Reviewerly/exHarmony huggingface dataset:
BeHAutH: A KNN-Based Classification Scheme for Behavior-Based Authentication in Web 3.0

Web 3.0 would allow users and processes to communicate and interpret data semantically. It is expected to employ web user (WU) behaviour data, which is sampled and collected at different time instants for verification. Recent approaches have suggested behaviour-based authentication (BBA), but most schemes suffer from a high rate of false positives. Motivated from the gap, the paper presents a scheme named BeHAutH, that constructs a two-factor authentication (2FA) scheme for WU, based on real-time keystroke dynamics. We employ the K-nearest neighbour classification (KNN) algorithm and compute the similarity across two vectors in K dimensions. The similarity is computed in terms of both Euclidean and Manhattan distance metrics. The scheme achieves an accuracy of 87% over 146 testing samples, and a recall value of 0.95, which address the false negative issues.
An Efficient Face-based Age Group Detector on a CPU using Two Perspective Convolution with Attention Modules

Age detection has become incredibly substantial in various scenarios such as video surveillance, forensic applications, and advertising platform. An age detector is expected to operate on low-cost devices or CPU devices to minimize the budget of the implementation system. This work presents an efficient face based age group detector (Age-CPU) that can operate fluidly on a CPU. It proposes two perspectives convolution architecture with depthwise global attention modules (2PDG) on this detector. It applies two kernel sizes to consider different sizes of the feature area of the object reinforced with enhancing block. The depthwise layer on the attention module helps the architecture extract features more focused and deeply. It convolves each channel with an individual depthwise kernel. The architecture is trained and validated on the UTKFace and FG-NET datasets. 2PDG acquires competitive accuracy compared to other competitors’ architectures on the datasets. Furthermore, the proposed detector can operate 100 frames per second on a CPU device, which is speedy to execute in real-time.
Q3DE: A fault-tolerant quantum computer architecture for multi-bit burst errors by cosmic rays

Demonstrating small error rates by integrating quantum error correction (QEC) into an architecture of quantum computing is the next milestone towards scalable fault-tolerant quantum computing (FTQC). Encoding logical qubits with superconducting qubits and surface codes is considered a promising candidate for FTQC architectures. In this paper, we propose an FTQC architecture, which we call Q3DE, that enhances the tolerance to multi-bit burst errors (MBBEs) by cosmic rays with moderate changes and overhead. There are three core components in Q3DE: in-situ anomaly DEtection, dynamic code DEformation, and optimized error DEcoding. In this architecture, MBBEs are detected only from syndrome values for error correction. The effect of MBBEs is immediately mitigated by dynamically increasing the encoding level of logical qubits and re-estimating probable recovery operation with the rollback of the decoding process. We investigate the performance and overhead of the Q3DE architecture with quantum-error simulators and demonstrate that Q3DE effectively reduces the period of MBBEs by 1000 times and halves the size of their region. Therefore, Q3DE significantly relaxes the requirement of qubit density and qubit chip size to realize FTQC. Our scheme is versatile for mitigating MBBEs, i.e., temporal variations of error properties, on a wide range of physical devices and FTQC architectures since it relies only on the standard features of topological stabilizer codes.
Recognizing Long-term Sleep Behaviour Change using Clustering for Elderly in Smart Homes

The need for smart healthcare tools and techniques has increased due to the availability of low-cost IoT sensors and devices and the growing aging population in the world. Early detection of health conditions such as dementia and Parkinsons are important for treatment and medication. Out of the many symptoms of such health conditions, one critical behavior is sleep activity changes. In this paper, we evaluate and apply an unsupervised machine learning: K-Means, to detect changes in long-term sleep behavior in the bedroom using smart-home motion sensors installed in 6 real-life single resident elderly homes for approximately three years. Our method analyses the transformation of clusters for a participant over three years, 2019, 2020, and 2021. This is done using three features: duration of stay, the hour of the day, and duration frequency. Data clustering is used to group durations of being in the bedroom at different hours of the day. This is done to see if there is a shift in these clusters for elderly persons with healthy aging and those developing health conditions like dementia and Parkinsons. We foresee that such methods to detect long-term behavior changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.
Development of "City of the Futureʺ Scene to Assess the User Experience in a Virtual Reality environment

Virtual reality is rapidly becoming a key method in digital medicine. It is a prospective area of research. The detailed examination of users and its impact on patients is required to introduce virtual reality in medicine. A detailed examination of virtual reality users, followed by an assessment of virtual reality effects, is required for wide adaptation of this technology. The virtual reality of 12 healthy subjects was examined. For this study, we used the custom-designed VR scene “City of the Future”. An HTC Vive Pro headset was used for VR immersion. A physical examination and neurological examination were performed using the State-Trait Anxiety Inventory, Visual analog scale, and Simulator Sickness Questionnaire. All subjects were divided into two groups based on the SSQ score. Group 1, with no or slight side effects, accounted for 58.3%. Group 2 included 41.7% participants with mild to moderate simulator disorder. The strain and trait anxiety level based on the State-Trait Anxiety Inventory in Group 2 with VR-related side effects was higher than in Group 1, which had no simulator sickness. We suppose that VR experience can have positive or negative user feedback or cause simulator sickness based on psychoemotional factors such as anxiety. Dynamic monitoring and the development of an adaptive system for users in the virtual reality environment, which will monitor the dynamic individual characteristics of the person, are needed. This will improve the adaptation mechanisms and lead to a positive virtual experience.
A Probabilistic Temporal Epistemic Logic: Strong Completeness

Abstract The paper offers a formalization of reasoning about distributed multi-agent systems. The presented propositional probabilistic temporal epistemic logic $\textbf {PTEL}$ is developed in full detail: syntax, semantics, soundness and strong completeness theorems. As an example, we prove consistency of the blockchain protocol with respect to the given set of axioms expressed in the formal language of the logic. We explain how to extend $\textbf {PTEL}$ to axiomatize the corresponding first-order logic.
Forensic Voice Comparison: Human-Supervised-Automatic Approach

The human-supervised-automatic analytical approach to forensic voice comparison in conjunction with the likelihood-ratio interpretive framework is described. Practitioner tasks are described, including adoption of relevant hypotheses for the case, assessment of the conditions of the questioned-speaker and known-speaker recordings in the case, and selection of data representing the relevant population and reflecting the conditions for the case. Software tools are also described. An example is provided of a forensic-voice-comparison system based on state-of-the-art automatic-speaker-recognition technology. Also described are the calibration and validation of that system using a benchmark dataset reflecting the conditions of a real forensic case.
Intra-Modal Constraint Loss for Image-Text Retrieval

Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains. With the development of convolutional and recurrent neural networks, the bottleneck of retrieval across image-text modalities is no longer the extraction of image and text features but an efficient loss function learning in embedding space. Many loss functions try to closer pairwise features from heterogeneous modalities. This paper proposes a method for learning joint embedding of images and texts using an intra-modal constraint loss function to reduce the violation of negative pairs from the same homogeneous modality. Experimental results show that our approach outperforms state-of-the-art bi-directional image-text retrieval methods on Flickr30K and Microsoft COCO datasets. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
SIMPPO

Serverless Function-as-a-Service (FaaS) offers improved programmability for customers, yet it is not server-"less" and comes at the cost of more complex infrastructure management (e.g., resource provisioning and scheduling) for cloud providers. To maintain service-level objectives (SLOs) and improve resource utilization efficiency, recent research has been focused on applying online learning algorithms such as reinforcement learning (RL) to manage resources. Despite the initial success of applying RL, we first show in this paper that the state-of-the-art single-agent RL algorithm (S-RL) suffers up to 4.8x higher p99 function latency degradation on multi-tenant serverless FaaS platforms compared to isolated environments and is unable to converge during training. We then design and implement a scalable and incremental multi-agent RL framework based on Proximal Policy Optimization (SIMPPO). Our experiments demonstrate that in multi-tenant environments, SIMPPO enables each RL agent to efficiently converge during training and provides online function latency performance comparable to that of S-RL trained in isolation with minor degradation (<9.2%). In addition, SIMPPO reduces the p99 function latency by 4.5x compared to S-RL in multi-tenant cases.
Towards the Conservation of Endangered Mammals using Single-stage Deep Neural Network

For the purpose of conserving and preserving endangered mammals in their habitat, their real-time detection is very crucial.This will help curb their involvement in accidents as well as help in tracking them in case they stray which consequently will be useful for the preservation of life and property as most of the mammals are known to destroy crops among other damages that can be caused by them.We deployed a single-stage deep neural network -You Only Look Once (YOLO) version 4 for the detection of four classes of mammals in real-time which will help in monitoring the animals as well as tracking their activities not excluding their security and protection from poisoning and being preyed upon.A system of this magnitude can be employed to replace the manual monitoring of these mammals and the efforts put in place to track their position which can be achieved using a video in this model.The choice of YOLO has been because of its applicability for the detection of the 80 classes of objects in the MS COCO dataset and the high speed it has in performing this task.To be used for our purpose, we carefully choose the components of the network and modified and retrained the model to suit our desire which is to detect 4 classes of mammals that we have in our dataset after they were annotated.This research presents a custom dataset for the detection of this class of animals in real-time with a laudable mean Average precision of 98.6% @ IoU = 0.5 and an average real-time speed of detection of 30.6 FPS on a GPU-enabled computer and over 20FPS for a computer that is only CPU-enabled.These results show that the model produced is applicable in real-time for detection both in GPU-and CPU-enabled devices.
Differential privacy performance evaluation under the condition of non-uniform noise distribution

Differential Privacy (DP) technology provides powerful and mathematically defined privacy protection for aggregated data. It makes the query results of adjacent datasets indistinguishable by introducing a proper noise level into the query results. The existing DP schemes usually uniformly add the noise which obeys a certain distribution to the target data set. Although they can effectively protect privacy, they may also lead to excessive privacy protection for local data in a data set, which greatly reduces the usability of query results. Based on the above issues, this paper proposes a novel DP scheme with non-uniform noise distribution. Our scheme divides the target data set and adds noise to specific data blocks. We analyzed the entire dataset to verify its privacy and usability. What we care about is whether the data set processed by the DP scheme with non-uniform noise distribution has sufficient privacy protection strength on the basis of improving usability. We obtain encouraging results from evaluating their performance on real data sets. Our results show that both theoretically and empirically, the scheme strikes a good balance between privacy and usability and can provide better privacy protection.
An Explainable Artificial-Intelligence-Based CNN Model for Knowledge Extraction From the Social Internet of Things: Proposing a New Model

Rich material is buried in the entity’s textual description information, its hierarchical-type information, and the graph’s topological structure information in the knowledge graph. As a result, these data can be a useful supplement to triple information in terms of improving performance. To appropriately exploit these social Internet of Things (IoT) data, entity details are first encoded using artificial-intelligence (AI)-based convolutional neural networks (CNNs). The unit vector and unit description vector are then projected into a given relational space using the hierarchical-type information, thus restricting its semantic content. The graph attention approach is then used to fuse the graph’s topological structure information to calculate the influence of various neighboring points on the entity. To deal with the data-sparse problem, the multihop relationship information among entities is calculated at the same time. Finally, a decoder is used to collect global information among dimensions. Link prediction experiments show that the multisource information combined knowledge representation learning (XAI-CNN) model based on explainable AI (XAI) can effectively use multisource social IoT information beyond triples and that other techniques may be better than the baseline model.
Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light

This paper proposes a brain-inspired navigation model based on absolute heading for the autonomous navigation of unmanned platforms. The proposed model combined the sand ant’s strategy of acquiring absolute heading from the sky environment and the brain-inspired navigation system, which is closer to the navigation mechanism of migratory animals. Firstly, a brain-inspired grid cell network model and an absolute heading-based head-direction cell network model were constructed based on the continuous attractor network (CAN). Then, an absolute heading-based environmental vision template was constructed using the line scan intensity distribution curve, and the path integration error was corrected using the environmental vision template. Finally, a topological cognitive node was constructed according to the grid cell, the head direction cell, the environmental visual template, the absolute heading information, and the position information. Numerous topological nodes formed the absolute heading-based topological map. The model is a topological navigation method not limited to strict geometric space scale, and its position and absolute heading are decoupled. The experimental results showed that the proposed model is superior to the other methods in terms of the accuracy of visual template recognition, as well as the accuracy and topology consistency of the constructed environment topology map.
Comparison of Feature Extraction Methods on Sentiment Analysis in Hotel Reviews

The development of technology causes things that done through meet in person or coming to a place can now be done by viewing information through gadgets or websites. Nowadays, to find out information about a place that provides accommodation for a vacation or a business visit, it can be done by accessing social media to see reviews from visitors who have visited the place, example, a hotel. Reviews given by hotel visitors are seen as more credible than information obtained from advertisements but the problem is that there are many reviews circulating on social media and it takes a time to analyze them. This study aims to analyze hotel reviews using the sentiment analysis method with the Support Vector Machine (SVM) approach. Sentiment analysis can be used to analyze the opinions of a large number of hotel visitors where it usually focuses on opinions that positive, negative and neutral. Before being analyzed with the support vector machine algorithm, 3 feature extraction methods will be used, namely Bag Of Words, TF-IDF and improvement TF-IDF to get the value of each word weight. The selection of these three methods is carried out by considering the influence of the presence of the same word feature in each review. In this comparison method, TF-IDF was found to be the best feature extraction method with 71.75% accuracy, 78.66% precision, 71.91% recall and 70.08% f1-score. The results obtained indicate that there are influence of features of the word in the hotel review data.
Object Relocation Visual Tracking Based on Histogram Filter and Siamese Network in Intelligent Transportation

Target detection and tracking algorithms are one of the key technologies in the field of autonomous driving in intelligent transportation, providing important sensing capabilities for vehicle localization and path planning. Siamese network-based trackers formulate the visual tracking mission as an image-matching process by regression and classification branches, which simplifies the network structure and improves the tracking accuracy. However, there remain many problems, as described below. (1) The lightweight neural networks decrease the feature representation ability. It is easy for the tracker to fail under the disturbing distractors (e.g., deformation and similar objects) or large changes in the viewing angle. (2) The tracker cannot adapt to variations of the object. (3) The tracker cannot reposition the object that has failed to track. To address these issues, we first propose a novel match filter arbiter based on the Euclidean distance histogram between the centers of multiple candidate objects to automatically determine whether the tracker fails. Secondly, the Hopcroft–Karp algorithm is introduced to select the winners from the dynamic template set through the backtracking process, and object relocation is achieved by comparing the Gradient Magnitude Similarity Deviation between the template and the winners. The experiments show that our method obtains better performance on several tracking benchmarks, i.e., OTB100, VOT2018, GOT-10k, and LaSOT, compared with state-of-the-art methods.
Evolutionary Computation

EC is a class of nature-inspired algorithms that maintains a population of candidate solutions (individuals) and evolves toward the best answer(s). It has been frequently used to solve difficult real-world optimization problems since it evolves numerous solutions at the same time, which contribute to the notable characteristic of EC as being frequently insensitiveness to local minimal.
Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection

The process of eliminating irrelevant, redundant and noisy features while trying to maintain less information loss is known as a feature selection problem. Given the vast amount of the textual data generated and shared on the internet such as news reports, articles, tweets and product reviews, the need for an effective text-feature selection method becomes increasingly important. Recently, stochastic optimization algorithms have been adopted to tackle this problem. However, the efficiency of these methods is decreased when tackling high-dimensional problems. This decrease could be attributed to premature convergence where the population diversity is not well maintained. As an innovative attempt, a cooperative Binary Bat Algorithm (BBACO) is proposed in this work to select the optimal text feature subset for classification purposes. The proposed BBACO uses a new mechanism to control the population’s diversity during the optimization process and to improve the performance of BBA-based text-feature selection method. This is achieved by dividing the dimension of the problem into several parts and optimizing each of them in a separate sub-population. To evaluate the generality and capability of the proposed method, three classifiers and two standard benchmark datasets in English, two in Malay and one in Arabic were used. The results show that the proposed method steadily improves the classification performance in comparison with other well-known feature selection methods. The improvement is obtained for all of the English, Malay and Arabic datasets which indicates the generality of the proposed method in terms of the dataset language.
Canonical Correlation Analysis With Low-Rank Learning for Image Representation

As a multivariate data analysis tool, canonical correlation analysis (CCA) has been widely used in computer vision and pattern recognition. However, CCA uses Euclidean distance as a metric, which is sensitive to noise or outliers in the data. Furthermore, CCA demands that the two training sets must have the same number of training samples, which limits the performance of CCA-based methods. To overcome these limitations of CCA, two novel canonical correlation learning methods based on low-rank learning are proposed in this paper for image representation, named robust canonical correlation analysis (robust-CCA) and low-rank representation canonical correlation analysis (LRR-CCA). By introducing two regular matrices, the training sample numbers of the two training datasets can be set as any values without any limitation in the two proposed methods. Specifically, robust-CCA uses low-rank learning to remove the noise in the data and extracts the maximization correlation features from the two learned clean data matrices. The nuclear norm and L1 -norm are used as constraints for the learned clean matrices and noise matrices, respectively. LRR-CCA introduces low-rank representation into CCA to ensure that the correlative features can be obtained in low-rank representation. To verify the performance of the proposed methods, five publicly image databases are used to conduct extensive experiments. The experimental results demonstrate the proposed methods outperform state-of-the-art CCA-based and low-rank learning methods.
Beam splitter as quantum coherence-maker

The aim of this work is to answer the question of how much quantum coherence a beam splitter is able to produce. To this end we consider as the variables under study both the amount of coherence of the input states as well as the beam splitter characteristics. We conclude that there is an optimal combination of these factors making the gain of coherence maximum. In addition, the two mode squeezed vacuum arises as the studied state most capable of gaining coherence when passing through a beam splitter. These results are qualitatively equivalent for the the l1-norm of coherence and for the relative entropy of coherence.