Reviewerly/exHarmony huggingface dataset:
AFFIRM: Provably Forward Privacy for Searchable Encryption in Cooperative Intelligent Transportation System

With the construction of intelligent transportation, big data with heterogeneous, multi-source and massive characteristics has become an important carrier of cooperative intelligent transportation systems (C-ITS) and plays an important role. Big data in C-ITS can break through the restrictions between regions and entities and then learning cooperatively by sharing data. In addition, the combined efficiency and information integration advantages of big data are conducive to the construction of a comprehensive and three-dimensional traffic information system and can enhance traffic prediction. However, such substantial sensitive data, mainly on the cloud infrastructure, exposes several vulnerabilities like data leakages and privacy breaks, especially when data is shared for cooperative learning purposes. To address this, this paper proposes a forward privacy-preserving scheme, named AFFIRM, for multi-party encrypted sample alignment adopting cooperative learning in C-ITS. By introducing the searchable encryption method, we realize the sample alignment of cooperative learning in the multi-party encrypted data space. AFFIRM ensures encrypted sample alignment under the condition of forward privacy security. We have formally proved that the proposed scheme satisfies both forward security and validity. We have assessed AFFIRM by validating the potential threat of malicious tampering by privacy attackers and malicious personnel search for the aligned sample data and verify it. Finally, we numerically tested and compared AFFIRM against the corresponding ones of some state-of-the-art schemes under various record sizes, servers and processing.
An Efficient Novel Approach for Detection of Handwritten Numericals Using Machine Learning Paradigms

The goal of recognizing handwritten numbers on paper is to extract the characteristics of the entered handwritten number. The human visual system is one of the wonders of the world. People can easily find and recognize the numbers as they are used for recognition and identification. Most people easily recognize numbers without effort. Humans are amazing at deciphering what our eyes show. All work is done subconsciously, and no additional training is required to recognize numbers. Generally, we do not need to assess the difficulty of a problem that our visual system solves. However, recognizing numbers for mechanical devices such as computers is not that easy. If we try to develop a computer program to detect numbers, the difficulty of visually recognizing patterns is significant. When it comes to coding, what seems simple when we see it will be quite complicated. Simple intuitions, for example, are for a system that recognizes strokes, circles, curves, straight lines, curves, and more. Example of recognizing a number like 9, you should recognize the loop at the top and a straight vertical bar at the bottom right. This is not easy to express algorithmically. That is why we came up with a prototype for number recognition. Although identification, classification and recognition are simple tasks with the help of machine learning, we are trying to develop a system that recognizes numbers as accurately as possible. The main goal of focusing on handwriting recognition is to learn about neural networks. Images are divided into pixels and recognized in pixel order.
Kinematically Adapted Sampling-Based Motion Planning Algorithm for Robotic Manipulators

We introduce modifications to the sampling-based motion planning approach in robotics to adapt the method to the positioning problem of robotic manipulators. The proposed method combines the information from configuration and task spaces of the mechanisms to cluster and subsequently reduce the number of the samples. The clustering process results in construction of a graph, dubbed kinematic graph $$\mathfrak {G}_k(\mathfrak {V}_k,\mathfrak {E}_k)$$ . We present a step by step instruction of the construction of the kinematic graph. The kinematic graph introduces interesting advantages to the planning algorithm. For instance, planning using the kinematic graph will sort the possibilities of sudden configuration changes, due to the surjection of forward kinematics function for robotic manipulators, in planning phase out. Moreover, combination of information from configuration and task spaces can be utilized to form the cost and heuristic functions for the heuristic search algorithms, like A $$^{\text {*}}$$ . Furthermore, the clustering and reduction of the number of the samples has direct effect on the solution depth, that is, the shortest path found by the search algorithm. This in turn reduces the expense of computation and worst-time complexity of the search algorithm. Finally, the information from the vertices of the kinematic graph can be used for a Boolean collision check of the sampled configuration, without the need of extra calls on the forward kinematics function.
Revisiting Stream-Cipher-Based Homomorphic Transciphering in the TFHE Era

Transciphering allows to workaround the large expansion of the size of FHE encrypted data, thanks to the use of symmetric cryptography. Transciphering is a recryption technique that delegates the effective homomorphic encryption to the cloud. As a result, a client only has to encrypt (once) a symmetric key $${\textsf {SYM}}.\!{\textsf {sk}}$$ under a homomorphic encryption system, while his payload data are encrypted under $${\textsf {SYM}}.\!{\textsf {sk}}$$ using the chosen symmetric encryption algorithm. In this work, we study the performances of some symmetric encryption algorithms in light of the TFHE cryptosystem and its properties. This allows us to unleash the use of additional existing symmetric algorithms which were not viable candidates for efficient encrypted domain execution with levelled-FHEs. In particular, we provide experimental evidences that Grain128-AEAD, a well established and well respected stream-cipher which is a finalist of the NIST competition for light-weight cryptography, is amenable to practical performances when run in the encrypted domain. As such, our work extends practical transciphering capabilities to include authenticated encryption for the first time.
Artificial Intelligence: Chatbot—The New Generation of Communication

Marketing tactics have changed in response to the growth of technology and its quick conversion in order to accomplish consumers’ requirements. Because of the rapid growth of artificial intelligence, companies and organizations are increasingly utilizing AI-guided chatbots to improve service on their portals (AI). The decline in face-to-face services as a result of the COVID-19 epidemic has hastened this tendency. Because of recent developments in AI and machine learning, as well as broad usage of the internet and messaging platforms, businesses are focusing on chatbots. Chatbots are computer programs powered by artificial intelligence that can communicate with humans and engage in active dialogue. Because they are aware of human capabilities, they are sometimes known as virtual assistants. Chatbot technology is essential for famous product lines to prosper in the coming years. The paper’s major goal is to identify chatbots as an AI technology and to find the link between the two terms to determine how they contribute to various industries and marketing tactics, to create a better understanding to be used as a means of customer communication. The paper’s findings demonstrate that chatbot technology could be a beneficial technology that enables consumer to communicate, but firms should pay close attention to their customers' perspectives and design chatbots more efficiently by incorporating AI upon their needs and goals.
How to Elicit Ownership and Agency for an Avatar Presented in the Third-Person Perspective: The Effect of Visuo-Motor and Tactile Feedback

The realism of experience of using a virtual body in virtual reality (VR) is associated with ownership (one’s self-attribution of a body) has been shown. However, whether or not ownership can be elicited for a virtual body presented by the third-person perspective is under debate. This study investigated the effect of multimodal presentations on the ownership of a male virtual body presented in the third-person perspective in three conditions (Visuo-tactile, visuo-motor, visuo-motor-tactile condition) (N = 40). We compared the illusory effect of ownership in the three conditions in male and female participants using a questionnaire and a 2 × 3 mixed-design ANOVA. Our study revealed that the male participants in the visuo-motor-tactile condition affirmed moderate (+1) of ownership, but the female participants did not. Ownership was significantly higher in the visuo-tactile (p < .01) and visuo-motor (p < .05) conditions. The results suggest that both visuo-motor synchrony and visuo-tactile feedback are essential factors to induce ownership to the virtual body in a third-person perspective. Moreover, our data suggest that matching the participant’s gender identity and the appearance of an avatar’s gender might be important for elicited ownership. Additionally, we evaluated the agency on the virtual body in the third-person perspective in the same three feedback conditions and found that only visuo-motor feedback is essential to elicit agency, unlike the causal factors of the ownership.
Reconciling Conflicting Interests in Data through Data Governance. An Analytical Framework (and a Brief Discussion of the Data Governance Act Draft, the Data Act Draft, the AI Regulation Draft, as well as the GDPR)

In the current European debate on how to tap the potential of data-driven innovation, data governance is seen to play a key role. However, if one tries to understand what the discussants actually mean by the term data governance, one quickly gets lost in a semantic labyrinth with abrupt dead ends: Either the concrete meaning remains unclear or when an explicit definition is given, it hardly describes the challenges, which are considered essential in this article, at least within the highly regulated EU Single Market. The terminological and conceptual ambiguity makes it difficult to adequately describe certain challenges for data governance and to compare corresponding solution mechanisms in terms of their conditions for success. This article, therefore, critically examines and further develops elements of data governance concepts currently discussed in Information Systems literature to better capture challenges for data governance with particular respect to data-driven innovation and conflicting interests, especially those protected by legal rights. To reach this aim, the article elaborates on a refined data governance framework that reflects practical experience and theoretical considerations particularly from the field of data protection and regulation of innovation. Against this background, the outlook briefly assesses the most relevant current draft laws of the EU Commission, namely: the Data Governance Act, the Data Act and the AI Regulation (especially the last one concerning the General Data Protection Regulation).
Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized.However, there is limited work that studies fairness using a multilingual and intersectional framework or on downstream tasks.In this paper, we introduce four multilingual Equity Evaluation Corpora, supplementary test sets designed to measure social biases, and a novel statistical framework for studying unisectional and intersectional social biases in natural language processing.We use these tools to measure gender, racial, ethnic, and intersectional social biases across five models trained on emotion regression tasks in English, Spanish, and Arabic.We find that many systems demonstrate statistically significant unisectional and intersectional social biases. 1
Review on Opportunities and Challenges of Blockchain Technology for Tourism Industry in Future Smart Society

Tourism is a trending industry in the current era of the economic growth of the country. Tourism can be explained as traveling to a place which is different from your home city or country for numerous reasons. But the tourism industry has been facing problems like Identity theft, Intermediaries, Payment failures, fake reviews, transparency, settlement, overbooking, policy, and most importantly safety concerns due to the COVID-19 pandemic. To overcome the above drawbacks, after referring to some of the research papers we have come up with a solution called Blockchain. Blockchain is a chain of blocks where each block contains a set of transactions and the blocks are connected using cryptography. Mainly Blockchain and tourism have the potential to show into a really advantageous combination as this technology will offer additional security, Safety, eradication of middleman, data integrity, decentralized business models, immutability, transparency, and accurate contact tracing app for Tourism Industry in the Future Smart Society. This paper gives a clear idea of how BLOBIM helps to overcome difficulties/challenges in tourism industries. Our future proposed architecture Blockchain-based integrated model removes the third party intervention and improves security, safety, trust, transparency, etc.
Human Activity Recognition Under Partial Occlusion

One of the major challenges in Human Activity Recognition (HAR) using cameras, is occlusion of one or more body parts. However, this problem is often underestimated in contemporary research works, wherein training and evaluation is based on datasets shot under laboratory conditions, i.e., without some kind of occlusion. In this work we propose an approach for HAR in the presence of partial occlusion, i.e., in case of up to two occluded body parts. We solve this problem using regression, performed by a deep neural network. That is, given an occluded sample, we attempt to reconstruct the missing information regarding the motion of the occluded part(s). We evaluate our approach using a publicly available human motion dataset. Our experimental results indicate a significant increase of performance, when compared to a baseline approach, wherein a network that has been trained using non-occluded samples is evaluated using occluded samples. To the best of our knowledge, this is the first research work that tackles the problem of HAR under occlusion as a regression problem.
Moroccan Carpets Classification Based on SVM Classifier and ORB Features

Moroccan carpets remain one of the major artisanal products that are very known worldwide. This encourages us to look forward to a system that gives people the ability to distinguish between the different types of Moroccan rugs. In this paper, we’ll be presenting a novel architecture for Moroccan carpet images classification. For this, we’ll first preprocess the images by applying the k-means clustering for segmentation. After that, we’ll use the ORB features detector that is going to help for sure by detecting the major features in each type of carpet. For this, we used to gather images from the web by uploading about 200 images for each type. The proposed model achieved an accuracy of 82%.
A Survey on Chaos-Based Cryptosystems: Implementations and Applications

Chaos theory is considered as a tool for studying the systems that show divergence and disorder. After having used discrete mathematics to deduce non-convergence situations, these theories are modeled in the form of a dynamic system and are applied in several domains such as electronic, mechanic, network security, etc. In network security domain, the development of new cryptosystems based on chaos is a relatively new area of research and is increasingly relevant. The essence of the theoretical and practical efforts in this field derive from the fact that these cryptosystems are faster than conventional methods, while ensuring performance of security, at least similar. In this paper, we discuss several proposals about chaos-based cryptosystem and pseudo-random number generator (PRNG). Moreover, topology and architecture of the proposed chaos systems are detailed. Finally, in order to show the more suitable system for encryption and secure communication, a synthesis comparison is presented and considered.
Image Encryption Using Chaotic Maps: A Survey

Images are widely used a data type for sharing information through the Internet. Designing and exploring security mechanisms to protect the image data during communication are matters of great interest among researchers and industry experts. Encrypting the images is a commonly used mechanism of securing them from different types of attacks. In this work, various image encryption algorithms employing chaotic map techniques are compared. It includes various security parameters in respect of chaotic maps and encryption algorithms for images. Chaotic maps are verified using tools like bifurcation and Lyapunov diagrams. Image encryption algorithms need to satisfy a comprehensive set of security parameters and tests—visual analysis, key sensitivity, keyspace, correlation coefficient, and histogram analysis. Finally, a tabular comparison of different encryption schemes is done using ‘NPCR’ and ‘UACI.’ Another comparison provides the different schemes’ correlation coefficients for horizontal, vertical, and diagonal adjacencies.
Introduction to Neural Network Intelligence

There was a great burst of deep learning industry in the past few years. Deep learning approaches have achieved outstanding results in computer vision, natural language processing, robotics, time series forecasting, and optimal control theory. However, there is no "silver bullet model" to solve all kinds of problems. Each problem and dataset needs a specific model architecture to achieve suitable performance. Machine learning models, especially deep learning models, have a lot of tunable parameters that can drastically affect the model performance. Those are model design, training method, model configuration hyperparameters, etc. The model optimization process is performed for each application and even each dataset. Data scientists and machine learning experts often spend a lot of time performing manual model optimization. This activity can be frustrating because it takes too much time and is usually based on an expert's experience and quasi-random search. Neural network intelligence (NNI) toolkit provides the latest state-of-the-art techniques to solve the most challenging automated deep learning problems. We’ll start exploring the basic NNI features in this chapter.
Is an anthropomorphic app icon more attractive than a non-anthropomorphic one A case study using multimodal measurement

This study investigates how an anthropomorphic app icon affects users' responses from an emotional standpoint. The design is a case-study/laboratory experiment in which 50 participants evaluated a commercially available weather app icon that had facial features (with an anthropomorphic appearance) and the same app icon but without the facial features (non-anthropomorphic appearance). The participants also selected one of the two apps presented on a mock app download interface. Multimodal measurement was used to measure users' responses, including: 1) subjective emotional experiences; 2) attitudes; 3) pupil dilation and facial electromyographic responses; 4) app-selection responses. The relationship between the anthropomorphic and non-anthropomorphic weather app icon designs and users' responses were analysed. Results showed that the anthropomorphic app icon induced positive emotions, enhanced favourable attitudes, and was selected for downloading more often than the non-anthropomorphic one. An implication is that weather app icons, and probably other icons, should be designed using anthropomorphic elements to attract users.
Spreading the Privacy Blanket:

In the shuffle model for differential privacy, n users locally randomize their data and submit the results to a trusted “shuffler” who mixes the results before sending them to a server for analysis. This is a promising model for real-world applications of differential privacy, as several recent results have shown that, in some cases, the shuffle model offers a strictly better privacy/utility tradeoff than what is possible in a purely local model. A downside of the shuffle model is its reliance on a trusted shuffler, and it is natural to try to replace this with a distributed shuffling protocol run by the users themselves. While it would of course be possible to use a fully secure shuffling protocol, one might hope to instead use a more-efficient protocol having weaker security guarantees. In this work, we consider a relaxation of secure shuffling called differential obliviousness that we prove suffices for differential privacy in the shuffle model. We also propose a differentially oblivious shuffling protocol based on onion routing that requires only $$O(n \log n)$$ communication while tolerating any constant fraction of corrupted users. We show that for practical settings of the parameters, our protocol outperforms existing solutions to the problem.
Wild OCR: Deep Learning Architecture for Text Recognition in Images

With the emergence of the Industry 4.0 known as “smart factory”, visual intelligence plays a vital role in various automation-related applications. Entrenched texts in images give plenty of information, which can be utilized in many applications. Various methods and technologies are existing in the realm of text extraction from the images. But due to diversity in arrangement of texts, scene background, shape, size, space, style variations, it is still a challenging task. In the past years, many researchers have been independently analyzing and solving issues related to detection and recognition of texts embedded in natural Images. This paper focuses on an end-to-end deep learning model for text detection and recognition in vehicle number plates. The proposed model’s objective is to read text information in challenging car number plates. The model has been compared across state-of-the-art models to understand its advantages and limitations in cases of horizontal, perspective texts along with Indian/foreign car number plates.
Removing Redundancies in Binary Images

Every day a huge amount of digital data is generated. Processing such big data encourages efficient data structure and parallelized operations. In this regard, this paper proposes a graph-based method reducing the memory requirement of the data storage. Graphs as a versatile representative tool in intelligent systems and pattern recognition may consist of many nonessential edges accumulating memory. This paper defines the structure of such redundant edges in the neighborhood graph of a 2D binary image. We introduce a novel approach for contracting the edges that simultaneously assists in determining the structurally redundant edges. In addition, finding a set of independent edges, the redundant edges are removed in parallel with the complexity $$\mathcal{O}(1)$$ . Theoretically, we prove that the maximum number of redundant edges is bounded by half of all edges. Practical results show the memory requirement decreases significantly depending on the input data in different categories of binary image data sets. Using the combinatorial map as the data structure, first the topological structure of the graph is preserved. Second, the method can be extended to higher dimensions (nD).