Reviewerly/exHarmony huggingface dataset:
Automating the expansion of a knowledge graph

In order to make computers understand human languages and to reason, human knowledge needs to be represented and stored in a form that can be processed by computers. Knowledge graphs have been developed for use as a form of the knowledge base for words and general relationships among words. However, they have two limitations. One is that the knowledge graph is limited in size and scope for most of the human languages. Another is that they are not able to deal with neologisms that form a part of the human common sense. Addressing these problems, we have developed and validated PolarisX which can automatically expand a knowledge graph, by crawling and analyzing the news sites and social media in real-time. We utilize and fine-tune the pre-trained multilingual BERT model for the construction of knowledge graphs without language dependencies. We extract new relationships using the BERT-based relation extraction model and integrate them into the knowledge graph. We verify the novelty and accuracy of PolarisX. It deals with neologisms and does not have language dependencies.
Embedding Recurrent Neural Networks in Wearable Systems for Real-Time Fall Detection

Accidental falls are the preminent cause of fatal injuries and the most common cause of nonfatal trauma-related hospital admissions among elderly adults. An automated monitoring system that detects occurring falls and issues remote notifications will prove very valuable to improve the level of care that could be provided to vulnerable people. The paper focuses on the wearable-device approach to real-time fall detection, and presents the design of an embedded software for wearable devices that are connected in wireless mode to a remote monitoring system. In particular, the work proposes the embedding of a recurrent neural network (RNN) architecture on a micro controller unit (MCU) fed by tri-axial accelerometers data recorded by onboard sensors. To address the feasibility of such resource-costrained deep learning approach, the paper presents a few general formulas to determine memory occupation, computational load and power consumption. The formulas have been validated with the implementation of the run-time detection module for the SensorTile® device by STMicroelectronics.
Achieving Intelligent Trust-Layer for Internet-of-Things via Self-Redactable Blockchain

The advances of artificial intelligence (AI) propels big data processing and transmission for Internet of Things (IoT), by capturing and structuring big data produced by heterogeneous devices. While applying blockchain to manage IoT devices and associated big data, the blockchain itself suffers from abuse of decentralization from anonymous users. Specifically, it has been utilized to facilitate black market trades and illegal activities. Ateniese et al. proposed using the chameleon hash (CH) to derive redactable blockchain (EuroS&P), which works by embedding a trapdoor in the basic hash function so that block content can be rewritten without causing major hard forks. In short, the redacted block hash remains unchanged. However, there is lacking intelligent design where any mistakes observed in the chain can be corrected universally and automatically. This creates disincentives to use redactable blockchain (RB) for managing big data or any data-driven business mainly due to ineffective chain redaction. To solve this problem, in this article, we propose the notion of the self-redactable blockchain (SRB) to support intelligent execution of chain redaction. Specifically, we propose the first revocable chameleon hash (RCH) to power RB. It enables an ephemeral trapdoor for finding collision without any co-operation. Periodical expiration is applied to committed hash and an ephemeral trapdoor to prevent any abuses of redaction power. We instantiate how to use our RCH to build SRB as an intelligent trust-layer for IoT. We also give a rigorous analysis as well as comprehensive experiments to validate our proposals. The evidence showed that our proposal is secure and acceptably efficient for IoT devices.
Robust Gradient Descent via Moment Encoding and LDPC Codes

This paper considers the problem of implementing large-scale gradient descent algorithms in a distributed computing setting in the presence of straggling processors. To mitigate the effect of the stragglers, it has been previously proposed to encode the data with an erasure-correcting code and decode at the master server at the end of the computation. We, instead, propose to encode the second-moment of the data with a low density parity-check (LDPC) code. The iterative decoding algorithms for LDPC codes have very low computational overhead and the number of decoding iterations can be made to automatically adjust with the number of stragglers in the system. For a random model for stragglers, we obtain the convergence guarantees for the proposed solution by viewing it as the stochastic gradient descent method. Furthermore, the proposed solution outperforms the existing schemes in a real distributed computing setup.
Improved emotion recognition in Spanish social media through incorporation of lexical knowledge

Emotions play an important role in human intelligence and behaviour and are a major vehicle for communication. Therefore, the integration of emotions in computational models can improve the human–computer interaction systems. In this paper, we present a study of different machine learning approaches to automatically recognise emotions in messages written in Spanish on social media. Although the computational treatment of emotion is more difficult than other sentiment analysis tasks, the baseline of some machine learning algorithms achieve an acceptable accuracy showing that it is possible to tackle the problem using some basic natural language processing techniques. In this study we have experimented with the integration of knowledge from different affective lexical resources. We conclude that the incorporation of lexical affective features leads to improvement over most baseline figures with significant improvement. Indeed, we observe that the use of resources generated particularly for emotion recognition in other languages than English is a promising approach to enhance basic machine learning systems. Particularly, we used a Spanish lexical resource and we notice that it always improves the results. In the best case, it improves 6.15% of the results obtained using the Naive Bayes classifier.
Forward Secure Public Key Encryption with Keyword Search for Outsourced Cloud Storage

Cloud storage has become a primary industry in remote data management service but also attracts security concerns, where the best available approach for preventing data disclosure is encryption. Among them the public key encryption with keyword search (PKSE) is considered to be a promising technique, since clients can efficiently search over encrypted data files. That is, a client first generates a search token when to query data files, the cloud server uses the search token to proceed the query over encrypted data files. However, a serious attack is raised when PKSE meets cloud. Formally speaking, the cloud server can learn the information of a newly added encrypted data file containing the keyword that previously queried by using the search tokens it has received, and can further discover the privacy information. To address this issue, we propose a forward secure public key searchable encryption scheme, in which a cloud server cannot learn any information about a newly added encrypted data file containing the keyword that previously queried. To better understand the design principle, we introduce a framework for constructing forward secure public key searchable encryption schemes based on attribute-based searchable encryption. Finally, the experiments show our scheme is efficient.
A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning–based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005–30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning–based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning–based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.
Operationalisation of soft skill attributes and determining the existing gap in novice ICT professionals

The manuscript presents an analysis of the perceived importance and existing gap of soft skills for new entrants in the information technology sector. It also examines the effect of various backgrounds on the perception of soft skills. Empirical examinations are carried out through independent t-test, and MANOVA indicates substantial dissimilarity in the perception of students and practitioners. While examining the effect of the background, practitioners responded to a similarity in the perceived importance of soft skills, irrespective of gender, management level and experience. Though students showed similarity in the preparedness of soft skills regardless of the gender or medium of education up to class 12th but demonstrated substantial differences due to institute during graduation. Hence findings indicate the importance of academic institutions for the development of soft skills. The outcome also depicts that academia should take necessary action to amend the method of imparting skills for enhancing employability in students. Further, the study also sublimes essential soft skill attributes.
A flexible reference point-based multi-objective evolutionary algorithm: An application to the UAV route planning problem

We study the multi-objective route planning problem of an unmanned air vehicle (UAV) moving in a continuous terrain. In this problem, the UAV starts from a base, visits all targets and returns to the base in a continuous terrain that is monitored by radars. We consider two objectives: minimizing total distance and minimizing radar detection threat. This problem has infinitely many Pareto-optimal points and generating all those points is not possible. We develop a general preference-based multi-objective evolutionary algorithm to converge to preferred solutions. Preferences of a decision maker (DM) are elicited through reference point(s) and the algorithm converges to regions of the Pareto-optimal frontier close to the reference points. The algorithm allows the DM to change his/her reference point(s) whenever he/she so wishes. We devise mechanisms to prevent the algorithm from producing dominated points at the final population. We also develop mechanisms specific to the UAV route planning problem and test the algorithm on several UAV routing problems as well as other well-known problem instances. We demonstrate that our algorithm converges to preferred regions on the Pareto-optimal frontier and adapts to changes in the reference points quickly.
Double Transfer Learning for Breast Cancer Histopathologic Image Classification

This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-trained with ImageNet dataset. We also use transfer learning on training a support vector machine (SVM) classifier on a tissue labeled colorectal cancer dataset aiming to filter the patches from a breast cancer HI and remove the irrelevant ones. We show that removing irrelevant patches before training a second SVM classifier, improves the accuracy for classifying malign and benign tumors on breast cancer images. We are able to improve the classification accuracy in 3.7% using the feature extraction transfer learning and an additional 0.7% using the irrelevant patch elimination. The proposed approach outperforms the state-of-the-art in three out of the four magnification factors of the breast cancer dataset.
Celebrities-ReID: A Benchmark for Clothes Variation in Long-Term Person Re-Identification

This paper considers person re-identification (re-ID) in the case of long-time gap (i.e., long-term re-ID) that concentrates on the challenge of clothes variation of each person. We introduce a new dataset, named Celebrities-reID to handle that challenge. Compared with current datasets, the proposed Celebrities-reID dataset is featured in two aspects. First, it contains 590 persons with 10,842 images, and each person does not wear the same clothing twice, making it the largest clothes variation person re-ID dataset to date. Second, a comprehensive evaluation using state of the arts is carried out to verify the feasibility and new challenge exposed by this dataset. In addition, we propose a benchmark approach to the dataset where a two-step fine-tuning strategy on human body parts is introduced to tackle the challenge of clothes variation. In experiments, we evaluate the feasibility and quality of the proposed Celebrities-reID dataset. The experimental results demonstrate that the proposed benchmark approach is not only able to best tackle clothes variation shown in our dataset but also achieves competitive performance on a widely used person re-ID dataset Market1501, which further proves the reliability of the proposed benchmark approach.
Cognitive Mapping and Planning for Visual Navigation

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: (a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and (b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as “going to a chair”. We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation.
Industrial revolution 4.0: implication to libraries and librarians

Purpose Industry 4.0 is a term for the so-called Fourth Industrial revolutions. It is the technological integration of cyber-physical systems (CPS) in the process of production. CPS enables internet-based process networking with all participants in the process of value creation. The industrial revolution is actually changing how we live, work and communicate. Many trades have highly been affected by 4IR, libraries are one of them. The libraries of twenty-first century are shifting their paradigms from traditional setup to modern information networking. As people and machines are connecting to each other at enormous speed, artificial intelligence, mobile computing, machine learning and automation of every trade have become a need of the day. Automation and artificial intelligence are change agents in 4IR that will make certain groups of employees redundant, replacing them with new workers with the needed skills or with machines that do the job cheaper. This paper aims to shed light on how the 4IR will “shape the future of education, gender, work and library services”. The purpose of this paper is to highlight the challenges being faced by the library and librarians in the age of Industrial 4.0 revolution in contemporary society. The purpose of this study is to review the past literature on Industrial Revolutions 4.0 in education and interlink them with Library services. Design/methodology/approach The current study performs a systematic and content-centric review of literature relevant to library services. The literature of this study is based on a six-stage approach to identify the design principles and technology trends of 4IR in education and Library services. Findings Industry 4.0 Revolution is the current trend of revolutionary technology, which has affected many services in this age of globalization. Similar, Library services have highly been affected by 4IR. An effort has been made to highlight the vari-ous challenges being faced by libraries and librarians in this age of information. Some solutions have been presented to the library professionals to overcome this technology to boost its services up to the entire satisfaction of the patrons. Research limitations/implications The strategic approach in this study can serve the academicians and practitioners in the field of librarianship as a stepping stone to develop a successful transition from traditional manufacturing into the industry 4.0. Originality/value The study is among the first to identify the challenges being faced by libraries and librarians in this age of Industrial revolutions.
Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations

Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers' properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers' locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers' locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.
eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.
Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach

Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, public safety. In this paper, we propose a comprehensive framework for detecting network anomalies using mobile traffic data: collecting data from the LTE Physical Downlink Control Channel (PDCCH) of different eNodeBs, we implement deep learning algorithms in a semi-supervised way to detect potential traffic anomalies that are generated, for example, by unexpected crowd gathering. With respect to other types of mobile dataset, using LTE PDCCH information, we are able to obtain fine-grained and high-resolution data for the users that are connected to the LTE eNodeB. Through a semi-supervised approach, algorithms are trained to detect anomalies using only one class of traffic samples. We design two algorithms based on stacked-LSTM Neural Networks: 1) LSTM Autoencoder (LSTM-AE), in which the objective is to reconstruct the traffic samples 2) LSTM traffic predictor (LSTM-PRED), where the goal is to predict the traffic in the next time-instants, based on historical data. In both cases, we analyze the reconstruction (or prediction) error to assess if the mobile traffic presents anomalies or not. Using the F1-score as metric, we demonstrate that the proposed methods are able to identify the anomalous traffic periods, beating a benchmark that comprises different state-of-the-art algorithms for anomaly detection.