Reviewerly/exHarmony huggingface dataset:
Supple: Multiagent Communication Protocols with Causal Types

A (communication) protocol captures how agents collaborate by specifying the messages they exchange. In particular, since the information content of messages characterizes the interactions a protocol specifies, message types can improve collaboration by strengthening the specification of what each agent may legitimately expect from another agent. In addition, in implementations, typing information can enable improved verification of agents. We introduce Supple, a protocol specification language that expresses message schemas with typed parameters. Supple enables definition of causal types for parameters that constrain how other parameters are computed in a protocol enactment. We give the formal semantics of Supple; characterize the liveness and safety of Supple specifications; and provide decision procedures for them.
Color Normalization of Blood Cell Images

Traditional microscopes have been used a long time in hematology for blood analysis, but during the last decade many laboratories have started to replace them with digital microscope systems. The appearance of blood cells in the digital images is very important to the end user, ideally they should be identical to how they would look in a traditional microscope. There are several digital microscope systems on the market today with various optics and illumination, which means that images from different systems do not look the same. This is a cumbersome problem in many ways. For example this means cell classification networks need to be trained for every single system. In this paper we investigate the possibility of using deep learning to transform images between digital systems. The main focus is on a cyclic network setup where it is possible to transform the images between two systems. We present two different networks, a cyclic network with a perceptual loss based on the VGG-16 network and a conditional version of a cyclic generative adversarial network (GAN). With these networks we obtain very good results that are better than previous methods for transforming blood cell images.
FORMATION OF INFORMATION COMPETENCE IN THE CONDITIONS OF LIBRARY EDUCATIONAL ENVIRONMENT

In the formation of information competence, the primary role is played by a «specially designed educational environment based on the interaction of the library and the educational institution». Therefore, each library has the opportunity to prepare an information-competent personality, even without resorting to the development of all sorts of new teaching methods for the formation information competence, but improving its educational environment, taking into account the need to raise this level. And, despite the fact that the process of creating an environment that will nourish information competence longer than the development of a new teaching methodology, the results will be more ambitious, since the environmental methodology is synergistic.
Implementing Augmented Reality in Learning

Technologies are changing and ever growing.One of the newest developing technologies is augmented reality (AR), which can be applied to many different existing technologies, such as computers, tablets, and smartphones.AR technology can also be utilized through wearable components, for example, glasses.Throughout this paper review on AR, the following aspects are discussed at length: research explored, theoretical foundations, applications in education, challenges, reactions, and implications.Several different types of AR devices and applications are discussed at length, and an in-depth analysis is done on several studies that have implemented AR technology in an educational setting.
Internet of Things : A Boon for Supply Chain Management

Data is the the new oil. The more data one has, the more powerful one is. According to knowledge (DIKW) pyramid, wisdom helps move human race forward, but data is at the base of the pyramid. Internet of Things (IoT) is capable of collecting vast amount of real-time data through sensors and convert data into information. Information gives way to knowledge and wisdom. Technology is beneficial as it saves time and provides comfort. So, the Internet of Things (IoT) with its evolution is rapidly becoming a basic feature from an optional or luxury feature. Manufacturing, logistics, and supply chain enterprises are aggressively moving to embrace modern technologies. Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Cognitive Intelligence (CI), and Big Data (BD), and Robotic Process Automation (RPA) are all buzzwords of today's supply chain management. Among all, IoT and Big Data are two fields of technologies which seem to be evolving very fast. IoT in supply chain can help organizations manage their operations from production through to delivery. RFID, GPS, and IVMT provided real-time data is helping supply chain managers to take smart decisions. Cloud based IoT coupled with data analytics and big data is definitely the next big thing in supply chain management. IoT provides end-to-end visibility in a supply chain from manufacturing through to delivery, hence, it provides accessibility to real-time data, hence, enabling quick decision making based on variations. This article talks about applications of IoT with special focus on supply chain management.
Performance Evaluation of Kernel-Based Feature Extraction Techniques for Face Recognition System

Face recognition is considered to be one of the most reliable biometrics where security issues are of concerned. Feature extraction which is a functional block of a face recognition system becomes a critical problem when there is need to obtain the best feature with minimum classification error and low running time. Most existing face recognition systems have adopted different non-linear feature extraction techniques for face recognition but identification of the most suitable non-linear kernel variants for these systems remain an open problem. Hence, this research work analyzed the performance of three kernel feature extraction technique (Kernel Principal Component Analysis, Kernel Linear Discriminant Analysis and Kernel Independent Component Analysis) for face recognition system. A database of 360 face images was created by obtaining facial images from LAUTECH Biometric Research Group consisting of six facial expressions of 60 persons. Images were preprocessed (gray scaling, cropping and histogram equalization) and the kernel variants were used to extract distinctive features and reduce the dimensionality of each of the images from 600x800 pixels to four smaller dimensions: 50x50, 100x100, 150x150 and 200x200 pixels. Euclidean Distance similarity measure was used for classification. The performance of the three kernel variants was evaluated for face recognition system using 180 images for training and 180 images for testing using the following metrics: Recognition Accuracy (RA) and Recognition Time (RT). Empirical result indicate that KLDA performs best for face recognition system with an average accuracy of 94.52%. The larger image dimension also results in better recognition performance. We intend to experiment on other classifiers for face recognition system in our future work. Keywords— Biometrics, Face, Feature extraction, Kernel, KICA, KPCA, KLDA, Linear, Non-linear;
Introduction to the thematic set: Progress in Remediation of Polluted Soils

Lead Guest Editor: Jaume BechCo-Guest Editors: Elena Korobova, M. Manuela Abreu, Carmen Perez-Sirvent, Hyo-Taek Chon and Nuria RocaThe collection of papers presented in this Thematic Set is an outcome of the SSS8.4 Session ‘Progress in remediation for soils polluted by potentially toxic elements’, presented at the European Geosciences Union (EGU) Conference held in Vienna, April 2017.Soils are essential components of the environment, the basis of terrestrial ecosystems and a crossroad of biogeochemical cycles at the lithosphere-hydrosphere-biosphere-atmosphere interface. Soils are a limited and vulnerable resource and soil quality must be preserved. Anthropogenic mismanagement through poor industrial and mining practices, overuse of agrochemicals, bad disposal of sewage sludge and waste, cause contamination, environmental and health concerns. Therefore soil pollution needs innovative and eco-friendly technologies for its remediation.This Thematic Set contains 13 papers written by authors from 12 countries: Armenia, Italy, Japan, Jordan, Korea, Mexico, Mongolia, Philippines, Russian Federation, Spain, Thailand and USA. This collection of original studies focuses on five topics: 1) sequential extraction/speciation, 2) phytoremediation, 3) oxygen content in iron sulphides, 4) stabilization of Zn compounds in a long incubation experiment, and 5) bioavailability of potential toxic elements.### Sequential extraction/speciationThere are five papers dealing with this topic:Burachevskaya et al. studied the Zn fractional distribution in artificially contaminated Haplic Chernozem in South Russia, using the Miller and Tessier methods of sequential extraction. The authors concluded that the content of Zn in the fraction bound to the removed component obtained by the Tessier method area was lower than that obtained by the Miller method.Minkina et al. researched the Zn speciation in Technosols of the Karabash copper smelter, Southern Urals, Russia, using the combined fractionation scheme and a set of X-ray synchroton …
Nonlinear Subspace Feature Enhancement for Image Set Classification

While several methods have been proposed for modeling and recognizing image sets, the success of these methods relies heavily on how well the image data follows the assumptions of the underlying models. Among the models that have been utilized by many image set classification methods, the physically inspired subspace model assumes that the images of an object lie on a union of low-dimensional subspaces. Despite their successful performance in controlled environments, the performance of such subspace-based classifiers suffers in practical unconstrained settings, where the data may not strictly follow the assumptions necessary for the subspace model to hold. In this paper, we propose Nonlinear Subspace Feature Enhancement (NSFE), an approach for nonlinearly embedding image sets into a space where they adhere to a more discriminative subspace structure. In turn, this improves the performance of subspace-based classifiers such as sparse representation-based classification. We describe how the structured loss function of NSFE can be optimized in a batch-by-batch fashion by a two-step alternating algorithm. The algorithm makes very few assumptions about the form of the embedding to be learned and is compatible with stochastic gradient descent and back-propagation. This makes NSFE usable with deep, feed-forward embeddings and trainable in an end-to-end fashion. We experiment with two different types of features and nonlinear embeddings over three image set datasets and we show that our method compares favorably to state-of-the-art image set classification methods.
Polynomial-Time Multi-Agent Pathfinding with Heterogeneous and Self-Interested Agents

This paper proposes a polynomial-time strategyproof mechanism that solves multi-agent pathfinding (MAPF) problems with heterogeneous and self-interested agents. In MAPF, agents need to reach their goal destinations while avoiding collisions between them. In this paper, we consider heterogeneous and self-interested MAPF. Agents are heterogeneous if the costs of traversing a given path differ between agents. In particular, we assume each agent has a private linear cost function of travel time. The proposed strategy-proof mechanisms aim to make agents truthfully declare the slope of the private linear cost function.
A Study of Methods Used to Teach Media-Language to Korean Learners for Academic Purposes

디지털식 전자 자료는 기존의 미디어 언어들의 대표적 특징인 선형적, 순차적, 연속적 구성 형태를 탈선형적, 비순차적, 비연속적 특성으로 변화시키고 있다. 학문목적 한국어의 학습자도 이러한 미디어의 상황에 보다 쉽고 빠르게 접근할 수 있는 능력을 요구받고 있는 점에서 미디어 언어 교육의 방법론이나 방향에 대한 보다 심도 있는 접근이 필요한 때이다. 미디어 언어는 메시지가 미디어를 통해 전달되는 과정에서 음성, 문자, 이미지, 영상 등으로 기호화되어 언어적인 작용을 하는 것으로, 미디어 언어 교육의 핵심적인 부분이 된다. 본고는 오늘날의 문식성 환경이 변화하고 있는 상황에서 한국어교육에서도 새로운 미디어 언어 교육이 필요하다는 점을 들어 앞으로 컴퓨터 혹은 디지털 미디어를 통한 다양한 학습 기기와 자료가 주요한 학습 교재가 될 때의 적절한 교수-학습 전략의 필요성을 제시하였다. 학습 매체의 다양화는 학습자들의 학습은 물론 학업 양상에 대한 전반적인 개념, 교육 내용의 목표와 목적까지도 여러 방향으로 변화시키고 있다. 이러한 상황들은 결국 한국어 교육의 교과목 수업, 특히 학문 목적 한국어의 교수-학습 전략의 질적 변화가 요구될 수 있다는 전제를 담고 있다. 이 같은 의미에서 미디어 언어교육을 위한 교육 방안으로 교수자는 우선 미디어 능력을 갖추고 학습자들이 어떠한 상황과 수준에서 무엇을 요구하는지를 파악하여 그에 맞는 교수-학습을 설계할 수 있어야 한다. 또한 학습자들에 대한 분석을 바탕으로 주어진 미디어를 교수-학습 진행의 측면에서 어떻게 효율적으로 교육할 수 있는가를 고려하고, 미디어의 기능적 역할뿐만 아니라 미디어가 전하는 내용을 통한 주제 설정과 이에 대한 토론 및 가치관 형성도 고려해야 한다.Digital resources are changing the existing media-language from linear, sequential, and continuous composition forms into non-linear, non-sequential, and non-continuous forms. Even Korean learners for academic purposes are demanding the available capacity in order to gain easier and faster access to this media situation. A deeper approach to the methodology or the direction of media-language education is necessary. Media-language carries out a linguistic function that is symbolized as voice, text, image, and video in the process of delivering a message through a particular medium. Thus, it becomes a key part of media-language education. Bearing in mind the necessity for new media-language education in the current context of Korean language education, given that the literacy environment is changing today, this study suggests the need for an appropriate teaching-learning strategy. This will prove to be vital when diverse learning devices and materials through computer or digital media become the major learning materials down the road. Diversification in learning media leads to changes not only in the learners modes of study, but also in the general concepts of learning, as well as the goals and objectives of educational content, in many ways. These situations are ultimately containing a prerequisite as saying of being likely to be requested a qualitative change in a teaching learning strategy for a curricular-subject class of Korean language education, especially for Korean language for academic purposes. An instructor needs to have media competence, and above all, be equipped with designing teaching-learning methods in line with such competence by grasping what the learners demand in each situation and level. There is a need to consider theme-setting in delivering the contents through different media, as well as the functional role of the mediaitself, and even the formation of a discussion and a value on this, with weighing how efficiently the given media based on an analysis on learners can be educated in the aspect of progressing teaching-learning.
Readability of the Transactional Texts in Bahasa Inggris Textbook for Grade X

This paper aims are lexical density and grammatical intricacy of the transactional texts in Bahasa Inggris as one of the English textbooks for senior high students (grade X) in Indonesia. The method applied is qualitative research method. The study supported by three methods in determining readability as proposed by Halliday (1985, 1994, 2004), Ure (1971), and a text analyzer adapted from www.usingenglish.com as the website that provided the tool. The data are taken from the textbooks as transactional texts. The texts obtained are six transactional texts. The results of the study have shown several valuable insights. Firstly, the transactional text that has the highest number of words (530 words) containing highest number of sentences could be categorized into the spoken text (lexical density= 26.44) and the level is under Easy Reading Range (Fog Index = 4.23) as described in T6. Secondly, the total unique words in transactional text has relation with hard words, lexical density and Fog Index as described in the T2. Thirdly, the lowest number of words could relate to the unique words and total number of sentences (as seen in T4). To choose the transactional text given as the material in Bahasa Inggris, it is suggested that the writer of the textbooks should be more thoughtful in deciding the transactional texts level chosen since the data shows that the level of the text is various (Fog Index 4.23 as the lowest – 10.25 as the highest) and the average transactional text level is 6.7.
Ontology Enhanced Mining of Multidimensional Association Rules from Data Cubes

Many approaches have provided applicable solutions to explain associations within data cubes. Nevertheless, there are still many issues causing users extra time to get real knowledge or even failing to obtain the useful knowledge they need. In this paper, we introduce an ontology based approach for multidimensional association rule mining that incorporates a domain ontology to help users in reducing the system resource consumption and improving the efficiency of the mining process. The structure of this ontology is illustrated and how it would be of benefit to the mining process is also demonstrated. Our experimental results prove that a significant improvement and efficiency is achieved using our suggested approach in comparison with those fitting in the same trend.
Filtering Specular Reflections by Merging Stereo Images

Due to the horizontal distance between the cameras taking stereo images, the position of specular reflections on objects differ in the two images of a stereo pair. In this paper we propose an algorithm to merge such stereo images in order to filter specular reflections by utilizing different types of disparity maps. Results show that merging stereo images reduces specular reflections by 75% in average.
The Role of the Dark Web in the Crime and Terrorism Nexus

&#x0D; &#x0D; &#x0D; On November 15, 2018 the Canadian Association for Security and Intelligence Studies (CASIS) Vancouver hosted its tenth roundtable meeting which covered “The Role of the dark web in the Crime and Terrorism Nexus.” The presentation was hosted by Dr. Richard Frank, an assistant professor in the School of Criminology at Simon Fraser University, as well as the Director of the International CyberCrime Research Centre (ICCRC). In the presentation, Dr. Frank began by explaining the operations of the dark web, and then moved on to discuss why the dark web cannot just be shut down, as well as actions law enforcement (policing) could take in order to counter the activities on the dark web. The subsequent roundtable discussion opened with an analysis of the operations of Silk Road, an online marketplace on the dark web that specializes in the sale of illegal drugs, weapons, and stolen identities. The topics of interest in the discussion were the effects of internet-based trade of illicit goods on organized crime and local drug markets, in addition to whether the dark web can be used constructively.&#x0D; &#x0D; &#x0D;
Sistem Tutorial Berbasis Kecerdasan Buatan Pada Proses Pengambilan Keputusan Perawatan dan Perbaikan Gitar

Guitar is a popular musical instrument in the world and is a metronome for every use in various music events and its correlation. As a metronome, the guitar must be well standardized on the quality of each part so that sound that comes in line with the user's expectations in this case is the guitarist. Damage to the guitar is something normal because of its intense use so it needs proper handling in the repair process. The easiest thing is to bring a broken guitar to the experts, but when there are not many guitar service experts or a long enough distance to reach it, then guitar repairs need to be done immediately. Therefore, it is necessary to develop a system that can act as a tutor in the maintenance and repair of guitars by utilizing artificial intelligence embedded in the system. With the help of artificial intelligence, it is expected that the system can assist in the decision making of guitar technicians in the process of making guitar repair decisions based on the symptoms that occur. Decision making used uses the certainty factor method based on certainty factors. After going through the equivalence partitioning testing process, in general this system produces a total percentage of 100% on the success of the item test by experts in the testing process of the 25 items tested. Thus the application meets the requirements for making the program, which is readable and valid.
Fast Control of JPEG Compression Rate

JPEG compression is broadly used for storing scanned images. This chapter describes a new rate control method for JPEG image compression. Similar to the vast majority of JPEG rate control approaches, the method solves the task of JPEG rate control by generating custom JPEG quantization tables (QTs). The method includes an adaptive bit count predictor training stage that is optionally followed by a rate-distortion optimization (RDO) stage. The training of the adaptive bit count predictor is based on a linear prediction model that uses either coefficient-wise average entropy or a ρ-parameter. The trained predictor is subsequently used in the RDO stage to estimate the JPEG bit count resulting from the application of a particular QT, thereby substantially speeding up the RDO algorithm. For the RDO stage, one of the two following algorithms is applied: Wu-Gersho or RD-OPT. The resulting hybrid design combines the strong points of each of the utilized approaches and mitigates their shortcomings, thereby providing a good trade-off between computational complexity, rate control accuracy and reconstructed image quality.
Web-based Alumni Network and Database Information System with Mobile Application

This study aimed to design, develop and evaluate a system that facilitate the network of alumni and a database system with the application of web technology and mobile phone. Alumni can build their network in the website or in the mobile apps in order to develop a network. The information of the alumni will be saved in the database of for several purposes. The system used HTML5, PHP programming and MySQL. This study utilized developmental method of research using researcher-made questionnaire which was administered to the set of alumni. Respondents were the fifty percent (50%) of the SLSU graduate studies alumni graduated from the school year 2010 until 2014. The statistical tools used were the percentage, frequency count, weighted mean and Wilcoxon rank test. Based on the findings of the study, the developed system is very effective, very accessible, very accurate and secure. Keywords: Alumni network, database information system, mobile application, web-based application, alumni tracer
Soft Labeling in Stochastic Shortest Path Problems

The Stochastic Shortest Path (SSP) problem is an established model for goal-directed probabilistic planning. Despite its broad applicability, wide adoption of the model has been impaired by its high computational complexity. Efforts to address this challenge have produced promising algorithms that leverage two popular mechanisms: labeling and short-sightedness. The resulting algorithms can generate near-optimal solutions much faster than optimal solvers, albeit at the cost of poor theoretical guarantees. In this work, we introduce a generalization of labeling, called soft labeling, which results in a framework that encompasses a wide spectrum of efficient labeling algorithms, and offers better theoretical guarantees than existing short-sighted labeling approaches. We also propose a novel instantiation of this framework, the soft-FLARES algorithm, which achieves state-of-the-art performance on a diverse set of benchmarks.