Reviewerly/exHarmony huggingface dataset:
The Handbook on Socially Interactive Agents

The Handbook on Socially Interactive Agents provides a comprehensive overview of the research fields of Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics. Socially Interactive Agents (SIAs), whether virtually or physically embodied, are autonomous agents that are able to perceive an environment including people or other agents, reason, and decide how to interact, and express attitudes such as emotions, engagement, or empathy. They are capable of interacting with people and each other in a socially intelligent manner using multimodal communicative behaviors with the goal to support humans in various domains. Written by international experts in their respective fields, the book summarizes research in the many important research communities pertinent for SIAs, while discussing current challenges and future directions. The handbook provides easy access to modeling and studying SIAs for researchers and students and aims at further bridging the gap between the research communities involved. In two volumes, the book clearly structures the vast body of research. The first volume starts by introducing what is involved in SIAs research, in particular research methodologies and ethical implications of developing SIAs. It further examines research on appearance and behavior, focusing on multimodality. Finally, social cognition for SIAs is investigated by different theoretical models and phenomena such as theory of mind or pro-sociality. The second volume starts with perspectives on interaction, examined from different angles such as interaction in social space, group interaction, or long-term interaction. It also includes an extensive overview summarizing research and systems of human-agent platforms and of some of the major application areas of SIAs such as education, aging support, autism or games.
Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.
OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers

We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS). OSFormer is based on two key designs. First, we design a location-sensing transformer (LST) to obtain the location label and instance-aware parameters by introducing the location-guided queries and the blend-convolution feed-forward network. Second, we develop a coarse-to-fine fusion (CFF) to merge diverse context information from the LST encoder and CNN backbone. Coupling these two components enables OSFormer to efficiently blend local features and long-range context dependencies for predicting camouflaged instances. Compared with two-stage frameworks, our OSFormer reaches 41% AP and achieves good convergence efficiency without requiring enormous training data, i.e., only 3,040 samples under 60 epochs. Code link: https://github.com/PJLallen/OSFormer .
VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection

This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects. The model learns a deep knowledge representation of the code syntax and semantics, which we leverage to train vulnerability detection classifiers. We evaluate our approach on binary and multi-class vulnerability detection tasks across several datasets (Vuldeepecker, Draper, REVEAL and muVuldeepecker) and benchmarks (CodeXGLUE and D2A). The evaluation results show that VulBERTa achieves state-of-the-art performance and outperforms existing approaches across different datasets, despite its conceptual simplicity, and limited cost in terms of size of training data and number of model parameters.
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions

Image-based virtual try-on aims to synthesize an image of a person wearing a given clothing item. To solve the task, the existing methods warp the clothing item to fit the person’s body and generate the segmentation map of the person wearing the item before fusing the item with the person. However, when the warping and the segmentation generation stages operate individually without information exchange, the misalignment between the warped clothes and the segmentation map occurs, which leads to the artifacts in the final image. The information disconnection also causes excessive warping near the clothing regions occluded by the body parts, so-called pixel-squeezing artifacts. To settle the issues, we propose a novel try-on condition generator as a unified module of the two stages (i.e., warping and segmentation generation stages). A newly proposed feature fusion block in the condition generator implements the information exchange, and the condition generator does not create any misalignment or pixel-squeezing artifacts. We also introduce discriminator rejection that filters out the incorrect segmentation map predictions and assures the performance of virtual try-on frameworks. Experiments on a high-resolution dataset demonstrate that our model successfully handles the misalignment and occlusion, and significantly outperforms the baselines. Code is available at https://github.com/sangyun884/HR-VITON .
TSR-YOLO: A Chinese Traffic Sign Recognition Algorithm for Intelligent Vehicles in Complex Scenes

Recognizing traffic signs is an essential component of intelligent driving systems’ environment perception technology. In real-world applications, traffic sign recognition is easily influenced by variables such as light intensity, extreme weather, and distance, which increase the safety risks associated with intelligent vehicles. A Chinese traffic sign detection algorithm based on YOLOv4-tiny is proposed to overcome these challenges. An improved lightweight BECA attention mechanism module was added to the backbone feature extraction network, and an improved dense SPP network was added to the enhanced feature extraction network. A yolo detection layer was added to the detection layer, and k-means++ clustering was used to obtain prior boxes that were better suited for traffic sign detection. The improved algorithm, TSR-YOLO, was tested and assessed with the CCTSDB2021 dataset and showed a detection accuracy of 96.62%, a recall rate of 79.73%, an F-1 Score of 87.37%, and a mAP value of 92.77%, which outperformed the original YOLOv4-tiny network, and its FPS value remained around 81 f/s. Therefore, the proposed method can improve the accuracy of recognizing traffic signs in complex scenarios and can meet the real-time requirements of intelligent vehicles for traffic sign recognition tasks.
ReCloud: Semantics-Based Word Cloud Visualization of User Reviews

User reviews, like those found on Yelp and Amazon, have become an important reference for decision making in daily life, for example, in dining, shopping and entertainment. However, large amounts of available reviews make the reading process tedious. Existing word cloud visualizations attempt to provide an overview. However their randomized layouts do not reveal content relationships to users. In this paper, we present ReCloud, a word cloud visualization of user reviews that arranges semantically related words as spatially proximal. We use a natural language processing technique called grammatical dependency parsing to create a semantic graph of review contents. Then, we apply a force-directed layout to the semantic graph, which generates a clustered layout of words by minimizing an energy model. Thus, ReCloud can provide users with more insight about the semantics and context of the review content. We also conducted an experiment to compare the efficiency of our method with two alternative review reading techniques: random layout word cloud and normal text-based reviews. The results showed that the proposed technique improves user performance and experience of understanding a large number of reviews.
Economic development and democracy: The modernization hypothesis in sub-Saharan Africa

Previous empirical literature focuses on income per capita as a measure for economic development. Using Lipset's modernization hypothesis as our theoretical framework, we contend that this measure does not capture the fundamental quality of economic development and as such may disadvantage low income regions when conducting empirical analysis. Our initial results using income per capita highlight this, showing a negative relationship between income per capita and democracy for sub-Saharan Africa between 1960 and 2010. However when we create a composite measure for economic development by employing the principle component analysis on the indicators that are suggested by Lipset, we obtain positive and significant results for democracy. This evidence suggests that we need to be wary of income per capita as a measure for economic development as the two are not synonymous. Income per capita may not capture other factors that also encompass development in a country.
The Content of Educational Programs in Technical Universities: Quality of Applying the Modern Professional Standards

The purpose of this study is to determine the methods of designing the normative content of education in Russian technical universities according to the Russian professional standards and the EU Qualification Framework.The content of the professional training of bachelors comprises four educational programs at four technical universities in the Republic of Tatarstan.Over 250 students took part in the survey aimed at assessing their professional skills and knowledge in compliance with professional standards by means of specific tasks designed for this purpose.This allowed us to determine the design of the program and to assess the educational staff awareness of the importance of professional standardization.According to the expert evaluation, the normative content of professional training in the university allows providing higher-level education in case of closer cooperation.
Diagnostic performance of CellaVision DM96 for Plasmodium vivax and Plasmodium falciparum screening in peripheral blood smears

Microscopic examination of blood smears is the standard method for malaria diagnosis but is labor-intensive and requires expert staff. CellaVision DM96 (CellaVision, Lund, Sweden) is a digital hematology analyzer available for advanced morphological analysis of blood films including intracellular parasites. Here, we evaluated the CellaVision DM96 Advanced RBC Application for malaria detection in stained peripheral blood (PB) smears. Two hundred and twenty thin PB smear slides (84 P. vivax, 14 P. falciparum, 122 negative controls) were stained with Wright-Giemsa using automated slidemaker/strainers of Beckman Coulter hematology systems (LH780, Beckman Coulter Inc., Miami, FL). The slides were automatically analyzed by CellaVision, and images were manually reviewed by experts. The results of automatic and manual detection by CellaVision were compared to those of microscopic examination. The sensitivity and specificity of automatic detection by CellaVision were 23.5% (23/98) and 81.1% (99/122), respectively. When CellaVision images were manually reviewed, the sensitivity and specificity increased to 65.3% (64/98) and 90.2% (110/122), respectively. The detection of P. falciparum showed the highest sensitivity by both the automated (33.3%) and the manual (85.7%) method. CellaVision misinterpreted malaria parasites as Howell-Jolly bodies in 57.1%, as Pappenheimer bodies in 84.7%, and as basophilic stipplings in 75.5% of the slides. Malaria diagnosis using CellaVision DM96 requires further improvements. Manual review improves CellaVision performance, but confirmation by conventional microscopy remains essential.
Breast Cancer Detection using Deep Convolutional Neural Network

Deep Convolutional Neural Network (DCNN) is considered as a popular and powerful deep learning algorithm in image classification. However, there are not many DCNN applications used in medical imaging, because large dataset for medical images is not always available. In this paper, we present two DCNN architectures, a shallow DCNN and a pre-trained DCNN model: AlexNet, to detect breast cancer from 8000 mammographic images extracted from the Digital Database for Screening Mammography. In order to validate the performance of DCNN in breast cancer detection using a big data , we carried out a comparative study with a second deep learning algorithm Stacked AutoEncoders (SAE) in terms accuracy, sensitivity and specificity. The DCNN method achieved the best results with 89.23% of accuracy, 91.11% of sensitivity and 87.75% of specificity.
Neural Network-based modeling methodologies for energy transformation equipment in integrated steelworks processes

The paper proposes a methodology for modeling of energy transformation equipment which are commonly found in integrated steelworks, mainly focusing on steam production in the Basic Oxygen Furnace and auxiliary boilers, the electric power production in off-gas expansion turbines and some relevant steam and electricity consumers. The modeling approach is based on standard neural networks and Echo State Networks (ESN) for forecasting the variables of interest. All the models are intended as processes predictors to be used in a hierarchical control strategy based on multi-period and multi-objective optimization techniques and model predictive control. The overall target is the optimization of the re-use of off-gas produced in integrated steelworks by minimizing costs and maximizing revenues. Training and validation of models have been carried out by exploiting real historical data provided by steelmaking companies and have been successful tested.
FFT multichannel interpolation and application to image super-resolution

This paper presents an innovative set of tools to support a methodology for the multichannel interpolation (MCI) of a discrete signal. It is shown that a bandlimited signal f can be exactly reconstructed from finite samples of gk (1 ≤ k ≤ M) which are the responses of M linear systems with input f. The proposed interpolation can also be applied to approximate non-bandlimited signals. Quantitative error is analyzed to ensure its effectiveness in approximating non-bandlimited signals and its Hilbert transform. Based on the FFT technique, a fast algorithm which brings high computational efficiency and reliability for MCI is presented. The standout performance of MCI is illustrated by several simulations. Additionally, the proposed interpolation is applied to the single image super-resolution (SISR). Its superior performance in accuracy and speed of SISR is demonstrated by the experimental studies. Our results are compared qualitatively and quantitatively with the state-of-the-art methods in image upsampling and reconstruction by using the standard measurement criteria.
A GLCM-Feature-Based Approach for Reversible Image Transformation

Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but als... | Find, read and cite all the research you need on Tech Science Press
Vision-Based Navigation of Omnidirectional Mobile Robots

This paper considers the problem of collision-free navigation of omnidirectional mobile robots in environments with obstacles.Information from a monocular camera, encoders, and an inertial measurement unit is used to achieve the task.Three different visual servoing control schemes, compatible with the class of considered robot kinematics and sensor equipment, are analysed and their robustness properties with respect to actuation inaccuracies discussed.Then, a controller is proposed with formal guarantee of convergence to the bisector of a corridor.The main controller components are a visual servoing control scheme and a velocity estimation algorithm integrating visual, kinematic and inertial information.The behaviour of all the considered algorithms is analised and illustrated through simulations both for a wheeled and a humanoid robot.The solution proposed as the most efficient and robust with respect to actuation inaccuracies is also validated experimentally on a real humanoid NAO.
Fuzzy String Matching Algorithm for Spam Detection in Twitter

In recent times one of the most popular Internet activity around the world is visiting online social websites. The number of users and time spent by users on these social networks is increasing exponentially. Moreover, users tend to rely on the trustworthiness of data present on these networks. But in wrong hands this trustworthiness can easily be exploited and used to spread spams. Users can easily be harassed by spam messages which waste time and can fool users to click on malicious links. Spam effects many different type of electronic communications including instant messaging, email and social networks. But due to open nature, huge user base and reliance on users for data, social networks are worst hit because of spams. To detect spams from the social networks it is desirable to find new unsupervised techniques which can save the training cost which is required in supervised techniques. In this article we present an unsupervised, distributed and decentralized technique to detect and remove spams from social networks. We present a new technique which uses fuzzy based method to detect spams, which can detect spams even from a single message stream. To handle huge data in networks, we implement our technique to work on MapReduce platform.
Detecting Demographic Bias in Automatically Generated Personas

We investigate the existence of demographic bias in automatically generated personas by producing personas from YouTube Analytics data. Despite the intended objectivity of the methodology, we find elements of bias in the data-driven personas. The bias is highest when doing an exact match comparison, and the bias decreases when comparing at age or gender level. The bias also decreases when increasing the number of generated personas. For example, the smaller number of personas resulted in underrepresentation of female personas. This suggests that a higher number of personas gives a more balanced representation of the user population and a smaller number increases biases. Researchers and practitioners developing data-driven personas should consider the possibility of algorithmic bias, even unintentional, in their personas by comparing the personas against the underlying raw data.
BalloonFAB

We propose an interactive system that allows common users to build large-scale balloon art based on a spatial augmented reality solution. The proposed system provides fabrication guidance to illustrate the differences between the depth maps of the target three-dimensional shape and the current work in progress. Instead of using color gradients for depth difference, we adopt a high contrast black and white projection of the numbers in considering balloon texture. In order to increase user immersion in the system, we propose a shaking animation for each projected number. Using the proposed system, the unskilled users in our case study were able to build large scale balloon art.
The influence of mobile instant messaging in language education: perceptions of current and future teachers

The globalisation of mobile instant messaging (MIM) in applications like WhatsApp encourages the use of a non-standard linguistic register. The purpose of this study is to determine and assess the perception of school teachers and teaching students on this issue in relation to the language education. This descriptive transectional study has been carried out with a non-probabilistic sample of 652 people, of whom 321 are active teachers at public schools (Infant, Elementary and Secondary School) in the province of Málaga (Spain), and 331 are students of the four courses in Primary Education at the University of Málaga. The data were collected through an own questionnaire, validated through a pilot test of the instrument, which yielded both qualitative and quantitative information. The results show no significant differences between the perceptions of teachers and students regarding the influence of MIM on the increasingly less normative linguistic uses of school-age children. Finally, the conclusion discusses the benefits and difficulties of the use of this kind of app in the classroom. Both groups agree on the need to work with this issue outside of school and to use instant messaging as a resource to improve language education.
On Sparse Linear Regression in the Local Differential Privacy Model.

In this paper, we study the sparse linear regression problem under the Local Differential Privacy (LDP) model. We first show that polynomial dependency on the dimensionality $p$ of the space is unavoidable for the estimation error in both non-interactive and sequential interactive local models, if the privacy of the whole dataset needs to be preserved. Similar limitations also exist for other types of error measurements and in the relaxed local models. This indicates that differential privacy in high dimensional space is unlikely achievable for the problem. With the understanding of this limitation, we then present two algorithmic results. The first one is a sequential interactive LDP algorithm for the low dimensional sparse case, called Locally Differentially Private Iterative Hard Thresholding (LDP-IHT), which achieves a near optimal upper bound. This algorithm is actually rather general and can be used to solve quite a few other problems, such as (Local) DP-ERM with sparsity constraints and sparse regression with non-linear measurements. The second one is for the restricted (high dimensional) case where only the privacy of the responses (labels) needs to be preserved. For this case, we show that the optimal rate of the error estimation can be made logarithmically dependent on $p$ (i.e., $\log p$ ) in the local model, where an upper bound is obtained by a label-privacy version of LDP-IHT. Experiments on real world and synthetic datasets confirm our theoretical analysis.