Reviewerly/exHarmony huggingface dataset:
Adaptive perceptual quantizer for high dynamic range video compression

Although high dynamic range (HDR) videos are a very fascinating way to represent real-world scenes, they need a huge amount of memory to store and transmit due to the high bit-depth. Thus, it is a major challenge for HDR video coding to efficiently compress them without sacrificing perceptual quality. Perceptual Quantizer (PQ) transfer function provides a solution to this problem, which is adopted as the HEVC Main 10 Profile-based Anchor. However, PQ is not adaptive to HDR contents, thus reducing the coding efficiency. In this paper, we propose adaptive transfer function based on PQ for HDR video compression, called adaptive PQ. Different from PQ which uses a fixed mapping curve from luminance to luma, the proposed transfer function adaptively maps luminance to luma according to HDR contents. Thus, adaptive PQ is able to efficiently utilize possible luma values. Moreover, adaptive PQ achieves better perceptual uniformity in the luminance range than PQ. Experimental results demonstrate that adaptive PQ achieves a significant performance improvement in HDR video coding over PQ in terms of visual quality and bitrate.
A DICTIONARY LEARNING APPROACH FOR FRACTAL IMAGE CODING

In recent years, sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, this paper investigates incorporating a dictionary learning approach into fractal image coding, which leads to a new model containing three terms: a patch-based sparse representation prior over a learned dictionary, a quadratic term measuring the closeness of the underlying image to a fractal image, and a data-fidelity term capturing the statistics of Gaussian noise. After the dictionary is learned, the resulting optimization problem with fractal coding can be solved effectively. The new method can not only efficiently recover noisy images, but also admirably achieve fractal image noiseless coding/compression. Experimental results suggest that in terms of visual quality, peak-signal-to-noise ratio, structural similarity index and mean absolute error, the proposed method significantly outperforms the state-of-the-art methods.
Curvelet transform based feature extraction and selection for multimedia event classification

Multimedia event classification has been one of the major endeavors in video event analysis. For event identification, feature extraction plays a critical role, merely distinguishing the right features becomes a challenging job. So, in this paper, feature extraction and selection for video event detection are proposed. For video event detection or classification, identification of object structure and its motion are a basic needs. So, for object detection, curvelet features are considered, due to its high directional selectivity and high anisotropic properties. Next, an algorithm for Shot Boundary Detection (SBD) called Motion based SBD (MSBD) is proposed to identify the shot boundaries. Also, to make the users to access the queried event in less time, an algorithm for representing few representative frames containing whole video content is proposed. To make an event search efficient, object-based features from dominant features are extracted and to enhance the features much more efficient and dominant, feature selection is performed using the ranking method. Lastly, an SVM classifier with RBF kernel is used for event classification. The proposed work is experimented using Columbia Consumer Video (CCV) dataset and evaluated using mean Average Precision (mAP) and find that, it outperforms various other existing methods.
Planning with actively eliciting preferences

Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without knowledge of the reasoning employed by the planner. We consider the problem of actively eliciting preferences from a human expert during the planning process. Specifically, we study this problem in the context of the Hierarchical Task Network (HTN) planning framework as it allows easy interaction with the human. We propose an approach where the planner identifies when and where expert guidance will be most useful and seeks expert’s preferences accordingly to make better decisions. Our experimental results on several diverse planning domains show that the preferences gathered using the proposed approach improve the quality and speed of the planner, while reducing the burden on the human expert.
DOPdefender: An approach to thwarting data-oriented programming attacks based on a data-aware automaton

In recent years, non-control data attacks have become a popular topic in the field of network security. These attacks, such as data-oriented programming (DOP), do not aim to circumvent the control-flow integrity (CFI) protections; rather, they need corrupt only the security-critical non-control data of the target program. Non-control data attacks have been shown to achieve Turing-complete computation. In this paper, we build a non-control data attack description model and analyse feasible defence strategies. We present a new program behaviour model, i.e., the data-aware finite-state automaton (dFSA). Based on the dFSA, we propose the DOPdefender method, which is a method of defending against non-control data attacks. DOPdefender monitors the target process and is aware of the security-critical non-control data that are expected to be operated at runtime, thus validating the legality of the operation on the security-critical non-control data. DOPdefender can prevent adversaries from corrupting the security-critical non-control data of the target program and defend against existing non-control data attacks. We evaluate our method on a Linux operating system. An effectiveness test and a performance test indicate the effectiveness of DOPdefender in thwarting non-control data attacks with a 28.4% runtime overhead on average for CPU-intensive programs and a 13.5% runtime overhead on average for I/O-intensive programs. In the Limitations Section, we discuss a solution to making our method scalable for large-scale programs under some guiding information.
The effect of velocity sparsity on the performance of cardinality constrained particle swarm optimization

The Particle Swarm Optimization (PSO) algorithm is a flexible heuristic optimizer that can be used for solving cardinality constrained binary optimization problems. In such problems, only K elements of the N-dimensional solution vector can be non-zero. The typical solution is to use a mapping function to enforce the cardinality constraint on the trial PSO solution. In this paper, we show that when K is small compared to N, the use of the mapped solution in the velocity vector tends to lead to early stagnation. As a solution, we recommend to use the untransformed solution as a direction in the velocity vector. We use numerical experiments to document the gains in performance when K is small compared to N.
How to Estimate Global Motion Non-Iteratively From a Coarsely Sampled Motion Vector Field

Nowadays, as the amount of video content increases, the importance of global motion estimation is increasing more and more. This paper considers motion estimation within a compressed domain and presents a robust non-iterative global motion estimation algorithm that efficiently eliminates outliers. The said algorithm is comprised of two parts. The first part uses the distinct-outlier mask based on statistical analysis to eliminate the distinct outliers and uses the current-object mask based on the reference-object mask to eliminate the object outliers. In the second part, the proposed median error designed to be robust to the outlier ratio has been used to estimate weights with higher values as the motion vector gets closer to global motion. Using these weights, the proposed algorithm can accurately estimate global motion non-iteratively, although the outlier ratio may change. The simulation results demonstrate that the proposed algorithm demonstrates the highest estimation accuracy and processing speed in synthetic motion fields as well as real video sequences.
Improved Bounds on the Threshold Gap in Ramp Secret Sharing

In this paper we consider linear secret sharing schemes over a finite field F q , where the secret is a vector in F q and each of the n shares is a single element of F q .We obtain lower bounds on the so-called threshold gap g of such schemes, defined as the quantity r -t where r is the smallest number such that any subset of r shares uniquely determines the secret and t is the largest number such that any subset of t shares provides no information about the secret.Our main result establishes a family of bounds which are tighter than previously known bounds for ≥ 2. Furthermore, we also provide bounds, in terms of n and q, on the partial reconstruction and privacy thresholds, a more fine-grained notion that considers the amount of information about the secret that can be contained in a set of shares of a given size.Finally, we compare our lower bounds with known upper bounds in the asymptotic setting.
Exploratory testing: Do contextual factors influence software fault identification?

Context: Exploratory Testing (ET) is a manual approach to software testing in which learning, test design and test execution occurs simultaneously. Still a developing topic of interest to academia, although as yet insufficiently investigated, most studies focus on the skills and experience of the individual tester. However, contextual factors such as project processes, test scope and organisational boundaries are also likely to affect the approach. Objective: This study explores contextual differences between teams of testers at a MedTec firm developing safety-critical products to ascertain whether contextual factors can influence the outcomes of ET, and what associated implications can be drawn for test management. Method: A development project was studied in two iterations, each consisting of a quantitative phase testing hypotheses concerning when ET would identify faults in comparison to other testing approaches and a qualitative phase involving interviews. Results: Influence on ET is traced to how the scope of tests focus learning on different types of knowledge and imply an asymmetry in the strength and number of information flows to test teams. Conclusions: While test specialisation can be attractive to software development organisations, results suggest changes to processes and organisational structures might be required to maintain test efficiency throughout projects: the responsibility for test cases might need to be rotated late in projects, and asymmetries in information flows might require management to actively strengthen the presence and connections of test teams throughout the firm. However, further research is needed to investigate whether these results also hold for non safety-critical faults.
Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning

Usually, real-world problems involve the optimization of multiple, possibly conflicting, objectives. These problems may be addressed by Multi-objective Reinforcement learning (MORL) techniques. MORL is a generalization of standard Reinforcement Learning (RL) where the single reward signal is extended to multiple signals, in particular, one for each objective. MORL is the process of learning policies that optimize multiple objectives simultaneously. In these problems, the use of directional/gradient information can be useful to guide the exploration to better and better behaviors. However, traditional policy-gradient approaches have two main drawbacks: they require the use of a batch of episodes to properly estimate the gradient information (reducing in this way the learning speed), and they use stochastic policies which could have a disastrous impact on the safety of the learning system. In this paper, we present a novel population-based MORL algorithm for problems in which the underlying objectives are reasonably smooth. It presents two main characteristics: fast computation of the gradient information for each objective through the use of neighboring solutions, and the use of this information to carry out a geometric partition of the search space and thus direct the exploration to promising areas. Finally, the algorithm is evaluated and compared to policy gradient MORL algorithms on different multi-objective problems: the water reservoir and the biped walking problem (the latter both on simulation and on a real robot).
Neuro-Fuzzy Models and Applications

The modern techniques of artificial intelligence have found application in almost all the fields of human knowledge. Among them, two important techniques of artificial intelligence, fuzzy systems (FS) and artificial neural networks (ANNs), have found many applications in various fields such as production, control systems, diagnostic, supervision, etc. They evolved and improved throughout the years to adapt arising needs and technological advancements. However, a great emphasis is given in the engineering field. The techniques of artificial intelligence based on fuzzy logic and neural networks are frequently applied together for solving engineering problems where the classic techniques do not supply an easy and accurate solution. Separately, each one of these techniques possesses advantages and disadvantages that, when mixed together, provide better results than the ones achieved with the use of each isolated technique. As ANNs and fuzzy systems have often been applied together, the concept of a fusion between them started to take shape. Neuro-fuzzy systems were born which utilize the advantages of both techniques. Such systems show two distinct ways of behavior. In a first phase, called learning phase, it behaves like neural networks that learn internal parameters off-line. Later, in the execution phase, it behaves like a fuzzy logic system. A neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. Neural networks and fuzzy systems can be combined to join its advantages and to cure its individual illness. Neural networks introduce its computational characteristics of learning in the fuzzy systems and receive from them the interpretation and clarity of systems representation. Thus, the disadvantages of the fuzzy systems are compensated by the capacities of the neural networks. These techniques are complementary, which justifies its use together. This chapter deals with an analysis of neuro-fuzzy systems. Benefits of these systems are studied with its limitations too. Comparative analyses of various categories of neuro-fuzzy systems are discussed in detail. Apart from these, real-time applications of such systems are also presented.
Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach

Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively utilize the word burstiness representation of documents and achieve decent performance, they do not explore the sequential information of text and relationships among synonyms. In this paper, the documents are modeled as the joint of bags of words, sequential features and word embeddings. We proposed Sequential Embedding induced Dirichlet Process Mixture Model (SiDPMM) to effectively exploit this joint document representation in text clustering. The sequential features are extracted by the encoder-decoder component. Word embeddings produced by the continuous-bag-of-words (CBOW) model are introduced to handle synonyms. Experimental results demonstrate the benefits of our model in two major aspects: (1) improved performance across multiple diverse text datasets in terms of the normalized mutual information (NMI); (2) more accurate inference of ground truth cluster numbers with regularization effect on tiny outlier clusters.
Connecting Researchers to Data Repositories in the Earth, Space, and Environmental Sciences

The Repository Finder tool was developed to help researchers in the domain of Earth, space, and environmental sciences to identify appropriate repositories where they can deposit their research data and to promote practices that implement the FAIR Principles, encouraging progress toward sharing data that are findable, accessible, interoperable, and reusable. Requirements for the design of the tool were gathered through a series of workshops and working groups as a part of the Enabling FAIR Data initiative led by the American Geophysical Union that included the development of a decision tree that researchers may follow in selecting a data repository, interviews with domain repository managers, and usability testing. The tool is hosted on the web by DataCite and enables a researcher to query all data repositories by keyword or to view a list of domain repositories that accept data for deposit, support open access, and provide persistent identifiers. Metadata records from the re3data.org registry of research data repositories and the returned results highlight repositories that have achieved trustworthy digital repository certification through a formal procedure such as the CoreTrust Seal.
A cloud-oriented integrity verification system for audio forensics

Integrity analysis is an important task in the field of audio forensics. Its aim is to verify if an evidence (i.e. audio recording) has been manipulated or not. With the aim of providing a solution for the chain of custody, we propose a system that encompasses capture module, the hash value of the audio recording, cloud storage and integrity verification module. In order to validate the properties of the proposed hash function and its execution time, several tests were performed. According to the results, the user of our system can demonstrate/verify the integrity of the evidence in any moment after the recording process.
Parallel transfer evolution algorithm

Parallelization of an evolutionary algorithm takes the advantage of modular population division and information exchange among multiple processors. However, existing parallel evolutionary algorithms are rather ad hoc and lack a capability of adapting to diverse problems. To accommodate a wider range of problems and to reduce algorithm design costs, this paper develops a parallel transfer evolution algorithm. It is based on the island-model of parallel evolutionary algorithm and, for improving performance, transfers both the connections and the evolutionary operators from one sub-population pair to another adaptively. Needing no extra upper selection strategy, each sub-population is able to select autonomously evolutionary operators and local search operators as subroutines according to both the sub-population’s own and the connected neighbor’s ranking boards. The parallel transfer evolution is tested on two typical combinatorial optimization problems in comparison with six existing ad-hoc evolutionary algorithms, and is also applied to a real-world case study in comparison with five typical parallel evolutionary algorithms. The tests show that the proposed scheme and the resultant PEA offer high flexibility in dealing with a wider range of combinatorial optimization problems without algorithmic modification or redesign. Both the topological transfer and the algorithmic transfer are seen applicable not only to combinatorial optimization problems, but also to non-permutated complex problems.
A new dcpo whose Scott topology is well-filtered but not sober

A topological space is sober if each of its nonempty closed irreducible sets is the closure of a unique singleton. A weaker topological property is the well-filteredness first considered by Heckmann. Johnstone constructed the first directed complete poset whose Scott topology is not sober. Heckmann asked whether the Scott topology of a directed complete poset is sober if it is assumed to be well-filtered. Kou constructed the first counterexample to give a negative answer. In this short note, based on our recent work on the dcpo models of topological space, we give another simpler example of directed complete poset whose Scott topology is well-filtered but not sober.
Modeling Player Knowledge in a Parallel Programming Educational Game

This article focuses on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracing player knowledge</i> in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the work is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from intelligent tutoring systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parallel</i> to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error. We also provide results from deployment of our system in a classroom environment.
Output field squeezing in a weakly-driven dissipative quantum Rabi model

We present a generalized method to address the squeezing spectrum of the output field for a weakly-driven, dissipative quantum Rabi model and calculate it for a range of coupling strengths covering strong, ultra-strong and deep-strong coupling regimes. The need for a generalization arises because of the periodic time dependence of the correlation functions due to counter rotating terms in the quantum Rabi model. We here adopt time-averaging over such correlation functions to obtain the squeezing spectrum, which may also be relevant to experimental situations. We compare the calculated squeezing spectra with those of the driven dissipative Jaynes–Cummings model to illustrate the departure from the latter as the interaction strength increases. Our calculation shows that the overall squeezing increases with the interaction strength after optimization over the system-bath coupling strength and the driving field strength.