Reviewerly/exHarmony huggingface dataset:
HTAC: Fine-Grained Policy-Hiding and Traceable Access Control in mHealth

As an emerging cryptographic primitive, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for the owner to share his personal health records (PHRs) in mobile healthcare systems (mHealth). Before deploying traditional CP-ABE in real mHealth applications, there are three concerns worth considering. First, the scale of pre-defined attribute universe is lack of scalability. Second, the plaintext access policy sent along with the ciphertext would leak the PHR owner’s privacy. Third, it is difficult to identify the malicious user who intentionally disclosed his (partial or modified) private key. In this paper, we present HTAC, a fine-grained policy-hiding and traceable access control scheme for mHealth. In HTAC, the attribute universe is exponentially large and unbounded. Each attribute is expressed by an attribute name and an attribute value. In the encryption phase, the value is hidden in the ciphertext and only the generic attribute name is exposed. The malicious user will be precisely identified by searching the identity linked with the suspicious private key in an identity table. We further extend HTAC by removing the identity table and assigning more explicitly responsibility for the authority and the trace center. Then the storage overhead of tracing the malicious users is constant. The security analysis and performance comparison indicate that HTAC and the extended scheme are secure and practicable for real mHealth.
Covert timing channel detection method based on time interval and payload length analysis

Information leakage is becoming increasingly serious in today’ s network environment. Faced with increasingly forceful network defence strategies, attackers are also constantly trying to steal important information from systems. As for security researchers, the most troublesome way of information stealing is the covert channel. Generally, the covert channel is divided into the covert storage channel (CSC) and the covert timing channel (CTC). For the covert storage channel, there are already many effective methods to detect it. However, the detection of the covert timing channel is still in the research stage. The basis for implementing the covert timing channel is to control the sending time of packets, so most researches about the covert timing channel detection are based on the time interval between packets. Based on this idea, we refer to the method adopted in the researches of the malicious traffic detection and propose a covert timing channel detection method based on the k-NearestNeighbor (kNN) algorithm. This method uses a series of statistics related to the time interval and payload length as features to train a machine learning model and using 10-fold cross-validation to improve model performance. The experiment result proves that the model has a great detection effect, the detection accuracy is 0.96, and the Area Under Curve (AUC) value the model is 0.9737.
An Autobiographical Design Study of a Long Distance Relationship

Long distance couples often face challenges in maintaining their relationship over distance because computer-mediated communication tools typically only support a limited range of relationship maintenance behaviors. To explore a broader design space that might help combat this problem, we conducted an autobiographical design study that explores the usage of a telepresence robot coupled with voice-activated smart home devices. The telepresence robot provided an embodiment for one remote partner who could talk through the robot to control the smart devices in the remote location. We studied how the setup was used by a long distance couple over a three month period to share their home and nurture and maintain their relationship. The study revealed how such a setup can promote feelings of ownership, belonging, and normalcy, as well as a diversity of interactions and social connections. Implications for design include the importance of supporting effortful, personalized, varied, and shared interactions.
Vehicle tracking with Kalman filter using online situation assessment

Vehicle tracking is an attractive problem in the field of public transportation with several research projects conducted using Kalman filter (KF) to tackle this. While a driver may act on his own decision, there exist parameters affecting his behavior so called situation assessment such as neighboring drivers, possible obstacles, or alternative routes changing over time. In this paper, utilizing online situation assessment (SA) inside Kalman filter is studied. Motion History Graph is used as online modeling of the history of the vehicle motions and is used to augment the estimation. Experimental results on video sequences from different datasets show an average 25 percent performance improvement when using online SA inside KF.
Using a Patch-Wise M-Net Convolutional Neural Network for Tissue Segmentation in Brain MRI Images

Accurate segmentation of brain tissues, such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), in magnetic resonance imaging (MRI) images, is helpful for the diagnosis of neurological disorders, such as schizophrenia, Alzheimer's disease, and dementia. Studies on MRI-based brain segmentation have received significant attention in recent years based on the non-invasive imaging and good soft-tissue contrast provided by MRI. A number of studies have used conventional machine learning strategies, as well as convolutional neural network approaches. In this paper, we propose a patch-wise M-net architecture for the automatic segmentation of brain MRI images. In the proposed brain segmentation method, slices from a brain MRI scan are divided into non-overlapping patches, which are then fed into an M-net model with corresponding ground-truth patches to train the network, which is composed of two encoder-decoder processes. Dilated convolutional kernels with different sizes are used in the encoder and decoder modules to derive abundant semantic features from brain MRI scans. The proposed patch-wise M-net overcomes the drawbacks of conventional methods and provides greater retention of fine details. The proposed M-net model was trained and tested on the open-access series of imaging studies dataset. The performance was measured quantitatively using the Dice similarity coefficient. Experimental results demonstrate that the proposed method achieves average segmentation accuracies of 94.81% for CSF, 95.44% for GM, and 96.33% for WM, meaning it outperforms state-of-the-art methods.
Blockchain meets IIoT: An architecture for privacy preservation and security in IIoT

Industry 4.0 articulates that modern intelligent machines are better than humans that are enough capable to capture and analyze data in real-time, as well as in communicating information that may be helpful to take business-related decisions faster. The Industrial Internet of Things (IIoT) relies on intelligent assets that communicate and store data, data communication infrastructures, analytics, and people for functioning. With so many constituents of the IIoT network, there is also an increased potential for security risks. Privacy and trust issues need to be addressed. Recently Blockchain Technology has shown tremendous growth due to its reliable nature. In this paper we introduce a blockchain technology-based architecture for IIoT with the aim to address a lot of significant security issues
Seeded graph matching via large neighborhood statistics

We study a noisy graph isomorphism problem, where the goal is to perfectly recover the vertex correspondence between two edge‐correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. We show that it is possible to achieve the information‐theoretic limit of graph sparsity in time polynomial in the number of vertices n . Moreover, we show the number of seeds needed for perfect recovery in polynomial‐time can be as low as in the sparse graph regime (with the average degree smaller than ) and in the dense graph regime, for a small positive constant . Unlike previous work on graph matching, which used small neighborhoods or small subgraphs with a logarithmic number of vertices in order to match vertices, our algorithms match vertices if their large neighborhoods have a significant overlap in the number of seeds.
Adversarial learning of sentiment word representations for sentiment analysis

Word embeddings are used to represent words as distributed features, which can boost the performance on sentiment analysis tasks. However, most word embeddings consider only semantic and syntactic information and ignore sentiment information. Words with opposite sentiment polarities can have similar word embeddings (e.g., happy and sad or good and bad) as they have similar contexts. For incorporating sentiment information into word vectors, some approaches to sentiment embeddings are proposed. Based on the end-to-end architectures, these methods typically take the sentiment labels of whole sentences as outputs and use them to propagate gradients that update the context word vectors. Therefore, if the polarities of context words are inconsistent, they will still share the same gradient for updating. To address this, we have proposed an adversarial learning method for training sentiment word embeddings, in which the discriminator is employed to force the generator to produce high-quality word embeddings by using semantic and sentiment information. Additionally, the generator applies the multi-head self-attention to re-weight the gradients so that sentiment and semantic information are efficiently captured. Comparative experiments have been conducted with the word- and sentence-level benchmarks. The results demonstrate that the proposed method has outperformed previous sentiment embedding training models.
Analysis and Design of Computational News Angles

A key skill for a journalist is the ability to assess the newsworthiness of an event or situation.To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item.As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles.In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios.This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space.
Data Mining Optimization uses C4.5 Classification and Particle Swarm Optimization (PSO) in the location selection of Student Boardinghouses

Abstract The purpose of this study is to select the location of student boarding houses using Particle Swarm Optimization (PSO) and C4.5 optimization techniques. The source of the data was obtained by observing and giving questionnaires to 150 respondents who were lodging in the Pematangsiantar-Simalungun area. from the data set of 81 records and using 5 parameters of assessment ((C1) water cleanliness, (C2) Facilities, (C3) Transportation, (C4) Security, and (C5) Conditions) obtained the results of modeling using the C4.5 + PSO algorithm has better accuracy is 97.78% compared to the C4.5 model whose accuracy is 97.53%. Thus, it is evident that the PSO applied to the weighting of the C4.5 attribute increases the value of accuracy..
Establishment of a remote diagnostic histopathology service using whole slide imaging (digital pathology)

Background Whole slide imaging (WSI) has diverse applications in modern pathology practice, including providing histopathology services to remote locations. Materials and methods Utilising an existing contractual partnership with a Northern Ontario group of hospitals, the feasibility of using WSI for primary diagnostic services from Toronto was explored by the dedicated working group. All aspects explored from information technology (IT), laboratory information system (LIS) integration, scanning needs, laboratory workflow and pathologist needs and training, were taken into account in the developing the rationale and business case. Results The financial outlay for a scanner was $CA180K (approximately £105.6 k) after discounts. There were no human resource requirements as staff were reorganised to cater for slide scanning. Additional IT/LIS costs were not incurred as existing connectivity was adapted to allow two site groups (gastrointestinal and skin) to pilot this study. Scanned slides were available for pathologist review 24–96 hours sooner than glass slides; there was a 2-day improvement for final authorised cases, and per annum savings were: $CA26 000 (£15.2 k) in courier costs, $CA60 000 (£35.2 k) travel and $CA45 000 (£26.4 k) in accommodation, meals and car rental expense. Conclusion WSI is a viable solution to provide timely, high-quality and cost efficient histopathology services to underserviced, remote areas.
Weakly-supervised multi-label learning with noisy features and incomplete labels

Weakly-supervised multi-label learning has emerged as a hot topic more recently. Most existing methods deal with such problem by learning from the data where the label assignments are incomplete while the feature information is ideal. However, in many real applications, due to the influence of occlusion, illumination and low-resolution, the acquired features are often noisy, which may reduce the robustness of the learning model. In this paper, to overcome the above shortcoming, we propose a novel weakly-supervised multi-label learning framework called WML-LSC, where the low-rank and sparse constrain schemes are jointly incorporated to capture the desired feature information. Specifically, we first decompose the observed feature matrix into an ideal feature matrix and an outlier matrix. Considering that similar instances usually share similar visual characteristics, we constrain the ideal feature matrix to be low-rank. Meanwhile, a reasonable assumption is that the noise is sparse compared with the feature matrix, which leads outlier matrix to be sparse. In addition, a linear self-recovery model is adopted to reconstruct the incomplete label assignment matrix by exploiting label correlations. Finally, the desired model is trained on the ideal feature matrix and the refined label matrix. Extensive experimental results demonstrate that our proposed method can achieve superior and comparable performance against state-of-the-art methods.
Exploring the Reflective Potentialities of Personal Data with Different Temporal Modalities

We describe a long-term field study of Olo Radio, a music player that lets people re-experience digital music they have listened to previously. Olo Radio offers different 'timeframe modes' for organizing one's personal listening history data, and for exploring possible connections among songs and across time. We deployed 5 Olo Radios in 5 households for 8 months to understand participants' experiences over time. Our goals are to: (i) investigate the reflective potentialities of personal data for memory- oriented music listening and (ii) empirically explore conceptual propositions related to slow technology. Findings revealed Olo Radio became highly integrated in participants lives and triggered reflection on past life experiences. They also showed that Olo Radio was perceived to subtly change over time, and open up different ways of experiencing time. Findings are interpreted to present opportunities for future HCI research and practice.
Multiple instance-based video anomaly detection using deep temporal encoding–decoding

In this paper, we propose a weakly supervised deep temporal encoding–decoding solution for anomaly detection in surveillance videos using multiple instance learning. The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance learning framework where we treat the video as a bag and video clips as instances in the bag. Our main contribution lies in the proposed novel approach to consider temporal relations between video instances in a weakly supervised setting. We deal with video instances (clips) as sequential visual data rather than a set of independent instances. We employ a deep temporal encoding–decoding network that is designed to capture spatio-temporal evolution of video instances over time. We also propose a new loss function that maximizes the mean distance between normal and abnormal instance predictions. The new loss function ensures a low false alarm rate which is crucial in practical surveillance applications. The proposed temporal encoding–decoding approach with the modified loss is benchmarked against the state of the art. The results show that the proposed method performs similar to, or better than the state-of-the-art solutions for anomaly detection in video surveillance applications and achieves the lowest false alarm rate on UCF-Crime dataset.
Use of Mobile Phone Apps for Contact Tracing to Control the COVID-19 Pandemic: A Literature Review

Background: Contact tracing is a widely adopted surveillance system that is used to identify, evaluate, and handle people who have been exposed to novel infectious diseases. The mobile phone apps using a digital technological system, called "proximity tracking," is used as a surveillance system to control the COVID-19 pandemic. Objective: This aim of this review is to examine the use of mobile phone apps for contact tracing to control the COVID-19 pandemic worldwide. Method: A search of different electronic databases, such as PubMed, PubMed Central, Google Scholar, and Google, was carried out using search items "mobile app," "tracing," and "COVID-19." The search was conducted between 18 and 31 May 2020. Findings: The search revealed that a total of 15 countries in the world developed and actively using 17 mobile apps for contact tracing to control the COVID-19 pandemic during the selected time frame. China and Malaysia were only using two apps. Out of 17 apps, three were protected by the country's data protection laws. The results indicate that the mobile apps were used to monitor self-isolated individuals, identify individuals not wearing masks, whether they had close contact with an infected person, provide exact time and place of the encounter, and possible risk of infection. Conclusion: Contact tracing is found to be an essential public health approach to fight the spread of COVID-19 pandemic and other novel infectious diseases. However, caution is warranted to generalize the usability of apps, especially in the LMICs, and to address the concerns regarding data anonymization, data privacy and usage, and data rights.
Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests

The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to random variation in test data, training data, or internal randomness of the learning algorithms. This study takes into account these issues during a comparison of ML algorithms for binary and multivariate classification. It helps in providing guidelines for statistical validation of results. The results obtained show that the performance measure of accuracy for one algorithm differs by critical difference (CD) than others over binary and multivariate datasets obtained from different application domains.
Building Codes Part-of-Speech Tagging Performance Improvement by Error-Driven Transformational Rules

To enable full automation, automated code compliance checking systems need to extract regulatory information in building codes and convert it to computable representations. This conversion is a natural language processing (NLP) task that requires highly accurate part-of-speech (POS) tagging results on building codes. Existing POS taggers, however, do not provide such accuracy on building codes. To address this need, the authors propose to improve the performance of POS taggers by error-driven transformational rules that revise machine-tagged POS results. The proposed method utilizes a syntactic and semantic rule-based, NLP approach combined with a structure that is inspired by transfer learning. This method generates a group of transformational rulesets, from simple ones to complex ones, that will convert machine taggers’ tagging results to their corresponding human-labeled gold standard. The transformational rules utilize syntactic and semantic information of domain texts. All rules are constrained not to introduce any new errors when fixing existing errors of machine taggers. The last ruleset, which fixes most common remaining errors in textual data after all other rules are applied, is exempted from this constraint. An experimental test on part-of-speech tagged building code (PTBC) data shows this method reduced 82.7% of errors in POS tagging results of building codes, which increased the POS tagging accuracy on building codes from 89.13% to 98.12%.
WITHDRAWN: Time pressure in human cybersecurity behavior: Theoretical framework and countermeasures

Cybersecurity is a growing concern for private individuals and professional entities. Reports have shown that the majority of cybersecurity incidents occur because users fail to behave securely. Research on human cybersecurity (HCS) behavior suggests that time pressure is one of the important driving factors behind non-secure HCS behavior. However, there is limited conceptual work to guide researchers and practitioners in this regard. Against this backdrop, we investigate how the impact of time pressure on HCS behavior can be conceptualized within an integrative framework and which countermeasures can be used to reduce its negative impact. Altogether, we conducted 35 interviews with cybersecurity experts, non-security professionals, and private users. The results of our study shed light on the theoretical pathways through which time pressure can affect different types of security behaviors and identify a range of operational, human, technical, and physical countermeasures with important implications for research and practice.
Credit Card Fraud Detection Using Lightgbm Model

With the rapid growth of online banking and shopping, many companies deploy their online transaction system and thus raising many issues of fraud online credit card transactions. In recent years, several studies have developed some data mining based method to overcome this problem. However, most of the studies used a small amount of data and feature engineering methods. Moreover, the learning models used by them are too weak to fit the large scale of data. This paper expands fraud detection strategy and proposed a detection algorithm using lightgbm. The dataset is IEEE-CIS Fraud Detection dataset provided by Vesta Corporation. The experiments indicated that our method outperformed the other classical methods like Support Vector Machine, Random Forest and Xgboost. Moreover, it also shows the feature importance of our feature engineering, which is valuable for feature selection and performance tuning.