Reviewerly/exHarmony huggingface dataset:
Security Analysis of Ye et al. Authentication Protocol for Internet of Things

Internet of Things (IoT) is a muti-layer technology that allows to devices (sensors and actuators) to connect and exchange data. In the last decade, IoT has known a great growth rapidly. As result of this exponential development, the collected data flows from one devices to others. IoT offers an opportunity to address some daily life problems such as water quality, air pollution, etc. Despite of the importance of the security to secure the sensitive data and guaranty the privacy in an open world, the security of IoT has not received mush importance and it has not in priorities of developers. Recently, Ye et al. have proposed an authentication and key agreement protocol for the perception layer of IoT. Then, they showed that their protocol is efficient and secured against various attacks. Nevertheless, in this paper, we demonstrated that Ye et al.'s protocol suffers from many problems. In the other hand, we suggest some solutions that can be applied to deal with discovered problems and enhance the protocol.
Game of protocols: Is QUIC ready for prime time streaming?

Summary Quick User Datagram Protocol (UDP) Internet Connections (QUIC) is an experimental and low‐latency transport protocol proposed by Google, which is still being improved and specified in the Internet Engineering Task Force (IETF). The viewer's quality of experience (QoE) in HTTP adaptive streaming (HAS) applications may be improved with the help of QUIC's low‐latency, improved congestion control, and multiplexing features. We measured the streaming performance of QUIC on wireless and cellular networks in order to understand whether the problems that occur when running HTTP over TCP can be reduced by using HTTP over QUIC. The performance of QUIC was tested in the presence of network interface changes caused by the mobility of the viewer. We observed that QUIC resulted in quicker start of media streams, better streaming, and seeking experience, especially during the higher levels of congestion in the network and had a better performance than TCP when the viewer was mobile and switched between the wireless networks. Furthermore, we measured QUIC's performance in an emulated network that had a various amount of losses and delays to evaluate how QUIC's multiplexing feature would be beneficial for HAS applications. We compared the performance of HAS applications using multiplexing video streams with HTTP/1.1 over multiple TCP connections to HTTP/2 over one TCP connection and to QUIC over one UDP connection. To that effect, we observed that QUIC provided better performance than TCP on a network that had large delays. However, QUIC did not provide a significant improvement when the loss rate was large. Finally, we analyzed the performance of the congestion control mechanisms implemented by QUIC and TCP, and tested their ability to provide fairness among streaming clients. We found that QUIC always provided fairness among QUIC flows, but was not always fair to TCP.
Layered approach for improving the quality of free-viewpoint depth-image-based rendering images

In free-viewpoint rendering systems, one of the most challenging goals is the creation of virtual views based on available color texture (RGB) and depth data. Conventional depth-image-based rendering (DIBR) approaches have assumed that the virtual camera can only be displaced horizontally, thus leading to fairly simple disocclusion artifacts. However, in free-viewpoint DIBR, the virtual camera can be positioned in an arbitrary way and the respective disocclusion artifacts can exhibit complicated anisotropic appearances. Consequently, conventional approaches for compensating disocclusion holes usually fail in such arbitrary camera motion. We present a disocclusion compensation technique based on texture inpainting. We propose a layered representation of both the color and depth images in local foreground, background, and undefined segments (a trimap). This representation allows for employing an efficient alpha-matting approach for reconstructing the underlying opacity layer followed by a background compensation and layered rendering. The performance of the proposed method is evaluated with respect to the state-of-the-art through objective and subjective tests. The achieved results, especially for large camera displacements, outperform the state-of-the-art. Those results assess the effectiveness of the proposed method and highlight the need for new quality metrics able to address the impairments of this type of content.
Understanding pilot response to flight safety events using categorisation theory

Categorisation theory explains our ability to recognise events in terms of a similarity overlap between either a prototypical, ideal case or a stored exemplar derived from experience. Evidence from aviation accident reports indicate that pilots are not always able to recognise flight safety events in real-time and this can lead to undesirable pilot behaviour. Flight safety events may not always arise in recognisable formats, especially as rare and unusual cue combinations are possible. Correspondence with prototypes or exemplars may be weak, creating borderline cases and harming recognition. In this article we extend categorisation theory to develop a new framework which characterises flight safety events. We model three case studies using the new framework to demonstrate how categorisation theory can be used to understand flight safety events of different types. Finally, we propose a roadmap for future research and discuss how categorisation theory could be applied to training or the organisation of flight crew reference material to improve response to inflight events.
Latin square experiment design in R

In this paper we will describe design of experiment by latin square method. Latin square is statistical test which is used in planning of experiment and is one of most accurate method. Programming language R is a tool which can be used for statistical tests and graphics. It has a wide range of applications both in research and practice. A practical example will be presented in this paper and its processing in the classical approach and in the programming language R.
The search of conditional outliers

There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly for two different purposes. On one hand, outliers are considered error measurement observations that should be rem oved from the analysis, e.g. robust statistics. On the other hand, outliers are the interesting observations, like in fraud detection, and should be modelled by some learning method. In this work, we start from the observation that outliers are affected by the so-called simpson paradox: a trend that appears in different groups of data but disappears or reverses when these groups are combined. Given a data set, we learn a regression tree. The tree grows by partitioning the data into groups more and more homogeneous of the target variable. At each partition defined by the tree, we apply a box plot on the target variable to detect outliers. We would expect that the deeper nodes of the tree would contain less and less outliers. We observe that some points previously signalled as outliers are no more signalled as such, but new outliers appear.
Safe and Efficient Implementation of a Security System on ARM using Intra-level Privilege Separation

Security monitoring has long been considered as a fundamental mechanism to mitigate the damage of a security attack. Recently, intra-level security systems have been proposed that can efficiently and securely monitor system software without any involvement of more privileged entity. Unfortunately, there exists no full intra-level security system that can universally operate at any privilege level on ARM. However, as malware and attacks increase against virtually every level of privileged software including an OS, a hypervisor, and even the highest privileged software armored by TrustZone, we have been motivated to develop an intra-level security system, named Hilps . Hilps realizes true intra-level scheme in all these levels of privileged software on ARM by elaborately exploiting a new hardware feature of ARM’s latest 64-bit architecture, called TxSZ, that enables elastic adjustment of the accessible virtual address range. Furthermore, Hilps newly supports the sandbox mechanism that provides security tools with individually isolated execution environments, thereby minimizing security threats from untrusted security tools. We have implemented a prototype of Hilps on a real machine. The experimental results demonstrate that Hilps is quite promising for practical use in real deployments.
Research on sports video image based on fuzzy algorithms

With the rapid development of network and multimedia technology, a large number of sports and national fitness information are stored in various fitness guidance systems in the form of video and pictures. In order to better promote public fitness and facilitate learning and viewing, sports video has a variety of needs of editing, segmentation and integration. Aiming at the shortcomings of current sports video image segmentation methods, such as rough segmentation results and high spatial distortion rate, a sports video image segmentation method based on fuzzy clustering algorithm is proposed. This paper introduces the basic theory of fuzzy clustering algorithm, establishes second-order fuzzy attributes with normal distribution and gray value by means of time-domain difference images, assigns the fuzzy attribute S membership function, then performs fuzzy clustering on time-domain difference images, and obtains the segmentation results of moving video images by edge detection. The experimental results show that the method has high spatial accuracy, good noise iteration performance and low spatial distortion rate, and can accurately segment complex moving video images to obtain high-definition images.
Aesop: A visual storytelling platform for conversational AI and common sense grounding

We present a new collaborative visual storytelling platform, Aesop, for direction and animation. Our system operates in two main modes, common sense grounding (annotation) and conversation. The Aesop system senses the human state and input using a natural language parser and human gesture monitorin g for natural interactions. The interface consists of a 3D animation software and a web controller to interact with the internal state of the system. For knowledge representation, we formulate novel knowledge graphs which enable spatio-temporal event representation. Aesop thus enables 3D spatial and temporal reasoning which are both essential for storytelling. Finally, the system utilizes a dialog manager to track the conversation state and manage goals. Aesop provides a rich platform that enables research in language, gestures, vision, and planning in the context of storytelling.
Demonstration of Advanced Open WDM Operations and Analytics, based on an Application-Extensible, Declarative, Data Model Abstracted Instrumentation Platform

We demonstrate a novel application-flexible data-model-abstracted and declarative software instrumentation platform that advances open-WDM multivendor operations and analytics. We validate its functional extensibility by implementing software-agent-based service provisioning and monitoring, and leveraging multiple open-source technologies.
Experimental study of photovoltaic panel mounting configurations for tube-shaped structures

Traditionally, solar photovoltaic panels have been installed on the top of buildings and oriented in a specific direction based on latitude. In modern structures, photovoltaic panels are being installed on different parts of buildings, resulting in panels mounted at various angles. This work investigates various photovoltaic panel mounting configurations on tube-shaped structures intended for emerging high-speed transportation systems operating in the north-south direction. The photovoltaic panel cases are compared in terms of power profile, total energy capture, energy storage system capacity, and equipment cost. Results are based on experimental data taken in Ulsan, Korea. Analysis and results indicate that the photovoltaic mounting case with one panel on the top of the tube at 0°, two adjacent panels at 45°, and two subsequent panels on the side of the tube at 90° yields the highest energy per investment cost.
A distant supervision method based on paradigmatic relations for learning word embeddings

Word embeddings learned on external resources have succeeded in improving many NLP tasks. However, existing embedding models still face challenges in situations where fine-gained semantic information is required, e.g., distinguishing antonyms from synonyms. In this paper, a distant supervision method is proposed to guide the training process by introducing semantic knowledge in a thesaurus. Specifically, the proposed model shortens the distance between target word and its synonyms by controlling the movements of them in both unidirectional and bidirectional, yielding three different models, namely Unidirectional Movement of Target Model (UMT), Unidirectional Movement of Synonyms Model (UMS) and Bidirectional Movement of Target and Synonyms Model (BMTS). Extensive computational experiments have been conducted, and results are collected for analysis purpose. The results show that the proposed models not only efficiently capture semantic information of antonyms but also achieve significant improvements in both intrinsic and extrinsic evaluation tasks. To validate the performance of the proposed models (UMT, UMS and BMTS), results are compared against well-known models, namely Skip-gram, JointRCM, WE-TD and dict2vec. The performances of the proposed models are evaluated on four tasks (benchmarks): word analogy (intrinsic), synonym-antonym detection (intrinsic), sentence matching (extrinsic) and text classification (extrinsic). A case study is provided to illustrate the working of the proposed models in an effective manner. Overall, a distant supervision method based on paradigmatic relations is proposed for learning word embeddings and it outperformed when compared against other existing models.
Identification of adaptive video streams based on traffic correlation

Due to the popularity of Dynamic Adaptive Streaming Over HTTP (DASH), broadband and Internet service providers’ links transmit mainly multimedia content. As the most popular providers encrypt their video services, the attempts to identify their traffic through Deep Packet Inspection (DPI) encounter difficulties. Therefore, encrypted DASH traffic requires new classification methods. In this work, we propose to identify DASH traffic taking into account statistical dependencies among video flows. For this purpose, we employ cluster analysis which can identify groups of traffic flows that show similarity using only the application level information. In our work, we applied three unsupervised clustering algorithms, namely MinMax K-Means, OPTICS and AutoClass, to classify video traces obtained from an emulated environment. The experimental results show that the employed algorithms are able to effectively distinguish video flows generated by different play-out strategies. The classification performance depends on the network conditions and parameters of the learning process.
Learning Fully Convolutional Network for Visual Tracking With Multi-Layer Feature Fusion

Convolutional neural networks are powerful models that yield hierarchies of features. In the paper, we present a new approach for general object tracking based on the fully convolutional network with multi-layer feature fusion. The designed network combines semantic information from deep, coarse layers, and appearance information from shallow, fine layers to make accurate pixel-wise objectness prediction. The network is first pretrained offline using a large set of videos with annotated heatmap groundtruths to obtain a general notion of foreground objects, and later fine-tuned using the first frame to adapt to the particular object instance. In online tracking, the location corresponding to the maximum target objectness in the search image is determined as the new target location, and the scale estimation is handled by incorporating a correlation filter branch into the network. An efficient updating strategy is proposed to further improve tracking performance. The extensive experiments performed on the widely used tracking benchmark OTB100 show that the proposed algorithm outperforms many other state-of-the-art trackers.
A sonography image processing system for tumour segmentation

A sonography image processing system for tumour segmentation is implemented by using the active contour model based on level set formulation. To achieve extraction of weak edges in speckle noisy images, the level set model called Edge Attraction Force on Chan and Vese model is developed. The cell competition algorithm is modified by adding the information of the cells’ edges. Calculus of variation is taken to derive the evolution equation of the active contour. Experiments demonstrate that the boundaries of tumours extracted by the proposed active contour are more accurate for conquering the drawbacks coming from weak edges. Experiments demonstrate that the boundaries of tumors extracted by the proposed active contour are accurate and the results avoid the drawbacks of the weak edges.
Decreasing case on characteristic endpoints question for iterative roots of PM functions

For PM functions of height 1, the existence of continuous iterative roots of any order was obtained under the characteristic endpoints condition. A natural open question about iterative roots without that condition was raised. This question was answered partially in the case that the function is increasing on its characteristic interval. In this paper, to the opposite, we consider the decreasing case and give the existence and nonexistence results for their iterative roots.
PixelSteganalysis: Pixel-Wise Hidden Information Removal With Low Visual Degradation

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10–20 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in both the decoded rate and the DT.
Fixing the Bugs in Software Projects from Software Repositories for Improvisation of Quality

Background: The study of bugs that are reported and close may indicate the growth and working of a software project. It may also indicate the quality of the project. Methods: As software projects grow, the number of bugs reported generally increases each year. To maintain quality, the developers have to resolve and close these increasing numbers of bugs. Results: The present paper discusses the relations between bugs being reported and bugs being closed. It also discusses some parameters related to the study of bugs. In this paper, new parameters have been introduced that help in the improvement of quality and this is the novelty of paper. Conclusion: The research mainly covers the problem of finding the relation of collaborative growth with the quality of software. The paper also covers improvements in parameters like the rate of bug fixing. It also discusses the significance of these parameters. We have suggested a new parameter called the bug closing rate. And this can be calculated in two ways.
A New Effective Shift Rule for M-Sequences

Pseudo-random sequences have been extensively used in design theory, coding theory, and cryptographic system.Pseudo-random sequences generator based on feedback shift register is an important component of a symmetric crypto-system, which is circuit equipment consisting of consecutive binary storage units regulated by a clock.As a class of important pseudo-random sequences, the M-sequences plays an important role in the systems of cryptography and communication.In this paper, a novel combinatorial method is proposed to construct (2 n -1)-length strings.This new construction method, the semi-prefer-XOR shift rule, is based on the structure of the induced digraphs of some shift operators.Thus, a large class of M-sequences is provided.
Three-States-Transition Method for Fall Detection Algorithm Using Depth Image

Currently, the proportion of elderly persons is increasing all over the world, and accidents involving falls have become a serious problem especially for those who live alone. In this paper, an enhancement to our algorithm to detect such falls in an elderly person’s living room is proposed. Our previous algorithm obtains a binary image by using a depth camera and obtains an outline of the binary image by Canny edge detection. This algorithm then calculates the tangent vector angles of each outline pixels and divide them into 15° range groups. If most of the tangent angles are below 45°, a fall is detected. Traditional fall detection systems cannot detect falls towards the camera so at least two cameras are necessary in related works. To detect falls towards the camera, this study proposes the addition of a three-states-transition method to distinguish a fall state from a sitting-down one. The proposed algorithm computes the different position states and divides these states into three groups to detect the person’s current state. Futhermore, transition speed is calculated in order to differentiate sit states from fall states. This study constructes a data set that includes over 1500 images, and the experimental evaluation of the images demonstrates that our enhanced algorithm is effective for detecting the falls with only a single camera.