Reviewerly/exHarmony huggingface dataset:
Identification of Activities of Daily Living through Artificial Intelligence: an accelerometry-based approach

The accelerometer is available on most of these mobile devices. It allows the acquisition and calculation of different physical parameters. Due to the use of pattern recognition, it also enables the identification of several Activities of Daily Living (ADL), such as walking, running, going downstairs, going upstairs, and standing. The feature extraction step performs the extraction of the five most significant distances between peaks, the average, standard deviation, variance and median of extracted peaks and raw data, and the maximum and minimum of raw data. The focus of this paper is the implementation of multiple artificial intelligence methods for the recognition of ADL, including Logistic Regression, Combined nomenclature rule inducer, Neural Network, Naive Bayes, Support Vector Machine, Decision Tree, Stochastic Gradient Descent, and k-Nearest Neighbor. The Decision tree reported the average accuracy of 85.22% between classes. This method also presents an F1-score value of 85,13% and a precision value of 85,08%. Nevertheless, the study has limitations associated with the use of mobile devices. The position and location of the device in the data collection phase need further investigation, and the system architecture demands higher energy consumption.
Design of Information System Vehicle Rental Based Web

Abstract The purpose of this study is to facilitate vehicle rental transactions between vehicle owners and tenants to make it easier for tenants to choose a vehicle that matches their needs such as engine capacity, number of seats, and so on. That way the tenant does not need to be bothered to come to the rental vehicle to minimize time. The method of this study used descriptive method where the writer conducts interviews directly with the owner who also rents the vehicle at SINAR XBZ to be used in case studies in this research. The results of this study are expected to create a vehicle rental website that can increase the attractiveness of consumers to rent vehicles quickly and reliably. Besides, it is expected that this website can make it easier for both vehicle rental owners and tenants so that the income from the vehicle rental can increase.
PassPage: Graphical Password Authentication Scheme Based on Web Browsing Records

This paper proposes a two-factor graphical password authentication scheme, PassPage, which is suitable for website authentication with enhanced security. It leverages the implicit memory based on the user's web browsing records. Whenever the user tries to log in, the server returns 9 small pages as a challenge, and asks the user to select all the pages the user has browsed besides inputting a text password. We performed user experiments on 12 volunteers. The experiment results showed that the average login success rate on a news website is steadily over 80% when the users are familiar with the login process, and the login success rate does not decrease sharply in 6 days.
On the Perception Analysis of User Feedback for Interactive Face Retrieval

In this article, we explore the coherence of face perception between human and machine in the scenario of interactive face retrieval. In the part of human perception, we collect user feedback to the stimuli of a target face and groups of displayed candidate face images in a face database with a large number of subjects. In the part of machine vision, we compare the benchmark features and general metrics to measure face similarity. We propose a series of coherence measurements to evaluate the statistic characteristic of human and machine face perception. We discover that despite the unfamiliarity of users to most faces in the database, the coherence between human and machine remains in a stable level across multiple variations in metrics, features, size of databases, and demographics. The simulation experiments with the coherence distributions demonstrate that the embedded information is valuable to speed up interactive retrieval. The comparisons over multiple parameter settings provide feasible instructions in designing the interactive face retrieval system with more consideration of human factors.
Automatic Face Expressions and Gesture Detection System Using Blockchain Security

Surveillance is the most outstanding space within the fashionable age because the world is enhancing technology. CCTV cameras are applied in several places wherever safety has a lot of importance. Manual examination appears to be monotonous and time-consuming. Security is determined in varied conditions like fraud detection, violent things, probabilities of an explosion, etc. The human being's face is an energetic object and includes a high degree of variability in its look. Surveillance videos have a significant contribution to unorganized data that is captured by the protection cameras. The unorganized data is needed solely at the time of crime occurrences. Face expressions, body gestures are extracted from these videos by using the Facial Expression Recognition and 3D CNN algorithms. The obtained facial expressions, body gestures can be tampered at any time by the malicious performers. The foremost keynote of this paper is providing security for the extracted facial expressions, body gestures by using Blockchain Technology which is stored in BigchainDB.
From Bedside to Bytes and Back: Data Quality and Standardization for Research, Quality Improvement, and Clinical Decision Support in the Era of Electronic Health Records*

Department of Pediatrics (Critical Care); and Department of Preventive Medicine (Health & Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL; and Division of Critical Care Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA *See also p. e572. Drs. Sanchez-Pinto and Dziorny involved in conception, design, and drafting the article for important intellectual content. Dr. Sanchez-Pinto’s institution received funding from R21HD096402 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development, and he received support for article research from the National Institutes of Health. Dr. Dziorny disclosed that he does not have any potential conflicts of interest.
VARTE:Trust evaluation model based on VAR for mobile medical information system

How to ensure the security of sensitive medical data is a challenging issue for medical information system. Trusted management technology, which is focusing on the information security and becoming the main research directions, provides the basis. By establishing trust model and realizing trust management, we can improve the security and reliability of medical information system deeply. As a result, the research on the application of trusted management technology in the medical security has become the hot point field. Many trust models have been proposed which are considered different factors or scenarios. In this paper, a trust evaluation model for user behavior in mobile medical information system is proposed. First, according to the characteristics of mobile medical information system, we chose three categories including eight indicators as the input: user parameter, operation parameter and content parameter. Second, the model called VARTE is based on the vector autoregressive (VAR), which is a method of multivariate time series analysis. VAR can consider more complex long-term factors than simple time decay used in other trust model. Finally, we compare VARTE with EigenTrust and the improvement of EigenTrust called EigenTrust-PRO under simulation conditions. Analysis and testing show that VARTE can effectively evaluate the trust value of user behavior, and has advantage in trust evaluation under the long-term and multi-factor influence.
Designing a Cyber Range Exercise for Educational Purposes

Cybersecurity is an emergent need in educational and industrial contexts. There is an increase need to train skilled people, students and employees on cybersecurity techniques. Training is one of the weaknesses identified within the industry especially by practitioners, and the use of cyber ranges is motivated. Cyber ranges are envisaged as infrastructures for training purposes. This paper provides an overview of cyber ranges, and a cyber range exercise design process illustrated with an example. This exercise is used in academic sector and it can be extrapolated to industrial contexts.
Comparison of Hybrid ACO-k-Means Algorithm and Graph Cut for MRI Images Segmentation

Image segmentation is the process of dividing an image into consistent and homogeneous in some characteristics regions. Image segmentation widely used in medical diagnostics. Segmentation algorithms are used for anatomical features ex-traction from medical images. The Hybrid Ant Colony Optimization (ACO) – k-means and Graph Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. There is no universal algorithm for medical image segmentation. In image processing and computer vision, segmentation is still a challenging problem in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Graph cut.
Scheduling Policies for Quantum Key Distribution Enabled Communication Networks

The upsurge of ubiquitous data-driven applications makes the need for super-secure communication networks more urgent than ever. Conventional encryption algorithm oriented cryptology would be hacked by cryptanalysis, hence is challenging to provide unconditional security. To tackle this unprecedented issue, quantum key distribution (QKD) has been envisioned as a promising physical layer security technology for the upcoming 6G era due to the unique properties of quantum physics, i.e., no-cloning and uncertainty. To accommodate as many security-sensitive applications as possible, in this letter, the secure key rate maximization problem for QKD enabled communication networks is investigated. By transforming the original problem into a maximum weight perfect matching problem of an undirected and non-bipartite graph, the associated quantum devices scheduling policy is optimally determined, along with the feasibility analysis as well as two simpler heuristic scheduling approaches. Numerical results demonstrate the performance of the devised scheduling strategies from comprehensive perspectives, such as achievable secure key rate and outage probability.
PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites

Phishing has grown significantly in the past few years and is predicted to further increase in the future. The dynamics of phishing introduce challenges in implementing a robust phishing detection system and selecting features which can represent phishing despite the change of attack. In this study, we propose PhishZip which is a novel phishing detection approach using a compression algorithm to perform website classification and demonstrate a systematic way to construct the word dictionaries for the compression models using word occurrence likelihood analysis. PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80.04%. We also propose the use of compression ratio as a novel machine learning feature which significantly improves machine learning based phishing detection over previous studies. Using compression ratios as additional features, the true positive rate significantly improves by 30.3% (from 51.47% to 81.77%), while the accuracy increases by 11.84% (from 71.20% to 83.04%).
Turkish Text Compression via Characters Encoding

In this paper, we suggest an efficient conversion for Turkish character’s string, from UTF-8 to ANSI character’s coding for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it’s a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy.
Deep Learning Applications in Medical Imaging

Machine learning is a technique of parsing data, learning from that data, and then applying what has been learned to make informed decisions. Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way, but it has different capabilities. The main difference between deep and machine learning is, machine learning models become well progressively, but the model still needs some guidance. If a machine learning model returns an inaccurate prediction, then the programmer needs to fix that problem explicitly, but in the case of deep learning, the model does it by itself. Automatic car driving system is a good example of deep learning. On other hand, Artificial Intelligence is a different thing from machine learning and deep learning. Deep learning and machine learning both are the subsets of AI.
Malaria Parasites Detection Using Deep Neural Network

Malaria is a dreadful infectious disease caused by the bite of female Anopheles mosquito, by the protozoan parasites of the genus Plasmodium. It's an epidemic disease and demands rapid and accurate diagnosis for proper intervention. Microscopic test on the thick and thin blood smear to detect the malaria and counts the infected cells is the gold standard for diagnosis of this disease. An automation process in the form of computer-aided diagnosis is much needed as it plays a vital role in fully or semi-automated diagnosis of diseases based on medical image information. Deep learning has vast ranging applications. This work is to build a convolutional neural network to expertly detect the presence of malaria parasitized cells in the thin blood smear. The authors construct the model as small and computationally efficient to obtain the highest level of accuracy possible.
An Intellectual Dual Axis Efficient Solar Tracking System by Using IoT Integrated Controller

The generation of power from the reduction of fossil fuels is the biggest challenge for the next half century. In this context solar tracking system is the best alternative to increase the efficiency of the photovoltaic panel. Solar trackers move the payload towards the sun throughout the day. Even in the hottest regions on the earth, the solar radiation flux available rarely exceeds 1 KW/M, which is insufficient for technological utilization. This problem can be rectified by an intellectual control algorithm, this paper proposed a Dual axis solar tracking system designed with IoT Arduino microcontroller plus Servo motor drive and BH1750 Light sensor. The results show that the solar tracking system increased the efficiency around 40% and energy received from the sun is improved from 9.00AM to 6.00PM. DC servo motor drive controlled by an IOT Arduino microcontroller drive unit that receives input signals from dedicated BH1750 Light sensor. This paper shows the results of solar tracking PV panel produced more energy than fixed one with about 59%.
Taxonomy of Link Based web Spammers using Mining Optimized PageRank Algorithm for e-Governance

Internet is an excellent and best way to represent and platform for sharing information for communication to each other in every field because without internet or technology not to share information as well. Using this technology user can facilitated by search engines in order to meet the user requirements in the hunt for in rank. Search engines are the “dragons” with the precious fortune that is useful information. It is a fact that web contains huge amount of information, but is customized to solve the user queries with less amount of results which is 10 to 20 pages. It is a responsibility of Search engines that they should rank the Web pages, as per the quality of data they contain. In network spam can extensively depreciate the excellence of result presented by search engine. It has been noticed that commercial search engines charge large amount of incentives for efficient spam detection. After using internet it has been considered as one of the major challenge in the industry of search engine. This is the problem of finding or reducing web spam over the internet that may be content spam or link spam. For this newly developed spam techniques, it is necessary to adapt Machine learning-based classification methods. After future technology we deal with the facts of uncertainty and vagueness one of the applications artificial intelligence with cognitive sciences is used as a tool for solving such kind of problem. Designing the series of algorithm to enhance the performance of the given problem is the main objective of this research.
Minimal Linear Codes From Vectors With Given Weights

Minimal linear codes as a special class of linear codes attract much interest for their application in secret sharing schemes and secure two-party computation. It is interesting to characterize minimal linear codes with kinds of mathematical tools and construct more minimal linear codes. This letter considers minimal linear codes constructed from the defining sets formed by vectors with given weights, follows the method in (“Minimal linear codes from characteristic functions, IEEE Trans. Inf. Theory. DOI: 10.1109/TIT.2020.2978387), and fully characterizes these minimal linear codes by weights.
Building Corpus in Bahasa Indonesia for Pornographic Indicated Website Content

Information technology and communication is indeed used as a benchmark for the progress of a country. One application of information and communication technology is internet use in the community. In addition to having a positive impact, technological developments have become a medium for cyber crime and the spread of negative content. At present, the government of the Republic of Indonesia is trying to block negative content on the internet. As of January 2019, based on the page https://trustpositif.kominfo.go.id, 780,586 negative content has been blocked. The most blocked is pornographic content, which is 773,517 websites. One solution to recognize and study content that is indicated by pornography is to build a corpus indicated by pornographic content using natural language processing to support the government in its efforts to protect the public regarding access to websites that are indicated by pornographic content. The research was conducted by crawling on 20 websites that indicated as a pornography content from https://trustpositif.kominfo.go.id, developing the corpus that indicated as a pornography content with natural language processing(NLP) and vectorization of words on the corpus results. The results of the study were identification of content indicated by pornography based on the corpus built.
IEye: Personalized Image Privacy Detection

Massive images are being shared via a variety of ways, such as social networking. The rich content of images raise a serious concern for privacy. A great number of efforts have been devoted to designing mechanisms for privacy protection based on the assumption that the privacy is well defined. However, in practice, given a collection of images it is usually nontrivial to decide which parts of images should be protected, since the sensitivity of objects is context-dependent and user-dependent. To meet personalized privacy requirements of different users, we propose a system IEye to automatically detect private parts of images based on both common knowledge and personal knowledge. Specifically, for each user's images, multi-layered semantic graphs are constructed as feature representations of his/her images and a rule set is learned from those graphs, which describes his/her personalized privacy. In addition, an optimization algorithm is proposed to protect the user's privacy as well as minimize the loss of utility. We conduct experiments on two datasets, the results verify the effectiveness of our design to detect and protect personalized image privacy.
What Motivates VSEs to Adopt an International Standard Such as ISO/IEC 29110? An Exploratory Analysis

Very Small Entities (VSEs) of México are important to the economy because of their contribution to the growing demand of software. The ISO/IEC 29110 series has become a highly adopted standard in México because it is one of the Quality Standards recognized by the government, the industry and academia. This is confirmed by the increasing number of VSEs certified to this standard since 2013. Over 46 of the 53 VSEs certified in Latin America are from México. The objective of this research was to perform an exploratory analysis of the factors that motivate and affect the motivation of VSEs in the implementation of this standard. To perform this research, a case study was conducted and a questionnaire to collect data was developed. The sample of our research is a set of 15 VSEs that have adopted the software Basic profile of ISO/IEC 29110 to improve their software development process. This research provides the factors that motivates positively and negatively the adoption of the ISO/IEC 29110 standard by VSEs.
A focused solution to the avoidance problem

Abstract In ML-style module type theory, sealing often leads to situations in which type variables must leave scope, and this creates a need for signatures that avoid such variables. Unfortunately, in general, there is no best signature that avoids a variable, so modules do not always enjoy principal signatures. This observation is called the avoidance problem. In the past, the problem has been circumvented using a variety of devices for moving variables so they can remain in scope. These devices work, but have heretofore lacked a logical foundation. They have also lacked a presentation in which the dynamic semantics is given on the same phrases as the static semantics, which limits their applications. We can provide a best supersignature avoiding a variable by fiat, by adding an existential signature that is the least upper bound of its instances. This idea is old, but a workable metatheory has not previously been worked out. This work resolves the metatheoretic issues using ideas borrowed from focused logic. We show that the new theory results in a type discipline very similar to the aforementioned devices used in prior work. In passing, this gives a type-theoretic justification for the generative stamps used in the early days of the static semantics of ML modules. All the proofs are formalized in Coq.
Fault Tree Analysis: Identifying Maximum Probability Minimal Cut Sets with MaxSAT

In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.