Reviewerly/exHarmony huggingface dataset:
Transformation of the Professional Competence of a Teacher in the Conditions of the Digital Overcoming of the Pandemic

Introduction. The pandemic caused by the spread and the necessary transition to e-learning as well as using distance educational technologies has actualized the problem of the professional competence of a school and college teacher. One of the particular issues of the above-mentioned problem is the juridical competence of a teacher in the context of a rapid digital transformation of the educational space. The purpose of the article is the theoretical understanding of the digital transformation of the juridical competence of a teacher in the context of digital overcoming of the educational consequences of the pandemic. The article actualizes the conceptual collisions of digital competence, as well as the core of the juridical competence of a teacher who has to get over with professional problems in the context of the development of digital transformation. Research methodology and methods. An analysis of the researchers works devoted to of the digital transformation of the educational system, instructive-methodological and state educational documents showed the need to review the content of the juridical, as a component of the professional, competence of a teacher in solving everyday professional tasks in the context of digital transformation. The article gives a local analysis of modern educational legislation and interstate legal acts regulating the common spots between digitalization in education and the professional competence of a teacher. Research results. As a result of the research, there was a thesis formulated about the systemic links of digital and juridical competence of a teacher. Conclusion. The part of the research devoted to the professional competence of a teacher contains a conclusion according to which the digital transformation of the professional competence of a teacher does not only consist of the methods and actions in information and communication sphere, digital environment, but also affects the key components of professional competence, including the juridical one.
AI for interactive performance: Challenges and techniques

AI techniques and systems are pervasive to the media and entertainment industry, with application ranging from chatbots and characters to games and virtual environments. A common feature characterising these applications is given by the intent to introduce a narrative element in the user experience, often conveyed through some type of performance. In this paper, we analyse the contribution of AI techniques in the design and realization of a dramatic performance, an interactive system participated by human performers and audiences through some type of enactment. Drawing on real applications developed for innovative performances, we propose an architectural model that forms the technical platform of the system, and discuss how it can be deployed using Artificial Intelligence techniques with reference to real, experimental applications created in the last two decades.
The Role of Contextual Word Embeddings in Correcting the ‘de/da’ Clitic Errors in Turkish

One of the most common spelling errors in Turkish is regarding the clitic `de/da'. People often misspell the `de/da' either by treating it as a suffix inappropriately when it should not, or by spelling it seperately when it should be a suffix. Since Turkish is a morphologically rich agglutinative language, detecting and identifying such errors are difficult. As such, many widely used spell correction tools do not handle such mistakes well. In this work, we show that a sequence tagger model that employs BERT model which produces word embeddings that consider the context of a word obtains higher performance compared to using non-contextual word embeddings instead. Training and evaluation tasks were performed with a dataset that was derived from a Turkish corpus using a special process in addition to a manually curated one. The contextual word embeddings obtained during this task are publicly shared with the research community.
Reducing Speckle in Ultrasound Images with Image Compounding

Speckle noise caused by random fluctuations in the medical ultrasound imaging system is one of the most important factors that reduce the resolution and contrast of ultrasound images. Distorted image quality can have a negative impact on the diagnostic process. Therefore, image compounding is often used to remove speckle noise in ultrasound images. In this study, the image warping method is investigated to correct the distortion in the ultrasound b-scan images generated using a cylindirical phantom by reducing speckle noise. The results show that by using a phantom with distinct edges, the image warping can effectively eliminate distortion in image compounding.
RGB-skeleton fusion network for spatial-temporal action detection

Due to most of the current algorithms use stacked RGB information for spatial-temporal action detection, the time sequence information is easily lost in the process of convolution and down-sampling, which makes it difficult to model spatial-temporal action and limits the development of action detection. Given the current advanced pose estimation algorithm that has achieved good detection accuracy, we propose an end-to-end network that fuses RGB with skeleton to solve the problem of spatial-temporal action detection. We use RGB to describe the appearance information of object and skeleton to describe the action information. Specifically, in the first part, we generate the initial classification and location proposals based on RGB information by the SSD network. Secondly, we generate frame-level skeleton information by the current advanced pose estimation algorithm, the skeleton helps the SSD network to filter negative samples during training, and then we stack the skeleton after completion and normalization, put it into LSTM network for classification. Finally, we fuse the outputs of the SSD network and LSTM network. We believe that the introduction of skeleton information can effectively address the problem of the insufficient capacity of RGB information for spatialtemporal action modeling. It is worth noting that our skeleton information is based on advanced attitude estimation algorithms rather than annotated. For the datasets, we select the single-person action videos in UCF101 and UCF50. The final experimental results show that our method can significantly improve the action modeling ability of the neural network, and show effective results in action detection.
Occurrence of Uranium in Groundwater from Cuddalore District Tamil Nadu Aided by Geospatial and Statistical Techniques

An attempt has been made to examine uranium distribution in groundwater from Cuddalore district, Tamil Nadu, India. Groundwater occurs under porous sedimentary, fractured, and weathered hard rock formations ranging in age from recent sediments to the oldest Archean formations. A total of 186 groundwater samples were collected during Pre- Monsoon (May) and Post-monsoon (January) and analyzed for major cations, anions, and uranium using standard procedures. Major anions and cations follow the order Cl- &gt;H4SiO4&gt;HCO3- &gt;NO3- &gt; Na+&gt; Ca2+&gt; Mg2+&gt;K+&gt;SO42- &gt; F-&gt; PO43- irrespective of seasons. Uranium in groundwater ranges from 0.1 micro gram per liter (µg/l ) to 24.67 µg/l with average 1.82 µg/l. The spatial representation maps isolated areas of higher and lower uranium and statistical analysis inferred uranium sources to the groundwater environment.
Towards Meaningful Software Metrics Aggregation

Aggregation of software metrics is a challenging task, it is even more complex when it comes to considering weights to indicate the relative importance of software metrics. These weights are mostly ...
A Joint Optimization Approach to Security- and Insurance Management System on Cloud

The fundamental point of this undertaking is to joined way to deal with protection and cyber insurance provisioning in the cloud based assets. Right now have introduced a joint way to deal with security and cyber insurance provisioning in the cloud. Utilizing a stochastic improvement, introduced a technique for ideally provisioning the two administrations despite vulnerability in regards to future valuing, approaching traffic and digital assaults. Hence, an application may make preparations for assaults by provisioning security administrations from suppliers, for example, Vast and Trend Micro. These administrations may take different structures; for example, secure information stockpiling, Identity Access Management (IAM), and interruption location administrations to mirror approaching traffic. And afterward cyber insurance is utilized to give express if malignant movement prompts money related misfortune. Protection inclusion might be first or outsider with, for example, burglary of cash and advanced resources, business interference, and digital blackmail, security breaks, loss of outsider information.
Mechanism Design with Blockchain Enforcement

We study the design of self-enforcing mechanisms that rely on neither a trusted third party (e.g., court, trusted mechanism designer) nor a long-term relationship. Instead, we use a smart contract written on blockchains as a commitment device. We design the digital court, a smart contract that identifies and punishes agents who reneged on the agreement. The digital court substitutes the role of legal enforcement in the traditional mechanism design paradigm. We show that, any agreement that is implementable with legal enforcement can also be implemented with enforcement by the digital court. To pursue a desirable design of the digital court, we study a way to leverage truthful reports made by a small fraction of behavioral agents. Our digital court has a unique equilibrium as long as there is a positive fraction of behavioral agents, and it gives correct judgment in the equilibrium if honest agents are more likely to exist than dishonest agents. The platform for smart contracts is already ready in 2020; thus, self-enforcing mechanisms proposed in this paper can be used practically, even now. As our digital court can be used for implementing general agreements, it does not leak the detailed information about the agreement even if it is deployed on a public blockchain (e.g., Ethereum) as a smart contract.
Productive Workforce: Portrait, Role and Strategy in Bogor Regency

The aim of this research is to describe the productive workforce based on its portrait, role and strategy in Bogor Regency. Based on portrait, the demographic changes are indicated by the increased productive age population and the decreased unproductive age population, then leading to the decreased dependency ratio. The sizes of productive aged population increase as consequences of the decreased fertility and mortality rates. Moreover, the sizes of productive aged population exceed the sizes of unproductive aged population. Based on role, productive working ages or productive workforce contribute its role to promote economic growth rate and GRDP per capita. The population structure changes contribute positively to promoting economic outputs, so that the dividend can be enjoyed from its demographic changes. Based on strategy, the role of productive workforce can be improved by investing labor supply, human capital, saving, and economic growth.
Methods toward Enhancing RSA Algorithm: A Survey

Cryptography defines different methods and technologies used in ensuring communication between two parties over any communication medium is secure, especially in presence of a third part. This is achieved through the use of several methods, such as encryption, decryption, signing, generating of pseudo-random numbers, among many others. Cryptography uses a key, or some sort of a password to either encrypt or decrypt a message that needs to be kept secret. This is made possible using two classes of key-based encryption and decryption algorithms, namely symmetric and asymmetric algorithms. The best known and the most widely used public key system is RSA. This algorithm comprises of three phases, which are the key generation phase, encryption phase, and the decryption phase. Owing to the advancement in computing technology, RSA is prone to some security risks, which makes it less secure. The following paper preview different proposals on different methods used to enhance the RSA algorithm and increase its security. Some of these enhancements include combining the RSA algorithm with Diffie-Hellman or ElGamal algorithm, modification of RSA to include three or four prime numbers, offline storage of generated keys, a secured algorithm for RSA where the message can be encrypted using dual encryption keys, etc.
Generalizing Local Density for Density-Based Clustering

Discovering densely-populated regions in a dataset of data points is an essential task for density-based clustering. To do so, it is often necessary to calculate each data point’s local density in the dataset. Various definitions for the local density have been proposed in the literature. These definitions can be divided into two categories: Radius-based and k Nearest Neighbors-based. In this study, we find the commonality between these two types of definitions and propose a canonical form for the local density. With the canonical form, the pros and cons of the existing definitions can be better explored, and new definitions for the local density can be derived and investigated.
Efficiently classify synthesized facial images generated by different synthesis methods

With image generation and manipulation as one of impressive progress of convolutional neural networks (CNNs), facial image synthesis methods, e.g., DeepFakes, pose serious challenges to social and personal security. Specifically, we find that (1) CNN-based synthesized facial image detection methods generally fail to identify synthesized images generated by other synthesis methods; (2) classical detection methods exploiting one-class support vector machines (SVMs) and traditional features of video clips fail when only one image is available. In view of the above challenges, we propose and experimentally verify a method combining CNNs features and one-class SVMs, which not only effectively detects synthesized facial images generated by different methods, but also has good robustness to the variances of the scene content.
Semantic Active Visual Search System Based on Text Information for Large and Unknown Environments

Different high-level robotics tasks require the robot to manipulate or interact with objects that are in an unexplored part of the environment or not already in its field of view. Although much works rely on searching for objects based on their colour or 3D context, we argue that text information is a useful and functional visual cue to guide the search. In this paper, we study the problem of active visual search (AVS) in large unknown environments. In this paper, we present an AVS system that relies on semantic information inferred from texts found in the environment, which allows the robot to reduce the search costs by avoiding not promising regions for the target object. Our semantic planner reasons over the numbers detected from door signs to decide either perform a goal-directed exploration towards unknown parts of the environment or carefully search in the already known parts. We compared the performance of our semantic AVS system with two other search systems in four simulated environments. First, we developed a greedy search system that does not consider any semantic information, and second, we invited human participants to teleoperate the robot while performing the search. Our results from simulation and real-world experiments show that text is a promising source of information that provides different semantic cues for AVS systems.
Eye-Tracking Technology in Online Real Estate Rental

In China in recent years, the rental housing market has boomed, but insufficient attention has been paid to microsubject tenants in the market, and there is a lack of research results on tenants’ decision-making processes. In keeping with the characteristics of China’s accommodation-renting population, this study takes as its research object graduating students, who form an important group in the housing rental market, and focuses on the information retrieval process underlying prospective tenants’ rental decisions. First, it investigates tenants’ concerns during the renting process by means of questionnaires. Second, using eye-tracking experiments, the real online renting process is simulated and tenants’ web listings are analyzed qualitatively and quantitatively. In the process of information search, the characteristics and rules of browsing the entry search page, the listings page, and the details page are obtained, and the factors that prospective tenants pay attention to in their search for rental information are obtained. The research results show that initial alphabetical sorting of the term search page can improve the subjects’ efficiency in locating the target keywords, the text information display area of the listings page receives more attention than others, and the real concern factors of the tenants on the page listing details are generally consistent with their selected factors but deviate slightly. Finally, the layout and display of web page information affect how subjects’ attention is distributed, and web page information presents a significant difference in attention between upper and lower pages.
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization

Mixed precision quantization improves DNN performance by assigning different layers with different bit-width values. Searching for the optimal bit-width for each layer, however, remains a challenge. Deep Reinforcement Learning (DRL) shows some recent promise. It however suffers instability due to function approximation errors, causing large variances in the early training stages, slow convergence, and suboptimal policies in the mixed-precision quantization problem. This paper proposes augmented DRL (ADRL) as a way to alleviate these issues. This new strategy augments the neural networks in DRL with a complementary scheme to boost the performance of learning. The paper examines the effectiveness of ADRL both analytically and empirically, showing that it can produce more accurate quantized models than the state of the art DRL-based quantization while improving the learning speed by 4.5-64 times.
Control synthesis for tasks with a given output function decentralized SISO-systems

Abstract For the control synthesis of multiply connected objects with many inputs and many outputs (MIMO) by importance of reducing computational costs by reducing accuracy, the method of decentralized control is used by dividing the original system into systems with one input and one output (SISO). The paper proposes an approach to the control synthesis of a decentralized SISO system, which is a part of a multi-connected MIMO object in conditions of impossibility of identifying model parameters. The paper considers the problem of control synthesis, which provides a given desired form of the output process.
Community-Based Data Justice: A Model for Data Collection in Informal Urban Settlements

Non-governmental organizations (NGOs) have become important curators of data from informal urban settlements. Given the absence of these communities in public datasets, they work to take informal settlements from a state of invisibility and injustice to one of visibility and justice, in and through data. The working premise of these NGOs is that by producing data about informal settlements, data can act as a “currency” through which individuals can access different forms of justice. However, the literature that studies datafication in marginalized urban communities shows this is not always the case. Data scholars have pointed out that datafication implies a series of risks, as well as new forms of exclusion and inequality for vulnerable populations and minorities. This paper studies, through the analysis of interviews of residents of informal settlements in Lima, Peru, whether intensive data collection in informal settlements is considered a process to access justice by vulnerable communities.The study concludes that in the short term, datafication does not give access to justice to vulnerable communities but in fact, deepens or reproduces instances of oppression by reinforcing the perception of their lack of knowledge, lack of capabilities or lack of authority to use data to lead their own development. However, participating in the process of datafication sparked an interest among community leaders about the different ways in which data could be used to further their capabilities, mobilize collective action and address their development needs. Community leaders are interested and willing to use data in constructive ways to collaborate with diverse actors and transform their conditions. However, this interest, power and potential needs to be activated through capability development and the cooperation of data partners willing to invest their resources to provide this training. The paper closes with a list of recommendations, suggested by community leaders from informal settlements, on how to build a community-based data collection model that redresses harms and individual forms of injustice from previous experiences.
Fuzzy Rules Reduction Based on Sparse Coding

With high-dimensional data appearing, the number of fuzzy rules increases which degrade the interpretability and increases the computation complexity of the fuzzy rule-based system. In this paper, we proposed a rule-reduced algorithm. Through the sparse encoding of the fuzzy basis functions (FBFs), rules are reduced. Least angle regression algorithm is proposed here to select the important rules. Compared with other sparse encoding algorithm, Least angle regression algorithm has the advantage of lower computation complexity and better performance. The experimental results show that our proposed algorithm has excellent performance, especially for high-dimension data.
TAGNet: Temporal Aware Graph Convolution Network for Clinical Information Extraction

In medical informatics, the most common task for data mining on electronic medical records (EMR) is disease auxiliary diagnosis, which predicts the health statuses of patients according to their historical EMRs. With the popularity of deep representation learning, researchers use deep learning-based methods to learn an effective representation from EMR data for dealing with target clinical prediction tasks. However, few work comes down to the underlying structures of EMR data, which is obvious and important in clinical practice. Therefore, this paper proposes a new method, called TAGNet, to learn a robust representation by utilizing both the structural information and the temporal information from EMR data. Verified by experiments, TAGNet achieves state-of-the-art performance on the real-world EMR dataset.
The Requirement Gatherers’ Approach to the 2019 Multi-Agent Programming Contest Scenario

The 2019 Multi-Agent Programming Contest (MAPC) scenario poses many challenges for agents participating in the contest. We discuss The Requirement Gatherers’ (TRG) approach to handling the various challenges we faced—including how we designed our system, how we went about debugging our agents, and the strategy we employed to each of our agents. We conclude the paper with remarks about the performance of our agents, and what we should have done differently.