Reviewerly/exHarmony huggingface dataset:
Learning to Detect Deceptive Opinion Spam: A Survey

With the development of e-commerce, more and more users begin to post reviews or comments about the quality of products on the internet.Meanwhile, people usually read previous reviews before purchasing online products.However, people are frequently deceived by deceptive opinion spam, which is usually used for promoting the products or damaging their reputations because of economic benefit.Deceptive opinion spam can mislead people's purchase behavior, so the techniques of detecting deceptive opinion spam have extensively been researched in past ten years.In particular, some work based on deep learning has been investigated in last three years for the task.However, there still lack a survey, which can systematically analyze and summarize the previous techniques.To address this issue, this paper first introduces the task of deceptive opinion spam detection.Then, we summarize the existing dataset resources and their construction methods.Third, existing methods are analyzed from two aspects: traditional statistical methods and neural network models.Finally, we give some future directions of the task.
Developing neural network models for early detection of cardiac arrest in emergency department

Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardiac arrest in emergency departments.This is a single-center electronic health record (EHR)-based study. The primary outcome was the development of cardiac arrest within 24 h after prediction. Three ANN models were trained: multilayer perceptron (MLP), long-short-term memory (LSTM), and hybrid. These were compared to other classifiers including the modified early warning score (MEWS), logistic regression, and random forest. We used AUROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the comparison.During the study period, there were a total of 374,605 ED visits and 2,910,321 patient status updates. The ANN models (MLP, LSTM, and hybrid) achieved higher AUROC (AUROC: 0.929, 0.933, and 0.936; 95% confidential interval: 0.926-0.932, 0.930-0.936, and 0.933-0.939, respectively) compared to the non-ANN models, and the hybrid model exhibited the best performance. The ANN classifiers displayed higher performance in most of the test characteristics when the threshold levels of the classifiers were fixed to display the same positive result as those at the three MEWS thresholds (score ≥ 3, ≥4, and ≥5), and when compared with each other.The ANN improves upon MEWS and conventional machine learning algorithms for the prediction of cardiac arrests in emergency departments. The hybrid ANN model utilizing both baseline and sequence information achieved the best performance.
Safe Machine Learning and Defeating Adversarial Attacks

Adversarial attacks have exposed the unreliability of machine-learning (ML) models for decision making in autonomous agents. This article discusses recent research for ML model assurance in the face of adversarial attacks.
What is a preattentive feature?

The concept of a preattentive feature has been central to vision and attention research for about half a century. A preattentive feature is a feature that guides attention in visual search and that cannot be decomposed into simpler features. While that definition seems straightforward, there is no simple diagnostic test that infallibly identifies a preattentive feature. This paper briefly reviews the criteria that have been proposed and illustrates some of the difficulties of definition.
Privacy-aware blockchain for personal data sharing and tracking

Abstract Secure data distribution is critical for data accountability. Surveillance caused privacy breaching incidents have already questioned existing personal data collection techniques. Organizations assemble a huge amount of personally identifiable information (PII) for data-driven market analysis and prediction. However, the limitation of data tracking tools restricts the detection of exact data breaching points. Blockchain technology, an ‘immutable’ distributed ledger, can be leveraged to establish a transparent data auditing platform. However, Art. 42 and Art. 25 of general data protection regulation (GDPR) demands ‘right to forget’ and ‘right to erase’ of personal information, which goes against the immutability of blockchain technology. This paper proposes a GDPR complied decentralized and trusted PII sharing and tracking scheme. Proposed blockchain based personally identifiable information management system (BcPIIMS) demonstrates data movement among GDPR entities (user, controller and processor). Considering GDPR limitations, BcPIIMS used off-the-chain data storing architecture. A prototype was created to validate the proposed architecture using multichain. The use of off-the-chain storage reduces individual block size. Additionally, private blockchain also limits personal data leaking by collecting fast approval from restricted peers. This study presents personal data sharing, deleting, modifying and tracking features to verify the privacy of proposed blockchain based personally identifiable information management system.
RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

Object recognition in real-world environments is one of the fundamental and key tasks in computer vision and robotics communities. With the advanced sensing technologies and low-cost depth sensors, the high-quality RGB and depth images can be recorded synchronously, and the object recognition performance can be improved by jointly exploiting them. RGB-D-based object recognition has evolved from early methods that using hand-crafted representations to the current state-of-the-art deep learning-based methods. With the undeniable success of deep learning, especially convolutional neural networks (CNNs) in the visual domain, the natural progression of deep learning research points to problems involving larger and more complex multimodal data. In this paper, we provide a comprehensive survey of recent multimodal CNNs (MMCNNs)-based approaches that have demonstrated significant improvements over previous methods. We highlight two key issues, namely, training data deficiency and multimodal fusion. In addition, we summarize and discuss the publicly available RGB-D object recognition datasets and present a comparative performance evaluation of the proposed methods on these benchmark datasets. Finally, we identify promising avenues of research in this rapidly evolving field. This survey will not only enable researchers to get a good overview of the state-of-the-art methods for RGB-D-based object recognition but also provide a reference for other multimodal machine learning applications, e.g., multimodal medical image fusion, audio-visual speech recognition, and multimedia retrieval and generation.
Scanet: Spatial-channel Attention Network for 3D Object Detection

This paper aims to achieve high-accuracy 3D object detection, in which we propose a novel Spatial-Channel Attention Network (SCANet), a two-stage detector that takes both LIDAR point clouds and RGB images as input to generate 3D object estimates. The first stage is a 3D region proposal network (RPN) in which we put forward a new Spatial-Channel Attention (SCA) module and an Extension Spatial Upsample (ESU) module. Using the pyramid pooling structure and global average pooling, the SCA module can not only effectively incorporate multi-scale and global context information, but also produce spatial and channel-wise attention to select discriminative features. The ESU module in the decoder can recover the lost spatial information caused by consecutive pooling operators to generate reliable 3D region proposals. In the second stage, we design a new multi-level fusion scheme for accurate classification and 3D bounding box regression. Experimental results demonstrate that SCANet achieves state-of-the-art performance on the challenging KITTI 3D object detection benchmark.
Explaining explainable AI

How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
HIC-net: A deep convolutional neural network model for classification of histopathological breast images

In this study, a convolutional neural network (CNN) model is presented to automatically identify cancerous areas on whole-slide histopathological images (WSI). The proposed WSI classification network (HIC-net) architecture performs window-based classification by dividing the WSI into a certain plane. In our method, an effective pre-processing step has been added for WSI for better predictability of image parts and faster training. A large dataset containing 30,656 images is used for the evaluation of the HIC-net algorithm. Of these images, 23,040 are used for training, 2560 are used for validation and 5056 are used for testing. HIC-net has more successful results than other state-of-art CNN algorithms with AUC score of 97.7%. If we evaluate the classification results of HIC-net using softmax function, HIC-net success rates have 96.71% sensitivity, 95.7% specificity, 96.21% accuracy, and are more successful than other state-of-the-art techniques which are used in cancer research.
Internet usage, innovation and human development nexus in Africa: the case of ECOWAS

Internet usage and the sporadic rise in the level of innovations have been major drivers of human progress in the last decades. These leaps in human intelligence have affected almost every area of human endeavour including education, health, finance, commerce, political engagement, and so on. With the United Nations' Sustainable Development Goals (SDGs), the Economic Community of West African States (ECOWAS) has adopted some strategies in order to ensure that ECOWAS member countries are not left behind. Thus, this study examines the influence of internet usage and innovation on human development in 15 ECOWAS countries. Using the fixed and random effects panel data techniques, the study finds that Internet usage, innovation and their interaction have significant and positive relationship with human development. Furthermore, the study unbundles the ECOWAS countries and finds that countries like Cape Verde had positive significant coefficient for the three cases examined, the results for some ECOWAS members were not significant, while few had significant negative results, which did not have influence on the overall result. Recommendations on how to increase innovation for human development in ECOWAS countries are highlighted in the study.
A Privacy-Preserving Outsourcing Scheme for Image Local Binary Pattern in Secure Industrial Internet of Things

In the era of Industrial Internet of Things (IIoT), huge amounts of data are generated, and companies are highly motivated to store the data on cloud servers for cost saving and efficient application. However, the IIoT data are always of great value. The direct outsourcing of such data can leak the important information of the companies and cause great business losses. A straightforward solution is to encrypt the data by using standard encryption methods before outsourcing. Nevertheless, this will make data utilization quite inconvenient. This paper focuses on the secure process of image data on cloud servers. Images are stored on cloud servers in encrypted form, and the local binary pattern feature can be directly extracted from the encrypted images for applications. The security analysis and experimental results demonstrate the security and effectiveness of our scheme.
Image super resolution by dilated dense progressive network

Image super-resolution (SR) is an interesting topic in computer vision. However, it remains challenging to achieve high-resolution image from the corresponding low-resolution version due to inherent variability, high dimensionality, and small ground targets images. In this paper, a new model based on dilated convolutional neural network is proposed to improve the image resolution. Recently, deep learning methods have led to significant improvements and completely outpace other models. However, these methods have not fully exploited all the features of the original low-resolution image, because of complex imaging conditions and the degradation process. To address this issue, we proposed an effective model based on dilated dense network operations to accelerate deep networks for image SR, which support the exponential growth of the receptive field parallel by increasing the filter size. In particular, residual network and skip connections are used for deep recovery. The experimental evaluations on several datasets prove the efficiency and stability of the proposed model. The proposed model not only achieves state-of-the-art performance but also has more efficient computation.
Behind the Curtain of the "Ultimate Empathy Machine"

Virtual Reality nonfiction (VRNF) is an emerging form of immersive media experience created for consumption using panoramic "Virtual Reality" headsets. VRNF promises nonfiction content producers the potential to create new ways for audiences to experience "the real"; allowing viewers to transition from passive spectators to active participants. Our current project is exploring VRNF through a series of ethnographic and experimental studies. In order to document the content available, we embarked on an analysis of VR documentaries produced to date. In this paper, we present an analysis of a representative sample of 150 VRNF titles released between 2012-2018. We identify and quantify 64 characteristics of the medium over this period, discuss how producers are exploiting the affordances of VR, and shed light on new audience roles. Our findings provide insight into the current state of the art in VRNF and provide a digital resource for other researchers in this area.
A Look into Programmers’ Heads

Program comprehension is an important, but hard to measure cognitive process. This makes it difficult to provide suitable programming languages, tools, or coding conventions to support developers in their everyday work. Here, we explore whether functional magnetic resonance imaging (fMRI) is feasible for soundly measuring program comprehension. To this end, we observed 17 participants inside an fMRI scanner while they were comprehending source code. The results show a clear, distinct activation of five brain regions, which are related to working memory, attention, and language processing, which all fit well to our understanding of program comprehension. Furthermore, we found reduced activity in the default mode network, indicating the cognitive effort necessary for program comprehension. We also observed that familiarity with Java as underlying programming language reduced cognitive effort during program comprehension. To gain confidence in the results and the method, we replicated the study with 11 new participants and largely confirmed our findings. Our results encourage us and, hopefully, others to use fMRI to observe programmers and, in the long run, answer questions, such as: How should we train programmers? Can we train someone to become an excellent programmer? How effective are new languages and tools for program comprehension?
Cognitive security paradigm for cloud computing applications

Summary This article presents new paradigms of confidential data protection. New classes of paradigms are dedicated to enhancing the already‐known cryptographic solutions belonging to the group of data sharing schemes. Data sharing schemes are dedicated to executing data protection tasks by means of splitting data into parts (called shadows) and distributing those shadows among a group of secret trustees. This process is enriched by linguistic and biometric solutions to guarantee protection of the shared data by means of biometric labeling or by means of user verification with the application of meaning interpretation of individual secret parts. The novelty in this solution is in the application of cognitive algorithms to describe correctly the shared secret. Cognitive paradigms guarantee an execution of information concealment protocols and their division at various levels of knowledge held by individual protocol participants. The paradigms of cognitive description of the concealed data have been introduced into a new class of data sharing protocols, ie, into the linguistic‐biometric threshold schemes. A special feature of the proposed solution is its universality, which is a possibility to apply the here‐discussed threshold schemes in the processes of data concealment with varying importance. At the same time, this solution can be applied to execute data protection tasks against unauthorized acquisition or disclosure. This paper will present also the possibilities of application of the paradigms presented here to conceal data dedicated to the data management processes.
Who Gets to Future?

This paper draws on a collaborative project called the Africatown Activation to examine the role design practices play in contributing to (or conspiring against) the flourishing of the Black community in Seattle, Washington. Specifically, we describe the efforts of a community group called Africatown to design and build an installation that counters decades of disinvestment and ongoing displacement in the historically Black Central Area neighborhood. Our analysis suggests that despite efforts to include community, conventional design practices may perpetuate forms of institutional racism: enabling activities of community engagement that may further legitimate racialized forms of displacement. We discuss how focusing on amplifying the legacies of imagination already at work may help us move beyond a simple reading of design as the solution to systemic forms of oppression.
MMAN: Multi-modality aggregation network for brain segmentation from MR images

Brain tissue segmentation from Magnetic resonance (MR) image is significant for assessing both neurologic conditions and brain disease. Manual brain tissue segmentation is time-consuming, tedious and subjective which indicates a need for more efficiently automated approaches. However, due to ambiguous boundaries, anatomically complex structure and individual differences, conventional automated segmentation methods performed poorly. Therefore, more effective feature extraction techniques and advanced segmentation models are in essential demand. Inspired by deep learning concepts, we propose a multi-modality aggregation network (MMAN), which is able to extract multi-scale features of brain tissues and harness complementary information from multi-modality MR images for fast and accurate segmentation. Extensive experiments on the well-known MRBrainS Challenge database corroborate the efficiency of the proposed model. Within approximately thirteen seconds, the MMAN can segment three different brain tissues from MRI data of each individual, that is faster than many existing methods. For the segmentation of gray matter, white matter, and cerebrospinal fluid, the MMAN achieved dice coefficients of 86.40%, 89.70% and 84.86%, respectively. Consequently, the proposed model outperformed many state-of-the-art methods and got the second place in the MRBrainS Challenge. Therefore, the proposed MMAN is promising for automated brain segmentation in clinical applications.
Binary spotted hyena optimizer and its application to feature selection

Spotted hyena optimizer (SHO) is a recently developed metaheuristic technique that mimics the hunting behavior of the spotted hyenas. However, it does not provide optimal solution for discrete problems. Therefore, a novel binary version of Spotted Hyena Optimizer is proposed in this paper. The binary version of SHO can deal with discrete optimization problems. In the proposed algorithm, tangent hyperbolic function is utilized to squash the continuous position and then these values are used to update the position of spotted hyenas. The prey searching, encircling, and attacking are three main steps of binary spotted hyena optimizer. The proposed algorithm has been compared with six well-known metaheuristic techniques over 29 benchmark test functions. The effects of convergence, scalability, and control parameters have been investigated. The statistical significance of the proposed approach has also been examined through ANOVA test. The proposed approach is also applied on feature selection domain. The performance of proposed approach has been compared with four well-known metaheuristic techniques over eleven UCI repository datasets. The experimental results reveal that the proposed approach is able to search the optimal feature set than the others.
Knowledge Based Recommender System for Academia Using Machine Learning: A Case Study on Higher Education Landscape of Pakistan

Allocation of courses and research students based on faculty's subject specialization and area of interest has always remained a challenging task for university administration due to the presence of academics' cross-domain interests, stale faculty resumes at university portals and changing the skill set demands from the industry. Collaborative filtering and content-based recommender systems have already been in use by the industry for recommending things, such as movies, news, restaurants, and shopping items to the users, and however, no one has utilized these off-the-shelf models for enhancing the student experience and improving the quality of higher education in academia. This paper presents a case study showcasing the use of probabilistic topic models for generating recommendations to users in academia through appropriate course allocation and supervisor assignment. The proposed system coined as ScholarLite harnesses the power of machine learning to extract research themes from faculty members' past publications, mines research interests from their resumes, and combines it with their educational background to generate recommendations for course teaching, research supervision, and industry-academia collaboration. We have shown the recommendation results on real-world data gathered from the higher education commission of the country and demonstrated that the proposed techniques are scalable across various programs offered by the universities and could be deployed in a small budget by universities for automating course and supervisor allocation procedures. The experiments confirm our performance expectation by showing good relevance and objectivity in results, thus making this decision management system more appealing for large-scale deployment and use by academia.
A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models

This work focuses on sentence-level aspect-based sentiment analysis for restaurant reviews. A two-stage sentiment analysis algorithm is proposed. In this method, first a lexicalized domain ontology is used to predict the sentiment and as a back-up algorithm a neural network with a rotatory attention mechanism (LCR-Rot) is utilized. Furthermore, two features are added to the backup algorithm. The first extension changes the order in which the rotatory attention mechanism operates (LCR-Rot-inv). The second extension runs over the rotatory attention mechanism for multiple iterations (LCR-Rot-hop). Using the SemEval-2015 and SemEval-2016 data, we conclude that the two-stage method outperforms the baseline methods, albeit with a small percentage. Moreover, we find that the method where we iterate multiple times over a rotatory attention mechanism has the best performance.