Reviewerly/exHarmony huggingface dataset:
Tell Me What You Want

Recommender systems are efficient exploration tools providing their users with valuable suggestions about items, such as products or movies. However, in scenarios where users have more specific ideas about what they are looking for (e.g., they provide describing narratives, such as "Movies with minimal story, but incredible atmosphere, such as No Country for Old Men"), traditional recommender systems struggle to provide relevant suggestions. In this paper, we study this problem by investigating a large collection of such narratives from the movie domain. We start by empirically analyzing a dataset containing free-text narratives representing movie suggestion requests from reddit users as well as community suggestions to those requests. We find that community suggestions are frequently more diverse than requests, making a recommendation task a challenging one. In a prediction experiment, we use embedding algorithms to assess the importance of request features including movie descriptions, genres, and plot keywords, by computing recommendations. Our findings suggest that, in our dataset, positive movies and keywords have the strongest, whereas negative movie features have the weakest predictive power. We strongly believe that our new insights into narratives for recommender systems represent an important stepping stone towards novel applications, such as interactive recommender applications.
Comparative Study on Application of Extended Kalman Filter and Unscented Kalman Filter in Target Tracking

Abstract This paper mainly describes the basic principles of extended Kalman filter and unscented Kalman filter and the application of them in target tracking based on observation distance At last the two algorithms are compared by Matlab software The simulation results show that unscented Kalman filter has better matching effect than extended Kalman filter and the former has smaller error variation and better convergence than the latter
How to Handle Data Management of Assisting Lifelogging Technologies from a User’s Point of View

A shift to higher proportions of older people and people in need of care requires new solutions and technologies with the potential to assist people in their everyday activities and to support them in being as independent and self-determined as possible. Lifelogging technologies have this potential by the collection, storage, and evaluation of personal data. Despite their potential, the users’ acceptance of such technologies is of great importance, in particular with regard to the technology’s handling of data security and privacy. For this reason, a quantitative study was carried out using an online questionnaire (N = 182), investigating two different application contexts of lifelogging technologies: a preventive context (frailty monitoring) and an assisting context related to patients suffering from dementia. Based on a preceding qualitative study, data access, purpose of data processing, duration as well as location of data storage were chosen as factors which were investigated, applying a conjoint analysis approach. The results revealed that the purpose of data processing and data access were the most decisive factors when users decide about the data management of lifelogging technologies and comparing the two contexts, contradicting decision patterns were found in particular for data access. Beyond these insights, user group specific decision patterns were identified for each of the application contexts. This study provides relevant insights into the users’ perspectives and requirements with regard to data management of lifelogging technologies, which should be taken into account for technology development and communication.
THE MEDIATING EFFECT OF PASTORAL STRESS AND SELF-ESTEEM IN THE RELATIONSHIP BETWEEN THE SENSE OF CALLING AND JOB SATISFACTION OF AN ASSISTANT PASTORS TO THE KOREAN CHURCH

This study aims to acknowledge the mediating effect of pastoral stress and self-esteem in the relationship between the sense of calling and job satisfaction of an assistant pastors to the Korean church. For this, 487 final copies were used for the final analysis for except the insincere ones. The results of the study are as follows. First, as a result of observing the kind of influence on an assistant pastors sense of calling, pastoral stress, self-esteem on job satisfaction, the explanatory power that explains an assistant pastors job satisfaction is high in the order of pastoral stress, self-esteem, and the sense of calling. Secondly, after observing the results of the intermediary role between the assistant pastors sense of calling, pastoral stress, self-esteem and job satisfaction, the sense of calling gives a significant influence on job satisfaction by the medium of pastoral stress and self-esteem, and it shows the indirect mediated effect. Based on the results of this study, it is expected that it will be used as a basic material for developing educational and counseling programs that can lower the pastoral stress of an assistant pastors and improve the sense of calling and self-esteem to increase the job satisfaction of an assistant pastors.
Bayesian Kernel Regression for Noisy Inputs Based on Nadaraya–Watson Estimator Constructed from Noiseless Training Data

In regression for noisy inputs, noise is typically removed from a given noisy input if possible, and then the resulting noise-free input is provided to the regression function. In some cases, however, there is no available time or method for removing noise. The regression method proposed in this paper determines a regression function for noisy inputs using the estimated posterior of their noise-free constituents with a nonparametric estimator for noiseless explanatory values, which is constructed from noiseless training data. In addition, a probabilistic generative model is presented for estimating the noise distribution. This enables us to determine the noise distribution parametrically from a single noisy input, using the distribution of the noise-free constituent of noisy input estimated from the training data set as a prior. Experiments conducted using artificial and real data sets show that the proposed method suppresses the overfitting of the regression function for noisy inputs and the root mean squared errors (RMSEs) of the predictions are smaller compared with those of an existing method.
Relationship Between Eye Movements and Individual Differences in Motion Sickness Susceptibility While Viewing Stereoscopic Movies Under Controlled Consciousness

In our previous study, we found it is possible to affect subjective feelings of visually induced motion sickness (VIMS) by controlling visually evoked postural responses (VEPRs) by changing conditions of consciousness of body sway. Thus, in this study, we verified the relationship between eye movements, body sway, and the susceptibility to motion sickness. Controlling consciousness of body sway while participants viewed stereoscopic movies revealed the involvement of three related theories for VIMS or motion sickness: sensory conflict theory, postural instability theory, and eye movement theory. Participants filled in a motion sickness susceptibility questionnaire short form (MSSQ-short). Thereafter, in the demonstration experiment, participants watched a three-dimensional movie consisting of several colored balls that moved sinusoidally at 0.25 Hz in the horizontal direction for 3 min after pre-instruction. Pre-instructions included "uncontrolled consciousness", "keep a static, upright posture", and "sway body in the same/opposite direction". This study recorded both body sway and electrooculography (EOG) data. Recorded EOG data were converted to eye movement velocity. The main result showed that susceptible participants exhibited a decreased frequency of optokinetic nystagmus (OKN) under conditions prone to the occurrence of OKN from correlated analysis for eye movements. Hence, we concluded that sensory conflict induced by decreases in the frequency of OKN is the primary cause of VIMS as OKN has a role in postural maintenance. This is a different hypothesis than the traditional eye movement theory.
A New Error-Modeling of Hardy’s Paradox for Superconducting Qubits and Its Experimental Verification

Hardy's paradox (equivalently, Hardy's non-locality or Hardy's test) [\href{https://link.aps.org/doi/10.1103/PhysRevLett.68.2981}{L. Hardy, Phys. Rev. Lett. \textbf{68}, 2981 (1992)}] is used to show non-locality without inequalities and it has been tested several times using optical circuits. We, for the first time, experimentally test Hardy's paradox of non-locality in superconducting qubits. For practical verification of Hardy's paradox, we argue that the error-modeling used in optical circuits is not useful for superconducting qubits. So, we propose a new error-modeling for Hardy's paradox and a new method to estimate the lower bound on Hardy's probability (i.e., the probability of a specific event in Hardy's test) for superconducting qubits. Our results confirmed the theory that any non-maximally entangled state of two qubits violates Hardy's equations; whereas, any maximally entangled state and product state of two qubits do not exhibit Hardy's non-locality. Further, we point out the difficulties associated with the practical implementation of quantum protocols based on Hardy's paradox and propose possible remedies. We also propose two performance measures for any two qubits of any quantum computer based on superconducting qubits.
Performance Analysis for Reed Solomon Codes and Bose Chaudhuri-Hocquenghem Codes in Digital Image Watermarking

The objective of this paper is to explore the use of error correction codes in digital image watermarking for purposes of copyright protection. A comparative study between two error correction codes, Reed Solomon Code and Bose Chaudhuri-Hocquenghem Code was conducted to identify a more suitable error correction code to be used for this application. A bitmap image watermark was encoded with each chosen Error Correction Code, respectively then embedded on a color JPEG image. The effectiveness of each code was determined by the BER value obtained when the host image was subjected to several image manipulation attacks.
Improved grey wolf optimisation algorithms

The grey wolf optimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta-heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out; and results show its better performance than the standard GWO algorithm and other well-known meta-heuristic algorithms
Secret Key based Sensor Node Security in the Internet of Things (IoT)

Security in Internet of Things (IoT) deals with safeguarding connected objects and networks. IoT involves the growing collection of objects known as things with unique identifiers. These objects have the ability to automatically transfer data over a network. Most of the increase in IoT communication comes from computing devices and embedded sensor systems which are used in industrial machine-to-machine (M2M) communication, home and building automation, vehicle to vehicle communication, smart energy grids, and wearable devices. Because the idea is to connect all physical objects to Internet, security has not always been measured in product design. The IoT products fail to select sufficiently strong security. Therefore, security is one of the important factors in IoT that should be taken care on priority basis. This study proposes IoT architecture which deals with security of devices. The main aim of this study is to make sure that an authentic device uploads the data and restrict false data being uploaded by devices trying to be an authentic device. The study presents a strong authentication technique based on secret keys which protects the identity of IoT devices in the network. The authentication and authorization techniques presented in this paper ensure that the right data is sent and received by right devices at right places and for right purpose.
Map Generation and Balance in the Terra Mystica Board Game Using Particle Swarm and Local Search

Modern board games offer an interesting opportunity for automatically generating content and models for ensuring balance among players. This paper tackles the problem of generating balanced maps for a popular and sophisticated board game called Terra Mystica. The complexity of the involved requirements coupled with a large search space makes of this a complex combinatorial optimisation problem which has not been investigated in the literature, to the best of the authors’ knowledge. This paper investigates the use of particle swarm optimisation and steepest ascent hill climbing with a random restart for generating maps in accordance with a designed subset of requirements. The results of applying these methods are very encouraging, fully showcasing the potential of search-based metaheuristics in procedural content generation.
Passive RFID-based Textile Touchpad

This paper presents the first prototype of a passive RFID-based textile touchpad. Our unique solution takes advantage of ICs from passive UHF RFID technology. These components are combined into a textile-integrated IC array, which can be used for handwritten character recognition. As the solution is fully passive and gets all the needed energy from the RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us.
A Data Hiding Scheme Based on Turtle-shell for AMBTC Compressed Images

Data hiding technology hides secret information into the carrier, so that when the carrier is transmitted over network, it will not attract any malicious attention.Using data compression, it is possible to reduce the data size into a small compressed code, which can effectively reduce the time when transmitting compressed code on the network.In this paper, the main objective is to effectively combine these two technologies.We designed a data hiding scheme based on two techniques which are turtle-shell information hiding scheme and absolute moment block truncation coding.The experimental results showed that the proposed scheme provided higher embedding capacity and better image quality than other hiding schemes which were based on absolute moment block truncation coding.
CRYPTOCURRENCIES AS A SUBJECT OF FINANCIAL INVESTMENTS. RISK ANALYSIS AND POTENTIAL BENEFITS ON THE EXAMPLE OF BITCOIN

The cryptocurrency market has been developing dynamically in recent years. The rapid development of the market is the result of increased interest in cryptocurrencies both from the entities treating it as a means of payment and from investors acquiring cryptocurrency for speculative purposes. Blockchain technology, on the basis of which cryptocurrencies are created, has gained acceptance in the financial industry and many entities are conducting advanced work on its use in their operations. On the other hand, numerous supervisors, including the European Banking Authority, the European Central Bank, the National Bank of Poland, and the Polish Financial Supervision Authority warn against investing in cryptocurrencies, indicating the numerous risks associated with such investments. The aim of the article is to analyze the potential risks and benefits of investing in cryptocurrencies. The main risks related to investments in cryptocurrency were analyzed on the example of bitcoin, and the rate of return and correlations with changes in the currency prices of other financial instruments were analyzed.
Exploring Users’ Eye Movements When Using Projection-Based Assembly Assistive Systems

Projection-based assistive systems have shown to be a promising technology to support workers during manual assembly processes in industrial manufacturing by projecting instructions into the working area. While existing studies have investigated various aspects of these systems, little research has been conducted regarding the way in which the user accesses the provided instructions. In this paper we analyze the eye movements of users during the repeated execution of an assembly task at a projection-based assistive system in order to gain insights into the utilization of the presented instructions. For this purpose, we analyzed eye tracking recordings from a user study with 15 participants to investigate the sequences in which the respective instructions are observed by the users. The results show a significantly lower number of nonlinear gaze sequences as well as a significantly higher number of steps without observing the instructions during the repeated use of the assistive system. In addition, there was a significantly lower task completion time during repeated use of the assistive system.
Using Multilayer Perceptron and Deep Neural Networks for the Diagnosis of Breast Cancer Classification

The very long delay that is suffered by patients of breast cancer in their early stages in low-income countries is due to access barriers and quality deficiencies in the care of cancer giving rise to the need for an alternative and efficient computer-based diagnostic system for the early detection and prevention of the disease. The early detection and improved therapy still remain crucial approaches for the prevention and cure of breast cancer. To this end, recent research looks into the development of different classifier models for the classification of breast cancer. This paper investigates the potentials of applying multiple neural network architectures with increased number of hidden layers and hidden units. The network architectures have one-hidden-layer, two-hidden-layer and three hidden layer (deep neural network) architectures respectively using the backpropagation training algorithm for the training of the models. The experimental results show that by applying this approach the models yield efficient and promising results.
An Aid for Speechless, Visually and Hearing Impaired Students in Classroom

Education develops a person's knowledge and prepares the individual to face the world confidently. Each individual's development automatically leads to social progress. Education to the visually and hearing impaired is no exception to the above statement. The current education system has not completely evolved to cater to the special needs of the visually and hearing-impaired students. An inclusive education system is an environment where the students with disabilities can fully participate in a classroom in equal competence with students without any disabilities. A successful inclusive education can happen only with the assistance of technology to aid the disabled students to primarily feel enabled in the peer group. The objective of this paper is to design and implement a low-cost wearable glove that will help the speechless, visually and hearing-impaired students communicate through automated sign language translational system. This system also encompasses an unassisted living aid for the disabled. The system is based on Arduino which includes flex sensors and Bluetooth.
The Problems with Usability Testing

Most books discussing User Experience (UX) or usability often dedicate sections or entire chapters to an introduction to usability testing as a valid and reliable method to identify issues that occur within a given interface or system [e.g., 1, 3, 6, 10]. Indeed, there is an entire book written about specific methods and application of usability testing [e.g., 6]. Despite the plethora of books discussing usability testing, Rolf Molich and his colleagues have found variance in the usability issues found even amongst agency experts [e.g., 12, 13, 14]. These variances suggest that usability testing might often be inconsistently applied across the UX field. From our experience in industry, we put forth in this paper commonly problems with usability testing, which can result in the inconsistencies applied across the UX field. These problems are contrasted against suggested protocol found in literature, which provides a basis to support discussion of solutions and provide recommendations to new researchers.
Identification technology of substation lightning overvoltage based on wavelet transform research and application

In the southern region, lightning strikes occur frequently, causing serious harm to the stable and safe operation of the power grid. In order to strengthen the lightning protection level of the line and save investment, it is necessary to strengthen the insulation of poles, towers, lines and adjacent parts that are frequently struck by lightning, especially in hilly and mountainous areas. Because many overvoltage monitoring devices have been installed in Sichuan, Yunnan and Shenzhen, and many overvoltage waveforms have been collected, it is necessary to identify the overvoltage. In this paper, the characteristic values of overvoltage are extracted by wavelet transform and classified by support vector machine. The measured lightning overvoltage waveform was used for training, and finally more than 4000 data collected from the substation were classified and identified. The recognition rate of this method is as high as 93%, which has good accuracy and good application prospect.
A Multi-component Attribute Network Embedding for Link Prediction

The problem of predicting the missing links between nodes or the links that may form in the future in a network is an important task of network analysis. However, the problem is not trivial because it is not easy to effectively capture the inherent properties of the network. In this paper, we propose a network embedding-based approach for predicting links. The proposed network embedding model (Multi-component Attributed Network Embedding, MANE) uses a unified graph representation to represent the network topology and node attribute information. Meanwhile, a random surfing model and positive point-wise mutual information (PPMI) matrix is used in the MANE to capture the structural information of graphs, and pairwise constraints are imposed to improve the quality of node representation. Extensive experimental results on six real-life datasets demonstrate a superior performance of our method over baselines in link prediction task.
A Field Theory for Multi-dimensional Scaling

An approach to multi-dimensional scaling is described which employs an analogy from the physics of conservative vector fields. This analogy allows the introduction of kinematic concepts into the data science problem in a natural way. Specific examples are presented. The method described here uses multi-dimensional scaling to introduce information redundantly into feature sets for classifier problems. This is empirically shown to have beneficial effects for certain difficult classification problems. This extends work done previously [1, 2] by using the posited physical analogy to make training more intuitive, efficient, and effective. A concept of super features is introduced and shown to improve classifier performance.