Reviewerly/exHarmony huggingface dataset:
Steganogaphy Using Mac-Independent Opportunistic Routing and Encoding (MORE) Protocol Based Communications

In this paper, we first propose an enhanced version of the proposed steganoraphic scheme using MORE protocol, increasing its security and performances. To do so, we use matrix permutation keys for the embedding (resp. extraction) process, and we give optimized embedding and retrieval algorithms leading to a 50% reduced time/computation complexity. Furthermore, we analyse the proposed schemes and discuss their vulnerability to steganalysis attacks, then we propose an enhancement against these attacks.
Grey Fault Detection Method Based on Application Interference Model in Cloud Storage

The existing failure detection technology in cloud storage system mainly used to identify abnormal states of nodes by analyzing log records and the usage trend of CPU, memory, disk space and other physical resources of nodes. Despite great efforts made by researchers in the field of failure detection, there are still the gray faults have not been detected, such as the effect of memory jitter, and the system does not consider it an abnormal fault. We found that the shortcoming of current fault detection techniques was that they do not consider the performance impact between different applications in the same node. The performance interference between different applications is due to the limitation of virtualization technology for physical resource isolation. Therefore, we propose a gray failure detection method based on application scenario modeling. This method automatically analyze the performance interference between applications, and establish the relationship model between application performance interference and gray fault for the application scenarios. And then it uses the relational model to perceive environment changes of performance interference, so as to detect the node's failure location by itself. The accuracy and timeliness of the proposed method is verified in the data collected in the docker-based virtual storage cluster environment, as well as in the Google cluster data. And the method can detect a gray fault in 6.4 seconds, and have high precision.
URL Scrutiny to Circumvent Phishing

Abstract Increasing the number of cyber-crimes and frauds it has become the need of hour to seriously consider the threat and make some solid security arrangement against it. The links or Uniform Resource locator (URL) can be classified as benign, spam or malicious thus informing the user beforehand about its safety. As emails are hook to attach malicious URL, luring to catch the users. Malicious URL are the most common tool used in breaking identity and attacking either the system database or some financial frauds. Instead of focusing the source of such links if the rigorous testing on various attributes is performed on the URL itself using Random forest algorithm for threat detection and Support Vector Machine (SVM) to consider even the extreme cases is expected to improve the coverage and thus can provide the system user with high level security from counterfeited URL.
Trial on Orientation Education used by Electric Blocks for AT Engineers Supporting Inclusive Society

An internship program based on welfare-industry-academia collaboration is important opportunity for engineering students to touch such support and assistive technology (AT) in various environments. In a previous study, we started an internship program at a special subsidiary company in order to foster engineers supporting an inclusive society. The special subsidiary company system is part of the unique welfare system in Japan. Most special subsidiary companies have a nice universal design environment including barrier-free environment to hire workers with various individualities. According to our previous study, the internship program gave student's participants a good opportunity to design and develop such as assistive device and universal support tools. On the other hand, the program required engineering knowledge and techniques up to a certain standard for the participants, so it had the disadvantage that participants were limited to senior students. Therefore, we considered an orientation education program of AT for beginners used by IoT (Internet of Things) blocks substituted for the AT device. IoT blocks are one of the programing teaching materials used with a tablet device for children, and each block has each a certain sensor. The IoT block can conceptionally correspond to elements of the assistive device. After implementing an orientation education for beginner grade college students, we questionnaired the participants and found that IoT blocks were one of the suitable teaching materials for AT learners.
Probabilistic Performance Profiling

A probabilistic model of maximal mean performance in endurance sports is presented. The joint distribution of three variables namely interval length, average power, and average heart rate is modeled using Gaussian processes. The model allows for prediction of maximal average performances even based on data from sub-maximal efforts.
Co-designing Media at the Local Level: The Social TV Framework

In this chapter, we propose an alternative view to design-driven participatory processes, which reclaims the cultural dimension and emphasises the central role of media and media contents in cultural production and participation. Social TV is a framework for social inclusion and community engagement based on different areas of knowledge and supports design practice. In particular, we focus on three: media studies, participatory video and narratives. The first part of the chapter defines the contribution of the three areas and the process for developing design-driven interventions; the second introduces three practice-based design research cases as examples of Social TV applied to place-based communities. In particular, one of those cases is the campUS Social TV, which experimented with actions for connecting on and off campus activities as a model for social inclusion. This is introduced in this chapter and will be developed further in the third section of the book.
The Power of Box Filters: Real-time Approximation to Large Convolution Kernel by Box-filtered Image Pyramid

This paper presents a novel solution for approximations to some large convolution kernels by leveraging a weighted box-filtered image pyramid set. Convolution filters are widely used, but still compute-intensive for real-time rendering when the kernel size is large. Our algorithm approximates the convolution kernels, such as Gaussian and cosine filters, by two phases of down and up sampling on a GPU. The computational complexity only depends on the input image resolution and is independent of the kernel size. Therefore, our method can be applied to nonuniform blurs, irradiance probe generations, and ray-traced glossy global illuminations in real time, and runs in effective and efficient performance.
The science of discovery – from Exploration 1.0 to Discovery 2.0

SummaryThe tools of modern exploration developed in the post war and cold war periods are no longer delivering mineral discoveries at the required rate for future economic production. The near surface search space is becoming mature. A new exploration toolkit backed by holistic systems thinking will be required to evolve Modern Exploration 1.0 into Discovery 2.0. Significant discoveries under moderate to deep cover will require new knowledge, new industry and government collaboration, new business and socio-economic models to deliver a step change in discovery rates.To develop Discovery 2.0 we will need to deal with both the good methods and failing thinking of the past. To be truly great at discovery in 21st century geoscientists will have to step up from being simply good explorers of the modern exploration era.Discovery thinking will need to embrace systems thinking, risk taking, and cope with ambiguity and uncertainty. Systematic area reduction may not be possible and exploration will rely on recognition of patterns more than anomalies. Lucky teams can be grown and forged by the right exploration leadership. Discoveries will come from good team based business decision making and risk management as much as brilliant geoscience and new technology. Better thinking and more thinking time will be critical factors.
CLOUDFALL 1.0: A Smart, Low-Cost, IoT-Based Fall-Detection Sensor Node

A fall can cause major problems if the injuries are left untreated too long and can possibly lead to serious brain or bone injuries, especially for the elderly. Injuries from a fall reduce the patient’s ability to enjoy social interaction and perform day-to-day activities; fear of future falls, isolation, and reduced independence can lead to depression. To reduce after-effects and injuries from falls, getting medical care as quickly as possible is vital. Smart IoT-based wearable sensors can provide a means to overcome the problems associated with a fall. Wearable sensor nodes make it possible to deliver a high degree of monitoring and reliability. This chapter focuses on the requirements and specifications of a low-cost, high-reliability, and energy-efficient sensor node, proposing a device to meet these requirements and exploring different parameters such as communication interface and processing speed. It also provides both hardware and software designs for a successful implementation of the wearable device in terms of quality of service (QoS). The design description provided clearly demonstrates that the proposed sensor node is energy efficient and better at detecting falls than other fall-detection systems.
Asymmetric Pyramid Based Super Resolution from Very Low Resolution Face Image

Most of the existing one-step upsampling super-resolution (SR) methods could not clearly reconstruct a higher resolution image from a very low-resolution image because there is not enough supervision information to be available. Inspired by the laplacian pyramid, we propose a novel Asymmetric and Progressive Face Super-Resolution Network (APFSRNet) to progressively reconstruct a super-resolution face image from a very low-resolution face image. To further improve the accuracy of the reconstruction, we use the densely connected layers to deepen our network which also alleviate the vanishing-gradient problem. We use the entire face image to train our network instead of using face image patches to maintain the global structure of the face image. Furthermore, we employ structural similarity index (SSIM) as a part of loss function to satisfy human observation. Our extensive experiments demonstrate the effectiveness of the proposed model qualitatively and quantitatively.
Learning Propagation for Arbitrarily-Structured Data

Processing an input signal that contains arbitrary structures, e.g., superpixels and point clouds, remains a big challenge in computer vision. Linear diffusion, an effective model for image processing, has been recently integrated with deep learning algorithms. In this paper, we propose to learn pairwise relations among data points in a global fashion to improve semantic segmentation with arbitrarily-structured data, through spatial generalized propagation networks (SGPN). The network propagates information on a group of graphs, which represent the arbitrarily-structured data, through a learned, linear diffusion process. The module is flexible to be embedded and jointly trained with many types of networks, e.g., CNNs. We experiment with semantic segmentation networks, where we use our propagation module to jointly train on different data -- images, superpixels, and point clouds. We show that SGPN consistently improves the performance of both pixel and point cloud segmentation, compared to networks that do not contain this module. Our method suggests an effective way to model the global pairwise relations for arbitrarily-structured data.
A Survey on Animal Voice Recognition: Mood and Behaviour using Machine Learning Approach

Abstract Voice recognition frameworks turned into the fundamental applications for discourse recognition innovation, a creature affirmation framework bolstered creature voice design recognition rule has been created. The proposed creature voice recognition framework uses the zero-cross rate, MelFrequency Cepstral Coefficient and Dynamic-Time wrap joint calculations in light of the fact that the instruments for recollecting the voice of the genuine creature. ZCR is utilized for the begin point recognition of testing voice indicated the commotion might be expelled. MFC is utilized for the strategy for quality extraction wherever an extra consolidated and less excess of the delegate voice might be accumulated from the testing voice. while the voice order will be finished by abuse DT WRAP rule. At that point the voice coordinating is done to distinguish and characterize the creature seen by the framework. The program made and data noted demonstrates the recognition framework works.
Research and Construction of Word Vector Extension Structure Based on Context

Abstract The CBOW (Continuous Bag-of-Words) model is a three-layer forward neural network that predicts the central word vector by the fixed-size window information. In fact, context has a very important role in understanding the meaning of words. However, the context information of the fixed window size is partial and it is not enough to represent the whole context. Due to the polysemy of Chinese words, the same word may have different semantics in different contexts, while the traditional CBOW method ignores the polysemy of words. Therefore, this paper proposes a context-based word vector extension structure for the above problems. The main contents are as follows: (1) Introducing the concept of context vector. The entire sentence in which the target word is located is represented by a vector. (2) Constructing the polysemy storage method of words, adding a contextual list to each word vector, so that the multiple semantics of the words can be effectively distinguished. (3) Based on the word vector extension structure of this paper, a new character vector generation model is proposed, and the effect of the new model and the traditional CBOW model in the news headline similarity sorting task is compared. The experimental results show that the new character vector generation model based on the extension structure of the word vector obtains better result.
Multidimensional Sparse Representation for Multishot Person Reidentification

Person reidentification is known as recognizing a subject in diverse scenes obtained from nonoverlapping cameras. To the best of our knowledge, the existing sparse representation approaches for person reidentification need an additional stage for combining a different level of features. In this letter, we propose a new sparse representation approach based on tensor along with the dictionary learning that is able to tackle both the sparse representation of features and the combination of different level of features, simultaneously. First, we construct the feature tensors using images of people. Subsequently, we learn a single cross-view invariant dictionary for representing images from different viewpoints in each tensor mode. The tensor representations of images alleviate the computational complexity of the conventional feature combination approaches, and enhance the reidentification of high dimensional data. Experimental results on iLIDS-VID dataset show the superiority of our method compared to some recent methods.
Automatic Inspection Verification Using Digital Certificate

Digital certificates are a core component in the provision of secure data communication. We propose the concept of digital certificate that can be used to provide client verification. Cryptographic methods are used to explore these aspects of data security and there are many drawbacks in current encryption algorithms in respect of security, real-time performance and so on. Among them, the elliptic curve cryptography (ECC) and Rivest, Shamir, Adelman (RSA) is evolving as important cryptographic methods since it offers high security. In our proposed system, Optical Character Recognition is used to convert the scanned image to extract the parameters in it and the parameters are encrypted and decrypted using RSA and ECC. So, encrypted digital certificate is created for each individual and stored in a private database for inspection purpose. The public or private sector organization verifies the digital certificate by sending a request to authorization server and the response is generated by referring the parameters of encrypted digital certificate with the parameters extracted from the local database. The main purpose of the digital certificate is to avert duplicates and forgeries.
An adaptive rendering framework for efficient synthetic light field generation

Real-time global illumination rendering is very desirable for emerging applications such as Virtual Reality (VR) and Augmented Reality (AR). However, client devices have difficulties to support photo-realistic rendering, such as Ray-Tracing, due to insufficient computing resources. Many modern frameworks adopted Light Field rendering to support device displaying. A Light Field can be precomputed and stored in the Cloud. During runtime, the display extracts the color information from the Light Field to generate arbitrary real time viewpoints or re-focusing within a predefined area. To efficiently compute the Light Field, We have combined DIBR (Depth-Image-Based-Rendering) and traditional ray-tracing in an adaptive fashion to synthesize images. By measuring the color errors during runtime, we adaptively determine a good balance between DIBR and Ray Tracing. We study several cases and problems when combing these algorithms and came up with a more efficient solution. To further optimize the computation efficiency, we also added a multi-level design to exploit the degree of shareable pixels among images to control the computation for error reduction. In addition, we also design an efficient multi-threading scheduling to show the scalability of our approach. Experiments show that when the number of threads is small, we achieved up to 3.24X speedup in Light Field generation for relative simple scenes like Cornell Box, and about 2X speed up for complex scenes like Conference Room or Sponza. In the multi-thread environment, our proposed scheme can offer about 2X speedup over traditional ones.
Controlled nth root of X gate on a real quantum computer

The qubit state is represented by a coherent superposition of its orthonormal basis states (|0〉 and |1〉). The probability distribution of a qubit is a discrete probability distribution of the qubits probability amplitudes. Setting a custom probability amplitudes allows a qubit to evolve to any desired state. In this paper a controlled n-root of X gate is simulated on a real quantum processor using IBMs Quantum Experience platform. A custom quantum gate could be constructed where the only inputs are the control and target qubits and the desired probability distribution for the target qubit.
An Improved Quantum Genetic Algorithm Based on Population Partition and Dynamic Probability Amplitude

Aiming at the problem that quantum genetic algorithm is easy to fall into local optimization in the process of function optimization under the original framework, an improved quantum genetic algorithm using population partition method and dynamic inverse probability amplitude strategy is proposed. In the process of optimization, the improved algorithm utilizes the superior individuals in each generation of populations to construct a population genotype suitable for evolution through the individual binary form. On this basis, replace the inferior individuals in the population with a certain proportion, and then construct a number of similar individuals that tend to the genotype of the superior individual. Therefore, the algorithm can continuously improve the adaptability of the problem and gradually converge to the optimal solution. At the same time, for the premature or local optimization problem that the algorithm may be trapped, in the period of algorithm stagnation, the population diversity is enriched by resetting the genotype of some individuals and narrowing the search space by using the current optimal solution.
Distortion scalable learned image compression

This work investigates a distortion scalable learned image encoder that, differently from other works, only encodes those most prominent feature maps that are found to benefit rate-distortion performance. This way, better suiting the texture complexity of content being coded and providing a competitive distortion scalable solution. Experimental results show that the proposed solution performs comparably to HEVC Intra with respect to the perceptually-inspired structural similarity (SSIM) index while being able to be optimized to suit a particular application.
Möbiusschleife: Beyond the Bounds of a Closed-Loop VR System

We propose a VR system “Möbiusschleife” to make the VR and real world directly interact and enlarge the boundary of the VR player’s field. Conventional VR systems generally adopt head- mounted display (HMD) to present altered experiences from the real, but external audiences cannot receive them. In this paper, we present a virtual “window” which provides bidirectional face-to- face interaction between the VR and real world with a touch display and webcam. We also prepare a simple stereoscopic display enabling the VR player and objects to move as if part of the VR world leaps into the real world. Lastly, we integrate all the above systems as a simple chat application with a virtual character.
Inuring

We present inuring, an attack-guided repair method for software vulnerabilities in n-variant systems. N-variant systems detect attacks that cause divergence in variant behavior, converting severe vulnerabilities (such as those that enable remote code execution) into less severe denial-of-service vulnerabilities. Inuring is a general technique for n-variant systems that uses information gleaned from an attack to perform a "live'' field repair of the underlying vulnerability, thereby obviating the denial-of-service attack. We present a case study of the use of inuring to protect against a powerful class of memory-corruption exploits in the Apache web server. Our demonstration leverages dappling, a new technique for provably secure memory layout in n-variant systems. With inuring and dappling we are able to guarantee strong protection and remediation for a class of write-what-where vulnerabilities in n-variant systems. Our case study illustrates the efficacy and efficiency of these techniques.
Segmented individual claim reserve estimation using BART refinement

We want to extend an individual claim reserving method proposed by [1] and also [2] by using segmented calculation. This method is an individual method of claims reserve estimation which involves detailed condition on claim characteristics in the calculation process. Data is divided into several segments according to combination of background variables. We then apply RDC method to find estimated IBNR and RBNS reserves for each segment. Bayesian Additive Regression Tree (BART) is used to refine the estimated reserves. This is because the estimation become unstable due to a lot of combination factors for each segment.