Reviewerly/exHarmony huggingface dataset:
Suspect Face Retrieval via Multicriteria Decision Process

Using human definable face attributes, this paper presents suspect face identification approach for criminal investigations. To facilitate this approach, Fuzzy Analytic Hierarchy Process (FAHP) weights were created to produce the salience of facial attributes. Facial Attributes, such as short, medium, and long, are given manually assigned weights based on membership grades. The accuracy of face retrieval from a face repository is evaluated through experiments. Along with eliminating the need for expert artists, the proposed method complements sketch-based face recognition by enabling investigators to search face repositories immediately without having to wait for sketches to be generated first.
Social Media Privacy Issues, Threats, and Risks

General knownledge presumes that social media users are increasing. As of October 2021, more than 4.5 billion people are using social networks. Popularly utilized for communication, knowledge participation, thoughts communication, videos, building networks, images, and so on. However, there is still a lack of knowledge of the consequences associated with the use of said programs, and the growth of users increases the possible vulnerabilities and attacks. This paper proposes some suggestions for mitigation of risks and threats when people publish anything online. The authors' contribution provided in this paper is the suggested solutions for each of the listed and discussed threats and risks.
An Enhanced Image Processing Model for Earlier Detection and Analysis of Diabetic Foot Hyperthermia Through Cognitive Approach

A neuropathic foot with a subsequent infection is always the most common indication for major limb amputation. The bulk of amputations is avoided if they are recognized early and promptly and aggressively. Thus, a computer-aided system integrated with infrarot thermographic cameras and cognitive computing must be used. Processing of medical images requires ongoing improvements in technology and applications to improve service quality in the medical industry. Infrared images of foot skin temperatures appeared to show a relationship between blood circulation and wound/ulcer presentation. This work is based mostly on the area of diabetes foot and follows the two research areas indicated above. The aim of this research is to enhance early identification of diabetic foot and reduce ulcers in diabetic foot.
Generating and Adapting to Diverse Ad Hoc Partners in <i>Hanabi</i>

Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose, and generates a population of diverse Hanabi agents using MAP-Elites. We also postulate that agents can benefit from a diverse population during training and implement a simple "meta-strategy" for adapting to an agent's perceived behavioral niche. We show this meta-strategy can work better than generalist strategies even outside the population it was trained with if its partner's behavioral niche can be correctly inferred, but in practice a partner's behavior depends and interferes with the meta-agent's own behavior, suggesting an avenue for future research in characterizing another agent's behavior during gameplay.
CTDS: Centralized Teacher With Decentralized Student for Multi-Agent Reinforcement Learning

Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has become one of the most widely used MARL paradigms. In CTDE, centralized information is dedicated to learning the allocation of the team reward with a mixing network, while the learning of individual Q-values is usually based on local observations. The insufficient utility of global observation will degrade performance in challenging environments. To this end, this work proposes a novel Centralized Teacher with Decentralized Student (CTDS) framework, which consists of a teacher model and a student model. Specifically, the teacher model allocates the team reward by learning individual Q-values conditioned on global observation, while the student model utilizes the partial observations to approximate the Q-values estimated by the teacher model. In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference. Our CTDS framework is generic, which is ready to be applied upon existing CTDE methods to boost their performance. We conduct experiments on a challenging set of StarCraft II micromanagement tasks to test the effectiveness of our method and the results show that CTDS outperforms the existing value-based MARL methods.
Suspicious Activity Detection in Surveillance Applications Using Slow-Fast Convolutional Neural Network

With the expansion in crime and abnormal human activities, extensive need has arisen to upgrade the cyber-physical security mechanism with artificial intelligence. Our research aims at proposing a model that can be installed in the CCTVs in any area and inform about the human activities happening in that particular area. We have developed a deep learning model, using a variant of slow-fast algorithm to detect and classify the unusual activity happening in the areas, which becomes tough and tedious with manual effort, thereby engaging large workforce. Our solution approach focuses on alleviating the problem using convolutional neural network. Our model uses a slow pathway and a fast pathway, wherein for analyzing stable or static content of the video, a low temporal resolution, with slow pathway is used. Also, another high temporal resolution with fast pathway is used for analyzing the active or dynamic data of video. These two pathways are combined by lateral connections to deliver global detection of the video. The experimental results show effectiveness of our method in detecting unusual movement and identification of suspicious objects in video recordings that may include both real-time live streams and prerecorded videos.
Target Recognition Technology Based on Improved Faster RCNN

Based on the Faster RCNN detector, this article first analyzes the advantages and disadvantages of using a variety of mainstream convolutional neural networks in target recognition. An improved model with “difficult samples” augmentation features and lighter network model is proposed. And through experiments, it is verified that this model has certain advantages in comprehensive evaluation indicators such as accuracy, recall, and detection speed compared with other models.
Quality Model for Educational Mobile Apps Based on SQuaRE and AHP

During the last decade, there has been an increase in the demand of mobile apps in different download platforms, like Google Play Store, Apple App Store and Amazon App Store. Because of this, different approaches related to mobile app quality have surged, which tend to focus on assessing the general quality of an app without focusing on a specific sector. Because of this reason, the motivation to develop a quality model for mobile apps for the educational sector arises, using the System and Software Quality Requirements and Evaluation (SQuaRE) guidelines and the Analytic Hierarchy Process (AHP) weighting technique; the latter was used to prioritize the quality attributes. A quality model has been obtained, which consists of 8 characteristics, 19 sub-characteristics and 30 metrics. In order to corroborate the proposed model, 4 research questions and 2 case studies related to 2 mobile apps have been established. The results proved that Google Classroom and Moodle have very similar scores in each assessed characteristic; in the case of Usability, there is a difference of 0.006 in favor of Moodle, for Portability, there is a difference of 0.150 in favor of Google Classroom, and for Security, regarding authentication rules and mechanisms, both apps obtained the same result.
A Novel Approach for Attack Tree to Attack Graph Transformation

Attack trees and attack graphs are both common graphical threat models used by organizations to better understand possible cybersecurity threats. These models have been primarily seen as separate entities, to be used and researched in entirely different contexts, but recently there has emerged a new interest in combining the strengths of these models and in transforming models from one notation into the other. The existing works in this area focus on transforming attack graphs into attack trees. In this paper, we propose an approach to transform attack trees into attack graphs based on the fundamental understanding of how actions are represented in both structures. From this, we hope to enable more versatility in both structures.
Teleportation of discrete-variable qubits via continuous-variable lossy channels

The Continuous-Variable (CV) quantum state of light allows for the teleportation of a Discrete-Variable (DV) photonic qubit. Such an operation is useful in the realm of hybrid quantum networks. However, it is known that the teleportation of a DV qubit via a Gaussian CV resource channel is severely limited under channel loss, with a teleportation fidelity beyond the classical limit unattainable for losses exceeding a small threshold of 0.5 dB. In this work, we present a new non-deterministic teleportation protocol that combines a Gaussian CV resource channel with a modified form of the Bell State Measurement that accommodates a DV-qubit fidelity beyond the classical limit for channel losses up to 20~dB. Beyond this orders of magnitude improvement, we also show how the use of non-Gaussian operations on the CV resource channel can lead to a DV-qubit fidelity approaching unity for any channel loss.
Construction of a Fuzzy Model for Contour Selection

The chapter discusses an algorithm for constructing a model based on Tsukamoto's fuzzy inference, which is used to highlight contour points in a grayscale image. The input data of the fuzzy inference model is a set of vectors generated for each pixel of the image, excluding those that are on the border of the image. Each vector has eight components, corresponding to the eight surrounding pixels. Each component contains the difference in brightness between the considered and the surrounding pixel. Pixel brightness differences are described by three fuzzy sets with trapezoidal membership functions. The output of the model is a sign of belonging of the considered pixel to the contour. The attribute of belonging is described by a fuzzy set. The initial knowledge base of the model consists of $${3}^{8}\times 2$$ production rules. The determination of the parameters of the membership functions is carried out using a genetic algorithm. A reduction of the knowledge base of the fuzzy model is proposed, which made it possible to reduce the number of rules used, which allows one to reduce the cost of computing resources.
Explorations in Graph-Based Ranking Algorithms for Automatic Text Summarization on Konkani Texts

The work presented in this paper is an attempt at exploring the field of automatic text summarization and applying it to Konkani language, which is one of the low-resource languages in the automatic text summarization domain. Low-resource languages are the ones that have none or a very limited number of existing resources available, such as data, tools, language experts, and so on. We examine popular graph-based ranking algorithms and evaluate their performance in performing unsupervised automatic text summarization. The text is represented as a graph, where the vertices represent sentences and the edges between a pair of vertices represent a similarity score computed using a similarity measure. The graph-based ranking algorithms then rank the most relevant vertices (or sentences) to include in a summary. This paper also examines the impact of using weighted undirected or directed graphs on the output of the summarization system. The dataset used in the experiments was specially constructed by the authors using books on Konkani literature, and it is written in Devanagari script. The results of the experiments indicate that the graph-based ranking algorithms produce promising summaries of arbitrary length without needing any resources or training data. These algorithms can be effortlessly extended to other low-resource languages to get favorable results.
The information association model of college students' archives based on a cloud computing management platform

In order to overcome the problems of slow response time and poor field integrity of the first level of the poor update effect of the student file service, this paper proposes an information association model of college student file based on the cloud computing management platform. To build a cloud computing management platform architecture, according to the characteristics of multi-source and heterogeneous information, judge whether the weight distribution is reasonable through the judgment matrix, calculate the user interest using the processed data, apply the Ebbinghaus forgetting formula to the user interest attenuation, build a complete file information association diagram by solving the association strength between file entities, and get a complete file information association diagram interest correlation analysis model. The experimental results show that it can modify personal space style and log, web service response is faster, service update effect is 7.0, and field integrity is about 7.8.
Consumers' intention to visit a robotic restaurant

The aim of this study is to identify the factors that influence consumers' intention to visit robot restaurants. Data is based on feedback from 283 experienced customers using the random sampling method. Structural equation modelling (SEM) and hypothesis testing were conducted to validate the model after checking the reliability and validity of the scale items. The study found that curiosity, status symbols and children's influence were significant and that better service and innovativeness did not play a significant role in explaining the visit intentions of Bangladeshi robotic restaurants. The findings provide clues as to what factors marketers should consider. This study is important because consumer interest in robotic restaurants and robotic services is increasing overall.
Facial landmark detection and geometric feature-based emotion recognition

Facial expression related to machine intelligence is a popular research area in emotion science, pain assessment, human behaviour analysis, virtual reality, etc. This paper aims at exploring a contour-based shape analysis from the viewpoint of geometric characteristics towards facial expression recognition. Since the facial landmark detection accuracy dramatically affects the final classification, a simple contour detection algorithm is used for identifying facial landmarks accurately. Spatial local and relative geometric features extracted with the neutral face as the reference are projected to the lower-dimensional space using stepwise linear discriminant analysis. The proposed system is tested and validated using backpropagation-based artificial neural network on JAFFE and MMI dataset with an average accuracy of 95.53% and 94.98%, respectively. The proposed scheme's recognition accuracy has been compared with the state-of-art methods, and the results show significant improvement in the proposed model over others using geometric features alone.
PTT5-Paraphraser: Diversity and Meaning Fidelity in Automatic Portuguese Paraphrasing

Paraphrasing is a fundamental technique for many text applications. Typically, this task is performed through models that perform lexical and translation operations, which tend to present a trade-off between meaning preservation and diversity. In this paper, we present a transformer-based approach called PTT5-Paraphraser, a PPT5 model fine-tuned on TaPaCo, a large corpus of paraphrases. PTT5-Paraphraser achieves good results according to a number of metrics, showing a good compromise between diversity and fidelity to the original meaning. Two human evaluations are made to explore the paraphrases produced by our model: the first analyzes their quality in terms of preserving the meaning and diversity of sentences, while the second compares automatically generated paraphrases with human-made ones. Finally, we perform a classification task, which shows that datasets augmented with paraphrases can substantially increase the performance of classifiers.
Equivalence between belief propagation instability and transition to replica symmetry breaking in perceptron learning systems

Binary perceptron is a fundamental model of supervised learning for the non-convex optimization, which is a root of the popular deep learning. Binary perceptron is able to achieve a classification of random high-dimensional data by computing the marginal probabilities of binary synapses. The relationship between the algorithmic instability and the equilibrium analysis of the model remains elusive. Here, we establish the relationship by showing that the instability condition around the algorithmic fixed point is identical to the instability for breaking the replica symmetric saddle point solution of the free energy function. Therefore, our analysis would hopefully provide insights towards other learning systems in bridging the gap between non-convex learning dynamics and statistical mechanics properties of more complex neural networks.
Newly learned novel cues to location are combined with familiar cues but not always with each other.

Mature perceptual systems can learn new arbitrary sensory signals (novel cues) to properties of the environment, but little is known about the extent to which novel cues are integrated into normal perception. In normal perception, multiple uncertain familiar cues are combined, often near-optimally (reliability-weighted averaging), to increase perceptual precision. We trained observers to use abstract novel cues to estimate horizontal locations of hidden objects on a monitor. In experiment 1, 4 groups of observers each learned to use a different novel cue. All groups benefited from a suboptimal but significant gain in precision using novel and familiar cues together after short-term training (3 ∼1.5 hr sessions), extending previous reports of novel-familiar cue combination. In experiment 2, we tested whether 2 novel cues may also be combined with each other. One pair of novel cues could be combined to improve precision but the other could not, at least not after 3 sessions of repeated training. Overall, our results provide extensive evidence that novel cues can be learned and combined with familiar cues to enhance perception, but mixed evidence for whether perceptual and decision-making systems can extend this ability to the combination of multiple novel cues with only short-term training. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Scalable verification of GNN-based job schedulers

Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive performance, concerns remain over whether these GNN-based job schedulers meet users’ expectations about other important properties, such as strategy-proofness, sharing incentive, and stability. In this work, we consider formal verification of GNN-based job schedulers. We address several domain-specific challenges such as networks that are deeper and specifications that are richer than those encountered when verifying image and NLP classifiers. We develop vegas, the first general framework for verifying both single-step and multi-step properties of these schedulers based on carefully designed algorithms that combine abstractions, refinements, solvers, and proof transfer. Our experimental results show that vegas achieves significant speed-up when verifying important properties of a state-of-the-art GNN-based scheduler compared to previous methods.
Blockchain-based seamless end-to-end shipping with android application

World trade has been influenced by the emergence of new technologies around the globe. Businesses are forced to adopt new technologies regardless of their expertise and capabilities, to outperform their counterparts. This leads to challenges, risks, and errors in various stages of shipping. It is possible to overcome these challenges by securing the shipments head-to-head. To implement this, the present study proposes a blockchain-based seamless end-to-end shipping approach that makes shipping more secured, transparent, reliable, etc. Hyperledger composer based on REST server has been adopted in the study to capture the shipping data of the participants, assets, and transactions. In addition, separate Android applications have been developed for managers and customers for effective order placement, management, and tracking. Validation of the order placement is presented in the study to confirm the reliability of the system. The results indicate that the shipment errors can be precisely determined through the proposed method.
An Integrated Facemask Detection with Face Recognition and Alert System Using MobileNetV2

COVID-19 pandemic has impacted the lives of individuals, organizations, markets, and the whole world in a way that has changed the functioning of all the systems. To get going, some try to adapt to working online, children started studying online and people started ordering food online. While this is still going on, there are many people whose jobs demand physical presence at workplaces and they have no choice but to be exposed to the virus while keeping our society functioning. People are needed to adapt to the new “normal” by practicing social distancing and wearing masks. Wearing masks is the most effective means of prevention of Covid-19. To ensure this, we built a web application that aims at keeping people advised to wear masks constantly with the help of an integrated facemask detection and face-recognition system. The proposed system initially detects whether the person in the real-time video feed is wearing a mask or not and then recognizes the face of the person if they are not wearing a mask. Finally, the proposed system alerts that specific violator to wear a mask through an auto-generated email to his personal email id. The application also allows the admin and the violators to log in and access the list of fines levied along with photo evidence.
Design of reasonable initialization weighted enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems

&lt;abstract&gt; &lt;p&gt;Interval type-2 fuzzy logic systems (IT2 FLSs) already become an emerging technology in recent years. As the most popular type-reduction (TR) algorithms, Karnik-Mendel (KM) algorithms own the advantage of maintaining the uncertainties flow in systems. This paper analyzes the initialization for KM types of algorithms. Furthermore, the weighting approaches of them are also given by means of the Newton-Cotes quadrature formulas. Importantly, the reasonable initialization weighted enhanced Karnik-Mendel (RIWEKM) algorithms are provided to complete the centroid type-reduction of IT2 FLSs. Three computer simulation experiments illustrate that, the proposed RIWEKM algorithms own both smaller absolute errors and faster convergence speeds in contrast to the EKM and RIEKM algorithms.&lt;/p&gt; &lt;/abstract&gt;