Reviewerly/exHarmony huggingface dataset:
Interactive image driven sound

This paper describes the work in progress of an interactive audiovisual synthesis application that explores the relationship between user interaction, image sequences and sound in the context of inter-active digital arts and media productions. The application generates aesthetically pleasing audiovisual output in interactive and real-time. It is based on an analysis of visual changes in the image sequences which are mapped to musical scales. The application is intended to become an integrated and modular toolkit that supports creative professionals in exploring different combinations of image and sound representations, for instance, in the context of mood representations, artistic ideation and content creation processes. The current implementation marks an initial step into this direction.
Twitter in Brazil: Discourses on China in Times of Coronavirus

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Feature Selection for Classification of Breast Cancer in Histopathology Images: A Comparative Investigation Using Wavelet-Based Color Features

Breast cancer is taken into account as one of the foremost common kinds of cancer occurring in women worldwide. This work is intended to develop an automated classification system in order to classify the breast tumor as benign or malignant. An online database with 645 benign and 1370 malignant histopathological images with 40X magnification is used in this work. A total of 174 color features based on wavelet transform are extracted. To work out the foremost relevant attributes for classifying breast tumors, feature selection is carried out. The best subset is used for feature choice (wrapper), whereas prime 18 features are used for filter-based methods. A back-propagation artificial neural network (BPANN) is used as the classifier. Various performance measures like accuracy, sensitivity, specificity, etc., are used for evaluating the performance of BPANN classifier after applying different feature selection techniques. Classification accuracy of 98.56% with tenfold data division protocol is obtained when all 174 color wavelet transform-based features were used for classification. However, by using different feature selection techniques, the highest accuracy is obtained as 95.83% for the wrapper approach when 18 relevant features were used.
FHE-Based Bootstrapping of Designated-Prover NIZK

We present a novel tree-based technique that can convert any designated-prover NIZK proof system (DP-NIZK) which maintains zero-knowledge only for single statement, into one that allows to prove an unlimited number of statements in ZK, while maintaining all parameters succinct. Our transformation requires leveled fully-homomorphic encryption. We note that single-statement DP-NIZK can be constructed from any one-way function. We also observe a two-way derivation between DP-NIZK and attribute-based signatures (ABS), and as a result derive now constructions of ABS and homomorphic signatures (HS). Our construction improves upon the prior construction of lattice-based DP-NIZK by Kim and Wu (Crypto 2018) since we only require leveled FHE as opposed to HS (which also translates to improved LWE parameters when instantiated). Alternatively, the recent construction of NIZK without preprocessing from either circular-secure FHE (Canetti et al. STOC 2019) or polynomial Learning with Errors (Peikert and Shiehian, Crypto 2019) could be used to obtain a similar final statement. Nevertheless, we note that our statement is formally incomparable to these works (since leveled FHE is not known to imply circular secure FHE or the hardness of LWE). We view this as evidence for the potential in our technique, which we hope can find additional applications in future works.
A Rule-Based Post-Processing Approach to Improve Persian OCR Performance

Optical Character Recognition (OCR) is a system to convert images including text into an editable text. Nowadays, the accuracy of these systems is acceptable in images with simple-structure and high quality. However, the performance degrades for images with complex-structure, low quality, and in the presence of noise, scratches, pictures, stamps, or other non-textual symbols. This paper proposes a Persian OCR post-processing technique to increase the accuracy of the OCR systems dealing with real-world challenging samples. The proposed method extracts five features in each line of the text and uses seven proposed rules to investigate whether that line should be ignored or not. To evaluate the proposed method, Khana (structural based) and Bina (deep learning-based) Persian OCR systems have utilized, and a dataset containing 200 complex-structure images and 100 simple-structure images have been collected. The accuracy of the Khana and Bina in images with complex-structure is 39% and 58%, respectively, while after applying the proposed post-processing method the accuracy increases to 93% and 91%, respectively.
MP3 Compression as a Means to Improve Robustness against Adversarial Noise Targeting Attention-based End-to-End Speech Recognition

Adversarial Examples represent an imminent security threat to any Machine Learning system. The present thesis addresses this issue by proposing MP3-compression as a potential measure to reduce the susceptibility of Automatic Speech Recognition (ASR) systems to be mislead by Audio Adversarial Examples (AAEs). In essence, we used the Fast Gradient Sign Method (FGSM) to generate untargeted AAEs in the form of adversarial noise added to original speech samples. We used a feature inversion procedure to convert the adversarial examples from the feature into the audio domain. Different from prior work, we targeted an end-to-end, fully neural ASR system (namely ESPnet) featuring a hybrid decoder enhanced with both Connectionist Temporal Classification (CTC) and Attention mechanisms. We found that MP3 compression applied to adversarial examples indeed reduces the recognition errors when compared to raw, uncompressed adversarial inputs. This result was validated by experiments with four ASR models trained on four types of audio data (uncompressed .wav format, as well as MP3 formats at three compression bitrates - 128, 64 and 24 kbps). Additionally, when we decoded compressed adversarial examples originating from a different audio format than the training data, in a train-test mismatch scenario, we observed a further alleviation in the error rates. In a parallel series of decoding experiments, we found that MP3 compression applied to speech inputs augmented with \textit{non-adversarial noise} triggers an opposite behaviour of the ASR systems, in which more transcription errors are achieved than for uncompressed noise-augmented inputs. This finding consolidates the previous ones by suggesting that MP3 encoding is effective in diminishing only the adversarial noise. Finally, a statistical test performed on the estimated Signal-to-Noise Ratio (SNR) of adversarial inputs confirmed that MP3-compressed adversarial samples had higher SNRs (hence less adversarial noise) than uncompressed adversarial inputs.
BitMint Hard Wallet: Digital Payment without Network Communication : No Internet, yet Sustained Payment Regimen between Randomness-Verifiable Hard Wallets

Identity-bearing digital money (e.g. BitMint) can be paid in a private transaction between payor and payee without reliance on network authentication. The payee must trust that the paid digital coin is bona fide. Presenting a hard wallet (HW) to generate the required trust. Payment issued from the HW can be taken in by a second hard wallet, which will further pay to a third hard wallet, creating a payment ecology of digital money for long periods without the benefit of a communication network. Payment may be tethered to eventual terms of redemption. The hard wallet may be personalized -- fitted with ownership security capability. The HW may be engineered in conjunction with a smart phone, so people can use a single device as a phone and as an off-line wallet.
A fundamental problem of hypothesis testing with finite inventory in e‐commerce

Abstract We draw attention to a problem that is often overlooked or ignored by companies practicing hypothesis testing (A/B testing) in online environments. We show that conducting experiments on limited inventory that is shared between variants in the experiment can lead to high false‐positive rates since the core assumption of independence between the groups is violated. We provide a detailed analysis of the problem in a simplified setting whose parameters are informed by realistic scenarios. The setting we consider is a two‐dimensional (2D) random walk in a semiinfinite strip. It is rich enough to take a finite inventory into account, but is at the same time simple enough to allow for a closed form of the false‐positive probability. We prove that high false‐positive rates can occur, and develop tools that are suitable to help design adequate tests in follow‐up work. Our results also show that high false‐negative rates may occur. The proofs rely on a functional limit theorem for the 2D random walk in a semiinfinite strip.
Investigation of English Communication Skills of University Students

This study aims to explore the factors affecting English spoken abilities of university level students who face problems in speaking adequate English. English is used in Pakistan as a professional tool, however, it is taught as a subject rather than a language that is the reason even at the university level, Pakistani students face problems in speaking English properly. The study further examines the impact of English speaking skills on one’s career and personality and to investigate different attainable ways for the students to develop their speaking skills. For this purpose, the total number of samplings (N=40) undergraduate respondents from different universities of Karachi were randomly preferred as a Sample of the study in which there were (n=18) males and (n=22) females under the age limit of 18 to 22. The study was based on online closed-ended questionnaire. Online Google Questionnaire form was utilized as a data collection tool. The study revealed that majority of the students face problem in speaking fluent English while they wish to be the fluent speakers. Additionally, the study found some phonetic and cognitive aspects i.e., pronunciation, grammar, listening and reading are important factors they face. The study further discovered different techniques for the students to develop their oral skills on their own. The improvement of English speaking builds up the confidence level of students which ultimately helps them to achieve successful career opportunities ahead.
Extracting correctly aligned segments from unclean parallel data using character n-gram matching

Training of Neural Machine Translation systems is a time- and resource-demanding task, especially when large amounts of parallel texts are used. In addition, it is sensitive to unclean parallel data. In this work, we explore a data cleaning method based on character n-gram matching. The method is particularly convenient for closely related language since the n-gram matching scores can be calculated directly on the source and the target parts of the training corpus. For more distant languages, a translation step is needed and then the MT output is compared with the corresponding original part. We show that the proposed method not only reduces the amount of training corpus, butalso can increase the system’s performance.
About conditions of controllability of ensamble trajectories of differential inclusion with delay

In this paper we consider the control problem of an ensemble of trajectories of differential inclusion with a delayed argument. The controllability conditions for the ensemble of trajectories from the initial state φ0(.) to a given terminal set Y = Y(t) are studied. The results are obtained by methods of the theory of differential inclusions and multi-valued analysis. Necessary and sufficient (φ0,Y)_ conditions for -controllability and complete Y-controllability are given. The significance of these results in studies of control problems under conditions of limited information is discussed.
Online Resources for E-Learning in Educational Institutions: A Case of Covid-19 Era

Since the Covid 19 outbreak, there have been over 30 million cases of covid-19 infections with death rate of about 925,000 globally. This has resulted to the outright lockdown of educational institutions. This has encouraged them to switch to e-learning where students can access online resources at the comfort of their homes. With e-learning platforms such as Course Management Systems (CMS), Messenger, E-learning 2.0, Skype, Google+, MySpace, blogs, Instant Messaging (IM), Wikis, Zoom, GoogleMeet, and Hi5, online resources such as e-books, e-magazines, e-newspaper, e-audio/ e-images, e-journals, e-databases, e-conference proceedings, e-reports, e-maps, e-pictures/photographs, e-manuscripts, e-theses, e- subject guide, e-newsletters and other internet/websites can be accessed for teaching and learning. But it is regrettably that most educational institutions are not implementing nor have access to developed online resources for e-learning activities for their students. Therefore, this paper discusses online resources and e-learning in the covid 19 lockdown period; e-learning platforms that are used to access online resources for e-learning; and the challenges of online resources for e-learning during covid 19 lockdown period. The paper concludes that with the advent of the ravaging Covid 19 virus which has claimed millions of lives, provision of online resources for e-learning has been implemented to cover the gap in the delivery of educational services. Recommendations were that government should ensure the provision of workable ICT policies that will encourage the use of online resources for e-learning in her educational institutions
On Size Generalization in Graph Neural Networks

Graph neural networks (GNNs) can process graphs of different sizes but their capacity to generalize across sizes is still not well understood. Size generalization is key to numerous GNN applications, from solving combinatorial optimization problems to learning in molecular biology. In such problems, obtaining labels and training on large graphs can be prohibitively expensive, but training on smaller graphs is possible. This paper puts forward the size-generalization question and characterizes important aspects of that problem theoretically and empirically. We prove that even for very simple tasks, such as counting the number of nodes or edges in a graph, GNNs do not naturally generalize to graphs of larger size. Instead, their generalization performance is closely related to the distribution of local patterns of connectivity and features and how that distribution changes from small to large graphs. Specifically, we prove that for many tasks, there are weight assignments for GNNs that can perfectly solve the task on small graphs but fail on large graphs, if there is a discrepancy between their local patterns. We further demonstrate on several tasks, that training GNNs on small graphs results in solutions which do not generalize to larger graphs. We then formalize size generalization as a domain-adaption problem and describe two learning setups where size generalization can be improved. First, as a self-supervised learning problem (SSL) over the target domain of large graphs. Second as a semi-supervised learning problem when few samples are available in the target domain. We demonstrate the efficacy of these solutions on a diverse set of benchmark graph datasets.
Cybersecurity in Corona Virus (COVID-19) Age

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CSSA'20

There has been a rapid growth in the use of symbolic representations along with their applications in many important tasks. Symbolic representations, in the form of Knowledge Graphs (KGs), constitute large networks of real-world entities and their relationships. On the other hand, sub-symbolic artificial intelligence has also become a mainstream area of research. This workshop brought together researchers to discuss and foster collaborations on the intersection of these two areas.
Autonomous Predictive Modeling via Reinforcement Learning

Building a robust predictive model requires an array of steps such as data imputation, feature transformations, estimator selection, hyper-parameter search, ensemble construction, amongst others. Due to this vast, complex and heterogeneous space of operations, off-the-shelf optimization methods offer infeasible solutions for realistic response time requirements. In practice, much of the predictive modeling process is conducted by experienced data scientists, who selectively make use of available tools. Over time, they develop an understanding of the behavior of operators, and perform serial decision making under uncertainty, colloquially referred to as educated guesswork. With an unprecedented demand for application of supervised machine learning, there is a call for solutions that automatically search for a suitable combination of operators across these tasks while minimize the modeling error. We introduce a novel system called APRL (Autonomous Predictive modeler via Reinforcement Learning), that uses past experience through reinforcement learning to optimize sequential decision making from within a set of diverse actions under a budget constraint. Our experiments demonstrate the superiority of the proposed approach over known AutoML systems that utilize Bayesian optimization or genetic algorithms.
Approaches to Modeling Blockchain Systems

Currently, blockchain technology has been actively developed due to decentralization and the possibility of organizing a safe, holistic exchange and storage of information. This technology is starting to be applied in various fields. At the same time, the task arises to consider the influence of this technology on the characteristics of the network to predict the behavior of traffic on the network and ensure the required indicators of the quality of services, as well as the stability of the state of the communication network elements during the operation of the distributed registry technology. However, to consider and analyze the effect of technology in a full-scale experiment is a laborious task that cannot always be performed, therefore, in this paper, the authors propose to consider approaches to modeling these systems and, as an alternative, propose using the Anylogic system.
The Development of Teaching Material for Short-Term Courses for Learning LCA and Future Tasks in its Online Implementation

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Electromagnetic wave pattern detection using cepstral features in the manufacturing field

In manufacturing fields such as factories, multiple wireless communication systems often operate in the same area simultaneously. Moreover, it is known that industrial equipment emits electromagnetic noise over channels for wireless communications [1]. In order to ensure reliable communications under such an environment, monitoring radio wave environments specific to each manufacturing field and finding channels and timing which enable stable communications are required. The authors have studied technologies to efficiently analyze a large amount of monitoring data including signals which show unknown spectrum such as wide band electromagnetic noise [2,3]. This paper proposes performing machine learning using cepstrum vectors as features to grasp types of noise and signals from data measured under environments in which electromagnetic noise and communication signals coexist. By using this features, the authors demonstrate that reduction of computational loads and improvement of detection accuracy can be expected.
Neograd: Gradient Descent with a Near-Ideal Learning Rate.

Since its inception by Cauchy in 1847, the gradient descent algorithm has been without guidance as to how to efficiently set the learning rate. This paper identifies a concept, defines metrics, and introduces algorithms to provide such guidance. The result is a family of algorithms (Neograd) based on a {\em constant $\rho$ ansatz}, where $\rho$ is a metric based on the error of the updates. This allows one to adjust the learning rate at each step, using a formulaic estimate based on $\rho$. It is now no longer necessary to do trial runs beforehand to estimate a single learning rate for an entire optimization run. The additional costs to operate this metric are trivial. One member of this family of algorithms, NeogradM, can quickly reach much lower cost function values than other first order algorithms. Comparisons are made mainly between NeogradM and Adam on an array of test functions and on a neural network model for identifying hand-written digits. The results show great performance improvements with NeogradM.
Wind Energy Resource Assessment of the South Gobi region in Mongolia

Due to an abundance of renewable energy resources, especially wind energy resource, the government of Mongolia is set a target to increase renewable energy share in total energy. Wind energy has the potential to make a major contribution in such an increased application of renewable energy. The recent years' experience of use of the first and only wind farm in Mongolia clearly showed the need for detailed wind resource study to successfully utilize wind energy in the future. The Mongolian University of Science and Technology in collaboration of North China Electric Power University and University of Inner Mongolia performing detailed wind resource assessment at South Gobi region. In this paper specifies the meteorological data to be collected for wind energy resource assessment, the processing methods of wind measurement data and the calculation methods of main parameters, the classification of wind power density, the reference criteria for wind energy resource assessment. The wind speed data collected from January 1 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sub> of 2015 to December 31 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sub> of 2015. The result of the current detailed long term wind monitoring will establish firm base for future wind energy development in the region in the scope of the Gobi tech Asia Super Grid initiative.
Combining Genetic Algorithms and Machine Learning for Exploring the Navigation Satellite Constellation Design Tradespace

Essential for services such as communication, navigation, and weather prediction, satellite constellations must minimize loss of coverage while being subject to constraints on space traffic and the number of satellites available. Genetic algorithms (GAs) offer a versatile method of optimization with demonstrated success in applied problems. In the case of a computationally expensive problem, the necessity of repeated fitness evaluations prevents convergence of a GA in a feasible timeframe. A potential solution to this dilemma is to implement a computationally-efficient surrogate model to estimate the computationally-expensive objective function value. Recent advances in machine learning methods, in particular neural networks, make them a compelling candidate as a surrogate function. A GA incorporating an ensemble of neural networks as a surrogate function is applied to the problem of constellation design. We demonstrate that through the incorporation of an ensemble of neural networks, we can improve function evaluation and optimization runtime for the GA.