vehicles.
Blockchain Technology : A Tool to Secure Data

Data security is the key to the development of modern Internet technology. The distributed, decentralized, and secured hashed mechanism of the blockchain gives a complete new point of view for the evolution of data security technology. Block chain technology is one of the major technological innovations of this century. In the last couple of years, the interest around blockchain technologies is increasing. Many implementation of blockchain technology are widely available today. Blockchain,the foundation of Bitcoin, has gain much attention in this era. Blockchain is an encrypted, immutable, distributed ledger, which allows transactions take place in a decentralized manner. Blockchain based applications expected to alter numerous fields including financial services, health care, entertainment media, Internet of Things (IoT), and many more. The Blockchain technology plays important role in the process of data security. In this paper, we will discuss about the research being done on this new domain of Computer Science. It is not only the most popular topic to discuss about, but is the most technological innovation, that is all set to reform the entire world.