Reviewerly/exHarmony huggingface dataset:
Authenticating Endpoints and Vetting Connections in Residential Networks

The security of residential networks can vary greatly. These networks are often administrated by end-users who may lack security expertise or the resources to adequately defend their networks. Insecure residential networks provide attackers with opportunities to infiltrate systems and create a platform for launching powerful attacks. To address these issues, we introduce a new approach that uses software-defined networking (SDN) to allow home users to outsource their security maintenance to a cloud-based service provider. Using this architecture, we show how a novel network-based two-factor authentication approach can be used to protect Internet of Things devices. Our approach works without requiring modifications to end-devices. We further show how security modules can enforce protocol messages to limit the attack surface in vulnerable devices. Our analysis shows that the system is effective and adds less than 50 milliseconds of delay to the start of a connection with less than 100 microseconds of delay for subsequent packets.
Progressive sketching with instant previewing

Sketch-based modeling uses freeform strokes as basic modeling metaphor and the modeling process typically involves two phases: sketching and sculpting. This paper presents a method to enhance the sketching phase to have the features of both sketching and sculpting. The key idea of the method is progressive sketching with instant previewing, which has the capacity of intuitively creating complex initial shapes that interpolate all the sketched strokes. The process lets the user iteratively sketch and meanwhile updates the reconstructed 3D shape instantly, which enables the user to be aware of the up-to-date shape. The underlying technique supporting this process is a novel surface construction algorithm that produces 3D triangular mesh models typically with only a single connected component and gradual shape changes during iterative sketching as well. To demonstrate the capability of the method, a sketching-based modeling prototype is also developed, which includes both sketching and sculpting functions. Experiments show that the proposed method can allow users to intuitively and flexibly create and edit 3D models even with complex topology, which is usually difficult for conventional sketch-based modeling systems.
BATON: Batch One-Hop Personalized PageRanks with Efficiency and Accuracy

Personalized PageRank (PPR) is a classic measure of the relevance among different nodes in a graph, and has been applied in numerous systems, such as Twitter's Who-To-Follow and Pinterest's Related Pins. Existing work on PPR has mainly focused on three general types of queries, namely, single-pair PPR, single-source PPR, and all-pair PPR. However, we observe that there are applications that rely on a new query type (referred to as batch one-hop PPR), which takes as input a set S of source nodes and, for each nodes E ϵ S and each of s's neighbor v, asks for the PPR value of v with respect to s. None of the existing PPR algorithms is able to efficiently process batch one-hop queries, due to the inherent differences between batch one-hop PPR and the three general query types. To address the limitations of existing algorithms, this paper presents Baton, an algorithm for batch one-hop PPR that offers both strong theoretical guarantees and practical efficiency. Baton leverages the characteristics of one-hop PPR to avoid unnecessary computation, and it incorporates advanced mechanisms to improve the cost-effectiveness of PPR derivations. Extensive experiments on benchmark datasets show that Baton is up to three orders of magnitude faster than the state of the art, while offering the same accuracy.
Cyber Sickness in Industrial Virtual Reality Training

The paper presents results of experimental studies of how cyber sickness (also known as the simulator sickness) can influence basic tasks performed by users of industrial VR (virtual reality) training simulations. Two application cases are presented – a simple walk around a virtual environment using two different movement possibilities and a more complex training process with object interaction involved. They were tested with representatives of real target groups – engineers and employees of production companies. The used equipment involved mobile and stationary headsets. The obtained results confirm initial expectations about stationary headsets being generally more safe for use regarding the cyber sickness issue. It was also found that continuous artificially induced movement in a VR application is generally less comfortable for the users and therefore unadvised to employ in training scenarios.
ZCopy-Vhost: Replacing Data Copy With Page Remapping in Virtual Packet I/O

Virtualization technology is the core technology of cloud computing. While virtualization technology offers flexibility in many ways, it also introduces additional performance overhead. For network systems, it needs to provide additional virtual switching, the virtual packet I/O, and other functions. Providing these features requires a lot of CPU resources. As cloud services grow, more and more CPU resources are being used to provide virtualization support, which means that fewer CPUs can be sold to tenants. Therefore, cloud service providers have been constantly seeking more efficient virtualization solutions for network systems. In this paper, we identify one of the main reasons for the large consumption of CPU resources in the virtual networking system - copy and propose to use memory page remapping technology to eliminate the copy. Moreover, to adapt to the zero-copy technology, we have integrated the technology with a mature virtual switch software in the host and a userspace protocol stack in the virtual machine. These components together build a more efficient network system in virtual environments. The evaluations with microbenchmarks and macrobenchmark show that our system performs much better than the state-of-the-art solutions.
Adaptive fuzzy neural production network with MIMO-structure for the evaluation of technology efficiency

The paper presents an example of modeling the algorithm operation. The paper analyses the modified Wang and Mendel MIMO-architecture of the adaptive fuzzy neural production network with a logical conclusion. It is distinguished by the automatic generation of a set of fuzzy rules based on a fuzzy decision tree and a hybrid algorithm for parameter adaptation by the neural network (centers, widths of membership functions, and conclusions) starting from the leaf nodes to the root nodes of the tree.
Thumbnail Image Selection for VOD Services

The booming of video on demand (VOD) provides numerous TV series and movies for users to watch anywhere at any time. As the amount of video contents available for viewing grows explosively, thumbnail image representation of video works as a surrogate and facilitates quick and easy video retrieval. Unlike previous work, which considers representativeness as the main criterion for thumbnail image selection, the work described in this paper incorporates attractiveness as an additional criterion. Our idea is based on the observation that thumbnail image for VOD services should not only convey the gist of the video but also intrigue the users. We propose a two-stage method that efficiently utilizes visual features to select a thumbnail image from each TV series. The effectiveness of the proposed method is verified by a subjective test. The results provide further insight into the user preference.
Memorability‐based image compression

This study is concerned with achieving the image compression using the concept of memorability. The authors have used memorability of an image, as a perceptual measure while image coding. In the proposed approach, a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely, memorability prediction and image compression is introduced. The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features. Based on these features, the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map. These memorability map values are used for optimised image compression. In order to validate the results, an eye tracking experiment with human participants is performed. The comparative analysis shows that the memorability-based compression outperforms the state-of-the-art compression techniques.
On construction of a cloud storage system with heterogeneous software-defined storage technologies

Abstract With the rapid development of networks and Information technologies, cloud computing is not only becoming popular, the types of cloud services available are also increasing. Through cloud services, users can upload their requirements via the Internet to the cloud environment and receive responses following post-processing, for example, with cloud storage services. Software-Defined Storage (SDS) is a virtualization technology for cloud storage services. SDS uses software to integrate storage resources and to improve the accessibility and usability of storage services. Currently, there are many different open source projects available for SDS development. This work aims to utilize these open source projects to improve the efficiency of integration for hardware and software resources. In other words, in this work, we propose a cloud storage system that integrates various open source SDS software to make cloud storage services more compatible and user friendly. The cloud service systems can also be managed in a more convenient and flexible manner. The experimental results demonstrate the benefits of the proposed system.
Linked USDL Extension for Cloud Services Description

Cloud computing has become the most influential paradigm in recent years, both in industry and academia. A Cloud provider delivers Cloud services to businesses or individuals. However, each Cloud provider uses its own techniques to describe their Cloud services. It is therefore difficult to compare Cloud offers and then provide the appropriate service to the user. Especially that the Cloud services can provide the same functionalities, but they differ by their quality of service, price, Cloud characteristics, service credibility, and so on. The variety of these techniques is due to the lack of Cloud service description standardization. To deal with such issues, we propose in this paper a Cloud service description ontology that assists the Cloud service publication, discovery and selection processes. The proposed description will be extended from the Linked USDL language to describe Cloud services thanks to its expressiveness by covering four aspects namely technical, operational, business and semantic.
Isopleth

Online resources can help novice developers learn basic programming skills, but few resources support progressing from writing working code to learning professional web development practices. We address this gap by advancing Readily Available Learning Experiences, a conceptual approach for transforming all professional web applications into opportunities for authentic learning. This article presents Isopleth, a web-based platform that helps learners make sense of complex code constructs and hidden asynchronous relationships in professional web code. Isopleth embeds sensemaking scaffolds informed by the learning sciences to (1) expose hidden functional and event-driven relationships, (2) surface functionally related slices of code, and (3) support learners manipulating the provided code representations. To expose event-driven relationships, Isopleth implements a novel technique called Serialized Deanonymization to determine and visualize asynchronous functional relationships. To evaluate Isopleth, we conducted a case study across 12 professional websites and a user study with 14 junior and senior developers. Results show that Isopleth’s sensemaking scaffolds helped to surface implementation approaches in event binding, web application design, and complex interactive features across a range of complex professional web applications. Moreover, Isopleth helped junior developers improve the accuracy of their conceptual models of how features are implemented by 31% on average.
Sketch/Image-Based 3D Scene Retrieval: Benchmark, Algorithm, Evaluation

Sketch/Image-based 3D scene retrieval is to retrieve man-made 3D scene models given a user's hand-drawn 2D scene sketch or a 2D scene image usually captured by a camera. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from either non-realistic 2D scene sketches or realistic 2D scene images. Due to the intuitiveness in sketching and ubiquitous availability in image capturing, this research topic has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR entertainment. To boost this interesting and important research, we build the currently largest and most comprehensive 2D scene sketch/image-based 3D scene retrieval benchmark1, develop a convolutional neural network (CNN)-based 3D scene retrieval algorithm and finally conduct an evaluation on the benchmark.
English oral evaluation algorithm based on fuzzy measure and speech recognition

In oral English, for the pronunciation of continuous reading and easy to confuse, the difficulty to understand the pronunciation and so on, this kind of spoken pronunciation has a direct influence on the accuracy of computer speech recognition technology. Therefore, based on the fuzzy measure and speech recognition technology, in this paper, the English oral evaluation algorithm is studied. Based on fuzzy measure, the fuzzy comprehensive evaluation algorithm is discussed. This paper focuses on how to solve the problem that the fuzzy measure cannot be fused, the optimization and updating scheme proposed from the algorithm flow and evaluation model, and an effective oral reading evaluation model is established by effectively evaluating the different oral continuous state, giving the corresponding evaluation results. The simulation results show that the optimization of oral English evaluation algorithm can provide a scientific evaluation method and improvement strategy for oral teaching and improving oral learning ability.
Image Denoising via Multi-Scale Gated Fusion Network

Deep convolutional neural networks have made significant progress in image denoising. However, in most cases, denoising methods using a single-stream structure with a single kernel size do not perform so well in integrating complementary contextual information; owing to the lack of this type of information, they may fail to reconstruct fine textures and patterns. To address this problem, we propose a multi-scale gated fusion network (MGFN) for image denoising, which learns direct end-to-end mappings from corrupted images to clean images. Our proposed network consists of several multi-scale mutually-gated (MM) blocks. In each MM block, we incorporate dilated convolution into a merge-and-run (MR) module to exploit multi-scale features in an effective way and further recognize useful features by filtration via a gating mechanism. Moreover, we propose a simple but effective loss function named dropout-loss to train the network. The extensive experiments on benchmark datasets show that our proposed method can well recover textures, yielding favorable performance against other state-of-the-art methods.
Evaluating the Effect of Feedback from Different Computer Vision Processing Stages

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms.
Exploiting Aesthetic Features in Visual Contents for Movie Recommendation

As one of the most widely used recommender systems, movie recommendation plays an important role in our life. However, the data sparsity problem severely hinders the effectiveness of personalized movie recommendation, which requires more rich content information to be utilized. Posters and still frames, which directly display the visual contents of movies, have significant influences on movie recommendation. They not only reveal rich knowledge for understanding movies but also useful for understanding user preferences. However, existing recommendation methods rarely consider aesthetic features, which tell how the movie looks and feels, extracted from these pictures for the movie recommendation. To this end, in this paper, we propose an aesthetic-aware unified visual content matrix factorization (called UVMF-AES) to integrate visual feature learning and recommendation into a unified framework. Specifically, we first integrate the convolutional neural network (CNN) features and aesthetic features into probabilistic matrix factorization. Then we establish a unified optimization framework with these features for the movie recommendation. The experimental results on two real-world datasets show that our proposed method UVMF-AES is significantly superior to the state-of-the-art methods on movie recommendation.
Quasi single-frame measurements with phase-stepped ESPI

Phase-stepped ESPI measurement techniques typically require the acquisition of sets of speckle images before and after specimen deformation. This process limits the use of the technique to quasi-static specimens. A novel technique is proposed here where speckle images are measured in a continuous phase-stepped sequence and where interpolation of nearby images is used to infer all four phase-stepped images corresponding to each measured image. This process effectively creates a single-frame ESPI measurement technique that is suitable for observing moving objects. Small phase changes measured by a high-speed camera are observed incrementally between successive images, with cumulative phase changes determined by summation. This approach avoids the need for phase unwrapping, and so retains an absolute zero phase datum. The sequence of small phase changes also preserves good correlation between successive measurements, thus enabling successful cumulative phase measurements over large specimen displacements, even into the mm range. The phase visibility at each pixel is available within every measured image, thereby enabling the use of pixel repair strategies if desired.
On the Data Limitation of Small-State Stream Ciphers: Correlation Attacks on Fruit-80 and Plantlet

Many cryptographers have focused on lightweight cryptography, and a huge number of lightweight block ciphers have been proposed. On the other hand, designing lightweight stream ciphers is a challenging task due to the well-known security criteria, i.e., the state size of stream ciphers must be at least twice the key size. The designers of Sprout addressed this issue by involving the secret key not only in the initialization but also in the keystream generation, and the state size of such stream ciphers can be smaller than twice the key size. After the seminal work, some small-state stream ciphers have been proposed such as Fruit, Plantlet, and LIZARD. Unlike conventional stream ciphers, these small-state stream ciphers have the limitation of keystream bits that can be generated from the same key and IV pair. In this paper, our motivation is to show whether the data limitation claimed by the designers is proper or not. The correlation attack is one of the attack methods exploiting many keystream bits generated from the same key and IV pair, and we apply it to Fruit-80 and Plantlet. As a result, we can break the full Fruit-80, i.e., the designers’ data limitation is not sufficient. We can also recover the secret key of Plantlet if it allows about $$2^{53}$$ keystream bits from the same key and IV pair.
Speech encryption using finite precision chaotic maps based stream ciphers

Efficient and simple design of speech encryption-based stream cipher is produced and suggested in this paper with name finite precision chaotic map-based stream cipher (FPCM-SC) system. Two pseudo random bits generator (PRBG) are built by hybrid chaotic maps using finite precision format and named finite precision chaotic XOR based PRBG (FPCXOR-BRPB) and finite precision chaotic hopping based PRBG(FPCH-PRBG). The statistical security analysis and speech residual unintelligibility measures are presented for the two proposed systems. The results show that key space for FPCXOR-PRBG is 214*32 and for FPCH-PRBG is 216*32 that are large enough to resist against all kind of brute force attack. Also, residual unintelligibility measures show that the speech signal is unintelligible signal and can't be listen by an attacker.
SBRNE: An Improved Unified Framework for Social and Behavior Recommendations with Network Embedding

Recent years have witnessed the fast growing and ubiquity of social media which has significantly changed the social manner and information sharing in our daily life. Given a user, social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in social media applications. Despite the extensive studies, few existing work has addressed both tasks elegantly and effectively. In this paper, we propose an improved unified framework for Social and Behavior Recommendations with Network Embedding (SBRNE for short). With modeling social and behavior information simultaneously, SBRNE integrates social recommendation and behavior recommendation into a unified framework. By employing users' latent interests as a bridge, social and behavior information is modeled effectively to improve performance of social and behavior recommendations all together. In addition, an efficient network embedding procedure is introduced as a pre-training step for users' latent representations to improve effectiveness and efficiency of recommendation tasks. Extensive experiments on real-world datasets demonstrate the effectiveness of SBRNE.
User Studies on End-User Service Composition

Context: End-user service composition (EUSC) is a service-oriented paradigm that aims to empower end users and allow them to compose their own web applications from reusable service components. User studies have been used to evaluate EUSC tools and processes. Such an approach should benefit software development, because incorporating end users’ feedback into software development should make software more useful and usable. Problem: There is a gap in our understanding of what constitutes a user study and how a good user study should be designed, conducted, and reported. Goal: This article aims to address this gap. Method: The article presents a systematic review of 47 selected user studies for EUSC. Guided by a review framework, the article systematically and consistently assesses the focus, methodology and cohesion of each of these studies. Results: The article concludes that the focus of these studies is clear, but their methodology is incomplete and inadequate, their overall cohesion is poor. The findings lead to the development of a design framework and a set of questions for the design, reporting, and review of good user studies for EUSC. The detailed analysis and the insights obtained from the analysis should be applicable to the design of user studies for service-oriented systems as well and indeed for any user studies related to software artifacts.