Reviewerly/exHarmony huggingface dataset:
SLIMSTORE: A Cloud-based Deduplication System for Multi-version Backups

Cloud backup is becoming the preferred way for users to support disaster recovery. In addition to its convenience, users are deeply concerned about reducing storage costs in the face of large-scale backup data. Data deduplication is an effective method for backup storage. However, current deduplicate methods lack the utilization of cloud resources to provide scalable backup service for cloud backup users, and cannot meet the biased preference for different backup versions. For new backup versions, users want higher deduplicate and restore speed to reduce the waiting time. Conversely, reducing storage costs is more necessary for old backup versions.In this paper, we present SLIMSTORE, with a cloud-based deduplication architecture that disassembles the system into a storage layer and a computing layer to support elastic utilization of cloud resources. We propose two types of processing nodes with different design focuses to meet the needs of cloud-based backup. The L-node exploits locality and similarity, and adopts a history-aware strategy to provide fast online deduplication service. L-node also optimizes online restoration to realize high restore efficiency. Meanwhile, the G-node provides exact deduplication offline for the old versions, and helps the restore performance of the new versions by optimizing their physical storage. We compare SLIMSTORE with some state-of-art deduplicate and restore methods. Experimental results show that SLIMSTORE can achieve fast deduplication, efficient restoration, and effective space reduction. Furthermore, SLIMSTORE attains scalable deduplication and restoration.
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization

Post-training quantization methods use a set of calibration data to compute quantization ranges for network parameters and activations. The calibration data usually comes from the training dataset which could be inaccessible due to sensitivity of the data. In this work, we want to study such a problem: can we use out-of-domain data to calibrate the trained networks without knowledge of the original dataset? Specifically, we go beyond the domain of natural images to include drastically different domains such as X-ray images, satellite images and ultrasound images. We find cross-domain calibration leads to surprisingly stable performance of quantized models on 10 tasks in different image domains with 13 different calibration datasets. We also find that the performance of quantized models is correlated with the similarity of the Gram matrices between the source and calibration domains, which can be used as a criterion to choose calibration set for better performance. We believe our research opens the door to borrow cross-domain knowledge for network quantization and compression.
Generator Available Inertia Estimation Based on Various Disturbance Measurements of PMU

Considering the inertia time-varying nature and its significance on frequency initial dynamics, it is meaningful to online real-time estimate available inertia with synchronous measurements. To avoiding the influence of neighboring connected systems on the inertia of specific concerned system, monitoring the system inertia is realized by estimating inertia of individual generator connected to it and aggregated them. Inertia estimation methods presented are swing equation based method with large disturbance measurements and system identification based method with ambient signals, which guarantee the accuracy and continuity of inertia monitoring respectively. The proposed generator inertia estimation system takes full advantage of effective information in PMU measurements, and achieves a useful input to adaptive protection and control application in a near-time way. The methods have been validated with the simulation of IEEE 39 bus system and PMU measurements in power grid in Shanghai, China.
Removing the Impulse Noise from Grayscaled and Colored Digital Images Using Fuzzy Image Filtering

Digital image is an important information source for various applications. Impulse noise may cause a blur to the image information that lead to lose its importance. Thus, the removal of this degradation is one of the areas that has faced a growing interest by researchers. This paper concerns to reduce Fixed and Random impulsive noises from a grayscaled and colored digital image. The fuzzy logic (FL) methods are applied to reduce these impulsive noises from the digital images. This method tries to detect the corrupted pixels before starting the filtration process and processes only the pixels which have been classified as a corrupted pixel. This is carried out by applying two algorithms: Impulse Noise Reduction for Colored Digital Images (INRC) and Fuzzy Random Impulse Noise Reduction (FRINR) algorithms in order to filter the grayscaled and colored images. To restore a digital image, a number of phases were performed where impulsive noise was added to the set of images in several ratios for each image (5%, 10%, ..., 80%), afterwards the previously mentioned algorithms were applied through a series of experiments and monitoring results and observations of each results of each experiment. The fuzzy methods gave densities of up to 50% of the noise. These obtained results are comparable to the results rates that accessed in other research papers in the world. Also, Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and universal image quality index (UIQ) measurements are used in this paper.
Studying the Applicability of Proof of Reputation(PoR) as an Alternative Consensus Mechanism for Distributed Ledger Systems

Blockchains combine several other technologies like cryptography, networking, and incentive mechanisms in order to support the creation, validation, and recording of transactions between participating nodes. A blockchain system relies on a consensus algorithm to determine the shared state among distributed nodes. An important component underlying any blockchainbased system is its consensus mechanism, which determines the characteristics of the overall system. This thesis proposes a reputation-based consensus mechanism for blockchain-based systems which we term Proof-of-Reputation(PoR) that uses the liquid rank algorithm where the reputation of a node is calculated by blending the normalized ratings by other nodes in the network for a given period with the reputation values of the nodes giving the ratings. The nodes with the highest reputation values eventually become part of the consensus group that determines the state of the blockchain.
Transforming ML Predictive Pipelines into SQL with MASQ

Inference of Machine Learning (ML) models, i.e. the process of obtaining predictions from trained models, is often an overlooked problem. Model inference is however one of the main contributors of both technical debt in ML applications and infrastructure complexity. MASQ is a framework able to run inference of ML models directly on DBMSs. MASQ not only averts expensive data movements for those predictive scenarios where data resides on a database, but it also naturally exploits all the "Enterprise-grade" features such as governance, security and auditability which make DBMSs the cornerstone of many businesses. MASQ compiles trained models and ML pipelines implemented in scikit-learn directly into standard SQL: no UDFs nor vendor-specific syntax are used, and therefore queries can be readily executed on any DBMS. In this demo, we will showcase MASQ's capabilities through a GUI allowing attendees to: (1) train ML pipelines composed of data featurizers and ML models; (2) compile the trained pipelines into SQL, and deploy them on different DBMSs (MySQL and SQLServer in the demo); and (3) compare the related performance under different configurations (e.g., the original pipeline on the ML framework against the SQL implementations).
Graph Restrictions for Signal Processing of Homicides Data

Relative to other types of crime, homicides are statistically more challenging due to its sparsity and low frequency. To overcome this problem and based on previous works, we propose a methodology that combines the signal processing capabilities of the Graph Laplacian of Gaussian (GLoG) filter and the inclusion of related types of crimes with kernel warping. We compare our model with the results obtained in those previous works and with benchmark models from other papers. We found that the combination of both methodologies results in an improvement of the model and fares well with the benchmark models.
Do Touch This: Turning a Plaster Bust Into a Tangible Interface

Plaster busts are common exhibition pieces in museums, but they usually are off-limits for touch. While modern exhibition concepts increasingly involve interaction, this rarely includes touching a plaster bust. However, there may be a wealth of information associated with these cultural heritage objects, e.g., their making and their creator’s craftsmanship, which tangible interaction could make accessible right on the object. We equipped plaster replicas of such a bust with capacitive sensors and developed a tangible prototype in collaboration with two domain experts from the art museum “Staatliche Antikensammlungen” (Munich, Germany), a curator and a sculptor, in an iterative design approach. Then we tested it in a lab study (N=12) and an in situ study in the museum (N=20). We describe our technical approach, which we also made public as a git project1, and discuss our study results about busts as tangible interfaces in the art of sculpting.
DBSpinner: Making a Case for Iterative Processing in Databases

Relational database management systems (RDBMS) have limited iterative processing support. Recursive queries were added to ANSI SQL, however, their semantics do not allow aggregation functions, which disqualifies their use for several applications, such as PageRank and shortest path computations. Recently, another SQL extension, iterative Common Table Expressions (CTEs), is proposed to enable users to perform general iterative computations on RDBMSs.In this work <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , we demonstrate how iterative CTEs can be efficiently incorporated into a production RDBMS without major intrusion to the system. We have prototyped our approach on Futurewei's MPPDB, a shared nothing relational parallel database engine. The implementation is based on a functional rewrite that translates iterative CTEs to other existing SQL operators. Thus, query plans of iterative CTEs can be optimized and executed by the engine with minimal modification to the code base. We have also applied several optimizations specifically for iterative CTEs to i) minimize data movement, ii) reuse results that remain constant and iii) push down predicates to avoid unnecessary data processing. We verified our implementation through extensive experimental evaluation using real world datasets and queries. The results show the feasibility of the rewrite approach and the effectiveness of the optimizations, which improve performance by an order of magnitude in some cases.
An Overview of Multi-population Methods for Dynamic Environments

Dynamic optimization problems (DOPs) can be found almost everywhere, from ship navigation at sea (Michalewicz et al. 2007) to aerospace design (Mack et al. 2007). In general terms, all aspects of science and engineering include the optimization of a set of complex problems, in which the objectives of the optimization, some restrictions, or other elements may vary over time. Since exact algorithms are impractical in dynamic environments, stochastic optimization techniques have gained much popularity. Among them, evolutionary computation (EC) techniques have attracted a great deal of attention due to their potential for solving complex optimization problems. Nevertheless, EC methods should undergo certain adjustments to work well when applying on DOPs. Diversity loss is by far the most severe challenge to EC methods in DOPs. This issue appears due to the tendency of the individuals to converge to a single optimum. As a result, when the global optimum is shifted away, the number of function evaluations (FEs) required for a partially converged population to relocate the optimum is quite harmful to the performance. In this chapter, we provide an overview of the multi-population methods for dynamic environments.
Adaptive random quantum eigensolver

We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. Specifically, we introduce a general method to parametrize and optimize the probability density function of a random number generator, which is the core of stochastic algorithms. We follow a bioinspired evolutionary mutation method to introduce changes in the involved matrices. Our optimization is based on two figures of merit: learning speed and learning accuracy. This method provides high fidelities for the searched eigenvectors and faster convergence on the way to quantum advantage with current noisy intermediate-scaled quantum computers.
Imagination as a phenomenon of cognition

This article discusses the problem of imagination as a holistic phenomenon of cognition based on the concept of corporeality of mind. Imagination becomes an instrument for enactive subject &amp;ndash; object interaction. They complement and revive each other in the activity of cognition and self-cognition. Imagination is a generative model of cyclical interaction between the subject and object in junction of the image and action. Imagination is a moment of visual culture, a means of shaping thoughts and feelings in the optical coherence of mental actions in the reproduction of the picture, scenic manifestations of the material in mental life of a person, interpretation of the imagery-symbolic language and action. Imagination creates the space of the game of feelings, mind, and body in the context of cognitive engagement of a person. The most vivid manifestation as a phenomenon of cognition imagination acquires in the practice of psychoanalysis. The scientific novelty of this work consists in the following statement: psychoanalytic description interprets imagination in realization of the image through body and mind. The article employs the method of enactive construction of knowledge, visualization and psychoanalytic description, which demonstrates imagination as an integrative dimension of a human, optically harmonizes body, thought, and external environment of a person. The article underlines the role of metaphors, transformation, and paradoxicality, which indicate the degree of depiction of the image through integration of the corporeal, social and imaginable in a circular, cyclical dependence. Imagination creates the syntheses of these dimensions in a &amp;ldquo;paradoxical system&amp;rdquo;, translation of the fiction into symbolic language, and symbolic substantiations of the living experience of a cognizing being. Imagination is the organic development of human nature. The interactant appears to be an external environment and part of the human organization that creates him through the living experience of cognition and self-cognition.
Model-Free-Adaptive-Control for Moving Object Detection in RGB Video Sequence

Moving object detection in video sequences is an important topic in the field of computer vision. The video background image extraction is the key step in most moving object detection methods. The traditional background image extraction methods are easy to cause model distortion when the video scene cannot meet its assumptions or model conditions. In this paper, the background image extraction method is proposed using model free adaptive control. The method combines RGB three-channel history and current data of pixels to represent and update background image. The proposed method that under different video sequences is compared with the traditional background image extraction methods. The results show that the method can extract the color background image and separated the color foreground image, and the foreground image is closer to the ground truth image of the video target.
Domain-Class Correlation Decomposition for Generalizable Person Re-Identification

Domain generalization in person re-identification is a highly important meaningful and practical task in which a model trained with data from several source domains is expected to generalize well to unseen target domains. Domain adversarial learning is a promising domain generalization method that aims to remove domain information in the latent representation through adversarial training. However, in person re-identification, the domain and class are correlated, and we theoretically show that domain adversarial learning will lose certain information about class due to this domain-class correlation. Inspired by causal inference, we propose to perform interventions to the domain factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> , aiming to decompose the domain-class correlation. To achieve this goal, we proposed estimating the resulting representation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z^{*}$</tex-math></inline-formula> caused by the intervention through first- and second-order statistical characteristic matching. Specifically, we build a memory bank to restore the statistical characteristics of each domain. Then, we use the newly generated samples <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lbrace z^{*},y,d^{*}\rbrace$</tex-math></inline-formula> to compute the loss function. These samples are domain-class correlation decomposed; thus, we can learn a domain-invariant representation that can capture more class-related features. Extensive experiments show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark.
Budget-Oriented Reliable WDO Algorithm for Workflow Scheduling in Cloud Systems

The Use of Cloud computing for solving complex scientific workflow applications has become very popular nowadays.Singh, Poonam Dutta, Maitreyee Aggarwal, Naveen The cloud infrastructure is equipped with elastic, heterogeneous and cost-efficient resource provision features. It enhances the computing by offering user an on-demand services through Internet. Scheduling these workflow applications under user-defined budget constraint is one of the main challenge in cloud environment. Also taking failure of computing resource into consideration, it becomes imperative to generate reliable schedule while meeting other scheduling attributes like cost, time and deadline. This paper presents a hybrid wind-driven optimization algorithm (HWDO) to generate reliable workflow schedule by maintaining budget within specified limit. The algorithm is simulated using WorkflowSim with real-world scientific applications. The results achieved substantiate 9–17% reliable schedule than other algorithms by meeting the budget constraint.
Efficient Two-stream Action Recognition on FPGA

Action recognition is an important research field that has many applications in surveillance, video search, autonomous vehicles, etc. However, current state-of-the-art action classifiers are still not widely adopted in embedded applications yet. The major reason is that action recognition needs to process both spatial and temporal streaming data to precisely identify actions, which is compute- intensive and power hungry. To solve this issue, researchers start using FPGA to run action recognition models with minimum power. In this paper, we propose a new hardware architecture of action recognition on FPGA. Our model is based on the popular two-stream neural network. By optimizing the optical flow and convolution operations in the temporal domain, our method can achieve similar accuracy with one order of magnitude less operations than other C3D baseline models. We have implemented our model on Xilinx Ultrascale+ ZCU102 and released the source code.
Blockchain Pharma

Blockchain, a distributed ledger technology (DLT) that sustained the creation of the first digital currency, Bitcoin, crosses many business areas, including healthcare, to promise better economic solutions. Blockchain generalized implementation is already a reality in Estonia, perhaps the most digitally advanced country globally, with proven healthcare results for its citizens. From a pharmaceutical industry perspective, blockchain offers solutions as diverse as the structuring of clinical trial protocols, the traceability of medicines along the supply chain, and intellectual property rights. Additionally, the DLT's cryptographic protocol, whose main characteristics are immutability, consensus, security, and transparency, may support both the web's decentralization and the transition to a Semantic Web, which is recognized by many as highly recommended.
Using item response theory in machine learning algorithms for student response data

Item Response Theory (IRT) is widely used to solve the problem measure of the hidden capabilities of a student based on historical data. But these methods have their limitations, which do not allow to obtain accurate results. In the paper, we propose combining IRT with machine learning algorithm to improve accuracy. IRT calculates the general abilities of students and grouping of students by abilities, this information is an additional feature for the machine learning algorithm. Three methods of machine learning were compared: logistic regression, XGBoost, LightGBM. LithtGBM performed best on three recall, precision, f1 metrics. This method can be used for adaptive learning systems that will allow students to choose the correct and fastest direction in learning subjects.
Line Weaver: Importance‐Driven Order Enhanced Rendering of Dense Line Charts

Abstract Line charts are an effective and widely used technique for visualizing series of ordered two‐dimensional data points. The relationship between consecutive points is indicated by connecting line segments, revealing potential trends or clusters in the underlying data. However, when dealing with an increasing number of lines, the render order substantially influences the resulting visualization. Rendering transparent lines can help but unfortunately the blending order is currently either ignored or naively used, for example, assuming it is implicitly given by the order in which the data was saved in a file. Due to the non‐commutativity of classic alpha blending, this results in contradicting visualizations of the same underlying data set, so‐called “hallucinators”. In this paper, we therefore present line weaver, a novel visualization technique for dense line charts. Using an importance function, we developed an approach that correctly considers the blending order independently of the render order and without any prior sorting of the data. We allow for importance functions which are either explicitly given or implicitly derived from the geometric properties of the data if no external data is available. The importance can then be applied globally to entire lines, or locally per pixel which simultaneously supports various types of user interaction. Finally, we discuss the potential of our contribution based on different synthetic and real‐world data sets where classic or naive approaches would fail.