Reviewerly/exHarmony huggingface dataset:
A high-performance FPGA architecture for Acceleration of SVM Machine Learning Training

Support Vector Machines (SVMs) is one of the most popular machine learning algorithms as it provides high-performance and needs minimal tuning. It can be used for classification, regression and other learning tasks. Its design makes the training of large datasets time-consuming, so it is important that solutions are developed that attempt to tackle this problem. One such solution is presented in this paper. Based on the LIBSVM library, one of the most popular and widely used implementations of SVMs, we developed the design that can be executed on an heterogeneous computing system, consisting of an FPGA accelerator card, along with CPU.The first step of the process is to determine the most computationally intensive and parallelizable part of the algorithm. That is found to be a matrix row computation, involving inner products and a mathematical function (one of exp, pow, tanh) for each of the elements. Then, we develop a kernel, that can fully utilize the abilities of the FPGA accelerator card, regarding data processing and computations. In our case that is the Alveo U200 Data Center accelerator card. The results show that the proposed implementation achieves up to 14x speedup in the row computation part of the algorithm compared to a multi-threaded CPU execution on a Ryzen3 2200G, a CPU with a base clock speed of 3.5 GHz.
An approach based on deep learning for Indian sign language translation

Purpose According to the Indian Sign Language Research and Training Centre (ISLRTC), India has approximately 300 certified human interpreters to help people with hearing loss. This paper aims to address the issue of Indian Sign Language (ISL) sentence recognition and translation into semantically equivalent English text in a signer-independent mode. Design/methodology/approach This study presents an approach that translates ISL sentences into English text using the MobileNetV2 model and Neural Machine Translation (NMT). The authors have created an ISL corpus from the Brown corpus using ISL grammar rules to perform machine translation. The authors’ approach converts ISL videos of the newly created dataset into ISL gloss sequences using the MobileNetV2 model and the recognized ISL gloss sequence is then fed to a machine translation module that generates an English sentence for each ISL sentence. Findings As per the experimental results, pretrained MobileNetV2 model was proven the best-suited model for the recognition of ISL sentences and NMT provided better results than Statistical Machine Translation (SMT) to convert ISL text into English text. The automatic and human evaluation of the proposed approach yielded accuracies of 83.3 and 86.1%, respectively. Research limitations/implications It can be seen that the neural machine translation systems produced translations with repetitions of other translated words, strange translations when the total number of words per sentence is increased and one or more unexpected terms that had no relation to the source text on occasion. The most common type of error is the mistranslation of places, numbers and dates. Although this has little effect on the overall structure of the translated sentence, it indicates that the embedding learned for these few words could be improved. Originality/value Sign language recognition and translation is a crucial step toward improving communication between the deaf and the rest of society. Because of the shortage of human interpreters, an alternative approach is desired to help people achieve smooth communication with the Deaf. To motivate research in this field, the authors generated an ISL corpus of 13,720 sentences and a video dataset of 47,880 ISL videos. As there is no public dataset available for ISl videos incorporating signs released by ISLRTC, the authors created a new video dataset and ISL corpus.
Towards A Smart Regulatory Compliance, The Capabilities of RegTech and SupTech

Global financial crisis made the financial regulation volume increase. Financial institutions needed to interpret new regulations and swiftly adjusted to them. RegTech, Regulatory Technology, was born to ease the regulatory requirements compliance process. RegTech was then also adopted by supervisor and/or regulator to supervise the compliance of financial institutions. The adoption of RegTech by supervisor is called SupTech (Supervisory Technology). RegTech and SupTech were discussed mostly in banking or financial research. This paper tries to bring more nuance to information technology e.g., the capabilities and their underpinning technology. Hence, RegTech/SupTech can be more discussed in computer science research. This paper also concludes the definition of RegTech/Suptech and states one core capability of RegTech with its four pillars.
MSCPDPLab: A MATLAB toolbox for transfer learning based multi-source cross-project defect prediction

Software defect prediction (SDP) plays an important role in allocating testing resources and improving testing efficiency. Multi-source cross-project defect prediction (MSCPDP) based on transfer learning refers to transferring defect knowledge from multiple source projects to the target project. MSCPDP has drawn increasing attention from academic and industry communities, and some MSCPDP methods have been proposed. However, most existing MSCPDP models are not open-source. MSCPDPLab replicates nine state-of-the-art MSCPDP models with unified interface and integrates the processes of data loading, model training and testing, and performance evaluation (including 13 performance measures). This paper describes the toolbox’s functionalities and presents its ease of use.
AuxBranch: Binarization residual-aware network design via auxiliary branch search

While network binarization is a promising method in memory saving and speedup on hardware, it inevitably leads to binarization residual of intermediate features, resulting in performance capability degradation. To alleviate the above issue, we focus on the network topology design scheme to the more suitable network structure for the extreme-low-bit scenario. In this paper, we propose the baseline-auxiliary expanding network design method to compensate for the binarization residual of features via searching for auxiliary branches, denoted as AuxBranch. The intermediate feature maps are reasonably enhanced by combining baseline and auxiliary features, mimicking the corresponding feature output of the full-precision network. In addition, we devise a hybrid performance estimator (PE) with three elements of preliminary accuracy, feature similarity, and computational complexity. The PE jointly performs an efficient architecture search for binarization baseline and enables automatic computation complexity adjustment under diverse constraints. Extensive experiments show that our approach is superior in terms of accuracy and computational performance, and is plug-and-play for different network backbones and binarization policies. Our code is available at https://github.com/VipaiLab/AuxBranch.
A semisupervised classification algorithm combining noise learning theory and a disagreement cotraining framework

In the era of big data, the data in many business scenarios are characterized by a small number of labelled samples and a large number of unlabelled samples. It is quite difficult to classify and identify such data and provide effective decision support for a business. A commonly employed processing method in this kind of data scenario is the disagreement-based semisupervised learning method, i.e., exchanging high-confidence samples among multiple models as pseudolabel samples to improve each model’s classification performance. As such pseudolabel samples inevitably contain label noise, they may interfere with the subsequent model learning and damage the robustness of the ensemble model. To solve this problem, a semisupervised classification algorithm based on noise learning theory and a disagreement cotraining framework is proposed. In this model, first, the probably approximately correct (PAC) estimation theory under label noise conditions is applied, the relationship between the label noise level and model robust estimation in the process of multiround cotraining is discussed, and a disagreement elimination algorithm framework based on multiple-model (feature argument and select (FANS) algorithm and L1 penalized logistics regression (PLR) algorithm) cotraining is constructed based on this theoretical relationship. The experimental results show that the algorithm proposed in this paper gives not only a high-confidence sample set that meets the upper bound constraint of the label noise level but also a robust ensemble model capable of resisting sampling bias. The work performed in this paper provides a new research perspective for semisupervised learning theory based on disagreement.
Graph Spring Network and Informative Anchor Selection for session-based recommendation

Session-based recommendation (SBR) aims at predicting the next item for an ongoing anonymous session. The major challenge of SBR is how to capture richer relations in between items and learn ID-based item embeddings to capture such relations. Recent studies propose to first construct an item graph from sessions and employ a Graph Neural Network (GNN) to encode item embedding from the graph. Although such graph-based approaches have achieved performance improvements, their GNNs are not suitable for ID-based embedding learning for the SBR task. In this paper, we argue that the objective of such ID-based embedding learning is to capture a kind of neighborhood affinity in that the embedding of a node is similar to that of its neighbors' in the embedding space. We propose a new graph neural network, called Graph Spring Network (GSN), for learning ID-based item embedding on an item graph to optimize neighborhood affinity in the embedding space. Furthermore, we argue that even stacking multiple GNN layers may not be enough to encode potential relations for two item nodes far-apart in a graph. In this paper, we propose a strategy that first selects some informative item anchors and then encode items' potential relations to such anchors. In summary, we propose a GSN-IAS model (Graph Spring Network and Informative Anchor Selection) for the SBR task. We first construct an item graph to describe items' co-occurrences in all sessions. We design the GSN for ID-based item embedding learning and propose an item entropy measure to select informative anchors. We then design an unsupervised learning mechanism to encode items' relations to anchors. We next employ a shared gated recurrent unit (GRU) network to learn two session representations and make two next item predictions. Finally, we design an adaptive decision fusion strategy to fuse two predictions to make the final recommendation. Extensive experiments on three public datasets demonstrate the superiority of our GSN-IAS model over the state-of-the-art models.
Employment of Blockchain Technology in the Field of Renewable Energy

Energy represents one of the biggest challenges of the 21st century due to the rapid growth of the global population and economy. However, gaining a Net-Zero emission is one of the main considerations when governments aim to address energy demands. For this, they aim to increase the role of renewable energy sources to solve these issues. But, it is also beneficial to obtain integrated energy systems with intelligent operations and control systems. The main aim of this paper is to provide a review on the role of blockchain in the renewable energy sector. In this regard, different application areas of the blockchain in renewable energy field are discussed, and then the challenges faced in this concept are discussed.
A non-interactive privacy preserved training technique based on hybrid deep learning

Training a neural network in a privacy preserved way enables efficient data transmission without the loss of sensitive information. There are many researches related to training a neural network for various purposes. While training a neural network for communication purposes, security stands as a major issue. Most of the available researches focus on training a network but includes communication overhead for the data owners due to frequent interactions between data owner and trainer during the training period. The proposed work aims to reduce this communication overheads of the data owners by avoiding frequent interaction with trainer in a privacy preserved way. The article proposes the Non-interactive Privacy-Preserved training (NPP) strategy where there is no interaction between data owners and trainers after acquiring the cipher text from the data owners. The three main entities such as the data owners, trainers and the Service Provider (SP) play pivotal roles in the systems’ operations. The Long Short-Term Memory (LSTM) neural network is proposed for the training purpose and the Adam optimization algorithm works on optimizing the gradients while training. The homomorphic encryption scheme encrypts the data in an additive manner and the training phase involves padding of the cipher-text that prevents the data to be revealed to SP. The gradient computation provides optimal gradients for every iteration in the training phase and hence the model works well with availed services from SP. After the training process, the network model is updated and then the services are availed to the trainer by the SP. The proposed NPP model is compared with the other existing models to prove the efficiency of the model in reducing communication overhead of the data owners.
Zero-power optical convolutional neural network using incoherent light

As a new high-speed intelligent computing method, optical neural network (ONN) has the advantage of realizing lower or even zero power computing. For example, the diffractive neural networks based on passive diffraction layer can work without power consumption. However, in the realization methods of current ONN, coherent light is used to carry input information, while the light in the natural scene is incoherent light, which makes it difficult to apply ONN to the actual optical scene for physical observation. In this paper, we propose an irregular incoherent optical convolutional neural network (I2OCNN). The network only uses the reflection and transmission properties of light to realize the controllable rearrangement of two-dimensional incoherent light field on a series of passive optical devices, so as to realize the cross interconnection of optical neurons, and thus realizes the optical convolution neural network of incoherent light with zero power consumption. Since the architecture is based on intensity modulation and incoherent superposition, incoherent light can be used to carry input signals, which solves the problem of practical application of ONN. In addition, the network can achieve irregular convolution. MNIST and Fashion MNIST were used to verify the image recognition capability of 7-layer I2OCNN, and the testing accuracy was 86.95% and 75.68%, respectively. Theoretical reasoning and simulation results show that this architecture can complete the basic image recognition tasks under the condition of incoherence and zero power consumption.
Smart Hospitality: Goodbye Virus!

One of the main impacts of technologies in the tourism sector is traveler empowerment. Technology has allowed travelers to be a lot more informed and to take a proactive role in organizing their own trip. Now, tourists are much more demanding, have several options to choose, and have more bargaining power. Tourist has also become not just a mere visitor, but a content generator sharing their experience with other travelers. This research intends to identify the adoption of technologies in the hotel and restaurant sector and secondly to identify smart technologies that can help to eradicate the virus and normalize the hospitality business. The research question is: How COVID-19 could be a trigger for the adoption of new technologies in the hospitality sector? The objectives are: (i) to identify new technologies already adopted in the hotel and restaurant areas, (ii) to pinpoint how new technologies could help in health and cleanness protocols, and (iii) to categorize changes in operational areas after the consecutive lockdowns. To achieve these goals, several case studies were collected to show and explain best practices for the future. The results are expected to help identify outcomes to build a survey to apply to hospitality professionals. Also, those outcomes will be useful in improving professionals’ tools, resources, and procedures to help to eradicate the COVID-19 virus.
Qualitative content analysis of Nigerian heads-of-state and presidents’ inaugural addresses: text mining, topic modelling and sentiment analysis

Political speech acts are critical for politicians launching a regime because they can provide information that can be used to control people's thoughts and opinions. The purpose of this study was to conduct a qualitative content analysis of the inaugural and ascension addresses of Nigerian heads of state and presidents. The textual data used in this analysis were the ascension and inaugural addresses of Nigerian Heads of State and Presidents from 1960 to 2019. They were extracted and analysed using text-mining techniques. Textual data were clustered about their topical content using Latent Dirichlet Allocation (LDA), and speech cohesion between these addresses was examined using a similarity matrix and heatmap. Furthermore, term frequency and association analyses were performed to examine the high-frequency terms (tokens) and the terms (tokens) that are strongly correlated within each of the ascension/inaugural addresses (corpus). The summarization of characters and words in the ascension and inaugural addresses reveals that the Civilian Presidents used more characters and words than the Military Heads of State. There was an increase in the number of characters and words in the ascension and inaugural addresses among those who had served the nation multiple times. The total sentiment score in the ascension/inaugural addresses from 1960 to 2019 by Civilian Presidents and Military Heads of State revealed that the Civilian Presidents expressed more trust, surprise, sadness, joy, fear, disgust and anticipation in their addresses than the Military Heads of State. The most occurring term (token) in the ascension/inaugural addresses was the word government which appeared 221 times. The most token in the corpus government was found to be moderately correlated with the following tokens: loss, existing and majority. Similarly, economic was found to be moderately correlated with these tokens: inflation, building, education, exchange, loan, workers and technical. In this study, all the ascension/inaugural addresses share similar topic distribution: as seen in Abacha's and Muritala's addresses; and Shonekan's inaugural address was very similar to Balewa, Azikwe and Babangida's addresses; Babangida's ascension, Abdulsalam's 1998 ascension, Jonathan's 2010 inaugural and Buhari's 2015 inaugural addresses discussed similar topics to Obasanjo's 1976 ascension address. The highest average sentiment score was observed in Obasanjo's 2003 inaugural address and the lowest score was in Buhari's 1983 ascension address. The sentiment score for the ascension/inaugural addresses showed that Civilian Presidents inaugural addresses expressed more positive, joy, trust and anticipation than Military Heads of State. These emotions showed that the Civilian President's inaugural addresses are better when compared to Military Heads of State in terms of the sentiment scores.
Time Traveling Towards a Climate-Neutral Society: An Interactive and Immersive Experience

The European Union aims to be climate-neutral by 2050—an economy with net-zero greenhouse gas emissions. RSE carries out research in the field of electrical energy with special focus on national strategic projects supported through the Fund for Research into Electrical Systems. The activity covers the entire supply system with an application-oriented, experimental, and system-based approach. The e-REAL’s time traveling immersive experience is based on RSE’s know-how and allows interactive and immersive experiences that are powerful ways to use relevant communication and foster people’s awareness about the European Union targets.
Data Visualization in the News Media: Trends and Challenges

Data visualization is applied intensively in the digital news media where it serves as an effective tool for storytelling and for facilitating explanations of complex or data-intensive information. However, research and theoretical studies of this type of narrative lag badly behind in terms of both their volume and importance. It is in response to this failure to keep pace that this chapter seeks to review academic and scientific production dedicated to understanding the news media, data visualization and, more specifically, information visualization, infographics, visual narratives, and visual storytelling. In so doing, our aims are to, first, delimit the state of the art of this discipline; second, describe emerging trends and the challenges faced when seeking to deploy data visualization, at the same time as identifying the technologies and protocols being employed to this end; and, third, determine the possibilities and limitations of its use for communication and journalism. We find that recent studies have focused primarily on infographics, data journalism, structured journalism, media literacy and the reader’s interpretation of visual data, the exploitation of visualization in the digital media, and visual analytics. Studies of data visualization technologies and tools have shown an interest above all in the application of big data strategies within the news media, as well as in the development of software and tools of visual analysis. Finally, our scientific database revealed a number of studies dedicated to visual analytics, the specific challenges faced, and the decision-making processes involved.
Establishment of Baseline Reference Geochemical Values in Tropical Soils of Western Ghats: Assessment of Periyar Basin with Special Reference to Contaminant Geochemistry

Abstract Based on the detailed geochemical studies of 184 soil samples from Periyar River Basin (PRB), a tropical monsoon dominated river basin (5398 km 2 ) in the southern western Ghats (WGs) of India, a baseline reference data is established. The soils are mildly acidic with sandy loam and silt loam facies in non‐monsoon to sandy loam and sandy clay loam in monsoon. The mean metal concentrations follow the upper continental crust and world shale values. The Geoaccumulation index (Igeo) shows unpolluted to moderately polluted category, except for Cu, Zn, and Ba, while Enrichment Factor (EF) indicates no to minor enrichment for all elements. Contamination factor (Cf) indicates low to considerable contamination for V, Rb, Sr, Ni, and low to very high contamination for Cu, Zn, and Ba. Three significant components are extracted by Principal Component Analysis (PCA), explaining 78.09% and 74.10% of the total variance for monsoon and non‐monsoon seasons. Ti, Al, Fe, Ca, Na, K, V, Cr, Ni, Sr, and Ba exhibited common source of origin while anthropogenic origin is identified for Zn and Cu. The study will provide valuable information into the pedological characteristics of WGs river basins.