Reviewerly/exHarmony huggingface dataset:
Assessment of anthropogenic contamination in an urban territory by the example of Blagoveshchensk city

The distribution of heavy metals (HM) was analyzed in soils of Blagoveshchensk. The main physicochemical features (pH, the content of organic substance, mobile forms of phosphorus and potassium, the exchangeable cations of calcium and magnesium, as well as the total content of Cu, Cr, Ni, Co, Pb, Mn, Cd, Zn) were studied in soil samples. The anthropogenic influence on the urban environment transforms the physicochemical properties of soils: alkalizes the soil cover, increases the organic matter content, exchangeable bases, and mobile forms of biogenic elements. The total content of studied HM in the soils of Blagoveshchensk exceeds their concentrations in soils of the Mukhinka background territory and shows the spatial heterogeneity of pollutants distribution in the upper soil layer. As proceeds from the comparison of the studied elements concentrations with their MPC/APC, the most polluted territories are confined to industrial zones. Four elements, i.e., Mn, Pb, Cd and Zn, are accumulated in urban soils. However, Cu, Ni, Co, Cr accumulation is not so important. The calculation results of total contamination index in the upper soil cover layer in Blagoveshchensk taking into account the HM toxicity coefficient prove that Blagoveshchensk soils are mainly classified as moderately hazardous and nonhazardous.&#x0D; The evidence on the total content of HM do not allow making conclusion about their geochemical behavior in soils and about possible transition to neighbor environment. That is why the most mobile and easily mobilized forms of heavy metals, i.e., water-soluble and specifically adsorbed forms, were extracted using the method of successive extractions. The mobility of metals was established to be higher in the urban soils as compared to the background soils. Among the studied elements, Cd, Pb and Zn proved to be the most hazardous, with their mobility being close to the average risk of being included in the nutrition chains. This may lead to the contamination of Amur cross-boundary river ecosystems with heavy metals in case the groundwater level rises in the Blagoveshchensk territory due to its waterlogging by the Zeya River water.
On Combining CNN With Non-Local Self-Similarity Based Image Denoising Methods

Despite the significant advances in convolutional neural network (CNN) based image denoising, the existing methods still cannot consistently outperform non-local self-similarity (NSS) based methods, especially on images with many repetitive structures. Although several studies have been given to incorporate NSS priors with CNN-based denoising,their improvement is generally insignificant when compared with the state-of-the-art CNN-based denoisers. In this paper, we suggest to combine CNN and NSS based methods for improved image denoising, resulting in an NSS-UNet architecture. Motivated by gradient descent inference of TNRD, both the current estimate and noisy observation are considered as the inputs to the CNN. To take the NSS prior into account, the result by NSS (e.g., BM3D or WNNM), is adopted as the initial estimate. And a modified UNet is presented for exploiting the multi-scale information. We evaluate the proposed method on three common testing datasets. The results clearly show that NSS-UNet outperforms the existing CNN and NSS based methods in terms of both PSNR index and visual quality.
Split-Attention Multiframe Alignment Network for Image Restoration

Image registration (or image alignment), the problem of aligning multiple images with relative displacement, is a crucial step in many multiframe image restoration algorithms. To solve the problem that most existing image registration approaches can only align two images in one inference, we propose a split-attention multiframe alignment network (SAMANet). Pixel-level displacements between multiple images are first estimated at low-resolution scales and then refined gradually with the increase in feature resolution. To better integrate the interframe information, we present a split-attention module (SAM) and a dot-product attention module (DPAM), which can adaptively rescale the cost volume features and optical flow features according to the similarity between features from different images. The experimental results demonstrate the superiority of our SAMANet over state-of-the-art image registration methods in terms of both accuracy and robustness. To solve the “ghosting effect” caused by pixelwise registration, we designed two “ghost” removal modules: warping repetition detection module (WRDM) and attention fusion module (AFM). WRDM detects “ghost” regions during the image warping process without increasing the time complexity of the registration algorithm. AFM uses an attention mechanism to rescale the aligned images and enables the registration network and the subsequent image restoration networks to be trained jointly. To validate the strengths of the proposed approaches, we apply SAMANet, WRDM and AFM to three image/video restoration tasks. Extensive evaluations demonstrate that the proposed methods can enhance the performance of image restoration algorithms and outperform the other compared registration algorithms.
Penulisan Soal Literasi Numerasi bagi Guru SD di Kabupaten Ponorogo

The change in curriculum aims to improve the quality of content and learning. One of the backgrounds of the curriculum revision is based on international tests on students’ competency and literation, such as PISA. In PISA, literacy-numeracy becomes one of the essential evaluation of mathematics competency. Therefore, the use of literacy problems should be administered in the primary school class. In order to implement the literacy-numeracy problem in the class well, the teachers are required to be able to develop functional literacy problems. This workshop on writing literacy-numeracy for elementary teachers in Ponorogo City was conducted in three stages. The first stage collected the fundamental knowledge of the teachers about what they understand about literacy-numeracy. The second stage was a discussion about the literacy-numeracy and its both background and classroom implementation, especially in elementary school. The last stage was the teachers’ workshop in arranging the literacy-numeracy problems. In the end, the problems developed by the teachers were analyzed based on literacy-numeracy criteria. Besides, the participants also gave feedback using a questionnaire as the information for the effectivity of the workshop.
IoT-Enabled Service for Crude-Oil Production Systems Against Unpredictable Disturbance

Internet of Things (IoT) has become a new paradigm of communication to reform traditional industries, in which distributed data automatically collected via IoT in a low cost enables many new IT services that were even impossible decades ago. This research reports on an IoT-enabled production management service for crude-oil industry. In practice, even if an optimal management decision is achieved, disruptions, such as possible oil-device failures, inclement weathers and other disturbances, arise frequently and then weaken efficiency and stability of supplement. With the help of IoT, a near-real-time management service comes into being, although the adoption of IoT brings new challenges to management of disruptions. The contributions of this article are as follows: First, a service framework is proposed for refinery which combines MQTT and Azure cloud, enabling reliable data/command delivery. Second, a smart disruption management service system is developed, which consists of monitor and alarm module, disruption management module, and rescheduling procedure module. The rescheduling procedure module takes into account the network of the refinery operations, and is easy to accommodate changes in the refinery configuration for unforeseen disruptions. The experimental results show that the proposed disruption management method balances efficiency and stability compared to traditional methods.
Creativity and Personality Traits as Foreign Language Acquisition Predictors in University Linguistics Students

Foreign (second) language (FL/SL) proficiency is one of the most important competencies for a modern person, and is necessary for both professional and personal fulfillment. The purpose of this study is to consider and compare personality traits and creativity as predictors of success in foreign language acquisition (FLA). The sample includes 128 (105 female and 23 male) first- and second-year university linguistics students. Creativity is measured by the Abbreviated Torrance Test for Adults (ATTA). The FFM personality traits are measured by the Russian NEO Five-Factor Inventory adaptation by S. Biryukov and M. Bodunov. To assess the level of FL proficiency, we used a traditional academic achievement indicator (the semester’s final grades in English), as well as the English teachers’ assessment of the level of language proficiency of their students according to the “Foreign Language Proficiency Scale” (10 indicators and total score). Descriptive statistics methods and a multiple regression analysis were used to process the data in the R software environment, version 3.5.2. The findings of our research showed that creativity indicators have a stronger but contradictory impact on the level of FL proficiency compared to personality traits. We suggest that teachers, most likely, lack knowledge on the manifestations of student creativity in the process of FL learning.
Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.
Image Text Deblurring Method Based on Generative Adversarial Network

In the automatic sorting process of express delivery, a three-segment code is used to represent a specific area assigned by a specific delivery person. In the process of obtaining the courier order information, the camera is affected by factors such as light, noise, and subject shake, which will cause the information on the courier order to be blurred, and some information will be lost. Therefore, this paper proposes an image text deblurring method based on a generative adversarial network. The model of the algorithm consists of two generative adversarial networks, combined with Wasserstein distance, using a combination of adversarial loss and perceptual loss on unpaired datasets to train the network model to restore the captured blurred images into clear and natural image. Compared with the traditional method, the advantage of this method is that the loss function between the input and output images can be calculated indirectly through the positive and negative generative adversarial networks. The Wasserstein distance can achieve a more stable training process and a more realistic generation effect. The constraints of adversarial loss and perceptual loss make the model capable of training on unpaired datasets. The experimental results on the GOPRO test dataset and the self-built unpaired dataset showed that the two indicators, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), increased by 13.3% and 3%, respectively. The human perception test results demonstrated that the algorithm proposed in this paper was better than the traditional blur algorithm as the deblurring effect was better.
Hybrid trajectory planning approach for roundabout merging scenarios

Despite the progress made in the Intelligent Transportation Systems (ITS) field, there is still a large amount of scenarios to be solved, such as urban roundabouts. In particular, the entrance must be emphasized due to most of accidents take place in this part. In such way, the article presents a method to solve the roundabout merging considering a nominal trajectory generated through Bézier curves combining with a Model Predictive Control (MPC) to assure safe future states. Tests are made using a real platform to execute the maneuver and a virtual platform to represent the other participant on the scenario. Experimental results demonstrate the good performance of the approach under merging scenarios.
VISUAL AUDIO COMMUNICATION DESIGN ON THE ROLE OF INFORMATION TECHNOLOGY ON STUDENT LIFE STYLE OF UNIVERSITAS RAHARJA

Abstract&#x0D; The development of information technology is increasingly rapidly from past to now create a condition in which people want to get at the information source needs want to easily, quickly and accurately. In college, technology is indispensable to meet the needs of students in the learning process and look for the information needed to meet the needs of learning. The utilization of current technology can create a good condition and also not good that can affect one's personality. The utilization of current technology can create a good condition and also not good that can affect one's personality. The design of audio visual communication leads to the college environment as a media monitoring technology role that students use to create a good lifestyle in the college environment in the learning process. The design of audio visual communication leads to a college environment as a media monitoring technology role that students use to create a good lifestyle in a college environment in the learning process, So that it can create a high-quality and interactive community college in the use of technology as a learning medium. The purpose of this research is as a form of understanding the role of technology as a medium that helps the learning process in college students, whether the technology that has been used so far is enough to help students in learning and Whether it is well directed or not by the presence of a system of learning that uses technology in higher education.&#x0D; Keyword: Media, Video information, Visual Information
An Adaptive Velocity Obstacle Avoidance Algorithm for Autonomous Surface Vehicles

This paper presents a new algorithm for a real-time obstacle avoidance for autonomous surface vehicles (ASV) that is capable of undertaking preemptive actions in complex and challenging scenarios. The algorithm is called adaptive velocity obstacle avoidance (AVOA) and takes into consideration the kinematic and dynamic constraints of autonomous vessels along with a protective zone concept to determine the safe crossing distance to obstacles. A configuration space that includes both the position and velocity of static or dynamic elements within the field-of-view of the ASV is supporting a particle swarm optimization procedure that minimizes the risk of harm and the deviation towards a predefined course while generating a navigation path with capabilities to prevent potential collisions. Extensive experiments demonstrate the ability of AVOA to select a velocity estimative for ASVs that originates a smoother, safer and, at least, two times more effective collision-free path when compared to existing techniques.
Automation in Moderation

This Article assesses recent efforts to encourage online platforms to use automated means to prevent the dissemination of unlawful online content before it is ever seen or distributed. As lawmakers in Europe and around the world closely scrutinize platforms’ “content moderation” practices, automation and artificial intelligence appear increasingly attractive options for ridding the Internet of many kinds of harmful online content, including defamation, copyright infringement, and terrorist speech. Proponents of these initiatives suggest that requiring platforms to screen user content using automation will promote healthier online discourse and will aid efforts to limit Big Tech’s power.In fact, however, the regulations that incentivize platforms to use automation in content moderation come with unappreciated costs for civil liberties and unexpected benefits for platforms. The new automation techniques exacerbate existing risks to free speech and user privacy and create ripe new sources of information for surveillance, aggravating threats to free expression, associational rights, religious freedoms, and equality. Automation also worsens transparency and accountability deficits. Far from curtailing private power, the new regulations endorse and expand platform authority to police online speech, with little in the way of oversight and few countervailing checks. New regulations of online intermediaries should therefore incorporate checks on the use of automation to avoid exacerbating these dynamics. Carefully drawn transparency obligations, algorithmic accountability mechanisms, and procedural safeguards can help to ameliorate the effects of these regulations on users and competition.
Malicious Account Detection on Twitter Based on Tweet Account Features using Machine Learning

As one of the most popular social media, Twitter is facing issues with the massive numbers of its users. This has led many to exploit the platform to perform cyber crime to other users. One of the cybercrime is the activity of malicious accounts. Malicious accounts such as spambots and fake followers can be problematic as they may harm other users. Spambots can send other users unwanted messages and fake followers can increase other accounts following numbers signaling trustworthiness or influence. Much research has been conducted to build a malicious account detector, but mostly use profile-based and graph-based features. On the other hand, malicious and genuine accounts can have distinct ways to tweet. In this research, we build a classification model using only account tweets. We also build further classification distinguishing fake followers and spambots from genuine accounts. In this research, maximum accuracy has been reached at 95.55% in malicious vs genuine account detection using tf-idf features and XGBoost algorithm and 95.2% in all three types of accounts using Word2Vec features and XGBoost algorithm.
A New Geometric-Oriented Minimum-Energy Perfect Control Design in the IMC-Based State-Space Domain

A new geometric approach providing the minimum-energy issue for inverse model control-related perfect regulation of linear time-invariant multi-input/single-output plants described in the discrete-time state-space framework is proposed in the paper. Recent results have shown that the minimum-norm T -inverse does not guarantee the minimum-energy perfect control design, which has been confirmed by heuristic studies only. The new proposal, postulated throughout the manuscript, certifies the potential of nonunique σ -inverse regarding the minimum-energy behavior of inverse model control-based structures. After application of the proposed geometric approach dedicated to some class of state-space systems, we can precisely calculate the total energy of the multivariable perfect control runs. Thus, the analytical new methodology allows to obtain the minimum-energy inverse model control schemes, what constitutes the main accomplishment of the paper. Additionally, the aim of future analytical exploration covering the entire class of right-invertible state-space systems is clearly focused.
Toward intelligent continuous assistance

Technology supported assistance is a research area dedicated to support both older adults and, at some level, their caregivers in a variety of situations and contexts. A number of projects doing detailed evaluation both with robots and/or ICT-based intelligent devices have identified as open challenges the need to guarantee both continuity and variability of service according to context interpretation. This paper starts from the willingness to study how both continuity and variability can be pursued by leveraging and integrating results from research areas like artificial intelligence (AI), cognitive systems, psychology and sensor networks. Some of these technological skills are needed for example by an assistive robot and still represent open challenges in AI. This paper presents a medium term research initiative aiming at synthesizing an enhanced (intelligent) control architecture for assistive robots that take advantage from the continuous flow of information provided by a sensor network. The paper presents two main results: (a) starting from the analysis of requirements coming from the real world, it envisages a conceptual cognitive architecture highlighting the functional requirements and the key capabilities characterizing an “ideal” intelligent assistive robot; (b) it presents a prototype of a testbed architecture called KOaLa (Knowledge-based cOntinuous Loop) which integrates sensor data representation, knowledge reasoning and decision making capabilities showing its novelty in a realistic scenario.
Adaptive game scoring with ambient music

Game scoring: music meets interactionMusic is an integral part of video games.Since the minimalistic beeps and bleeps of the earliest games the overwhelming majority come with their own more or less elaborate music.Similar to early film music, 1 early game music served as a replacement for missing sound effects, which was still the case in many video games of the 1990s. 2For several decades, gaming hardware was hardly able to produce naturalistic sound effects due to technological limitations.Sound synthesis technology for games of this time included standard waveform oscillators (such as square, saw and triangle), limited amplitude and frequency modulation, and basic filters (offered by only few sound chips).The stylized sound effects they produce established a unique aesthetic that continues today as part of our media culture, for instance in the chiptune genre.The uniqueness of these aesthetics becomes salient when experienced in other media contexts where naturalistic sounds are more common, such as in films, e.g. in The Super Mario Bros. Super Show!, 3 an animated television show that is based on the eponymous Nintendo games series. 4 These sound effects are closer to musical idents than filmic Foley aesthetics and can still be found today, for instance when games reward 1