Reviewerly/exHarmony huggingface dataset:
Secure Sharing of Spatio-Temporal Data through Name-based Access Control

As more and more mobile data is collected continuously in space and time for a variety of purposes such as health monitoring and driving behavior tracking, people are increasingly concerned about their privacy when sharing their data. To minimize privacy leakage, data owners often want to restrict the access to their data based on space and time constraints while meeting each data user's specific needs. In this paper, we introduce an access control system over Named Data Networking (NDN) that allows data owners to schematize and control data access at a fine granularity with respect to time, space, and user needs. More specifically, we designed spatio-temporal access control policies using hierarchically structured names, extended Name-based Access Control to support spatio-temporal policies, and incorporated publish-subscribe functionality for real-time data sharing. Moreover, we developed an NDN-based prototype based on our design and evaluated its performance in sharing both real-time and historical data.
Fuzzy Enhancement Technique of Face Images

Nowadays, different systems and applications secure personal data using the person's biometric features. Face, fingerprint, hand geometry, palm print, iris, and voice signatures are examples of biometric traits. This paper presents a fuzzy enhancement technique for generating high-contrast and clear face images. Since images are captured in various settings, the output images may have poor quality and low contrast. Evaluation metrics such as entropy, spectral entropy, contrast, average gradient and edge magnitude are used for measuring the performance of the proposed technique. The obtained results are compared with those of the recent enhancement techniques.
AI Facilitator Allows Participants to Conduct a Friendly Discussion and Contribute to Feasible Proposals

This paper added an AI facilitator function to a discussion board system (DBS) for the purpose of guiding a discussion in an appropriate direction. Each participant who uses the DBS on their individual screen can express their own opinions by moving words to boxes made for each item to be discussed. The improved DBS (ver. 2.1) presented comments to those participants who had not spoken or operated any words on the screen for a designated period of time. When all participants in the discussion placed the same word in the same box, the word color changed to green. Here, four participants held an online discussion with their cameras off using DBS ver. 2.1. The results of a questionnaire showed that the prompts from DBS encouraged the participants to talk and add the words to the boxes. The sight of the green-colored text gave participants a feeling of pleasure, knowing that the other participants agreed with their opinion. Contrary to the experimenter’s hypothesis, there were no disagreements, because all participants recognized an idea as “excellent.” However, the idea came to be a realistic and immediately feasible proposal, because the participants discussed it by asking each other whether they had placed the same word in the same box, although the underlying assumption of DBS is that the participants will not disclose to each other the words they put into the boxes.
Replacing Out-of-Vocabulary Words with an Appropriate Synonym Based on Word2VnCR

The most typical problem in an analysis of natural language is finding synonyms of out-of-vocabulary (OOV) words. When someone tries to understand a sentence containing an OOV word, the person determines the most appropriate meaning of a replacement word using the meanings of co-occurrence words under the same context based on the conceptual system learned. In this study, a word-to-vector and conceptual relationship (Word2VnCR) algorithm is proposed that replaces an OOV word leading to an erroneous morphemic analysis with an appropriate synonym. TheWord2VnCR algorithm is an improvement over the conventional Word2Vec algorithm, which has a problem in suggesting a replacement word by not determining the similarity of the word. After word-embedding learning is conducted using the learning dataset, the replacement word candidates of the OOV word are extracted. The semantic similarities of the extracted replacement word candidates are measured with the surrounding neighboring words of the OOV word, and a replacement word having the highest similarity value is selected as a replacement. To evaluate the performance of the proposed Word2VnCR algorithm, a comparative experiment was conducted using the Word2VnCR and Word2Vec algorithms. As the experimental results indicate, the proposed algorithm shows a higher accuracy than the Word2Vec algorithm.
Key Techniques of Upper Limb Rehabilitation Training System based on Kinect

Rehabilitation training for patients with upper limb functional impairment is generally based on rehabilitation machinery equipment and the experience does not meet the needs of users. This paper proposes and designs a rehabilitation training system that can carry out independent training and intelligentassessment. Collected by the sensor system Kinect human motion information, and the key points of the skeleton data processing based on time series data smoothing algorithm, based on the upper limb motion recognition method of classification algorithm, experiments show that the algorithm is optimized bone data, the system can effectively help patients to perform upper limb rehabilitation training, which has good application value, realizes the guidance of the patient's rehabilitation process through human-computer interaction, realizes the continuous interaction of human and machine and completes the patient's independent rehabilitation training.
Epidermal Systems and Virtual Reality: Emerging Disruptive Technology for Military Applications

This review study, presented at the 2 nd World Conference on Advanced Materials for Defense (AuxDefense 2020), focuses on skin as sensory interface and explores the latest discoveries in bioelectronic science. The work analyzes at what extent invisibility is possible by emulating nature, and if military applications can really benefit from technology that combines epidermal systems and virtual reality — and from next generation of wearable textile computing technologies.
PerformEyebrow: Design and Implementation of an Artificial Eyebrow Device Enabling Augmented Facial Expression

In communication, it is important to clearly convey one’s own emotions to others and to accurately read the emotions intended by others. There are two ways to communicate emotions: nonverbal information and verbal information. The psychologist Merhrabian argues that non-verbal information is more important than verbal information, and that visual information such as facial expressions and gestures play a major role in inferring emotions. However, there are times when our own emotions are not understood by others, leading to misunderstandings. In this study, we propose an artificial eyebrow shape control device, PerformEyebrow, which extends the wearer’s facial expression by presenting various eyebrow shapes. The eyebrows are drawn with thermochromic ink so that their shape can be changed dynamically. It takes about 9 s for the eyebrows of PerformEyebrow to change shape. In order to confirm the usefulness of the device as an expression enhancing device, we conducted an experiment to evaluate the impression made by eyebrow shape when the eyebrow shape was changed by wearing PerformEyebrow.
Showcasing Nursing Leadership and Contributions to a University's COVID-19 Student Contact Tracing Process

The COVID-19 pandemic was an unprecedented event that impacted every segment of healthcare, including universities preparing healthcare professionals. Instituting processes to coordinate student return to campus and ongoing COVID-19 testing and contract tracing challenged university campuses, but also brought opportunities for collaboration. This article reports on the experiences of one nonprofit private higher education university in management of the COVID-19 testing and contact tracing that were led by school of nursing faculty and nursing leadership.
Classification Performance Evaluation on Diagnosis of Breast Cancer

Cancer, which has many different types such as breast, pleural, and leukemia, is one of the common health problems of today. Most of them cause pain and treatment processes are so challenging. Medical authorities report that the diagnosis of cancer at early stages has a positive effect on medical treatments' success. On the way to design a computer-aided cancer diagnosis system about breast cancer to support the decisions of doctors about medical treatments, classification performances of six classifiers are investigated in this study. For this purpose, classifier models have been created with machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF); and deep learning algorithms such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory Network (LSTM). Two different open-source breast cancer datasets were used namely Wisconsin and Coimbra on experiments. Accuracy (Acc), Sensitivity (Sens), Precision (Pre), F-measure (F) were used as performance criteria. As a result of the tests, Acc values between 85% and 98% were obtained in the Coimbra breast cancer dataset; while Acc values were obtained between 92% and 97% in Wisconsin. According to the results obtained, it is seen that deep learning algorithms (RNN, GRU, and LSTM) are more successful than machine learning algorithms (SVM NB and RF). Among the deep learning algorithms, LSTM is more successful.
Seclusive Cache Hierarchy for Mitigating Cross-Core Cache and Coherence Directory Attacks

Cross-core cache attacks glean sensitive data by exploiting the fundamental interference at the shared resources like the last-level cache (LLC) and coherence directories. Complete non-interference will make cross-core cache attacks unsuccessful. To this end, we propose a seclusive cache hierarchy with zero storage overhead and a marginal increase in on-chip traffic, that provides non-interference by employing cache-privatization on demand. Upon a cross-core eviction by an attacker core at the LLC, the block is back-filled into the private cache of the victim core. Our back-fill strategy mitigates cross-core conflict based LLC and coherence directory-based attacks. We show the efficacy of the seclusive cache hierarchy by comparing it with existing cache hierarchies.
LambdaKube - A Functional Programming Approach in a Distributed Realm

Containerization has become an essential approach in modern software engineering. Docker is a widely-used solutio for separate services (like database, backend, etc.) and start the as a standalone, isolated process instance on the same host kernel. Kubernetes is distributed approach over Docker, it supports multiple hosts for the deployment. Kubeless in new approach that aims at the functionwise deployment, so every subprogram can be deployed, scaled, operated separately, thererfore a functional programming approach can be realized in a modern, highly distributed realm. In this paper, we propose our solution that is based on the constructs of functional programming (e.g. map-reduce transformations). We take advantage of the Kubeless system and provide a functional programming framework called LambdaKube.
Brown Hands Aren’t Terrorists: Challenges in Image Classification of Violent Extremist Content

The Internet eases the broadcasting of data, information, and propaganda. The availability of myriad social media has turned the spotlight on violent extremism and expanded the scope and impact of ideology-oriented acts of violence. Automated image classification for this content is a highly sought-after goal, yet raises the question of potential bias and discrimination in case of incorrect classification. A requirement for addressing, and potentially counter-acting, bias, is the existence of a reliable training dataset. To demonstrate how such a dataset can be developed for highly sensitive topics, this article operationalizes the process of human-coding images posted on the open social web by violent religious extremists into four master categories and four subcategories. We concentrate on the group ISIS due to their prolific digital content creation. The developed training dataset is used to train a convolutional neural network to automatically detect extremist visual content on social media and determine its category. Using inter-coder reliability, we show that the training data can be reliably coded despite highly nuanced data and the existence of various categories and subcategories.
Multi-View 3D Human Pose Tracking Based on Evolutionary Robot Vision

Human life expectancy is at present the maximum in recorded history. However, a disadvantage is that the elderly are increasingly displaying cognitive disabilities. Studies have shown that physical exercises such as calisthenics can potentially prevent disabilities. Meanwhile, existing systems for evaluating human pose focus mainly on accuracy and omit convenience and efficiency. To solve this issue, in this paper, we propose a framework for rapidly estimating three-dimensional human pose from two camera views. It is based on an evolutionary algorithm. This system can be applied straightforwardly to inexpensive smart devices and used to evaluate multiple individuals’ calisthenics with two or more smart devices.
Eye-Tracking Analysis from a Flight-Director-Use and Pilot-Monitoring Study

Eye tracking may be a useful tool to investigate pilot monitoring and develop and conduct training. There is increased interest from airlines to use eye-tracking technologies in flight simulators. However, much is still unknown about how to best utilize eye-tracking data in pilot training. This paper presents eye-tracking results from a pilot-monitoring training study with 19 pilots. All pilots completed 15 monitoring challenges across four operational scenarios in a B737-700 full flight simulator. In addition, the study investigated the impact of having the flight director engaged or disengaged on the pilot monitoring side in the final approach. It was hypothesized that pilots would focus less on the primary flight display with the flight director off and look more around in the cockpit. To assess this, pilots performed half of the scenarios with the flight director on and half with the flight director off. However, pilots monitoring tended to look less at the primary flight display with the flight director on as indicated by lower Proportion Dwell Times, contrary to the hypothesis. Next, eye-tracking data were analyzed from two monitoring challenges involving waypoint restrictions and two involving extending the flaps at appropriate airspeeds. Pilots that successfully completed the challenges appeared to focus more on areas of interest that contained the most relevant information to successfully complete the challenge. In addition, successful pilots seemed to adapt their monitoring strategy more to the challenge at hand as observed by a distinct shift in focus on either the primary flight display or the navigation display depending on the challenge. Our findings suggest the importance of flexible gaze allocation across specific situations and raise the question whether and to what degree prespecified patterns of eye fixation can be identified and trained.
Psychiatry Transcript Annotation: Process Study and Improvements

Background: The demand for psychiatry is increasing each year. Limited research has been performed to improve psychiatrist work experience and reduce daily workload using computational methods. There is currently no validated tool or procedure for the mental health transcript annotation process for generating “gold-standard” data. The purpose of this paper was to determine the annotation process for mental health transcripts and how it can be improved to acquire more reliable results considering human factors elements. Method: Three expert clinicians were recruited in this study to evaluate the transcripts. The clinicians were asked to fully annotate two transcripts. An additional five subjects were recruited randomly (aged between 20-40) for this pilot study, which was divided into two phases, phase 1 (annotation without training) and phase 2 (annotation with training) of five transcripts. Kappa statistics were used to measure the inter-rater reliability and accuracy between subjects. Results: The inter-rater reliability between expert clinicians for two transcripts were 0.26 (CI 0.19 to 0.33) and 0.49 (CI 0.42 to 0.57), respectively. In the pilot testing phases, the mean inter-rater reliability between subjects was higher in phase 2 with training transcript (k= 0.35 (CI 0.052 to 0.625)) than in phase 1 without training transcript (k= 0.29 (CI 0.128 to 0.451)). After training, the accuracy percentage among subjects was significantly higher in transcript A (p=0.04) than transcript B (p=0.10). Conclusion: This study focused on understanding the annotation process for mental health transcripts, which will be applied in training machine learning models. Through this exploratory study, the research found appropriate categorical labels that should be included for transcripts annotation, and the importance of training the subjects. Contributions of this case study will help the psychiatric clinicians and researchers in implementing the recommended data collection process to develop a more accurate artificial intelligence model for fully- or semi-automated transcript annotation.
Ontology-Based Modeling of Cloud Application Using Security Patterns

The acceptance of various computing aspects of cloud-based systems gets hampered by the evolution of various security threats. Security and privacy issues are considered as the primary challenges to an adoption of cloud computing. In order to develop a secure cloud, there is a need of proper analysis of security threats and their associated detection as well as prevention techniques. In this study, an attempt has been made to offer an ontology-based analysis and design approach for the cloud security. The security provision of cloud systems is accomplished by using security patterns, which are often specified by using UML (Unified Modeling Language) diagrams. In order to specify cloud pattern notations, a semantic modeling approach i.e., Web Ontology Language (OWL) has been considered. Description Logic (DL) has been considered for analyzing security requirements, which is supported by the OWL editor i.e., Protégé.
On the Success Probability of Three Detectors for the Box-Constrained Integer Linear Model

This paper is concerned with detecting an integer parameter vector inside a box from a linear model that is corrupted with a noise vector following the Gaussian distribution. One of the commonly used detectors is the maximum likelihood detector, which is obtained by solving a box-constrained integer least squares problem, that is NP-hard. Two other popular detectors are the box-constrained rounding and Babai detectors due to their high efficiency of implementation. In this paper, we first present formulas for the success probabilities (the probabilities of correct detection) of these three detectors for two different situations: the integer parameter vector is deterministic and is uniformly distributed over the constraint box. Then, we give two simple examples to respectively show that the success probability of the box-constrained rounding detector can be larger than that of the box-constrained Babai detector and the latter can be larger than the success probability of the maximum likelihood detector when the parameter vector is deterministic, and prove that the success probability of the box-constrained rounding detector is always not larger than that of the box-constrained Babai detector when the parameter vector is uniformly distributed over the constraint box. Some relations between the results for the box constrained and ordinary cases are presented, and two bounds on the success probability of the maximum likelihood detector, which can easily be computed, are developed. Finally, simulation results are provided to illustrate our main theoretical findings.
Exploration of Norms and Policies in Digital Fashion Domain Using Semantic Web Technologies

Nowadays, one of the problems in policy notice of fashion companies is that they are not provided in a machine-readable format; therefore, they cannot be searched and monitored by computers. The fact that most of the fashion brands transform their sales into digital format magnifies the importance of automation in policies management for the future of digital business in general and of fashion companies in particular. In this paper, we explore the use of privacy policies of companies in the digital fashion domain not only to protect customer’s data but for making it feasible for companies to understand their customer’s needs easier and faster. We describe how the Open Digital Rights Language (ODRL), a W3C recommendation for expressing policies using Semantic Web Technologies, can be applied in the field of digital fashion. We then discuss the required components for making it possible to use such a policy language for monitoring and enforcement services.
Between Dissent and Consensus, Resistance and Counter-Resistance: Design Practice as a Common Project-Process for Plural Possibilities of Being and Becoming

This paper is a contribution to the discussion on the ethical and political limitations of institutionalised, dominant design practices and on the need to rethink the ways in which they operate. It points out that institutionalised design processes act as a dispositive of power that not only capture and colonise forms of life, but that also shape territories, bodies and languages through normative models that are exogenous to them. This discussion is crucial when thinking about the role that design has played in nurturing current crises. This paper is an inquiry into the possibility of design practice that is not institutionalised either by sovereign designing designers or by subordinated designed users, but that constitutes itself according to dynamics where design emerges as a common project-process of creative possibilities of being and becoming. Crucial aspects for a non-institutionalised design practice are identified through the analysis of a design experience with communities in Rio de Janeiro favelas. This paper shows how this design experience is based on a design approach that, through discursive structures, dynamically supports and is informed by dissent and consensus, and by the interplay between resistance and counter-resistance.
Facial Age Estimation Using Convolution Neural Networks

Age-related analysis has been a concern in the current years because many implementations have a great significance. The techniques of facial age prediction and classification are commonly used in the recent years for vitality applications but these techniques are time-consuming. The deep algorithms demonstrated superior efficiency compared to other approaches in order to solve the problem of age estimation. Herein, an age classification model is proposed using the mechanisms of deep age estimation in this article. This work introduces an age recognition model that helps to classify a person's image into a suitable aging group. The proposed model achieved better results in the prediction process with an accuracy reached 85.7% using InceptionV4. The main difference between this work and the relevant related works is that this work focuses on highlighting the performance of four pre-trained models three of them have different architectures; ResNet50, ResNet101, Sequeeze1_0, and InceptionV4. In deep age evaluation schemes, we look at previous study initiatives and current common datasets.