Reviewerly/exHarmony huggingface dataset:
Assessing Groundwater Quality for Multiuse and Geochemical Evolution in the South Banat Area of Serbia, Pannonian Basin

The study includes an assessment of groundwater quality from one shallow and two deeper aquifers in the Danube left bank, under the conditions of coexistence of the open coal mine, arable lands with increasing irrigation needs and potential regional water supply system for the Banat region.The spatial distribution maps of the targeted parameters were used as a starting point in the identification of nine zones in the study area.The Piper plot and the specific ionic plots pointed out that dissolution of carbonate and silicate minerals, as well as cation exchange, plays a significant role in the groundwater chemistry.Although the geological and hydrogeological complexity of the study area highlighted the considerable vertical and horizontal zonations when it comes to groundwater quality, waters have a desirable quality for drinking while for irrigation the eastern zone of the study area is better.Vertical mixing of shallow and deeper groundwaters confirmed the post-sedimentary faulting and erosion of coal seams in the central part of the study area.Generally, the results of determining the origin and quality of groundwaters throughout the study area could greatly help in making decisions regarding the regional water supply and expansion of irrigated lands in the future.
Modern libraries and the challenges

The authors discuss the main challenges for the libraries related to the libraries’ mission, their role and place in the modern society. The brief historical review of librarianship is given; the key factors of its transformation are revealed. The focus is made on the libraries – government authorities interaction and, in particular, on the so-called optimization of public libraries which primarily means the decreasing library network. The well-shaped state policy in librarianship is lacking today which impedes development of an upgraded unified concept. By examining the ratio of information and cultural &amp;educational functions of the public libraries, the authors conclude on the need for some balance based on social demand, target and resource potential of each library. The transformation of the concept of the library profession and related problems of library education as the foundation for preservation and development of human resources in the library sphere, are also discussed. The libraries’ principal response to the challenges lies in the theoretical substantiation, testing in practice, mobility and relevance.
How "True Bitcoiners" Work on Reddit to Maintain Bitcoin

After the meteoric rise in price, and subsequent public interest, of the cryptocurrency Bitcoin, a developing body of work has begun examining its impact on society. In recent months, as Bitcoin's price has rapidly declined, uncertainty and distrust have begun to overshadow early enthusiasm. In this late-breaking work, we investigated one of the largest and most important Bitcoin online communities, the r/Bitcoin Reddit forum. A vocal subgroup of users identify themselves as "true Bitcoiners", and justify their continued devotion to Bitcoin. These subreddit participants explained and justified their trust in Bitcoin in three primary ways: identifying characteristics of beneficial versus harmful Bitcoin users, diminishing the importance of problems, and describing themselves as loyal to Bitcoin over time.
Improving Students’ Interest in Learning English by Using Games

One of the teachers' efforts to improve students' interest in English is learning English through games. The learning process by playing games can provide more interactive English learning atmosphere so that it is expected to improve the ability of students in vocabulary development. Learning through games can provide several benefits. First, what is learned by students is not only in the form of mere knowledge of reason but experienced in real terms; such experiences are difficult to forget. Second, the lessons provided are pleasantly accepted, because they are related to the nature of the game which is entertaining and joyful. Thus, the possibility of student rejection of what is taught can be minimized. Third, because the game is fun, playing at the same time generates great interest for students about certain things or topics. Based on the reasons, it is expected that through these fun activities that is games; students' interest in learning English can be improved.
Robust correlation filter tracking with multi-scale spatial view

With extensive applications, visual tracking has already become one of the most important research focuses in computer vision. Due to such interference as serious occlusion or severe illumination change and so on, the appearance model of the target tends to vary heavily, posing great challenges on tracking. However, a majority of existing tracking methods have difficulties in detecting the above interference under the single spatial view, affecting the performance of tracking method apparently. In this paper, a robust correlation filter tracking method with multi-scale spatial view (RCFMSV) is proposed in which a group of multi-scale spatial filters of different view areas is established. There are two models in RCFMSV, one is detection model of multi-scale spatial view (DMMSV), which is responsible for the interference detection with the help of different sensitivity of the spatial view in different scales. The other is on-line location model of multi-scale spatial view (On-line LMMSV), which is mainly used to perform collaborative location by introducing the method of pre-location and adopting the multi-scale spatial view around the target as a reference to realize a more accurate tracking method. Extensive tracking experiments have been conducted on the proposed algorithm in object tracking benchmark and detailed comparative analysis between this algorithm and the state-of-the-art methods also have been made. It is confirmed by the experiments and analysis that the RCFMSV tracking method proposed in our work is competitive with the state-of-the-art methods in tracking performance.
An evolutionary hybrid algorithm for complex optimization problems

This paper describes an evolutionary hybrid model linking two algorithms: Particle Swarm Optimization (PSO) and Simulated Annealing (SA).The basic idea behind using a hybrid model is improving the reliability of the obtained results from our first model, namely MPSO (Modified PSO) based on PSO algorithm, by adding SA algorithm which is quite popular for its powerful feature of effective escaping from the trap of local minima.MPSO model uses the concept of evolutionary neighborhoods associated to parallel computation, to overcome to the two essential disadvantages of PSO: high running time and premature convergence.The presented algorithm has two essential operations: first running PSO algorithm in parallel using the new concept of evolutionary neighborhood to obtain a global best solution, then improving the results with SA algorithm to get the global optimal solution.By testing this hybrid algorithm (H-MPSO-SA) on a set of standard benchmark functions and according to the obtained results, the program have given satisfactory results of the hybrid model compared to the basic PSO and MPSO algorithms.
Comparative study between teachers and students on acceptance and use of technologies for educational purposes in the Chilean context

Nowadays, it is expected that technologies be integrated in teaching and learning processes. To achieve this goal, it is also necessary to recognize a series ofpersonal, institutional and formation factors that influences in the integration of these technologies. This study explores chilean university teachers and students perceptions about technology valuation, uses and acceptation for educational matters. Main results shows a high level of access to technology, especially the computer and smartphones, this last one highly valuated for academic achievement. Teachers had higher expectations for the use of ICT in teaching, using more diversity of them with multiple purposes, unlike students that presented a reduction of use, although with a high valuation of technologies. It is highlighted the smartphone valuation as a means of ubiquitous, mobile and contextualized learning. Also, a positive correlation is showed between perception of facility and utility of use of the Moodle platform, and a positive perception about b-learning models. Finally, the implications of the study for the processes of integration of ICTs in higher education contexts are presented and future lines of research are enunciated.
Towards Ontological Support for Journalistic Angles

Journalism relies more and more on information and communication technology (ICT). New journalistic ICT platforms continuously harvest potentially news-related information from the internet and try to make it useful for journalists. Because the information sources and formats vary widely, knowledge graphs are emerging as a preferred technology for integrating, enriching, and preparing journalistic information. The paper explores how journalistic knowledge graphs can be augmented with support for news angles, in order to help journalists detect newsworthy events and present them in ways that will interest the intended audience. We argue that finding newsworthy angles on news-related information is important as an example of a more general problem in information science: that of finding the most interesting events and situations in big data sets and presenting those events and situations in the most interesting ways.
Robust Variational Autoencoder

Machine learning methods often need a large amount of labeled training data. Since the training data is assumed to be the ground truth, outliers can severely degrade learned representations and performance of trained models. Here we apply concepts from robust statistics to derive a novel variational autoencoder that is robust to outliers in the training data. Variational autoencoders (VAEs) extract a lower-dimensional encoded feature representation from which we can generate new data samples. Robustness of autoencoders to outliers is critical for generating a reliable representation of particular data types in the encoded space when using corrupted training data. Our robust VAE is based on beta-divergence rather than the standard Kullback-Leibler (KL) divergence. Our proposed lower bound lead to a RVAE model that has the same computational complexity as the VAE and contains a single tuning parameter to control the degree of robustness. We demonstrate the performance of our $β$-divergence based autoencoder for a range of image datasets, showing improved robustness to outliers both qualitatively and quantitatively. We also illustrate the use of our robust VAE for outlier detection.
Human activity recognition via smart-belt in wireless body area networks

Human activity recognition based on wireless body area networks plays an essential role in various applications such as health monitoring, rehabilitation, and physical training. Currently, most of the human activity recognition is based on smartphone, and it provides more possibilities for this task with the rapid proliferation of wearable devices. To obtain satisfactory accuracy and adapt to various scenarios, we built a smart-belt which embedded the VG350 as posture data collector. This article proposes a hierarchical activity recognition structure, which divides the recognition process into two levels. Then a multi-classification Support Vector Machine algorithm optimized by Particle Swarm Optimization is applied to identify five kinds of conventional human postures. And we compare the effectiveness of triaxial accelerometer and gyroscope when used together and separately. Finally, we conduct systematic performance analysis. Experimental results show that our overall classification accuracy is 92.3% and the F-Measure can reach 92.63%, which indicates the human activity recognition system is accurate and effective.
Error detection algorithm for Lempel-Ziv-77 compressed data

In this study, we develop a novel error detection algorithm for Lempel-Ziv-77 (LZ77) compressed data. In the proposed algorithm, additional bits are not used to detect bit errors, unlike in conventional methods such as checksum, cyclic redundancy check, Hamming code, and repetition code. We also introduce eight special features of LZ77-compressed data for detecting the presence of errors. We demonstrate the feasibility of the algorithm based on simulations and evaluate it using two publicly available databases comprising the Calgary and Canterbury corpora. The error detection rate using the proposed algorithm is below those of conventional methods, but the compression ratio is better. The application of a parity bit in the algorithm improves the error detection performance. The number of redundant bits increases owing to the insertion of the parity bit, but the code rate is still greater than or equal to 0.9, whereas conventional methods obtain code rates less than 0.9. Simulations demonstrate that the algorithm obtains significant performance improvements when a parity bit is periodically inserted. In particular, we achieve an error detection rate of 100% using the parity bit when the number of bit errors is greater than seven.
Shadow-camera based solar nowcasting system for shortest-term forecasts

The rapid growth of solar power generation and the variable nature of the solar resource pose challenges for our electricity grids.Forecasting future changes in the irradiance might help to cost-efficiently manage this variability both for photovoltaic and concentration solar plants as well as grids with high solar penetrations.So far, for shortest-term forecasts with lead times of a few minutes, all-sky imager based nowcasting systems are used.However, due to the complexity of dynamically changing 3d cloud shapes as well as certain geometrical effects such as self-occlusion or near-horizon saturation, all-sky imager based nowcasting systems exhibit inherent weaknesses.Here, we present a novel system to generate shortest-term solar forecasts, which is located at Plataforma Solar de Almería in southern Spain.This approach is based on downward-facing cameras (shadow cameras), taking images of the ground.From these images, spatially resolved irradiance maps are derived.By tracking cloud shadows, future irradiances are predicted.A demonstration system is achieved, which provides shortest-term forecasts for the next 2 min.To the best of our knowledge, this is the first time such a system is developed.We benchmark several possible algorithmic approaches on 16 days and compare the deviations to a state-of-the-art all-sky imager based nowcasting system on 22 days.The root-mean-squared deviation (RMSD) of this shadow camera based nowcasting system for direct normal irradiance (DNI) and 1-min temporal averages is 15.6 % for lead times of 2 min (MAD, DNI: 9.6 %).In comparison to an all-sky imager system, this is an improvement as the all-sky imager system only reaches 22.0 % RMSD and 14.8 % MAD (both DNI).This demonstrates the feasibility and attractiveness in terms of accuracy of the proposed concept.
A Global Co-Saliency Guided Bit Allocation for Light Field Image Compression

Light field is the most prospective technology for interactive and immersive visual applications. and light field image is an intermediate data format that demands a large amount of storage space and higher transmission bandwidth. Therefore, compression of light field images is highly desired for further applications. In this paper, we propose a co-saliency guided bit allocation scheme with constraints of consistency among sub-aperture images. Firstly, saliency is jointly detected on color and depth images of sub-aperture by improving our previous model. The obtained pixel-wise co-saliency map is converted into block-wise via K-means clustering. In this way, the saliency weight of each coding tree unit (CTU) can be calculated. Then, target bits of each CTU are initially determined by the weight of each block. The allocation is adjusted dynamically under the guidance of co-saliency map and the image texture complexity. The experimental results show that BD-PSNR of 0.384 dB can be achieved for the salient region at the cost of less than 0.107 dB decrease for the whole image compared to HTM anchor. Moreover, subjective quality of proposed scheme outperforms the anchor for the salient region, and there is no noticeable distortion for non-salient region.
THE FORMATION OF TRANSLATORS’ RESEARCH COMPETENCE AT THE UNIVERSITIES OF UKRAINE

This study explores the essence and the structure of the translators’ research competence, as well as the development of the system for its formation. We have developed a system of 12 interrelated segments of the research skills of translator: the development of the initial research base, the study of theoretical information, data collection (translation), data collection (didactics), data processing, pedagogical model of the experimental forming stage, argumentation of personal position, generalization of experimental data, forecasting the research prospects, presenting the research results, publication of research results, self-esteem. In accordance with the scientific positions, we offered to evaluate the level of formation of research skills in accordance with the criterion of the performance accuracy of skills presented in each segment. The obtained data revealed low, medium, high levels of formation of research skills. We have evaluated the level of students’ knowledge about the main categories of research activity. In order to form the research competence of translators, we have developed the tasks aimed at the formation of research skills, knowledge about the organization of research activities in the field of translation studies.
Decoding the New World Language: Analyzing the Popularity, Roles, and Utility of Emojis

Emojis have quickly become a universal language that is used by worldwide users, for everyday tasks, across language barriers, and in different apps and platforms. The prevalence of emojis has quickly attracted great attentions from various research communities such as natural language processing, Web mining, ubiquitous computing, and human-computer interaction, as well as other disciplines including social science, arts, psychology, and linguistics.
Coherent Semantic Attention for Image Inpainting

The latest deep learning-based approaches have shown promising results for the challenging task of inpainting missing regions of an image. However, the existing methods often generate contents with blurry textures and distorted structures due to the discontinuity of the local pixels. From a semantic-level perspective, the local pixel discontinuity is mainly because these methods ignore the semantic relevance and feature continuity of hole regions. To handle this problem, we investigate the human behavior in repairing pictures and propose a fined deep generative model-based approach with a novel coherent semantic attention (CSA) layer, which can not only preserve contextual structure but also make more effective predictions of missing parts by modeling the semantic relevance between the holes features. The task is divided into rough, refinement as two steps and model each step with a neural network under the U-Net architecture, where the CSA layer is embedded into the encoder of refinement step. To stabilize the network training process and promote the CSA layer to learn more effective parameters, we propose a consistency loss to enforce the both the CSA layer and the corresponding layer of the CSA in decoder to be close to the VGG feature layer of a ground truth image simultaneously. The experiments on CelebA, Places2, and Paris StreetView datasets have validated the effectiveness of our proposed methods in image inpainting tasks and can obtain images with a higher quality as compared with the existing state-of-the-art approaches.
Fact Checking Misinformation Using Recommendations from Emotional Pedagogical Agents

Dealing with complex and controversial topics like the spread of misinformation is a salient aspect of our lives. In this paper, we present initial work towards developing a recommendation system that uses crowd-sourced social argumentation with pedagogical agents to help combat misinformation. We model users’ emotional associations on such topics and inform the pedagogical agents using a recommendation system based on both the users’ emotional profiles and the semantic content from the argumentation graph. This approach can be utilized in either formal or informal learning settings, using threaded discussions or social networking virtual communities.
60‐1: <i>Invited Paper:</i> Highly Transparent AMOLED Display with Interactive System

A new transparent display system with human‐computer interaction (HCI) will become the key technology of next generation display. We have developed an interactive transparent display system with 17″ transparent AMOLED. The transmittance of the transparent AMOLED can achieve 70.6% and the brightness is higher than 483 cd/m 2 . Furthermore, the interactive transparent AMOLED system has successfully verified in intelligent aquarium and ecological box with real‐time interactive operation.
Semantic gaze labeling for human-robot shared manipulation

Human-robot collaboration systems benefit from recognizing people's intentions. This capability is especially useful for collaborative manipulation applications, in which users operate robot arms to manipulate objects. For collaborative manipulation, systems can determine users' intentions by tracking eye gaze and identifying gaze fixations on particular objects in the scene (i.e., semantic gaze labeling). Translating 2D fixation locations (from eye trackers) into 3D fixation locations (in the real world) is a technical challenge. One approach is to assign each fixation to the object closest to it. However, calibration drift, head motion, and the extra dimension required for real-world interactions make this position matching approach inaccurate. In this work, we introduce velocity features that compare the relative motion between subsequent gaze fixations and a finite set of known points and assign fixation position to one of those known points. We validate our approach on synthetic data to demonstrate that classifying using velocity features is more robust than a position matching approach. In addition, we show that a classifier using velocity features improves semantic labeling on a real-world dataset of human-robot assistive manipulation interactions.
Computer Modeling of Aerosol Emissions Spread in the Atmosphere

The paper deals with the development of mathematical model and numerical algorithms for solving the problem of transfer and diffusion of aerosol emissions in the atmospheric boundary layer. The model takes into account several significant parameters such as terrain relief, characteristics of underlying surface and weather-climatic factors. A series of numerical experiments were conducted based on the given model. The obtained results presented here show how these factors affect aerosol emissions spread in the atmosphere.