Reviewerly/exHarmony huggingface dataset:
Generating Realistic African Fashion Designs for Men using Deep Convolutional Generative Adversarial Networks

The African fashion industry has continued to thrive over the years and has gained recognition globally. However, the design and manufacturing process still follows the conventional methodology. Some studies have been done on image synthesis using deep learning techniques for the progress of the fashion industry with little or none of the studies on African fashion. African fashion, especially Nigerian fashion lacks an online presence in terms of artificially generated realistic fashion styles. Since fashion is a way a people express themselves through clothing, footwear, makeup or hairdo in a specific time, place, and environment, this work aims to develop a system that would promote the diversity of the Nigerian people, reduce inequality and promote economic growth in line with the sustainable development goals. In this work, we developed a system that can generate new African fashion designs for men by applying the Deep Convolutional Generative Adversarial Networks (DCGAN) using a locally curated image data from publicly accessible and open-source images and the AFRIFASHION40000 dataset. The method involves designing a Male Fashion Generative Framework (MFGF), building an image generation model that can synthesize new fashion images and a neural style transfer model that can style the generated image. The model trained on the AFRIFASHION40000 dataset was found to perform better than the locally curated dataset as indicated by a lower generator loss and a higher discriminator loss. This study provides a solution to the problem of generating new designs for men in the Nigerian fashion industry and highlights the importance of integrating deep learning into the African fashion industry. The result shows a generation of low resolution images that represents different fashion styles for African men. It also provides a framework for future research. The findings can be beneficial to fashion designers, manufacturers, and consumers looking for new and innovative designs.
Examining Various Search Algorithms in AI With Appropriate Literature and Their Performances Against a Human Agent in the Snake Game

Artificial intelligence (AI) is one of computer science's most crucial subfields. AI applications are growing rapidly. Games are the main place where artificial intelligence is actually used. AI is used to create the game's non-player characters, also referred to as NPCs. Although there are a lot of alternative ways to implement AI in a game, the search strategy is by far the most common. A variety of different search algorithms can be used to construct AI in video games. This paper's main objective is to use the snake game as a comparative tool to examine the differences between search algorithms employed by human agents and those used in AI. This paper presents detailed assessments of informed and uninformed search strategies as well as experiments on Hamiltonian search. This paper also addresses a few widely used techniques, like finite state machines, behavior trees, tree search, etc., frequently used while creating AI. We get to the conclusion that some algorithms are better candidates for use as search algorithms based on the results of thorough expla-nations and trials. We found that the Human Agent performs poorly when compared to the Depth-First Search, Breadth-First Search, Hamiltonian Search, and Best-First Search algorithms, while the A * Search algorithm surpasses them all significantly.
From humans to robots with the SAHR project

Researchers in the SAHR project are studying how humans acquire fine motor skills and looking to develop new learning and control algorithms for robots, as Professor Aude Billard explains.
Improving Pedestrian Attribute Recognition with Dual Adaptive Fusion Attention

As one of the important fields of computer vision research, pedestrian attribute recognition has received increasing attention on researchers at domestic and foreign. However, obtaining long-distance pedestrian information on actual scenes has problems, such as lack of information, incomplete feature extraction, and low attribute recognition accuracy. To address these issues, we proposed a Dual Adaptive Fusion Attention and Criss-Cross Attention Module (DAFCC). This module contains two sub-modules: First, the dual adaptive fusion attention module automatically adjusts the weights of attributes in different scales, then fusion the different scale features and makes attribute extraction more complete. Second, we employ criss-cross attention to extract rich contextual information, which is beneficial for visual understanding. By training on the public PA-100K, RAP and PETA datasets, the mean accuracies achieved 81.09%, 81.44% and 85.94%, respectively. Extensive experimental results show that the method has strong competitiveness among many current classical algorithms.
An Unstructured Data Desensitization Approach for Futures Industry

The development of technologies of Big Data and artificial intelligence provides powerful boost to financing institutions on data digging, while also bringing challenges to prevent private data disclosures. Data desensitization technology is one of the ways to protect private data. Compared to structured data desensitization technologies, unstructured data desensitization technologies are still facing some challenges. On one hand, the accuracy of text recognition from images, voices and videos and other types of unstructured data seriously affects the performance of desensitization. On the other hand, conventional sensitive information recognition methods, which are rules and matching-based, often offer unacceptable desensitized results when facing complicated financial data. Due to such issues, this paper proposes a completely new method for unstructured data desensitization. By first using the evaluation model based on multi-level fine-grained verification for text conversion accuracy to improve the accuracy of text recognition, followed by introducing a sensitive information recognition model based on hybrid analysis to reduce the rates of missed and false detection on sensitive information recognition, this unstructured data desensitization method achieved satisfactory results on real datasets.
Boolean-Arithmetic Equations: Acquisition and Uses

Motivated by identifying equations to automate the discovery of conjectures about sharp bounds on combinatorial objects, we introduce a CP model to acquire Boolean-arithmetic equations (BAE) from a table providing sharp bounds for various combinations of parameters. Boolean-arithmetic expressions consist of simple arithmetic conditions (SAC) connected by a single commutative operator such as ‘ $$\wedge $$ ’, ‘ $$\vee $$ ’, ‘ $${{\,\mathrm{\oplus }\,}}$$ ’ or ‘ $$+$$ ’. Each SAC can use up to three variables, two coefficients, and an arithmetic function such as ‘ $$+$$ ’, ‘−’, ‘ $$\times $$ ’, ‘floor’, ‘ $$\bmod $$ ’ or ‘ $$\min $$ ’. We enhance our CP model in the following way to limit the search space: (i) We break the symmetries linked to multiple instances of similar SACs in the same expression. (ii) We prevent the creation of SAC that could be simplified away. We identify several use cases of our CP model for acquiring BAE and show its applicability for learning sharp bounds for eight types of combinatorial objects as digraphs, forests, and partitions.
A combinatorial empowerment-cloud model-based prediction method for artificial intelligence manufacturing evaluation

The level of development of artificial intelligence in manufacturing industry directly affects the development of the country, is the centralized embodiment of the comprehensive strength of the country, directly provides technical, material and resource support for the development of the national economy and is the most basic industry that drives economic development. In this context, this paper builds an evaluation index system based on hierarchical analysis method and cloud model theory, uses fuzzy hierarchical analysis method to determine the weight values of indicators at all levels, then outputs the standard cloud diagram and evaluation cloud diagram by building a cloud model to visually reflect the fit, and finally introduces examples to verify the scientific and rationality of the model method, and through data Finally, we introduce examples to verify the scientific and reasonableness of the model method, and reflect the superiority of the model in prediction and evaluation through the comparison of data.
STEGANOGRAPHY USING LSB TECHNIQUE: IMPLEMENTATION, ANALYSIS, AND FUTURE PERSPECTIVES

Steganography, the usage of codes, is cryptography's sin-ister relative.Steganography is meant to provide secrecy while cryptography offers privacy.Steganography is a secret communication technique.Steganography is the practise of concealing a message in a suitable carrier, such as an image or an audio file.The carrier can then be delivered to a recipient without letting on to its secret message.In this page, we've attempted to clarify the many methods for im-plementing steganography utilising "multimedia" files (text, still images, and audio).Steganalysis is a freshly developed area of data processing that aims to recognise steganographic coverings and, if possible, extract messages from them.It resembles cryptanalysis in the field of cryptography.The method is an age-old rising monster that has come to ev-eryone's attention due to its recent intrusion into the field of digital communication security.The goal is to conceal the message's existence in addition to preventing it from being read.
Uncertainty-estimation with normalized logits for out-of-distribution detection

Out-of-Distribution (OOD) detection is critical for preventing deep learning models from making incorrect predictions, especially in safety-critical applications such as medical diagnosis and autonomous driving. However, neural networks often suffer from overconfidence, making high confidence predictions for OOD data that are never seen during training and may be irrelevant to training data. Determining the reliability of the prediction is still a difficult and challenging task. To address this challenge, we propose a new method called Uncertainty-Estimation with Normalized Logits (UE-NL) for robust learning and OOD detection. The method has three main benefits: (1) Neural networks with UE-NL treat every In Distribution (ID) sample equally by predicting the uncertainty score of input data and the uncertainty is added into SoftMax function to adjust the learning strength of easy and hard samples during training phase, making the model learn robustly and accurately. (2) UE-NL enforces a constant vector norm on the logits to decouple the effect of the increasing output’s norm from optimization process, which causes the overconfidence issue to some extent. (3) UE-NL provides a new metric, the magnitude of uncertainty score, to detect OOD data. Experiments demonstrate that UE-NL outperforms existing methods on common OOD benchmarks and is more robust to noisy ID data that may be misjudged as OOD data by other methods.
A SlowFast behavior recognition algorithm incorporating motion saliency

This paper first analyzes three major problems that can be encountered in video behavior recognition tasks: sampled blocks cannot be focused on motion regions, global motion affects recognition results, and the network's spatio-temporal modeling capability is weak. To address these three problems, we propose the SlowFast behavior recognition algorithm (MASlowFast) that incorporates motion saliency as an application scenario for mine personnel safety behavior recognition. The specific solutions are the sampling method based on motion saliency, the extraction of motion boundary features, and the spatio-temporal segmentation strategy of fast and slow channels. Finally, we validated the effectiveness and accuracy of the algorithm in this paper by ablation experiments on UCF101 dataset and HMDB51 dataset.
Abnormal behavior identification of examinees based on improved YOLOv5

Traditional examination rooms rely on invigilators to monitor examinees in real time and use cameras to help invigilate the exam, which is prone to problems such as incomplete monitoring, inadequate response to cheating. This paper builds an abnormal behavior recognition model in examination room based on LOLOv5 and the cascading attention mechanism. The model effectively improves the backbone network of YOLOv5, and combines the cascading attention mechanism to enhance the features. Finally, the model is tested on the self-created dataset. The results show that the examinees abnormal behavior detection results of the proposed model are P (92.53%), mAP (93.52%), fps (0.547). Compared with several classical abnormal behavior detection algorithms, the proposed algorithm has higher accuracy and recognition speed.
A Proposed Architecture of Mobile Service Provider Based Interactive Systems

Chatbots are basically designed to imitate any human interaction to automate interaction and support. Numerous benefits of chatbots has led to its increased usage by different organizations to provide essential assistance to its various customers. The current interactive systems are mainly developed from two main concepts namely NLP and Machine Learning. Despite of this, there are certain challenges and limitations of current chatbots. In this paper we will review some of the current chatbots with their limitations and highlight some of the errors identified through survey analysis of different chatbots of some companies like Airtel, Vodafone and Jio etc. A survey is done to analyze the chatbots on various metrics.
Similarity Measurement on Logo Image Using CBIR (Content Base Image Retrieval) and CNN ResNet-18 Architecture

In this study aimed to measure the level of similarity between two logos, both those that look different and those that look the same. This can be realized by forming a logo image database that is stored in a logo image database derived from various existing logo image data. This research uses 4 logo images as data testing and 210 image data for the database as data training. All of the logo images used come from the Ministry of Law and Human Rights of the Republic of Indonesia (Kemenkumham RI) West Sumatra Regional Office. The size images are color images with a pixel size of 320 x 320 pixels, the purpose of which is for the process of dimensional uniformity of the images to be studied. This research uses Content Base Image Retrieval (CBIR) method to search for images from a large image database than using Convolutional Neural Network (CNN) type Residual Network (ResNet-18) Architecture to get the similarity score accurately. The result of this implementation is the formation of an automatic distribution of training images and validation images with 147 training image data values (70%) and 63 validation images (30%) of the 210 existing images. The result of this research is producing the algorithm to implement the method and the tool software application to measure the similarity of logo images. The accuracy of this tool is 93.65% with a total of 84 iterations.
Toward Computationally Efficient Path Generation and Push Planning for Robotic Nonprehensile Manipulation

Robots have been involved in our daily life more and more. This involvement comes along with a necessity for endowing robots to interact with real-world objects that are novel to them. Among many others, pushing is one of the important tasks for robotic arms (or manipulators) as it has been widely used in the pipeline of the more complicated tasks (e.g., grasping, singulation, reorienting, and similar other tasks.), In this paper, we present simple yet efficient path generation and push planning algorithms for pushing an object from a given point to a target point in the given grid map with static obstacles. Our path generation module aims to generate waypoints taking into account obstacles and quantizing them into circular regions in order to reduce the computational cost. The path is formed using the waypoints by minimizing the designed cost function. We also define a cost function for planning pushing steps aiming to both minimize the number of pushing actions and deviation from the path generated. We present comparative numerical results using simulated data. Our preliminary findings show promising results leaving room for further improvements.
Causal Reasoning over Probabilistic Uncertainty

A system deployed in the real world will need to handle uncertainty in its observations and interventions. For this, we present an approach to introduce uncertainty of state variables in causal reasoning using a constructivist AI architecture. Open questions of how noisy data can be handled and intervention uncertainty can be represented in a causal reasoning system will be addressed. In addition, we will show how handling uncertainty can improve a system’s planning and attention mechanisms. We present the reasoning process of the system, including reasoning over uncertainty, in the form of a feed-forward algorithm, highlighting how noisy data and beliefs of states can be included in the process of causal reasoning.
Design and Practice of Green Data Center Construction Scheme with High Energy Saving and High Reliability

With the continuous development of China's economy, China's energy consumption increases, and the problem of energy shortage becomes more and more obvious. Today, with the increasingly serious global energy crisis and the increasing awareness of environmental protection and energy conservation, people are beginning to realize that the impact of large data centers on the environment can not be ignored. In order to maximize energy utilization, green energy conservation has become the goal of current data center construction, and green data center has become the core concept of current data center construction. Based on this, this paper builds the system model of the new green data center, and designs and implements the task scheduling strategy in the data center. At the same time, in order to eliminate the extra overhead caused by task migration, this paper uses the dynamic load forecasting method to forecast the data center load intensity in a short time, so as to determine the best time for the load to switch between servers. Experimental results show that the accuracy of this algorithm is high, and the scheduling effectiveness can reach 95.14%. In this paper, the design and practice of high energy-saving and high reliability green data center construction scheme has certain practical significance, in order to provide some reference for related research.
Tunisia’s economic development: why better than most in the Middle East but not East Asia authors: Mustapha K. Nabli and Jeffrey B. Nugent

Nabli's and Nugent's book is a multi-dimension review of Tunisia's growth and development. It follows several lines of enquiry which include a historical, political and institutional analysis, as well as an analysis of the economic policies pursued in different periods. It concludes with an assessment of Tunisia's recent descent in political turmoil and regression, and its prospects for recovery.
Oware is Strongly Solved

On 23 January 2021, Xavier Blanvillain solved the Abapa variant of the classic strategic game Oware, with a single computer and with multi-threaded, brute-force algorithms. The results of all 827,240,309,058 possible positions were calculated. It has been determined that if each player makes only perfect moves, the games end in a draw. In other words, the starting player doesn’t have a first-move advantage and should open the game with the right-most pit. In situations with 2, 3, 4, 7 or 9 seeds remaining on the board, the resolution required human intervention to force the results of repeating positions, known as loops. An 11-seed loop and a 13-seed loop, previously unknown to Oware players, were discovered during the research. Although these loops cannot be solved with Abapa rules, the research proved that it was possible to continue the resolution. The end result shows that Oware was solved strongly.
An Improved Lightweight Cryptography Based on One-Time Pad for IoT Devices

In the era of the Internet of Things, collisions between the increasing number of IoT devices and associate security issues become more and more intense. Due to the limited resources, applying standard encryption cryptography on IoT devices is not effective and feasible. This article proposes an improved One-Time Pad encryption scheme to overcome this dilemma. By rearranging the message into matrix form to conveniently introduce the parity check bits generating method in three-dimensional parity check and then combined with the indicator matrix sent by the server instead of the master key, a novel key generating scheme is put forward. And to further reduce the data storage, novel encryption and decryption process are proposed. The indicator matrix generated by the server also increases the security level by controlling the linear transformation of the plaintext while complying with the conditions of a security One-Time-Pad. Compared with another lightweight cryptography utilizing One-Time Pad, this scheme shows better performance on data storage, data transmission, and security level.
非線形力ルマンフィルタを用いた車いすの動作推定

This paper proposes a method for estimating the movement of a wheelchair using inertial sensors, even in the absence of GPS. A plant model was constructed by a kinematics model which includes wheelchair’s body section and two wheels, and constraint equations of differential type two-wheeled vehicle model. Simulating wheelchair tennis motion was performed and measured with an inertial sensor and optical motion capture. By applying the extended Kalman filter to the model described above, the wheelchair motion was accurately estimated for 20 seconds in duration.
Non-Fungible Token (NFT) Overview Research Trends

Non-Fungible Tokens (NFTs) now becomes the latest digital currency phenomenon.This phenomenon began in 2014 and widely used in nowadays.Based on this phenomenon, this research was carried out.The research conducted to do overview research related to Non-Fungible Tokens based on the SCOPUS database from 2017-2021.From the search results of the SCOPUS database, it was found that there were 68 studies related to Non-Fungible Tokens from 2017-2021.The most numerous documents are conference papers (N=37) and the publication source with the most documents (N=5) is Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics.The country with the most Non-Fungible Token keyword research is the United States (N=14).While the subject area of research that discusses the most Non-Fungble Token is Computer Science (N = 54).Based on the results, the research trend over Non Fungible Token is related to 2 cluster which is about Blockchain and Technology.So, there is there are still many opportunities for other research to be carried out outside the two clusters and their relationships.
Parallel, Distributed and Federated Learning for applying PRIM in Big Data

Training models in machine learning often requires big computational capacities, even more when it involves Big Data.The Patient Rule Induction Method (PRIM), introduced as a bump hunting algorithm and a subgroup discovery method, already consumes a lot of computing resources when searching for the rules.In this computational research area, several methods have been developed to handle the problem of computing a big dataset: parallel learning, distributed learning and federated learning.In this paper, we investigate the literature review of these three methods to define each one and the differences between them, and then we determine which of these methods is best suited for the implementation of PRIM in big dataset for out futur works in Big Data.