Reviewerly/exHarmony huggingface dataset:
Detecting Screen Presence with Activity-Oriented RGB Camera in Egocentric Videos

Screen time is associated with several health risk behaviors including mindless eating, sedentary behavior, and decreased academic performance. Screen time behavior is traditionally assessed with self-report measures, which are known to be burdensome, inaccurate, and imprecise. Recent methods to automatically detect screen time are geared more towards detecting television screens from wearable cameras that record high-resolution video. Activity-oriented wearable cameras (i.e., cameras oriented towards the wearer with a fisheye lens) have recently been designed and shown to reduce privacy concerns, yet pose a greater challenge in capturing screens due to their orientation and fewer pixels on target. Methods that detect screens from low-power, low-resolution wearable camera video are needed given the increased adoption of such devices in longitudinal studies. We propose a method that leverages deep learning algorithms and lower-resolution images from an activity-oriented camera to detect screen presence from multiple types of screens with high variability of pixel on target (e.g., near and far TV, smartphones, laptops, and tablets). We test our system in a real-world study comprising 10 individuals, 80 hours of data, and 1.2 million low-resolution RGB frames. Our results outperform existing state-of-the-art video screen detection methods yielding an F1-score of 81%. This paper demonstrates the potential for detecting screen-watching behavior in longitudinal studies using activity-oriented cameras, paving the way for a nuanced understanding of screen time's relationship with health risk behaviors.
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction

Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding interactions without considering other relation types like agonist, antagonist, etc. In addition, existing methods either heavily rely on high-quality domain features or are intrinsically transductive, which limits the capacity of models to generalize to drugs/genes that lack external information or are unseen during the training process. To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features. Moreover, the model strengthened the relations on the drug-gene graph through a communicative message passing mechanism. To evaluate our method, we compiled two new benchmark datasets from DrugBank and DGIdb. The comprehensive experiments on the two datasets showed that our method outperformed state-of-the-art baselines in the transductive scenarios and achieved superior performance in the inductive ones. Further experimental analysis including LINCS experimental validation and literature verification also demonstrated the value of our model.
F2VAE

Recommendation algorithms are widely used nowadays, especially in scenarios of information overload (i.e., when users have too many options to choose from), due to their ability to suggest potentially relevant items to users in a personalized fashion. Users, nevertheless, might be considered as separated in groups according to sensitive attributes, such as age, gender or nationality, and the recommendation process might be biased towards one of these groups. If observed, this bias has to be mitigated actively, or it can propagate and be amplified over time. Here, we consider a relevant difference of recommendation quality among groups as unfair, and we argue that this difference should be maintained as low as possible. We propose a framework named F2VAE for mitigating user-oriented unfairness in recommender systems. The framework is based on Variational Autoencoders (VAE) and it introduces two extra terms in VAE's standard loss function, one associated to fair representation and another one associated to fair recommendation. The conflicting objectives associated to these terms are discussed in details in a series of experiments considering the bias associated to the users' nationality in a music consumption dataset. We recall recent works proposed for generating fair representations in the context of classification, and we adapt one of these methods to the recommendation task. F2VAE was able to increase the precision by approximately 1% while reducing the unfairness by 21% when compared to standard VAE.
Health Monitoring System for Cancer Care Using IoT

We are living in a new era of computing technology named the internet of things (IoT). It is also known as the Universal Neural Network of smart sensing devices. This enables interoperability, sensor-to-sensor communication, exchange of information, and data movement to build up a trustworthy patient glance framework. The goal is that social insurance expert can easily monitor their patients, who are either hospitalized or executing their regular day-by-day life exercises. In the present scenario, the whole world is facing serious healthcare challenges with the rapid increase of population, which leads to different chronic diseases like cancer due to poor living style, poor sanitation, polluted water, and air. The cancer disease spread throughout the society which high demands the medical facility during the last several years. All healthcare systems are hospital-centric where the patient has to visit the hospital to check the status of the disease at regular intervals, which is a physician and disease-centric where the patient has no direct involvement except the physical presence for clinical observation. The basic challenges found such as: (a) patient/doctor limited time interaction; (b) adherence monitoring; (c) larger geriatric population; (d) urbanization; (e) healthcare work staff shortage during the emergency; and (f) increase medical expenses. To achieve success against the above challenges, the application of IoT 204may be used to provide seamless communication among the patient/doctor with communication objects. This will enhance the eminence of human life with smart connected sensors and it will play an important role to save the precious life of the human being. Sensor readings can be used effectively in diagnosing a cancer patient with its severity during the critical stage, and it will help the doctor to reduce the interconnectivity gap between hospital and patient. A health monitoring system for cancer care using cloud-based IoT with a wireless sensor network (WSN) is proposed to enhance the healthcare solution. Here cloud services are used to achieve transmission of data accurately, a good decision-making environment to enhance the cancer treatment facility.
Implementation of Medical Image Watermarking using RDWT and SVD for Secure Medical Data Transmission in Healthcare Systems

In telemedicine, the authenticity and integrity of medical images must be safeguarded. Copyright protection is provided through robust medical image watermarking (MIW) methods, and the original pictures may be retrieved at the receiver's end. But existing algorithms have limits in terms of balancing the tradeoff between robustness, imperceptibility, and embedded capacity. Aside from that, most MIW algorithms aren't built for color images. This article proposes a novel MIW technique based on the redundant discrete wavelet transform (RDWT) with singular value decomposition (SVD) to increase their performance in preserving medical color picture information. First and foremost, the RDWT -SVD is a reliable solution as compared to the conventional DWT. Second, modifying the wavelet domain coefficient ensures that integer values in the spatial domain change and that the watermarking process is reversible. Finally, the embedding approach makes full advantage of the original image's features and watermarking. The simulation results showed that the proposed method decreases the amount of original picture change and improves imperceptibility as compared to the conventional approaches.
Content-based Movie Recommender System Using Keywords and Plot Overview

Big tech companies like Amazon, Netflix and Google have tons of data and are still successful in providing specific products and services correctly as per user requirements. This is made possible by the recommendation algorithms that feed on the data we provide, in turn, enabling them to produce accurate results. Movie recommendation systems aspire to help cinema geeks by proposing movies of their penchant, devoid of them needing to do the standard long and arduous method of selecting from huge sets of movies that go up to millions and is onerous and frustrating. In this paper, we aspire to diminish human endeavor by recommending them movies based on their interests. To resolve such troubles, we have built a model using a content-based approach. The idea behind this model is to recommend a movie based on descriptions of movies. Using the movie &#x201C;GoldenEye&#x201D; as an example, we obtained the result as &#x201C;Skyfall&#x201D; with a similarity score of 66.73&#x0025; using CountVectorizer, 13.14&#x0025; using Jaccard Recommender, 14.34&#x0025; using TF-IDF Keywords, 9.87&#x0025; using TF-IDF Plot Overview and 71.9&#x0025; using Google Form responses.
UK Information Commissioner: halt police rollout of face ID

The UK Government's privacy watchdog, Information Commissioner Elizabeth Denham, has called for a halt in the police rollout of facial recognition technology.
“Voting System using Multichain Blockchain and Fingerprint Verification”

Nowadays, crypto currency has become a trending theme within the software package globe. Crypto currency may be a digital quality that’s meant to operate as a dealings medium that utilizes sturdy cryptography to secure asset exchange and make sure plus transfer. Crypto currency is in addition spoken as digital suburbanised cash. Block chain stores knowledge concerning dealings which will be accustomed assess transaction trait. This voting system deals with multi chain block chain technology. We can define block chain as a digital transaction which is used to record financial transactions also as totally different transactions. As a result of the knowledge keep within the block chain isn’t related to personal identifiable information, it’s such a fanonymity attribute. Blockchain allows dealings and verification to be clear. The options of this block chain technology are useful in powerful voting system,robustness, obscurity and transparency. The electoral system is our country’s core. Veri- fication of fingerprints employed in this theme to authenticate identity of electors.
Blockchains

We will begin our journey with a brief introduction to Ethereum and blockchain technology in general, starting with Bitcoin. We will explain what makes a blockchain and when it makes sense to use one. We will also cover some cryptography basics before we start and later provide an overview of decentralized application development before we implement our first one in Chapter 2 .
A Novel Adaptive Gradient Compression Approach for Communication-Efficient Federated Learning

Federated learning is a recent advance in privacy-preserving deep learning. However, recent surveys show that federated learning still has prohibitive communication costs. Since the communication bandwidth of terminal devices is usually quite limited, improving the communication efficiency is critical to model updating. To solve the above issues, we present a novel adaptive gradient compression algorithm, based on non-Independent, Identically Distributed(non-IID) and unbalanced data distributions in federated learning to provide a unique compression rate for each client. Through comparative experiments, we prove that the performance of the adaptive gradient compression algorithm in federated learning is superior to the fixed compression rate method, which significantly improves the communication efficiency while preserving the model accuracy.
EMRVisualization for Patient Progress Tracking: Interface Design and System Implementation

In a treatment process, a doctor has to view a lot of medical data about symptoms, test results, drugs and their dosages in a period of time. It is convenient for the doctor to view and search for any information he/she needs if all the medical data of each patient are visualized appropriately. For that purpose, we propose EMRVisualization, an electronic medical record visualization system, to visualize all the related medical data in an integrative manner. The system provides an interactive visualization with accurate data at different detail levels, quick access and convenience for a doctor to track the progress of each patient over time. Its development is detailed in this paper. Its demonstration with the real data of gastroenterological Vietnamese patients in Thong Nhat Hospital, Ho Chi Minh City, Vietnam, is conducted. It also shows that every interaction of a doctor can be accomplished in at most two steps.
Real-time anomaly detection in gas sensor streaming data

In order to improve the timeliness and accuracy of coal mine gas disaster risk assessments, it is important to detect anomalies in the gas sensor streaming data in real-time. In this paper, the support vector regression (SVR) algorithm combined with the normal statistical distribution technique is used to establish a real-time anomaly detection model for gas sensor streaming data. Furthermore, a prototype system for the real-time anomaly detection in gas sensor streaming data that is built using the stream processing framework Spark Streaming is presented. Experiments show that the real-time anomaly detection system can periodically update the anomaly detection model and determine anomalies in the sensor streaming data in real-time. For a window size of 9, an update cycle of 1 and an anomaly threshold of 0.95, the anomaly detection model is better than the boxplot, the statistical analysis and the clustering algorithm regarding the prediction precision and accuracy.
Creative Collaborative Virtual Environments

The authors propose to define creative collaborative virtual environments (CCVEs) as platforms for collaborative and distributed creation in online communities. This will be established by examining virtual worlds as agents of change towards new creative and collaborative models. CCVEs are grounded on three key elements: creation, collaboration, and distribution. These relate not only to the technical but also to the social layers of virtual online communities. Shared creativity and distributed authorship are approached as examples of specific dynamics rooted upon these three elements. The concept of CCVE is important to the design of emerging virtual worlds, specifically regarding the preservation of affordances for collaborative creativity. Discussion based on these observations demonstrates how collaborative creation of new content and meaning takes place in CCVEs, and how they transform communicative and creative agency in digital communities.
Research in User-Centered Design 2009 to 2018: A Systematic Keyword Network Analysis

User-centered design (UCD) has become an important concept in Human-Computer Interaction (HCI) and other disciplines. While there is abundant UCD research, keyword analysis research has been less studied even though keywords are important for achieving better understanding of UCD. Therefore, this study provides keywords network a visual analysis of UCD articles published between 2009 and 2018 to answer the following questions: (1) What UCD-related keywords have been studied and in which disciplines? and (2) How have keywords been connected to on another? The study analyzed 304 keywords articles from IEEE, ACM, and ScienceDirect that included “UCD” in their titles. It utilized Gephi 0.9.2 to visualize keyword frequencies, relationships, and authors’ disciplines. The findings presented that the five most frequently mentioned keywords regarding UCD were “usability,” “HCI,” “User Experiences,” “User-Centered,” and “User Interfaces”. The top five most identified disciplines in the UCD articles were Computer Science, Design, Engineering, Education, and Psychology. In visualizing this data, we created a keyword hierarchy with various sizes of texts and circles, and we denoted various relationship levels between keywords by different weights of edges. This visualization of the selected 43 keywords shows a clear relationship between keywords in which UCD is strongly related to usability, UX, user-centered, HCI, Persona, prototype, interaction design, interface design, assistive technology, design thinking. The findings can be valuable in understanding the current UCD research mainstream for researchers and designers pursuing interdisciplinary approaches.
Honour, hate and violence in social media: insights from India

Honour-based hate content is predominantly generated from family hate content and may affect humanity. In the Indian context, analysis of multiple resources such as literature, reported articles and social media sites pertinent to honour-based hate content is less. Therefore, the purpose of this paper is to identify and understand the influencing factors and emotions of honour-based hate content. A review of literature, news articles on honour killing and the analysis of Twitter content are made to attain the purpose. In India, factor like marrying a person against family members' ideologies is observed as dominating among other factors of honour-based hate content. It has been also observed that emotions such as anger, fear, disgust and sadness are used to express hate. Possible impacts of honour-based hate content on family and society are discussed. The analysis of emotions about honour and hate content increases novelty of the article.
Mineralogy

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Demographical gender prediction of Twitter users using big data analytics: an application of decision marketing

Twitter text is difficult to analyse due to the non-standard and unstructured data. Twitter does not accumulate user gender information as do other popular social media platforms. The demographic feature prediction and additional informative content are important for advertising, custom-made marketing and authorised investigation from the social medium. The proposed statistical method with real-time analysis using big data technologies is able to predict the gender of Twitter users. Gender prediction is performed using the naive Bayes classifier to address systemic issues, and Apache Hive is used to solve data cleaning, storage and processing issues. The proposed method is a speedy, easy-to-implement with pre-processing, close to state-of-the-art document text categorisation method using big data technologies.
A wavelet and adaptive threshold-based contrast enhancement of masses in mammograms for visual screening

The screening of mammograms is a difficult task for the radiologist, due to variation in contrast and homogeneous structure of the masses and surrounding breast tissues. Therefore, an adaptive threshold-based contrast enhancement method is proposed in this paper for enhancement of suspicious masses in mammograms. Homomorphic filtering and wavelet-based denosing has been used prior to enhancement in the describe method. The approach contains, artefact suppression using pre-processing. Then wavelet transform is applied on the preprocessed mammogram, homomorphic filter is used to filter the approximate coefficient and wavelet shrinkage operator is applied on detail coefficients for denoising. Finally, contrast enhancement approach is used to enhance the suspicious region based on adaptive threshold technique. Two databases, namely Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS), were used to test proposed method. The obtain results using proposed method gives better visibility for suspicious masses for all types of mammograms.
Structure in Neural Activity during Observed and Executed Movements Is Shared at the Neural Population Level, Not in Single Neurons

(Cell Reports 32, 108006-1–108006-14.e1–e6; August 11, 2020) In the version of this article originally published online, an outdated version of the Supplemental Information PDF was included with the published article. A corrected version now appears with the article online. The production team apologizes for this error. Structure in Neural Activity during Observed and Executed Movements Is Shared at the Neural Population Level, Not in Single NeuronsJiang et al.Cell ReportsAugust 11, 2020In BriefWe can understand actions and learn movements by observing them. These processes are thought to involve neurons in motor cortex that are active during both movement observation and execution. Jiang et al. find that, when observing and executing movements, motor cortical neurons exhibit shared structure through consistent covariation and dynamics. Full-Text PDF Open Access
Naïve Bayes Classifiers For Tweet Sentiment Analysis Using GPU

The use of computers to solve problems has been done for all areas of work. Along with this, demanded faster computing process. To perform sentiment analysis of data obtained from the internet. Data taken from micro-blogging which at this time became the most popular communication tool and favored by internet users. The method used to construct the classification model of training data in this research is Naive Bayes Method. Training data is collected by utilizing the crontab facility with query emoticons and national media accounts linked to the Twitter API. The collected data will pass certain preprocessing before the training. The weighting feature used is the term frequency with TF-IDF. All data used in this research is a tweet that is delivered in Bahasa Indonesia. From the implementation results obtained 96.61% accuracy for sequential classification conducted using GPU GeForce 930M.
Foundations of Deep Learning and Its Applications to Health Informatics

Deep learning, a subfield of artificial intelligence, has shown exponential growth in health care applications. With the improvement of computational power and advances in big data, deep learning has made a significant impact on bioinformatics. Deep learning algorithms make use of neural networks and can process many features when dealing with unstructured data. Deep learning models can derive indispensable results by using the right models. In this chapter, we provide a brief discussion on the foundations of deep learning and highlight the applications of deep learning in healthcare. We anticipate this chapter can serve as a reference for researchers who are working in bioinformatics.
FPGA Design of Encryption Speech system using Synchronized Fixed-Point Chaotic Maps Based Stream Ciphers

In this work, speech encryption using synchronized fixed-point chaotic map-based stream ciphers (SFPCM-SC) is suggested. Five chaotic maps named quadratic, henon, logistic, lozi and duffing are synchronized by using master-slave synchronization technique and then used to generate the pseudo random bit generator (PRBG) using fixed-point converter. The PRBG is then Xor-ed with digitized speech signal where the encrypted signal is created. In the other side, the same map is synchronized with the master one and used to recover the original speech signal. First this work is simulated by using MATLAB and then built the design using Xilinx system generator (XSG). Finally, the hardware co-simulation is applied for the proposed system by using FPGA SP605 XC6SLX45T device. The results show that the error between master and slave become zero after a small period and the original speech signal is recovered with real time environment in successful.