Reviewerly/exHarmony huggingface dataset:
A New 3-pass Zero-knowledge Lattice-based Identification Scheme

It is very important that the communicating parties are able to verify their identity in order to communicate with each other properly. Identification schemes are used for this purpose. The identification schemes, widely used in applications, are based on the hardness of the discrete logarithm or integer factorization problem. Shor proposed an algorithm that solve discrete logarithm and factorization problems by using quantum computers in polynomial-time. Therefore, there is a huge need for identification schemes which can be used post-quantum. In this paper, the identification schemes based on hardness of different lattice problems are surveyed. In addition, a new zero-knowledge lattice-based identification scheme is proposed.
“Loku” application for culinary location determination

The culinary business a few years back is showing a very significant development. Many things to be considered by culinary entrepreneurs, one of which is the business location. Location is a driver of cost and income, so locations often have the power to create business strategies. Strategic business location aims to maximize profits. The application "Luku" is a decision support application to choose the location of culinary business. This application was developed using Analytic Hierarcy Process (AHP) method. With this application is expected business actors can determine the location of the most strategic business in supporting his business accurately from several alternatives where the existing business. The test results show that the sequence generated from the created system is the same as the order created by the expert.
Self-orthogonal codes from orbit matrices of Seidel and Laplacian matrices of strongly regular graphs

<p style='text-indent:20px;'>In this paper we introduce the notion of orbit matrices of integer matrices such as Seidel and Laplacian matrices of some strongly regular graphs with respect to their permutation automorphism groups. We further show that under certain conditions these orbit matrices yield self-orthogonal codes over finite fields <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{F}_q $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M2">\begin{document}$ q $\end{document}</tex-math></inline-formula> is a prime power and over finite rings <inline-formula><tex-math id="M3">\begin{document}$ \mathbb{Z}_m $\end{document}</tex-math></inline-formula>. As a case study, we construct codes from orbit matrices of Seidel, Laplacian and signless Laplacian matrices of strongly regular graphs. In particular, we construct self-orthogonal codes from orbit matrices of Seidel and Laplacian matrices of the Higman-Sims and McLaughlin graphs.
Personality Based Lipstick Color Recommender System using K-Nearest Neighbors Algorithm

Lipstick is a lip color which available in many colors. A research said instant valuation of woman personality can be figured by their lipstick color choice. Therefore there is a necessity to use the right lipstick color to obtain a harmony between personality and appearance. This experiment was conducted to give lipstick color recommendation by using K-Nearest Neighbors algorithm, and Myers-Briggs Type Indicator (MBTI) personality test instrument. The system was built on Android application. Euclidean distance value is affected by 5 factors which are age, introvert, sensing, thinking, and judging. Lipstick color recommendation is obtained by fetching 7 training data with nearest Euclidean distance when compared to personality test result. The colors used in this experiment are nude, pink, red, orange, and purple. After evaluation, it is obtained the application’s accuracy of 87.38% which considered as good classification, both precision and recall with 75.68% which considered as fair classification. The score for software quality is 79.13% which considered as good quality.&#x0D; Keywords—K-Nearest Neighbors, Data Mining, Myers-Briggs Type Indicator,Recommender System, Lipstick.&#x0D;
Collaborative Filtering System

Recommendation systems using machine learning created. Recommendation systems can be gathered around 3 main topics. Content-based, collaborative filtering and hybrid suggestion systems. Collaborative filtering system is a system where suggestions are made using similar users. In this article, the effects of the change of hidden factor number on error metrics for collaborative suggestion system are examined. In addition, the learning rate of error metrics was examined by keeping the number of hidden factors constant. The proposed suggestion system has 40 hidden factors and the most successful result is obtained by MAE error metric. MSE error metric, the most successful learning rate graph was obtained. The results showing the data of the suggestion system during training are explained with graphics.
Review of Blockchain Literature – Its Application and Acceptance

Aim/Purpose: To understand the current state of the body of literature in blockchain technology and propose dimensions for acceptance. Background: Blockchain technology has large promise to replace centralized and even distributed database systems. Its premise focuses on issues such as transparency, immutability, and privacy of transactions. Created for bitcoin, researchers and practitioners have begun to see its potential in different areas and industries. Its acceptance is still debatable as there are a number of issues still to be resolved. Methodology: We conducted a literature review to assess the size and scope of the body of research in the are of blockchain applications and carried a conceptual analysis for blockchain acceptance. Contribution: We provide an assessment of the body of literature in the are of blockchain and cluster the number of articles according to application groupings. We show that research in blockchain cannot be considered that it even started due to its diverse, scattered and weak related studies. At the same time, blockchain is still faced with a lot of resistance yet no one is studying its acceptance. We therefore propose dimension for its acceptance, as adapted from the very rich area of ecommerce research. Findings: Body of research is at its infancy. Research is scattered and weak. Most research is related to bitcoin and cryptocurrency. There is a great need to study the application of blockchain outside its current focus on cryptocurrency. Areas of study of blockchain applications include internet of things, energy and finance – other areas are identified. Dimensions for blockchain acceptance are proposed and include: reputation, risk, usefulness, and intentions. Recommendations for Practitioners: Blockchain has great potential to be applied in areas such as medicine, aviation and disaster relief. In terms of blockchain characteristics, practitioners have a lot of room to innovate in various approaches such as the blockchain hashing algorithm, smart contracts and peer validation. These do not need to be fixed but can vary based on the business characteristics. Recommendations for Researchers: Research in the application of blockchain can be considered as not even started. The application of blockchain is an open playground which today few have stepped in to enjoy. Research opportunities include but are not limited to energy consumption of blockchain transactions, dimensions to blockchain adoption and acceptance, adaptability of blockchain to various industries, and innovation in blockchain characteristics such as new approaches to peer validation of transactions.
Bitcoinomics 101: Principles of Bitcoin's Supply, Demand &amp;amp; Price

Drawing on some some early literature in economics we develop a framework that assists the consideration of Bitcoin as a novel economic good. We highlight an essential tension between scarcity and concentration as having an overarching impact on Bitcoin's value. In conjunction with the structural features of its blockchain and consensus protocol, as instantiated by the mechanism of its code, the role of provisioning for future demand and production places a keener emphasis on the role of inventory management. This creates an interesting dynamic in Bitcoin between its structurally regulated supply and organic reservation demand for bitcoins by miners on the one hand and its broader market demand on the other. Understanding the features of this dynamic as a foundation for Bitcoin gives us some simple insights on its price path.
Trajectory Planning for a Pseudo Omnidirectional Vehicle using Particle Swarm Optimization

We propose an online motion planning approach for a pseudo omnidirectional vehicle based on particle swarm optimization. Therefore, we first describe the principles behind the optimization process. Afterwards we derive representations for the vehicle’s movement based on the description of the position of the instantaneous center of motion. Then, the mathematical operators used in the optimization process for the trajectories are described with regard to the previously derived representations. The costfunction is explained with focus on the new opportunities in the movement of the vehicle like e.g. driving sideways. However, the extra degree of freedom not only brings benefits, but also complicates the generation of trajectories for the initial particle swarm. Therefore we describe how to efficiently sample omnidirectional trajectories and also trajectories for certain well known gaits like Ackermann driving. Finally, the approach is evaluated in simulation showing the full maneuverability of the vehicle.
Semi-Automatic Pallet Pick-up as an Advanced Driver Assistance System for Forklifts

This paper presents a novel advanced driver assistance system for human-operated forklifts in logistical scenarios. The system helps the operator to pick up wooden pallets by performing a collision-free insertion of the forks into the pallet. For this purpose, the assistance system takes over the steering function of the vehicle while it approaches the pallet. A 3D camera-based system is used to detect and localize the pallets while driving. For every localized pallet, a trajectory is calculated which has to fulfill certain conditions. These include an at least curvature continuous course and limiting vehicle parameters, such as a limited steering angle. Furthermore, the necessary user interface for the correct selection of the target pallet and the activation of the assistance function is presented. Finally, a technical method is described to realize the steering intervention on an existing forklift.
Prediksi Kelulusan Tepat Waktu Menggunakan Metode C4.5 DAN K-NN (Studi Kasus : Mahasiswa Program Studi S1 Ilmu Farmasi, Fakultas Farmasi, Universitas Muhammadiyah Purwokerto)

The graduation profile is one of the key elements for the accreditation standard of higher education. It mirrors the performance of the applied educational system within a period of time. The better it is, the better the accreditation will be. In support of this, a graduation prediction may be conducted to the academic database of the students. It is of pivotal to trace and classify the historical data into the data training and data testing, thus, to predict the on time-graduation. The step is importantly done to help decide the better management of learning processes. This study was therefore done to analyse certain variables applied to predict the on time-graduation using the algorythms of C.45 and K-Nearest Neighbour (K-NN). The data mining was done to the academic database of the students of the Pharmacy study programme, Pharmacy Faculty, Muhammadiyah University of Purwokerto by adding certain variables into the process. The data was then classified into the data training and data testing. Backward selection was done to select the best and most influential variables for the dataset. The study further resulted that by using the algorhythm of C.45 and backward selection, the accuracy of the graduation reached 92.75%. It is different from the acurracy the K-NN and backward selection showed that reached 96.14%. The result confirmed that the KNN showed the better accuracy than the C.45. It considerably benefitted the study programme to make better decisions on increasing the quality of services, in particular that of leraning processes.
Prospects for Chatbots

At the end of the second decade of the digital millennium, the flow of daily human and business life is increasingly dependent on the development and invention of information and communication technologies. One of the innovations is chatbot, which is software that performs tasks as an automated information assistant with a wide range of applications and fields. This article reviews the nature and characteristics of chatbots and, based on their imposition and development in electronic exchange channels, comments are made on their potential impact on the retail and outsourcing business related to customer service.
Measurement of deformation and strain field of cantilever beam under forced vibration

Based on digital image correlation and binocular stereo vision, a method is proposed for measuring full field deformation and strain of cantilever beam under forced vibration. First, in the actual measurement, the traditional correlation function with linear model may not be reliable to describe the deformation and light intensity change of subset. Therefore, the radial basis function is introduced into the correlation function to specify the weight of each pixel in the subset, which improves the reliability of the correlation matching. Second, the rigid motion of the excitation source is monitored by sticking a marker at the fixed end. By measuring the position of the marker in the process of vibration, the motion direction of the excitation source can be obtained and then define the new Z-axis along the motion direction. After transformation of the coordinate system, the pure deformation at any position of the beam surface is equal to the displacement at that point minus the displacement of the excitation source. Finally, the deformation and maximum principal strain field of cantilever beam under sinusoidal excitation at different frequencies is compared and analyzed. The method can provide a reference for the deformation measurement under forced vibration.
Vietnamese Span-based Constituency Parsing with BERT Embedding

Syntactic structure of sentences obtained from Constituency Parsing is fundamental information in many Natural Language Processing tasks. However, due to the lack of available resources and the complex linguistic features of Vietnamese, the research into Constituency Parsing has not received enough attention in this language. To the best of our knowledge, the study presented in this paper is one of the first investigations to explore this task in Vietnamese. In this work, we present a Spanbased approach which focuses on representing spans through the use of contextualized pre-trained embeddings to obtain optimal parse trees for Vietnamese sentences. The conducted experiments indicate that our system achieved promising results on the VLSP Vietnamese Treebank dataset by significantly outperforming existing methods. The results of this study support the view that encoding context information into the representation of words is effective in improving the parsing performance of Vietnamese. Consequently, this idea can be generalized to apply to other tasks such as Dependency Parsing or other low-resource languages.
A Dual-Modal Assistive Technology Employing Eyelid and Head Movements

This work presents a new hands-free, dualmodal Eyelid-Head Drive System (EHDS) technology designed to help people with disabilities interact with their environment. The EHDS combines the Eyelid Drive System (EDS), which is based on inductive sensing of eyelid movement, with head movements measured by an accelerometer. The EHDS offers a wide range of useful applications, including personal computer (PC) access and control by emulating a mouse. As a proof-of-concept, a hardware prototype was constructed with commercial off-the-shelf components and non-prescription glasses. We describe the hardware and software modifications done to convert the EDS into the EHDS. The prototype was then used to perform trials on four human subjects. The total time taken to complete the given task and accuracy are reported. The average information transfer rate (ITR) achieved over all trials was 46.1 bits/min (10.6 letters/min).
Monetary policy transmission mechanism, innovative banking system and economic growth dynamics in Nigeria

Due to mismanagement alongside poor formulation and implementation of monetary and fiscal policies, level of economic development in Africa remains low. This study argued that African countries have great potentials to accelerate and sustain economic growth through effective monetary policies due to huge market potentials, growing population, and availability of manpower, untapped natural resources and other growth advantages provided effective policies are enacted. Against this backdrop, this study investigated the effect of monetary policy transmission mechanism and innovation in the banking system on economic growth in Nigeria. Data sourced from the Nigerian Central Bank Statistical Bulletin and World Development Indicators between 1981-2015 was engaged. The study adopted a vector auto-regression and auto-regressive distribution lag approaches for its analysis. The study revealed that, due to the large informal banking sector, monetary policies have not been effective and also supervisory and intermediary financial institutions lack dependence due to frequent government interventions.
INTERPERSONAL ANALYSIS OF DONALD TRUMP’S EMOTIONAL LANGUAGE

The victory of Donald Trump in 2016 US Presidential Election marks the rise of Post-truth era at which objective truth is less important than the emotional truth . This paper analyses interpersonal metafunction of his emotional languages in his campaign speeches. The data are obtained from his speech transcripts. The emotion involves fear, warmth, and humour. They are detected using theory of prototype scenario developed by Wierzbicka (1992) and is analyzed using the perspective of Halliday and Matthiessen’s Systemic Functional Linguistics (SFL) (2004). The analysis focuses on Mood, Modality, and Appraisal Technique. The finding shows the use of all Mood system to convince his audiences, the dominant use of modal item will as median value of obligation, and various use of repetition technique.
Research on Interactive System of Science Exhibition Based on Leap Motion

Abstract Objective In order to explore the application of non-contact interaction in Science exhibition, an interactive system of science exhibits based on leap motion technology is proposed. Method Based on the Leap motion-based Science exhibition interactive system, the design of a “planetary story” science exhibit with interactive experience is evaluated according to three interactive experience indicators: realism level, learning pressure level and pleasure level. In this way, the feasibility of Leap motion technology in the popular science interactive system is verified. Result The use of the Leap motion gesture interaction library, combined with Unity3D development tools, to design science exhibits. Conclusion By proposing an Science exhibition interactive system based on Leap motion, it provides a design theory for the development of popular science exhibits. At the same time, the combination of Leap motion and popular science exhibits improves the user’s interactive experience and meets users’ interest in popular science content.
Chemical and Isotope Studies of Zakher Lake and Its Possible Risk on the Geoenvironment, Al Ain, United Arab Emirates

This study concerns the occurrence of pollutants in Zakher Lake, Al Ain, UAE, which are relevant to the water supply and impact on ecosystem. Lake water and sediment samples were collected from the lake and were analyzed for different chemical and physical properties. In-situ measurements of temperature, pH, EC and TDS and Rn-222 activity were carried out using a WTW-COND-330I and RAD7 instrument with errors of &lt; 5%. The analyses of trace and major elements were performed using inductively coupled plasma optical emission spectroscopy (ICP-OES) Varian 715 with errors &lt; 5%. The results reveal high concentrations of Na, K, Mg and Ca, Fe, Mn, Al, Sr, Ba and Pb in water samples of the lake, which are higher than permissible limits for drinking and irrigation. Variable concentrations of these elements were found in the sediment samples. In addition, radon isotope (Rn-222) data in the lake water indicate activity values below the detection limit of the instrument. The high concentrations of major and trace elements support the idea that the water is polluted. The sources of water to the lake are considered mainly anthropogenic due to the relatively high activity of Rn-222 in surrounding groundwater wells that is not detected in the lake water. Consequently, the lake water is not suitable for drinking, domestic or irrigation purposes and may be considered as a source of environmental hazard in the area.
Using Ensemble Technique to Improve Multiclass Classification

Many real world applications inevitably contain datasets that have multiclass structure characterized by imbalance classes, redundant and irrelevant features that degrade performance of classifiers.Minority classes in the datasets are treated as outliers' classes.The research aimed at establishing the role of ensemble technique in improving performance of multiclass classification.Multiclass datasets were transformed to binary and the datasets resampled using Synthetic minority oversampling technique (SMOTE) algorithm.Relevant features of the datasets were selected by use of an ensemble filter method developed using Correlation, Information Gain, Gain-Ratio and ReliefF filter selection methods.Adaboost and Random subspace learning algorithms were combined using Voting methodology utilizing random forest as the base classifier.The classifiers were evaluated using 10 fold stratified cross validation.The model showed better performance in terms of outlier detection and classification prediction for multiclass problem.The model outperformed other well-known existing classification and outlier detection algorithms such as Naïve bayes, KNN, Bagging, JRipper, Decision trees, RandomTree and Random forest.The study findings established that ensemble technique, resampling datasets and decomposing multiclass results in an improved classification performance as well as enhanced detection of minority outlier (rare) classes.