Reviewerly/exHarmony huggingface dataset:
Performance analysis of an energy-saving strategy in cloud data centers based on a MMAP[K]/M[K]/<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3419" altimg="si2.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo linebreak="goodbreak" linebreakstyle="after">+</mml:mo><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> non-…

With the development of cloud computing, big data, artificial intelligence and other next generation information technologies, the scale of the data center industry worldwide is growing rapidly, resulting in a sharp increase in energy consumption. In order to efficiently reduce the idle energy consumption in cloud data centers, in this paper, we propose an energy-saving strategy based on two-threshold hysteresis cluster scheduling mechanism, which enables the reserved cluster to be dynamically switched on and off with load variation. In addition, the tasks to be processed are classified into real-time tasks and non-real-time tasks. To keep track of the two classes of tasks and model the correlated traffic in cloud data centers, we describe the arrival flow of tasks as a Marked Markovian Arrival Process (MMAP). Accordingly, we develop a non-preemptive priority queue as the system model to capture the working principle of the proposed strategy. By using the matrix-geometric solution and Gauss–Seidel method, the steady-state distribution of the system model is analyzed, and some key Quality of Service (QoS) metrics and Total Cost of Ownership (TCO) metrics are calculated. Results of numerical experiments show that, under the proposed energy-saving strategy, the overall power consumption in a small cloud data center can be reduced by an average of 30.20% under different scenarios, and the larger the cloud data center scale, the more obvious the energy saving effect. The results also confirm the impact of inter-class correlations of the input process on performance metrics. Furthermore, we identify the Pareto optimal solutions for trading off the overall power consumption and average waiting time of real-time tasks.
RETRACTED: Part II: a step-by-step guide to latent class analysis

Latent Class Analysis (LCA) is an analytical approach for the identification of more homogeneous subgroups within an otherwise dissimilar patient population. In the current paper, Part II, we present a practical step-by-step guide for LCA of clinical data, including when LCA might be applied, selecting indicator variables, and choosing a final class model. We also identify some common pitfalls of LCA, and some related solutions.
Multitask Personalized Recognition of Emotions Evoked by Textual Content

In this paper, we present the studies on personalized emotion recognition in text perception in the multitask setting. We analyze the performance of the joint learning of the model within groups of similar tasks (sub-multitask approach) and for all tasks learned together (full multitask) versus the classical approach based on separate models dedicated to each individual task (single-task). Additionally, we use our novel personalized deep learning architectures capable of recognizing emotions from the user’s perspective through the calculated Human Bias and learned vector representation of the user. These methods are combined with state-of-the-art linguistic models based on transformers to represent the textual content. The results show a significant gain in emotion recognition quality in both sub-multitask and multitask scenarios. In each setup, personalization yields a significant quality gain, which is essential from the perspective of AI systems responding to the emotions of a specific user.
AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times

The world faces the continuously increasing issue of a lack of skilled employees, staff migration, and turnover. It is strengthened by unexpected situations such as wars, pandemics, and other civilization crises. Solutions are sought and researched in various branches of industry and academia, including engineering, social sciences, management, and political and computer sciences. From the viewpoint of this paper, this is a side topic of Industry 4.0 and, more specifically, sustainability in working environments, and the issue is related to production employees who perform manual operations. Some of the tasks cannot be carried out under robotization or automation; therefore, novel human-work support tools are expected. This paper presents such highly demanded support tools related to augmented reality (AR) and artificial intelligence (AI). First, a panoramic literature review is given. Secondly, the authors explain the main objective of the presented contribution. Then the authors’ achievements are described—the R&amp;D focus on such solutions and the introduction of the developed tools that are based on AR and AI. Benefits connected to the AR-AI technology applications are presented in terms of both time savings with the tool usage and job simplification, enabling inexperienced, unskilled, or less skilled employees to perform the work in the selected manual production processes.
Fabulating Biodata Futures for Living and Knowing Together

A growing number of design researchers explore engagement with and through biodata. To help make sense of this growing space, we synthesize three emergent themes: (1) expanding notions of biodata and bodies, (2) attending to a greater diversity of human bodies and experiences with biodata, and (3) biodata collaborations between human and non-human bodies. We illustrate these themes with selected design examples. From this synthesis, we develop three interconnected fabulations reimagining alternative engagements with biodata: Weaving Alongside, Diffracting Selves, and Collective Affect. Our discussion unpacks conceptual work of the fabulations, offering invitations for design research to explore alternative ways of living and knowing together with biodata.
A Peer-to-Peer Smart Food Delivery Platform Based on Smart Contract

The era of mobile information has arrived, and people’s lifestyles have undergone tremendous changes. Ordering takeaways through takeout apps on smartphones is one of them. However, most existing takeaway platforms charge high commissions in the middle. There are many fake reviews in restaurants, the authenticity of restaurant ratings is low, and the recommended dishes have low customer satisfaction. This paper aims to solve this problem by introducing a peer-to-peer architecture based on blockchain smart contracts. The proposed architecture leverages the automation of smart contracts to provide autonomous, commission-free food ordering and delivery services. In addition, the smart contract reward mechanism is used to collect order information and rating information, and a deep learning recommendation model is introduced to analyze the data to recommend restaurants and menus to the client accurately. To demonstrate the usability and efficiency of the proposed method, we conducted a case study using public chain-based technologies. At the same time, comprehensive evaluation experiments are carried out, and the results show the importance of the proposed food delivery system.
Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency

Feature matching is a key method of feature-based image registration, which refers to establishing reliable correspondence between feature points extracted from two images. In order to eliminate false matchings from the initial matchings, we propose a simple and efficient method. The key principle of our method is to maintain the topological and affine transformation consistency among the neighborhood matches. We formulate this problem as a mathematical model and derive a closed solution with linear time and space complexity. More specifically, our method can remove mismatches from thousands of hypothetical correspondences within a few milliseconds. We conduct qualitative and quantitative experiments on our method on different types of remote-sensing datasets. The experimental results show that our method is general, and it can deal with all kinds of remote-sensing image pairs, whether rigid or non-rigid image deformation or image pairs with various shadow, projection distortion, noise, and geometric distortion. Furthermore, it is two orders of magnitude faster and more accurate than state-of-the-art methods and can be used for real-time applications.
Learning view-specific labels and label-feature dependence maximization for multi-view multi-label classification

Multi-view multi-label learning tasks often appear in various critical data classification scenarios. Each training sample has multiple heterogeneous data views associated with multiple labels in this learning framework simultaneously. Nevertheless, most existing methods do not consider that a single view cannot fully predict all unknown labels caused by non-aligned views, which leads to insufficient consideration of the relationship between the features and labels of each view, and the learning effect is not ideal. In this paper, we develop a novel method that uses view-specific labels and label-feature dependence maximization. Concretely, we first assume that each view and its corresponding label space have a smooth local structure. In this way, the view-specific label learning model is constructed, enhancing the consistency and complementarity of label space information. Then, multiple multi-label classifiers are constructed by maximizing label-feature dependence. Finally, the linear classification model is extended to the nonlinear, and the prediction stage is combined with the contribution weight of each view. The results of several benchmark datasets show that our proposed method is significantly more effective than the state-of-the-art methods.
Recursion synthesis with unrealizability witnesses

We propose SE2GIS, a novel inductive recursion synthesis approach with the ability to both synthesize code and declare a problem unsolvable. SE2GIS combines a symbolic variant of counterexample-guided inductive synthesis (CEGIS) with a new dual inductive procedure, which focuses on proving a synthesis problem unsolvable rather than finding a solution for it. A vital component of this procedure is a new algorithm that produces a witness, a set of concrete assignments to relevant variables, as a proof that the synthesis instance is not solvable. Witnesses in the dual inductive procedure play the same role that solutions do in classic CEGIS; that is, they ensure progress. Given a reference function, invariants on the input recursive data types, and a target family of recursive functions, SE2GIS synthesizes an implementation in this family that is equivalent to the reference implementation, or declares the problem unsolvable and produces a witness for it. We demonstrate that SE2GIS is effective in both cases; that is, for interesting data types with complex invariants, it can synthesize non-trivial recursive functions or output witnesses that contain useful feedback for the user.
Exploring warehouse management practices for adoption of IoT-blockchain

The warehousing sector has been experiencing technological up-gradation for the last few years. To enable a technology-friendly environment, warehousing companies have begun to use management practices like the omnichannel, shared economy, intelligent bins, etc. The current study used recent literature to develop a conceptual model by identifying modern warehousing practices and then used structural equation modelling to examine the relationship between these practices and IoT-blockchain adoption to assess the impact on organisational business performance. Further, the data was acquired from the Indian warehousing organisation to analyse the proposed model. The study’s outcome shows that financial and infrastructure practices significantly impact the adoption of IoT blockchain, which further impacts business performance. The findings also imply that SC managers should incorporate operational, organisational, security, and flexibility practices into their work culture since these practices (factors and items) would build the foundation for IoT blockchain deployment. The outcome will aid practitioners by identifying appropriate management strategies and practices for performance improvement.
Reducing false positives in bank anti-fraud systems based on rule induction in distributed tree-based models

Fraud detection in bank payments transactions suffers from a high number of false positives. To deal with this problem, we introduce a rules generation framework for a fraud-detection system – an automatic rules generation using distributed tree-based ML (machine learning) algorithms such as Decision Tree, Random Forest and Gradient Boosting, where the components of expert rules are used as the features for the model. This approach is a combination of statistical and expert-based approaches. We apply it to the bank's card transaction data. Our dataset covers February 2021 and consists of more than 20 mil. records including information on clients, transactions, and merchants. The autogenerated rules were aimed at improving FPR (false positive rate) business-metric. The framework was tested in a real fraud-monitoring system of large bank throughout half of the year. The rules obtained using this framework proved to be satisfactory efficient while having tangible business effect.
The usability of ClimApp: A personalized thermal stress warning tool

ClimApp was developed to assist individuals in reducing the risk of exposure to thermal stress and to supply the user with individualized advice and recommendations. The tool integrates several human thermal models and heat indices with the local weather forecast and goes on to provide users with a prediction of their thermal stress based on their activity level, clothing level, and heat acclimatization input. Despite its innovative and interactive functionality, ClimApp’s utility and ease of use should be considered to increase its usability. The usability of ClimApp was evaluated over two iterations: one usability lab test (n = 10) and one field test (n = 38) where first-time users completed tasks in ClimApp related to navigation, perceived ease of use, and perceived usefulness. Activity theory guided the analysis, and a directed qualitative content analysis was applied to evaluate the usability of ClimApp. The results suggest that there is room for improvement when assessing the universal design aspects, navigation, and information complexity, yet the participants perceived the tool as useful in the situated context.
LedgerView: Access-Control Views on Hyperledger Fabric

We present LedgerView -- a system that adds access control views to permissioned blockchains. The approach is motivated by an AT&T application of tracking refurbished devices. A blockchain is a decentralized tamper-resistant ledger managed by a group of peers. It is used in many applications for storing and sharing sensitive information, e.g., monetary transactions, health records, personal documents, etc. But in blockchain, all the peers see all the stored transactions, while in some applications, access to sensitive information should be limited, that is, concealed from peers and users who do not have proper access permissions. In database management systems, sets of records that are visible to some users and concealed from others are defined by views, but existing blockchain systems lack such access-control capabilities. Thus, in this paper, we introduce access-control views for Hyperledger Fabric. We present two types of views -- irrevocable and revocable, according to whether access to sensitive information can or cannot be revoked. We explain how to implement the two types of view by using cryptographic hash functions and encryption keys, and we show how to support Role-Based Access Control (RBAC). Experiments with supply chain transactions illustrate the incurred costs of the views in LedgerView, including latency, transaction rate and storage overhead.
Model scheduling and sample selection for ensemble adversarial example attacks

• To our knowledge, we are the first to study model scheduling for multi-stage adversarial example ensemble attacks. We propose an effective model scheduling strategy by simultaneously considering two criteria: decision boundary similarity and model diversity. • To further reduce overheads and improve transferability, we propose a novel sample selection strategy for multi-stage ensemble attacks, which can pick out those legitimate examples for adversarial example generation. • Extensive experiments demonstrate that our model scheduling based ensemble attack performs better than the state-of-the-art attacks SCES, SMBEA and EnsembleFool, and the proposed sample selection strategy can improve attack success rate by about 138%. Adversarial examples refer to the malicious inputs that can mislead deep neural networks (DNNs) to falsely classify them. In practice, some adversarial examples are transferable and hence can deceive different target models. In multi-stage ensemble adversarial example attacks, adversaries can generate strongly transferable adversarial examples through iteratively perturbing legitimate examples to attack well-trained source models in a white-box manner. Limited by computational and memory resources (e.g., GPU memory), however, adversaries cannot handle all models and all legitimate examples at a time. This brings an important but never studied research issue: how to optimally schedule source models and appropriately select samples to improve adversarial example transferability and reduce unnecessary computational overheads? To shed light on this problem, we develop a novel multi-stage ensemble adversarial example attack method based on our proposed strategies of model scheduling and sample selection. The first strategy schedules source models to be attacked in every stage, based on the criteria of decision boundary similarity and model diversity. The second selects input samples to be handled by ensemble attacks, according to their sensitivity level for adversarial perturbations. To our knowledge, we are the first to study model scheduling and sample selection for multi-stage ensemble attacks. We conduct extensive experiments on three datasets with a variety of source and target models. Experiments show that our model scheduling based ensemble attack outperforms the all-model ensemble attack and the state-of-the-art ensemble attacks SCES, SMBEA and EnsembleFool in transferability. Moreover, our sample selection strategy improves attack success rate by about 138 % .
Islaris: verification of machine code against authoritative ISA semantics

Recent years have seen great advances towards verifying large-scale systems code. However, these verifications are usually based on hand-written assembly or machine-code semantics for the underlying architecture that only cover a small part of the instruction set architecture (ISA). In contrast, other recent work has used Sail to establish formal models for large real-world architectures, including Armv8-A and RISC-V, that are comprehensive (complete enough to boot an operating system or hypervisor) and authoritative (automatically derived from the Arm internal model and validated against the Arm validation suite, and adopted as the official formal specification by RISC-V International, respectively). But the scale and complexity of these models makes them challenging to use as a basis for verification.
MOESI-prime

Prior work shows that Rowhammer attacks---which flip bits in DRAM via frequent activations of the same row(s)---are viable. Adversaries typically mount these attacks via instruction sequences that are carefully-crafted to bypass CPU caches. However, we discover a novel form of hammering that we refer to as coherence-induced hammering, caused by Intel's implementations of cache coherent non-uniform memory access (ccNUMA) protocols. We show that this hammering occurs in commodity benchmarks on a major cloud provider's production hardware, the first hammering found to be generated by non-malicious code. Given DRAM's rising susceptibility to bit flips, it is paramount to prevent coherence-induced hammering to ensure reliability and security in the cloud.
Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterations.
Dual graph-regularized sparse concept factorization for clustering

The concept factorization algorithm has received widespread attention and achieved remarkable results in the field of clustering. However, when modeling this clustering algorithm, it is necessary to initialize two new low-dimensional matrices that are independent of the objective matrix and continuously approximate the objective matrix through alternating iterative updating, thus inevitably introducing some noise factors that are undesirable for the model. Especially in the objective function constructed by square loss, the noise factors have a more significant influence on the clustering performance. To solve this issue, a dual graph-regularized sparse concept factorization (DGSCF) algorithm is proposed in this paper. In addition to maintaining the geometric structure of the data using dual graph regularization, DGSCF adopts an optimization framework based on l1 and Frobenius norms, which enhance the ability of feature selection and sparsity to eliminate the influence of noise factors on the algorithm performance. The corresponding alternating iterative updating rules and convergence proof of the DGSCF are provided. Finally, experiments on eight public datasets show its effectiveness and superiority.
A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.