Reviewerly/exHarmony huggingface dataset:
Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization

Leading cloud providers recently introduced a new instance type named burstable instances to better match the time-varying workloads of tenants and further reduce their costs. In the research community, however, little has been done to understand burstable instances from a theoretical perspective. This paper presents the first unified framework to model, analyze, and optimize the operation of burstable instances. Specifically, we model the resource provisioning of burstable instances in different service classes, identify key performance metrics, and derive the performance given the resource provisioning decisions. We then characterize the equilibrium behind tenants' responses to the prices offered for different burstable instance service classes, taking into account the impact of tenants' actions on the performance achieved by each service class. In addition, we investigate how a cloud provider can leverage the knowledge of this equilibrium to find the prices that maximize its total revenue. Finally, we validate our framework on real traces and demonstrate its usage to price a public cloud.
Quantum IoT: A Quantum Approach in IoT Security Maintenance

Securing Internet of things is a major concern as it deals with data that are personal, needed to be reliable, can direct and manipulate device decisions in a harmful way. Also regarding data generation process is heterogeneous, data being immense in volume, complex management. Quantum Computing and Internet of Things (IoT) coined as Quantum IoT defines a concept of greater security design which harness the virtue of quantum mechanics laws in Internet of Things (IoT) security management. Also it ensures secured data storage, processing, communication, data dynamics. In this paper, an IoT security infrastructure is introduced which is a hybrid one, with an extra layer, which ensures quantum state. This state prevents any sort of harmful actions from the eavesdroppers in the communication channel and cyber side, by maintaining its state, protecting the key by quantum cryptography BB84 protocol. An adapted version is introduced specific to this IoT scenario. A classical cryptography system ‘One-Time pad (OTP)’ is used in the hybrid management. The novelty of this paper lies with the integration of classical and quantum communication for Internet of Things (IoT) security.
Online Multiple Maneuvering Vehicle Tracking System Based on Multi-Model Smooth Variable Structure Filter

Autonomous vehicles need a real-time traffic tracking system in order to interact with multiple moving vehicles in urban situations. Most existing works rely on camera sensors to interpret the surrounding environment. However, camera sensors degrade in conditions of lighting, shadows, and extreme weathers such that they can hardly detect objects. The light detection and ranging (LiDAR) sensors promise to be a good substitute, as they enable highly precise and robust localization across a wide range of conditions. This paper presents a new LiDAR-based online multiple maneuvering vehicle tracking problem and proposes a novel online multi-model smooth variable structure filter to address the problem. The real-time experiments show that our method is able to deliver superior performance compared to other conventional methods.
Nonhomogeneous Noise Removal From Side-Scan Sonar Images Using Structural Sparsity

The image quality of side-scan sonar (SSS) is determined by its operating frequency. SSS operating at a low frequency produces low-quality images due to high levels of noise. This noise is randomly generated from a number of different sources, including equipment noise and underwater environmental interference. In addition, to compensate for transmission loss in a received signal, the signal is amplified by time-varied gain correction, and consequently, SSS images contain nonhomogeneous noise, unlike natural images whose noise is assumed to be homogeneous. In this letter, a structural sparsity-based image denoising algorithm is proposed to remove nonhomogeneous noise from SSS images. The algorithm incorporates both local and nonlocal models in the structural features domain in order to guarantee sparsity and enhance nonlocal self-similarity. Using structural features also preserves fine-scale structures, leading to denoised images with natural seabed textures. The patch weights in the nonlocal model are corrected in consideration of the nonhomogeneity of the noise. Experimental results show that the proposed algorithm is qualitatively and quantitatively comparable to conventional algorithms.
OrthographicNet: A Deep Transfer Learning Approach for 3-D Object Recognition in Open-Ended Domains

Nowadays, service robots are appearing more and more in our daily life. For this type of robot, open-ended object category learning and recognition is necessary since no matter how extensive the training data used for batch learning, the robot might be faced with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new object</i> when operating in a real-world environment. In this article, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OrthographicNet</i> , a convolutional neural network based model, for 3-D object recognition in open-ended domains. In particular, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OrthographicNet</i> generates a global rotation- and scale-invariant representation for a given 3-D object, enabling robots to recognize the same or similar objects seen from different perspectives. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning object recognition performance and scalability in open-ended scenarios. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OrthographicNet</i> demonstrates the capability of learning new categories from very few examples on-site. Regarding real-time performance, three real-world demonstrations validate the promising performance of the proposed architecture.
Readability and word complexity of SERPs snippets and web pages on children’s search queries

Purpose The purpose of this paper is to analyze the readability and level of word complexity of search engine results pages (SERPs) snippets and associated web pages between Google and Bing. Design/methodology/approach The authors employed the Readability Test Tool to analyze the readability and word complexity of 3,000 SERPs snippets and 3,000 associated pages in Google and Bing retrieved on 150 search queries issued by middle school children. Findings A significant difference was found in the readability of SERPs snippets and associated web pages between Google and Bing. A significant difference was also observed in the number of complex words in snippets between the two engines but not in associated web pages. At the engine level, the readability of Google and Bing snippets was significantly higher than associated web pages. The readability of Google SERPs snippets was at a much higher level than those of Bing. The readability of snippets in both engines mismatched with the reading comprehension of children in grades 6–8. Research limitations/implications The data corpus may be small. Analysis relied on quantitative measures. Practical implications Practitioners and other mediators should mitigate the readability issue in SERPs snippets. Researchers should consider text readability and word complexity simultaneously with other factors to obtain the nuanced understanding of young users’ web information behaviors. Additional theoretical and methodological implications are discussed. Originality/value This study measured the readability and the level of word complexity embedded in SERPs snippets and compared them to respective web pages in Google and Bing. Findings provide further evidence of the readability issue of SERPs snippets and the need to solve this issue through system design improvements.
Modified Firefly Algorithm With Chaos Theory for Feature Selection

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.
BEEF: Balanced English Explanations of Forecasts

The problem of understanding the reasons behind why different machine learning classifiers make specific predictions is a difficult one, mainly because the inner workings of the algorithms underlying such tools are not amenable to the direct extraction of succinct explanations. In this paper, we address the problem of automatically extracting balanced explanations from predictions generated by any classifier, which include not only why the prediction might be correct but also why it could be wrong. Our framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Balanced English Explanations of Forecasts</i> , can generate such explanations in natural language. After showing that the problem of generating explanations is NP-complete, we focus on the development of a heuristic algorithm, empirically showing that it produces high-quality results both in terms of objective measures—with statistically significant effects shown for several parameter variations—and subjective evaluations based on a survey completed by 100 anonymous participants recruited via Amazon Mechanical Turk.
Robust Super-Resolution Gan, with Manifold-Based and Perception Loss

Super-resolution using deep neural networks typically relies on highly curated training sets that are often unavailable in clinical deployment scenarios. Using loss functions that assume Gaussian-distributed residuals makes the learning sensitive to corruptions in clinical training sets. We propose novel loss functions that are robust to corruptions in training sets by modeling heavy-tailed non-Gaussian distributions on the residuals. We propose a loss based on an autoencoder-based manifold-distance between the super-resolved and high-resolution images, to reproduce realistic textural content in super-resolved images. We propose to learn to super-resolve images to match human perceptions of structure, luminance, and contrast. Results on a large clinical dataset shows the advantages of each of our contributions, where our framework improves over the state of the art.
A Conditional Deep Generative Model of People in Natural Images

We propose a deep generative model of humans in natural images which keeps 2D pose separated from other latent factors of variation, such as background scene and clothing. In contrast to methods that learn generative models of low-dimensional representations, e.g., segmentation masks and 2D skeletons, our single-stage end-to-end conditional-VAEGAN learns directly on the image space. The flexibility of this approach allows the sampling of people with independent variations of pose and appearance. Moreover, it enables the reconstruction of images conditioned to a given posture, allowing, for instance, pose-transfer from one person to another. We validate our method on the Human3.6M dataset and achieve state-of-the-art results on the ChictopiaPlus benchmark. Our model, named Conditional-DGPose, outperforms the closest related work in the literature. It generates more realistic and accurate images regarding both, body posture and image quality, learning the underlying factors of pose and appearance variation.
Key Exchange using Ternary system to Enhance Security

This paper presents how the security of key distribution protocols can be enhanced using the ternary arithmetic conversion, along with shared keys between communicating parties. It is shown that both the transmitting and the receiving party can calculate his own private key without exchanging it during the unsecured channel. Moreover, it is explained how the binary private keys can be independently generated by both communicating parties directly from the public ternary keys. The private keys are used to encrypt messages and allow secure communication through open untrusted channels. In addition, it is shown how addressable cryptographic tables technology can be exploited to design protocols that exchange safely the shared keys.
A Continuation Method for Graph Matching Based Feature Correspondence

Feature correspondence lays the foundation for many computer vision and image processing tasks, which can be well formulated and solved by graph matching. Because of the high complexity, approximate methods are necessary for graph matching, and the continuous relaxation provides an efficient approximate scheme. But there are still many problems to be settled, such as the highly nonconvex objective function, the ignorance of the combinatorial nature of graph matching in the optimization process, and few attention to the outlier problem. Focusing on these problems, this paper introduces a continuation method directly targeting at the combinatorial optimization problem associated with graph matching. Specifically, first a regularization function incorporating the original objective function and the discrete constraints is proposed. Then a continuation method based on Gaussian smoothing is applied to it, in which the closed forms of relevant functions with respect to the outlier distribution are deduced. Experiments on both synthetic data and real world images validate the effectiveness of the proposed method.
Automatically detecting the scopes of source code comments

Comments convey useful information about the system functionalities and many methods for software engineering tasks take comments as an important source for many software engineering tasks such as code semantic analysis, code reuse and so on. However, unlike structural doc comments, it is challenging to identify the relationship between the functional semantics of the code and its corresponding textual descriptions nested inside the code and apply it to automatic analyzing and mining approaches in software engineering tasks efficiently. In this paper, we propose a general method for the detection of source code comment scopes. Based on machine learning, our method utilized features of code snippets and comments to detect the scopes of source code comments automatically in Java programs. On the dataset of comment-statement pairs from 4 popular open source projects, our method achieved a high accuracy of 81.45% in detecting the scopes of comments. Furthermore, the results demonstrated the feasibility and effectiveness of our comment scope detection method on new projects. Moreover, our method was applied to two specific software engineering tasks in our studies: analyzing software repositories for outdated comment detection and mining software repositories for comment generation. As a general approach, our method provided a solution to comment-code mapping. It improved the performance of baseline methods in both tasks, which demonstrated that our method is conducive to automatic analyzing and mining approaches on software repositories.
Induction in Saturation-Based Proof Search

Many applications of theorem proving, for example program verification and analysis, require first-order reasoning with both quantifiers and theories such as arithmetic and datatypes. There is no complete procedure for reasoning in such theories but the state-of-the-art in automated theorem proving is still able to reason effectively with real-world problems from this rich domain. In this paper we contribute to a missing part of the puzzle: automated induction inside a saturation-based theorem prover. Our goal is to incorporate lightweight automated induction in a way that complements the saturation-based approach, allowing us to solve problems requiring a combination of first-order reasoning, theory reasoning, and inductive reasoning. We implement a number of techniques and heuristics and evaluate them within the Vampire theorem prover. Our results show that these new techniques enjoy practical success on real-world problems.
Wishful Thinking and IT Threat Avoidance: An Extension to the Technology Threat Avoidance Theory

In this research, we report the empirical findings of a vignette study designed to examine how volitional computer users cope with information technology (IT) threats based on the technology threat avoidance theory (TTAT). We incorporate the construct of wishful thinking, an important form of emotion-focused coping behavior that is rarely studied, along with problem-focused coping into a research model to examine the interaction between the two coping approaches. The results reveal that wishful thinking weakens the effect of perceived avoidability on avoidance motivation. In turn, wishful thinking is positively affected by perceived threat and this effect is stronger when a user has low risk tolerance. Although perceived avoidability can reduce wishful thinking, its effect is fully mediated by perceived threat. This study highlights the important role of wishful thinking in affecting users' security behavior. It not only corroborates, but also extends the extant propositions of TTAT, thus making a significant contribution to IT security research and practice.
Three-weight codes, triple sum sets, and strongly walk regular graphs

We construct strongly walk-regular graphs as coset graphs of the duals of three-weight codes over $$\mathbb {F}_q.$$ The columns of the check matrix of the code form a triple sum set, a natural generalization of partial difference sets. Many infinite families of such graphs are constructed from cyclic codes, Boolean functions, and trace codes over fields and rings. Classification in short code lengths is made for $$q=2,3,4$$ .
Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions

The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pixel-level annotation process is very expensive and time-consuming. To reduce the cost, the paper proposes a semantic candidate regions trained extreme learning machine (ELM) method with image-level labels to achieve pixel-level labels mapping. In this work, the paper casts the pixel mapping problem into a candidate region semantic inference problem. Specifically, after segmenting each image into a set of superpixels, superpixels are automatically combined to achieve segmentation of candidate region according to the number of image-level labels. Semantic inference of candidate regions is realized based on the relationship and neighborhood rough set associated with semantic labels. Finally, the paper trains the ELM using the candidate regions of the inferred labels to classify the test candidate regions. The experiment is verified on the MSRC dataset and PASCAL VOC 2012, which are popularly used in semantic segmentation. The experimental results show that the proposed method outperforms several state-of-the-art approaches for deep semantic segmentation.
Noncyclic geometric quantum computation with shortcut to adiabaticity

Fast and robust quantum gates is the cornerstone of fault-tolerance quantum computation. In this paper, we propose to achieve quantum gates based on non-cyclic geometric evolution. Dynamical phase during the evolution is cancelled by spin-echo process and the adiabatic control can be sped up through shortcut to adiabatic manner. Different from geometric gates based on cyclic evolution, the superiority of non-cyclic scheme is that the operation time is proportional to the rotation angle (but not the geometric phase) of quantum state. Therefore, the non-cyclic scheme becomes fairly fast in the case of quantum gates with small rotation angle which will be more insensitive to the decoherence and leakage to the states outside the computational basis. The proposed scheme is also robust against random noise due to the geometric characteristic of projective Hilbert space. Since the refined proposed scheme is fast and robust, it is an particularly suitable way to manipulate the physical systems with weak nonlinearity, such as superconucting systems.
A new approach for probability calculation of fuzzy events in Bayesian Networks

In this study, we propose an approach that incorporates the advantages of fuzzy events and fuzzy probabilities, as they have not been used together before in the literature for Bayesian Networks. We focus on the fuzzy events in Bayesian Networks and calculate their fuzzy marginal and fuzzy conditional probabilities through Buckley's fuzzy probability method. We intend to show that this approach can be used for achieving more interpretable results for real life problems and represent the uncertainty better. Moreover, detailed information in the data can be used more effectively.
Becoming a professional digital competent teacher

The aim of this Norwegian study is to focus on challenges and possibilities concerning professional digital competence (PDC) for teacher education encountering students in transition between the position as private and professionals. Students need to gain a self-understanding of who they are and how to react in a profession where there few correct answers- A report from the Norwegian Center of ICT underlines that student teachers should develop PDC. The report says nothing about how this competence should be attained. Working with cases has proved to be a possible way to prepare for future work as teachers. Included in the interviews with focus on previous experiences, are cases with ethical dilemmas concerning teachers' use of SNS. Findings show that SNS is used for private purposes. Students have experienced negative messages posted in SNS. They have little experience with using SNS for pedagogical purposes and they do not want to be friends with their pupils. Reflection on cases including ethical dilemmas is regarded as a useful way to gain a self-understanding as professional, digital competent teachers. The conclusion is that teacher education should create possibilities for reflection based on personal experiences and cases including ethical dilemmas connected to SNS.