Reviewerly/exHarmony huggingface dataset:
Clustering method for censored and collinear survival data

Abstract In this paper we propose a Dirichlet process mixture model for censored survival data with covariates. This model is suitable in two scenarios. First, this method can be used to identify clusters determined by both the censored survival data and the predictors. Second, this method is suitable for highly correlated predictors, in cases when the usual survival models cannot be implemented because they would be unstable due to multicollinearity. The Dirichlet process mixture model links a response vector to covariate data through cluster membership and in this paper this model is extended for mixtures of Weibull distributions, which can be used to model survival times and also allow for censoring. We propose two variants of this model, one with a shape parameter common to all clusters (referred to as a global parameter) for the Weibull distributions and one with a cluster-specific shape parameter. The first satisfies the proportional hazard assumption, while the latter is very flexible, as it has the advantage of allowing estimation of the survival curve whether or not the proportional hazards assumption is satisfied. We present a simulation study and, to demonstrate the applicability of the method in practice, a real application to sleep surveys in older women from The Australian Longitudinal Study on Women’s Health. The method developed in the paper is available in the R package PReMiuM.
Prospects of Augmented Reality in Physical Stores’s using Shopping Assistance App

This paper discusses about the enhancement of shopping behavior through mobile application as a personal assistant for the shopper’s in a physical retail outlet by giving various information regarding the products, offers, discounts when they scan a product through mobile. The mobile app uses augmented reality added with in-store GPS technology to track the movements of the shoppers and analyze the buying behavior and shopping pattern. The target design of the conceptual framework is for large in store retail setup like for.eg, hyper market, shopping mall and supermarket.
Developing Non-Stochastic Privacy-Preserving Policies Using Agglomerative Clustering

We consider a non-stochastic privacy-preserving problem in which an adversary aims to infer sensitive information S from publicly accessible data X without using statistics. We consider the problem of generating and releasing a quantization X̂ of X to minimize the privacy leakage of S to X̂ while maintaining a certain level of utility (or, inversely, the quantization loss). The variables S and X are treated as bounded and non-probabilistic, but are otherwise general. We consider two existing non-stochastic privacy measures, namely the maximum uncertainty reduction L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (S → X̂) and the refined information X̂) (also called the maximin information) of S. For each I (S; privacy measure, we propose a corresponding agglomerative clustering algorithm that converges to a locally optimal quantization solution X̂ by iteratively merging elements in the alphabet of X. To instantiate the solution to this problem, we consider two specific utility measures, the worst-case resolution of Xby observing X̂ and the maximal distortion of the released data X̂. We show that the value of the maximin information I (S; X̂) can be determined by dividing the confusability graph into connected subgraphs. Hence, I (S; X̂) can be reduced by merging nodes connecting subgraphs. The relation to the probabilistic information-theoretic privacy is also studied by noting that the Gács-Körner common information is the stochastic version of I and indicates the attainability of statistical indistinguishability.
Comparative Study for Predicting the Severity of Cyberbullying Across Multiple Social Media Platforms

Initially, cyberbullying detection was mostly done by explicit reporting of inappropriate content by users on the internet, but victims usually are not in the mental space to report such instances, which ultimately go unnoticed by the social media websites. A need for automated methods of cyberbullying detection was realized but the current detection methods require massive improvements to correctly determine the extent of cyberbullying. Our paper aims to identify instances of cyberbullying across various social media platforms and classify them based on the level of severity of cyberbullying. We wish to provide a comparative study of various traditional machine learning and deep learning models that can be used for the above objective.We also wish to release 3 datasets that are annotated in 4 labels based on the severity of cyberbullying (none, low, medium, and high) to be used for future work by the interested person(s). Since young adults are very vulnerable to such incidents, the paper aims to work on social media platforms trending amongst the youth, like Reddit, Twitter, and Formspring.
Automatic Knowledge Graph Construction Based on Relational Data of Power Terminal Equipment

In the last years, Knowledge Graph (KG) has been widely applied in domain knowledge modeling, and therefore has received more attention in the Ubiquitous Power Internet of Things (UPIoT) that consists of a large number of power terminal equipment and sensing devices. In this paper, we propose an automatic framework to construct power terminal equipment knowledge graph by extracting ontological information and instantiating objects from the relational data of power terminal equipment. First, the ontological concepts and the hierarchy between concepts are built by analyzing the data schemas of different types of devices and their properties. Then the knowledge graph for different types of device objects is constructed by instantiating every object and their binary relations. In addition, we give the relevant algorithms of automatically constructing the complete knowledge graph of power terminal equipment. We argue that the proposed approach will greatly facilitate the information sharing of multi-source heterogeneous data of power terminal equipment on the environment of UPIoT.
Comparison between the geochemical response of BLEG and fine fraction stream sediments to mineralization in the Eastern Black Sea region, Turkey

Stream sediments are the preferred sampling method in regional geochemical exploration for Au deposits in regions displaying well-defined drainage systems. Analysis of either bulk leach extractable gold (BLEG) and aqua regia-extractable multi-element analysis of fine fraction sediments are two common analytical approaches. This study examines the efficiency of detecting known ore deposits and prospects using parametric (Q-Q) and nonparametric (U-spatial and C-A fractal) modeling of a large compilation of company stream sediment and rock chip geochemical data from exploration programs in the Eastern Black Sea region of Turkey, to identify geochemical patterns related to major precious and base metal deposits. The median and 95th percentile values for Au are similar across the different lithological groups, although BLEG values are high in granite and volcanic-dominated terranes. In general, C-A and U-spatial modeling of individual elements concentrations and PCA scores were more effective than Q-Q plots in separating populations associated with the effects of mineralization and delivered lower estimates for thresholds to the dominant "background" population. There is strong spatial association between the anomalous geochemical populations (especially for PCA scores) and the locations of the majority of known Au ± Ag and Cu ± Pb ± Zn deposits in the study area. Most of the known Au mineral deposits occur in catchments with marginal to strongly anomalous Au concentrations in BLEG or the <180 μm fraction. Known Pb and Cu deposits (of various genetic types) are generally contained in catchments displaying the third or higher C-A fractal or U-spatial populations. BLEG was marginally more effective than <180 μm in detecting catchments with known mineralization.
Privacy-Preserving Similarity Computation in Cloud-Based Mobile Social Networks

A growing number of mobile social network applications are taking advantage of cloud computing to store profiles of end users and run protocols which are compute-intensive.We focus on an application scenario of similarity computation between two users.To protect data security and privacy, mobile users encrypt their sensitive profiles before outsourcing to the cloud and different users choose different encryption keys.Three challenges need attention -how to compute on encrypted profiles under different keys, how to allow mobile users to stay offline during execution of the protocol, and how to select similarity metric.Existing schemes either rely on multi-key fully homomorphic encryption with one server or partially homomorphic encryption with two non-colluding servers.To balance computational complexity on one server and communication overhead between two servers, we put forward a privacy-preserving similarity computation protocol which supports both homomorphic additions and one homomorphic multiplication.We conduct security analysis and experimental evaluation of our scheme.The results show that our protocol is provably secure and runs reasonably fast, and thus can be applied in practice.
SmartChama

In Kenya there are over 300,000 "chamas". These groups provide social and financial support for their members and have around $3.4 billion in assets. Despite widespread mobile phone use in Kenya, there are few efforts to develop digital services for chamas. We describe the design, and formative evaluation of 'SmartChama'---a prototype mobile app developed to understand activities in chamas and how to digitize them. We used contextual inquiry to understand how seven chama groups (n=48 participants) used our prototype to manage and track their finances. Based on our findings, we iterated SmartChama's design and then evaluated it with seven groups. Findings from this evaluation suggest different chama activities that should be considered when designing digital financial systems for these groups. We discuss these design implications and offer new perspectives on designing financial systems in Kenya.
MalPortrait: Sketch Malicious Domain Portraits Based on Passive DNS Data

Malicious domain detection is of great significance for cybersecurity. Most prior works detect malicious domains based on individual features, which are only related to the attributes of domains themselves and can be easily changed to avoid detection. To solve the problem, we propose a novel system called MalPortrait, which combines individual features and association information of domains to detect malicious domains. In MalPortrait, we show the association information among domains by a domain association graph where vertices represent domains and edges connect domains resolved to the same IP. Based on the graph, we combine individual features (e.g., string-based, network-based) of each domain and its association information to generate new features. Compared with individual features, the new features are harder to be tampered with and can help determine whether a domain is malicious from a more comprehensive perspective. We evaluate MalPortrait on the passive DNS traffic collected from real-world large ISP networks. Our experimental results show that MalPortrait can accurately identify malicious domain names with a precision of 96.8% and a recall of 95.5%. Compared with prior works, MalPortrait performs better and hardly relies on additional knowledge (e.g., IP reputation, Domain whois).
The Developing Bodily Self: How Posture Constrains Body Representation in Childhood

Adults’ body representation is constrained by multisensory information and knowledge of the body such as its possible postures. This study ( N = 180) tested for similar constraints in children. Using the rubber hand illusion with adults and 6‐ to 7‐year olds, we measured proprioceptive drift (an index of hand localization) and ratings of felt hand ownership. The fake hand was either congruent or incongruent with the participant’s own. Across ages, congruency of posture and visual–tactile congruency yielded greater drift toward the fake hand. Ownership ratings were higher with congruent visual–tactile information, but unaffected by posture. Posture constrains body representation similarly in children and adults, suggesting that children have sensitive, robust mechanisms for maintaining a sense of bodily self.
Combining Character and Word Embeddings for Affect in Arabic Informal Social Media Microblogs

Word representation models have been successfully applied in many natural language processing tasks, including sentiment analysis. However, these models do not always work effectively in some social media contexts. When considering the use of Arabic in microblogs like Twitter, it is important to note that a variety of different linguistic domains are involved. This is mainly because social media users employ various dialects in their communications. While training word-level models with such informal text can lead to words being captured that have the same meanings, these models cannot capture all words that can be encountered in the real world due to out-of-vocabulary (OOV) words. The inability to identify words is one of the main limitations of this word-level model. In contrast, character-level embeddings can work effectively with this problem through their ability to learn the vectors of character n-grams or parts of words. We take advantage of both character- and word-level models to discover more effective methods to represent Arabic affect words in tweets. We evaluate our embeddings by incorporating them into a supervised learning framework for a range of affect tasks. Our models outperform the state-of-the-art Arabic pre-trained word embeddings in these tasks. Moreover, they offer improved state-of-the-art results for the task of Arabic emotion intensity, outperforming the top-performing systems that employ a combination of deep neural networks and several other features.
Using Camera Array to Detect Elderly Falling and Distribute Alerting Media for Smart Home Care

Towards an aging society, applying information and communication technologies to advance elderly's home care services to be smart, i.e., smart home care, becomes a significant research theme. Considering falling is specially dangerous to the elderly, immediately detecting the elderly's fall and alerting exigency events to whom have concerns about, e.g., hospitals, healthcare center and family members, is crucial. In this paper, we take advantage of the Internet of Things (IoT) technology to develop smart home care services and help to distribute instant information to remote users outside the home domain. We propose a camera-built peer-to-peer (P2P) network system on which a camera array will cooperate in detecting falling, broadcasting alert events and distributing captured media streams in an elderly activity area. Multiple cameras with Wi-Fi and IoT functions can organize an in-house P2P network where cameras can publish, subscribe and relay media content. In addition, a gateway device in charge will send that information to the remote user. For a proof-of-concept implementation, our study develops an application-level prototype based on the P2P networking techniques. A practical scenario of elderly fall detection and alerting media distribution services in elder living environments is demonstrated to highlight the functionality of this system architecture for smart home care services.
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles

Machine reading comprehension is a natural language understanding task where the computing system is required to read a text and then find the answer to a specific question posed by a human. Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models. Furthermore, machine reading comprehension (MRC) for the health sector has potential for practical applications; nevertheless, MRC research in this domain is currently scarce. This article presents UIT-ViNewsQA, a new corpus for the Vietnamese language to evaluate MRC models for the healthcare textual domain. The corpus consists of 22,057 human-generated question-answer pairs. Crowd-workers create the questions and answers on a collection of 4,416 online Vietnamese healthcare news articles, where the answers are textual spans extracted from the corresponding articles. We introduce a process for creating a high-quality corpus for the Vietnamese machine reading comprehension task. Linguistically, our corpus accommodates diversity in question and answer types. In addition, we conduct experiments and compare the effectiveness of different MRC methods based on the neural networks and transformer architectures. Experimental results on our corpus show that the MRC system based on ALBERT architecture outperforms the neural network architectures and the BERT-based approach, an exact match score of 65.26% and an F1-score of 84.89%. The best machine model achieves about 10.90% F1-score less efficiently than humans, which proves that exploring machine models on UIT-ViNewsQA to surpass humans is challenging for researchers in the future. Our corpus is publicly available on our website: http://nlp.uit.edu.vn/datasets for research purposes.
AnytimeNet: Controlling Time-Quality Tradeoffs in Deep Neural Network Architectures

Deeper neural networks, especially those with extremely large numbers of internal parameters, impose a heavy computational burden in obtaining sufficiently high-quality results. These burdens are impeding the application of machine learning and related techniques to time-critical computing systems. To address this challenge, we are proposing an architectural approach for neural networks that adaptively trades off computation time and solution quality to achieve high-quality solutions with timeliness. We propose a novel and general framework, AnytimeNet, that gradually inserts additional layers, so users can expect monotonically increasing quality of solutions as more computation time is expended. The framework allows users to select on the fly when to retrieve a result during runtime. Extensive evaluation results on classification tasks demonstrate that our proposed architecture provides adaptive control of classification solution quality according to the available computation time.
RDBN: Visual relationship detection with inaccurate RGB-D images

Traditional visual relationship detection methods only use RGB information to train the semantic network, which do not match human habits that we combine RGB information with Depth information to perceive the world, thus, there is not enough generalization ability (zero-shot performance) to extract the visual relationships in practical scenes. To solve this problem, a novel visual relationship detection framework based on RGB-D images is proposed in this paper. Since it is difficult to get accurate depth maps from complex scenes, we propose a fuzzy strategy based method to represent Depth features of inaccurate depth maps which are independent of manual depth annotations. In particular, we formulate the RGB-Depth-Balanced-Network (RDBN) which can simultaneously process RGB features and the corresponding estimated depth maps to counter the inaccuracy of depth maps and extract semantic information by the only input of monocular RGB images. In experiments, we conduct ablation experiments to analyze functions of different visual components to demonstrate the effectiveness of our RDBN. Furthermore, we show that RDBN outperforms state-of-the-art visual relationship detection methods on Visual Relationship Dataset (VRD) and UnRel Dataset when tackling the visual relationship detection task of zero-shot learning in specific depth conditions, and the task of image retrieval among unusual relationships.
Post Quantum Security Solution for Data Aggregation in Wireless Sensor Networks

Data aggregation has been one of the most widely researched topics for Wireless Sensor Network (WSN) as it helps in increasing the effective life time of the network and reduces the packet transmission overhead. Besides other challenges, security is one of the serious challenges during data aggregation. Numerous solutions on security for data aggregation for WSN are based on homomorphic encryption technique or Elliptic Curve Cryptography (ECC) based solutions and thus are vulnerable to attacks by quantum machines. This paper presents a post-quantum security solution using lattice cryptography. The proposed solution is applying Learning with Errors over Rings (R-LWE) for encryption of data in the data aggregation process at the gateway module of WSN. Security analysis shows that the proposed scheme provides confidentiality, integrity and authenticity during communication. Performance analysis shows that the proposed scheme is lightweight and shows better performance compared to Elliptic Curve Elgamal (ECEG) cryptography based scheme and symmetric homomorphic based scheme.
Overview of Segmentation X-Ray Medical Images Using Image Processing Technique

Abstract Image processing techniques have been used in a wide variety of applications nowadays to enhance the quality of raw image data. Today, image segmentation or detection of x-tray medical imaging is very popular and challenges task in order to improve the diagnosis and analysis result. An x-ray image is one of the oldest photographic films that is mostly used in medical diagnosis and treatment. An x-ray image is a very useful modality for the physicians and doctors to determine and analyze the bone fracture, which is an important symptom used for diagnosis, but x-ray produces an only medium quality image, which will normally affect the information of the image. This article provides a review study of the medical image segmentation. Based on this study, the advantages and drawback each method clearly explained. This article presents an exhaustive review of these studies and suggests a direction for future developments in order to propose segmentation methods.
Detecting Video Speed Manipulation

Manipulated videos are frequently an important part of misinformation campaigns. While much attention has recently focused on sophisticated threats such as deepfakes, the majority of videos used in misinformation campaigns have been created using relatively simple manipulations that can be produced by commonly available editing software. One important manipulation that has been used in previous misinformation attempts is altering the speed of a video. In the previous 18 months, widely circulated videos have had their speed manipulated to make Speaker of the House Nancy Pelosi appear disoriented, and to make reporter Jim Acosta appear to act aggressively toward a White House staffer. Currently, however, there are no approaches to accurately detect video speed manipulation that can be deployed at scale. In this paper, we propose new algorithms to detect video speed manipulation and to estimate the rate by which a video's speed has been modified. To do this, we identify a new trace left by video speed manipulation and show how it can be extracted from a video. Our approaches to trace extraction, speed manipulation detection, and manipulation rate estimation are computationally efficient and can be run in a matter of milliseconds. We present experimental results that show that our proposed approach can detect manipulated videos with up to 99% accuracy.