Reviewerly/exHarmony huggingface dataset:
A Tool-Based Approach for Essentializing Software Engineering Practices

Software Engineers work using highly diverse methods and practices, and general theories in software engineering are lacking. A recent attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering. Essence is a method-agnostic progress management framework and a meta-method for Software Engineering (SE). However, tooling for Essence is still lacking. Without dedicated tools and other instruments, a meta-method such as Essence is cumbersome to utilize by practitioners and students. Indeed, Essence currently suffers from a lack of widespread practitioner adoption. In this paper, we thus present an Open Source tool for essentializing methods and practices: Essencery. We conduct a qualitative evaluation of the tool through a quasi-formal experiment and a set of semi-structured interviews. Based on this data, we improve Essencery iteratively before it is utilized in a large-scale project-based course as a proof of concept.
Pembuatan Peta Timbul Sebagai Media Pembelajaran Geografi

This article aims to improve the ability of teachers and students to make media maps arise and improve teacher understanding and ability in terms of understanding pedagogical aspects that can be seen from the process of designing a Learning Implementation Plan (LIP) especially in terms of choosing a model or learning method. This activity was held at SMAN 2 using training methods, question and answer, and practice. In accordance with the objectives of the implementation of the activity, it is expected that teachers and students have an increased ability in terms of mastery to make map media arise. Then the teacher can also improve the teacher's understanding/ability in terms of understanding pedagogical aspects that can be seen from the process of designing the LIP especially in terms of choosing a learning model. By carrying out training to improve teacher skills in developing learning media, Geography Teachers are expected to have sufficient competence in terms of designing and utilizing media so that quality learning is created.
Helping the helpers: Evaluating the impact of a controlled language checker on the intralingual and interlingual translation tasks involving volunteer health professionals

Cochrane is a non-profit organization which mainly relies on volunteer health professionals for the production, simplification, evaluation, and multilingual dissemination of high-quality health content. The approach that Cochrane volunteers adopt for the simplification (or intralingual translation) of English health content is non-automated and involves the manual checking and implementation of plain language guidelines. This study investigated whether and to what extent the introduction of a controlled language (CL) checker – which would make the simplification approach semi-automated – increased authors’ satisfaction and machine translation (MT) quality. Twelve Cochrane authors completed a standardized questionnaire and answered follow-up questions on their level of satisfaction and preferences. Forty-one Cochrane evaluators assessed the quality of the Spanish MT outputs of simplified texts. Authors showed a preference for the introduction of a CL checker. Differences in MT quality scores were slight.
Looking for Transaction Data Pattern Using Apriori Algorithm with Association Rule Method

Abstract The purpose of this research is to find patterns in transaction data so that it can be useful information. The author used data mining in the analysis of transaction data with the association rule method and a priori algorithm. The results of this study state that making the right decision for the marketing strategy. This study will analyze transaction data at XYZ dining places and find relationships between items purchased by consumers.
Learning Word Embeddings Using Spatial Information

This study proposes a word embedding learning method for intelligent document search which can consider the meanings of place-related named entity word. In the previous method, the word embeddings were learned based on the context in which the words emerged. However, because the previous method missed the spatial information, the word embeddings that share similar contexts (e.g., universities) become similar even though the entities of the words were different (e.g., “” (The University of Tokyo) and “” (Kyoto University)). In the proposed method, the word embeddings are learned based on the spatial information data, which comprise both the object names and coordinates. Therefore, even if the words share similar contexts, the proposed method can learn different word embeddings for different words if those entities are different. The proposed method is evaluated using the synonym search task. As a result, it was observed that the mean reciprocal rank (MRR) for the evaluation data improved using the proposed method in comparison with the previous method. Furthermore, the proposed method improved the MRR by 177% maximally, i.e., from 0.151 to 0.418, in comparison with the previous method.
Interactive Spatial Storytelling for Location-Based Games: A Case Study

In this paper we discuss the distinctive role of interactive stories in the design of location-based games. Interactive spatial storytelling becomes a powerful tool for creating experiences of mixed reality where the story is related to specific facts that occurred in the same location in which the player is physically present. Moreover, player’s immersion in mixed reality is increased when she follows or enacts as a real character of a historically documented story, infused with imaginative elements in accordance to the objectives of the game. Following this line of thought, we introduce the location-based mobile game “Inside Old Town of Athens”, and its accompanying website, in which we creatively combine elements and principles of interactive storytelling with the distinctive attributes of location-based games that generate site-specific experiences. We also, discuss our design choices that concern the integration of the story scenario and game structure with the route in the area of Plaka, in Athens, as well as the graphic and interaction design of the mobile application that stimulate and enable the player to act in both digital and physical space.
Four Constructions of Asymptotically Optimal Codebooks via Additive Characters and Multiplicative Characters

In this paper, we present four new constructions of complex codebooks with multiplicative characters, additive characters, and quadratic irreducible polynomials and determine the maximal cross-correlation amplitude of these codebooks. We prove that the codebooks we constructed are asymptotically optimal with respect to the Welch bound. Moreover, we generalize the result obtained by Zhang and Feng and contain theirs as a special case. The parameters of these codebooks are new.
Research on the recognition technology of human relationship in face image based on deep learning

Abstract Face images contain rich and complex information, and similar genes are directly expressed as facial resemblance. In this paper, based on the theoretical background of biology and aiming at the wider application of artificial intelligence in the field of face image analysis, deep learning based recognition of the kinship of people in face images is studied in depth. Kinship recognition is a new and challenging research. In order to recognize the kinship between characters from face images, this paper defines a new correlation feature to represent the correlation between two characters. Based on the idea of multi-layer self-encoder to input and reconstruct the target, and combined with the powerful feature extraction ability of deep convolutional neural network, this paper designs a new deep convolutional self-coding neural network, and the correlation feature is the activation value of the deep hidden layer of this deep neural network. The deep convolutional self-coding neural network USES back propagation algorithm in a supervised way. With the deepening of the network hierarchy, the identity features of representative characters are extracted from face images step by step, and the identity features of a pair of characters are fused into the associated features that represent the relationship between them. Based on deep learning, the associated features extracted from face images can effectively identify a given character relationship.
Research on Online Comment Negative Emotion Warning Based on Machine Learning from the Perspective of Big Data

With the rapid development of information technology, the amount of data has grown exponentially. The birth of cloud computing directly sent us into the era of big data. In view of the uncontrollable factors of online commentary, how to effectively mine the hidden consumer experience and control the negative experience communication from massive online reviews has become the key to building a dynamic â€œgreenâ€ network environment in current big data era. This paper is based on machine learning to mine online comments and proposes an online commentary emotional early warning model, which realizes online emotional monitoring based on intelligent information processing to a certain extent.
Certifiably Pseudorandom Financial Derivatives

Arora et al. [S. Arora, B. Barak, M. Brunnermeier, and R. Ge, Comm. ACM, 54 (2011), pp. 101--107] showed that taking computational complexity into account, a dishonest seller could strategically place lemons in financial derivatives to make them substantially less valuable to buyers. We show that if the seller is required to construct derivatives of a certain form, then this phenomenon disappears. In particular, we define and construct pseudorandom derivative families, for which lemon placement only slightly affects the values of the derivatives. Our constructions use expander graphs. We study our derivatives in a more general setting than Arora et al. In particular, we analyze arbitrary tranches of the common collateralized debt obligations (CDOs) when the underlying assets can have significant dependencies.
The Researches on Path Planning Algorithm for Distribution Vehicles Based on Automata

With the rapid development of logistics, customers can enjoy more convenient service. But meanwhile, the market has also put higher requirements forward the timeliness of cargo distribution. Traffic demand has increasing continuously, which has led people to tend to buy more motor vehicles. And the increasing vehicle ownership has brought great pressure on urban transportation, causing traffic congestion to become increasingly serious. However, traffic congestion sometimes has influence on the timeliness of cargo distribution. This article is based on the theory of automata and applies the real-time communication positioning technology of vehicle networking to simulate the running status of the distribution vehicles in the road network. Based on this, it designs a dynamic scheduling algorithm for the distribution vehicles' driving path, and it can optimize distribution vehicles' driving path in real time, and improve the efficiency of distribution.
Authors, Genres, and Audiences

I will begin this scholarly chapter in literary criticism and theory with examples of another genre, the joke, more specifically, the subgenre of “X-walks-into-a bar” jokes. Although such an opening isn’t standard practice, it isn’t shocking either: I’m far from the first to begin a scholarly chapter with a joke or two (or four), and I certainly won’t be the last. I’m confident that you are not inferring that I’ve completely misread my assignment for this volume, even as you withhold other judgment until you see me use the jokes in the service of my argument about genre. In other words, though my beginning with this meta-commentary and going on to the jokes slightly bend the generic boundaries of the scholarly chapter, you and the genre itself can readily accommodate that bending.
Anticorruption personality orientation as an innovative component of teaching of cadets of educational organizations of the Federal Penal Service of Russia

Fight against corruption is one of global problems of modern society, that is why law-enforcement agencies have the most important objective of developing effective and innovative psychological and educational technologies of fighting against corruption-related offences. It is especially important to form rejection of corruption by would-be law-enforcement officials (specialists of the Ministry of Internal Affairs, the Federal Security Service, the Federal Penal Service, the Investigation Committee, the Judicial System, the Prosecutor’s Office), as life and health of the population of Russia depends on the legitimacy of their decisions. One of the ways to fight against corruption in organizations of higher education can be forming of students’ anticorruption personality orientation which is realized by the authors as integrative personality education providing anti-corruption legislation compliance in the sphere of professional occupation by means of structural components of personality orientation (motivational-axiological, emotional-volitional, cognitive, spiritual and moral) which are being formed in the process of studying in the higher education establishment.
Towards Self-Aware Heatstroke Early-Warning System Based on Healthcare IoT

This paper addresses a preliminary model for self-aware heatstroke early-warning system based on the healthcare internet of things (IoT). Basically, heatstroke is a syndrome caused by human's body overheating, commonly due to lengthy exposure or physical exertion in excessive temperatures. Now-a-days, heatstroke is one of the most critical issues in human health due to global warming. The major goal of this research is to save valuable lives by proposing a self-aware heatstroke early-warning system by applying an advancement of healthcare IoT and machine learning technique. The proposed system is an easy to wear and cost-effective system that even middle class people can afford it. The system will use real-time sensor data obtained from the users in real-time outdoor and indoor experiment. In this system, we use an Arduino prototype which contains three different types of sensors including (a) temperature sensor, (b) pulse sensor and (c) breath sensor. In addition to this, the usage of analytics here can enhance accuracy and real-time capability, allow in-advance detection, and enable personalization.
Artificial Neural Network for the Left Ventricle Detection

Machine learning has proved its effectiveness through its application in medicine. Neural networks have been used for solving different dilemma in the medical field such as image analysis and diagnosis. The object detection is a common field in computer vision practices, for detecting some important objects in images or videos. In medicine, there is also a need for object detection such as, organ detection. Thus, different types of deep networks have been used in medicine for detecting organs or some parts of organs such as ventricles in heart images. This paper introduces a system based on neural network, used for automated detection in MRI cardiac images of left ventricles. Our work relies upon backpropagation neural network with a sliding window used to go through the images to find and detect the left ventricle. Firstly, network is operated in order to classify left ventricle and non-left ventricle images using a backpropagation techniques neural network. MATLAB program is run and furthermore, BPNN is automatically exercised several times before the detection process is carried out. The trained network is then validated by testing it on unseen data to gain a good generalization capability. The recognition rate achieved by BPNN is 100% on training data, 89.23% on testing data. The trained network is then used to determine if a left ventricle is contained or not in the sampled region. Moreover, the developed system seems to perform effectively in detecting left ventricles with up to 6% salt and pepper noise.
Anomaly Detection for Water Supply Data using Machine Learning Technique

Abstract The advent of the era of big data brings new challenges and opportunities to water data processing. Abnormal detection or authenticity verification of water supply data becomes an urgent problem in natural resource data processing. This article addresses the issue of anomaly detection for a set of measured water supply data that provided by the Ministry of Water Resources of China. The machine learning technique, namely, One-Class support vector machine, is used for anomaly detection, and a detailed analysis of the features of anomalies is also provided. Experiments on three situations, namely, one dimensional situation, three dimensional situation and seven dimensional situation are carried out to analyse the features of anomalies. The experiment results revealed that the cases with entries of large values more than 10 times of median, zero entries together with some large values and ascending or descending sequences tend to have high abnormality scores.
Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

Establishing an accurate inverse model is a key problem in the design of adaptive inverse controllers. Most real objects have nonlinear characteristics, so mathematical expression of an inverse model cannot be obtained in most situation. A Takagi–Sugeno(T-S)fuzzy model can approximate real objects with high precision, and is often applied in the modeling of nonlinear systems. Since the consequent parameters of T-S fuzzy models are linear expressions, this paper firstly uses a fuzzy c-regression model (FCRM) clustering algorithm to establish inverse fuzzy model. As the least mean square (LMS) algorithm is only used to adjust consequent parameters of the T-S fuzzy model in the process of parameter adjustment, the premise parameters are fixed and unchanged in the process of adjustment. In this paper, the back propagation (BP) algorithm is applied to adjust the premise and consequent parameters of the T-S fuzzy model, simultaneously online. The simulation results show that the error between the system output controlled by proposed adaptive inverse controller and the desired output is smaller, also the system stability can be maintained when the system output has disturbances.
Archimedean spiral based artificial bee colony algorithm

Swarm intelligence techniques are popular meta-heuristics which are developed to solve very complex problems that are not decipherable using traditional optimization approaches. In this class, artificial bee colony is latest and efficient algorithm that is able to solve complex problems. During solution search process it perform exploration in the early iterations, while should utilize the identified area in later stage. By analyzing the ABC, it is clear that the position update process depends on the difference of two random vector multiplied by a random component. So the step size of ABC is exceedingly hinge on these arbitrary components that leads to skipping of good solutions. For that reason, in this paper, Archimedean Spiral inspired Local Search (ASLS) algorithm is united with ABC and the new algorithm is labelled as Archimedean Spiral based ABC (ASABC) algorithm. In ASABC, in the current swarm only good solutions get chance to exploit the nearby search space using ASLS local search approach in every iteration. To demonstrate the performance of ASABC, experiment performed over 10 recognized benchmarks and compared with basic ABC, Best-so-far ABC and Modified ABC. Outcomes prove that ASABC algorithm may be considered as a viable algorithm for optimization problems.
Parallel computing algorithm for real-time mapping between large-scale networks

In this paper, we propose a scalable massively-parallel algorithm to solve the general mapping problem in large-scale networks in real-time. The proposed parallel algorithm takes advantage of GPU architecture and launches millions of workers to calculate values on a target network simultaneously. Threads are managed through the SIMT execution model and target values are updated through atomic operations. Our experiments show the proposed algorithm can accomplish network mapping (find importance weights for links in a real-world large-scale shared-mobility network) with more than 2 million weights within 1.82 μs (microsecond-level), which is truly real-time. The algorithm performance suggests that mapping computations may no longer be the bottleneck in highly dynamic network-centered problems, as the computations can be completed faster than the solid state drive (SSD) read access latency. Compared to serial algorithms, the speedup is more than 12,000 times. The proposed algorithm is also scalable. Results on simulated data show that even when the network size grows exponentially, microsecond-level computing performance can still be obtained, and even more than 190,000 times speedup can be achieved. The proposed algorithm can serve as a cornerstone for ultra-fast processing of highly dynamic large-scale networks.
A Two Stage Framework for Detection and Segmentation of Writing Events in Air-Written Assamese Characters

Gestural air-writing involves the process of writing continuous characters or words in free space using hand or finger motion. It differs from traditional pen-based writing from the fact that it does not contain delimiting points which helps in demarcation of valid writing segments. Thus, in gestural air-writing, detection of meaningful writing events from a continuous gestural sequence containing irrelevant writing movements is an intricate task which needs special attention. This paper presents an automatic method of gesture spotting and segmentation which identifies the meaningful air-written character segments confined within a continuous character pattern using a hybrid spatiotemporal and statistical feature set. A sliding window-based approach is employed for extracting the writing events from a continuous stream of hand-motion data, suppressing the superfluous idle data points. Consecutive writing events are then categorized into valid character segments and redundant ones. The relative performance of the proposed system is examined by taking various Assamese characters into consideration. Experimental results reveal that the proposed model achieves an overall segment error rate of 1.31%.
Learning Medical Diagnosis via Scaled Convex Hull-Based SK Algorithm

Machine learning algorithms, especially support vector machines (SVMs), were from the very beginning designed and used to analyze medical datasets. As a geometric dual representation of SVMs, Schlesinger-Kozinec (SK) algorithm achieves classification by solving the nearest point pair between convex hulls. However, the initial SK algorithm is only for linear separable problems. For non-separable problems, it needs to perform appropriate convex hull transformation, i.e., so-called scaled convex hull (SCH). This paper first gives the theory foundation of scaled convex hull. Then, the SK algorithm as well as its kernelization process is further explained. At last, we employ the SCH and the kernel SK (KSK) algorithm to carry out medical diagnostic tasks. The results show that compared with other well-known machine learning algorithms, the SCH-based KSK algorithm can achieve higher prediction accuracy.