Reviewerly/exHarmony huggingface dataset:
Large-scale Cost-aware Classification Using Feature Computational Dependency Graph

With the rapid growth of real-time machine learning applications, the process of feature selection and model optimization requires to integrate with the constraints on computational budgets. A specific computational resource in this regard is the time needed for evaluating predictions on test instances. The joint optimization problem of prediction accuracy and prediction-time efficiency draws more and more attention in the data mining and machine learning communities. The runtime cost is dominated by the feature generation process that contains significantly redundant computations across different features that sharing the same computational component in practice. Eliminating such redundancies would obviously reduce the time costs in the feature generation process. Our previous Cost-aware classification using Feature computational dependencies heterogeneous Hypergraph (CAFH) model has achieved excellent performance on the effectiveness. In the big data era, the high dimensionality caused by the heterogeneous data sources leads to the difficulty in fitting the entire hypergraph into the main memory and the high computational cost during the optimization process. Simply partitioning the features into batches cannot give the optimal solution since it will lose some feature dependencies across the batches. To improve the high memory and computational costs in the CAFH model, we propose an equivalent Accelerated CAFH (ACAFH) model based on the lossless heterogeneous hypergraph decomposition. An efficient and effective nonconvex optimization algorithm based on the alternating direction method of multipliers (ADMM) is developed to optimize the ACAFH model. The time and space complexities of the optimization algorithm for the ACAFH model are three and one polynomial degrees less than our previous algorithm for the CAFH model, respectively. Extensive experiments demonstrate the proposed ACAFH model achieves competitive performance on the effectiveness and much better performance on the efficiency.
Cyber-Space and Its Menaces

In today’s world, cyber security is a major concern to protect resources of networks, confidential data and vital information in an organization and prevent these from getting into the wrong hands. This paper aims to bring into light, the different types of cyber threats, their respective solutions and preventive measures to overcome from them. Besides that, it also describes the different facets of cyber-crime and status of cyber-security across the globe. Now-a-days, with the increased growth of internet usage, cyber security is not limited to a personal workstation, but is also used to prevent information retrieval from personal mobile devices like tabs and cell phones because they have become a very crucial medium of information transfer due to the current advancements in technology. Security is also required for various applications such as shopping, banking transactions, education, public services to name a few. With increased usage worldwide, internet is also known to have vulnerability issue. The vulnerability issue consists of various cybercrimes namely fraudulent banking transactions, known as phishing, HTML injections, SQL Injections etc. which are prevalent these days. Cyber security is a matter of concern, due to the quick and continuous growth of complexity levels of the threats. In order to solve cyber-security related issues, the security researchers’ community comprising of the government sector, academia and the private sector must work together to recognize the evolving threats to the computing world and provide methods to stop this menace.
Texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization for stereo matching

In stereo matching, energy-minimization optimizers such as dynamic programming (DP) could achieve efficient disparity estimation. However, DP-based optimizers still suffer from relatively high energy due to too many artificial penalty parameters and a complex energy function, inevitably leading to matching failure in depth discontinuities, occlusions, and low-textured regions. To reach dense high-accuracy disparity estimation, we propose an effective stereo-matching method using texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization. First, pixel adjustment is introduced to the gradient difference calculation so that initial matching accuracy, especially in low-textured regions, can be improved. For determining the degree of pixel adjustment, a gray-gradient co-occurrence matrix and the fuzzy c-means method are adopted to differentiate between two varieties of reference images based on textural property. Second, the support weight in local methods and the smoothness constraints in global optimizations are leveraged. Color information and the confidence map of the input image are combined to form the support weight. By ameliorating the smoothness constraint function during weighted horizontal DP passes, the adverse effects of excessive penalty can be reduced. Third, a postprocessing method is proposed for refining results in both occluded and low-textured regions. The proposed method considers test cases from the Middlebury v.2 and v.3 datasets and can outperform the state-of-the-art stereo-matching algorithms.
ASK Approach: A Pre-migration Approach for Legacy Application Migration to Cloud

Legacy application migration is a mammoth task, if migration approach is not well thought at the very start, i.e. pre-migration, and supported by robust planning especially at pre-migration process area. This paper proposes a mathematical pre-migration approach, which will help the enterprise to analyse existing/legacy application based on the application’s available information and parameters an enterprise would like to consider for analysis. Proposed pre-migration assessment will help in understanding the legacy application’s current state and will help in un-earthing the information with respect candidate application. Proposed pre-migration approach will help to take appropriate well-informed decision, whether to migrate or not to migrate the legacy application. As it is said that application migration is a journey, if kick-started once, needs to reach its destination else it can result into a disaster hence pre-migration is one of the important areas of migration journey.
Interference of Quantum States and Superposition Principle in Probability Representation of Quantum Mechanics

The superposition of pure quantum states explicitly expressed in terms of a nonlinear addition rule of state density operators is reviewed. The probability representation of density matrices of qudit states is used to formulate the interference of the states as a combination of the probability distributions describing pure states. The formalism of quantizer–dequantizer operators is developed. Examples of spin-1/2 states and f-oscillator systems are considered.
Enhanced Shortened Uniform Resource Locator Based on Phishing and Malware Detection Algorithm-A Secure Model

Security plays an important role in most of the applications like banking, military etc, In that, URL shortener is one of a kind. Any issues in security may causes damages to the applications and it lead to the dissatisfaction among the users. So in the existing URL shortener there are security issues which cause threats. URL Shortener is used to convert longer form of URL into shortened form of the URL. Users deal with many important URL's in their day-to-day life. They are very long and annoying for the user to remember. It is the technique on the www in which URL can be made significantly short and can be redirected to the destination page. Often the redirected domain name is shorter than the original one. A shortened URL is more user friendly and it looks very neat, trimmed and attractive for the user. URL Shortener can be also used to beautify the link and track clicks to the link. Shortened URL can be abused by redirecting to phishing and malware websites which may cause harm to the users systems. Because they have no clue about the name of the destination website. So hackers may steal the credentials of the users and gain authorized access to the system which leads to damages in the system. This is the problem in the existing URL shortener in the market like bitly, clkim, cloudApp, Droplr, Geniuslink. To overcome the issues present in the existing systems, the idea behind this paper is to provide secure model for shortened URL which provides user awareness and confidence about the shortened URL's. The main feature added is to verify whether the original website contains any malware and spurious infections in the system.
GANEx: A complete pipeline of training, inference and benchmarking GAN experiments

Deep learning (DL) is one of the standard methods in the field of multimedia research to perform data classification, detection, segmentation and generation. Within DL, generative adversarial networks (GANs) represents a new and highly popular branch of methods. GANs have the capability to generate, from random noise or conditional input, new data realizations within the dataset population. While generation is popular and highly useful in itself, GANs can also be useful to improve supervised DL. GAN-based approaches can, for example, perform segmentation or create synthetic data for training other DL models. The latter one is especially interesting in domains where not much training data exists such as medical multimedia. In this respect, performing a series of experiments involving GANs can be very time consuming due to the lack of tools that support the whole pipeline such as structured training, testing and tracking of different architectures and configurations. Moreover, the success of generative models is highly dependent on hyper-parameter optimization and statistical analysis in the design and fine-tuning stages. In this paper, we present a new tool called GANEx for making the whole pipeline of training, inference and benchmarking GANs faster, more efficient and more structured. The tool consists of a special library called FastGAN which allows designing generative models very fast. Moreover, GANEx has a graphical user interface to support structured experimenting, quick hyper-parameter configurations and output analysis. The presented tool is not limited to a specific DL framework and can be therefore even used to compare the performance of cross frameworks.
Fast Compressed Self-indexes with Deterministic Linear-Time Construction

We introduce a compressed suffix array representation that, on a text T of length n over an alphabet of size $$\sigma $$, can be built in O(n) deterministic time, within $$O(n\log \sigma )$$ bits of working space, and counts the number of occurrences of any pattern P in T in time $$O(|P| + \log \log _w \sigma )$$ on a RAM machine of $$w=\Omega (\log n)$$-bit words. This time is almost optimal for large alphabets ($$\log \sigma =\Theta (\log n)$$), and it outperforms all the other compressed indexes that can be built in linear deterministic time, as well as some others. The only faster indexes can be built in linear time only in expectation, or require $$\Theta (n\log n)$$ bits. For smaller alphabets, where $$\log \sigma = o(\log n)$$, we show how, by using space proportional to a compressed representation of the text, we can build in linear time an index that counts in time $$O(|P|/\log _\sigma n + \log _\sigma ^\epsilon n)$$ for any constant $$\epsilon >0$$. This is almost RAM-optimal in the typical case where $$w=\Theta (\log n)$$.
An Effective Reversible Data-Hiding in Encrypted Images Using Memory Cellular Automata

This article attempts to propose an effective reversible data-hiding in encrypted images using memory cellular automata. The image content owner encrypts the original image using a Paillier homomorphic encryption algorithm with an encryption key and uploads the encrypted image into the cloud server. Then, the data hider embeds additional data into the encrypted image with an embedding key. Whenever the user gets both keys, the original image can be recovered without any errors. Compared to existing reversible data-hiding in encrypted image methods, the proposed method has better embedding rates, embedding payloads, and high-security performance.
Novel Class Detection with Concept Drift in Data Stream - AhtNODE

Data stream mining has become an interesting analysis topic and it is a growing interest in data discovery method. There are several applications supporting stream data processing like device network, electronic network, etc. Our approach AhtNODE (Adaptive Hoeffding Tree based NOvel class DEtection) detects novel class in the presence of concept drift in streaming data. It addresses there are three challenges of streaming data: infinite length, concept drift, and concept evolution. This approach automatically detects the novel class whenever it arrives in the data stream. It is a multi-class approach that distinguishes novel class from existing classes. The authors tend to apply the Adaptive Hoeffding Tree as a classification model that is also used to handle the concept drift situation. Previous approaches used the ensemble model to handle concept drift. In AHT, classification is done in the single pass. The experiment result proves the effectiveness of AhtNODE compared to existing ensemble classifier in terms of classification accuracy, speed and use of memory.
Pre-trained Word Embedding based Parallel Text Augmentation Technique for Low-Resource NMT in Favor of Morphologically Rich Languages

Recently, neural machine translation (NMT) has made a remarkable achievement. However, performance of NMT is highly influenced by the size of training parallel text. The required amount of parallel text is not available for low-resource languages. The issue of low-resource NMT exacerbated if at least one of paired languages is morphologically rich. Inspired by the capabilities of pre-trained word embedding models, we propose simple yet effective word embedding based parallel text augmentation technique. The proposed technique is vital to enlarge size of limited parallel text for low-resource NMT. To this end, we adopt publicly available pre-trained word embedding models that favor morphologically rich languages. Accordingly, we replace each word in the original text with the n most similar words in the vector space (in this study n=3). For experimental analysis, we simulate low-resource languages by using publicly available parallel texts for German↔English, Turkish↔English and Finnish↔English language pairs. We use the original parallel texts as a seed for generating synthetic parallel texts and for training character-level NMT that deemed as baseline models. The obtained results show that our augmentation techniques improve performance of baseline and dummy source sentence based NMT models by up to 5.9 CHRF scores.
A Novel Hybrid User Authentication Scheme Using Cognitive Ambiguous Illusion Images

Text-based passwords are most common and easy to use butDabeer, Sumaiya are difficult to memorize and remember. Moreover,Ahmad, Mahira they are prone to attacks like shoulderSarosh Umar, Mohammad surfing and brute-force. On the other hand, graphical passwords are easy to remember and memorize. But theyHasan Khan, Muneeb are still not commonly used as they have some issues like increased user login time, and small password space. In today’s scenario where number of data breaches is increasing, more secure authentication schemes are needed to ensure the authenticity of a user. In this paper, we propose a novel hybrid user authentication scheme by integrating both text-based and graphical password schemes to make authentication system stronger and resistant to attacks. Our scheme has two steps of authentication, in which at the first step, the user has to recognize and select his appropriate image among the blurred images and in the next step, the user has to enter the tag associated with the selected image. Only after successful completion of the two steps, the user is authenticated. The images used as a part of graphical password scheme are cognitive ambiguous illusion images. The basic idea behind using these images is that they are perceived by different users differently depending on how they visualize the image. To evaluate the effectiveness of the proposed scheme, an experiment was conducted on the setup and the results obtained were promising.
The Covering Path Problem on a Grid

This paper introduces the covering path problem on a grid (CPPG) that finds the cost-minimizing path connecting a subset of points in a grid such that each point that needs to be covered is within a predetermined distance of a point from the chosen subset. We leverage the geometric properties of the grid graph, which captures the road network structure in many transportation problems, including our motivating setting of school bus routing. As defined in this paper, the CPPG is a biobjective optimization problem comprising one cost term related to path length and one cost term related to stop count. We develop a trade-off constraint, which quantifies the trade-off between path length and stop count and provides a lower bound for the biobjective optimization problem. We introduce simple construction techniques to provide feasible paths that match the lower bound within a constant factor. Importantly, this solution approach uses transformations of the general CPPG to either a discrete CPPG or continuous CPPG based on the value of the coverage radius. For both the discrete and continuous versions, we provide fast constant-factor approximations, thus solving the general CPPG.
Broadmark: A Testing Framework for Broad‐Phase Collision Detection Algorithms

Abstract Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions.
Cross-Domain Authentication and Interoperability Scheme for Federated Cloud

Collaboration of the clouds has revolutionized the IT industry whereby different service providers can come together with an agreement to leverage the services from each other and make efficient use of various available resources. For such type of collaborations, there is different working environments with different authentication protocols. In heterogeneous domain, two major network authentication protocols such as X.509 and Kerberos 5 have been widely used. X.509 is asymmetric-based, whereas the Kerberos 4 is symmetric-based. Therefore, communication between these protocols was a challenging task. But with the emergence of Kerberos 5, the problem of communication between two domains have been resolved to a large extent as Kerberos 5 does support asymmetric encryption. Also, one of the ever existing issued in any domain is of the security. Each user needs to prove its identity before accessing any resources. This authentication phase in multiple domain environment becomes a biggest challenge. Another issue is of interoperability, which is strongly required to meet the user needs in the collaborative structure of clouds. The proposed model enables cross-domain mutual authentication between X.509 domain and Kerberos 5 domain using Elliptic Curve Cryptography(ECC) as well as Public Key Cryptography for Initial Authentication (PKINIT).
PDF-DS: Privacy-Preserving Data Filtering for Distributed Data Streams in Cloud

Many real-world applications, from traditional wireless sensor networks to today's Internet of Thing (loT), generate a large amount of data streams distributively. To meet challenges of handling distributed data streams, deploying data streams management systems on public clouds is a prevalent choice. However, as data streams may contain sensitive information, appropriate privacy protection mechanisms must be in place when sending these data to the cloud. Aiming to utilize data outsourced to the cloud without disclosing their privacy, a number of functional encryption schemes have been proposed. Nevertheless, these existing schemes either only consider centralized data source or require pre-defined indexes. This paper proposes a privacy-preserving filtering scheme for distributed data streams outsourced to the public cloud. Our scheme allows cloud servers to filter out corresponding data streams directly over encrypted data. Our scheme enables each data source to encrypt its data independently, and thus the compromise of one data source will not reveal the privacy of others. Thorough analysis and experimental evaluation are carried out to demonstrate the security, effectiveness, and efficiency of our scheme.
Taxonomy of Payment Structures and Economic Incentive Schemes in Internet

Recently the study of economics of internet has emerged as an emerging field of study. The workstations being distributed across the network along with the users having varied interests has made this study very important from an information security and policy designing point of view. The main purpose of any framework design is to keep up with the security standards of confidentiality, integrity and availability without being an overburden on the deployer. The same goes for the users, the Quality of Service (QoS) should be in accordance with what they pay for. The concept of “tragedy of commons” plays an important role in distributing the limited resources of the internet. In this, the users because of their own self-interest destroy the collective interest of a community sharing the resource. A sustainable pricing strategy is the one which is able to cater to the competitive advantage of different network providers offering the same set of services but on varied prices. A pricing mechanism will help in differentiating the services offered to the users, but another important task is of fixing the incentives. The pricing strategy plays a very important role in facilitating varied kinds of QoS requirements. Security professionals have realized that while designing any security mechanism it is vital to keep in consideration the “theory of mind” which explains the way the attackers and benign users take decision to deceive of remain loyal to the system. So, studying the incentive and payment structure from economic point of view is important.