Reviewerly/exHarmony huggingface dataset:
Real-Time Body Orientation Recognition for Customer Pose Orientation

Background/Purpose: One of the most significant areas in marketing is consumer position analysis. Retailers can assess the extent of client interest in the goods based on customer pose data. Due to occlusion and left-right similarity difficulties, pose estimation is problematic. We describe a CNN-based solution that includes the body orientation and visibility mask to overcome these two challenges. It provides global information about posture configuration using simple gaits in a retail setting. When a person looks to the right, for example, the left side of his or her body is hidden by the body orientation. In the same way, the person faces the camera, the right shoulder will most likely be on the image's left side. A novel Deep Neural Network design is used to merge body orientation and local joint connections. Second, the visibility mask simulates each joint's occlusion state. Because body orientation is the major source of self-occlusion, it is inextricably tied to it. Detecting an occluding object (such as a shopping cart in a retail setting) might provide give visibility mask prediction clues. Global body position, local joint connections, client mobility, and occluding obstructions are all taken into account in the final advised method. Finally, we run a number of comparison tests to see how effective our technique is.Objective: This work presents customer posture estimation, and a visibility mask to build a prototype for inner and self-occlusion. It also concentrates on local joint connections, global body orientation, and customer mobility.Methodology: The suggested technique is depicted in its entirety in Figure-2. To figure out which portion of the human picture is concealed, we employ stance markers to identify the viewable areas. The landmarks in the occlusion zone have a lower confidence score when we extract posture landmarks. As a result, it is possible to obtain visible masks incorporating occlusion information. We employ visible signs to assist the hidden individual in three ways. To begin, visible masks are utilized to detect viewable parts and to construct spatial masks that filter noise caused by occlusions.Findings/Results: The proposed method outperforms to overcome left-right similarity difficulties, the network incorporates body orientation information, and the visibility mark layers are introduced into the network to enhance the efficiency of occluded joints.Conclusion: For customer pose estimation, A novel architecture using the concepts of Deep Learning is proposed and the occluding object detection clearly provides inter-occlusion by object cues. As a result, local joint connection, the global body orientation, and occluding object and human motion.
Ml-Kfhe: Multi-Label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter

Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is a recent ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This work proposes a multi-label version of KFHE, ML-KFHE, demonstrating the effectiveness of the KFHE method on multi-label datasets. Two variants are introduced based on the underlying component classifier algorithm, ML-KFHE-HOMER, and MLKFHE-CC which uses HOMER and Classifier Chain (CC) as the underlying multi-label algorithms respectively. ML-KFHE-HOMER and ML-KFHE-CC sequentially trains multiple HOMER and CC multi-label classifiers and aggregates their outputs using the sensor fusion properties of the Kalman filter. Extensive experiments and detailed analysis performed on thirteen multi-label datasets and eight other algorithms, including state-of-the-art ensemble methods, show that for both versions, the ML-KFHE framework improves the ensembling process significantly with respect to bagging based combinations of HOMER and CC, thus demonstrating the effectiveness of ML-KFHE. Also, the ML-KFHEHOMER variant was found to perform consistently and significantly better than the compared multi-label methods including existing approaches based on ensembles.
Hotel Intelligent System Design Based on Machine Learning Technology

With the rapid development of my country’s economy and the continuous improvement of people's living standards, the hotel industry has developed rapidly, and more and more star-rated luxury modern high-end products have appeared. How to improve the hotel's service level and work efficiency is a very important issue. To this end, this article intends to start with machine learning technology to conduct intelligent research on hotels. The research purpose of this article is to improve the management service level and work efficiency of the hotel. This article mainly uses experimental testing and comparison methods to analyze the performance of the hotel's intelligent system. Compare the time and load capacity of the system with the user's expected value, highlighting the characteristics of the system's performance. The experimental results show that the time-consuming of the system is less than 0.7, and the carrying capacity is 29 more on average. This is enough to show the feasibility of the hotel's intelligent system.
The Overheard Text Map: A Voice Communication System that Displays Word Clouds of a Conversation in a Virtual Room to Participants Outside the Room

This paper developed a voice communication system, the Overheard Text Map (OTM), which displays the frequent words of a conversation in a virtual space. Participants who engage in voice communication using the OTM can create conversation rooms. They may move freely among the rooms, if they mutually agree to do so, holding internal departmental meetings, group discussions at seminars, poster sessions, and informal gatherings, for example. The OTM displays frequent words in the current conversation to the participants outside the corresponding room using a word cloud. The word cloud selects multiple words with a high frequency of occurrence in the text data of conversations and displays them in a size corresponding to their frequency. In this way, participants can obtain an overview of the conversation in the room without joining it. Therefore, it is expected that the participants will find it easier to choose and join a room with conversations that match what they want to talk about. In the experiment, participants watched the word clouds and chose an appropriate room where participants were having a conversation that matched what they wanted to talk about. The results showed that word clouds created based on discussions are useful for people who want to join an appropriate conversation room.
Spam and Ham Classification by Multinomial Naïve Bayes Classification in Text Data

Attacking Mobile phones is increasingly alarming especially infringing through many applications. Attacking is being done through clicking hyperlinks, which are transmitted through messaging systems as content. The Mobile phones literally are being made un-operational through these kinds of cyber-attacks. This project aims to build machine learning model that learns to detect the attacking through malware caused within messaging that contains different kinds of sources that include text, video and audio. The model aims to learn the attacking caused through malware and then trigger an action that counter the attack based on its type.
PattGAN: Pluralistic Facial Attribute Editing

Facial attribute editing has witnessed substantial progress due to the increasing power of generative adversarial networks.However, existing methods mainly focus on improving the quality of facial attribute editing but ignore the diversity of facial attribute editing, which can only generate a single editing result by binary attribute labels and cannot reveal the diversity of attribute styles.To overcome this limitation, we propose a novel pluralistic facial attribute editing approach termed PattGAN (pluralistic attribute GAN).Instead of directly using the binary attribute labels to guide facial attribute editing, PattGAN first learns the disentangled representation of facial attributes by the binary attribute labels and then uses the disentangled representation of attributes to guide facial attribute editing.To this end, an independent encoder called the "attribute encoder" is introduced to extract distributions of specific attributes from face images.Furthermore, a novel swapping strategy is designed to assist the attribute encoder in modeling the disentangled representation of facial attributes by enhancing the model's ability to learn pluralistic attributes.Coupled with the classification loss, the attribute encoder can accurately separate attribute-related information from face images.Both qualitative and quantitative experiments on the CelebA datasets show that PattGAN can achieve diverse face editing results by different exemplars.In summary, rather than other state-of-the-art methods, PattGAN performs better in diversity facial attribute editing.
Don’t Wait Until the Accident Happens: Few-Shot Classification Framework for Car Accident Inspection in a Real World

Car accident inspection is a binary classification task to recognize whether a given car image includes a damaged surface or not. While the prior studies utilized various computer vision algorithms under the fully supervised, high data availability regime, these studies bear several limits for application in the real world. First, acquiring a large amount of car accident images is challenging due to their scarcity. Second, the supervised classifier would fail to recognize a sample not seen a priori. To improve the aforementioned drawbacks, we propose a few-shot classification framework for the accident inspection task and illustrate several takeaways to the practitioners. First, we designed a few-shot classification framework and validated our approach precisely identifies the accident, although the practitioner has a few accident images. Second, we analyzed the fine-grained discriminative characteristics between normal and accident images; thus, fine-grained feature extractor architecture is adequate for our accident inspection task. Third, we scrutinized optimal image resizing strategy varies along with the feature extractor architecture; therefore, we recommend that practitioners be cautious in handling real world car images. Lastly, we analyzed a larger number of acquired accident images that are advantageous in a few-shot classification. Based on these contributions, we highly expect further studies can realize the benefits of automated car part recognition in the real world shortly.
Automated Evolution Planning for Evolutionary Architecture

Software systems often evolve over time due to frequent changes in user requirements. Many functional changes result in refactoring of the architectural design, which dramatically impacts the software system. Evolutionary architecture is a design principle that helps to support making frequent changes. The key aspect of evolutionary architecture is defining fitness functions to ensure that the changes meet the goals to be achieved. However, planning the evolution of architectural design as incremental steps remains a challenge. This paper presents an approach to automatically generating evolution plans to refactor the architectural design aimed at supporting new functionalities. Formal modelling has been applied to allow functional properties to be verified against the design. With the generated evolution plan, we can determine the safe path to evolve the software system with minimal risk of failures. We have evaluated the accuracy and effectiveness of the evolution plan generated by our approach for six real-world software systems. Our experimental results showed that the proposed approach is effective in generating evolution plans. Moreover, we are able to identify the most suitable planning strategy, which generates the evolution plan with minimal system interruptions.
Computer Assisted Unsupervised Extraction and Validation Technique for Brain Images from MRI

Magnetic Resonance Imaging (MRI) of human is a developing field in medical research because it assists in considering the brain anomalies. To identify and analyze brain anomalies, the research requires brain extraction. Brain extraction is a significant clinical image handling method for quick conclusion with clinical perception for quantitative assessment. Automated methods of extracting brain from MRI are challenging, due to the connected pixel intensity information for various regions such as skull, sub head and neck tissues. This paper presents a fully automated extraction of brain area from MRI. The steps involved in developing the method to extract brain area, includes image contrast limited using histogram, background suppression using average filtering, pixel region growing method by finding pixel intensity similarity and filling discontinuity inside brain region. Twenty volumes of brain slices are utilized in this research method. The outcome is achieved by this method is approved by comparing with manually extracted slices. The test results confirm the performance of this strategy can effectively section brain from MRI.
Friendship Prediction in Social Networks Using Developed Extreme Learning Machine with Kernel Reduction and Probabilistic Calculation

The social network remains a highly dynamic object. Friendship prediction presents a significant problem in the research in network application in general and in social networking applications in particular. It involves analyzing an existing network graph and predicting more links inside the graph that were not identified before. Various models and approaches were developed for this purpose. Similarity-based models were used extensively, mainly they suffered from non-capability of handling the changing nature of the graph. Other models have supervised models that require training on labelled data. However, they need the extraction of many features to achieve satisfying performance. This work provides a novel implicit link prediction probabilistic reduced kernel extreme learning machine named ILP-PRKELM. Unlike the traditional supervised model of link prediction, ILP-PRKELM is attributed to the capability of achieving absolute accuracy with less number of features. Experimental results showed the superiority of ILP-PRKELM with an accomplished accuracy of 84.6 and 78.6 for Last.fm and Douban respectively, which is equivalent to 2% improved accuracy over the benchmarks.
Value-Based Software Engineering: A Systematic Mapping Study

ContextIntegrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure software development activities address all key critical stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE), ObjectiveTo provide an overview of VBSE with respect to the research efforts that have been put into VBSE to date.MethodWe conducted a mapping study to classify evidence on value definitions, quality of studies, VBSE principles and practices, research topics, research methods, research types, contribution facets, and scientometric analysis on publication venues, citations and researchers ranking.ResultsBased on the 143 studies, the results showed that the term “value” has not been clearly defined in many VBSE studies. Value-based (VB) Requirements Engineering (26%) and VB Planning and Control (16%) were the two principles and practices mostly investigated, whereas VB Risk Management and VB People Management were the least researched.ConclusionsThe main emphasis of VBSE research is placed on the early phases of software engineering (requirements engineering and planning/control). Case-study was the research method used the most (38%). The majority of studies contribute towards methods and processes, while very few studies propose metrics and tools.
Deep Reinforcement Learning Based Regional Coverage Path Planning for Uvms Organism Target Capture

The robotic organism captures in the oceanic aquatic field can greatly improve the fishing efficiency and economics of seafood. The purpose for path planning of underwater vehicle manipulator system (UVMS) is to capture the most organisms in the shortest time with no obstacle collisions. This paper has proposed a novel cost modelling algorithm for the collision avoidance, manipulation and vehicle movement during the target capture process. On the underwater unknown and blurred environment, a novel double value iteration network with long short-term memory (LSTM) network has been proposed on the basis of value iteration networks. With the LSTM network, the proposed double value iteration network can predict the blurred and unknown environment, plan capture strategy with the calculated collision risk and movement cost. Moreover, a novel cooperative path planning model for task allocation has been proposed to improve capture efficiency. Simulations on robot and multiple robots capture planning have verified the network performance.
A Cybersecurity Incident Classification Integrating the Perspectives of Perpetrators and Target Companies

All companies and supply chains will undergo cybersecurity incidents, and public companies, in particular, are attractive targets for perpetrators. The US Securities and Exchange Commission (SEC) is currently recommending rules for disclosing these events, but after a material cybersecurity incident, organizations will finetune the information disclosure level to avoid creating roadmaps for future events. This study proposes a bottom-up classification for cybersecurity incidents that integrates the perspectives of perpetrators and targeted companies and considers such a roadmap dilemma. We applied direct content analysis to the filings of approximately 1,100 companies in the SEC’s EDGAR database to create a dataset with 102 material cybersecurity incidents spanning the last decade (2011-2021). Latent Class Analysis was applied to classify these events, proposing four descriptive classes. Public companies usually disclose the selected variables, which are strongly associated with the company sector. Our classification could be beneficially integrated within enhanced future SEC disclosure rules, and it assists public companies with peer experience in addressing the roadmap dilemma.
Iterating on multiple collections in synchrony

Abstract Modern programming languages typically provide some form of comprehension syntax which renders programs manipulating collection types more readable and understandable. However, comprehension syntax corresponds to nested loops in general. There is no simple way of using it to express efficient general synchronized iterations on multiple ordered collections, such as linear-time algorithms for low-selectivity database joins. Synchrony fold is proposed here as a novel characterization of synchronized iteration. Central to this characterization is a monotonic isBefore predicate for relating the orderings on the two collections being iterated on and an antimonotonic canSee predicate for identifying matching pairs in the two collections to synchronize and act on. A restriction is then placed on Synchrony fold, cutting its extensional expressive power to match that of comprehension syntax, giving us Synchrony generator . Synchrony generator retains sufficient intensional expressive power for expressing efficient synchronized iteration on ordered collections. In particular, it is proved to be a natural generalization of the database merge join algorithm, extending the latter to more general database joins. Finally, Synchrony iterator is derived from Synchrony generator as a novel form of iterator. While Synchrony iterator has the same extensional and intensional expressive power as Synchrony generator, the former is better dovetailed with comprehension syntax. Thereby, algorithms requiring synchronized iterations on multiple ordered collections, including those for efficient general database joins, become expressible naturally in comprehension syntax.
Data Anomaly Detection for Structural Health Monitoring by Multi-View Representation Based on Local Binary Patterns

Structural health monitoring (SHM) systems provide opportunities to understand the structural behaviors remotely in real-time. However, anomalous measurement data are frequently collected from structures, which greatly affect the results of further analyses. Hence, detecting anomalous data is crucial for SHM systems. In this article, we present a simple yet efficient approach that incorporates complementary information obtained from multi-view local binary patterns (LBP) and random forests (RF) to distinguish data anomalies. Acceleration data are first converted into gray-scale image data. The LBP texture features are extracted in three different views from the converted images, which are further aggregated as the anomaly representation for the final RF prediction. Consequently, multiple types of data anomalies can be accurately identified. Extensive experiments validated on an acceleration dataset acquired on a long-span cable-stayed bridge highlight the advantages of the proposed method. State-of-the-art performances are achieved by the proposed method, demonstrating its effectiveness and generalization ability.
New Collective Signatures Based on the Elliptic Curve Discrete Logarithm Problem

There have been many digital signature schemes were developed based on the discrete logarithm problem on a finite field. In this study, we use the elliptic curve discrete logarithm problem to build new collective signature schemes. The cryptosystem on elliptic curve allows to generate digital signatures with the same level of security as other cryptosystems but with smaller keys. To extend practical applicability and enhance the security level of the group signature protocols, we propose two new types of collective digital signature schemes based on the discrete logarithm problem on the elliptic curve: i) the collective digital signature scheme shared by several signing groups and ii) the collective digital signature scheme shared by several signing groups and several individual signers. These two new types of collective signatures have combined the advantages of group digital signatures and collective digital signatures. These signatures have a fixed size and do not depend on the number of members participating in the creation of the final collective signature. One of the advantages of the proposed collective signature protocols is that they can be deployed on top of the available public key infrastructures.
Lattice Distances in 3-Dimensional Quantum Jumps

For Q a lattice polytope and $$x\not \in Q$$ a lattice point, we say that (Q, x) is a quantum jump if $${{\,\mathrm{conv}\,}}(Q \cup \{x\})$$ contains exactly one more lattice point than Q. Usually this can only happen when the lattice distance between x and Q is somehow bounded. In this paper I collect several results and information on the bound for that distance in 3-dimensional quantum jumps, and the consequences on the distance between the boundary of a polytope and its interior lattice points.
Explanation of deep learning models

Explainability should take into account both interpretability and completeness. The interpretability is the human understanding of the structure of a system. And the completeness is the ability to explain how a system works in an accurate way. In the explainable model based on deep learning, we will explain how to provide explanatory power in two aspects. Explainable models based on deep learning are roughly divided into two methods: Activation-Based Methods (ABMs) and Backpropagation-Based Methods (BBMs). Representative examples of ABMs include Class Activation Map and Gradient-Class Activation Map, and representative examples of BBMs include Layer-wise Relevance Propagation, Deep Learning Important Features, and SmoothGrad.