Reviewerly/exHarmony huggingface dataset:
Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.
A Lightweight Attribute-Based Security Scheme for Fog-Enabled Cyber Physical Systems

In this paper, a lightweight attribute-based security scheme based on elliptic curve cryptography (ECC) is proposed for fog-enabled cyber physical systems (Fog-CPS). A novel aspect of the proposed scheme is that the communication between Fog-CPS entities is secure even when the certification authority (CA) is compromised. This is achieved by dividing the attributes into two sets, namely, secret and shared, and subsequently generating two key pairs, referred to as the partial and final key pairs, for each entity of the Fog-CPS system. Unlike existing attribute-based encryption (ABE) and identity-based encryption schemes, in the proposed scheme, each entity calculates the final public key of the communicating CPS devices without the need of generating and transmitting digital certificates. Moreover, the proposed security scheme considers an efficient and secure key pair update approach in which the calculation overhead is limited to one group element. To show the effectiveness of the proposed scheme, we have calculated and compared the memory and processing complexity with other bilinear and elliptic curve schemes. We have also implemented our scheme in a Raspberry Pi (3B+ model) for CPS simulations. The proposed scheme guarantees the confidentiality, integrity, privacy, and authenticity in Fog-CPS systems.
Method of Constructing an Asymmetric Human Bronchial Tree in Normal and Pathological Cases

The goal of the study is the analytical design of the full asymmetric human bronchial tree (irregular dichotomy) for healthy patients and patients with obstructive pulmonary diseases. For this purpose, the author has derived the special analytical formulas. All surfaces of the bronchial tree are matched with the second-order smoothness (there are no acute angles or ribs). The geometric characteristics of the human bronchial tree in the pathological case are modeled by a “starry” shape of the inner structure of the bronchus; a level of the pathology is defined by two parameters: bronchus constriction level and level of distortion of the cylindrical shape of the bronchus. Closed analytical formulas allow a researcher to construct the human bronchial tree of an arbitrary complexity (up to alveoli); moreover, the parametric dependences make it possible to specify any desirable level of airway obstruction.
PEMANFAATAN E-LEARNING BAGI PARA PENDIDIK DI ERA DIGITAL 4.0

In this digital era or Industrial 4.0 revolution, technological advances become things that cannot be solved in human life. Teachers must learn better in learning, need to utilize optimal learning resources, this is important, because the effectiveness of learning is also included by the willingness and ability to utilize learning media, such as the web (google class). In this era students are required to have certain skills, besides those educators are also required to have several competencies that must be possessed so that the e-learning program that they run can run well. An educator or teacher must be able to learn to be there to learn, be creative and fun. Keywords- Digital Era, Education, Usefulness of E-Learning, Teacher
Cross-lingual

Clustering unlinkable entity mentions across documents in multiple languages (cross-lingual NIL Clustering) is an important task as part of Entity Discovery and Linking (EDL). This task has been largely neglected by the EDL community because it is challenging to outperform simple edit distance or other heuristics based baselines. We propose a novel approach based on encoding the orthographic similarity of the mentions using a Recurrent Neural Network (RNN) architecture. Our model adapts a training procedure from the one-shot facial recognition literature in order to achieve this. We also perform several exploratory probing tasks on our name encodings in order to determine what specific types of information are likely to be encoded by our model. Experiments show our approach provides up to a 6.6% absolute CEAFm F-Score improvement over state-of-the-art methods and successfully captures phonological relations across languages.
Pulsed Waveforms and Intermittently Nonlinear Filtering in Synthesis of Low-SNR and Covert Communications

In traditional spread-spectrum techniques, a wideband transmit signal is obtained by modulating a wideband carrier by a narrowband signal containing a relatively low-rate message. In the receiver, the respective demodulation/despreading restores the information-carrying narrowband signal. In this paper, we introduce an alternative approach, where the low-rate information is encoded directly into a wideband waveform of a given bandwidth, without physical “spreading” of the carrier's frequency. The main advantages of this approach lie in extended options for encoding the information, and in retaining a reversible control over the temporal and amplitude structures of the modulating wideband waveforms. Significant “excess bandwidth” (over that needed to carry the information) enables us to use allpass filters to manage statistical properties and time-domain appearances of these waveforms without changing their spectral composition. For example, a mixture of transmitted waveforms can be shaped as a low-crest-factor signal (e.g. to reduce the burden on the power amplifier), and/or made statistically indistinguishable from Gaussian noise (e.g. for covert transmissions and physical layer steganography), while the selected components of the received waveform can be transformed into high-crest-factor pulse trains suitable for multiplexing and/or low-SNR communications. Further, control over the temporal and amplitude structures of wideband waveforms carrying low-rate information enables effective use of nonlinear filtering techniques. Such techniques can be employed for robust real-time asynchronous extraction of the information, as well as for separation of wideband signal components with identical spectral content from each other. This can facilitate development of a large variety of low-SNR and covert communication configurations.
Use of surveillance technology to enhance exposure management for healthcare workers during the COVID-19 pandemic

We read with interest the recent article by Wee et al. describing the contact tracing processes for a COVID-19 case among their institution's healthcare workers (HCWs), a security guard working in the emergency department's 'fever area', who was picked up by universal screening of all ancillary HCWs [1]. We commend the contact tracing team for their robust epidemiological investigation and contact tracing processes to prevent nosocomial transmission of COVID-19. We wish to add that surveillance technology, such as real-time location systems (RTLS), may be a valuable addition to contact tracing practices [2].
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models’ discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.
Capturing Long-Range Dependence and Harmonic Phenomena in 24-Hour Solar Irradiance Forecasting: A Quantile Regression Robustification via Forecasts Combination Approach

The global horizontal irradiance data recorded at the earth's horizontal surface is a mixture of deterministic (extra-terrestrially) and stochastic components due to the ever-changing atmospheric conditions. The Box-Jenkins short memory stochastic models and their hybrid versions have been used successfully to forecast solar irradiance data. However, these models lack robustness and faulter for distant horizon forecasting such as more than 2 hours in the hourly case. Using a quantile regression model as both a robust benchmark and robustification model, we remedy this drawback by using long memory models and their hybrid versions in 24 hour ahead irradiance forecasting at three sites in South Africa (RVD, UPR and SUN). Results show that the inherent long memory is anti-persistent in all models but one that shows persistence. The forecasts from these long memory-based models are generally within the prediction interval of the benchmark model forecasts for two sites. Based on the RMSE, rRMSE, MAE, rMAE and the pinball loss function at the central quantile level and two extreme quantile levels, forecasts obtained through robustification via forecasts combinations with the quantile regression yield the best performance. The predictive ability of the best model before forecasts combination against the quantile regression model combined (robustified) model based Diebold-Mariano test rejected the null hypothesis of the same accuracy. As a follow up to distinguish which has a superior predictive accuracy, “murphy diagrams” were used. For the RVD and UPR sites, the model based on forecast combination has the best predictive accuracy while the quantile regression based benchmark model has the best accuracy for the SUN data.
Automatic Medical Image Report Generation with Multi-view and Multi-modal Attention Mechanism

Medical image report writing is a time-consuming and knowledge intensive task. However, the existing machine/deep learning models often incur similar reports and inaccurate descriptions. To address these critical issues, we propose a multi-view and multi-modal (MvMM) approach which utilizes various-perspective visual features and medical semantic features to generate diverse and accurate medical reports. First, we design a multi-view encoder with attention to extract visual features from the frontal and lateral viewing angles. Second, we extract medical concepts from the radiology reports which are adopted as semantic features and combined with visual features through a two-layer decoder with attention. Third, we fine-tune the model parameters using self-critical training with a coverage reward to generate more accurate medical concepts. Experimental results show that our method achieves noticeable performance improvements over the baseline approaches and increases CIDEr scores by 0.157.
SQL injection attacks countermeasures assessments

&lt;span&gt;SQL injections attacks have been rated as the most dangerous vulnerability of web-based systems over more than a decade by OWASP top ten. Though different static, runtime and hybrid approaches have been proposed to counter SQL injection attacks, no single approach guarantees flawless prevention/ detection for these attacks. Hundreds of components of open source and commercial software products are reported to be vulnerable for SQL injection to CVE repository every year. In this mapping study, we identify different existing approaches in terms of the cost of computation and protection offered. We found that most of the existing techniques claim to offer protection based on the testing on a very small or limited scale. This study dissects each proposed approach and highlights their strengths and weaknesses and categorizes them based on the underlying technology used to detect or counter the injection attacks.&lt;/span&gt;
Artificial Intelligence in economic decision making: how to assure a trust?

Motivation: The decisions made by modern ‘black box’ artificial intelligence models are not understandable and therefore people do not trust them. This limits down the potential power of usage of Artificial Intelligence.Aim: The idea of this text is to show the different initiatives in different countries how AI, especially black box AI, can be made transparent and trustworthy and what kind of regulations will be implemented or discussed to be implemented. We also show up how a commonly used development process within Machine Learning can be enriched to fulfil the requirements e.g. of the Ethics guidelines for trustworthy AI of the High-Level Expert Group of the European Union. We support our discussion with a proposition of empirical tools providing interpretability.Results: The full potential of AI or products using AI can only be raised if the decision of AI models are transparent and trustworthy. Regulations which are followed over the whole life cycle of AI models, algorithms or the products they using these are therefore necessary as well as understandability or explainability of the decisions these models and algorithms made. Initiatives on every level of stakeholders started, e.g. international level on the European Union, country level, USA, China etc. as well on a company level. The post-hoc local interpretability methods could and should be implemented by economic decision makers to provide compliance with the regulations.
FRIEND RECOMMENDATION USING GRAPH MINING ON SOCIAL MEDIA

Recommendation system is an important type of machine learning algorithm that provide precise suggestions to the users. Recommendation systems are used in innumerable types of areas such as generation of playlists, music and video services like Jio savaan, wynk, amazon prime music etc., and products recommendation for users in e-commerce applications and commercial applications. The recommendations that are provided by various types of applications increases the speed for identifying and makes easier to access the products that users are interested in. For each user, the recommendation system is capable of envisaging the future predilections on a set of items and recommend the top items. In several industries, recommendation systems are very useful as they generate huge amount of income and this type of industries can stand uniquely from competitors. Due to cumbersome number of items that each user can find in the web, the impact of recommendation system has been increased in the internet. Recommendation systems are used for custom-made navigation by getting huge amount of data particularly in social media domain for recommending friends. A recommendation system act as a subclass for the information filtering system that pursue to predict the rating. The similarity measures that are calculated in this research are Jaccard distance and Otsuka-Ochiai coefficient. The feature extractions that are used in this paper are Adar index, PageRank, Katz centrality, Hits score. Now a days many research people are implementing different types of algorithms in various domains for recommendation systems.
Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.
Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia

Abstract Background Several climatologists and experts in the renewable energy field agree that GHI and DNI calculation models must be revised because of the increasingly unpredictable and powerful climatic disturbances. The construction of analytical mathematical models for the prediction of these disturbances is almost impossible because the physical phenomena relating to the climate are often complex. We raise the question over the current and future PV system’s sustainable energy production and whether climate disturbances will be affecting this sustainability and resulting in supply decline. Methods In this paper, we tried to use deep learning as a tool to predict the evolution of the future production of any geographic site. This approach can allow for improvements in decision-making concerning the implantation of solar PV or CSP plants. To reach this aim, we have deployed the databases of NASA and the Tunisian National Institute of Meteorology relating to the climatic parameters of the case study region of El Akarit, Gabes, Tunisia. In spite of the colossal amount of processed data that dates back to 1985, the use of deep learning algorithms allowed for the validation of the previously made estimates of the energy potential in the studied region. Results The calculation results suggested an increase in production as it was confirmed by the 2019 measures. The findings obtained from the case study region were reliable and seemed to be very promising. The results obtained using deep learning algorithms were similar to those produced by conventional calculation methods. However, while conventional approaches based on measurements obtained using hardware solutions (ground sensors) are expensive and very difficult to implement, the suggested new approach is cheaper and more convenient. Conclusions In the existence of a protracted controversy over the hypothetical effects of climate change, making advances in artificial intelligence and using new deep learning algorithms are critical procedures to strengthening conventional assessment tools of the production sites of photovoltaic energy and CSP plants.
Active Learning for Imbalanced Ordinal Regression

Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes.Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems.In this article, we address an active learning based solution for imbalanced OR problem.We propose an active learning algorithm for OR (AL-OR) to select the most informative samples from unlabeled samples, mark them and add them to the training set.Based on AL-OR, we put forward an improved active learning for imbalanced OR (IAL-IOR), which further adjust the sampling strategy of AL-OR dynamically to make the training data as valuable and balanced as possible.Recall rate for multi-classification and new mean absolute error are designed to measure the performance of the algorithms.To the best of our knowledge, our algorithm is the first algorithm for imbalanced OR in algorithm level.The experimental results show that the proposed algorithms have faster convergence and much better generalization ability than the classical methods and the state-of-the-art methods under the evaluation measurements for imbalance problems.In addition, we also proved the effectiveness of our algorithms by statistical analysis.
Analysis of data dictionary formats of HIV clinical trials

Background Efforts to define research Common Data Elements try to harmonize data collection across clinical studies. Objective Our goal was to analyze the quality and usability of data dictionaries of HIV studies. Methods For the clinical domain of HIV, we searched data sharing platforms and acquired a set of 18 HIV related studies from which we analyzed 26 328 data elements. We identified existing standards for creating a data dictionary and reviewed their use. To facilitate aggregation across studies, we defined three types of data dictionary (data element, forms, and permissible values) and created a simple information model for each type. Results An average study had 427 data elements (ranging from 46 elements to 9 945 elements). In terms of data type, 48.6% of data elements were string, 47.8% were numeric, 3.0% were date and 0.6% were date-time. No study in our sample explicitly declared a data element as a categorical variable and rather considered them either strings or numeric. Only for 61% of studies were we able to obtain permissible values. The majority of studies used CSV files to share a data dictionary while 22% of the studies used a non-computable, PDF format. All studies grouped their data elements. The average number of groups or forms per study was 24 (ranging between 2 and 124 groups/forms). An accurate and well formatted data dictionary facilitates error-free secondary analysis and can help with data de-identification. Conclusion We saw features of data dictionaries that made them difficult to use and understand. This included multiple data dictionary files or non-machine-readable documents, data elements included in data but not in the dictionary or missing data types or descriptions. Building on experience with aggregating data elements across a large set of studies, we created a set of recommendations (called CONSIDER statement) that can guide optimal data sharing of future studies.