Reviewerly/exHarmony huggingface dataset:
Rényi Entropy and Variance Comparison for Symmetric Log-Concave Random Variables

We show that for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha &gt;0$ </tex-math></inline-formula> the Rényi entropy of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is minimized, among all symmetric log-concave random variables with fixed variance, either for a uniform distribution or for a two sided exponential distribution. The first case occurs for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \in (0,\alpha ^{\ast}]$ </tex-math></inline-formula> and the second case for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \in [\alpha ^{\ast},\infty$ </tex-math></inline-formula> ), where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha ^{\ast}$ </tex-math></inline-formula> satisfies the equation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \log \alpha ^{\ast}= (\alpha ^{\ast}-1) \log 6$ </tex-math></inline-formula> , that is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha ^{\ast} \approx 1.241$ </tex-math></inline-formula> . We deduce that the one-sided exponential distribution minimizes Rényi entropy of order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \geq 2$ </tex-math></inline-formula> among all log-concave random variables with fixed variance.
Speech Recognition Based Prescription Generator

Lots of cases have been recorded that are caused by misinterpretation of illegible handwritten prescriptions. In this paper, we propose a mobile device-based speech recognition approach to help doctors utter prescription and get the prescription in tabularized format rather than writing it manually. We start with data collection for the task at hand, which is a Natural Language Processing task. This is followed by its preprocessing and further, we use the Stack-Propagation approach to address the joint task of intent detection and slot filling. After refining our dataset and model parameters as well as the training parameters we finally achieve acceptable results. The scope of this paper is discussed, and future improvement plans are laid.
Image Information Assistance Neural Network for VideoPose3D-based Monocular 3D Pose Estimation

3D pose estimation based on a monocular camera can be applied to various fields such as human-computer interaction and human action recognition. As a two-stage 3D pose estimator, VideoPose3D achieves state-of-the-art accuracy. However, because of the limitation of two-stage processing, image information is partially lost in the process of mapping 2D poses to 3D space, which results in limited final accuracy. This paper proposes an image-assisting pose estimation model and a back-projection based offset generating module. The image-assisting pose estimation model consists of a 2D pose processing branch and an image processing branch. Image information is processed to generate an offset to refine the intermediate 3D pose produced by the 2D pose processing network. The back-projection based offset generating module projects the intermediate 3D poses to 2D space and calculates the error between the projection and input 2D pose. With the error combining with extracted image feature, the neural network generates an offset to decrease the error. By evaluation, the accuracy on each action of Human3.6M dataset gets an average improvement of 0.9 mm over the VideoPose3D baseline.
Social Media Use During Social Gathering Among Youth: A Study on Moghbazar Area of Dhaka City

The rise of social media and continuous technological advances have had a significant impact on how people communicate. Social media sites include social networking sites like Facebook, Instagram, and WhatsApp, and any website that allows social interaction. Many studies have been conducted on the impact of social media, but this study is specifically based on the impact of social media on social gatherings in Dhaka. The primary objective of this study is to examine how the virtual world reshapes the way of social gathering. In this study, an online survey was administered to find out the level of engagement of social media during a social gathering. The researcher collected a total of 120 respondents using the purposive sampling method. Findings suggest that social media intensely affects social gatherings. Despite respondents' awareness of the decrease of face-to-face communication as a result of social media, more than 74 percent of respondents continue to use social media during a social gathering. Use of social media sites found to be harnessing the quality of conversation. While technology facilitates instant communication, it also harms regular interaction.
Autoencoders Without Reconstruction for Textural Anomaly Detection

Automatic anomaly detection in natural textures is a key component within quality control for a range of high-speed, high-yield manufacturing industries that rely on camera-based visual inspection techniques. Targeting anomaly detection through the use of autoencoder reconstruction error readily facilitates training on an often more plentiful set of non-anomalous samples, without the explicit need for a representative set of anomalous training samples that may be difficult to source. Unfortunately, autoencoders struggle to reconstruct high-frequency visual information and therefore, such approaches often fail to achieve a low enough reconstruction error for non-anomalous pixels. In this paper, we propose a new approach in which the autoencoder is trained to directly output the desired per-pixel measure of abnormality without first having to perform reconstruction. This is achieved by corrupting training samples with noise and then predicting how pixels need to be shifted so as to remove the noise. Our direct approach enables the model to compress anomaly scores for normal pixels into a tight bound close to zero, resulting in very clean anomaly segmentations that significantly improve performance. We also introduce the Reflected ReLU output activation function that better facilitates training under this direct regime by leaving values that fall within the image dynamic range unmodified. Overall, an average area under the ROC curve of 96% is achieved on the texture classes of the MVTecAD benchmark dataset, surpassing that achieved by all current state-of-the-art methods.
Discriminative Patch Descriptor Learning With Focal Triplet Loss Function

This paper proposes a focal triplet loss function for discriminative patch descriptor learning. The standard triplet loss function usually restrains the distance difference between the matching samples and the non-matching ones. However, along with the training procedure, the majority of triplets in each batch tend to satisfy the constraint of the loss function and produce low loss values, leading to a masquerade that the model is well-trained. To address this problem, the focal triplet loss function is proposed in this paper to weaken the impact of the easy triplets and focus training on the hard ones. By emphasizing the importance of hard triplets on the model training, the proposed loss forces the descriptor vectors with fixed dimension to carry more discriminative information from the patches. With the benefits of the focal mechanism, the proposed method achieves better performance compared to the state-of-the-art on UBC dataset for image matching task. Furthermore, to demonstrate the effectiveness of the proposed method, we extend the focal triplet loss on the cross-model retrieval task. The experimental results indicate that the proposed method can also be used to improve visual-semantic embedding learning.
Saturated Region Recovery in Tone-Mapped HDR Images

Tone-mapping is one of the prevailing methods to overcome high dynamic range imaging limitations over low dynamic range display devices, but the tone-mapped output image may suffer from saturated regions with texture and color information loss. In this paper, a novel approach is proposed to solve the so-called clipping problem in tone-mapped high dynamic range images. A successful saturation correction framework, which relies on linear embeddings, difference of pixel intensities and gradient-guided block-search, is developed as a post-processing technique to tone-mapping. Experimental results demonstrate that the proposed method successfully recovers clipped regions for the saturation problem in tone-mapped output images while avoiding artifacts.
Game Feature Validation of a Real-Time Game Space with an eXtended Classifier System

The objective of this paper is the proposal of a new approach for the game feature validation of a game space with the eXtended Classifier System (XCS) algorithm. For initial "proof-of-concept" evaluation we used the game space of the Tic-Tac-Toe game, which was placed in a context of real-time characteristics. Evaluation was done with the XCS algorithm without pre-processing internal knowledge programmed (online mode). Evaluation data results were acquired under real-time constraints. No-loss-strategy was implemented for the game, with various scenarios tested to find out if the algorithm has the capability of finding invalid game feature design flaws. Results indicated that the XCS algorithm is able to deliver stable validation of game feature testing results, can be a powerful tool and, therefore, worthy of further research in the Gaming domain. Confirmation of this core concept testing with this type of algorithm (and similar ones) is necessary, since it paves the way for further research in more complex game environments, where the dimension size, the number of aspects, game states and game actions rises intensely.
COVID in the classroom

research-article Free AccessCOVID in the classroom Share on Author: Alan G. Labouseur Marist College Marist CollegeView Profile Authors Info & Claims ACM InroadsVolume 12Issue 3September 2021 pp 32–38https://doi.org/10.1145/3477055Online:14 August 2021Publication History 0citation187DownloadsMetricsTotal Citations0Total Downloads187Last 12 Months187Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Optimization of Encrypted Communication Length Based on Generative Adversarial Network

With the development of artificial intelligence and cryptography, intelligent cryptography will be the trend of encrypted communications in the future. Abadi designed an encrypted communication model based on a generative adversarial network, which can communicate securely when the adversary knows the ciphertext. The communication party and the adversary fight against each other to continuously improve their own capabilities to achieve a state of secure communication. However, this model can only have a better communication effect under the 16 bits communication length, and cannot adapt to the length of modern encrypted communication. Combine the neural network structure in DCGAN to optimize the neural network of the original model, and at the same time increase the batch normalization process, and optimize the loss function in the original model. Experiments show that under the condition of the maximum 2048-bit communication length, the decryption success rate of communication reaches about 0.97, while ensuring that the adversary’s guess error rate is about 0.95, and the training speed is greatly increased to keep it below 5000 steps, ensuring safety and efficiency Communication.
Dynamic Uncertain Causality Graph Applied to the Intelligent Evaluation of a Shale-Gas Sweet Spot

Shale-gas sweet-spot evaluation as a critical part of shale-gas exploration and development has always been the focus of experts and scholars in the unconventional oil and gas field. After comprehensively considering geological, engineering, and economic factors affecting the evaluation of shale-gas sweet spots, a dynamic uncertainty causality graph (DUCG) is applied for the first time to shale-gas sweet-spot evaluation. A graphical modeling scheme is presented to reduce the difficulty in model construction. The evaluation model is based on expert knowledge and does not depend on data. Through rigorous and efficient reasoning, it guarantees exact and efficient diagnostic reasoning in the case of incomplete information. Multiple conditional events and weighted graphs are proposed for specific problems in shale-gas sweet-spot evaluation, which is an extension of the DUCG that defines only one conditional event for different weighted function events and relies only on the experience of a single expert. These solutions make the reasoning process and results more objective, credible, and interpretable. The model is verified with both complete data and incomplete data. The results show that compared with other methods, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising auxiliary tool for shale-gas sweet spot evaluation.
Toric Codes from Order Polytopes

We investigate a class of linear error correcting codes in relation with the order polytopes. In particular we consider the order polytopes of tree posets and bipartite posets. We calculate the parameters of the associated toric variety codes.
How subjective information with AI for digital revolution

This paper summarizes the relationship of subjective information with artificial intelligence (AI) technology and points out how the role of subjective information and its position in AI. Eventually, the characteristic of digital era is the “softening of the theories and hardening of the experiences”. Subjective information is widely used in digital revolution for transforming the qualitative estimations into quasi-quantitative solutions, such as the empirical methods in decision making for quantitative management, etc., it will be the transferor for realizing it. The theoretical formulation of how subjective information is digitized through “Fuzzy-AI Model” for digital revolution is presented in this paper; it has becoming a universal problem solver of utilizing AI technology for quantizing the degree uncertainties in decision-making and fuzzy estimation. Besides, the “Big Data” searching will heavily depend on the completeness of its source information, yet “subjective information” approach can directly predict human thinking or the internal law of complicated objective events into an explicit digital form, for the completeness of source information to make the correct and comprehensive “Big Data” prediction possible. Practical case studies are presented.
Sparse Reconstruction for Radar Imaging Based on Quantum Algorithms

The sparse-driven radar imaging can obtain the high-resolution images about target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.
Graph-Based Method for the Interpretation of User Activities in Serious Games

Computer-based training systems are of growing importance nowadays. Demand for remote skill and knowledge validating solutions is higher than ever before. Serious games provide safe and replayable conditions for teaching and practice. The main issue with education founded on serious games paradigm is the evaluation of the players. Virtual environments allow for various activities that are hard to grasp and quantify automatically. Gameplay log files are usually filled with an excess of trivial activities that don’t provide vital information about user’s skills or knowledge in the relevant domain. In this paper, the authors propose a novel method for classification and interpretation of user activities in computer-based training systems. The innovation of the presented idea lies in the combination of layered graphs data structures, and Bayesian inference about the user’s skills and knowledge.
A validation of QDAcity-RE for domain modeling using qualitative data analysis

Abstract Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to single-case case studies. While these exploratory cases are valuable for an initial assessment, the evaluation of the efficacy of QDA to solve the suggested problems is restricted by the common single-case case study research design. Using our own method, called QDAcity-RE, as the example, we present an in-depth empirical evaluation of employing qualitative data analysis for domain modeling using a controlled experiment design. Our controlled experiment shows that the QDA-based method leads to a deeper and richer set of domain concepts discovered from the data, while also being more time efficient than the control group using a comparable non-QDA-based method with the same level of traceability.
CMF: Cascaded Multi-Model Fusion For Referring Image Segmentation

In this work, we address the task of referring image segmentation (RIS), which aims at predicting a segmentation mask for the object described by a natural language expression. Most existing methods focus on establishing unidirectional or directional relationships between visual and linguistic features to associate two modalities together, while the multi-scale context is ignored or insufficiently modeled. Multi-scale context is crucial to localize and segment those objects that have large scale variations during the multi-modal fusion process. To solve this problem, we propose a simple yet effective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple atrous convolutional layers in parallel and further introduces a cascaded branch to fuse visual and linguistic features. The cascaded branch can progressively integrate multi-scale contextual information and facilitate the alignment of two modalities during the multi-modal fusion process. Experimental results on four benchmark datasets demonstrate that our method outperforms most state-of-the-art methods. Code is available at https://github.com/jianhua2022/CMF-Refseg.
Towards Improving Identity and Access Management with the IdMSecMan Process Framework

In today’s networks, administrative access to Linux servers is commonly managed by Privileged Access Management (PAM). It is not only important to monitor these privileged accounts, but also to control segregation of duty and detect keys as well as accounts that potentially bypass PAM. Unprohibited access can become a business risk. In order to improve the security in a controlled manner, we establish IdMSecMan, a security management process tailored for identity and access management (IAM). Security management processes typically use the Deming Cycle or an adaption for continuous improvements of products, services, or processes within the network infrastructure. We adjust a security management process with visualization for IAM, which also shifts the focus from typical assets to the attacker. With the controlled cycles, the maturity of IAM is measured and can continually advance. This paper presents and applies the work in progress IdMSecMan to a motivating scenario in the field of Linux server. We evaluate our approach in a controlled test environment with first steps to roll it out in our data center. Last but not least, we discuss challenges and future work.
A Bengali Text Summarization Using Encoder-Decoder Based on Social Media Dataset

Text summarization is one of the strategies of compressing a long document to create a version of the main points of the original text. Due to the excessive amount of long posts these days, the value of summarization is born. Reading the main document and obtaining a desirable summary, time and trouble are worth it. Using machine learning and natural language processing built an automated text summarization system can solve this problem. So our proposed system will distribute an abstractive summary of a long text automatically in a period of some time. We have done the whole analysis with the Bengali text. In our designed model, we used chain to chain models of RNN with LSTM in the encrypting layer. The architecture of our model works using RNN decoder and encoder, where the encoder inputs text document and generates output as a short summary at the decoder. This system improves two things, namely summarization and establishing benchmarks performance with ignoble train loss. To train our model, we use our dataset that was created from various online media, articles, Facebook, and some people's personal posts. The challenges we face most here are Bengali text processing, limited text length, enough resources for collecting text.
Some Principles of Building Steganographic Communication Networks

This paper proposes a model of interaction of steganographic systems, which formalizes the description of the principles of construction of steganographic telecommunications. This model is based on the principle of decomposition of the OSI model and includes seven levels: application level, representation level, stegosession level, stegotransport level, stegonetwork level, stegochannel level and stegocontainer level. The main functions of all levels are determined. The article presents the order of interaction of steganographic systems at all levels. A method of redundant data encoding is proposed for the stegochannel level. It is based on an unconventional method of applying codes of the Residue Number System (RNS). The data represented by the RNS code is embedded in steganographic containers. The advantages of the proposed method of coding are increasing the stability of the steganographic channel to the destructive effects of interference and the attackers. An example of the functioning of a steganographic communication network is given. The process of encapsulation and decapsulation of steganographic data at seven levels of the interaction model of steganographic systems is presented. The order of addressing steganographic packets is formulated (in general outline.