Reviewerly/exHarmony huggingface dataset:
Image Denoising with Self-adaptive Multi-UNET Valve

Image denoising has been a classic problem in the field of image processing and computer vision, with the underlying aim of estimating the original image by suppressing noise from the noise-contaminated version of the image. Different intrinsic and extrinsic conditions may cause noise in image that in real-life situations are often not avoidable and therefore image denoising techniques are absolutely essential. UNET is an autoencoder style architecture and some researchers have recently used UNET for image denoising. As with most denoising approaches, UNET is also not robust to noise types beyond what it has seen during the training. The simplest solution to cater for different types of noise is to use multiple denoising models, each of which is trained to denoise a specific type of noise. The denoising model to be used at testing time, however, depends on the distribution of noise present in the test image which is not known beforehand and thus the solution is not practical. To overcome this limitation, we propose Multi-UNET Valve (MUNET Valve), a novel approach that uses a special neural network called Valve, which employs latent features of different UNETs as control signals to automatically determine the distribution of noise during testing and to efficiently integrate different UNETs into the denoising pipeline. This makes the system self adaptive to varying types of noise and experimentation on Brain CT Images proves that our model outperforms the state of the art with average PSNR 30.8.
Requirements Engineering Technology for the IoT Software Systems

Contemporary software systems (CSS) – such as the internet of things (IoT) based software systems – incorporate new concerns and characteristics inherent to the network, software, hardware, context awareness, interoperability, and others, compared to conventional software systems. In this sense, requirements engineering (RE) plays a fundamental role in ensuring these software systems' correct development looking for the business and end-user needs. Several software technologies supporting RE are available in the literature, but many do not cover all CSS specificities, notably those based on IoT. This research article presents RETIoT (Requirements Engineering Technology for the Internet of Things based software systems), aiming to provide methodological, technical, and tooling support to produce IoT software system requirements document. It is composed of an IoT scenario description technique, a checklist to verify IoT scenarios, construction processes, and templates for IoT software systems. A feasibility study was carried out in IoT system projects to observe its templates and identify improvement opportunities. The results indicate the feasibility of RETIoT templates' when used to capture IoT characteristics. However, further experimental studies represent research opportunities, strengthen confidence in its elements (construction process, techniques, and templates), and capture end-user perception.
University of Regensburg @ SwissText 2021 SEPP-NLG: Adding Sentence Structure to Unpunctuated Text

This paper describes our approach (UR-mSBD) to address the shared task on SentenceEnd and Punctuation Prediction in NLG Text(SEPP-NLG) organised as part of SwissText2021. We participated in Subtask 1 (fully un-punctuated sentences – full stop detection) andsubmitted a run for every featured language(English, German, French, Italian). Our sub-missions are based on pre-trained BERT mod-els that have been fine-tuned to the task at hand.We had recently demonstrated, that such an ap-proach achieves state-of-the-art performancewhen identifying end-of-sentence markers onautomatically transcribed texts. The differenceto that work is that here we use language-specific BERT models for each featured lan-guage. By framing the problem as a binarytagging task using the outlined architecture weare able to achieve competitive results on theofficial test set across all languages, with Re-call, Precision, F1 ranging between 0.91 and0.96 which makes us joint winners for Recallin two of the languages. The official baselinesare beaten by large margins.
Synbit: synthesizing bidirectional programs using unidirectional sketches

We propose a technique for synthesizing bidirectional programs from the corresponding unidirectional code plus a few input/output examples. The core ideas are: (1) constructing a sketch using the given unidirectional program as a specification, and (2) filling the sketch in a modular fashion by exploiting the properties of bidirectional programs. These ideas are enabled by our choice of programming language, HOBiT, which is specifically designed to maintain the unidirectional program structure in bidirectional programming, and keep the parts that control bidirectional behavior modular. To evaluate our approach, we implemented it in a tool called Synbit and used it to generate bidirectional programs for intricate microbenchmarks, as well as for a few larger, more realistic problems. We also compared Synbit to a state-of-the-art unidirectional synthesis tool on the task of synthesizing backward computations.
Mobile game-based learning with Opi app

Smartphones have been an alternative resource in emergency remote learning. In this paper, we introduce the Opi application, an educational game aimed at students from the 1st to the 5th grade of elementary school. The application was developed with the objective of contributing to the engagement and motivation of children in the context of remote classes during the Covid-19 pandemic, in addition to promoting the participation of parents in the process. The main functionalities and characteristics of Opi are described from a mobile game-based learning framework. We report the usability evaluation carried out with children and parents, listing the problems found and the suggestions and feedback. It ends with some lessons learned on the design and evaluation for children in the context of the pandemic and of mobile game-based learning.
Significance of Reverse Side Analysis in Revealing Interpolation as Well as Relative Age of Writings – Illustration through Forensic Examination of Documents

Letters, figures or words are sometimes fraudulently added to a contract or other legal documents after it has been signed or produced. This can drastically change the intent of the document. It is more difficult to detect additions that are skillfully added to a document than erasures. It becomes the task of the document examiner to determine if the addition or alteration was made after the document was executed or these were the part and parcel of the document earlier. The forensic document experts can examine, detect and interpret the evidence of alteration by means of additions/insertions, as well as relative age of writings and spuriousness of the documents even in the absence of samples. Besides examining the different types of forgery involved in the writings/ signatures, the ink of the writings, misalignment between the letters, figures, words as well as lines, crowdedness of the writing, rhythm of the writing1, detection and analysis of indented and other accidental markings in the front as well as back side of the document under observation need to be carefully and thoroughly examined. In this study, the systematic method of identification of fraudulent nature of the document with respect to alteration by means of additions/ insertions/interpolation, as well as relative age of writings are discussed.
Can reactive synthesis and syntax-guided synthesis be friends?

While reactive synthesis and syntax-guided synthesis (SyGuS) have seen enormous progress in recent years, combining the two approaches has remained a challenge. In this work, we present the synthesis of reactive programs from Temporal Stream Logic modulo theories (TSL-MT), a framework that unites the two approaches to synthesize a single program. In our approach, reactive synthesis and SyGuS collaborate in the synthesis process, and generate executable code that implements both reactive and data-level properties. We also present a tool, temos, that combines state-of-the-art methods in reactive synthesis and SyGuS to synthesize programs from TSL-MT specifications. We demonstrate the applicability of our approach over a set of benchmarks, and present a deep case study on synthesizing a music keyboard synthesizer.
Accuracy evaluation model of oral English translation based on semantic analysis

In order to solve the problem of low semantic feature extraction accuracy and evaluation efficiency in traditional oral English translation accuracy evaluation model, this paper proposes and constructs an oral English translation accuracy evaluation model based on semantic analysis. Semantic analysis method combined with word frequency weight is used to construct word frequency feature matrix, and singular value decomposition method is used to solve the feature matrix to obtain semantic features. According to the result of word score calculation, the weight of oral English translation words is calculated. According to the results of vocabulary weight calculation, the translation accuracy evaluation model is constructed to complete the evaluation of oral English translation accuracy. Experimental results show that, compared with the traditional evaluation model, this model has higher semantic feature extraction accuracy and accuracy evaluation efficiency, so the evaluation performance of the text model is stronger.
QR Bar-Code Designed Resistant against EM Information Leakage

A threat of eavesdropping display screen image of information device is caused by unintended EM leakage emanation. QR bar-code is capable of error correction, and its information is possibly read from a damaged screen image from EM leakage. A new design of QR bar-code proposed in this paper uses selected colors in consideration of correlation between the EM wave leakage and display color. Proposed design of QR bar-code keeps error correction of displayed image, and makes it difficult to read information on the eavesdropped image.
Retraction: Chen et al. Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks. Appl. Sci. 2019, 9, 2316

The published article Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks [...]
Algorithms of modified Hirsch index for the formation of ratings of university teachers in terms of publications and their quotes

Objective: To suggest an algorithm for assessing the results of the scientiﬁc activity of a teacher drawing on published works and their quotes, which should make it possible to consider the total quoting of signiﬁcant publications determined by the Hirsch algorithm. To create a more advanced approach to the effective assessment of scientists’ publication activities compared to the Hirsch number. Methods: A systematization approach is used to systematize the total citation array, by separating it into the following citation arrays: 1) the basic, deﬁned Hirsch index; 2) signiﬁcant publications; 3) intensity of work. The resulting arrays formed the basis of the creation of three indices: h – Hirsch’s index, g – an index of signiﬁcant publications and p – intensive work index. These indexes allowed us to determine the Euclidean norms gh – the index of basic signiﬁcant publications and hp – the intensive work index of a teacher. The above indices make it possible to differentiate the teachers’ publication activities and produce better ranking in the team. Results: The ratings of a team of authors formed by the sample from Russian Science Citation Index using the Hirsch index and the gh-index are built. It is shown that more efﬁcient compared to the Hirsch index is the proposed approach. Practical importance: The described approach to the formation of the rating position of the authors of publications in the creative team is based on a simple calculation and comparison of the modiﬁed Hirsch indices
Real World Robustness from Systematic Noise

Systematic error, which is not determined by chance, often refers to the inaccuracy (involving either the observation or measurement process) inherent to a system. In this paper, we exhibit some long-neglected but frequent-happening adversarial examples caused by systematic error. More specifically, we find the trained neural network classifier can be fooled by inconsistent implementations of image decoding and resize. This tiny difference between these implementations often causes an accuracy drop from training to deployment. To benchmark these real-world adversarial examples, we propose ImageNet-S dataset, which enables researchers to measure a classifier's robustness to systematic error. For example, we find a normal ResNet-50 trained on ImageNet can have 1%$\sim$5% accuracy difference due to the systematic error. Together our evaluation and dataset may aid future work toward real-world robustness and practical generalization.
Employer Brand and Employee In-Role Performance: A Moderated Mediation Model of Employee's Self- Efficacy and Work Engagement

This study investigates the relationship between employer brand and employee in -role performance through the mediating role of employee’s work engagement and the moderating role of employee’s self-efficacy. It employs the integrated framework of the job demand- resource model, social exchange theory, and behavioral plasticity theory to explain these relationships. A structured questionnaire was used to collect primary data from 337 employees who work in several humanitarian organizations in Jordan. After checking the questionnaire’s validity and reliability, the research hypotheses were tested using the regression analysis and macro process plugin. The results revealed that there is a direct relationship between employer brand and employee in-role performance, and the employee’s work engagement is a mediator to this direct relationship. Furthermore, the results revealed that this relationship is stronger when an employee’s self-efficacy is low rather than high. The findings of this study pose a framework for humanitarian employees to strengthen their work engagement and in -role performance by offering employer brand activities to those individuals who have a low level of self-efficacy.
Outline of Guideline for Seismic Response Analysis Method Using 3D Finite Element Model of Reactor Building

Abstract In the seismic safety assessment and design of building structures in nuclear facilities, lumped mass models are conventionally used. However, they cannot possess the required high-accuracy evaluation of nuclear facilities, such as the local response at the equipment location in a reactor building. From this point of view, a seismic response analysis method using a three-dimensional finite element (3D FE) model is indispensable. Furthermore, because analysis results obtained using 3D FE models vary to a large degree depending on the experience and knowledge of analysts, the quality of analysis results should be ensured by developing a standard analysis method. In the Japan Atomic Energy Agency, we have developed a guideline for seismic response analysis methods that adopt 3D FE models of reactor buildings, and we plan to publish it as JAEA report. The guideline consists of a main body, commentary, and several appendixes; it also includes procedures, recommendations, points of attention, and a technical basis for conducting seismic response analysis using 3D FE models of reactor buildings. In this paper, the outline of the guideline and analysis examples based on the guideline are presented.
Breaking Down Multilingual Machine Translation

While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning. These training settings expose the encoder and the decoder in a machine translation model with different data distributions. In this paper, we examine how different varieties of multilingual training contribute to learning these two components of the MT model. Specifically, we compare bilingual models with encoders and/or decoders initialized by multilingual training. We show that multilingual training is beneficial to encoders in general, while it only benefits decoders for low-resource languages (LRLs). We further find the important attention heads for each language pair and compare their correlations during inference. Our analysis sheds light on how multilingual translation models work and also enables us to propose methods to improve performance by training with highly related languages. Our many-to-one models for high-resource languages and one-to-many models for LRL outperform the best results reported by Aharoni et al. (2019).
Handling Support Cycles and Collective Interactions in the Logical Encoding of Higher-Order Bipolar Argumentation Frameworks

In our paper [], we have proposed a logical encoding of argumentation frameworks with higher-order interactions (i.e. attacks or supports whose targets are arguments or other attacks or supports) with an evidential meaning for supports, such frameworks are called REBAF. With this encoding, we are able to characterize the semantics of REBAF under the form of specific logical models. Nevertheless this encoding has two important drawbacks: first the handling of support cycles has some weaknesses and secondly the collective interactions (i.e. attacks or supports whose source is a set of arguments and not only one argument) are not taken into account. The current paper proposes an improvement of this encoding for solving these drawbacks.
Adversarial Deception on Deep-Learning Based Radio Waveforms Classification

Deep learning has achieved superior performance on radio signal modulation classification. However, its security and reliability are vulnerable due to its data-oriented learning strategy. In this paper, the vulnerability of deep neural network for radio classification is investigated. Based on the radar and communication waveforms, slight optimal perturbations are generated and added onto the original signals by searching for the saliency map. The simulated results show that the deep neural network will suffer degradation of classification accuracy due to adversarial deception both for radio signals with high and low signal-to-noise ratio.
How Deep Learning Works: Inside the Neural Networks that Power Today's AI

Each neuron in an artificial neural network sums its inputs and applies an activation function to determine its output. This architecture was inspired by what goes on in the brain, where neurons transmit signals between one another via synapses.
Power Management for Data Centers: Challenges and Opportunities

This article presents a broad view of the management of the design and implementation of power systems for data centers. The article outlines many challenges that are present because of the demanding requirements of data centers both in design and management and then introduces the opportunities that recent technological advances have made possible. We present several new approaches of ownership and responsibilities that directly affect the financial viability of data centers.
Identification and Prediction Methods of Financial Anti-fraud

Nowadays, evaluating and identifying the potential fraud risk of borrowers effectively and calculating the fraud probability of them are the basis and significant steps of credit risk management in modern financial institutions before issuing loans.This paper mainly studies the statical analysis of the historical loan data of financial institutions based on the idea of unbalanced data classification and establishes the prediction model of loan fraud through random forest, decision tree and regression algorithm.The prediction performance of random forest algorithm is better than the other two mentioned methods.Additionally, it may obtain the feature that have a remarkable impact on the final fraud by ranking the importance of those features, which leads to a more effective judgment on the credit risk in the financial field.
On the advantages of stochastic encoders

Stochastic encoders have been used in rate-distortion theory and neural compression because they can be easier to handle. However, in performance comparisons with deterministic encoders they often do worse, suggesting that noise in the encoding process may generally be a bad idea. It is poorly understood if and when stochastic encoders do better than deterministic encoders. In this paper we provide one illustrative example which shows that stochastic encoders can significantly outperform the best deterministic encoders. Our toy example suggests that stochastic encoders may be particularly useful in the regime of perfect perceptual quality.
Innovative technologies in modern higher education: European experience and Ukrainian context

This study examines the issues of implementing innovative technological solutions in modern higher education, considering the accumulated European experience and in the modern Ukrainian context. The relevance of this study is conditioned by the wide spread of innovative technological solutions in the European higher education system in general and Ukraine in particular, as well as the need for a deep and comprehensive study of this process considering the prospects for the technological development of modern higher education and accumulated European experience in this field. The purpose of this study is to determine the fundamental factors that influence the development of technological innovations in the modern higher education system. The main results of this study should be the definition of the essence of innovative technologies in the modern system of higher education, the types and categories of modern innovations, as well as the main trends in academic research in this field.
Updates to Information Standards and Standardization Efforts

Abstract This panel, sponsored by the ASIS&amp;T Standards Committee, has two purposes. First, the panel reports the most recent activities of the national and international standards in the field of information science and technologies in which the ASIS&amp;T Standards Committee and members have been involved. Second, the panel will focus on a number of significant standardization efforts, their important roles, and the challenges in dealing with semantic conflicts while addressing inclusion and relevance to ensure non‐bias in information representation and the FAIRness of data. Those efforts have led to the globally adopted standard systems, vocabularies, and schemas, which will be introduced by this panel.