Reviewerly/exHarmony huggingface dataset:
An Experimental Study on the Effects of Digital Signature Writing Speed and Writing Posture on Pen Pressure Based on Statistics and Neural Network

This paper studies the effects of writing posture and writing speed on the pen pressure of electronic signature handwriting, and further finds out the characteristics that are not easily affected in the pen pressure. Fifteen volunteers were required to write same simple and complex signatures with electromagnetic digital pressure sensitive pen at three speeds and two postures.Monofactor analysis and matched sample t-test are used to statistically analyze the stability of the average pen pressure of simple signatures and the influence of writing speed and posture on the pen pressure. It is concluded that writing signature in standing posture will lead to the increase of average pressure, the impact of writing speed on average pressure is not statistically significant, peak trend of pressure curve is least affected by writing speed and writing posture. A pressure curve recognition model based on LSTM is proposed. After pen pressure signal through peak extraction and Z-score standardization, the different optimization learning algorithm, hidden layer neuron number and regularized dropout are adjusted to optimize the writer recognition model. Experimental results show that LSTM neural network identifies simple signatures under the Adam algorithm, the number of hidden neurons is 250, and dropout rate is 0.5. The recognition rate was the highest, 87.52%; Complex signature recognition based on LSTM neural network under Adam algorithm, when the hidden layer neuron is 250 and the dropout rate is 0.5, the recognition rate is the highest, 89.54%, which proves the stability and particularity of pressure signals of different people under different writing conditions.The model could be used as a complementary technique to prove the peak trend of pressure curve is relatively stable and is not easily affected by writing posture and speed , improve the objectivity of signature examination.
Biolaw Beyond Biology: Artificial Intelligence and Smart Robots

Biolaw presupposes a well-determined concept of “life,” which is given by life sciences. In general, “life” means organic entities, forming self-reproducing functional systems, with the presence of DNA; that concept embraces microorganisms, plants, and animals, including humans.
Role of Mobile Phones in Creating Environmental Awareness Among Fishers in the Indus Delta of Pakistan

Mobile phones changed the patterns of communication and flow of information in human society. This study assesses their importance in creating awareness about issues related to the natural environment and climate change, where responsiveness at a community is essential. It examines particularly the role of mobile phones in creating environmental awareness among fishers in the Indus Delta region of Pakistan. Mobile usage patterns, mobile phones as a source of environmental information and the use of social media for environmental discourse among the fishing communities are analysed. The results indicate that mobile communication is ranked as the fifth major source of information about environmental problems in the Indus Delta after personal observation, television, radio and newspapers. Although mobile phones offer high potential for receiving, accessing and reporting information about environmental issues through the use of social media, they are still not widely used for such purposes in these fishing communities.
Image Encryption and Decryption Using Chaotic Bimodal Quadratic Map

The transmission of images covers the highest percentage of data, and thus, the protection of the image becomes one of the key security concerns. The chaotic cryptographic algorithms forms an efficient way to achieve secure image encryption because of its unique properties like, sensitivity to initial conditions, and randomlike behavior. In this paper, an encryption scheme based on two chaotic maps, a 1-D Logistic map and a 2-D Difference map, is proposed which makes use of a secret key, generated from the maps. The image encryption and decryption are implemented using MATLAB, and its performance is analyzed using various tests which includes key sensitivity analysis, PSNR, correlation coefficient analysis, key space analysis, and statistical analysis using NPCR, UACI, and MAE tests. The uniform distribution of the histogram and the simulation results indicate a reduction in the possibility of static attacks, high accuracy, and resistance against noise.
The Impact of Digitalization

Abstract Previous energy transformations have been driven by the exploitation of a new energy resource. In contrast, although the digitalization of energy is delivering cost reductions in the supply and transportation of energy; digitalization is truly transformational because it brings the demand side into play, facilitating the move to a more integrated, highly flexible and customer-centric energy system which will ultimately unlock deep decarbonization of our societies. This fertile new energy landscape will be dominated by data-rich organizations able to establish direct relationships with consumers, resulting in the provision of new services which enable the integration of energy consumption with weather-dependent renewable production. Much of the focus is on the intelligent management of demand and supply from connected buildings and electric vehicles, leading to a proliferation of new business models. This increased connectivity comes with risks: increased danger of cybersecurity attacks and disruption of existing business relationships, staffing and organizational structures and a re-positioning of the role of the customer at the heart of the system. This commercial disruption is inherent to the digital transformation of the energy sector: an essential step in the transition to a carbon-neutral global economy.
Brainstorming Fuzzy Cognitive Maps for Camera-Based Assistive Navigation

Motivated by the brainstorming process of human beings, a novel learning Fuzzy Cognitive Map (FCM) model named Brainstorming Fuzzy Cognitive Map (BFCM) is proposed. The proposed model is based on a state-of-the-art optimization algorithm, named Determinative Brain Storm Optimization, which is utilized to automatically adapt the weights of the FCM structure. In this study, BFCM is applied for safe outdoor navigation of visually impaired individuals. This application ensures the avoidance of static obstacles in an unknown environment, by taking into consideration the output of an obstacle detection system based on a depth camera. The simulation results show that the proposed model can effectively assist the users to avoid static obstacles and safely reach a desired destination, and they promise a wider applicability of the model to other domains, such as robotics.
Machine Learning for Classification of DOS Attack in Smart Healthcare Networks

Healthcare industry has taken 360-degree change when it comes to managing, analyzing and leveraging healthcare data. With 5G technology increased data rates, more reliability and greater capacity, the healthcare system can provide remote services for patients. For remote monitoring of a patient’s health, real time data delivery is a must. The crucial requirement of the current time in the healthcare industry is the security of the patient’s sensitive and critical data against potential threats. Therefore, it is important that we have security mechanisms ensuring not only authorized parties have access to a patient’s sensitive data and medical information but also preserve its privacy and security. By 2022, cybercrime is predicted to cost \$6 trillion each year. Healthcare industry is continually changing and adopting new aspects in technological transformations. In recent years there has been a broad adoption of Machine learning approaches because of their high level performance in healthcare services starting from the prediction of heart arrest, to medical imaging for detection of tumors and even infections like COVID. AI could help the Healthcare Industry protect their patients’ data as well as secure their 5G network of computers across their organization. We have discussed Machine Learning techniques for Data security and privacy and a case study for detecting the intensity of DDOS attack using Decision Tree algorithm on Healthcare application network data. We have also proposed a model which includes 4 interconnecting lifecycle stages- collecting the data, storing the data, processing the data along with the analysis stage and creating knowledge from those data.
ResRnnNet: Learning to Reconstruct the 3D Room Layout from a Single RGB Panorama

Zhao, Shaonan Li, Wei3D room layout reconstruction from a single RGB panoramic image has been an emerging research topic in recent years. To achieve better prediction accuracy, in this paper, we propose a new approach to predict 3D room layout from a single panoramic image. Our reconstruction flow follows a common framework which is same as LayoutNet [9] and HorizonNet [4]; however, we redesign a new deep learning architecture with recurrent neural networks (RNNs) encoder–decoder as an extension for keypoints refinement and use a gradient ascent optimization algorithm to minimize the similar loss. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy.
A Machine Learning Approach to Detect Accounting Frauds

This paper introduces a new fraud prediction model to the accounting literature using machine learning (ML). This model, which we refer to as LogitBoost, combines ensemble learning, one of the most powerful ML methods, and logistic regressions. We show, using seven alternative measures assessing the ability to detect fraud, that our model outperforms the methods based solely on logistic regressions or other ML methods used by prior literature. Additionally, our model outperforms the others in predicting fraud beyond the current accounting period. Importantly, our method relies on a lower number of predictors than those used in prior ML research, thus minimizing concerns over multicollinearity and potential overfitting associated with machine learning methods.
Advanced Models for Video Question Answering

In Chap. 8 , we introduced several traditional models for video question answering tasks based on the encoder-decoder framework. However, other models exist beyond this framework, which exhibit fine architectures and performances. In this chapter, we categorize these methods into four categories, i.e., attention on spatiotemporal features, memory networksMemory networks, spatiotemporal graph neural networksSpatiotemporal graph neural networks and multitask pretraining and discuss the characteristics of these frameworks.
Occluded and Tiny Face Detection with Deep and Shallow Features Fusion and Compensation

Xu, Zhuofan Bai, Huihui Xiao, Jimin Jie, Feiran Zhao, YaoFace detection has been widely developed in the past decades. However, detecting occluded and tiny faces still remains great challenges. Previous methods solve these problems to a certain extent, but they still have two main shortcomings. Firstly, the feature fusion has positive significance for multi-scale detection, but since the features in deep and shallow layers are essentially different, blindly feature fusion from different layers may lead to the interferences. Secondly, the interrelation between the background and the faces is easy to be neglected, so that some features belonging to faces may be misclassified to background. In order to solve the first problem, we present two special feature pyramid networks to adaptively integrate the deep and shallow features, respectively, named SFPN and DFPN. SFPN focuses on the fusion of the shallow features while DFPN concentrates on the deep features fusion. As for the second problem, we come up with a module named background-assisted compensation module (BACM), which can enhance the interrelation between the background and the faces and compensate for the face features that are classified into the background mistakenly. Our detector has achieved superior performance compared with other corresponding methods on wider face dataset.
Blockchain Implementations and Use Cases for Inhibiting COVID-19 Pandemic

The SARS COV-2 or COVID-19 epidemic has created a global health crisis that is having a profound effect on our daily livelihood and globalization. Hospitals across the globe are currently facing tremendous problems in delivering treatment to COVID-19 patients. Clinical trials and research are always lengthy processes and the exchanges of trailed data among the untrusted parties need a trusted source that would provide immutable transactions with a minimum time stamp. Thus, the objectives of this review are to explore the current focus of Blockchain-based research for inhabiting the COVID-19 pandemic and to devise all possible use cases that can be used to create a Blockchain-based pandemic data sharing and management system. To attain these objectives, a total of 19 articles have been reviewed following the Systematic Literature Review (SLR) process. As outcomes, this study highlighted four focused objectives concurrently being exposed by existing studies and revealed eight Blockchain use-cases to develop a future system for COVID-19 pandemic data sharing and management system with enhanced security.
Analysis and Architecture Design of a Large-Scale Event-Centric Knowledge Graph System for Dispute Resolution

Exploiting natural language processing and machine learning techniques, knowledge graph (KG) can transform disorganized raw data into structured knowledge. Recently, KG has been applied in various fields, such as intelligent search, question answering systems and so on. Most of them are usually entity KGs, which can be denoted by triples including head entities, tail entities and relations between them. This kind of KGs generally focus on named entities, e.g. people, organizations, places. With the development of the Internet and social media, large amounts of news spread rapidly and widely. Both academia and industry have particularly concerned of how to accurately mine the information and acquire the details of related events from lots of news. Besides that, events of disputes or conflicts may bring threats to social security. As a result, deeply analyzing and mining information of conflicts and disputes can effectively improve the ability of social governance. In this context, we introduce an event-centric knowledge graph to assist solving the problem. In this paper, we first discuss the architecture of our proposed event-centric knowledge graph for dispute resolution, which is suitable for large-scale data processing. Then we introduce the methods and modules to construct the proposed architecture. For each module, we discuss the details of our building methods. To this end, we capture a complete view of the proposed system.
Visualization and Self-Organising Maps for the Characterisation of Bank Clients

The analysis and detection of fraudulent patterns in banking transactions are of most importance. However, it can be a laborious and time-consuming task. We propose a visualization tool—VaBank—to ease the analysis of banking transactions over time and enhance the detection of the transactions’ topology and suspicious behaviours. To reduce the visualization space, we apply a time matrix that aggregates the transactions by time and amount values. Additionally, to provide a mechanism to characterises the different sub-sets of transactions and facilitate the distinction between common and uncommon transactions, we represent the transactions’ topology through matrix and force-directed layouts. More specifically, we present: (i) a visual tool for the analysis of bank transactions; (ii) the characterisation of the transactions’ topology through a self-organising algorithm; (iii) the visual representation of each transaction through a glyph technique; and (iv) the assessment of the tool effectiveness and efficiency through a user study and usage scenario.
Person Re-Identification Using LBPH and K-Reciprocal Encoding

Individual re-identification proof (Re-ID) targets recovering an individual of interest across different non-covering cameras.With the recent development of technological algorithm and expanding request of intelligence video observation, it has acquired fundamentally expanded interest in the computer vision.Person re-identification is characterized as the issue of perceiving an individual caught in different occasions and additionally areas more than a few nonoverlapping camera sees, thinking about a huge arrangement of up-and-comers.This issue influences essentially the administration of disseminated, multiview observation frameworks, in which subjects should be followed across better places, either deduced or on-the-fly when they travel through various areas.Reidentification proof is a truly challenging issue, as more often than not individuals can be caught by a few low goal cameras, under impediment conditions, severely (and not quite the same as view to see) enlightened, and in differing presents.In this context an encoding technique K-reciprocal results using the LBPH (Local Binary Patterns Histogram) Algorithm has been proposed.This work aims to obtain a genuine image match more prone to the probe in the K-corresponding closest neighbour.When probe image is given, complementary is encoded with the k-equal nearest neighbours into a vector to rerank using the Jaccard matrix.The obtained result is a combination of a Mahalanobis metric, the Jaccard metric, and the LBPH algorithm.The reranking activity needs no Human interference in producing an appropriate enormous scale dataset.The performance of rank-1 metrics 77.27, 61.90, 76.34 &.55.11 percentage is achieved for large-scale Market-1501, CUHK03, MARS, and PRW datasets.The other metrics used for person re-id named mAP recorded 65.01%, 61.21%, 68.21% and 38.13% for the same dataset in that order.
Voice and Text Based Sentiment Analysis Using Natural Language Processing

We live in an era where the business in all sectors is booming, hence the companies providing services like sentiment analysis have a neck and neck competition to stay in power in the market. Analysis of public opinion enables firms, companies, and other organizations to recognize clients’ selection of items, brands, and services rendered by the above-mentioned organizations in online platforms and forums. Valuable insights can be drawn from the feedback and reviews written or spoken by the users. Sentiment analysis considers the abstract data in an articulation, that is, the suppositions, evaluations, feelings, opinions, attitude of the user or their perspectives toward the products, entities, and services provided by numerous organizations operating in the market. Sentiments of the users can be expressed thereby classifying them as positive, negative, or neutral. In this work, a voice-based sentiment analysis system, two different approaches are used so as to know their working mechanism and to compare them. The first approach is to get the voice input, convert it to text and analyze that text. The second approach is to get the voice input and analyze the sentiment in the voice without converting it to text. In addition to it, the Naïve Bayes classifier and the decision tree classifier are used for the creation of the model, which in turn is instrumental for performing the sentiment analysis.
An Architecture for Food Product Recommendation Focusing on Nutrients and Price

Part of the world’s population is nutrient deficient, a phenomenon known as hidden hunger. Poor eating conditions cause this deficiency, leading to illnesses and recovery difficulties. Malnourished patients are more easily affected by Covid-19 and have a difficult recovery after the illness. An effective food choice has the price and nutritional value of food products as the most relevant factors, with the price being the most relevant, considering the context of countries such as Brazil. Thus, having identified a scenario in which the access and food price mainly cause malnutrition. This work proposes an architecture, called Nutri’n Price, to recommend high nutritional foods with low costs. The architecture encompasses a network of ontologies, inference algorithms, information retrieval and collaborative filtering techniques to recommend the best foods according to nutrient choice, price, and user contextual information. A prototype of a mobile application was developed to evaluate the feasibility of the proposed architecture.
Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources Using Autonomous Surface Vehicles

The process of monitoring and patrolling large water resources is a major challenge in their conservation process. The use of Autonomous Surface Vehicles equipped with water quality sensor modules and an appropriate patrolling policy serves as an early warning system for contamination of lakes and rivers, affected by spills or toxic algae blooms. This work proposes a method to obtain a collision-free patrolling policy that addresses the environmental information gathering with a redundancy criterion useful in two cases: a static scenario, where pollution variables do not change, and a dynamic scenario, where revisiting areas is important to update the state of the system. Using entropy as a measure of the non-gathered information, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism, alongside a visual description of the state with noisy neural networks. The obtained results demonstrate the usefulness of the proposed algorithm since known coverage strategies are outperformed both in the accuracy of the obtained contamination model by a 13% improvement and by a 37% improvement in the detection of anomalous contamination peaks.
H3Rec: Higher-Order Heterogeneous and Homogeneous Interaction Modeling for Group Recommendations of Web Services

Recommendations are important web services in the era of information explosion. Particularly, group recommendations aim to suggest new items to groups such that the members of groups are likely interested in. However, existing works still suffer from sparsity and cold-start issues (e.g., cold-start groups or items) for groups with few interactions on items. Most of them model the preferences or features of entities (i.e., users, items and groups) from heterogeneous interactions (i.e., user-item, group-item and user-group interactions) between two distinct types of entities, while ignoring the homogeneous interactions (i.e., user-user, item-item and group-group interactions) between entities of one type. To this end, we propose a new model, called H3Rec, which learns the representations of entities by developing two graph embedding layers based on an interaction graph of all entities. Specifically, the two graph embedding layers make full use of the hidden information in the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">igher-order</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eterogeneous and</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">omogeneous interactions</i> of the graph. Therefore, H3Rec can alleviate the sparsity and cold-start issues and improve the performance of group recommendations. The experimental results on two real world datasets in different domains show the superiority of H3Rec in group recommendations, especially for cold-start groups and items.