Reviewerly/exHarmony huggingface dataset:
Conversational Voice Assistants and a Case Study of Long-Term Users: A Human Information Behaviours Perspective

This paper describes the human information behaviours of long-time users of voice assistants or chatbot software that focuses on voice interaction to retrieve information, and which operate on technologies that use artificial intelligence to understand and act upon the information requests of the user through a conversational interface. The paper reports on a case study based on 10 interviews with adult participants who have used voice assistants in their homes for at least six months. Interviews were semi-structured and focused on connecting to the users’ and their family’s experiences of using voice assistants in their daily lives. Insights from our findings focus on two areas, how the humanness of voice assistants and their perceived personality affect information retrieval, and secondly, the difficulties in transitioning from text-based information seeking to speech-based interactions. This paper makes a case for expanding human information behaviour studies to include interactive conversational information retrieval devices such as voice assistants.
Continuous monitoring with machine learning and interactive data visualization: An application to a healthcare payroll process

This paper presents a framework for proactive and intelligent continuous control monitoring (CCM) that helps management gain higher assurance over business processes and alleviate information overload. We adopt a design science approach towards systematically developing CCM artifacts, including operation and internal control violation display and multidimensional anomaly detection. We illustrate the design with an implementation project whereby a CPA firm, the firm's healthcare sector client, and the research team work together to improve the assurance provided by payroll reviews. This study contributes to the CCM literature by envisioning that interactive data visualization and machine learning technologies can alleviate information overload for management in CCM. Second, we provide real-world evidence on the improvement brought to economic and behavioral aspects of the control monitoring process compared to the traditional approach. We show that emerging technologies substantially improve the efficiency and effectiveness of risk assessment, anomaly detection, and loss prevention. We also contribute to control monitoring practice by providing guidance on artifact development and application for practitioners to follow.
A new feature selection method based on frequent and associated itemsets for text classification

Summary Feature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low‐quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this article focuses on the association analysis theory in data mining to select important features. In this study, a novel feature selection method based on frequent and associated itemsets (FS‐FAI) for text classification is proposed. FS‐FAI seeks to find relevant features and also takes feature interaction into account. Moreover, it uses association as a metric to evaluate the relativity between the target concept and feature(s). To evaluate the efficacy of the proposed method, several experiments were conducted on a BBC dataset from the BBC news website and SMS spam collection dataset from the UCI machine learning repository. The obtained results were compared to well‐known feature selection methods. The reported results demonstrated the effectiveness of the proposed feature selection method in selecting high‐quality features and in handling redundant information in text classification.
Traffic Monitoring from the Perspective of an Unmanned Aerial Vehicle

The paper is focused on the development of the experimental web-based solution for image processing from the perspective of an Unmanned Aerial Vehicle (UAV). Specifically, the research is carried out as part of the broader study on drone utilization in traffic at the Technical University of Kosice. This contribution explores the possibility of using the UAV as a tool to detect the temporal state of the traffic in multiple locations. Road traffic analysis is enabled through the detection of vehicles from the user-defined region of interest (ROI). Its content then serves as the input for motion detection, followed by the detection of vehicles using the YOLOv4 model. Detection of other types of objects is possible, thus making the system more universal. The vehicle is tracked after recognition in two consecutive frames. The tracking algorithm is based on the calculation of the Euclidean distance and the intersection of the rectangles. The experimental verification yields lower hardware requirements for CPU and GPU by about two FPS when using optimization techniques, such as ROI or reference dimensions of objects. The accuracy of detection and the subsequent tracking of cars reaches almost 100% while providing accurate trajectory determination.
DNAS: Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation

Deep learning methods, especially deep convolutional neural networks (DCNNs), have been widely used in high-resolution remote sensing image (HRSI) semantic segmentation. In literature, most successful DCNNs are artificially designed through a large number of experiments, which often consume lots of time and depend on rich domain knowledge. Recently, neural architecture search (NAS), as a direction for automatically designing network architectures, has achieved great success in different kinds of computer vision tasks. For HRSI semantic segmentation, NAS faces two major challenges: (1) The task’s high complexity degree, which is caused by the pixel-by-pixel prediction demand in semantic segmentation, leads to a rapid expansion of the search space; (2) HRSI semantic segmentation often needs to exploit long-range dependency (i.e., a large spatial context), which means the NAS technique requires a lot of display memory in the optimization process and can be tough to converge. With the aforementioned considerations in mind, we propose a new decoupling NAS (DNAS) framework to automatically design the network architecture for HRSI semantic segmentation. In DNAS, a hierarchical search space with three levels is recommended: path-level, connection-level, and cell-level. To adapt to this hierarchical search space, we devised a new decoupling search optimization strategy to decrease the memory occupation. More specifically, the search optimization strategy consists of three stages: (1) a light super-net (i.e., the specific search space) in the path-level space is trained to get the optimal path coding; (2) we endowed the optimal path with various cross-layer connections and it is trained to obtain the connection coding; (3) the super-net, which is initialized by path coding and connection coding, is populated with kinds of concrete cell operators and the optimal cell operators are finally determined. It is worth noting that the well-designed search space can cover various network candidates and the optimization process can be done efficiently. Extensive experiments on the publicly open GID and FU datasets showed that our DNAS outperformed the state-of-the-art methods, including artificial networks and NAS methods.
Identify Representative Samples by Conditional Random Field of Cancer Histology Images

Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment plan as well. To detect abnormality in histopathology images with prevailing patch-based convolutional neural networks (CNNs), contextual information often serves as a powerful cue. However, as whole-slide images (WSIs) are characterized by intense morphological heterogeneity and extensive tissue scale, a straightforward visual span to a larger context may not well capture the information closely associated with the focal patch. In this paper, we propose a novel pixel-offset based patch-location method to identify high-representative tissues, with a CNN backbone. Pathology Deformable Conditional Random Field (PDCRF) is proposed to learn the offsets and weights of neighboring contexts in a spatial-adaptive manner, to search for high-representative patches. A CNN structure with the localized patches as training input is then capable of consistently reaching superior classification outcomes for histology images. Overall, the proposed method has achieved state-of-the-art performance, in terms of the test classification accuracy improvement to the baseline by 1.15-2.60%, 0.78-1.78%, and 1.47-2.18% on TCGA public datasets of TCGA-STAD, TCGA-COAD, and TCGA-READ respectively. It also achieves 88.95% test accuracy and 0.920 test AUC on Camelyon 16. To show the effectiveness of the proposed framework on downstream tasks, we take a further step by incorporating an active learning model, which noticeably reduces the number of manual annotations by PDCRF to reach a parallel patch-based histology classifier.
Secure biometric-based access control scheme for future IoT-enabled cloud-assisted video surveillance system

We propose a new access control mechanism for video surveillance system based on user's biometrics, which allows to access real-time video surveillance data from deployed Internet of Things (IoT) smart devices. For this purpose, mutual authentication is performed between a user and accessed smart devices via the cloud server(s) and the session keys are then established to make secure communication. The proposed scheme allows accessing real time video and older videos that are in cloud servers, and also user's credential update phase. Through the security analysis, it has been shown that the proposed scheme is robust against a variety of potential passive/active attacks. A detailed comparative study shows the efficacy of the proposed scheme as compared to other existing solutions. Finally, the real testbed experiments have been carried out for secure surveillance system using Raspberry PI devices as IoT devices, user's devices and servers to show its practical application.
Quantum separability criteria based on realignment moments

Quantum entanglement is the core resource in quantum information processing and quantum computing. It is an significant challenge to effectively characterize the entanglement of quantum states. Recently, elegant separability criterion is presented in [Phys. Rev. Lett. 125, 200501 (2020)] by Elben et al. based on the first three partially transposed (PT) moments of density matrices. Then in [Phys. Rev. Lett. 127, 060504 (2021)] Yu \emph{et al.} proposed two general powerful criteria based on the PT moments. In this paper, based on the realignment operations of matrices we propose entanglement detection criteria in terms of such realignment moments. We show by detailed example that the realignment moments can also be used to identify quantum entanglement.
Real‐time recognition and warning of mask wearing based on improved YOLOv5 R6.1

Since the new crown epidemic, mask-wearing has become a new normal in people's work and life. The inspection mechanism for mask-wearing at the entrance and exit of public places is seriously insufficient. The phenomenon of “pick-up on entry” has led to the severe formalization of mask-wearing inspection. Manual detection of mask-wearing in an open and dynamic crowded environment is unrealistic, which is not only time-consuming and labor-intensive but also cannot achieve early warning throughout the entire process. In response to this problem, this paper proposes a real-time recognition and early warning method for mask-wearing in an open, dynamic, complex environment based on improved YOLOv5 R6.1. First, replacing the first Conv structure of the backbone network in the YOLOv5 R6.1 model with an improved Stem structure to minimize the computational overhead while improving the performance. Then by normalizing the data, the random erasure data expansion technique is used to enhance the antiocclusion robustness of the algorithm. Finally, according to the mask-wearing specification in the training data set, optimizing and adjusting the anchor box parameters of the YOLOv5 R6.1 model to improve the model's ability to recognize small targets. The experiments are based on open data sets, and the results show that the mean precision (mAP), precision, and recall of this method reach 92.9%, 94.1%, and 88.5% on average, and the average frames per second (FPS) reaches 117. Moreover, the mAP and FPS are improved by an average of 6.5% and 474% compared with algorithms based on RetinaNet, Attention-Retina, Single Shot multibox Detector, Fast-RCNN, YOLOv4, and YOLOv5.
Privacy‐preserving CNN feature extraction and retrieval over medical images

Online medicine diagnosis based on pathological images has been regarded as a pervasive method due to the advances in electronic healthcare and Internet of Things (IoT), however, it also causes storage and computing stress on the local IoT devices. To solve this problem, a nature way is to outsource images to cloud servers. Unfortunately, a range of security and privacy issues arise while delegating both storage and computing to the untrusted external servers. In this paper, we present a privacy-preserving feature extraction and retrieval scheme over medical images, which allows images storage and processing on two separate cloud servers. We share the images using secret sharing technology and design a set of secure two-party computation protocols between the two cloud servers. Then a privacy-preserving convolutional neural networks (CNN) framework is constructed to achieve feature extraction, classification and retrieval of images in the encrypted domain. We analyse and evaluate our scheme in terms of both security and efficiency. The results indicate that the proposed secure protocols in our scheme can significantly reduce the computation overhead while protecting the privacy of images as well as the data generated during execution on cloud servers and final results. The performance of our scheme in image feature extraction, classification and retrieval is at a similar level comparable to the scheme based on original CNN in plaintext.
HealthFort: A Cloud-Based eHealth System With Conditional Forward Transparency and Secure Provenance via Blockchain

In this paper, we propose a servers-aided password-based subsequent-key-locked encryption mechanism to ensure the confidentiality of outsourced electronic health records (EHRs). The encryption mechanism achieves conditional forward transparency: a doctor can only access a patient's EHRs related to the current diagnosis with the patient's delegation. It also achieves portability: to delegate a doctor for accessing a specific part of EHRs, the patient only needs to send one key (at most 256 bits) in addition to the delegation information to the doctor; the patient does not need to maintain any secret in a local device. Then, we propose a blockchain-based secure EHR provenance mechanism, where a data structure of EHR provenance record is designed to precisely reflect the EHRs’ provenance information; a smart contract on a public blockchain is deployed to secure both EHRs and the corresponding provenance records. Finally, we develop a cloud-based eHealth system, dubbed HealthFort, based on the two mechanisms. Security analysis and comprehensive performance evaluation are conducted to demonstrate that HealthFort is secure and efficient.
A Distance Metric for Uneven Clusters of Unsupervised K-Means Clustering Algorithm

In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origin, rather than a cluster closer to the origin. The proposed metric is based on the Canberra distances and it is useful for cases that require unequal size clusters. This metric can be used in connected autonomous vehicle wireless networks to classify mobile users such as pedestrians, cyclists, and vehicles. We use a combination of mathematical and exhaustive search to establish its validity as a true distance metric. We compare the K-Means algorithm using the proposed distance metric with five other distance metrics for comparison. These metrics include the Euclidean, Manhattan, Canberra, Chi-squared, and Clark distances. Simulation results depict the effectiveness of our proposed metric compared with the other distance metrics in both one-dimensional and two-dimensional randomly generated datasets. In this paper, we use three internal evaluation measures namely the Compactness, Sum of Squared Errors (SSE), and Silhouette measures. These measures are used to study the proper number of clusters for each of the K-Means algorithms and also select the best run among multiple centroid initializations. The elbow method and the local maximum approach are used alongside the evaluation measures to select the optimal number of clusters.
Logical Characterizations of Crisp Bisimulations in Fuzzy Description Logics

Fuzzy description logics (FDLs) are useful for dealing with fuzzy terminological knowledge for domains with linked data. Logical similarity or indiscernibility between individuals with respect to a given FDL is a fuzzy measure, which becomes crisp when the logic is extended with the Baaz projection operator. The measure is closely related to bisimulation. While logical indiscernibility is defined semantically, its corresponding notion based on bisimulation enables the computation. In this article, we study crisp bisimulations between fuzzy interpretations in FDLs with the Baaz projection operator under a general semantics based on an abstract algebra of fuzzy truth values. We define such bisimulations for a large class of FDLs with a rich set of well-known concept and role constructors, including qualified/unqualified number restrictions, nominals and the role constructors that correspond to the program constructors of propositional dynamic logic. We formulate and prove their logical characterizations, including the invariance of concepts under crisp bisimulations and the Hennessy–Milner property of crisp bisimulations. Such logical characterizations do not depend on a concrete semantics, such as the Gödel, Łukasiewicz, and product semantics. Based on crisp bisimulations, we also study indiscernibility of individuals in FDLs with the Baaz projection operator. An interesting consequence of our results states that, when restricting to the considered FDLs and image-finite fuzzy interpretations that are witnessed and modally saturated, indiscernibility of individuals is independent from the underlying algebra of fuzzy truth values in the case without number restrictions, and it is the same for both the Gödel and product semantics in the case with number restrictions.
Artificial Intelligence-Based Algorithm for Resources Allocation

In today's world the information should be available at any time and any place. From the user's point of view, these requirements are mandatory even if the traffic is growing and the user's experience is hyper-contextualized., personalized and mostly dynamic. From an application services provider point of view, in software defined networks and cloud intelligent managed data rooms, the networking architecture is required to combine central management and network residency. To provide the above mentioned services, the telecommunications networks have to provide maximum flexibility. The same mandatory functionality is required for clustered architectures used in cloud service providers' data rooms. This maximum resilience must involve two aspects: flexibility seen as accepting many different possible configurations and flexibility as the required time for these configurations to be applied to equipment in order to take effect. A possible solution to these requirements, is to separates the data plane forwarding the traffic from the control plane who's taking the decisions based on different criteria. In the meantime the control plane evolves and becomes a complete centralized management solution. This paper presents two new ideas and their implementation in an operational test data room. The first one consist of a data processing technique and algorithm that leads to dynamic resource allocation. The second implementation has as a main focus on the artificial intelligence AI-based trend prediction module that takes the decisions for the resources allocation algorithm. This research is part of a project developed by a joint team composed by cloud services provider in France and computer science specialists in Romania.
ImprovedGCN: An efficient and accurate recommendation system employing lightweight graph convolutional networks in social media

Graph Convolutional Networks (GCNs) have emerged as a hot topic of interest for collaborative filtering among researchers in the recent past. The research which exists in literature and is applied to recommendation does not analyze all the facets of GCN, as GCN is introduced for graph classification activities. It is observed that the two facets of GCNs namely, feature transformation and non-linear activation have a small influence on increasing the effectiveness of collaborative filtering (CF). Furthermore, the inclusion of these two facets increases the complexity of training and even decreases the recommendation performance. In this paper, a novel approach namely Improved Graph Convolutional Network (ImprovedGCN) has been proposed which only makes use of the important part of GCN termed neighborhood aggregation for CF. The aforesaid model can be implemented and trained which leads to significant improvements as compared to a similar approach termed Neural Graph Collaborative Filtering (NGCF).
Electric Bicycle Detection Based on Improved YOLOv5

Electric bicycle has become one of the main travel tools for residents because of its convenience, economy . However, on the occasion of the prosperity and development of the electric bicycle market, electric bicycles are frequently parked and placed disorderly, entering elevators and charging indoors in violation of regulations, and the number of casualties caused by electric bicycle fires has increased significantly. In order to improve the management efficiency of electric bicycle parking and charging, this paper presents an intelligent electric bicycle detection algorithm based on improved YOLOv5. We use GhostNet to improve the backbone network of YOLOv5s to make the model lighter. In order to make the prediction box more consistent with the real box, the loss function is changed to CIOU loss. And use QFocal Loss to learn better classification score and positioning quality. The experimental results show that the improved YOLOv5s algorithm proposed in this paper reduces the number of parameters by 23.6%, and the detection map of electric bicycle can reach 93.3%. Therefore, the algorithm can not only ensure the accuracy, but also save memory and computing resources, make the network model lighter and more suitable for the deployment of mobile terminals.