Reviewerly/exHarmony huggingface dataset:
Bitcoin Price Forecast Using Quantitative Models

Bitcoin was designed to grow in value indefinitely. The smallest quantity, known as a Satoshi, is equal to 0.00000001 of a Bitcoin. It’s obvious that its creator imagined a deflationary system for his creation, one that could help it reach ever increasing values.In the moment that a Satoshi has grown to be worth a dollar, a Bitcoin will equal to $ 100,000,000 (one hundred million dollars!). This is the value that Mr. Satoshi Nakamoto had in mind for his own Bitcoin.Why is it possible for Bitcoin to reach this value? What is the “fundamental value” of Bitcoin?In this paper we aim to build a framework that permits to answer this question.First step of our framework in based on “The value of scarcity”. The concept of scarcity is well present and known in Commodities, such as with Gold, Silver, Palladium or Platinum.These precious materials are all the more precious the more scarce their production is. In fact, there is a mathematical model known as Stock to Flow, that estimates price based on the quantity already present in the world (Stock) with the quantity that is extracted every year (Flow).Second step is explaining the “bubbles” (or the “waves”, considering Shiller’s narrative environment) in a fundamental picture based on the “halving rule”.Third step is the determinants of the Bitcoin demand and price. The “rate of adoption” model.Fourth step is the “cost and revenues of production” model based on mining process, and the hash rate. The supply chain of bitcoin model is built to create price growth.
VR interface design for promoting exercise among the elderly

By the 21century, the elderly population will exceed the total population of the last century. The issue of caring for the elderly has resulted in the need for health care, assisted living, and independence of the elderly. Previous research has confirmed that introducing health technology at an early stage can encourage the elderly to continue their activities, promote their independence and reduce the cost of social care. In this study, which applies the advantages of virtual reality (VR) technology, Unity 3D software is used to design VR dumbbell motion software for the elderly. This software can provide positive excitation and enable the elderly to maintain continuous motion. The objectives of this research are as follows: (1) to construct VR prototypes; (2) to test their usability (i.e., operating efficiency, visibility, and error rate); and (3) to study the emotional response of interfaces. This study adopted the Self-Assessment Manikin to measure emotion and the questionnaires for user interaction satisfaction to measure usability. Interface experts were also invited to conduct experiments, fill in questionnaires and provide interviews. The comparison of the VR application programs with physical dumbbells yields the following conclusions. The VR application programs are more pleasant. The real water dumbbell interface feels slightly stronger. The interface learning performance of the VR application programs is better. The preliminary conclusions drawn from this study can be used as a reference for designers. Scholars are expected to conduct further explorations.
Visual Analogy: Deep Learning Versus Compositional Models

Author(s): Ichien, Nicholas; Liu, Qing; Fu, Shuhao; Holyoak, Keith; Yuille, Alan; Lu, Hongjing | Abstract: Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by computing similarities between structured representations of analogs? We address this question by comparing human performance on visual analogies created using images of familiar three-dimensional objects (cars and their subregions) with the performance of alternative computational models. Human reasoners achieved above-chance accuracy for all problem types, but made more errors in several conditions (e.g., when relevant subregions were occluded). We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations. The compositional model based on part representations, but not the deep learning models, generated qualitative performance similar to that of human reasoners.
Error Resilient Space Partitioning (Invited Talk)

In this paper we consider a new type of space partitioning which bridges the gap between continuous and discrete spaces in an error resilient way. It is motivated by the problem of rounding noisy measurements from some continuous space such as ℝ^d to a discrete subset of representative values, in which each tile in the partition is defined as the preimage of one of the output points. Standard rounding schemes seem to be inherently discontinuous across tile boundaries, but in this paper we show how to make it perfectly consistent (with error resilience e) by guaranteeing that any pair of consecutive measurements X₁ and X₂ whose L₂ distance is bounded by e will be rounded to the same nearby representative point in the discrete output space. We achieve this resilience by allowing a few bits of information about the first measurement X₁ to be unidirectionally communicated to and used by the rounding process of the second measurement X₂. Minimizing this revealed information can be particularly important in privacy-sensitive applications such as COVID-19 contact tracing, in which we want to find out all the cases in which two persons were at roughly the same place at roughly the same time, by comparing cryptographically hashed versions of their itineraries in an error resilient way.The main problem we study in this paper is characterizing the achievable tradeoffs between the amount of information provided and the error resilience for various dimensions. We analyze the problem by considering the possible colored tilings of the space with k available colors, and use the color of the tile in which X₁ resides as the side information. We obtain our upper and lower bounds with a variety of techniques including isoperimetric inequalities, the Brunn-Minkowski theorem, sphere packing bounds, Sperner’s lemma, and Cech cohomology. In particular, we show that when X_i ∈ ℝ^d, communicating log₂(d+1) bits of information is both sufficient and necessary (in the worst case) to achieve positive resilience, and when d=3 we obtain a tight upper and lower asymptotic bound of (0.561 …)k^{1/3} on the achievable error resilience when we provide log₂(k) bits of information about X₁’s color.
TESTING A POTENTIAL INTERACTIVE METHOD OF DICTOGLOSS TO IMPROVE LISTENING SKILLS

This paper reports investigation of potential way to increase listening skills using Dictogloss as interactive method for EFL learners. The objective of this research is to find out whether dictogloss as an interactive method to improve or not listening skills of the learners. Mixed method was applied in this research started with an experimental design to test the implementation of Dictogloss for EFL learners and analyzed quantitatively. The implementation was carefully designed and applied for eight time meetings through online learning. Then, a qualitative analysis utilized to explore respond of learners after experiencing dictogloss in a virtual class. The populations of EFL learners were the students of SMAN 13 Bone at the eleventh grade and 30 learners were selected as sample by using purposive sample. The result demonstrated that the implementation score of post-test by 73.02 is greater than pre-test by 58,86. The quantitative analysis also proved a significant improvement after implementation. Where, the result of qualitative analysis found that interactive method of dictogloss was very helpful to improve listening skills of EFL learners. The testing has revealed that interactive method of dictogloss work out to improve listening skills of EFL learners. This demonstrates a potential for general applicability to English as a Foreign Language learners particularly to senior high school level which delivered not only by means of face to face class but also in online learning environment.
An exploratory model for sediment-hosted lead and zinc deposits by using the fractal method in Yazd block- Central Iran

Structure processes have a significant role in forming sediment-hosted lead and zinc deposits. On other words, they are formed in an individual pattern located in the related rift basins. So, the first-order, the second-order, and the third-order basins have been created. Faults operate as the conduct of ore-bearing fluid and the bounding-the second-order basins. By starting rifting, hydrothermal fluid enters a sedimentary basin and as a result of the extending faults, it creates convection cells confined by faults. Therefore, structural processes are the main control factor for these deposits. Also, factors controlling the mineralization are recognized by spatial analysis methods. In this study, to identify controlling factors and present an exploratory pattern for sediment-hosted Pb-Zn deposits in the southern part of the Yazd Block, the fractal (Box- counting) method has been used. This method was utilized to estimate the distance of sub-basins which are the host of sediment-hosted Pb and Zn mineralization for the first time. The results of this method depicted three different populations representing three factors controlling the mineralization in the studied area. These results were consistent with the basin structure which has been formed from three sub-basins. Therefore, three populations obtained from the fractal analysis showed the dimensions of three sub-basins in the studied area. The distance between mineral deposits in the third-order basin obtained about 8 Km while it is about 31 Km for the second-order basins. So, three second-order basins were recognized in which the distance between sub-basin numbers 1 and 2 is about 30 Km while it is about 60 Km between sub-basin numbers 2 and 3. Based on this exploratory model, there could be another second-order basin between sub-basin numbers 2 and 3. According to reports and literature, no other deposit has been discovered between them until now. So, based on the suggested model, there is a possibility of other deposits occurring between sub-basins numbers 2 and 3.
Evaluating Recursive Backtracking Depth-First Search Algorithm in Unknown Search Space for Self-learning Path Finding Robot

Various path or route solving algorithms have been widely researched for the last 30 years. It has been applied in many different robotic systems such as bomb sniffing robots, path exploration and search rescue operation. For instance, an autonomous robot has been used to locate and assist a person trapped in the jungle or building to exit. Today, numerous maze solving algorithms have been proposed based on the some information available regarding the maze or remotely control. In real scenario, a robot is usually placed in an unknown environment. It is required for the robot to learn the path, and exhibit a good decision making capability in order to navigate the path successfully without human’ assistance. In this project, an Artificial Intelligence (AI) based algorithm called Recursive Backtracking Depth First Search (RBDS) is proposed to explore a maze to reach a target location, and to take the shortest route back to the start position. Due to the limited energy and processing resource, a simple search tree algorithm has been proposed. The proposed algorithm has been evaluated in a robot that has the capability to keep track of the path taken while trying to calculate the optimum path by eliminating unwanted path using Cul-de-Sac technique. Experimental results have shown that the proposed algorithm can solve different mazes. The robot has also shown the capability to learn and remember the path taken, to return to the start and back to target area successfully.
THE EFFECT OF AUDIO, VIDEO AND MOTIVATION ON STUDENTS’ LISTENING COMPREHENSION ACHIEVEMENT

ABSTRACTThe objectives of this study were to explain whether : (1) the students’ achievement in listening comprehension taught by using video was significantly higher than that taught by audio (2) the students’ achievement taught to high motivation significantly was higher than that to students’ low motivation? (3) therewas any significant interaction among teaching media (video and audio) and motivation in listening comprehension achievement. The research design is experimental research by using factorial design 2x2. There were 72 students from MAS YMPI Kota Tanjungbalai as sample of this research. The students were divided into two groups. The first group was treated by using Audio and the second group was treated by using Video. Students’ achievement in listening comprehension was measured by using multiple choice tests. The data were analyzed by applying Two-Ways ANOVA. The result revealed that (1) students’ listening comprehension achievement taught by using video wassignificantly higher than that taught by using audio with Fobservedwas higher than that Ftable at level significance α = 0.05 (Fobserved = 155.7&gt;Ftable = 3.98). (2) There was significant difference of students achievement in listening comprehension with high motivation was taught by using video was higher than that low motivation by using audio with Fobserved is higher than Ftable at the level significance α = 0.05 (Fobserved = 93.4&gt;Ftable = 3.98). (3) There was significance interaction among media (audio and video) and students’ motivation on students’ achievement in listening comprehension with Fobservedwas lower than Ftable at level significance α = 0.05 (Fobserved = 1.2 &lt;Ftable = 3.98). Thus, media (audio and video) and students’ motivation significantly affect the students’ achievement in listening comprehension. Keywords: Audio, Video, Motivation, Listening Comprehension
A STUDY ON DEEP LEARNING ALGORITHMS FOR MULTIMODAL AND MULTILINGUAL CYBERBULLYING DETECTION

With the increased utilization of the internet and social media platforms, can foster destructive or harmful behaviors such as cyberbullying. Cyberbullying poses signicant threat to physical and mental health of the victims. There is a demand for automatic detection and prevention of cyberbullying. In Social networks, there is a big challenge to detect the cyber bullying event and to control all the cyberbullying content and languages that users post. Due to complexity of multiple languages and cross-mix languages used in cyberbullying, the detection has remained only mildly satisfying. And also recently, images and videos dominate the social feeds in addition to text messages and comments. Machine learning and deep learning techniques can be helpful to detect the bullies and can generate a model to automatically detect multi-lingual cyberbullying actions. Deep neural architectures are useful to model, learn and fuse multi-modal data for cyber bullying detection. This paper proposes a detailed review on machine and deep learning approach for detecting and preventing multimodal and multilingual cyberbullying.
Decision making patterns and techniques widely used in war games

One of the methods used to prepare and increase the capability of military commanders in the field of decision-making, decision-making and feedback from decisions in universities, research institutes or military training centers, is the method of war game or situation simulation. War game is divided into different categories, each of which has its own characteristics and features. In this research, we have used the library approach to collect literature in the field of war games and by classifying them, the present study has become an applied research whose achievements can be used in future planning and planning in war game design, implementation and training centers. In this research, data collection has been done by field and library methods. The result of the research identifies the types of decision-making patterns and models in war games and helps planners to design, execute and train war games in order to improve the ability of commanders to provide appropriate solutions by recognizing decision-making patterns.
INCREASING STUDENTS’ VOCABULARY BY USING WORD CARDS

The purpose of this study was to find out whether the use of Word Cards is able to increase students’ vocabulary. The students at SMP NEGERI 6 KAKAS in academic year 2019/2020 were chosen as the subject of this study, specified to the seventh grade that consisted of 18 students. This research used pre-experimental design with one group pretest and post-test. This study consisted in five meetings. First the researcher gave a pretest next treatment and the last posttest. The technique in collecting data used test as instrument in form of matching item test consisted with 25 numbers. The result of this study found that Word Cards was effective in increasing students’ vocabulary where post-test was higher than pre-test. The mean score pre-test was 8.27 and the mean score of post-test was 14.61. It can be stated that Word Card is considered effective to increase students’ vocabulary.
IMPLEMENTATION OF THE ACADEMIC INFORMATION SYSTEM (SIAKAD) AND THE QUALITY OF ACADEMIC SERVICES ON USER SATISFACTION MEDIATED DECISION MAKING (CASE STUDY ON 3 PTS IN PEKALONGAN RESIDENCY AREA)

This research intends to determine the effect of the implementation of academic information systems and the quality of academic services on user satisfaction mediated by decision making. The sample of respondents in this research was 155 respondents with an error rate of 5%. Testing the data in this research using validity tests, reliability tests, and multiple linear regression tests using the SPSS version 19 program. The development of information technology that is rapidly growing has a positive impact on organizations or companies such as universities, the existence of an academic information system can help students’ users in making decisions, especially in the academic field. In order to achieve the expected goals, the academic information system needs to be evaluated on the implementation of the system and the quality of services provided by the campus to its users. The existence of an evaluation of academic information systems and service quality will have an impact on user satisfaction, this has become a phenomenon in research conducted by researchers. The result of this research is that the user satisfaction variable does not mediate the academic information system (SIAKAD) variable on decision making, but the user satisfaction variable mediates the effect of service quality on decision making.
A MID-AIR WORD GESTURE AND VOICE ALERT SYSTEM FOR PHYSICALLY CHALLENGED USING MACHINE LEARNING

: Motion gestures provide a complementary modality for general human-computer interaction. Motion gestures are meant to be simple so that a user can easily memorize and perform them. However, motion gestures themselves are not expressive enough to input text for motion-based control. We define “air-writing” as writing letters or words in free space. Air-writing is especially useful for user interfaces that do not allow the user to type on a keyboard. To develop a Machine Learning based system that uses gestures to perform functions. There are a lot of gesture-based applications existing in today’s world but every time we want to use a gesture-controlled application, we need to learn the predefined gestures and the functionality is also limited to the defaults provided with it. For this project, we aim to build a system that can be trained to recognize the gestures we make and perform the dedicated function we decide for it. We will be demonstrating this by training the system to recognize letters by the gestures we make in the air.To build this system, we will be using an Arduino board interfaced with an accelerometer. The device can be attached to the user’s hand. The accelerometer will provide input to the microcontroller about the hand’s coordinates. The algorithm will pick up this data and maintain a database to recognize each gesture differently. Once we train the system with the same gesture multiple times it will gather enough data to have an estimate of what the gesture should look like. The inputted letters are then converted to voice.
Gloves: a COVID-19-driven habit nurses need to break

The pandemic has blurred understanding of guidance on glove use, so it’s time to be clear on what is needed
Optimized Attendance System Using Face Recognition

The purpose of this paper is to look into a new and improved method for marking the attendance of students gathering the attendance in the traditional way is accomplished by the expenditure of a sizeable amount of time and effort on the part of the teacher or administrator in charge. Apart from a roll call or passing around an attendance sheet, other methods of attendance gathering, like iris scanning, fingerprint recognition and other biometric based systems have been developed. However, these systems are costly to deploy and maintain on a large scale. To counter these disadvantages, a system based on another parameter like facial recognition can be used, involving phases such as face detection, face cropping, image resizing, image normalization, image de-noising along with feature extraction. Finally features from processed image will be given as a training data to the learning model and further classified as the means to mark attendance. To this end, the algorithms such as the Viola Jones object detection algorithm can be utilized in obtaining the intended results. In addition, this system will also solve the problem of maintaining manual records of the rising number of students in universities.
THE EFFECTIVENESS OF IMPLEMENTATION OF CHARACTER BUILDING TRAINING WITH PARTICIPATION LEARNING METHOD (CASE STUDY: WORKSHOP 21st CENTURY FLUENCY SKILL EDUCATION, LIFELONG GUIDELINES, AND LIFE SKILLS (UNIVERSAL VALUES) IN MEDAN

The researcher sees the importance of the participatory learning method in character-building training as an effective method to build motivation and creativity so that cooperation and efforts to support each other. The purpose of this qualitative research and case study survey is to determine the participants' perceptions of the effectiveness of character-building training using the participatory learning method. The research sample consisted of ten participants and a trainer from a purposive sample, and the primary data collection tool was semi-structured interviews. The findings in the study based on the results of interviews with participants were positive comments or sound judgments that occurred during the training and respect, courtesy, and supportive behavior shown by participants when interacting with other participants..
Estimating Food Volume in a Bowl Based on Geometric Image Features

Image-based dietary assessment is important for health monitoring and obesity management because it can provides quantitative food intake information. In this thesis, a novel image processing method that estimates the volume of food within a circular bowl (i.e., the top rim of the bowl is a circle) is presented. In contrast to the Western culture where circular plates are most commonly used as food containers, circular bowls are the primary food containers in Asian and African culture. This thesis focuses on estimating the volume of amorphous food (i.e., food without a clear shape, such as a bowl of cereal) instead of food with usual shapes (e.g., an apple). Four geometric features of the food, namely food orientation, food area ratio, normalized curvature and normalized shape vertex, are extracted from 2D images. Based on these features, food volume is estimated using a linear or quadratic regression model. Our experiments show that, for 135 images of six different foods in a bowl of known shape, the mean absolute percentage error of our estimation was less than 20\%, evaluated using a five-fold cross-validation technique.