Reviewerly/exHarmony huggingface dataset:
NVGraph: Enforcing Crash Consistency of Evolving Network Analytics in NVMM Systems

Many complex networks can be modeled as evolving graphs. Existing in-memory graph data structures are designed for DRAM. Thus, they cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent for NVMM. (2) NVMM writes and reads and may incur higher latency than DRAM. In this article, we propose a novel persistent evolving graph data structure, named NVG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAPH</sub> , for both computing and in-memory storage of evolving graphs in NVMM. We devise NVG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAPH</sub> as a multi-version data structure, wherein a minimum of one version of its data is stored in NVMM to provide the desired durability at runtime for failure recovery, and another version is stored in both DRAM and NVMM to reduce the NVMM-induced memory latency. NVG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAPH</sub> is also a partitioned data structure. We dynamically transform the layout of NVG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAPH</sub> (e.g., changing the size of its partition in DRAM) exploiting network properties and data access patterns of workloads. For the evaluation of NVG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAPH</sub> , we implement four representative real-world graph applications: pagerank, BFS, influence maximization, and rumor source detection. The experimental results show that the performance of NVG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAPH</sub> is comparable to other in-memory data structure (e.g., CSR and LLAMA) while using 70 percent less DRAM. It scales well up to 10 billion edges and 201 snapshots and supports crash consistency. It offers up to the 21X speedup of execution time compared to the scale-up graph computation approaches (e.g., GraphChi and X-stream).
The impact of lexicographic parsimony pressure for ORDER/MAJORITY on the run time

While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations. A naturally occurring problem is that of bloat, that is, the unnecessary growth of solution lengths, which may slow down the optimization process. So far, the mathematical runtime analysis could not deal well with bloat and required explicit assumptions limiting bloat. In this paper, we provide the first mathematical runtime analysis of a GP algorithm that does not require any assumptions on the bloat. Previous performance guarantees were only proven conditionally for runs in which no strong bloat occurs. Together with improved analyses for the case with bloat restrictions our results show that such assumptions on the bloat are not necessary and that the algorithm is efficient without explicit bloat control mechanism. More specifically, we analyzed the performance of the (1+1) GP on the two benchmark functions Order and Majority. When using lexicographic parsimony pressure as bloat control, we show a tight runtime estimate of O(Tinit+nlog⁡n) iterations both for Order and Majority. For the case without bloat control, the bounds O(Tinitlog⁡Tinit+n(log⁡n)3) and Ω(Tinit+nlog⁡n) (and Ω(Tinitlog⁡Tinit) for n=1) hold for Majority.1
Human Intelligence Meets Smart Machine: A Special Event at the IEEE International Conference on Systems, Man, and Cybernetics 2018

Machine learning is a popular approach for cybernetics systems and has always been considered as an important research topic in the computational intelligence area. The Open Go Darkforest (OGD) cloud platform, a machine?human co-learning model, was established to help human players learn and analyze the game Go based on their learning behavior and performance. To evaluate humans' learning progress, a special event—Human and Smart Machine Co-Learning @ IEEE SMC 2018—was held at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) in Miyazaki, Japan, in October 2018.
Virtual Reality as a Means of Teaching Contemporary Chemistry

The fundamental challenge for understanding and thus for teaching chemistry is that chemical processes at the atomic level are all inaccessible to sensory experience and must therefore be represented by models. For learners these models are often difficult to understand and to use, as they pose high demands regarding cognitive and spatial ability as well as abstraction. This applies especially when it comes to current developments and research topics of chemistry, like nanoscience. This leads to a situation where modern chemistry and chemical research is more and more inaccessible for learners. Using learning environments that utilize Virtual Reality may help to overcome this problematic situation as they allow new ways of visualization, a more direct interaction between learner and chemical object and are open to more game-based approaches. By using VR-technology in combination with aspects of actual chemical research topics, chemistry education students may gain better understanding of modern chemistry. As a result, they should be better prepared to realize modern chemistry lessons in the future, that deliver a realistic view of modern chemistry, cover topics of actual relevance and use digital methods that foster learning. In a first attempt to realize such an approach in chemistry teacher education, a virtual reality game was created and embedded in a course on chemistry education. The aim is to present the students a kind of real situation with aspects of modern chemistry, where they have to act as a forensic scientist. Additionally, they should use this VR game as basis for conceptualizing teaching materials for chemistry lessons at school and as a means to promote their digital competencies.
An Analytical Game for Knowledge Acquisition for Maritime Behavioral Analysis Systems

The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach.
Contrast Enhancement Using Quantile Separation and Bi-Histogram Equalization

One of the most common methods of contrast enhancement is Histogram Equalization (HE). But it has problems of over-enhancement owing to its global nature and the mean of output brightness does not correlate with the input. Quantile based HE combats both these problems by applying histogram equalization on local sub-histograms. Due to dynamic scaling of the intensity probability distribution, the average brightness of the output correlates with the input image, and at the same time, improves AMBE and MSSI, but falls short in maximising PSNR. As per the current scenario, LQSWDHE has improved upon the short comings of normal Histogram Equalization and has shown better results than competing algorithms. But the algorithm, when simulated for values of Q (number of values), PSNR value appears to decrease, which is undesirable. Therefore the scope of this paper is to propose an improvement to LQSWDHE so as to improve the PSNR value while maintaining the AMBE and MSSI parameters, as they indicate how similar the output is to the input. This has been achieved by the proposed algorithm by introducing a Brightness Preserving Bi-Histogram Equalization block before the original histogram equalization step in the algorithm.
On the Difference Between Finite-State and Pushdown Depth

This paper expands upon existing and introduces new formulations of Bennett’s logical depth. A new notion based on pushdown compressors is developed. A pushdown deep sequence is constructed. The separation of (previously published) finite-state based and pushdown based depth is shown. The previously published finite state depth notion is extended to an almost everywhere (a.e.) version. An a.e. finite-state deep sequence is shown to exist along with a sequence that is infinitely often (i.o.) but not a.e. finite-state deep. For both finite-state and pushdown, easy and random sequences with respect to each notion are shown to be non-deep, and that a slow growth law holds for pushdown depth.
Combinatorial Artificial Bee Colony Optimization with Reinforcement Learning Updating for Travelling Salesman Problem

The Artificial Bee Colony algorithm is originally designed for solving numerical optimization problems, whereas the Travelling Salesman Problem is classified as a combinatorial optimization one. This paper proposes a combinatorial variant of the Artificial Bee Colony algorithm by using reinforcement learning update. Reinforcement values are introduced, and positive reinforcement is given to the selected cities when a better solution is found by an employed bee. On the contrary, negative reinforcement is assigned to the selected cities when a worse solution is found. Onlooker bees then select cities to update their solutions according to these reinforcement values. The performance of the proposed algorithm is tested on six benchmark problems. The results show that the algorithm with the reinforcement learning update provides better solutions than the algorithm without the reinforcement learning update in five out of six benchmark problems. The convergence rates of the algorithm with the reinforcement learning update are also faster than those of the algorithm without the reinforcement learning update.
Leveraging Image Processing Techniques to Thwart Adversarial Attacks in Image Classification

Deep Convolutional Neural Networks (DCNNs) are vulnerable to images that have been altered with well-engineered and imperceptible perturbations. We propose three color quantization pre-processing techniques to make DCNNs more robust to adversarial perturbation including Gaussian smoothing and PNM color reduction (GPCR), color quantization using Gaussian smoothing and K-means (GK-means), and fast GK-means. We evaluate the approaches on a subset of the ImageNet dataset. Our evaluation reveals that our GK-means-based algorithms have the best top-1 accuracy. We also present the trade-off between GK-means-based algorithms and GPCR with respect to computational time.
Bi-Decoder Augmented Network for Neural Machine Translation

Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder–decoder framework is the mainstream among all the methods. It is obvious that the quality of the semantic representations from encoding is very crucial and can significantly affect the performance of the model. However, existing unidirectional source-to-target architectures may hardly produce a language-independent representation of the text because they rely heavily on the specific relations of the given language pairs. To alleviate this problem, in this paper, we propose a novel Bi-Decoder Augmented Network (BiDAN) for the neural machine translation task. Besides the original decoder which generates the target language sequence, we add an auxiliary decoder to generate back the source language sequence at the training time. Since each decoder transforms the representations of the input text into its corresponding language, jointly training with two target ends can make the shared encoder has the potential to produce a language-independent semantic space. We conduct extensive experiments on several NMT benchmark datasets and the results demonstrate the effectiveness of our proposed approach.
Modeling singular mineralization processes due to fluid pressure fluctuations

Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in response to earthquakes or fault rupture are primarily responsible for changes in solubility and trigger transient physical and chemical variations in ore-forming fluids that enhance the mineralization process. Based on this general concept, we investigated mineral precipitation processes driven by rapid fluid pressure reductions by coupling mineralization to a cellular automaton model to reveal the nonlinear mechanism of the orogenic gold mineralization process using simulation. In the model, fluid pressure can increase to the rock failure condition, which was set as lithostatic pressure at a depth of 10 km (270 MPa), due to either porosity reduction or dehydration reactions. Rapid drops in pressure resulting from fault rupture or local hydrofracture may induce repeated gold precipitation. The geochemical patterns generated by the model evolve from depletion to enrichment patterns, and from spatially random to spatially clustered structures quantified by multifractal models and geostatistics. Results show how metal elements self-organize to form high metal concentration patterns displaying self-similarity and scale-invariance. These transitions are attributed to the growth and coalescence of sub-networks with different fluid pressures up to the percolation threshold, resulting in a wide range of fluid pressure reductions and gold precipitation in the form of clusters. The results suggest that cyclic evolution of fluid pressure and its effects on gold precipitation systems can effectively mimic the repeated mineralization superposition process, and generate complex geochemical patterns characterized by a multifractal model. The nonlinear behavior exhibits scale-invariance and self-organized critical threshold, where mineral phase separations result from fluid pressure reductions associated with fault failure.
Preference Elicitation within Framework of Fully Probabilistic Design of Decision Strategies

The paper proposes the preference-elicitation support within the framework of fully probabilistic design (FPD) of decision strategies. Agent employing FPD uses probability densities to model the closed-loop behaviour, i.e. a collection of all observed, opted and considered random variables. Opted actions are generated by a randomised strategy. The optimal decision strategy minimises Kullback-Leibler divergence of the closed-loop model to its ideal counterpart describing the agent's preferences. Thus, selecting the ideal closed-loop model comprises preference elicitation. The paper provides a general choice of the best ideal closed-loop model reflecting agent's preferences. The foreseen application potential of such a preference elicitation is high as FPD is a non-trivial dense extension of Bayesian decision making that dominates prescriptive decision theories. The general solution is illustrated on the regulation task with a linear Gaussian model describing the agent's environment.
Bringing semantics to multicloud service compositions

Summary Over the last decade, cloud computing has emerged as a new paradigm for delivering various on‐demand virtualized resources as services. Cloud services have inherited not only the major characteristics of web services but also their classical issues, in particular, the interoperability issues and the heterogeneous nature of their hosting environments. This latter problem must be taken into account when composing various cloud services, in order to answer users' complex requirements. Moreover, leading cloud providers started to offer their services across multiple clouds. This adds a new factor of heterogeneity, as composition engines must take into consideration the heterogeneity not only at the service level (eg, service descriptions) but also at the cloud level (eg, pricing models, security policies). In this context, the semantics of multicloud actors must be incorporated into the multicloud service composition (MCSC) process. However, most existing approaches have treated the semantic service composition in traditional single‐cloud environments. The few works in multicloud settings have ignored the semantics of cloud zones and resources. Moreover, they often focus on the general aspect of MCSC (eg, horizontal or vertical compositions). Even the few researchers who have addressed both vertical and horizontal service compositions, conducted their research studies in the context of single‐ cloud environments, which were proven to be unrealistic and offer limited quality of service (QoS) and security support. To ensure a high interoperability when composing services from multiple heterogeneous clouds and to enable a horizontal/vertical semantic service compositions, we take advantage of a standardized and semantically enriched generic service description, including all aspects (technical, operational, business, semantic, contextual) and supporting different cloud service models (SaaS, PaaS, IaaS, etc). We also incorporate Semantic Web Rule Language into the MCSC process to enable not only rule‐based reasoning about various composition constraints (eg, QoS constraints, cloud zones constraints) but also to provide accurate semantic matching of cloud services' capabilities. Conducted experiments have proven the ability of our approach to combine high‐quality services from the optimal number of clouds.
Neural Social Recommendation With User Embedding

Social information is usually jointly utilized with rating information to help the traditional recommendation system providing more personalized services, while how to make full use of social information to build better recommendation models still faces lots of challenges.In this paper, we propose a novel social recommendation model taking advantage of both deep and shallow model, with deep autoencoders acting as the nonlinear feature extractor and MF-based method being used to depict the user's preferences.Then, motivated by the idea of word2vec, we provide an appealing method that embeds users into latent space and meanwhile preserves the structural information of social networks during the embedding.Also, the embedded latent features of each user are corresponding to the dual roles the user plays in the recommendation.Furthermore, we design a loss function for the holistic training of the model, and our loss function is made up mainly of three parts which embody the effects of different factors on the rating predictions.Specifically, the loss function (i) captures the personal preference from user-item adoption matrix based on matrix factorization, (ii) discriminates the two different social functions of users and further evaluates the effects of interpersonal influence through user embedding and social influence matrix, (iii) and avoids overfitting by imposing a quadratic regularization penalty.As a result, our model can predict the missing ratings with the MF-based method by consuming the latent features of users and items extracted by the deep model.The experiments show that our method outperforms existing methods and performs well on cold start users.
Tuning Runtimes in Open Source FaaS

A dynamically expanding area of cloud computing is Function as a Service (FaaS). FaaS allows customers to develop, run, and manage application functionalities on cloud infrastructure without the burden of building and managing a virtual infrastructure. The vast majority of FaaS services used in production are provided by public cloud operators, but a growing number of open source FaaS frameworks offer an alternative deploying on-premises FaaS services. FaaS frameworks support different programming language runtimes. The performance of such systems is dependent on these language runtimes. Our goal is to show and analyze this dependency, and provide insights to the important aspect when performance is essential. This paper provides a measurement based evaluation of the capabilities of different language runtimes in FaaS frameworks. We evaluate three different workloads (echo, compute intensive, and data intensive) on the selected runtimes.
Automatic characteristic-calibrated registration (ACC-REG): Hippocampal surface registration using eigen-graphs

In this paper, we propose an efficient algorithm, the ACC-REG, to automatically extract intrinsic key characteristics on hippocampal mesh surfaces and hence compute an accurate registration mapping between them. Given a pair of hippocampal surface mesh, the proposed algorithm constructs the eigen-graphs, an intrinsic feature on the surface, on each surface as its representative. The eigen-graphs are then calibrated along the longitudinal direction of the hippocampal surfaces. Accurately corresponded intrinsic characteristics on each hippocampus can thus be extracted. As a result, the two surfaces can be registered with improved accuracy and low computation cost. Experiments on ADNI data demonstrate the effectiveness of the proposed ACC-REG model over existing methods.
Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.