Reviewerly/exHarmony huggingface dataset:
A Study on Effectiveness to Evaluate Competencies by Student Survey Approach

The generic skills are important capabilities for engineers. We can measure them in the PROG competency test that is accepted as an objective evaluation method for the generic skills in society, but it costs every time and takes time to feed results back to students. On the other hands, in a student achievement survey method by a questionnaire survey, we can measure the generic skills of students easily and feed results back to them quickly, but it lacks in objectivity. In this paper, we analyze correlations between the generic skills and the total capabilities for working members of society in the student achievement survey method by students themselves and the PROG competency scores. As a result, we could find some relationships between the generic skills and the total capabilities for working members of society and ‘the fundamental competency to deal with the people around him/her’ , ‘the fundamental competency to face oneself ’ , and the PROG competency total score.
Structural and Philosophical Features of an Effective Communication Space for Cultivating Sensibility on Rivers and River Management

In the area of community-based natural resource management, the increasing difficulty of maintaining community groups and for them to keep carrying out their activities is a serious challenge. In order to search for a way to solve this situation, this study aims at describing the key structural and philosophical points of an effective communication space that cultivates a supportive network among the community groups, and amongst the individual members of the groups. The name of the focused communication platform is the Iikawa and Iikawa-dukuri Workshop. This is a long lasting annual award selection workshop event where the participants are encouraged through gaining confidence and feeling rewarded for their work caring for their local rivers. Through document analysis and in-field research, this study shows that there is a well-designed spatial forum with an open and creative structure, and a future-oriented philosophy that maintains moral safety in the communication during the workshop.
Attributes Consistent Faces Generation Under Arbitrary Poses

Generating visual images automatically given semantic attributes description helps building friendly human-machine interface as criminal suspects depicting and customer-oriented product design. However, this is a hard task since there exist great semantic and structure gaps between attributes description and visual images, which further cause great inference uncertainty in transforming from attribute description to vivid visual images. We aim to reduce the posterior distribute complexity given attributes (P(I|A)) by exploring face structure knowledge and imposing semantic consistency constraint in an end-to-end learning fashion. Our contributions are three-fold: (1) we address the semantic and structure consistent problem in attribute-conditioned image generation; (2) the proposed method can generate attribute consistent face images with high quality in both detailed texture and also clear structure; (3) we provide an interface for generating attribute consistent images with diverse poses.
MR <sup>2</sup>

In this paper we discuss the design and implementation of a preliminary Mixed Reality interface for Microsoft Hololens, supporting medical records retrieval and navigation. The goal is to ease common activities in the dynamic clinical context, using facial recognition and multimodal commands for accessing medical records on the fly through a free-hand interaction.
Software Estimation Framework for Mobile Application Projects

As mobile devices are becoming the primary access channels for information, the authors need to have accurate effort estimation model for mobile application projects. In this paper the authors discuss “Mobile application estimation framework” that was designed based on 14 mobile application projects and was validated against 5 mobile application projects. In this paper the authors discuss the estimation framework for both native/hybrid mobile application projects and mobile web application projects. The proposed “Mobile application estimation framework” provides comprehensive coverage for various factors involved in mobile estimation such as layer-wise components, horizontal components and others. The estimation framework also considers the cost drivers and is used as effort adjustment factor. The proposed mobile application estimation framework achieved the MMRE of 0.207 with pred (0.3) of 80%.
Object-Audio Capture System for Sports Broadcasting

Object-audio workflows for traditional flat broadcasts have recently appeared after the introduction of new audio formats, such as MPEG-H and Atmos. These formats enable the creation of object-based mixes that can be dynamically rendered by the end user depending on their reproduction hardware. Until very recently, only post-produced content was being created for these formats, but new broadcast standards in the U.S. and Asia, as well as new hardware encoding engines, have made live sports production in these formats more feasible. These formats allow for a fuller, more immersive sound design and allow for some possibilities of personalization. The issue then arises on how to capture live action from the field that would provide these object-audio workflows with the desired isolated sounds and accompanying metadata. Current capture systems provide a suboptimal amount of isolation from the crowd to highlight individual action sounds and dialog from the field. Also, in most cases, placing traditional microphones near the action is not possible. In this paper, we present new microphone techniques and systems enabling better performance for sound capture that fulfill the needs of future object-audio broadcast formats. This includes beamforming techniques, automatic steering, and systems management of microphone arrays.
Cooperative Adversarial Network for Accurate Super Resolution

Convolutional neural networks for super-resolution (SR) have recently improved global pixel similarity. To estimate visually pleasing SR images, general SR methods focus on optimizing a network by minimizing the feature loss provided by a pre-trained classification network. These estimated SR images are usually enriched with high-frequency texture details, but accompanied with large global pixel error. In this paper, we design a cooperative adversarial network (CAN) for SR, which consists of two sub-networks: chaser and enhancer. The chaser estimates visually pleasing SR images by optimizing feature loss provided by the enhancer, and the enhancer promotes the SR images of the chaser with less global pixel error. In addition, the enhancer fuses multiple residual features from different layers to reserve different level details for the SR images. In order to merge the two sub-networks into a unified network, we define an equilibrium ratio of the pixel-wise loss of chaser to enhancer for controlling alternate process of three training units. Compared to existing multi-network models, our CAN shows competitive performance with less parameters.
Identity evaluation of moving vehicles tracking using hue and slope value on wireless sensor network

Moving vehicle detection is a very important task in intelligent transportation system and video surveillance system. In this paper, we proposed the identity evaluation method for checking on whether or not the same vehicle as time progresses. The proposed method is composed of background/foreground, the moving vehicle detection with colour-based shadow removal, and the tracking process. To verify the identity evaluation, we adopt the moving vehicle features including the hue value, a slope of moving distance and the movement factor. Some experiments are conducted so as to verify the proposed algorithm, and as a result, the identity evaluation of moving vehicles is well detected and tracked with a unique value of moving vehicle features.
Word-to-word Machine Translation: Bilateral Similarity Retrieval for Mitigating Hubness

Abstract Nearest neighbor search is playing a critical role in machine word translation, due to its ability to obtain the lingual labels of source word embeddings by searching k Nearest Neighbor ( k NN) target embeddings from a shared bilingual semantic space. However, aligning two language distributions into a shared space usually requires amounts of target label, and k NN retrieval causes hubness problem in high-dimensions feature space. Although most the best- k retrievals get rid of hubs in the list of translation candidates to mitigate the hubness problem, it is flawed to eliminate hubs. Because hub also has a correct source word query corresponding to it and should not be crudely excluded. In this paper, we introduce an unsupervised machine word translation model based on Generative Adversarial Nets (GANs) with Bilingual Similarity retrieval, namely, Unsupervised-BSMWT. Our model addresses three main challenges: (1) reduce the dependence of parallel data with GANs in a fully unsupervised way. (2) Significantly decrease the training time of adversarial game. (3) Propose a novel Bilingual Similarity retrieval for mitigating hubness pollution regardless of whether it is a hub. Our model efficiently performs competitive results in 74min exceeding previous GANs-based models.
African Linguistics in the Americas

Intelligence community (IC) and by extension the American government, Christian missions, Peace Corps and numerous research initiatives form the braid of North American scholarship on African languages. Mainstream North American linguistics continued to be relatively uninterested in Africa until recently. African languages gained importance when they proved relevant to linguistic theory, exhibiting features that caused theoreticians to reformulate their thinking. Interest was further stimulated when funding became available for the documentation of endangered languages. Missionaries have been more constant in their attention, although that work has changed to converge as missionaries, too, focused on literacy, language documentation and linguistic analysis. The study of African languages in Latin America had in focus the Spanish and Portuguese varieties spoken in former European colonies. In Brazil, research on African languages gave first priority to the lexicon and discussed the possibility of the Brazilian Portuguese having undergone a creolization process. In the other countries of Latin America, the situation is similar in the sense that, except the Creole of Colombia, the African presence was considered only to be found in the lexicon, which did not significantly modify the Hispanic varieties of the continent. In addition to the search for grammatical features of African languages in the linguistic varieties spoken in Latin America, there is still a strong interest in the identification of lexical elements mainly of Bantu origin.
An Efficient Framework Based on Cloud Computing Integrated With Internet of Things Technology for Intelligent Waste Management

This chapter presents techniques based on internet of things and cloud computing-driven waste management. The data of the World Bank says that the municipal solid waste generation by the year of 2025 will be 1.42 kg/capital per day in the urban residential areas, with the increase in cost of about $375.5 billion that has a major rise from an annual of $205.4 billion in the year 2012. Due to the high population with the extreme consumption of goods and services, this leads to a strong association among the income levels, quality of life, and waste generation. In the present situation, more than 50% of the total population is living in the cities. In the governance aspect, it is said that the cost of waste management will be highly expensive. This chapter deals with the effective waste management with the implementation of internet of things (IoT)-based cloud technology with the machine learning algorithm that could be highly intellectual in the management of waste.
Karar vericinin tercih ettiği çözümü bulmak için filtreleme yardımlı etkileşimli bir yöntem

 An interactive algorithm to find the best solution of a decision maker  Designed for multiple criteria problems in discrete solution space  Good convergence performance and speed  Application to university selection problem with five criteriaMany decision making problems involve multiple criteria that need to be considered simultaneously, and these criteria generally conflict with each other.It is a difficult process for a decision maker (DM) to reach a final solution in such problems.In this study, a new interactive approach is proposed to converge towards the most preferred solution of the DM among discrete alternatives that are evaluated with multiple criteria.Figure A. Main steps of the proposed approachPurpose: For multiple criteria choice problems, this study proposes an approach that distinguishes a highly preferred solution of the DM with low cognitive burden. Theory and Methods:At each iteration of the proposed interactive approach, the DM evaluates a small number of solutions and selects the most preferred one.The preferences of the DM are modeled by a weighted Lα function and parameters of this function (weight values and α) are estimated iteratively.Weighted Lα function is flexible and capable of representing a wide variety of preference structures with the use of different parameters.The solutions presented to the DM are determined with a filtering procedure to enhance convergence performance. Results:The proposed approach is tested with different sets of university data from the rankings of Times Higher Education.The results show that in various problem settings, the approach successfully converges to the most preferred solution of the DM with low number of iterations.The approach is also shown to outperform benchmark algorithms. Conclusion:Various forms of DM preferences can be represented through a weighted Lα preference function, and the proposed approach is able to find a highly preferred solution of the DM for different forms.DM is required to perform a simple task in the approach and the number of iterations is low.
TDMA Device Identification Using Continuity of Carrier Phase

Abstract Specific emitter identification provides the capability to distinguish radio emitters with the external features carried by the received waveforms. As existing features are not specific to time-division multiple access (TDMA) devices, it is yet intractable to discern the emitter in the case of low signal-to-noise rates or short durations. In this paper, we propose a novel characteristic based on the continuity of carrier phase and explore its application on TDMA device identification. The characteristic reflects a fact whether adjacent time slots are assigned to the same user terminal, which reveals a potential link between slots even if the protocol is unknown. To apply it to TDMA device identification, we augment the typical SEI scheme with two subsystems, Decision and Correction, in which the recognition results can be corrected to improve the accuracy. Simulation results demonstrate that the characteristic is resilient against the ambient noise, and it evidently improves the recognition accuracy.
IoT BASED HEAT STROKE PREDICTION SYSTEM FOR HEART PATIENT

Abstract: These days we have an increased number of diseases including increased risk of heart attacks. Many people among us lose their life to heart attacks. These because of their diet, age, less physical activity and many other factors. Heart attack also caused due to heat stroke. Heat stroke is a type of severe heat illness it occurs due to high external temperatures or physical exertion, risk factors include heat waves, high humidity, certain drugs such as diuretics, beta blockers, or alcohol, heart disease and skin disorders .To overcome and help our society from heat stroke for heart patient, we are developing such as a system which will help to decrease the death rate and early detection on heat stroke based on IoT. Wireless Sensor Network (WSNs) in health care system has yielded a tremendous effort in recent years. Still, in most of tasks like sensor data processing, health state decisions making and emergency messages sending are completed by a remote location. Transmitting and handling with an enormous number of data from body sensors consume a lot of communication resource,   which can bring an affliction to the remote server and delay the decision time and notification time. Keyword: WSNs   , IoT, beta blockers, sensors, Heat stroke.
10‐1: Can We Overcome the Challenges on the Path to Consumer Adoption of AR Headsets?

This paper will give an overview of what are the requirements for consumer adoption, and address the technical challenges associated with displays and optical systems to better explain the projected trends for the upcoming years for consumer adoption.
Feature Points Matching Algorithm based on Homography Constraint and Gray Scale Truncation Number

Abstract A measure function is used to judge whether match in boost matching. In this paper, we offer a kind of new measure function called GST. We simply use the relationship between gray differences and threshold of the points of domain tests the matching degree. We use homography and GST to improve the number and precision of correspondences of SIFT matching. Firstly, homography is estimated using the result of SIFT matching by VRANSAC. Then, wrong correspondences are eliminated by homography. Finally, we use GST and homography to boost matching the wrong correspondences. Experiments with the Oxford datasets show that our method can get better performance than the existed methods.
Improving Native Language Identification Model with Syntactic Features: Case of Arabic

In this paper, we present a method based on machine learning for Arabic native language identification task. We expose a hybrid method that combines surface analysis in texts with an automatic learning method. Unlike the few techniques found in the state of the art, the features selection phase allowed improving performances. We also show the impact of syntactic features for native language identification task. Therefore, the obtained results outperformed those provided by the best methods used for Arabic native language detection.
Exploring the Use of the Phoneme Frequency Scale Method in Determining Word Difficulty Levels and Readability Scores

Readability of texts are often dependent on the level of difficulty of words. Current methods in assessing the level of difficulty of words include word frequency assessments. This paper takes on a different approach in assessing level of difficulty of words by analyzing the frequency of the phonetic composition of words. The frequency ranking of the 44 phonemes in American English is obtained from analyzing the phoneme composition of the top 5000 most common words. The obtained phoneme frequency scale is tested in experiments using the CEFR (Common European Framework of Reference for Languages) for word difficulty and comparing with the Spache score, Flesch-Kincaid Grade Level, and New Dale-Chall score for text readability assessments. Results showed that the Phoneme Frequency Scale approach produces similar scores as the Spache score. This result will help in applications e-learning in determining user language level and ability.
Development of a Font Comparison System Using Similarity Metrics

Various measures are being used to select the best font for the content. However, the degree of similarity calculated using different measures is different for the same font. In this research, we propose a font comparison system considering different similarity comparison methods by ranking similarities for each font. For measuring similarity, we use MSE, PSNR, SSIM, and MSSSIM for image quality assessment, as well as Euclidean distance and cosine similarity in t-SNE for dimensionality reduction. Relative relations among fonts are obtained by averaging similarity rankings of different similarity comparison methods.
Hybrid Temporal Situation Calculus

We present a hybrid discrete-continuous extension of Reiter’s temporal situation calculus, directly inspired by hybrid systems in control theory. While keeping to the foundations of Reiter’s approach, we extend it by adding a time argument to all fluents that represent continuous change. Thereby, we ensure that change can happen not only because of actions, but also due to the passage of time. We present a systematic methodology to derive, from simple premises, a new group of axioms which specify how continuous fluents change over time within a situation. We study regression for our new hybrid action theories and demonstrate what reasoning problems can be solved. Finally, we show that our hybrid theories indeed capture hybrid automata.
The Object Model of the Subject Domain with the Use of Semantic Networks

Object-oriented development of software systems and theircomponents is based on the application of object-oriented models, technologies and tools that support them. Real systems are characterized by complexity, which is caused by problems describing the properties and behavior of subject domain objects. A description of the subject domain is given in the natural language, which can be considered an input language at the stage of objectoriented analysis. Syntax-oriented processing of sentences in the input language.forms the output of the model software system. The conversion of the inputlanguage into a certain output is reduced to the construction of some translator,which, for any transformations of the sentences of the input language, uses thestructure of this sentence. The ideas on which this concept is based include theideas of formal grammars and semantic calculations for symbolic chains. Thesequestions are the basis of the work under consideration.
Improvement of General Secret Sharing Scheme Reducing Shares Distributed to Specified Participants

In secret sharing schemes for general access structures, an important issue is the number of shares distributed to each participant. However, in general, the existing schemes are impractical in this respect when the size of the access structure is very large. In 2015, a secret sharing scheme that can reduce the number of shares distributed to specified participants was proposed (the scheme A of T15). In this scheme, we can select a subset of participants and reduce the number of shares distributed to any participant who belongs to the selected subset though this scheme cannot reduce the number of shares distributed to every participant. In other words, this scheme cannot reduce the number of shares distributed to each participant who does not belong to the selected subset. In this paper, we modify the scheme A of T15 and propose a new secret sharing scheme realizing general access structures. The proposed scheme can reduce the number of shares distributed to each participant who does not belong to the selected subset as well. That is, the proposed scheme is more efficient than the scheme A of T15.