Reviewerly/exHarmony huggingface dataset:
A study of librarians' perceptions and adoption of Web 2.0 technologies in academic libraries in Akwa Ibom State, Nigeria

This paper surveyed the librarians' perceptions and adoption of Web 2.0 technologies in academic libraries in public institutions in Akwa Ibom State, Nigeria. The study specifically examined the constructs of librarians' perceptions of Web 2.0 Technologies in line with a model modified by the researcher; assessed the Web 2.0 tools adopted in the libraries; and identified the constraints to the adoption of the Web 2.0 technologies in the six (6) academic libraries in public institutions in Akwa Ibom State, Nigeria. The entire population was 60 professional librarians with at least a bachelor's degree in Library and Information Science. The study targeted professional librarians because it is assumed they have the qualifications, experience and exposure needed in the adoption of Web technologies. The population was used as sample since the size was small and manageable. The data collected using researcher-developed questionnaire was analyzed with descriptive statistics in form of frequency counts, simple percentage and chart. The findings revealed that majority of librarians have the perception that Web 2.0 technologies are useful and could enhance their job performance; that Web tools are clear and easy to understand; that fellow librarians, library users, host institutions, etc., expect them to use the tools. However, the result showed a low level of web technologies adoption across the libraries. The major constraints to adoption of Web 2.0 technologies in academic libraries in Akwa Ibom State include high cost of technology, poor access to web tools, budget constraints, inadequate training for librarians, and restrictive ICT policies. The recommendations made were proper funding, staff training and re-training, update of library facilities to include web 2.0 offerings, etc. Areas for further studies were also suggested.
Gaussian fuzzy theoretic analysis for variational learning of nested compositions

This paper introduces a variational analysis approach to the learning of a deep model formed via a nested composition of mappings. The fuzzy sets, being characterized by Gaussian type of membership functions, are used to represent unknown functions associated to the layers of the model. The learning of the deep model would require a quantification of the uncertainties on the signals across the layers of the deep model. We derive analytically the mathematical expressions for membership functions using variational optimization to quantify the uncertainties on variables. The most significant feature of the learning approach is that all of the unobserved variables and parameters, associated to the deep model, are averaged out where the averages are computed taking into account the uncertainties (on variables and parameters). The uncertainties are quantified by means of fuzzy sets with membership functions optimally learned from the observed data. A rigorous mathematical treatment of the learning problem results in the development of a competitive classification algorithm. The study is a theoretical contribution to the field of fuzzy machine learning, nevertheless, offering practical machine learning algorithms.
Improved YOLOv3 with duplex FPN for object detection based on deep learning

YOLOv3 is a deep learning-based real-time object detector and is mainly used in applications such as video surveillance and autonomous vehicles. In this paper, we proposed an improved YOLOv3 (You Only Look Once version 3) applied Duplex FPN, which enhanced large object detection by utilizing low-level feature information. The conventional YOLOv3 improved the small object detection performance by applying FPN (Feature Pyramid Networks) structure to YOLOv2. However, YOLOv3 with an FPN structure specialized in detecting small objects, so it is difficult to detect large objects. Therefore, this paper proposed an improved YOLOv3 applied Duplex FPN, which can utilize low-level location information in high-level feature maps instead of the existing FPN structure of YOLOv3. This improved the detection accuracy of large objects. Also, an extra detection layer was added to the top-level feature map to prevent failure of detection of parts of large objects. Further, dimension clusters of each detection layer were reassigned to learn quickly how to accurately detect objects. The proposed method was compared and analyzed in the PASCAL VOC dataset. The experimental results showed that the bounding box accuracy of large objects improved owing to the Duplex FPN and extra detection layer, and the proposed method succeeded in detecting large objects that the existing YOLOv3 did not.
Unified database for ship monitoring and diagnostic system

With the advancement of IOT and their integration on board the ship, the need to create and adopt a unified database to store the measurements made is becoming more and more acute. The existence of a unified and standardized such database will lead to easier and faster integration of different origin systems for monitoring, analysis, diagnostics and control in the future. In this article an analysis of the existing ones is made and an exemplary dynamic database is prepared to meet the set requirements.
A novel zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging

The authenticity and copyright protection of volumetric medical images has become extremely important when these images are distributed online for diagnosis and education purpose. Compared to the authenticity and copyright protection of conventional images, there are two additional challenges for protecting the volumetric medical images. On one hand, the content of the protected medical images must be distortion-free to ensure unbiased diagnosis. On the other hand, it requires enhanced distinguishability to avoid misclassification of non-protected images into the protected set because volumetric medical images of different persons in the same modality share similar visual structures. To address these issues, a novel multi-slice feature based zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging is proposed. In this scheme, ring statistics are deployed to guarantee both the watermarking distinguishability and robustness. In addition, an intra-slice variation based mechanism is designed to further enhance the watermarking distinguishability. Finally, a logistic-logistic system based chaotic map is used to ensure the watermarking security. Our experimental results demonstrate that the proposed scheme not only satisfies the lossless quality requirement but also ensures the watermarking distinguishability and robustness, which outperforms the state-of-the-art schemes.
Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images

Accurate and real-time detection of airplanes, cars, and ships in remote sensing images is an important but challenging task that plays an important role in both military and civilian life. The most challenging issues posed by this task are the intensive and tiny size of objects and the complexity of application scenarios. In this letter, we propose a multi-vision small object detector that can rapidly and accurately detect airplanes, cars, and ships in remote sensing images. We make the following three contributions: a multiscale residual block (MRB) is proposed, whereby dilated convolution is employed in a cascade residual block to capture multiscale context information, thus improving the feature representation ability of convolutional neural networks; a multiscale receptive field enhancement module (MRFEM) is proposed that combines features obtained using dilated convolution at different dilation rates to further enhance the multiscale feature representation of the remote sensing targets; and a multi-vision network (MVNet) is presented that uses multiple low-level feature maps with multi-branch convolution to detect small objects. Experimental results show that the proposed method can achieve a significant mean average precision (mAP) of 94.70% in remote sensing images and can run at 24 FPS on a single NVIDIA 1080Ti GPU.
Simple and scalable blockchain with privacy

The three primary requirements in privacy coin are sender anonymity, receiver anonymity, and transaction amount obscurity. In the process of achieving these three requirements, it can result in some side-effects. Some of the side-effects include large blockchain sizes, unpruneable blockchain, unverifiable total coin mintage, and complicated protocol. In this paper, we propose a coin privacy solution that fulfills these three requirements without side-effects. Compared to other solutions, our solution has a bigger anonymity set, easier to understand, and comes with prunable blockchain. However, transactions involving fractionation amount are rounded up to the nearest available denomination. This rounding up is acceptable because it is a similar problem with cash that been used for centuries. Our protocol is an extension of bitcoin P2PKH and CoinJoin features. The CoinJoin is secured with a CoinJoinID, which cryptographical hard to reproduce. We also have proven the security of the pruning process. Other related issues are also described in this paper.
Narrative Maps

Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.
Implicit Intention Communication for Activities of Daily Living of Elder/Disabled People to Improve Well-Being

The term activities of daily living (ADL) refers to those daily activities that a person can complete without outside assistance. Persons with hearing loss may have significant adverse effects on all developmental fields, including language, cognitive, social, emotional, and behavioral function. Because of these possible developmental delays, individuals who are deaf or hard to hear are at higher risk of having psychological health issues as opposed to their usually causing peer issues relative to their typically developing peers. A growing number of older people have become a severe problem, particularly those living alone and apart from the rest of their family. It is convincing yet challenging to track these men and a burden on their families. The deaf people reveal significant challenges in communicating with caregivers since barriers to communication in life have been linked to their poor physical and mental health. Often, the assessment of people’s mental and behavioral problems has traditionally relied on the questionnaire from home friends. Despite the need to pay high salaries for caregivers, society is getting complicated. Nevertheless, trouble to find well-trained caregivers, and most of them lack caregiving jobs. Furthermore, a shortage of caregivers will soon become a serious problem and it can be prevented by using non-verbal communication. This chapter proposes a novel method for understanding a human user’s implicit intent. This method defines the user ‘s purpose to identify the appropriate home environment activities/requirements that will help the elder with the eye-gaze movement point them out to fulfill his/her intent.
Comparison of ROS Local Planners with Differential Drive Heavy Robotic System

This work aims to analyze the performance of ROS local planners with a differential drive heavy robotic system. Intel Realsense D435i depth camera and T265 tracking camera are used as the primary sensor source for navigation with real-time appearance-based mapping and localization technique. This work has studied the performance of DWA, TEB, and EBand local planners under two experiments. Obtained results are used to analyze the local planners based on how well it follows the global planners, their obstacle avoidance capability, time consumption, goal-reaching accuracy, and the quality of motion generation. Obtained results indicate that DWA and TEB local planners are more suitable for the large differential drive robot setup.
BloTISRT

Computer and network technology is developing rapidly, meanwhile various cyber security incidents have occurred frequently. New vulnerabilities and many new security risks have emerged endlessly, which have caused serious threats to cyber security and business security. So the role and value of threat intelligence are increasing. However, there are still some problems of threat intelligence. The threat intelligence information can be tampered in the existing sharing mechanism. At the same time, there is no corresponding feedback mechanism for the quality of the threat intelligence information provided by the intelligence providers. There is a lack of credible judgment and corresponding incentive mechanism for threat intelligence providers. So, in order to solve these problems, a blockchain-based threat intelligence sharing and rating technology is proposed in this paper. Based on the characteristics of blockchain such as openness, consensus, autonomy and decentralization, trustlessness, non-tampering and traceability, the paper uses blockchain technology to build blocks of threat intelligence information, including IP address information, domain name information, URLs Information, security incident information, vulnerability information, threat intelligence source, user's evaluation, credibility of threat intelligence, contribution rate of threat intelligence, credibility of threat intelligence sources and contribution rate of threat intelligence sources. A threat intelligence sharing and rating system based on blockchain is designed in the paper, and the corresponding threat intelligence sharing method and rating method based on blockchain are given. It also proposes the smart contract for the threat intelligence sharing and rating in the paper. It can acquire and analyze the latest and most valuable threat intelligence information timely and effectively, so as to perform protection and emergency response timely, and promote the continuous and effective development of the closed loop of the entire threat intelligence ecosystem. The experimental environment and smart contract are designed, and its effectiveness is proved.
Process Synthesis and Design Problems Based on a Global Particle Swarm Optimization Algorithm

Many process synthesis and design problems in engineering are actually mixed integer nonlinear programming problems (MINLP), because they contain both continuous and integer variables. These problems are generally recognized to be complex and intractable by virtue of the combinatorial characteristic. In order to effectively solve process synthesis and design problems, a global particle swarm optimization (GPSO) algorithm is proposed in this paper. GPSO algorithm makes two improvements on original particle swarm optimization (PSO) algorithm: first, it introduces a global inertia weight, which is beneficial for improving its global searching capacity during the whole optimization process; second, it adopts a mutation operation with a small probability, which enables the GPSO algorithm to get rid of the local optimum easily. Simulation results show that the GPSO algorithm has high efficiency on finding the optimal solutions, and it has stronger convergence than the other four particle swarm optimization algorithms.
Multi‐scale Information Assembly for Image Matting

Abstract Image matting is a long‐standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images. We argue that the foreground objects can be represented by different‐level information, including the central bodies, large‐grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi‐scale information assembly framework (MSIA‐matte) to pull out high‐quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different‐level foreground expression, then combine them by our well‐designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA‐matte, and we can achieve state‐of‐the‐art performance compared to most existing matting networks.
A strategic decision-making architecture toward hybrid teams for dynamic competitive problems

Advances in artificial intelligence create new opportunities for computers to support humans as peers in hybrid teams in several complex problem-solving situations. This paper proposes a decision-making architecture for adaptively informing decisions in human-computer collaboration for large-scale competitive problems under dynamic environments. The proposed architecture integrates methods from sequence learning, model predictive control, and game theory. Computers in this architecture learn objectives and strategies from experimental data to support humans with strategic decisions while operational decisions are made by humans. The paper also presents data-driven methods for partitioning tasks among a team of computers in this architecture. The generalized methodology is illustrated on the real-time strategy game Starcraft II. The results from this application show that low-performing players can benefit from the game-theoretic decision support whereas this support can be overly conservative for high-performing players. The proposed approach provides safe though suboptimal suggestions particularly against an opponent with an unknown level of expertise. The results further show that problem solution with a team of computers based on non-intuitive task partitioning significantly improves the quality of decisions compared to an all-in-one solution with a single computer.
Unsupervised Meta-Domain Adaptation for Fashion Retrieval

Cross-domain fashion item retrieval naturally arises when unconstrained consumer images are used to query for fashion items in a collection of high-quality photographs provided by retailers. To perform this task, approaches typically leverage both consumer and shop domains from a given dataset to learn a domain invariant representation, allowing these images of different nature to be directly compared. When consumer images are not available beforehand, such training is impossible. In this paper, we focus on this challenging and yet practical scenario, and we propose instead to leverage representations learned for cross-domain retrieval from another source dataset and to adapt them to the target dataset for this particular setting. More precisely, we bypass the lack of consumer images and directly target the more challenging meta-domain gap which occurs between consumer images and shop images, independently of their dataset. Assuming that datasets share some similar fashion items, we cluster their shop images and leverage the clusters to automatically generate pseudo-labels. Those are used to associate consumer and shop images across datasets, which in turn allows to learn meta-domain-invariant representations suitable for cross-domain retrieval in the target dataset. The features and code are available at https://github.com/vivoutlaw/UDMA.
Multi-dimensional trust for context-aware services computing

The paper addresses the problem of trust management of cloud, fog and IoT services in dynamically changing environments. The continuous dynamic environment is one of the challenges that trustworthy services management in the cloud, fog and IoT settings faces. Services in such an environmental context have difficulty securing an acceptable quality of service (QoS). This article proposes a trust management framework that establishes service trust by considering the direct trust from the truster (subjective trust), aggregating referrals about the service in a collusion-resistant manner (objective trust), and bootstrapping new services. We introduce a subjective trust model based on the formalism of dependency networks to dynamically predict the provided QoS in response to context environment changes. The proposed approach leverages the dependency relations that exist among the QoS metrics and environmental context variables. The novelty at the subjective trust level lies in considering the dynamic cyclic dependency relations that enhances the prediction accuracy. However, subjective trust based on direct interactions could be insufficient to make the trust estimate credible. Hence, on top of the subjective layer, we propose an objective trust management model resilient to collusion attacks by leveraging the power of mass collaboration among referees. Finally, we propose a bootstrapping mechanism that is resilient to the white-washing attacks by observing the behaviours of newcomer services with no trust resources using the concept of social adoption to estimate their initial trust values. Experiments conducted on real-life and synthetic datasets demonstrate the effectiveness of our approach compared with state-of-the-art approaches. We used the statistical log score to assess the model’s prediction accuracy and employed the estimation error of the objective trust as indicated by referees relative to the one calculated by the multi-round simulation. The ROC (Receiver Operating Characteristic) curves are finally used to measure the accuracy of the classifier used in the proposed trust bootstrapping mechanism in providing accurate initial trust values. The main findings of the paper are around the new trust model of cloud, fog and IoT services considering their dynamically changing environments. The first finding is that the prediction of the provided QoS shows better results when it is dynamic and responds to context environment changes by leveraging the dynamic dependency network linking the QoS metrics and context variables of the environment. The second finding is that the objective trust performs better when it is resilient to collusion attacks by leveraging the power of mass collaboration among referees. The third finding is that the bootstrapping mechanism that observes the behaviours of new comer services with no trust resources using the concept of social adoption to estimate their initial trust values excels by being resilient to white watching attacks.