Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Self-Tallying Voting Protocol in Decentralized IoT

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. To address these issues, in this article, we introduce a framework of the self-tallying voting system in decentralized IoT based on blockchain. We propose a concrete construction and prove that the proposed system satisfies all the security requirements, including fairness, dispute-freeness, and maximal ballot secrecy. We simulate the algorithms on a laptop, an Android phone, and a Raspberry Pi to test the time consumption and evaluate the gas cost of each algorithm in a private blockchain as well. The implementation results demonstrate the practicability of our system.
SCT: Set Constrained Temporal Transformer for Set Supervised Action Segmentation

Temporal action segmentation is a topic of increasing interest, however, annotating each frame in a video is cumbersome and costly. Weakly supervised approaches therefore aim at learning temporal action segmentation from videos that are only weakly labeled. In this work, we assume that for each training video only the list of actions is given that occur in the video, but not when, how often, and in which order they occur. In order to address this task, we propose an approach that can be trained end-to-end on such data. The approach divides the video into smaller temporal regions and predicts for each region the action label and its length. In addition, the network estimates the action labels for each frame. By measuring how consistent the frame-wise predictions are with respect to the temporal regions and the annotated action labels, the network learns to divide a video into class-consistent regions. We evaluate our approach on three datasets where the approach achieves state-of-the-art results.
Enhancing Student Motivation with use of Augmented Reality for Interactive Learning in Engineering Education

In Engineering Education, learning by doing is of paramount importance for supplementing the theoretical concepts with technical introduction to maximize the learning outcome. However, in many fields, the students are not able to apply their knowledge in practical ways in spite of having theoretical understanding of the fundamentals. This can be mainly attributed to the lack of motivation due to absence of interactive course content required for proper visualization of the concepts through student engagement in the classroom. As a result, deep understanding of the concepts is not possible for those who are unable to grasp the delivered content using traditional ways of learning. If the students are provided with such tools that they are able to visualize and interact with what is taught in the classroom, their motivation towards learning can be enhanced. Augmented Reality, being a visualization technology, can be adopted in classroom settings to fulfill this goal. This paper is focused on the use of Augmented Reality as a tool for interactive learning in various fields of engineering education, and its contribution towards student motivation in classroom scenarios.
Medical Information Extraction in the Age of Deep Learning

Objectives: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes—diseases and drugs (or medications)—and relations between them. Methods: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence. Results: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies. Conclusions: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.
Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images

The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image. The main aim of this research is to improve the robustness of the classifier used by comparing six different first-order stochastic gradient-based optimizers to select the best for this particular dataset. The dataset used to train the classifier is the PatchCamelyon public dataset, which consists of 220,025 images to train the classifier; the dataset is composed of 60% positive images and 40% negative images, and 57,458 images to test its performance. The classifier was trained on 80% of the images and validated on the rest of 20% of the images; then, it was tested on the test set. The optimizers were evaluated based on their AUC of the ROC curve. The results show that the adaptative based optimizers achieved the highest results except for AdaGrad that achieved the lowest results.
Digital Teaching Competence of University Teachers: A Systematic Review of the Literature

The digital teaching competence is an emerging issue in scientific literature. However, despite existence of different frameworks that define it, most of them are focused on the pre-university level. This article presents the results of a systematic review of the literature on the digital teaching competence of university teachers, in the Web of Science and Scopus scientific databases. According to the results, university teachers must be competent enough to meet the new challenges of today's digital society. This digital competence, both technical and pedagogical, allows teachers to enrich their teaching, develop the digital competence of their students and continue to develop professionally. Regarding their level of digital teaching competence, most university teachers seem to have adequate technical digital competence. Nevertheless, the results regarding the pedagogical use of technologies are different, with lower levels being found in the use of technologies for their own teaching development.
A Step by Step Guide for Choosing Project Topics and Writing Research Papers in ICT Related Disciplines

ICT is fast-growing and changing field. A lot of researches are being done in various area of ICT, and results are presented in various platforms like conferences, journal and books. This is common observations in the publications from developing countries (especially in sub – Saharan africa) are not being published in reputed and established publishers even their technical/experiments are good. This is due to lack of several factors including professional presentation, the novelty of the topic, quality of literature review etc. This work guides final year bachelor’s students, PG students (masters and PhD) and young researchers, especially working in computing-related disciplines, on how to convert their project works into quality publications. The authors provide details on how these researchers can select suitable project topics, do a proper review, write up the key components of a paper and present their results in an appropriate form (that is, writing style starting from abstract to conclusion). This paper also presents and guides on how to write various types of review papers.
Lattice-Based Public Key Searchable Encryption from Experimental Perspectives

Public key Encryption with Keyword Search (PEKS) aims in mitigating the impacts of data privacy versus utilization dilemma by allowing any user in the system to send encrypted files to the server to be searched by a receiver. The receiver can retrieve the encrypted files containing specific keywords by providing the corresponding trapdoors of these keywords to the server. Despite their merits, the existing PEKS schemes introduce a high end-to-end delay that may hinder their adoption in practice. Moreover, they do not scale well for large security parameters and provide no post-quantum security promises. In this paper, we propose two novel lattice-based PEKS schemes that offer a high computational efficiency along with better security assurances than that of the existing alternatives. Specifically, our NTRU-PEKS scheme achieves 18 times lower end-to-end delay than the most efficient pairing-based alternatives. Our LWE-PEKS offers provable security in the standard model with a reduction to the worst-case lattice problems. We fully implemented our NTRU-PEKS scheme and benchmarked its performance as deployed on Amazon Web Services cloud infrastructures.
Segmentation and Recognition of Basic and Transitional Activities for Continuous Physical Human Activity

Human activity recognition (HAR) is a hot research topic which aims to understand human behavior and can be applied in various applications. However, transitions between activities are usually disregarded due to their low incidence and short duration when compared against other activities, while in fact, transitions can affect the performance of the recognition system if not dealt with properly. In this paper, we propose and implement a systematic human activity recognition method to recognize basic activities (BA) and transitional activities (TA) in a continuous sensor data stream. First, raw sensor data are segmented into fragments with sliding window and the features are constructed based on window segmentation. Then, cluster analysis with K-Means is used to aggregate activity fragments into periods. Next, generally, realize the classification of BA and TA according to the shortest duration of the BA, and then deal with the hidden phenomenon of BA. Third, the fragments between adjacent BA are evaluated to decide whether they are TA or disturbance process. Finally, random forest classifier is used to accurately recognize BA and TA. The proposed method is evaluated on the public dataset SBHAR. The results demonstrate that our method effectively recognizes different activities and can deliver high accuracy with all activities considered.
A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers

This study examines all known methods that have been proposed in the literature to identify clear-sky periods in historical solar irradiance time series. Two different types of clear-sky detection (CSD) methods are discussed: those (16 total) that attempt to isolate periods of 1-min or more cloudless conditions, and those (5 total) that only attempt to detect clear-sun periods. All methods are found to rely on a diversity of inputs and on a variety of tests that typically examine the smoothness of the temporal variation of global and/or direct irradiance. Using samples of a few days with variable cloudiness, it is shown that these methods all have obvious strengths and weaknesses. Although this justifies a detailed validation to determine which method(s) could be best suited in the practice of solar radiation modeling or other applications, the current lack of appropriate equipment at high-quality reference radiometric stations prevents such an endeavor. Only a preliminary study is conducted here at seven stations of the SURFRAD network in the U.S., where 1-min irradiance measurements are available, along with sky data from a Total Sky Imager (TSI). The many limitations of the latter prevent its data to be considered “ground truth” here. Nevertheless, the comparison of the results from all CSD methods and 1.2 million TSI observations from all SURFRAD sites provides important qualitative and quantitative information, using a variety of performance indicators. Overall, two CSD methods appear more robust and are recommended, pending better high-resolution and high-performance cloud observations from modern sky cameras to redo these tests.
Gated recurrent neural network with sentimental relations for sentiment classification

Gated recurrent neural networks (GRNNs) have been very successful in sentiment classification due to their ability to preserve semantics over time. However, modeling sentimental relations such as negation and intensification under a recurrent architecture remains a challenge. In this work, we introduce a gated recurrent neural network with sentimental relations (GRNN-SR)1to capture the sentimental relations’ information from sentiment modifier context and model their effects in texts. At each time step, GRNN-SR separately encodes the information of sentiment polarity and sentiment modifier context. The new sentiment inputs are modified multiplicatively by the previous encoded sentiment modifier context before they are updated into current states of sentiment polarity, which is more effective than the approach of traditional GRNNs. The experimental results show that our model not only can capture sentimental relations but also is an improvement over state-of-the-art gated recurrent neural network baselines.
Big Data and analytics in tourism and hospitality: a perspective article

Purpose This study aims to discuss the evolution of Big Data (BD) and Analytics in the tourism and hospitality field. It analyses the important role that BD has played so far in tourism and hospitality research and delineates how it might evolve in the future. Design/methodology/approach In line with the Platinum Jubilee Special Issue of Tourism Review , this work consists of a critical and conceptual analysis including a mini literature review of recent work in the area at the intersection of BD and tourism and hospitality research. Findings Findings suggest that tourism and hospitality scholars are increasingly aware of and adopting BD approaches to retrieve, collect, analyse, report and visualise their data. However, a number of avenues for improvement in the use and interpretation of BD and BD analytics as both sets of methods and technology need to be developed. Moreover, BD analytics promise to enhance a number of digital technologies in tourism and hospitality such as AI and IoT that heavily rely on data. As such, the authors envision that a new digital entrepreneurship field might be shaped within the tourism and hospitality literature. Research pathways for future inquiry at the intersection of BD and tourism and hospitality are outlined. Originality/value While thinking retrospectively about research revolving around BD and its role in the tourism and hospitality research field so far, this study also addresses the challenges pertaining to how BD research will be conducted in the next seven decades within tourism and hospitality.
Inter Blockchain Communication

Blockchain technology is growing massively where the number of blockchain platforms and decentralized applications are increasing rapidly in the last years. However, most of the existing blockchain networks are operating in a standalone environment isolated from each other, which increases scalability and connectivity issues in the current blockchain platforms as well as limiting the blockchain adoption in industry ecosystems. In the current phase, different blockchain networks don't have mutual trust where they cannot interact with each other and their capacity level has only reached a level similar to LAN. Due to the high barriers between the independent isolated blockchain platforms, researchers have started to focus on the concept of Blockchain interoperability. Blockchain interoperability is the ability of connecting multiple blockchain networks together, which significantly increases and solves scalability and connectivity issues in the blockchain platforms. Given the potential of blockchain interoperability and cross blockchain communication, many researchers are working on finding the optimal cross blockchain communication solution. As blockchain interoperability is emerging as an essential blockchain feature, the number of proposed blockchain interoperability solutions have been increasing within the last few years. In this paper, a survey of all the available cross blockchain communication solutions are discussed with a comparison of the proposed architectures.
Technology integration for students’ information and digital literacy education in academic libraries

Purpose Digital literacy has become a widely studied topic for connecting people, participation in government and public affairs. It encourages library professionals to train students and effectively acquire, preserve and disseminate knowledge. Academic libraries involve using digital technology improved the students’ level of digital literacy and technological skills. This paper aims to understand the students’ technological competence in using database resources and searching for online information. In addition, to evaluate the training contents and designated instructors on digital literacy education, the study also focuses on digitally exposed students’ learning behavior, the library management and students’ academic interaction on new technological trends. Design/methodology/approach This study uses a quantitative method for which primary data were collected from university students using a structured research questionnaire. Through targeted random sampling techniques, 1,500 printed questionnaires were distributed among students, and 1,266 (84.4%) responses were received. The authors applied descriptive statistics to analyze the demographic information of male and female students. The statistical technique Chi-square test ( χ 2 ) was applied using SPSS to determine the association between variables. Moreover, Bonferroni correction for the new a value [modified = 0.05/number of possible comparisons/analysis for each question shown in Tables (II-IX)] was also used to avoid a family-wise error. Findings The results inferred a strong association between students’ technological skill with using digital tools, utilization of database resources and browsing networked information on the Web. The association between universities in terms of recruiting designated instructors on digital literacy education, introducing ICT based curriculum and the association between library management in maintaining a regular training program on digital literacy demonstrated a negative association. Besides, the findings disclosed that student’s digital exposure is not the forcing factor in developing technological competence, but found to be a more encouraging factor. It was also concluded that library technological involvement significantly improves student technology-based learning skills. Practical implications The results obtained after the analyzed data may help library management in providing long-term strategy for improving digital literacy education and training for students in the effective utilization of technology for better academic performance and social integration. Originality/value The study was based on quantitative methods using a questionnaire. Extensive database coverage and digital literacy skills based on coach input can improve student performance and academic quality.
A Novel Approach for Activity Recognition with Down-Sampling 1D Local Binary Pattern Features

The sensors on the mobile devices directly reflect the physical and demographic characteristics of the user. Sensor signals may contain information about the gender and movement of the ...
Oslcfit (organic simultaneous LSTM and CNN Fit): A novel deep learning based solution for sentiment polarity classification of reviews

Review sentiment influences purchase decisions and indicates user satisfaction. Inferring the sentiment from reviews is an essential task in Natural Language Processing and has managerial implications for improving customer satisfaction and item quality. Traditional approaches to polarity classification use bag-of-words techniques and lexicons combined with machine learning. These approaches suffer from an inability to capture semantics and context. We propose a Deep Learning solution called OSLCFit (Organic Simultaneous LSTM and CNN Fit). In our architecture, we include all the components of a CNN until but not including the final fully connected layer and do the same in case of a bi-directional LSTM. The final fully connected layer in our architecture consists of fixed length features from the CNN, and features for both variable length and temporal dependencies from the bi-directional LSTM. The solution fine-tunes Language Model embeddings for the specific task of polarity classification using transfer learning, enabling the capture of semantics and context. The key contribution of this paper is the combination of features from both a CNN and a bi-directional LSTM into a single architecture with a single optimizer. This combination forms an organic combination and uses embeddings fine-tuned to the reviews for the specific purpose of sentiment polarity classification. The solution is benchmarked on six different datasets such as SMS Spam, YouTube Spam, Large Movie Review Corpus, Stanford Sentiment Treebank, Amazon Cellphone & Accessories and Yelp, where it beats existing benchmarks and scales to large datasets. The source code is available for the purposes of reproducible research on GitHub.1
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data

Machine Learning (ML) models are difficult to maintain in production settings. In particular, deviations of the unseen serving data (for which we want to compute predictions) from the source data (on which the model was trained) pose a central challenge, especially when model training and prediction are outsourced via cloud services. Errors or shifts in the serving data can affect the predictive quality of a model, but are hard to detect for engineers operating ML deployments.