Reviewerly/exHarmony huggingface dataset:
Security Approaches and Crypto Algorithms in Mobile Cloud Storage Environment to Ensure Data Security

Mobile Cloud Storage Architecture is a promising service platform provided by CSPs to store and access resources on-demand via the internet. Information and data stored on these large storage platforms can be well managed than locally stored. Cloud storage services use deduplication for saving bandwidth and storage. However, using these services pose lots of doubts and challenges in the minds of its users in relation to security, privacy, reliability and integrity of their stored data and information. An adversary can exploit side-channel information in several attack scenarios when deduplication takes place at the client side, leaking information on whether a specific plaintext exists in the cloud storage. In this paper, different security techniques and algorithms adopted to enhance security, reliability and integrity of data and information stored in the cloud are discussed. Also, an analysis of trending issues with regards to security of data and information stored in the cloud as well as the reliability and integrity of these storage platforms provided by CSPs are discussed extensively..
New noise reduction method based on FPGA for a stereoscopic video system

A 3D reality world used for medical therapies needs clear images without defective and noisy pixels. This is essential for portative equipments used by several patients inside the same building. To reduce the resources of the system, a corrective and denoising implementation has been implemented based on FPGA. Two low resources algorithms are proposed for defective pixel correction and noise reduction. The correction algorithms are integrated according to ”Bayer Color Filter Array” for stereoscopic video system respecting real-time constraints. State-of-the-art denoising algorithm shows that implementations use too many resources for the target system, therefore a new low resource denoising algorithm is presented. It is derived from the efficient Non-Local Means (NLM) denoising algorithm which is hardly implementable in a FPGA system. The new denoising method discussed in this paper does not use any multiplier and only consumes 3.8k ALUTs.
Histopathological Image Classification: Defying Deep Architectures on Complex Data

Automatic analysis of medical images is a challenging research which requires both the skill of a pathologist and computer vision knowledge to develop efficient systems. In this work, we have taken up the task of classifying different types of cell nuclei in histopathological Colon Cancer Images. We aim to show the relevance and effect of a complex histopathological dataset on the performance of current deep learning architectures. We have experimented with pre-trained (on ImageNet) AlexNet, VGG16, and VGG19 architectures and applied transfer learning approach to train these architectures. On the basis of the results obtained on the Histopathological image dataset, while using fine tuned AlexNet, VGG16, and VGG19 architectures; the suitability of using pure architectures is somehow questionable and these state of the art algorithms straightaway cannot be used for the sophisticated classification of very complex cancer tissue dataset. Comparative evaluation of the above state of the art methods have been done and the possibility of devising hybrid deep architectures is investigated thereof.
Method Level Code Smells: Chernoff Face Visualization

Software that is badly written and prone to design problems often smells. Code smells results in design anomalies that make software hard to understand and maintain. Several tools and techniques available in literature helps in detection of code smells. But the severity of the smells in the code is often not known immediately as it lacks visualization. In this paper, two method level code smells namely long method and feature envy are visualized using chernoff faces. Techniques proposed in literature either use knowledge driven approach or data driven approach for code smell detection. In the proposed approach a fusion of both knowledge and data driven approach is used to identify the most relevant features. These most relevant features are mapped to the 15 desired features of chernoff faces to visualize the behavior of the code. The result shows that almost 95% of the smells are visualized correctly. This helps in analyzing the programmer's capability in maintaining quality of source code.
Generalized Smart Traffic Regulation Framework with Dynamic Adaptation and Prediction Logic Using Computer Vision

Traffic signals are essential to ensure safe driving at road intersections and to maintain a constant flow of vehicles in a convenient manner. However, sometimes inefficient traffic control themselves restrict the constant flow creating commotions and delays. Therefore, in this work, we are introducing a generalized smart traffic regulation algorithm (G-STRA) which is going to consider the real-time traffic density using image processing at each lane, to reduce the wait time and improve the total throughput. The system will be smart enough to identify vehicles of importance (VoIs), to give them extra clearance from the regular commuting vehicles. The G-STRA is called “smart” as it will adapt itself with the phases of the day, days of importance (DoIs), the condition of road, and weather and the type of commotion to predict the type of flow it should maintain learning from its past experiences.
Embedding Gestalt Principles to Hierarchical Distance Dependent Nonparametric Bayesian Model for Video Segmentation

Gestalt is a psychology term meaning “unified whole” which refers to the theories of visual perception developed in the 1920s. These theories attempt to describe how people tend to organize visual elements into groups or unified wholes when certain principles are applied. These principles such as similarity, common fate, continuation and etc. are intuitive to understand. However the challenge is to encode Gestalt theory as the principle to construct a computational model for visual process. In the domain of computer vision, visual processing tasks such as segmentation benefits greatly from spatio-temporal information from videos. Hence, we propose to study video segmentation where the number of objects is unknown. We achieve this by formulating a hierarchical nonparametric Bayesian model. Our model contains three key features (1) it embeds Gestalt principles as the prior of the model (2) it is a distance dependent nonparametric Bayesian model where the spatial temporal order of the data points matters. (3) it is a hierarchical model where we considered both the local and global aspects. We show that that our unsupervised generative model share similar results in human visual segmentation tasks as well as some psychology experiments.
Zero

This paper proposes a method of zero-watermarking scheme to generate a unique digital identification tag having patient's identity without tampering the details of the medical information present in the image. Using the features of the original medical image, the digital identification tag is encrypted and a unique master key is created which can be decrypted by using the original medical image at the receiver's end. This is performed by using strategic decomposition of digital wavelet transform along with singular value decomposition to obtain a unique identity vector which is used in a function along with the image's watermark to create the master share for that image. Images of the fundus comprise of very fine features like macula, optic disks and blood vessels. Traditional watermarking techniques might tamper with the crucial information stored in the medical image. Even the smallest of changes can result in noise and distortion of the fine features of the medical image and can affect the final decision which would be made by the doctor. The method proposed in this paper does not tamper with any details of the image in order of watermarking the digital identification tags onto the image.
Examining DES-based Cipher Suite Support within the TLS Ecosystem

In July 2018, over a decade after the DES encryption algorithm was retired, 3DES was also officially deprecated. While previous work suggests a successful deprecation of DES, with fewer than 1% of observed SSL/TLS handshakes using some form of DES up until 2018, such work tends to be limited in scope and does not necessarily capture the true persistence of DES across the entire TLS ecosystem. In this paper, we actively investigate online support for DES and DES-derivative ciphers by querying IP addresses responsive to port 443 connection attempts. To achieve this, we design and implement our own Internet scanning tool built upon ZMap and attempt to negotiate handshakes exclusively using DES ciphers. In total, we have scanned over 31 million unique IP addresses and found that nearly half of them can still successfully establish an HTTPS connection using at least one DES cipher. Moreover, we also find that many servers still support DES40 (which can be broken in seconds) and anon ciphers (which offer no certificate verification and are vulnerable to man-in-the-middle attacks). Our investigation demonstrates the biases and misunderstandings in previous weak cipher studies within the TLS ecosystem, and discloses the severity of this problem by targeting DES-based cipher suites.
Sports Vision Based Tennis Player Training

Sport vision based tennis player training is proposed to accelerate players’ skill-up in tennis play with instructions provided by the proposed sport vision based system. In sports, gaze, dynamic visual acuity, eye movement and viewing place are important. In sports vision, Static eyesight, Dynamic visual acuity, Contrast sensitivity, Eye movement, Deep vision, Instant vision, Cooperative action of eye, hand and foot, and Peripheral field are have to be treated. In particular for the tennis, all of the items are very important. Therefore, sports vision based tennis player training system is proposed. Through experiment, it is found that the proposed system does work well for improvement of tennis players’ skills.
Perceptual video encryption using orthogonal matrix

In this paper, the properties of rotation matrices are employed to construct special orthogonal matrices that are used to encrypt and decrypt video with varying perceptibility. Square orthogonal matrices of varying sides are formulated as cipher key matrices. The encryption algorithm uses these matrices to encrypt plain video into cipher video and transmit while the decryption algorithm uses the transpose of the cipher key matrices to recover the original video at the other end of the communication channel. Degradation in visual quality is controlled by varying cipher key dimension and the number of repetitions. Barring any noise in the communication channel, the proposed method retrieves the original video free of any error. Analysis of results show that the proposed method is key sensitive and resistant to chosen/known plain-text attacks.
Discovering Data Discrepancies during Deselection: A Study of GreenGlass, Aleph, and Due Date Slips Circulation Data

Amid a weeding project, librarians at a state college suspected GreenGlass’s circulation data was inaccurate. This study compared GreenGlass’s and Aleph’s circulation statistics for a random sample of books. It also determined if GreenGlass’s list of books with zero uses included curriculum related works using keyword searching. The study compared GreenGlass’s and due date slips’ circulation data for curriculum related titles. Some GreenGlass circulation data was erroneous. Additionally, curriculum related books that circulated were on GreenGlass’s zero use list. The study’s results helped retain pertinent titles and highlighted the inadequacy of relying exclusively on circulation data to weed monographs.
Argumentación y cambio comportamental

In this paper I show that the schemes of practical argumentation provide an adequate framework for understanding the causes of patterns of behavior and for the elaboration of refutations tending to their modification. With this purpose, (1) I introduce the problem; (2) I present the relationship between the general form of institutional facts and the forms or schemes of argumentation; (3) I expose the forms of the arguments for the realization of actions that integrate the Macagno and Walton’s (2018) modular approach; (4) I summarize the factors of behavioral change determined by Bicchieri (2019) and link them to the different schemes of practical argumentation that make up the modular approach referred to in the third section; as well as, finally, (5) I link some strategies of the design of interventions for the promotion of behavioral change with some critical questions associated with some argumentation schemes.
Rendering of Virtual Walking Sensation by a Vestibular Display

The current study describes a method for rendering the sensation of walking using a vestibular display (a motion seat) with three degrees of freedom. The sensations evoked by the vestibular display and real walking were investigated using a psychophysical method. The optimal motion of the vestibular display to induce the sensation of walking on a level surface and walking up/down stairs was obtained using the method of adjustment. The results revealed that an amplitude less than 10% of real motion was appropriate for the passive presentation of walking. A low level of asymmetry was observed in the speed and acceleration of the motion trajectory for the stair walking presentation. This finding suggested that a smaller amount of return motion compared with forward (first half) motion in a reciprocal motion for each step was appropriate for inducing the sensation of walking up/down stairs.
Data Structures for Direct Spanning Tree Representations in Mutation-based Evolutionary Algorithms

Optimization methods for spanning tree problems may require efficient data structures. The node-depth-degree representation (NDDR) has achieved relevant results for direct spanning tree representation together with evolutionary algorithms (EAs). Its two mutation operators have average time O(√n), where n is the number of vertices of the graph, while similar operators implemented by predecessor arrays, a typical tree data structure, have time O(n). Dynamic trees are also relevant when investigating tree representations since they have low time complexity, but there is no proper extension of them for EAs. Using aspects of both a dynamic tree and NDDR, namely, Euler tours and structural sharing, we propose a data structure called 2LETT, whose mutation operators have time O(√n) in the worst case. Experiments with the mutation operators using 2LETT, predecessor arrays, and NDDR are carried out for graphs with up to 300000 vertices. For a mutation operator that exchanges any two valid edges, predecessor arrays present the best performance for random trees with fewer than 10000 vertices; while 2LETT has the best performance for trees with more than 10000 vertices. Especially, noteworthy is the fact that 2LETT is the only structure whose running time is independent of tree diameter.
Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images

Prostate cancer (PCa) is the most common oncological disease in Western men. Even though a significant effort has been carried out by the scientific community, accurate and reliable automated PCa detection methods are still a compelling issue. In this clinical scenario, high-resolution multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, also enabling quantitative studies. Recently, deep learning techniques have achieved outstanding results in prostate MRI analysis tasks, in particular with regard to image classification. This paper studies the feasibility of using the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the fully-connected architecture commonly used in the last layers of Convolutional Neural Networks (CNNs). The experimental phase considered the PROSTATEx dataset composed of multispectral MRI sequences. The achieved results show that, on the same non-contrast-enhanced MRI series, SLM outperforms with statistical significance a state-of-the-art CNN trained with backpropagation. The SLM performance is achieved without pre-training the underlying CNN with backpropagation. Furthermore, on average the SLM training time is approximately 14 times faster than the backpropagation-based approach.
Implementation and Analysis of Quantum Fourier Transform Gates Over FPGA Framework

Since the emergence of the Shor's factoring algorithm, the research over Quantum Fourier Transform (QFT) has motivated the broad attention in the dilemma of the efficient quantum computation. For the purpose of exploring the sizable extent for their exponential growth and the massive computational steps, this paper confers the Field Programmable Gate Arrays (FPGA) implementation of the QFT and, investigate the association between the logic density, complexity and power consumption. In this context, reusability as well as compensating resource utilization is discussed together with the observing issues.
Senior’s Acceptance of Head-Mounted Display Using Consumer Based Virtual Reality Contents

Head-mounted display has been commercialized and have made a considerable market in the virtual reality industry. While many seniors have interest to the consumer head-mounted display, the opportunity to experience the head-mounted display is still very limited. Hence this paper aims to resolve whether the current contents or products are acceptable and to clarify the challenges to discuss solutions. To investigate on this issue, an event to experience head-mounted display took place within a regional event for seniors at Kashiwa city in Japan. During the event, voluntary participants were asked to answer the questionnaires. The questionnaire was gathered from 5 seniors over age 60 and 10 young under the age 60. The seniors acceptance level was not so high in contrast to the young. However, positive results indicated seniors enjoyed experiencing the head-mounted display contents, willing to experience more opportunities, and also highly recommend to experience to others.
Review on Text Watermarking Resistant to Print-Scan, Screen-Shooting

Although the development of the Internet has enabled more and more texts to be transmitted in electronic forms, printed documents are still an irreplaceable form of transmission. Whether it is the copyright protection of book authors or the prevention of the leakage of confidential documents, there is an urgent need for text watermarking resistant to print-scan attacks. Due to the complex influence of print-scan process and the particularity of text carrier, to design a text watermarking method resistant to print-scan is a complex project. The emergence of screen-shooting attacks also brings new challenges and requirements for text watermarking. In this paper, we study the technologies and theories of text watermarking resistant to print-scan in detail and roughly discuss the screen-shooting resilient watermarking, then review the existing research results in text watermarking robust to print-scan operations and digital watermarking robust to screen-shooting. The advantages, disadvantages and contributions of these results are also analyzed.
Towards Extreme-Resolution Image Registration with Deep Learning

Image registration plays an important role in comparing images. It is particularly important in analysing medical images like CT, MRI and PET, to quantify different biological samples, to monitor disease progression, and to fuse different modalities to support better diagnosis. The recent emergence of tissue clearing protocols enable us to take images at cellular level resolution. Image registration tools developed for other modalities are currently unable to manage images of entire organs at such resolution. The popularity of deep learning based methods in the computer vision community justifies a rigorous investigation of deep-learning based methods on tissue cleared images along with their traditional counterparts. In this paper, we investigate and compare the performance of a deep learning based registration method with traditional optimization based methods on samples from tissue-clearing methods. From the comparative results it is found that a deep-learning based method outperforms all traditional registration tools in terms of registration time and has achieved promising registration accuracy.
Single-wavelength and multi-parallel dotted- and solid-lines for dense and robust active 3D reconstruction

A dense one-shot scanning technique that is robust to subsurface scattering is proposed. In this technique, a novel pattern, consisting of multiple parallel dotted lines and solid lines, that are aligned alternately, is proposed. To project such a pattern efficiently, a single-wavelength laser-based pattern projector is developed. To detect patterns robustly from captured images, a black and white camera attached with a narrow-band-path filter is used in conjunction with our novel deep learning based algorithm, which is based on a convolutional neural network (CNN). Because the detected lines must be identified for shape reconstruction, we apply a gap-coding technique, which is originally based on a grid-line pattern, to the dot pattern. To this end, we introduce a virtual grid-line structure, which is generated from the dot pattern. Additionally, we propose a calibration algorithm specialized for our system, where the pattern is static and shared with the shape reconstruction algorithm, i. e., correspondence problem remains. For a solution, gap-coding is further applied to find correspondences under epipolar constraints. The experimental results of scanning real objects are presented to demonstrate the effectiveness of our calibration and reconstruction techniques.
Adaptation of learning agents through artificial perception

The process of online reinforcement learning also creates a stream of experiences that an agent can store to re-learn from them. In this work, we introduce a concept of artificial perception affecting the dynamics of experience memory replay, which induces a secondary goal-directed drive that complements the main goal defined by the reinforcement function. The different perception dynamics are capable of inducing different “personality” types able to govern the agent behavior, possibly enabling it to exhibit an improved performance over an environment with specific characteristics. Experimental results show that different personalities show different performance levels when facing environment variations, therefore, showcasing the influence of artificial perception in agent’s adaptation.