Reviewerly/exHarmony huggingface dataset:
Enhancing Hierocrypt-3 Performance by Modifying Its S-Box and Modes of Operations

Human relationships rely on trust, which is the reason that the authentication and related digital signatures are the most complex and confusing areas of cryptography. The Massage Authentication Codes (MACs) could be built from cryptographic hash functions or block cipher modes of operations. Substitution-Box (S-Box) is the unique nonlinear operation in block ciphers, and it determines the cipher performance. The stronger S-Box, the stronger and more secure the algorithm. This paper focuses on the security considerations for MACs using block cipher modes of operations with the Hierocrypt-3 block cipher. the Hierocrypt-3 could be considered as a week cipher. It could be enhanced by changing its S-Box with a new one that has better performance against algebraic attack with using different modes of operations. The mathematical model for the new S-Boxes with its properties is provided. The result of this change appeared in the mirror of Average Strict Avalanche Criterion (SAC) and some other properties. SAC could be improved from 0.80469 to 0.16406. The Hierocrypt-3 could be enhanced for more security.
Classifying LPI signals with transfer learning on CNN architectures

Due to the increased deployment of low probability of intercept radar systems, recognition and classification of low probability of intercept signals has developed an increased importance for electronic warfare systems. Recent results showed that combining time-frequency transformations such as Choi-Williams distribution with convolutional neural networks yield high accuracy. Since training convolutional neural networks is a time consuming task, we propose to use transfer learning on pre-trained convolutional neural network architectures. Furthermore, we compare these retrained neural network to the neural network trained with randomly initialized weights. We will demonstrate that the required training time for the transfer learning method is significantly shorter. Moreover, classifying time-frequency images based on Choi-Williams distribution achieves for both weight initialization methods an accuracy of over 99%.
Distilling Content from Style for Handwritten Word Recognition

Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes. Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both the generative process and the recognition efficiency in the IAM dataset.
Mining Self-similarity: Label Super-Resolution with Epitomic Representations

We show that simple patch-based models, such as epitomes (Jojic et al., 2003), can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks. We derive a new training algorithm for epitomes which allows, for the first time, learning from very large data sets and derive a label super-resolution algorithm as a statistical inference over epitomic representations. We illustrate our methods on land cover mapping and medical image analysis tasks.
A Survey of Super-Resolution Based on Deep Learning

Image super-resolution (SR) is an important low-level visual task in the field of image processing. It is used to enhance the resolution of images or videos and has a wide range of applications. In recent years, many researchers have begun to apply deep learning-based methods to SR task, which can significantly improve the quality of restored images. In this paper, we will introduce the concept of image super-resolution task, several typical CNNs based on supervised SR, unsupervised SR as well as the future research directions of SR.
AI, visual imagery, and a case study on the challenges posed by human intelligence tests

Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone’s mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven’s Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.
Finger-Ray Interaction using Real Hand in Handheld Augmented Reality Interface

Abstract Augmented Reality (AR) is the technology that augments the real world by using virtual information of 3D objects overlaid on a view of the real environment. This will create an immersive and intuitive experience. As handheld devices are widely used and now has climb with increasing demand of specifications, its interaction based on touch is a natural and appealing input style for the application of AR. Furthermore, most heuristic studies of interaction in AR usually focus on interactions with AR Target close to the user, generally within arm’s reach. As the user’s move farther away, the effectiveness and usability of the interaction modalities may be different. This study explores handheld AR interaction using real hand gesture at a distance in a room-scale setup. Our aim is to investigate the effectiveness of performing selection while being far away from the 3D object and performing selection task towards object that is being occluded. As object manipulation is one of the key features to explore for mobile handheld AR, we hope our study can give some contribution towards its practical use, especially for real-world assembly structure. Hence, our paper proposes finger ray interaction technique for real hand in handheld AR interface to work for selecting objects at distances ranging from 3 feet to 8 feet and when the object being occluded ranging from 20% to 80% occluded.
A Fully Secure KP-ABE Scheme on Prime-Order Bilinear Groups through Selective Techniques

Key-policy attribute-based encryption (KP-ABE) is the cryptographic primitive which enables fine grained access control while still providing end-to-end encryption. Although traditional encryption schemes can provide end-to-end encryption, users have to either share the same decryption keys or the data have to be stored in multiple instances which are encrypted with different keys. Both of these options are undesirable. However, KP-ABE can provide less key overhead compared to the traditional encryption schemes. While there are a lot of KP-ABE schemes, none of them simultaneously supports multiuse of attributes, adaptive security, monotone span programs, and static security assumption. Hence, we propose a fully secure KP-ABE scheme for monotone span programs in prime-order group. This scheme uses selective security proof techniques to obtain the requisite ingredients for full security proof. This strengthens the correlation between selective and full security models and enables the transition of the best qualities in selective security models to fully secure systems. The security proof is based on decisional linear assumption and three-party Diffie–Hellman assumption.
Polymorphic types and effects with Boolean unification

We present a simple, practical, and expressive type and effect system based on Boolean constraints. The effect system extends the Hindley-Milner type system, supports parametric polymorphism, and preserves principal types modulo Boolean equivalence. We show how to support type inference by extending Algorithm W with Boolean unification based on the successive variable elimination algorithm. We implement the type and effect system in the Flix programming language. We perform an in-depth evaluation on the impact of Boolean unification on type inference time and end-to-end compilation time. While the computational complexity of Boolean unification is NP-hard, the experimental results demonstrate that it works well in practice. We find that the impact on type inference time is on average a 1.4x slowdown and the overall impact on end-to-end compilation time is a 1.1x slowdown.
Contextualisation of Data Flow Diagrams for Security Analysis

Data flow diagrams (DFDs) are popular for sketching systems for subsequent threat modelling. Their limited semantics make reasoning about them difficult, but enriching them endangers their simplicity and subsequent ease of take up. We present an approach for reasoning about tainted data flows in design-level DFDs by putting them in context with other complementary usability and requirements models. We illustrate our approach using a pilot study, where tainted data flows were identified without any augmentations to either the DFD or its complementary models.
Solar image deconvolution by generative adversarial network

With Aperture synthesis (AS) technique, a number of small antennas can assemble to form a large telescope which spatial resolution is determined by the distance of two farthest antennas instead of the diameter of a single-dish antenna. Different from direct imaging system, an AS telescope captures the Fourier coefficients of a spatial object, and then implement inverse Fourier transform to reconstruct the spatial image. Due to the limited number of antennas, the Fourier coefficients are extremely sparse in practice, resulting in a very blurry image. To remove/reduce blur, "CLEAN" deconvolution was widely used in the literature. However, it was initially designed for point source. For extended source, like the sun, its efficiency is unsatisfied. In this study, a deep neural network, referring to Generative Adversarial Network (GAN), is proposed for solar image deconvolution. The experimental results demonstrate that the proposed model is markedly better than traditional CLEAN on solar images.
A Survey of Distinctive Prominence of Automatic Text Summarization Techniques Using Natural Language Processing

There are multitudinous Indian languages coming up on the web each day. These languages are basically providing information to the people in their regional languages. This paper explores various text summarization techniques that have been used in existing work done in this area. The main intention of text summarization of documents is that to have important sentences sorted and the document is minimized by eliminating redundant sentences as well as less important data. In any respective language, the text summarization is done in two ways: abstractive and extractive. Different methods can be used for summing up any document. These methods can be targeted as clustering, classification, and tokenizing methods. The text summarization field came in limelight these days because of the increasing amount of web content. So users possess many advantages of summarizing text as (a) it reduces reading time. (b) The summary will prove whether the article is worth reading or not. (c) It also helps people to find out the abstract for their work. (d) It reduces search time while researching documents.
Virtual Reality in Environmental Education for Manufacturing Sustainability in Industry 4.0

Industry 4.0 is not only a digital transformation of manufacturing process. It also deals with the environmental modification and is focused on sustainable development. The use of eco-technologies reduces the environmental modification and leads to formation of a new type of education in the field of environmental protection. Environmental engineers should be able to respond to modern challenges. Virtual learning technologies will engage students in environmental management and allow students to immediately get information about current global environmental issues, to find solutions to complicated problems, collect data and share their results with a wider audience. This study critically examines virtual learning environment as experimental approach to environmental education on the basis of previous studies and the authors' own experience. The aim of this paper is to consider the perspectives and limits of the virtual learning technologies.
Clustered entropy for edge detection

The quantity of information in images can be evaluated by means of the Shannon entropy. When dealing with natural images with a large scale of gray levels, as well as with images containing textures or suffering some degradation such as noise or blurring, this measure tends to saturate. That is, it reaches high values due to a large amount of irrelevant information, making it useless for measuring significant information. In this paper we present a new information measure, the clustered entropy. This information measure, based on clustering local histograms, has a zero value for quasi-homogeneous regions and reaches high values for regions containing edges. The clustered entropy is used in this paper as an edge detector, by centering a sliding window on every pixel of an image, and calculating the clustered entropy of the corresponding histogram. A search for local maxima throughout the resulting matrix of entropies provides the final image of edges. The mathematical properties of clustered entropy are studied, a comparison between the clustered and the normal entropy is done, and some comparative experiments of edge detection are shown in this paper.
Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates

In this paper, we introduce a novel approach to predictive modeling for software engineering, named Learning From Mistakes (LFM). The core idea underlying our proposal is to automatically learn from past estimation errors made by human experts, in order to predict the characteristics of their future misestimates, therefore resulting in improved future estimates. We show the feasibility of LFM by investigating whether it is possible to predict the type, severity and magnitude of errors made by human experts when estimating the development effort of software projects, and whether it is possible to use these predictions to enhance future estimations. To this end we conduct a thorough empirical study investigating 402 maintenance and new development industrial software projects. The results of our study reveal that the type, severity and magnitude of errors are all, indeed, predictable. Moreover, we find that by exploiting these predictions, we can obtain significantly better estimates than those provided by random guessing, human experts and traditional machine learners in 31 out of the 36 cases considered (86 percent), with large and very large effect sizes in the majority of these cases (81 percent). This empirical evidence opens the door to the development of techniques that use the power of machine learning, coupled with the observation that human errors are predictable, to support engineers in estimation tasks rather than replacing them with machine-provided estimates.
Scrum Evaluation to Increase Software Development Project Success: A Case Study of Digital Banking Company

Agile can help companies respond to change. Agile offers flexibility to introduce necessary changes in the middle of development process. Scrum is an agile framework for developing and sustaining complex problems and more efficient in terms of cost and time. XYZ Digital Bank uses Scrum as its software development method, but tasks that are committed are often found carried over to the next or several sprints. This study aims to identify solutions that can address the problem of sprint execution on Scrum and increase project delivery time. A literature review was carried out on previous studies and found five factors of sprint execution problems, i,e., problem during performing self-organization, team lack of cross-functionality, changes in the middle of the development process, the scope creep, and bad planning at testing stage.
Improving the performance of reference-frame-independent quantum key distribution through a turbulent atmosphere

Reference-frame-independent quantum key distribution (RFI-QKD) can dispense with the requirements of active alignment on reference frames between legitimate users, which is beneficial for free-space implementation. However, the fluctuating transmittance due to atmospheric turbulence still remains a great challenge for improving the performance and has been seldom addressed. In this paper we extend the recently proposed prefixed-threshold real-time selection method to RFI-QKD while combining practical consideration of the transmittance probability distribution model based on the finite aperture theory. Through numerical simulations, we present an estimation for the variance of the log-normal model with respect to distance and receiving aperture radius, and demonstrate the effectiveness of using this method in the RFI protocol. Considering the finite-key effects, simulation results show that the gap of the key rate with different reference frame deviations can be alleviated by increasing the data size. By adopting this method one can tolerate more serious transmission loss, especially in strong turbulence cases, which is helpful for future free-space experimental designs.
WITHDRAWN: A comprehensive study on recommendation systems their issues and future research direction in e-learning domain

With the advent of World Wide Web a significant number of institutions, universities and companies have started doing their business online. However, in this scenario a huge amount of items are presented to the user online which makes it difficult for a user to choose the right item which best matches with their preferences. This motivates researchers to build a recommendation engine which suggests right items to users based on their profile. A recommendation systems help out the end user by presenting a list of items to choose from the vast repository of items. The function of a recommendation system is to predict the ratings of unseen items by a user and recommends the top N items which might be of interest to the end user. Today’s recommendation system based their recommendations solely on users’ profile which disregards other crucial information about users’ preferences which could further improve the quality of recommendations. This users’ profile is not sufficient to make good quality of recommendations and can be further enriched by exploiting additional information about learners such as learners’ contextual data, tagging data of learner in social networking sites and learners’ semantic information underlying a website among others. Keeping this in mind, new recommendation approaches need to be developed which can extract and combine this additional data about learners into the recommendation process. This paper aims to survey existing recommendation techniques, their algorithms, discusses their issues and provides research directions for developing smart E-Learning recommender systems.
Trace-Driven Optimization on Bitrate Adaptation for Mobile Video Streaming

Mobile video streaming occupies three-quarters of today's cellular network traffic. The quality of mobile videos becomes increasingly important for video providers to attract more users. For example, they invest in network bandwidth resources and conduct adaptive bitrate techniques to improve video quality. Prior adaptive bitrate (ABR) algorithms perform well under given throughput traces on broadband and WiFi networks. They may perform poorly for mobile video streaming due to the high network dynamics of cellular networks. To study the properties of throughput traces under cellular networks, we collect 4G network throughput traces for over four months in two large cities, Beijing and Suzhou in China. We derive the environment-specific Markov property of throughputs in the dataset. Accordingly, we propose NEIVA, an environment identification based technique to adaptively predict future throughput for different types of environments. We also implement NEIVA and integrate it with the state-of-the-art ABR algorithm, model predictive control (MPC) approach in our testbed for experiments. By emulating mobile video streaming under throughput traces in our dataset, NEIVA achieves 20 - 25 percent improvement on throughput prediction accuracy comparing to baseline predictors. Meanwhile, NEIVA achieves 11 - 20 percent user QoE improvement over MPC with baseline predictors.
A Deep Learning Classification Approach for Short Messages Sentiment Analysis

In today's world, we humans have been communicating with each other through calls, social media applications like whatsapp, facebook, twitter etc. From the social media apps we get social media data from those applications and check what sentences are positive and negative sentiment using sentiment analysis and using deep learning methods like deep neural networks for using the Hindi tweets dataset and classifying them under positive or negative sentiment polarity from twitter accounts.