Reviewerly/exHarmony huggingface dataset:
AI-Driven Data Monetization: The Other Face of Data in IoT-Based Smart and Connected Health

As the trajectory of the Internet of Things (IoT) moving at a rapid pace and with the rapid worldwide development and public embracement of wearable sensors, these days, most companies and organizations are awash in massive amounts of data. Determining how to profit from data deluge can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business. The market has already seen a level of monetization across vertical domains in the form of layering connected devices with a variety of Software-as-a-Service (SaaS) choices, such as subscription plans or smart device insights. Out of this arena is evolving a “machine economy” in which the ability to correctly monetize data rather than simply hoard it, will provide a significant advantage in a competitive digital environment. The recent advent of the technological advances in the fields of big data, analytics, and artificial intelligence (AI) has opened new avenues of competition, where data are utilized strategically and treated as a continuously changing asset able to unleash new revenue opportunities for monetization. Such growth has made room for an onslaught of new tools, architectures, business models, platforms, and marketplaces that enable organizations to successfully monetize data. In fact, emerging business models are striving to alter the power balance between users and companies that harvest information. Start-ups and organizations are offering to sell user data to data analytics companies and other businesses. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by: 1) utilizing it to make better business decisions or improve processes; 2) surrounding flagship services or products with data; or 3) selling information to current or new markets. This article will address all important aspects of IoT data monetization with more focus on the healthcare industry and discuss the corresponding challenges, such as data management, scalability, regulations, interoperability, security, and privacy. In addition, it presents a holistic reference architecture for the healthcare data economy with an in-depth case study on the detection and prediction of cardiac anomalies using multiparty computation (MPC) and privacy-preserving machine learning (PPML) techniques.
The Group Loss for Deep Metric Learning

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that “similar objects should belong to the same group”, the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We show state-of-the-art results on clustering and image retrieval on several datasets, and show the potential of our method when combined with other techniques such as ensembles. To facilitate further research, we make available the code and the models at https://github.com/dvl-tum/group_loss .
How digitalization affects insurance companies: overview and use cases of digital technologies

Abstract The digital transformation is of increasing relevance for insurance companies’ business models. It leads to opportunities as well as challenges, especially for IT departments as core enablers or preventers. Against this background, the aim of this paper is to provide a comprehensive overview of digital technologies (e.g., artificial intelligence, cloud computing) and the resulting use cases for the insurance industry. To this end, we conduct a review of academic articles, industry studies and publications of the supervisory authorities. We point to the resulting requirements for an insurer’s IT and find many interdependencies between the digital technologies. Our results therefore emphasize the importance of a holistic digital strategy.
Keyfilter-Aware Real-Time UAV Object Tracking

Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate boundary effect, yet introducing undesired background distraction. Existing frame-by-frame context learning strategies for repressing background distraction nevertheless lower the tracking speed. Inspired by keyframe-based simultaneous localization and mapping, keyfilter is proposed in visual tracking for the first time, in order to handle the above issues efficiently and effectively. Keyfilters generated by periodically selected keyframes learn the context intermittently and are used to restrain the learning of filters, so that 1) context awareness can be transmitted to all the filters via keyfilter restriction, and 2) filter corruption can be repressed. Compared to the state-of-the-art results, our tracker performs better on two challenging benchmarks, with enough speed for UAV real-time applications.
Smart Contract Repair

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks, which may enable attackers to steal valuable assets of involving parties. There is, therefore, a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated smart contract repair approach that is also gas-aware. Our repair method is search-based and searches among mutations of the buggy contract. Our method also considers the gas usage of the candidate patches by leveraging our novel notion of gas dominance relationship . We have made our smart contract repair tool SCRepair available open-source, for investigation by the wider community.
A framework utilizing augmented reality to improve critical thinking ability and learning gain of the students in Physics

Abstract Physics is a branch of science that deals with different properties of energy and matter. Most of the principles of Physics are based on Mathematics, Mechanics, Optics, Electricity, Magnetism, and Thermodynamics. It is often difficult for students to grasp concepts as they cannot visualize the phenomena, resulting in compounding the problem of lack of interest in STEM subjects. Augmented reality (AR) can be effective in providing better visualization and interaction with real‐like three‐dimensional virtual objects that can ease the learning experience. In this paper, an AR‐based learning environment is developed to help students understand concepts of the magnetic field, electric current, electromagnetic waves, Maxwell's equations, and Fleming's rules for electromagnetism. An experimental study was conducted to determine the impact of AR intervention on student's learning and critical thinking capabilities. The study was conducted among 80 engineering students, who were distributed into two different groups: the AR teaching group ( N = 40) and the conventional teaching group ( N = 40). The AR teaching group was instructed through the AR‐based learning environment while the conventional teaching group students were taught using a conventional teaching approach. The experimental results indicate that the AR‐based learning environment has a significant positive impact on the critical thinking and learning gain of the students. The AR experience helped the students in visualizing the abstract concepts of Physics and enhanced their understanding.
Convolutional Neural Network Based Classification of App Reviews

An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user's experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and non-textual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.
Computer-aided diagnosis of liver lesions using CT images: A systematic review

Medical image processing has a strong footprint in radio diagnosis for the detection of diseases from the images. Several computer-aided systems were researched in the recent past to assist the radiologist in diagnosing liver diseases and reducing the interpretation time. The aim of this paper is to provide an overview of the state-of-the-art techniques in computer-assisted diagnosis systems to predict benign and malignant lesions using computed tomography images. The research articles published between 1998 and 2020 obtained from various standard databases were considered for preparing the review. The research papers include both conventional as well as deep learning-based systems for liver lesion diagnosis. The paper initially discusses the various hepatic lesions that are identifiable on computed tomography images, then the computer-aided diagnosis systems and their workflow. The conventional and deep learning-based systems are presented in stages wherein the various methods used for preprocessing, liver and lesion segmentation, radiological feature extraction and classification are discussed. The review suggests the scope for future, work as efficient and effective segmentation methods that work well with diverse images have not been developed. Furthermore, unsupervised and semi-supervised deep learning models were not investigated for liver disease diagnosis in the reviewed papers. Other areas to be explored include image fusion and inclusion of essential clinical features along with the radiological features for better classification accuracy.
Proof-of-Event Recording System for Autonomous Vehicles: A Blockchain-Based Solution

Autonomous vehicles are capable of sensing their environment and navigating without any human inputs. However, when autonomous vehicles are involved in accidents between themselves or with human subjects, liability must be indubitably decided based on accident forensics. This paper proposes a blockchain-inspired event recording system for autonomous vehicles. Specifically, we design the mechanism of “Proof-of-Event” with a dynamic federation consensus to achieve indisputable accident forensics by providing trustable and verifiable event information. We propose a dynamic federation consensus scheme to verify and confirm the new block of event data in an efficient way without any central authority. We conduct numerical analyses and prototyped experiments based on the proposed fast leader election algorithm and the Hyperledger Fabric blockchain network. The results show that our system is effective and feasible in generating and storing accident records in blockchain-based vehicular networks. The security capability of the proposed scheme is also discussed against multiple threats and attack scenarios.
The Impacts of Communication Ethics on Workplace Decision Making and Productivity

Communication is the first most important tool towards workplace success. Effective communication ethics increases workplace productivity in various ways, as the management and the employee’s alike employee is capable of making the right decisions concerning various issues within a workplace. Effective commination ethics govern all the decisions made within any workplace situation. It is established that ethics guide and direct all the business operations, an element that is crucial in the overall success of any business. The database has been reviewed to inform the system review. Besides, the finding was that communication ethics is key in any business decision-making process. There is a need for any workplace management to understanding the role of effective workplace communication ethics and culture or integrate such ethics in everyday practice within the workplace. The systematic review does reveal the impact of workplace communication ethics on the decision-making and the overall productivity of a workplace.
Multimodal Representation with Embedded Visual Guiding Objects for Named Entity Recognition in Social Media Posts

Visual contexts often help to recognize named entities more precisely in short texts such as tweets or snapchat. For example, one can identify "Charlie'' as a name of a dog according to the user posts. Previous works on multimodal named entity recognition ignore the corresponding relations of visual objects and entities. Visual objects are considered as fine-grained image representations. For a sentence with multiple entity types, objects of the relevant image can be utilized to capture different entity information. In this paper, we propose a neural network which combines object-level image information and character-level text information to predict entities. Vision and language are bridged by leveraging object labels as embeddings, and a dense co-attention mechanism is introduced for fine-grained interactions. Experimental results in Twitter dataset demonstrate that our method outperforms the state-of-the-art methods.
Weakly-Supervised Video Object Grounding by Exploring Spatio-Temporal Contexts

Grounding objects in visual context from natural language queries is a crucial yet challenging vision-and-language task, which has gained increasing attention in recent years. Existing work has primarily investigated this task in the context of still images. Despite their effectiveness, these methods cannot be directly migrated into the video context, mainly due to 1) the complex spatio-temporal structure of videos and 2) the scarcity of fine-grained annotations of videos. To effectively ground objects in videos is profoundly more challenging and less explored.
Enhancing the alignment between target words and corresponding frames for video captioning

Video captioning aims at translating from a sequence of video frames into a sequence of words with the encoder-decoder framework. Hence, it is critical to align these two different sequences. Most existing methods exploit soft-attention (temporal attention) mechanism to align target words with corresponding frames, where the relevance of them merely depends on the previously generated words (i.e., language context). As we know, however, there is an inherent gap between vision and language, and most of the words in a caption belong to non-visual words (e.g. “a”, “is”, and “in”). Hence, merely with the guidance of the language context, existing temporal attention-based methods cannot exactly align target words with corresponding frames. In order to address this problem, we first introduce pre-detected visual tags from the video to bridge the gap between vision and language. The reason is that visual tags not only belong to textual modality, but also can convey visual information. Then, we present a Textual-Temporal Attention Model (TTA) to exactly align the target words with corresponding frames. The experimental results show that our proposed method outperforms the state-of-the-art methods on two well known datasets, i.e., MSVD and MSR-VTT. 1
A Competitive Mechanism Multi-Objective Particle Swarm Optimization Algorithm and Its Application to Signalized Traffic Problem

In this paper, a modified Competitive Mechanism Multi-Objective Particle Swarm Optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume.
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based human action recognition has attracted much attention with the prevalence of accessible depth sensors. Recently, graph convolutional networks (GCNs) have been widely used for this task due to their powerful capability to model graph data. The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons. Thus, previous methods mainly focus on the design/learning of the graph topology. But once the topology is learned, only a single-scale feature and one transformation exist in each layer of the networks. Many insights, such as multi-scale information and multiple sets of transformations, that have been proven to be very effective in convolutional neural networks (CNNs), have not been investigated in GCNs. The reason is that, due to the gap between graph-structured skeleton data and conventional image/video data, it is very challenging to embed these insights into GCNs. To overcome this gap, we reinvent the split-transform-merge strategy in GCNs for skeleton sequence processing. Specifically, we design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths. Extensive experiments demonstrate that our network outperforms state-of-the-art methods by a significant margin with only 1/5 of the parameters and 1/10 of the FLOPs.
Meta‐analysis and mega‐analysis: A simple introduction

Abstract Statistical methods are increasingly being used to integrate findings from the ever‐expanding universe of empirical research. Meta‐analysis encompasses various techniques for synthesizing summary statistics, and mega‐analysis pools raw data across studies. This paper offers an introduction to meta‐analysis and mega‐analysis that complements the study of analysis of variance (ANOVA). After a brief conceptual discussion, we provide simple numerical examples.
MiNet

Click-through rate (CTR) prediction is a critical task in online advertising systems. Existing works mainly address the single-domain CTR prediction problem and model aspects such as feature interaction, user behavior history and contextual information. Nevertheless, ads are usually displayed with natural content, which offers an opportunity for cross-domain CTR prediction. In this paper, we address this problem and leverage auxiliary data from a source domain to improve the CTR prediction performance of a target domain. Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad. In order to effectively leverage news data for predicting CTRs of ads, we propose the Mixed Interest Network (MiNet) which jointly models three types of user interest: 1) long-term interest across domains, 2) short-term interest from the source domain and 3) short-term interest in the target domain. MiNet contains two levels of attentions, where the item-level attention can adaptively distill useful information from clicked news / ads and the interest-level attention can adaptively fuse different interest representations. Offline experiments show that MiNet outperforms several state-of-the-art methods for CTR prediction. We have deployed MiNet in UC Toutiao and the A/B test results show that the online CTR is also improved substantially. MiNet now serves the main ad traffic in UC Toutiao.
Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs, and available bandwidth. This article proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. The results demonstrate that the HMAO algorithm is more effective than two baseline algorithms of genetic algorithm (GA) and fast elitist non-dominated sorting genetic algorithm (NSGA-II) in solving the large-scale optimization problem of resource allocation. Furthermore, we provide the performance comparison of the HMAO algorithm with two heuristic Greedy and Viterbi algorithms in on-line resource allocation.