Reviewerly/exHarmony huggingface dataset:
Multi-scale Autoencoders in Autoencoder for Semantic Image Segmentation

Semantic image segmentation is essential for scene understanding. Several state-of-the-art deep learning-based approaches achieved remarkable results by increasing the network depth to improve performance. Using this principle, we introduce a novel encoder-decoder network architecture for semantic image segmentation of outdoor scenes called SAsiANet. SAsiANet utilizes multi-scale cascaded autoencoders at the decoder section of an autoencoder to achieve high accuracy pixel-wise prediction and involves exploiting features across multiple scales when upsampling the output of the encoder to obtain better spatial and contextual information effectively. The proposed network architecture is trained using the cross-entropy loss function but without incorporating any class balancing technique to the loss function. Our experimental results on two challenging outdoor scenes: the CamVid urban scenes dataset and the Freiburg forest dataset demonstrate that SAsiANet provides an effective way of producing accurate segmentation maps since it achieved state-of-the-art results on the test set of both datasets, $$72.40\%$$ mIoU, and $$89.90\%$$ mIoU, respectively.
Hybrid Recommender System via Personalized Users’ Context

Abstract In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit the user’s available time, location, mood and emotions. Conducted experiments for evaluation showed that the proposed method outperforms the other baselines.
A regularized point-to-manifold distance metric for multi-view multi-manifold learning

In this paper, we propose a regularized point-to-manifold distance metric to measure the distance between the unknown query object and object-specific manifolds for the task multi-view multi-manifold learning. Our metric determine the class of query object based on the class of objects that have smallest average geodesic distance from it. To do this, we propose our proposed method with the two features: 1. We automatically discover the object-specific manifolds. In this section, we focus on the fundamental problem of efficiently selecting a uniform class-consistent neighbors from all available poses for graph-based multi-manifold learning methods in a supervised manner. Also, we extract the most distinctive exemplars from the manifold of each object that cover the possible variations on pose angles. To make a distinction between some object-specific pose-inconsistent and object-inconsistent pose-consistent that may be very close, we utilize the total variation regularized least squares problem to representing each object in a weighted sum of its class-consistent neighbors under different poses. 2. We use the information of k objects to decide about the class of query object. We measure the distance between the unknown object and k exemplars of each object-specific manifold to find the closest manifold as the class of query object. Numerical experiments on several benchmark multi-view datasets are reported, which provide excellent support to the proposed methods. In the mean, our neighborhood graph can improve the SH-NGC and UTDTV, as new supervised multi-manifold learning and unsupervised multi-view multi-manifold learning methods, more than 2.6 and 7%, respectively. Also, in object recognition, our proposed method achieves more than 5% better results respect to the best result of state-of-the-art graph-based manifold learning methods.
Do Ethics Matter in Persuasive Technology?

This paper aims to discuss how ethics has been addressed within the persuasive technology field and to explore whether ethics is generally applied in persuasive technology (PT) or simply recognized by academics as an important perspective. The paper is based on a literature review of the past 13 years of Persuasive Technology conference papers. The themes identified from the literature review are presented along with summaries of defining works within the field which have contributed to the discussion of ethics. This is followed by a discussion and reflection on the findings of the literature review. Finally, we conclude that ethics does matter but we argue that ethics has not been adequately addressed in the field of PT and that ethical considerations regarding the rights of the designers need to be researched.
The Applications of Model Driven Architecture (MDA) in Wireless Sensor Networks (WSN): Techniques and Tools

Wireless Sensor Networks (WSNs) comprise several sensor nodes that work under certain operational constraints. The traditional software development approaches do not perform well while dealing with the complexity and real time properties of WSNs. Consequently, Model Driven Architecture (MDA) is commonly applied in WSN to verify the required system constraints in preliminary development periods. As MDA is highly suitable development approach for WSN, there is a strong need to explore and summarize the latest MDA trends in the field of WSN. Therefore, this article performs a Systematic Literature Review (SLR) to identify 27 research studies available during 2013-2018. This leads to classify the recognize studies into four MDA categories and five WSN groups. Moreover, 24 available tools are identified and organized into Model-driven (10), WSN-related (9) and other (5) groups. Furthermore, 12 tools developed by the researchers through the combination of MDA and WSN concepts are presented. In addition, MDA based algorithms (2) and protocols (2) for WSN are presented. Finally, comparative analysis of developed/proposed tools is performed to analyze the benefits and limitations of MDA for WSN. It is concluded that the major MDA attributes like reusability and early design verification are fully exploited in the domain of WSN. However, it is always challenging to choose right modeling and transformation approaches due to the diverse characteristics of WSN.
Dual Anti-counterfeiting of QR Code Based on Information Encryption and Digital Watermarking

The QR code is widely used in modern internet life as its fast speed of response, large capacity of information and the wide range of coding. Therefore, it’s an important means to improve the anti-counterfeiting performance of QR code and prevent QR code from being copied and tampered in order to improve the security of information. Based on the coding principle and identification method of two-dimensional codes, this paper proposes a dual anti-counterfeiting method with information encryption and digital watermarking for QR code. Firstly, an encryption algorithm based on RSA is used to encrypt authorization information. Then, a corresponding watermark image is generated based on the encrypted information. Finally, an anti-print embedding and extraction image watermarking method combined with discrete wavelet transform (DWT) and singular value decomposition (SVD) is introduced. The extracted watermark and decrypted information are compared and verified to achieve the dual security of QR code. The result of the attack experiment shows that the digital watermark has strong robustness and invisibility. The anti-counterfeit test results verified the feasibility of the proposed method.
Assessment of Faster R-CNN in Man-Machine Collaborative Search

With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when do such expert systems enhance human performance in a fine-grained small target visual search task. We set up a 2 session factorial experimental design in which humans visually search for a target with and without a Deep Learning (DL) expert system. We evaluate human changes of target detection performance and eye-movements in the presence of the DL system. We find that performance improvements with the DL system (computed via a Faster R-CNN with a VGG16) interacts with observer's perceptual abilities (e.g., sensitivity). The main results include: 1) The DL system reduces the False Alarm rate per Image on average across observer groups of both high/low sensitivity; 2) Only human observers with high sensitivity perform better than the DL system, while the low sensitivity group does not surpass individual DL system performance, even when aided with the DL system itself; 3) Increases in number of trials and decrease in viewing time were mainly driven by the DL system only for the low sensitivity group. 4) The DL system aids the human observer to fixate at a target by the 3rd fixation. These results provide insights of the benefits and limitations of deep learning systems that are collaborative or competitive with humans.
Motion Planning and Control with Randomized Payloads Using Deep Reinforcement Learning

In this study, we present a unified motion planner with low-level controller for continuous control of a differential drive mobile robot under variable payload values. Our deep reinforcement agent takes 11 dimensional state vector as input and calculates each wheel's torque value as a 2 dimensional output vector. These torque values are fed into the dynamic model of the robot, and lastly steering commands are gathered. In previous studies, intersection navigation solutions that uses deep - RL methods, have not been considered with variable payloads. Our study is focused specifically on service robotic applications where payload is subject to change. To the best of our knowledge, this is the first study in the literature that investigates intersection - navigation problem under variable payloads using deep-RL. In this paper, deep-RL based motion planning is performed by considering both kinematic and dynamic constraints. According to the simulations in a dynamic environment, the agent successfully navigates to target with 98.2% success rate in test time with unseen payload masses during training. Another agent is also trained without payload randomization for comparison. Results show that our agent outperforms the other agent, that is not aware of its own payload, with more than 40% gap.
Evaluation of wavefront errors introduced by pattern distortion of computer-generated holograms

The computer-generated holograms (CGH) are the core component to solve the problem of complex surface measurement. Pattern distortion is an important error source that affects its testing accuracy. This paper presents an evaluation method for the influence of pattern distortion on wavefront error. Firstly, the technical means of characterizing pattern information of CGH by optical element detection system was expounded. Then, the simulation model of wavefront error distribution was established to study the effect of pattern distortion on CGH testing accuracy. Finally, we verified the feasibility of the evaluation method based on the experimental research.
Evolutionary Algorithms for Convolutional Neural Network Visualisation

Deep Learning is based on deep neural networks trained over huge sets of examples. It enabled computers to compete with—or even outperform—humans at many tasks, from playing Go to driving vehicules. Still, it remains hard to understand how these networks actually operate. While an observer sees any individual local behaviour, he gets little insight about their global decision-making process. However, there is a class of neural networks widely used for image processing, convolutional networks, where each layer contains features working in parallel. By their structure, these features keep some spatial information across a network’s layers. Visualisation of this spatial information at different locations in a network, notably on input data that maximise the activation of a given feature, can give insights on the way the model works. This paper investigates the use of Evolutionary Algorithms to evolve such input images that maximise feature activation. Compared with some pre-existing approaches, ours seems currently computationally heavier but with a wider applicability.
The Design and Use of Assessment Frameworks in Digital Curation

To understand and improve their current abilities and maturity, organizations use diagnostic instruments such as maturity models and other assessment frameworks. Increasing numbers of these are being developed in digital curation. Their central role in strategic decision making raises the need to evaluate their fitness for this purpose and develop guidelines for their design and evaluation. A comprehensive review of assessment frameworks, however, found little evidence that existing assessment frameworks have been evaluated systematically, and no methods for their evaluation. This article proposes a new methodology for evaluating the design and use of assessment frameworks. It builds on prior research on maturity models and combines analytic and empirical evaluation methods to explain how the design of assessment frameworks influences their application in practice, and how the design process can effectively take this into account. We present the evaluation methodology and its application to two frameworks. The evaluation results lead to guidelines for the design process of assessment frameworks in digital curation. The methodology provides insights to the designers of the evaluated frameworks that they can consider in future revisions; methodical guidance for researchers in the field; and practical insights and words of caution to organizations keen on diagnosing their abilities.
Bi-objective Analysis of an Adaptive Secure Data Storage in a Multi-cloud

Security issues related to cloud computing as well as all solutions proposed in the literature are one of the high topics for research. However, there are many unsolved problems regarded to cloud storage. In this paper, we focused on an adaptive model of data storage based on Secret Sharing Schemes (SSS) and Residue Number System (RNS). We proposed five strategies to minimize information loss and time to data upload and download into the cloud. We evaluate these strategies on seven Cloud Storage Providers (CSPs). We study a correlation of system settings with the probability of information loss, data redundancy, speed of access to CSPs, and encoding/decoding speeds We demonstrate that strategies that consider CSPs with the best upload access speeds and then, after storing, migrate to the CSPs with the least probability of information loss or best download speeds show better performance behavior.
Robust and fast object tracking via co-trained adaptive correlation filter

Recently, convolutional neural network (CNN) model based on correlation filter has achieved great success in object tracking. However, most of these methods relying on correlation filter train many classifiers individually, and fail to cope with significant appearance change of targets and complex challenging environment. In this paper, we propose a robust and fast object tracking method by training only two discriminative classifiers jointly. It poses a Tikhonov regularization on the ridge regression where each tracker tries to correct the other one during tracking. In addition, an online detector is adopted to re-detect objects in case of tracking failures and a target pyramid is builded to estimate the optimal scale of target. Extensive experiments on OTB-2013, OTB-2015 and VOT-2016 demonstrate that our method outperforms other state-of-the-art trackers with fast speed.
Intelligent construction of English-Chinese bilingual context model based on CBR

To promote the development of English-Chinese bilingual context, a CBR-based bilingual contextual intelligent model is constructed. This paper first expounds the development history of the bilingual context in English and Chinese. Concerning contextual knowledge, a CBR-based bilingual context syst em is designed. Using machine learning theory, the bilingual context of English and Chinese is analyzed, which provides a good environment for language learning. In this paper, a data mining algorithm is applied to optimize and update the algorithm flow and evaluation model. By evaluating the development pattern of bilingual context effectively, an effective recognition model is established. In the bilingual context intelligent model test, the practicability and efficiency test of the model shows that CBR has good practicability and is worth further promotion.
Cost-sharing models: experience of the Anatolian University Libraries Consortium

This study examines the cost-sharing models used by the Anatolian University Libraries Consortium (ANKOS) to determine the amounts that university libraries pay for each consortial subscription. A survey was conducted among library directors and managers of ANKOS member universities to evaluate cost-sharing models in order to create improved models. The data obtained from the questionnaire were analyzed in SPSS, the first time that these cost-sharing models have been assessed on the national level. The results of the study are presented for the benefit of other consortia and scientists/researchers and library professionals working in the field. This article takes a historical and descriptive approach and uses statistical methods to analyze the results of the survey. It also focuses on obtaining objective results from the Consortium members' feedback. As with other library consortia all over the world, many different cost-sharing models have been used by ANKOS. As a result of this study, the most suitable models have been determined for different scales of academic libraries. In negotiations with publishers and vendors, priority will be given to price models preferred by member institutions. The material presented should be useful for all academic librarians concerned with consortium systems, cost-sharing, and e-resource management, especially in the developing world. This paper also provides some key points for academic librarians in the process of establishing a consortium. This paper is an example of successful academic library consortium activities in a developing country and examines, in detail, the developments, opportunities, and challenges experienced since the establishment of the consortium.
Network Location Privacy Protection with Multi-access Edge Computing

Real world incidents show leaking of communication and location privacy can lead to serious consequence. Many research efforts have tried to anonymize individual user from aggregated location data. However, just the communication between the mobile user and their peers could reveal important privacy of the mobile user, such as location history. In this paper, we proposed a scalable system built with Multi-access Edge (MEC) that can hide the mobile user’s network location and traffic from communication peers. This system creates a dynamic distributed proxy network for each mobile user to minimize performance overhead and operation cost.
Designing Representations of Behavioral Data with Blended Causality: An Approach to Interventions for Lifestyle Habits

Many personal informatics systems present users’ behavioral data in numbers or graphs for their reflection, which may not be effective on a daily basis because people do not always act like data scientists. Representation of behavioral data in virtual environments can provide information at a glance. Grounded in conceptual blending theory, insights from social psychology, and existing persuasive design principles, this article is conceptual-theoretical. It argues that representations should be designed like virtual consequences of behavior and related to users’ existing knowledge of comparable cause-effect relationships in order to prompt one’s imaginative beliefs about the behavioral-virtual causality. It proposes a framework that guides designing representations of behavioral data, including (1) identifying scenarios with comparable causality, (2) examining and grounding the mappings in embodied experiences, (3) performing blends between the behavior and the identified scenario, with different virtual consequences corresponding to different user behaviors, and (4) rendering virtual consequences as feedback that dynamically anchors the scenario for similar blends in users. Design cases are presented and analyzed to demonstrate how embodied mappings can be constructed for interventions for lifestyle habits.
Why Joanie Can Encrypt

Email privacy is of crucial importance. Existing email encryption approaches are comprehensive but seldom used due to their complexity and inconvenience. We take a new approach to simplify email encryption and improve its usability by implementing receiver-controlled encryption: newly received messages are transparently downloaded and encrypted to a locally-generated key; the original message is then replaced. To avoid the problem of moving a single private key between devices, we implement per-device key pairs: only public keys need be synchronized via a simple verification step. Compromising an email account or server only provides access to encrypted emails. We implemented this scheme on several platforms, showing it works with PGP and S/MIME, is compatible with widely used mail clients and email services including Gmail, has acceptable overhead, and that users consider it intuitive and easy to use.
A privacy-preserving certificateless two-party authenticated key exchange protocol without bilinear pairing for mobile-commerce applications

In recent times, several three-party authenticated key exchange (3PAKE) and two-party authenticated key exchange (2PAKE) protocols have been proposed to establish secret session key for confidentiality of transactions among clients in mobile commerce (m-commerce) environments. However, the involvement of a trusted server (TS) in every communication as well as the numerous communication rounds and many message exchanges in 3PAKE make them unsuitable for m-commerce applications. In this work, we propose an efficient and privacy-preserving 2PAKE protocol based on certificateless public key cryptography using elliptic curve cryptography with only one communication round and one message exchange between two clients. The protocol is provably secure, autonomous and provides conditional, location and identity privacies. The performance analysis shows that the proposed protocol has better efficiency than the existing related protocols in terms of computation cost and communication overheads.
Strong Adequacy and Untyped Full-Abstraction for Probabilistic Coherence Spaces

Abstract We consider the probabilistic untyped lambda-calculus and prove a stronger form of the adequacy property for probabilistic coherence spaces (PCoh), showing how the denotation of a term statistically distributes over the denotations of its head-normal forms. We use this result to state a precise correspondence between PCoh and a notion of probabilistic Nakajima trees, recently introduced by Leventis in order to prove a separation theorem. As a consequence, we get full abstraction for PCoh. This latter result has already been mentioned as a corollary of Clairambault and Paquet’s full abstraction theorem for probabilistic concurrent games. Our approach allows to prove the property directly, without the need of a third model.