Reviewerly/exHarmony huggingface dataset:
Bodylab

Bodylab is a virtual reality system for creating full-body virtual reality avatars. It provides anatomically realistic mannequins as canvases, which can be painted and textured, sculpted and deformed, adorned with objects and animated with particle effects. The artist can work from a first or third person perspective, and variety of interaction modalities are made available. To assess the system, we invite costume designers and theatrical artists to provide expert reviews. We also describe the use of Bodylab in a live dance performance. We believe Bodylab to be the first virtual reality tool for in virtuo painting and sculpting of full-body avatars.
Implementasi Keamanan Aset Informasi Steganografi Menggunakan Metode Least Significant Bit (LSB)

Data security is an important factor in electronic transactions because data is sent should be known only to those who are authorized to receive information. Many aspects of data security must be maintained, such as: authenticity, integrity, non-rejection, authority, security, privacy, and access control. The purpose of this study is to create a security system in protecting data and access that requires system security. The process in this study is the creation of an Encoder and Decoder application where in this Encoder Process we insert an image then we set the pixel size and insert Text in the image. At the Decoder stage an image that has gone through the previous Encoder Process can be displayed again to see the result of the Text inserted in the image.
Intelligent photography software information processing system design based on data analysis

The rapid advancement of technology has led to the proliferation of digital photography and the accumulation of vast amounts of image data. In order to effectively manage and utilize this data, intelligent photography software information processing systems based on data analysis techniques have become essential. This paper presents a comprehensive exploration of the design and implementation of such a system, focusing on key components such as data collection and preprocessing, feature extraction, image classification, and recommendation algorithms. The results demonstrate that the k-means algorithm is effective in clustering similar data points and achieving high accuracy and precision. Based on the evaluation results, the study concludes by providing insights into the design of an intelligent photography software information processing system. The k-means algorithm can be utilized to effectively analyze and process photography data.
Modern Progressive Pitfalls of Cyber Attacks on the Digital World

It is widely acknowledged that in recent years the number of cyber-attacks has increased exponentially. Given the ever-increasing rates of internet usage due to the rapid “digitalization” of lifestyles and the recent global pandemic COVID-19 which created the need to transition from the physical to the digital world (e.g. telecommuting), a natural corollary is the exponential increase in cyber-attacks and their sophistication. A subset of targeted attacks is distinguished by the skill and sophistication of the methods used, the highly organized and technically skilled teams that carry out such attacks, and the potential funding and involvement of government agencies. This threat scenario is described as Modern Progressive Pitfall (MPP). Initially, the main target was government agencies and critical infrastructure, but over time their target zone appears to have expanded to include organizations, businesses, and ordinary citizens. Being state-sponsored, MPP actors have highly sophisticated tools and software at their disposal. This is evidenced by the advanced malware and techniques they use to achieve their well-defined objectives. MPP attacks are one of the most significant cyber threats today.
Adaptive Teaching for Cross-Domain Crowd Counting

The main challenge of Unsupervised Domain Adaptation (UDA) crowd counting is the large domain gap between a synthetic domain with annotations (source) and a real-world domain of interest without annotations (target). Previous mainstream UDA crowd counting methods either employ feature alignment or a semi-supervised learning paradigm via pseudo-labels. We for the first time combine both of their advantages and propose an Adversarial Mean Teacher (AMT) framework. On the one hand, we optimize the student model with domain adversarial learning. On the other hand, we feed perturbed target images to the teacher model to generate pseudo-labels. Furthermore, to improve the quality of the pseudo-labels, we propose an Adaptive Teaching (AT) module, consisting of pseudo-label refinement and credible pseudo-label selection. Concretely, we first generate two candidate pseudo-labels from the prediction of the teacher model and obtain a refined pseudo-label by mixing them at the pixel-level. Moreover, we introduce an auxiliary task of foreground-background classification to assist credible region selection and only activate supervision signals on those regions. Extensive experiments on four real-world crowd counting benchmarks demonstrate the effectiveness of our method namely Cross-Domain Adaptive Teacher (CDAT). The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Yankeegsj/CDAT</uri> .
Research on automatic registration of driverless vehicle data

Driverless vehicle data is an important data for remote dispatching platform to manage and maintain vehicles. Aiming at problems such as complex data, information islands and management difficulties of driverless vehicles, using the dataoriented architecture (DOA) idea, construct an automatic registration model based on the data registration center (DRC), to realize automatic registration and synchronous update of vehicle data; Analyze the basic attributes and common elements of driverless vehicle data, and on the basis of existing research, propose a registration standard for driverless vehicle data. The experimental results show that the automatic registration model can realize the automatic collection and registration of vehicle data and write it into DRC, and the remote dispatching platform can better manage and maintain remote driverless vehicles, improving dispatching efficiency.
Image matching based on traditional algorithm and convolutional neural network

In recent years, with the progress of neural networks, people have gradually shifted their research focus from words to images, precisely because of the emergence of convolutional neural networks, which can convert the information of images into an easy-to-understand form and make it easier for people to solve problems in the image field. Image matching is the most fundamental research direction in the field of images. With the development of technology, the algorithms applied to image matching have gradually changed from traditional sift and orb algorithms to convolutional neural network-based algorithms, such as superPoint, superGlue, D2net and other models. Although these models have certain advantages over the traditional algorithms, there are still problems that the high-level features of the image are not closely related to the underlying features. To address these problems, this paper will use the traditional algorithm to fuse the features extracted by the convolutional neural network model to improve the matching accuracy, and combine the attention mechanism in the convolutional neural network to make better extraction of the easily ignored areas in the image. The proposed method is experimented on the Hpatches dataset and Brown dataset and achieved good results with an accuracy of 91.23% and 92.36%, respectively.
Multimodal Interactive Supervised Pedestrian Detection Based on yolov5

Pedestrian detection and recognition is one of the important and fundamental tasks of environmental awareness in autonomous driving. Some existing research methods are based on visual images (RGB) for detection, but detection methods that only use visual images cannot meet harsh detection environments, such as in cloudy, rainy, foggy, and poor lighting environments, where the detection effect is poor. In recent years, fusion detection based on visual images (RGB) and thermal images (Thermal) has received increasing attention, but there are still some problems in fusion strategies. The purpose of RGB Thermal pedestrian detection is to fuse complementary visible light images and thermal infrared information to improve the performance of pedestrian detection in day and night environments [1]. In recent years, many experts and scholars have done a lot of research work on this issue [2]–[6], effectively integrating the two modal data of RGB and thermal infrared images, and have achieved some results. However, these methods directly use fused features for pedestrian detection, without considering the problem that the quality of the resulting fused features may not be high. Therefore, multimodal feature fusion pedestrian detection structures need to improve the quality of fusion features, which plays a crucial role in the research of multimodal data fusion pedestrian detection in autonomous driving. The Cross-Modal Supervision (CMS) model designed and implemented in this article has been experimentally verified on the public Kaist [7] dataset. The experimental results show that the accuracy of the cross modal supervised model on the Kaist dataset reaches 53.68°/ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</inf> and the miss rate decreases to 46.32°/ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</inf> .
aMLLibrary: An AutoML Approach For Performance Prediction

aMLLibrary is an open-source, high-level Python package that allows the parallel building of multiple Machine Learning (ML) regression models. It is focused on performance modeling and supports several methods for feature engineering/selection and hyperparameter tuning. The library implements fault tolerance mechanisms to recover from system crashes, and only a simple declarative text file is required to launch a full experimental campaign for all required models. Its modular structure allows users to implement their own plugins and model-building wrappers and easily add them to the library. We test aMLLibrary on building the performance models of neural networks and image processing applications, with the best model produced often having less than 20% prediction error.
KRRNet: Keypoint Relational Regression Network for Bottom-up Anchor-free Object Detection

Anchor-free detection methods identify different objects by perceiving bounding box keypoints without predefined anchor boxes, which have attracted much attention due to their straightforward design and comparable performance. Currently, most anchor-free methods detect bounding box corners to regress object locations. In clutter environments, the bounding box corners may lie in background regions, which have limited relation with the object itself. In addition, the relationships between object keypoints are always neglected, potentially affecting the perceptibility of the detector for high-precision object detection. In this paper, we propose the Keypoint Relational Regression Network (KRRNet) to detect object keypoints with semantic relations instead of bounding box corners. The relational regression head is designed to enhance the keypoint relationship exploration capability and reason accuracy object locations. Moreover, the random background sampling strategy is proposed to sample negative background points around foreground object regions and form point pairs with object keypoints. Then, KRRNet can explicitly learn discriminative feature embedding from contrastive learning to pull close the positive pairs and push apart the negative pairs, resisting the influence of surrounding complex environments. KRRNet can be trained on one Nvidia RTX 3090 GPU and achieves a single-scale test AP of 48.9% and multi-scale test AP of 50.6% on the MS-COCO test-dev with the backbone of Hourglass-104, surpassing state-of-the-art bottom-up anchor-free detector using the same backbone.
RISMCNet: A Two-Timescale CSI Feedback Solution for RIS-aided Multi-Carrier systems

In this letter, a novel deep learning-based solution named RISMCNet is introduced to tackle the challenge of high overhead and low accuracy in RIS-aided multi-carrier communication channel state information (CSI) feedback. The proposed method utilizes the two-timescale feature of the RIS-aided systems by designing separate feedback periods for the base station (BS)-RIS and RIS-user equipment (UE) channels. RISM-CNet leverages the sparsity of the angular-delay domain channel and apply an attention-based encoder at the UE end to compress the channel for limited feedback overhead. A multi-resolution decoder is designed at the BS end to recover high-precision CSI. Simulations results reveal that RISMCNet outperforms conventional compressive sensing (CS)-based and deep learning-based methods in CSI recovery accuracy. Moreover, RISMCNet achieves 270 to 810 times faster running times compared to CS-based methods.
The Convergence Criteria of Monetary Integration and Economic Performance of Sub-Saharan African Countries

Based on the experience of countries that have adopted the use of a single currency, there is no doubt that monetary integration remains a key strategy for Sub-Saharan African (SSA) countries to transform themselves into a strong united bloc of developed nations and a global force. Therefore, this study examines the impact of the convergence criteria of monetary integration on the economic performance of SSA countries from 1999 to 2021, using Panel Autoregressive Distributed Lag Model (PARDL). About nine SSA countries from the four regions of SSA were randomly selected, and the countries include: Nigeria, South Africa, Angola, Kenya, Lesotho, Ghana, Ethiopia, Democratic Republic of Congo, and Central African Republic. In order to achieve the main objective of this study, the annual panel data were collected from World Bank database on the variables such as Gross Domestic Product growth rate (GDPG), Inflation Rates (INFR), Real Exchange Rates (REXR), and Fiscal Deficit (FDEF). The panel unit root and Johansen Fisher cointegration tests were conducted on all the variables. While the variables exert mixed order of integration, there exists a long-run relationship among the variables. The PARDL results reveal that the inflation rate has an insignificant positive effect on the GDP growth rate in the long run and an insignificant negative effect in the short run. The result further shows that in both the long and short run, the real exchange rate has an insignificant negative impact on the economic performance of SSA countries. The fiscal deficit result shows a significant positive impact on the GDP growth rate in SSA countries both in the long run and short run. The study, therefore, recommends that government should formulate a policy that would strengthen local currencies against foreign currencies, probably by fixing the exchange rates. If this is done, the inflation rate will be tamed through various productions of goods and services at reduced costs.
Analyzing the Effectiveness of the Marketing Strategies, Market Orientation, and Entrepreneurship on Company Performance through the Mediating Role of Entrepreneurial Marketing

Based on the entrepreneurial marketing model, this study examines the effects of marketing strategies, market orientation, and entrepreneurship on function companies in Jay Industrial Town, Isfahan. Seven hypotheses were developed for this purpose. In order to collect the necessary information for testing the hypothesis, a researcher-made questionnaire containing 34 questions in 6 dimensions was used and distributed among 110 company managers working at Jay Industrial Town in Isfahan as a statistical sample of the research. For data analysis and hypothesis testing, structural equation modeling by partial least squares method using Smart PLS software was used. The results of the research hypothesis testing show that there is a positive and significant effect between all the variables in the case of entrepreneurial marketing, except for the entrepreneurship orientation or tendency. Also, there is a positive and significant effect between all the variables in relation to the company’s performance.
PENERAPAN ARSITEKTUR MODEREN PADA PERPUSTAKAAN DAERAH PROVINSI GORONTALO

As a source of literacy, a library plays an essential role in developing the quality of human resources. Complete facilities and infrastructures will aid in the improvement of the quality of the human resources in an area. Gorontalo has a regional library, yet the facilities and infrastructures are poor and not in accordance with applicable standards under government regulations regarding National Standards for Provincial Libraries. This library’s planning aimed to obtain a design that complies with the mentioned standards and adequate facilities and infrastructure. The data collection method employed in this design was through field observation, which entails reviewing fields condition and collecting the data by interviewing relevant parties. The data was also collected by identifying aspects the could be applied in the design, including technical aspects and structuring design aspects under the criteria. The outcome was analyzed to develop a design concept based on the theme of Modern Architecture. The final step of design was a regional library that met all applicable criteria and was equiooed with infrastructures to support the needs of Gorontalo’s communities. The reports result was in the form of a design and its implementation towards the design of the Regional Library of Gorontalo Province.
Challenges in cloud infrastructure and scientific data: technical proposals and tools applied to the SciELO database

Introduction: Cloud computing applications have significant relevance for scientific databases based on research data. This work aims to report the proposed requisites and steps of technologies and tools to develop cloud computing applications based on research data for the SciELO database. Methods: We have used commercial cloud infrastructure and proposed a step-by-step method that could be applied to unstructured databases aiming at a comprehensive data model. Results: We have iteratively looked at data entries and developed SQL scripts that build a data model for the SciELO database. All the scripts are made public. Conclusion: Despite the data sovereignty concerns, commercial cloud services are a good option for short term projects. Also, the sequence of steps and general structure of the scripts can be used to make other similar open databases available and useful.
On Counterfactual Reasoning

Abstract Counterfactual reasoning has always played a role in human life. We ask questions like, “Could it have been different?,” “Under which conditions might/would it have been different?,” and “What would have happened if … ?” If we do not find an answer, i.e., what we accept as an answer, we may start reasoning . Reasoning means introducing new information or assumptions, new questions, new answers to new questions, and so on. From a formal point of view, reasoning may be compared with moving stepwise toward a destination in a path system without ever achieving an overview of the system. Seen in this way, reasoning is an activity with its own rationale, which must be studied from the agent’s own perspective. This paper explores the following query: Which conditions are necessarily fulfilled when the act of posing a specific question, or of introducing a particular piece of information or assumption, etc., may count as a step toward the answer to the initial question?
Artificial Intelligence Technology and Ecological Transition -Analysis and Criticism-

Artificial intelligence (AI) refers to an application capable of processing tasks which are currently performed satisfactorily by human beings insofar as they involve high-level mental processes such as perceptual learning or the organization of memory (Marvin Lee Minsky, 1956). Until now, research in this field has shown a difficulty in validating and certifying artificial intelligence systems at the service of decarbonization, ecological and energy transition objectives. In this context, this article focuses on an effective analysis of 05 of today’s most popular AI technologies in the field of environment, Artificial Neural Networks, fuzzy logic, Case-based reasoning, the multi-agent system and the process of natural language. The results show that our analysis can be beneficial for developers to select the appropriate technology for a reliable and successful implementation of artificial intelligence.
Moroccan dialect NLP resources for Data Engineering and Intelligent Systems

Dialectal resources can provide valuable information for scientific research in many domains. They can play an important role in scientific research, especially in the fields of linguistics, sociology, anthropology, psychology, digital transformation, and artificial intelligence. NLP can also play an important role in decision-making by enabling the analysis of large volumes of textual data to extract relevant information. Data relevance is a key factor in decision-making, and companies wishing to join the trend must have all NLP resources at their disposal. In this paper, we will present the different resources and systems developed for Data Engineering and Intelligent Systems for Moroccan dialects.
Commonsense-Aware Attentive Modeling for Humor Recognition

Laughter has positive effects on health. Humor is an important component of daily communication and usually causes laughter. Hence, we can expect that the infusion of humor in a dialogue system will improve the user’s physical and mental satisfaction. Since even humans have difficulty comprehending humor, appropriate knowledge is essential for humor understanding. In this paper, commonsense-aware modules are extrapolated to Pre-trained Language Models (PLMs) to provide external knowledge. We specifically extract keywords from a text and use COMET to obtain embeddings that represent the commonsense associated with the keywords. We attempt to detect humor that is not detectable by PLM alone. Our approach enables the model to access commonsense knowledge. Compared to the baseline, the number of humor detections increases, and recall is improved without a significant decrease in precision. Our best model significantly improves recall by 4.4% for a 0.4% reduction in precision in the HaHackathon dataset and by 20.3% for an 8.4% reduction in precision in the Humicroedit dataset compared to the baseline. We also observe the changes in prediction and processing speed so as to analyze the characteristics of the proposed method and the issues for its social implementation.
BİR E-TİCARET SİTESİNİ TAVSİYE ETME VE SİTEDEN TEKRAR SATIN ALMA NİYETİ İLE SİTEYE DUYULAN GÜVEN ARASINDAKİ İLİŞKİ VE DUYULAN GÜVENİ ETKİLEYEN FAKTÖRLER ÜZERİNE BİR ARAŞTIRMA

This study examines the effect of the user trust established by an e-commerce site on the users' repurchase intention from this website and their recommending behavior to others. The study's primary purpose is to reveal the factors building trust. The relationship between the websites’ having a live-chat (chatbot)/correspondence (WhatsApp) module, whether they respond to e-mails and the trust of users in the website were examined through a total of 1648 tests of 10 different websites. According to the results, the fact that an online shopping site responds to e-mails positively affects users’ trust in that site and, therefore, their repurchase intention from the site again and recommendation it to others. However, having a chatbot does not significantly affect the trust in the website. Also, the opinions of the users were revealed after the qualitative assessment of the open-ended questions asked to the participants in the survey.