Reviewerly/exHarmony huggingface dataset:
Random number generation from a quantum tunneling diode

Random numbers are important in many activities, including communication, encryption, science, gambling, finance, and decision making. There is a strong demand for a hardware random number generator that could support cryptographic applications. In this work, we propose a quantum tunneling diode as a source of true randomness achieved by applying electrical current sweeps through the device and then harnessing a time-counting unit to measure fluctuation of current flows. Our approach can be implemented with inexpensive electronics and could be integrated into systems that require random numbers such as portable communication devices.
Robust generative adversarial network

Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations. Most existing works focus on stabilizing the training of the discriminator while ignoring the generalization properties. In this work, we aim to improve the generalization capability of GANs by promoting the local robustness within the small neighborhood of the training samples. We also prove that the robustness in small neighborhood of training sets can lead to better generalization. Particularly, we design a robust optimization framework where the generator and discriminator compete with each other in a \textit{worst-case} setting within a small Wasserstein ball. The generator tries to map \textit{the worst input distribution} (rather than a Gaussian distribution used in most GANs) to the real data distribution, while the discriminator attempts to distinguish the real and fake distribution \textit{with the worst perturbation}. We have proved that our robust method can obtain a tighter generalization upper bound than traditional GANs under mild assumptions, ensuring a theoretical superiority of RGAN over GANs. A series of experiments on CIFAR-10, STL-10 and CelebA datasets indicate that our proposed robust framework can improve on five baseline GAN models substantially and consistently.
Easy and Efficient Hyperparameter Optimization to Address Some Artificial Intelligence “ilities”

Artificial Intelligence (AI), has many benefits, including the ability to find complex patterns, automation, and meaning making.Through these benefits, AI has revolutionized image processing among numerous other disciplines.AI further has the potential to revolutionize other domains; however, this will not happen until we can address the "ilities": repeatability, explain-ability, reliability, use-ability, trust-ability, etc. Notably, many problems with the "ilities" are due to the artistic nature of AI algorithm development, especially hyperparameter determination.AI algorithms are often crafted products with the hyperparameters learned experientially.As such, when applying the same algorithm to new problems, the algorithm may not perform due to inappropriate settings.This research aims to provide a straightforward and reliable approach to automatically determining suitable hyperparameter settings when given an AI algorithm.Results, show reasonable performance is possible and end-to-end examples are given for three deep learning algorithms and three different data problems.
Decision support system for determining chili land using weighted product method

Chili is one of the most consumed food commodities in Indonesia. Thus, chili is very influential to the economic, also inflation and deflation of an area. In order for the needs of chili to be fulfilled and to be able to export, the production must be increased with one consideration being the selection of the right land. The right land must meet the criteria agreed upon by experts, there is land height, soil pH, nutrient value and ambient temperature. This research aims to create a system to support the decision of chili crop land selection using the weighted product method. By entering land data according to the criteria, the user can easily find out which land has the potential to have a higher suitability for chili plants. The result of this system is in the form of land ranking that can help users decide which land to use for chili cultivation.
Knowledge Sharing Network in a Community of Illicit Practice: A Cybermarket Subreddit Case

Often neglected in the literature about communities of practice is the fact that online knowledge-sharing communities thrive among illicit collectives whose activities are stigmatized or outlawed.This paper focuses on a knowledge-sharing community of users who engage in illegal practices by examining the ways in which the community's network structure changes when a high-stakes, uncertain event-the July 2017 shutdown of the dark web market Alphabay-occurs.This study compares the discussion network structures in the subreddit r/AlphaBay during pre-shutdown days (the "routine" period) and shutdown days (the "market defect" period) and offers a content analysis of the knowledge and resources shared by users during these periods.Several differences were observed: (a) the network structure changed such that the network size grew while becoming more centralized; (b) new crisis-specific players emerged; (c) types of knowledge shared during the market defect period was qualitatively different from the routine period.
YouTube Children’s Videos: Development of a Genre under Algorithm

YouTube children's video has been claimed to have a preponderance of violent, disturbing or otherwise inappropriate content.To assess this claim, we conduct a content analysis of a sample of children's videos published between January 2016 and December 2018.Our analysis reveals an evolving ecosystem involving a variety of production modes and messages which nonetheless bears the heavy imprint of the algorithm-centered commercial incentives of marketing to children and attracting YouTube advertising.Hence, while content formerly causing public concern appears to be effectively policed at this juncture, algorithmic incentives do appear to distort children's content in potentially unhealthy ways.
Prefeasibility Study of Photovoltaic Power Potential Based on a Skew-Normal Distribution

Solar energy does not always follow the normal distribution due to the characteristics of natural energy. The system advisor model (SAM), a well-known energy performance analysis program, analyzes exceedance probabilities by dividing solar irradiance into two cases, i.e., when normal distribution is followed, and when normal distribution is not followed. However, it does not provide a mathematical model for data distribution when not following the normal distribution. The present study applied the skew-normal distribution when solar irradiance does not follow the normal distribution, and calculated photovoltaic power potential to compare the result with those using the two existing methods. It determined which distribution was more appropriate between normal and skew-normal distributions using the Jarque–Bera test, and then the corrected Akaike information criterion (AICc). As a result, three places in Korea showed that the skew-normal distribution was more appropriate than the normal distribution during the summer and winter seasons. The AICc relative likelihood between two models was more than 0.3, which showed that the difference between the two models was not extremely high. However, considering that the proportion of uncertainty of solar irradiance in photovoltaic projects was 5% to 17%, more accurate models need to be chosen.
Applying Software Quality Criteria to Blockchain Applications: A Criteria Catalog

The selection of the suitable blockchain software ecosystem has become very complex, given the growing market.More and more products with different functionality (mainly consensus algorithms and smart contracts) are available on the market.To identify the correct blockchain system for the respective application, a catalog of criteria with a focus on software quality is developed in this work.This catalog supports the selection of the right application and can be individually weighted.
Coalgebraic Operational Semantics for an Imperative Language

Operational semantics is a known and popular semantic method for describing the execution of programs in detail. The traditional definition of this method defines each step of a program as a transition relation. We present a new approach on how to define operational semantics as a coalgebra over a category of configurations. Our approach enables us to deal with a program that is written in a small but real imperative language containing also the common program constructs as input and output statements, and declarations. A coalgebra enables to define operational semantics in a uniform way and it describes the behavior of the programs. The state space of our coalgebra consists of the configurations modeling the actual states; the morphisms in a base category of the coalgebra are the functions defining particular steps during the program's executions. Polynomial endofunctor determines this type of systems. Another advantage of our approach is its easy implementation and graphical representation, which we illustrate on a simple program.
SKI: A New Agile Framework that Supports DevOps, Continuous Delivery, and Lean Hypothesis Testing

This paper explores the need for a new process framework that can effectively support DevOps and Continuous Delivery teams.It then defines a new framework, which adheres to the lean Kanban philosophy but augments Kanban by providing a structured iteration process.This new Structured Kanban Iteration (SKI) framework defines capabilitybased iterations (as opposed to Kanban-like no iterations or Scrum-like time-based sprints) as well as roles, meetings and artifacts.This structure enables a team to adopt a well-defined process that can be consistently used across groups and organizations.While many of SKI's concepts are similar to those in found in Scrum, SKI's capability-based iterations can support the demands of product development as well as operational support efforts, and hence, is well suited for DevOps and Continuous Delivery.SKI also supports lean hypothesis testing as well as more traditional software development teams where capability-based iterations are deemed more appropriate than timebased sprints.
Adaptive particularly tunable fuzzy particle swarm optimization algorithm

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms have been being studied extensively in recent years. In this study, a modified version of PSO algorithms is presented and is named as Adaptive Particularly Tunable Fuzzy Particle Swarm Optimization (APT-FPSO). In it, the global and personal learning coefficients of every single particle are tuned adaptively and particularly, at an individual extent, within each iteration with the aid of fuzzy logic concepts. Ample statistical evidence is provided indicating that the proposed algorithm further improves the potentialities and capabilities of the standard PSO.
Part-Based Enhanced Super Resolution Network for Low-Resolution Person Re-Identification

Person re-identification (REID) is an important task in video surveillance and forensics applications. Many previous works often build models on the assumption that they have same resolution cross different camera views, while it is divorced from reality. To increase the adaptability of person REID models, this paper focuses on the low-resolution person REID task to relax the impractical assumption when traditional low-resolution person REID models are under pixel-to-pixel supervision in low and high resolution pedestrian image pairs. In addition, they are easily influenced by the global background, illumination or pose variations across camera views. Therefore, we propose a Part-based Enhanced Super Resolution (PESR) network by employing a part division strategy and an enhanced generative adversarial network to boost the unpaired pedestrian image super resolution process. Specifically, the part-based super resolution network transforms low resolution image in probe into high resolution without any pixel-to-pixel supervision and the part-based synthetic feature extractor module can learn discriminative pedestrian feature representation for the generated high resolution images, which employ a part feature connection loss as constraint to conduct matching for person re-identification. Furthermore, evaluations on four public person REID datasets demonstrate the advantages of our method over the state-of-the-art ones.
Open Domain Chatbot Based on Attentive End-to-End Seq2Seq Mechanism

Chatbot as a conversational system that can interact with human naturally is a Natural Language Processing task that require modeling semantics of complicated relationships of the language for communication. Various attempts have been made to reduce the complexities (language understanding, feature extraction, domain recognition, intent detection, semantic slot filling and language generation) of training text-based Chatbot. While Traditional machine learning models are usually unable to be truly generic, recent advances in deep learning allow end-to-end models to be trained with large dataset. We train an open domain Chatbot end-to-end by directly mapping input tags as sequences to generate optimized output sequence tags. Our model is trained on Google Tensorflow framework running GPUs with deep neural architecture of sequence-to-sequence and attention mechanism. The interaction of the chatbot with human performed common sense reasoning. It also achieved a competitive result when quantified based on Human Performance Evaluation. However, we suggest networked reinforcement learning to incrementally update the consistency drawbacks towards achieving a generic Chatbot.
Deep learning based facial expressions recognition system for assisting visually impaired persons

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.
The meaning of soft skills in the modern professional career

This article identifies the importance of students’ soft skills building for education and their future career. Modern professional activity does not require only specific knowledge, but also soft skills necessary for specific professional field. Presently, soft skills play vital roles in the workplace for they raise professional levels and promote successful careers. Therefore, the purpose of higher education is to produce specialists able to work in competitive environments, which require introducing technologies that form students’ soft skills. In the article, we have justified the choice of the most valuable students’ personal and professional qualities required for forming soft skills in the learning process. The importance of self-organization and ability to work on a team has been revealed on the basis of analyzing psychological and pedagogical literature. The relevance of self-organization and working collaboratively has been supported by data obtained from the soft skills ratings provided by Russian and western employers. We found that soft skills based on the quality of self-organization and working on a team are in the Top-10 of analyzed ratings demonstrating their importance in Russia’s modern labor market. Consequently, the assessment technology aimed at forming students’ personal and professional skills will be developed and tested during final stages of our research.
Application of recommendation system with AHP method and sentiment analysis

Over time, people needs are increasing. Needs that must be met, often cause problems in the people in determining a choice. People must make the right choice and according to their needs. Not an easy thing for people to make these choices. Therefore, a recommendation system is needed to support people in making decisions that fit their criteria. This research provides a system that can provide recommendations for decision support people according to their criteria, which are web-based. The decision-making system in this research uses the analytical hierarchy process (AHP) method. AHP is a multi-criteria decision-making method, which in this research one of the criteria is using sentiment analysis. Sentiment analysis is the process of understanding, extracting, and processing textual data to get sentiment information from an opinion sentence. The opinion sentiment value of each alternative will be included in the AHP calculation to get the best alternative recommendations according to the criteria of people. The result of this research is that the system can provide recommendations to people or users according to their criteria and alternatives as well as public opinion about each alternative.
Employing Shadows for Multi-Person Tracking Based on a Single RGB-D Camera

Although there are many algorithms to track people that are walking, existing methods mostly fail to cope with occluded bodies in the setting of multi-person tracking with one camera. In this paper, we propose a method to use people's shadows as a clue to track them instead of treating shadows as mere noise. We introduce a novel method to track multiple people by fusing shadow data from the RGB image with skeleton data, both of which are captured by a single RGB Depth (RGB-D) camera. Skeletal tracking provides the positions of people that can be captured directly, while their shadows are used to track them when they are no longer visible. Our experiments confirm that this method can efficiently handle full occlusions. It thus has substantial value in resolving the occlusion problem in multi-person tracking, even with other kinds of cameras.
Semiotic Representation of Gender in Google Emojis: A Liberal Feminist Perspective

Modern technology has undoubtedly changed the way we communicate.Emoji is the latest development that crosses all language barriers and serves as a global language.The present study seeks to investigate how gender is semiotically represented in Google female emojis.The study is interpretive and qualitative in nature.By using Kress & Leeuwen (2006) the Grammar of Visual Design model, the study analyzed the stereotypical and professional (newly introduced) Google emojis for female and attempted to spot the tenets of liberal feminist movement.The revision of emojis by Google, with a belief that 'this will empower young women and better reflect the pivotal roles women play in the world' bespeaks the flourishing global liberal feminist movement.This acknowledgement is a feather in liberal feminist movement's cap in that gender roles are considered social constructs.The new emojis duly construct women as coming out of the stereotypical womanish stuff and entering into professions that were once male-dominant.
Vision: a web service for face recognition using convolutional network

This paper proposes a face recognition module built as a web service. We introduce a novel design and mechanism for face recognition on a web platform and to memorize most recent users for the user. This web service is called Vision and developed using the Flask and TensorFlow deep learning framework. The face recognition process is powered by FaceNet deep convolutional network model. The face recognition process done by Vision could also be utilized for user authentication and user memorization, both done in on a web platform. As a demonstration of concept and viability, in this study, Vision is integrated into a web-based voice chatbot. The testing and evaluation of Vision's face recognition process show an overall F-score of one for all test scenarios.
Teacher and Students’ Perception on Using Kahoot! for English Learning

The purpose of this research was to determine (1) Teacher's perception on using Kahoot!for English learning; (2) Students' perceptions on using Kahoot!for English learning; and(3) Constraints encountered while using Kahoot!for English learning.This is a quantitative descriptive research.The research subjects were one English teacher and 32 students of Class X MIPA 1 of SMA 4 Singaraja.Data collection was conducted using a closed questionnaire, structured interviews, and nonparticipatory observation.The results highlight teacher and students' perceptions on the use of Kahoot! is very good.The constraint encountered by the teacher is slow internet connection when accessing Kahoot!
Content adaptive single image interpolation based super resolution of compressed images

Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling .This paper focuses on upscaling of compressed images based on Interpolation techniques. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super-resolution (SR) methods .The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on single image and uses the content of the original image only; therefore the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.
Identification of various image retargeting techniques using hybrid features

Image retargeting refers to manipulating an input image to adapt its display on diverse screens with distinct spatial resolutions and aspect ratios. However, it might also be used for malicious image forgeries such as object removal. There exist several types of image retargeting techniques, yet most existing image retargeting detection approaches expose only single type of image retargeting. This paper proposes a blind forensics approach to identify three image retargeting techniques including seam carving, scaling and scale-and-stretch. Since image retargeting usually leads to local texture distortion and global structure deformation, a 512-dimensional feature set, which is made up of LBPCO (co-occurrence of local binary pattern) and BSIF (binarized statistical image features), is designed to expose image retargeting artifacts. Support vector machine (SVM) is exploited as pattern classifier to identify three image retargeting techniques. Compared with existing works, the proposed approach not only detects image retargeting forgery, but also identifies specific image retargeting technique.