Reviewerly/exHarmony huggingface dataset:
Iton: End-to-End Audio Generation with Ito Stochastic Differential Equations

This paper proposes an end-to-end speech audio generation model called ITON based on Ito stochastic differential equations (SDE). At the time of inference, ITON takes raw text as input and corresponding voice as output, and there is no process of generating mel-spectrogram in the middle. ITON uses an encoder-decoder-dual denoisers structure. The function of the encoder is to learn the representations of the text, pitch, and energy information to obtain the embedding of the prosodic text. The decoder is used to find the mapping between the text and mel-spectrogram hidden representations. The main denoiser of the dual is used to predict the gradient of a certain wave’s log probability (a.k.a score) on the wave data manifold. The auxiliary denoiser is used to fill the information gap between text and wave, and it is used to calculate the gradient of the log probability of mel-spectrogram. The diffusion processes in the two denoisers are based on Ito SDEs. Experimental results show that ITON achieves state-of-the-art MOS of 4.51 on public data set LJSpeech.
Waste Heat Reutilization and Integrated Demand Response for Decentralized Optimization of Data Centers

In recent years, the energy consumption of data centers (DCs) has increased rapidly. Since the demand response (DR) capability of DCs is considerable, DCs play an important role in DR programs. In this work, we further focus on the DCs’ integrated demand response (IDR) capability which owes to its temporally and spatially shiftable electricity and heat. To make full use of waste heat retained in the coolant, we design a waste heat recovery process for waste heat reutilization. Moreover, we propose a decentralized optimization algorithm based on the alternating direction multiplier method (ADMM) to protect the inner sensitive data of DCs. Numerical results show that energy purchase costs can be effectively reduced for DCs with IDR capability. It is also proved that the reutilized waste heat constitutes an important part of the heat supply. Besides, the convergence performance of the proposed algorithm is verified based on large quantities of simulations.
The Effects of ICT on Economic Growth in the MENA Countries: Does Governance Matter?

We examine the impacts of Information and Communications Technology (ICT) usage and ICT investment on economic growth in 16 MENA countries throughout 1995-2018. We also investigate the moderating role of the quality of national governance on the association between ICT usage, ICT investment, and economic growth. Applying the panel ARDL method, the results suggest that ICT usage plays a significant role in contributing to higher economic growth, whereas ICT investment does not have a direct impact on economic growth. In addition, we find that improvements in the quality of governance increase the effectiveness of ICT usage and investment on economic growth. Our results are of importance for policymakers interested in improving the effectiveness of ICT’s contribution to economic growth.
A Feature Selection Method Based on Binary Quantum Generalized Normal Distribution Algorithm Andmutual Information Theory

The enormous growth in data sources leads to essential demand for data analysis. For machine learning techniques such as classifcation, the size and the right selected features have great inï¬‚uences on the accuracy of the machine model. In this paper, a new feature selection method based on quantum-behavior of binary Generalized Normal Distribution Optimization algorithm ( GNDO ) and mutual information theory called MBQGNDO is proposed. The proposed method introduces quantum theory into GNDO to improve the searching process and prevent premature convergence. In addition, the fltering method based on mutual information theory is presented to greatly reduce the dimensionality of the problem. The experiments are conducted on twenty-four datasets from UCI repository to evaluate and compare the performance of the MBQGNDO against other methods from the literature. The results show that MBQGNDO superior other methods in terms of reducing number of features and also higher level of accuracy in most datasets.
A Multidata-Source Privacy-Preserving Approach: A Semisupervised Learning-Based Model for Migrating Data Annotation

In the era of big data, a single computing node can no longer meet the demand, and a large quantity of data or larger models often have higher requirements on computing resources. Therefore, practical data application scenarios usually adopt distributed machine learning methods, and when the data have privacy-sensitive properties, the central scheduling of distributed machine learning will create a great risk of privacy leakage to user data. In this paper, a multidata-source privacy-preserving model is designed based on the PATE model combined with distributed machine learning ideas. We call the model in this paper the multiparty semisupervised-based knowledge transfer learning privacy-preserving model (MSSKT). In this method, the predicted privacy data model is trained on the multiteacher party, and the predicted results are sent to the student party for aggregation. The privacy property of the "student" model can be understood intuitively: there is no single teacher and therefore no single dataset that determines student training, and it can be understood formally as differential privacy (DP). Thus, even if an attacker attacks both the "student" and the single "teacher" model that is not publicly available, the privacy protection of sensitive data still holds. Through our experiments, the results confirm the effectiveness of this approach.
A Data Selection Framework to Improve Unsupervised Sentence Simplification Using Fine-Tuned Bert

Word suggestion in unsupervised sentence simplification is mostly done without considering the context. In this paper, we propose a technique that merges pre-trained BERT models with a successful edit-based unsupervised sentence simplification model to bring context-awareness into the simple word suggestion functionality. More importantly, we present that only by fine-tuning the BERT model on simple English sentences, simplification results can be improved and even outperform some of the competing supervised methods. Finally, we introduce a framework that involves filtering an arbitrary amount of unlabeled in-domain text for tuning the model in situations where labeled data, as simple and complex, is scarce. This preprocessing step also speeds up the training process by ignoring fine– tuning on unnecessary samples.
Center Suppressed Graph Convolution Tracking

Relying on the deep network, mainstream tracking methods have achieved high performance by obtaining extensive information, none of which considers the relationship among features yet. Based on the insight that the feature constraint relationship in the physical world is valuable, we propose a Center Suppressed Graph Convolution Tracking architecture, modelling relationships in features to exploit the neighborhood's information well. In the architecture, a self-integrated and a cross-context graph module are explored to weight the surrounding information and integrate them into the feature region so that the network can fully perceive the local region and surrounding feature to enhance representation ability. Modules apply the modified cosine similarity and distance prior knowledge to model the relationship among different regions, which can suppress the over-high self-response, resulting in significant information aggregation. Further, a transformer-based correlation network is designed to achieve cross-correlation substituting for conventional depth-wise convolution to build more effective correlation features. Extensive experiments are conducted on public tracking benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) performance with fast-tracking speed.
Blockchain Sharding Scheme for Agricultural Machinery Scheduling System

HighlightsBlockchain sharding scheme for agricultural machinery scheduling systemHaotian Yang,Liangmin Wang,Lu Liu,Chenbo Ding,Longxia Huang• The proposed framework aims to develop an optimal blockchain sharding scheme for agricultural machinery scheduling system to improve the throughput of the scheduling system and alleviate the storage pressure of scheduling users.• The proposed system designs a data coordination scheme suitable for cross-region scheduling of agricultural machinery, which does not need to rely on high-performance hardware for calculation, and realizes cross-shard/cross-region scheduling transactions with low overhead and high throughput.• The framework is implemented in Hyperledger Fabric, including one At-Large Shard for identity management and data coordination, and multiple regional sharding systems, which achieves high performance through parallel processing of scheduling data.
A Comprehensive Study Based on Societal Question And Answer System Using Natural Language Processing with Topic Modelling for Job Assistantship In the World Industry

This breakthrough study explores public opinion on technical words or topics using data from social media. Individuals seeking work assistance must consider public opinion on the existence of terminological or technical obstacles, as well as their impact on the environment and society. Public assistance is also required for the execution of legislation and mitigation programmes. A public opinion study is required to better understand the social environment and social processes. Researchers are paying special attention to social media data because it provides extremely significant information on public perceptions and responses to conflicting socio-technical terminology or issues, such as quorums, stack overflows, and yahoo! It also responds to Twitter and other social media platforms, and it's frequently used to monitor and analyze how society reacts to natural and social calamities. Search for keywords or topics in topic models to identify distinct topics to get the most out of social media data. In classic topic models, users may provide an erroneous number of subjects, resulting in unsatisfactory grouping results. For data retrieval and cluster trend identification in these scenarios, exact representations are essential. Unmarked and incorrect texts or subjects are related to suitable ways to model themes utilizing NLP and NLTK models to tackle the existing challenge. As mentioned in the related works section on NLP and subject modelling, we can leverage your application with a list of references based on who is discussing in the public question and answer system to overflow the stack. This study discusses the country's public Q&A framework and examines the evolution of key issues and projects, with an emphasis on automatically disseminating relevant customer responses and determining what information items you require. The next generation of global empowerment technology has access to housing and work prospects. Finally, the results of the experiments suggest that employing theme models to process natural language improves accuracy in eliciting more acceptable responses for accommodations and interviews.
Extra, Extra, Crypto…Read All About It Cryptic Cross Words

Of course, Bitcoin isn’t the only crypto – the proliferation of ‘alt coins’ reaching a peak with nonsense such as Dogecoin, to the extent that altcoins were satirised (and commentators and financial journalists reported the satire as being real – it’s always a good indication that you shouldn’t be investing when you reach the point at which when you can’t distinguish reality from satire), when a new coin was issued and all that was known about it was that it was a coin “for carrying an undertaking of great advantage, but nobody to know what it is.” (Chancellor)
Architecture, Operation and Implementation for Blockchain-Driven Turnkey Project Under I4.0 Workshop Based on Rami 4.0

Turnkey project has always been an important part of the rapid construction of production system. However, in the I4.0 workshop, during the implementation of turnkey project, there is no unified system construction and delivery standard, and there is no system configuration architecture that meets the information security. According to the I4.0 manufacturing requirements of node scalability, trusted delivery and standard reference in the turnkey process, a reference information architecture of blockchain-driven turnkey project is proposed to establish a turnkey service system with AAS as the delivery core, so that enterprises can quickly integrate into the turnkey process: (1) Take RAMI4.0 and I4.0 component as the engineering construction framework and supporting technology, by analyzing the bottleneck of blockchain in solving the I4.0 workshop, integrating the open blockchain architecture characteristics of BC-Based smart contract and Tangle-Based extensible IIoT-node, and establish complete solution to turnkey project information architecture under the I4.0 workshop; (2) Combined with the current turnkey project, this paper proposes and studies the AAS modeling method supporting blockchain verification and the delivery constraint mechanism based on smart contract, and designs the blockchain-based architecture, operation and implementation process with AAS as the delivery core; (3) By giving a development operation case of turnkey project of I4.0 workshop customized production line, it helps more SMEs to quickly integrate into the turnkey project system.
Object Parsing Expressions for Unplanned, Unmodified, and Incremental Grammar Reuse

Developing families of software languages requires, among others, composable grammar definitions. Object Parsing-Expression Grammars (OPEGs) serve as such grammars that can be composed without preplanning and in an unmodified manner, either via grammar unions or via fine-grained grammar transformations. In addition, OPEGs help avoid typical pitfalls (abstraction mismatches) of using intermediate parse representations (e.g., parse trees) when parsing to object graphs. The paper documents the design and implementation of OPEGs on top of a packrat parser as well as advanced features of OPEGs (e.g., handling multi-value properties, non-positional parsing). An OPEG implementation is available as part of DjDSL, a development system for domain-specific languages (DSLs).
Coreference Resolution for Measuring Sentiment in Financial News

We adopt a machine learning-based algorithm to identify direct and indirect company mentions in newspaper articles. We identify the importance of properly matching text segments to their respective companies when estimating human-perceived newspaper sentiment. Previous research has developed methods for measuring text sentiment and has revealed its relation to company returns. We further establish that knowing whether a text segment refers to a particular company is as important for estimating sentiment as the choice of the sentiment measure. Incorporating our methods into finance sentiment measures will significantly improve how accurately news sentiment both explains current and predicts future returns.
Acmrvd: Augmented Confidence Map Based Roboust Vehicle Detection

Vehicle detection in real time scenario is challenging because of the presence of multiple type vehicles with different speed, shapes, structure etc. This paper presents a new method called Augmented Confidence Map based Robust Vehicle Detection (ACMRVD). To reduce the adverse effect of different speed, shapes, structures, and presence of several vehicles in single image, we introduce the concept of augmentation which highlight region of interest containing the vehicles. The augmented map is generated by exploring the combination of multi-resolution analysis and maximally stable extremal regions (MR-MSER). The output of MR-MSER is supplied to fast CNN to generate confidence map, which results in candidate regions. Furthermore, unlike existing models follow conventional way for vehicle detection, we explore the combination of rough set and fuzzy based deep learning model for robust vehicle detection. To show the effectiveness of the proposed method, we conduct experiments on our own dataset captured by drones and on several benchmark datasets of vehicle detection, namely, KITTI and DETRAC. The results on our own dataset and benchmark dataset show that the proposed method outperforms the existing methods in terms of time efficiency and detection rate.
Visual Decision Aids: Improving Laypeople’s Understanding of Forensic Science Evidence

Forensic science plays an important role in the criminal justice system, however research and miscarriages of justice have demonstrated that laypeople can easily misunderstand the results of forensic tests. Given the importance of these test results, interdisciplinary oversight groups have called for clearer expression of forensic tests’ corresponding error rates. Meanwhile, a large body of research in the medical domain suggests that visual decision aids can improve understanding of statistical information. Seeking to apply decision aids to the forensic domain, we present three preregistered experiments (N = 879) demonstrating that visual decision aids may indeed improve understanding of forensic science evidence. A mini meta-analysis across the three experiments comparing control conditions to visual aids demonstrated a medium effect size of g = 0.35. Therefore, decision aids represent a promising, easy-to-implement way to improve laypeople’s understanding of forensic science evidence, thereby potentially preventing associated miscarriages of justice.
Quadriwave Lateral Shearing Interferometer Based on a Crystal Orthogonal Shear Displacer

To effectively solve the transmissivity limitation problems of two-dimensional gratings used for the QWLSI in aspherical surface measurement, and the non-uniformity of diffraction light intensity. A novel and simple quadriwave lateral shearing interferometer (QWLSI) for aspherical surface based on a crystal orthogonal shear displacer (OSD) is proposed. The OSD system is formed by adopting two-crystal polarization parallel beam displacers (PBDs), which can make it achieve the orthogonal shearing in the x- and y-directions simultaneously. The lateral shearing interference waves of four-beam overlapped replicas are simply generated in OSD orthogonal directions without any bulky and complicated optical components. when the overlapped replicas incident to the polarization angle of the analyzer set as 45° or 135°, the synchronous QWLSI interferogram information can be obtained in real-time. This QWLSI interferogram itself carries less noise, and a high-precision phase profile can be obtained by simple spectrum denoising. In this study, the proposed method was theoretically described and verified by measuring aspheric samples discussed with comparison to the measurement results with a well-established interferometer. This interferometry has made it possible to improve the stability and feasibility of aspherical surface testing experiments.
English Word Stress Production of Male and Female Madurese Students

Pronunciation is very important in speaking. Most of English learners usually face some difficulties in pronouncing English words properly especially in word stress. One of the influential factors is because of first language interference. Therefore, the objective of this study was to analyze studentsâ€™ stress production of two, three, four, five, and six syllable words in order to explain the possible factors that influence the Madurese male and female students in pronouncing English word and to analyze studentsâ€™ stress production of syllable words in order to explain the influence of gender on studentsâ€™ pronunciation. This research employed qualitative descriptive research. The subjects of this study were 20 students of Madura Islamic University. The data were collected by using questionnaires, studentsâ€™ recording and table. The result of this study shows that Madurese dialect gives significant positive influence on two syllable words stress on first syllable. It is because in Madurese dialect sound system, stress in two syllable words are frequently produced on first position. Meanwhile, male and female students were almost the same in pronouncing stress correctly on three until four syllable words. On the contrary, they put stress improperly on words that consist of five to six syllables. In this case, the students put the stress correctly and incorrectly from three until six syllable words, it does not indicate that it was influenced by Madurese local language. The possible factors affecting the studentsâ€™ word stress is because of motivation, age, target language exposure, lack of pronunciation training and lack of knowledge about the rules of word stress. Last, Gender does not influence much in pronunciation moreover in word stress. In segmental feature, female studentsâ€™ pronunciation consonant is better than that of male students but not in vowel. The students who pronounce correctly were the students who always train themselves. Pedagogically, this study can be the additional information to develop male and female studentsâ€™ stress production.
Medical Image Retrieval using Two Dimensional PCA

Medical image analysis will be used to develop image retrieval system to provide access to image databases using extracted features. Content Based Image Retrieval (CBIR) is used for retrieving similar images from image databases. During the last few years, medical images are grown and used for medical image analysis. Here, we are proposed that medical image retrieval using two dimensional Principal Component Analysis (2DPCA). For extracting medical image features, 2DPCA has advantageous that evaluates accurate covariance matrix easily as much smaller and also requires less time for finding Eigen vectors. Medical image reconstruction is performed with increased values of 2DPCA and observed from results that reconstruction accuracy improves with increase of principal component values. Retrieval is performed for transformed image space by calculating the Euclidean Distance(ED) between 2DPCA values of unknown images with database images. Minimum distance classifier is used for retrieval, which is simple classifier. Simulation results are reported by considering different medical images and showed that simulation results provide increased retrieval accuracy. Further, Segmentation of retrieved medical images is obtained using k-means clustering algorithm.
Teknologi Informasi Dalam Pendidikan

The development of technology at this time has entered the realm of the world of education, various forms of technological progress in the world of education can be accessed easily and whenever used to be used. Formerly difficult things to do now can be easily obtained, examples such as reading books, we no longer need to go to the library to buy books or buy books to be able to read books, we just open the smartphone we have and access via the internet by opening an E-book or electronic books. Not only that, the convenience provided with today's technology makes technology users spoiled, for example as we are currently being attacked by the covid-19 virus outbreak that supports us having to open the crowd for now as stated by the health protocol currently. However, with technology we can study at home and still be able to interact with friends and teachers, only with dakam, videos called online or online learning systems. Keywords : Education, Tecnology, Information