Reviewerly/exHarmony huggingface dataset:
Sparse Shield

With the rise of social media users and the general shift of communication from traditional media to online platforms, the spread of harmful content (e.g., hate speech, misinformation, fake news) has been exacerbated. Harmful content in the form of hate speech causes a person distress or harm, having a negative impact on the individual mental health, with even more detrimental effects on the psychology of children and teenagers. In this paper, we propose an end-to-end solution with real-time capabilities to detect harmful content in real-time and mitigate its spread over the network. Our main contribution is Sparse Shield, a novel method that out-scales existing state-of-the-art methods for network immunization. We also propose a novel architecture for harmful speech mitigation that maximizes the impact of immunization. Our solution aims to identify a set of users for which to move harmful content at the bottom of the user feed, rather than censoring users. By immunizing certain network nodes in this manner, we minimize the negative impact on the network and minimize the interference with and limitation of individual freedoms: the information is not hidden but rather not as easy to reach without an explicit search. Our analysis is based on graphs built on real-world data collected from Twitter; these graphs reflect real user behavior. We perform extensive scalability experiments to prove the superiority of our method over existing state-of-the-art network immunization techniques. We also perform extensive experiments to showcase that Sparse Shield outperforms existing techniques on the task of harmful speech mitigation on a real-world dataset.
Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis

The recent increase in user interaction with social media has completely changed the way customers communicate their opinions, questions, and concerns to brands. For this reason, many companies have established on the top of their agendas the necessity of analyzing the high amounts of user-generated content data in social networks. These analyses are helping brands to understand their customers’ experiences as well as for maintaining a competitive advantage in the sector. Due to this fact, this study aims to analyze and characterize the public opinions from the messages posted by Twitter users while addressing customer services. For this purpose, this study carried out a content analysis of a customer service platform. We extracted the general users’ viewpoints and sentiments of each of the discussed topics by using a wide range of techniques, such as topic modeling, document clustering, and opinion mining algorithms. For training these systems and drawing conclusions, a dataset containing tweets from the English-speaking customers addressing the @Uber_Support platform during the year 2020 has been used.
Transfer Learning from Audio Domains a Valuable Tool for Structural Health Monitoring

Today, the application of artificial neural network tools to define models that mimic the dynamic behavior of structural systems is a wide-spread approach. A fundamental issue in developing these strategies for damage assessment in civil structures is represented by the unbalanced nature of the available databases, which commonly contain plenty of data coming from the structure under healthy operational conditions and very few samples from the system in unhealthy conditions since the structure would have failed by that time. Consequently, the learning task, carried on with standard deep learning approaches, becomes case-dependent and tends to be specialized for a particular structure and for a very limited number of damage scenarios. This work presents a framework for damage classification in structural systems intended to overcome such limitations. In this methodology, the model is trained to gain knowledge in the learning task from a rich acoustic dataset (source domain), acquiring higher-level features characterizing vibration traits from a rich acoustic dataset. This knowledge is then transferred to a target domain, with much less training data, such as a structural system, in order to classify its structural condition. The framework starts with constructing a time-delay neural network (TDNN) structure, trained on the VoxCeleb dataset, in the speech domain. The input of the network consists of Cepstral and pitch features extracted from the audio records. Higher-level features, the x-vectors, speaker embeddings, capturing neural outputs of the network’s intermediate layers, are derived and then used to train a probabilistic linear discriminant analysis (PLDA) model to provide a probabilistic discriminant model for speaker comparison. These features collect generic information regarding the source domain and characterize a classification process based on the frequency content of signals, which is not strictly dependent on the original acoustic domain. Because of the non-case-dependent nature of the x-vector embeddings (features), they can be used to train an alternative PLDA model to address a damage classification task, considering vibration measurements coming from a different system, a structural one which represents the target domain. The simulated data from the 12 degrees of freedom benchmark shear-building structure provided by the IASC-ASCE Structural Health Monitoring Group are studied to verify the proposed framework’s effectiveness.
Highly-accurate binary tiny neural network for low-power human activity recognition

An ultra low power hardware implementation of Human Activity Recognition systems imposes very tight constraints. Therefore it requires a very thoughtful balancing between highly accurate results and the reduction of the allocated physical resources. Deep Learning models provide the highest accuracies, however their typical computational complexity and memory requirements are not easily deployable in handheld or wearable devices which embody very constrained memory and computation capabilities. In this work, we introduce a new HAR system built with a new Hybrid Binarized Neural Network suitable for a very compact and ultra low-power hardware implementation. The system receives data from MEMS based inertial sensors and makes the acquisitions independent from the sensor spatial orientation and the gravity acceleration signal. The system has been trained and validated on the PAMAP2 and SHL dataset. In both cases, it achieves accuracies higher than 99% in the best case, with different input sensor configurations. A custom circuit has been implemented, which extensively shares circuitry between the different functional sub-modules of the system to minimize the amount of mapped physical resources. The FPGA implementation on a Xilinx Artix 7 achieves a total power dissipation of 72 mW and occupies 6788 LUTs.
University Educational Environment in the Information Exchange Agents Evaluations

The multi-agent approach in the study of university learning environment opens up the prospect of constructive analysis of the different information interconnections in process professional training specialist. The authors aim to identify criteria for assessing the quality of the learning environment and meaningful motivations of participants in the educational process. E-learning at a level mediated by computer software technology is accompanied by a transformation of the established system of relationships of traditional participants in the educational process. Students, teachers, leaders who enter into interactions within the educational system of the university have different goals that determine their attitude to the E-learning environment. The distance E-learning format adds a new level of communication to the student–teacher relationship system, which has its own technological and semantic features and creates barriers to information and cognitive exchange. In this regard, the study of the university educational environment as a multi-agent system and comparative analysis of the positions of its participants are relevant. The study is based on a methodology of system approach. The empirical base is compiled by survey data of representative groups, obtained by the method of online and offline questionnaires. The research results can serve as a basis for improving the educational policy of the university and technologies for information support of the educational process. The revealed tendencies of target coincidence and problem points in information exchange agents interaction are a tool for predicting changes in the university electronic environment to increase the effectiveness of professional training of a qualified specialist.
Nature inspired optimization tools for SVMs - NIOTS

Support Vector Machines (SVMs) technique for achieving classifiers and regressors. However, to obtain models with high accuracy and low complexity, it is necessary to define the kernel parameters as well as the parameters of the training model, which are called hyperparameters. The challenge of defining the more suitable value to hyperparameters is called the Parameter Selection Problem (PSP). However, minimizing the complexity and maximizing the generalization capacity of the SVMs are conflicting criteria. Therefore, we propose the Nature Inspired Optimization Tools for SVMs (NIOTS) that offers a method to automate the search process for the best possible solution for the PSP, allowing the user to quickly obtain several sets of good solutions and choose the one most appropriate for his specific problem.•The PSP has been modeled as a Multiobjective Optimization Problem (MOP) with two objectives: (1) good precision and (2) low complexity (low number of support vectors).•The user can evaluate multiple solutions included in the Pareto front, in terms of precision and low complexity of the model.•Apart from the Adaptive Parameter with Mutant Tournament Multiobjective Differential Evolution (APMT-MODE), the user can choose other metaheuristics and also among several kernel options.
The Effective Factors on Continuity of Corporate Information Security Management: Based on TOE Framework

In the Fourth Industrial Revolution era, data-based business management activities among enterprises proliferated are mainly based on digital transformation. In this change, the information security system and its operation are emphasized as essential business activities of enterprises the research aims to verify the relationship among the influence factors of corporate information security management based on the TOE framework. This study analyzes the effects of technical, organizational, and environmental factors on the intention, strengthening, and continuity of information security management. To this, a survey was conducted on professional individuals who are working in areas related to information security in organizations, and 107 questionnaires were collected and analyzed. According to major results of the analysis on adopted hypotheses. In results, as to the intention of information security management, organization and environment factors were influential. In the other side, technology and environment factors were affected to the strengthening of information security management. Hence this study pointed out that the environmental factors are most significant for the information security administration of an organization. In addition, it turned out that the strengthening of information security management was influential on the continuity of information security management more significantly than the intention of information security management.
Semi-Supervised Ensemble Learning for Dealing with Inaccurate and Incomplete Supervision

In real-world tasks, obtaining a large set of noise-free data can be prohibitively expensive. Therefore, recent research tries to enable machine learning to work with weakly supervised datasets, such as inaccurate or incomplete data. However, the previous literature treats each type of weak supervision individually, although, in most cases, different types of weak supervision tend to occur simultaneously. Therefore, in this article, we present Smart MEnDR, a Classification Model that applies Ensemble Learning and Data-driven Rectification to deal with inaccurate and incomplete supervised datasets. The model first applies a preliminary phase of ensemble learning in which the noisy data points are detected while exploiting the unlabelled data. The phase employs a semi-supervised technique with maximum likelihood estimation to decide on the disagreement rate. Second, the proposed approach applies an iterative meta-learning step to tackle the problem of knowing which points should be made correct to improve the performance of the final classifier. To evaluate the proposed framework, we report the classification performance, noise detection, and the labelling accuracy of the proposed method against state-of-the-art techniques. The experimental results demonstrate the effectiveness of the proposed framework in detecting noise, providing correct labels, and attaining high classification performance.
An improved watermarking algorithm for robustness and imperceptibility of data protection in the perception layer of internet of things

Emergence of Internet of Things (IoT) and modern digital applications such as digital financial services and deliveries make it easy to reproduce and re-distribute digital contents thus give room to so many copyright violations of illegal use of contents that need to be resolved. Researcher have been presenting the watermarking algorithms to prevent these illicit activities to a document before distribution. However, several issues have been identified for the digital transactions in the IoT. Thus, this research proposes a new text document image watermarking algorithm which emphasizes on two most important measures, visual quality, and robustness. To boost these measures, third least significant bit has been used for insertion. In addition, to further strengthen the technique, the Pascal Triangle is applied to determine the best position for embedding. Experimental results on the standard dataset have revealed that the proposed watermarking has achieved very encouraging results with PSNR and NCC averaged 54.95db and 0.98, respectively.
Improving the Efficiency of Solar Power Plants Based on Forecasting the Intensity of Solar Radiation Using Artificial Neural Networks

The method of forecasting of the generation of solar power plants on the basis of the theory of artificial neural networks is proposed. Creating a predictive model has been carried out in the STATISTICA software package. To predict the energy of solar radiation for 24 hours in advance, the so-called “time window” method was used. To predict the intensity of solar radiation, measurements of hydro meteorological station were used. During simulation, the width of the input “time window” was set to 24 hours, the width of the output “time window” was set to 1 hours. Thus, the “time window” was shifted 24 times. The practical results of the article are offered to the use of enterprises operating solar power plants.
Modeling of Passenger Transport Logistics Based on Intelligent Computational Techniques

Abstract The paper describes the analysis of the application of classic and optimized triangular norms in the hierarchical structure of weighted fuzzy Petri nets on the example of passenger transport logistics. The research is presented in a scheme with several levels of tasks. Each task includes some knowledge sets which were previously given by the experts and set into tables of “Object–property” type. It leads to the creation of a hierarchical structure of weighted fuzzy Petri net model which includes 125 possible combinations of triples of functions for calculation processing. A classic triple of functions (ZtN, GtN, ZsN) is tested and compared with an optimized triple (ZtN, ZtN, LsN). Additional verification methods are applied to confirm the practical usefulness of these triples. These techniques imply the use of additional triples which can be associated with each other along with practically efficient mathematical calculations.
A User Authentication and Access Control Scheme for IoT-Based Healthcare Using Blockchain

IoT devices do not possess the potential to protect themselves from risk of the attackers as they are resource-constrained. Blockchain is arising as a decentralized and distributed technology with proficiency in delivering secure management, access control and user authentication for protecting data and services of IoT devices, guaranteeing integrity, confidentiality and availability. IoT-based healthcare applications has many benefits like reduced cost of healthcare, improved quality, remote monitoring of patients etc. Ensuring a robust and secure interactions between patient and healthcare providers is very important to protect sensitive medical data. A distributed and reliable user authentication and access control scheme to be used in IoT based Healthcare is designed and implemented here with the help of local gateways directly interfaced to smart contract based Ethereum Blockchain. The local gateways can manage multiple local IoT devices and improve scalability. This reduces the overhead of performing resource-consuming authentication tasks and blockchain-communication at the IoT devices. To exhibit the working of the framework, a case study is presented with two laptops and a Raspberry Pi.
Urban Traffic Surveillance (UTS): A fully probabilistic 3D tracking approach based on 2D detections

Urban Traffic Surveillance (UTS) is a surveillance system based on a monocular and calibrated video camera that detects vehicles in an urban traffic scenario with dense traffic on multiple lanes and vehicles performing sharp turning maneuvers. UTS then tracks the vehicles using a 3D bounding box representation and a physically reasonable 3D motion model relying on an unscented Kalman filter based approach. Since UTS recovers positions, shape and motion information in a three-dimensional world coordinate system, it can be employed to recognize diverse traffic violations or to supply intelligent vehicles with valuable traffic information. We build on YOLOv3 as a detector yielding 2D bounding boxes and class labels for each vehicle. A 2D detector renders our system much more independent to different camera perspectives as a variety of labeled training data is available. This allows for a good generalization while also being more hardware efficient. The task of 3D tracking based on 2D detections is supported by integrating class specific prior knowledge about the vehicle shape. We quantitatively evaluate UTS using self generated synthetic data and ground truth from the CARLA simulator, due to the non-existence of datasets with an urban vehicle surveillance setting and labeled 3D bounding boxes. Additionally, we give a qualitative impression of how UTS performs on real-world data. Our implementation is capable of operating in real time on a reasonably modern workstation. To the best of our knowledge, UTS is to date the only 3D vehicle tracking system in a surveillance scenario (static camera observing moving targets).
ProAI: An Efficient Embedded AI Hardware for Automotive Applications – a Benchmark Study

Development in the field of Single Board Computers (SBC) have been increasing for several years. They provide a good balance between computing performance and power consumption which is usually required for mobile platforms, like application in vehicles for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). However, there is an ever-increasing need of more powerful and efficient SBCs which can run power intensive Deep Neural Networks (DNNs) in real-time and can also satisfy necessary functional safety requirements such as Automotive Safety Integrity Level (ASIL). "ProAI" is being developed by ZF mainly to run powerful and efficient applications such as multitask DNNs and on top of that it also has the required safety certification for AD. In this work, we compare and discuss state of the art SBC on the basis of power intensive multitask DNN architecture called Multitask-CenterNet with respect to performance measures such as, FPS and power efficiency. As an automotive supercomputer, ProAI delivers an excellent combination of performance and efficiency, managing nearly twice the number of FPS per watt than a modern workstation laptop and almost four times compared to the Jetson Nano. Furthermore, it was also shown that there is still power in reserve for further and more complex tasks on the ProAI, based on the CPU/GPU utilization during the benchmark.
Towards Facial Biometrics for ID Document Validation in Mobile Devices

Various modern security systems follow a tendency to simplify the usage of the existing biometric recognition solutions and embed them into ubiquitous portable devices. In this work, we continue the investigation and development of our method for securing identification documents. The original facial biometric template, which is extracted from the trusted frontal face image, is stored on the identification document in a secured personalized machine-readable code. Such document is protected from face photo manipulation and may be validated with an offline mobile application. We apply automatic methods of compressing the developed face descriptors to make the biometric validation system more suitable for mobile applications. As an additional contribution, we introduce several print-capture datasets that may be used for training and evaluating similar systems for mobile identification and travel documents validation.
A Design Framework for Smart Ration Shop Using Blockchain and IoT Technologies

The Public Distribution System (PDS) distributes the subsidized food and non-food items to poor populations through fair price shops (FPS). The PDS has been criticized for its urban bias and its failure to serve the underprivileged sections of the community effectively. The Current system manual-based data management and ledger management gives rise to much corruption in the process of extricating the poor from those who are less needy. There are chances for the block market because of the current methodology of data management. This article proposes a blockchain technology-based smart ration shop system that uses immutable smart contract-based transactions to overcome the issues. The proposed mechanism will provide more transparency and enable access to all the transactions by everyone. The system utilizes IoT technology for asset tracking while moving the food items from distribution centers to a particular ration shop, which avoids malpractice during the transportation of the goods. Voice-based and text-based user-friendly interfaces in local languages were also developed to provide an effective interaction and improve the usability of the system. The performance of the proposed blockchain network is evaluated and reported.