Reviewerly/exHarmony huggingface dataset:
Understanding the Experiences of Remote Workers: Opportunities for Ambient Workspaces at Home

As the COVID-19 pandemic has forced many to work remotely from home, collaborating solely through digital technologies, a growing population of remote home workers are faced with profound wellbeing challenges. Passive sensing devices and ambient feedback have great potential to support the wellbeing of the remote workers, but there is a lack of background and understanding of the domestic workplace in terms of physical and affective dimensions and challenges to wellbeing. There are profound research gaps on wellbeing in the domestic workplace, with the current push for remote home and hybrid working making this topic timely. To address these gaps and shape a starting point for an “ambient workspaces” agenda, we conducted an exploratory study to map physical and affective aspects of working from home. The study involved both qualitative and quantitative measures of occupant experience, including sensor wristbands, and a custom web application for self-reporting mood and aspects of the environment. It included 13 participants for a period of 4 weeks, during a period of exclusive home working. Based on quantitative and qualitative analysis, our study addresses wellbeing challenges of the domestic workplace, establishes correlations between mood and physical aspects, and discusses the impact of feedback mechanisms in the domestic workplace on the behavior of remote workers. Insights from these observations are then used to inform a future design agenda for ambient technologies that supports the wellbeing of remote workers; addressing the design opportunities for ambient interventions in domestic workspaces. This work offers three contributions: 1) qualitatively and quantitatively informed understandings of the experiences of home-workers; 2) a future design agenda for “ambient home workspaces”; and 3) we propose three design concepts for ambient feedback and human–AI interactions in the built environment, to illustrate the utility of the design agenda.
Constructing Fast Approximate Eigenspaces With Application to the Fast Graph Fourier Transforms

We investigate numerically efficient approximations of eigenspaces associated with symmetric and general matrices. The eigenspaces are factored into a fixed number of fundamental components that can be efficiently manipulated which we consider to be extended orthogonal Givens or scaling and shear transformations. The number of these components controls the trade-off between approximation accuracy and the computational complexity of projecting on the eigenspaces. We write minimization problems for the single fundamental components and provide closed-form solutions. Then we propose algorithms that iteratively update all these components until convergence. We show results on random matrices and an application on the approximation of graph Fourier transforms for directed and undirected graphs.
Active clustering data streams with affinity propagation

Most existing applications have a large number of evolving data streams. Clustering data streams is still a critical problem for these applications as the data are evolving and changes over time. Most existing algorithms are unsupervised learning in which background information is useless. This paper proposes an active clustering algorithm for data stream based on the affinity propagation method, referred to as AAPStream. The affinity propagation aims to identify exemplars and create clusters based on these exemplars. Thus, the objective is to get the most informative exemplars to create the streaming model and predict the new arrival data. We conduct a set of experiments on real-world datasets to compare our algorithm with a state-of-the-art algorithm, and the experimental results show the effectiveness of the proposed algorithm.
Differential Privacy-Preserving User Linkage across Online Social Networks

Many people maintain accounts at multiple online social networks (OSNs). Multi-OSN user linkage seeks to link the same person’s web profiles and integrate his/her data across different OSNs. It has been widely recognized as the key enabler for many important network applications. User linkage is unfortunately accompanied by growing privacy concerns about real identity leakage and the disclosure of sensitive user attributes. This paper initiates the study on privacy-preserving user linkage across multiple OSNs. We consider a social data collector (SDC) which collects perturbed user data from multiple OSNs and then performs user linkage for commercial data applications. To ensure strong user privacy, we introduce two novel differential privacy notions, ϵ-attribute indistinguishability and ϵ-profile indistinguishability, which ensure that any two users’ similar attributes and profiles cannot be distinguished after perturbation. We then present a novel Multivariate Laplace Mechanism (MLM) to achieve ϵ-attribute indistinguishability and ϵ-profile indistinguishability. We finally propose a novel differential privacy-preserving user linkage framework in which the SDC trains a classifier for user linkage across different OSNs. Extensive experimental studies based on three real datasets confirm the efficacy of our proposed framework.
A survey on QoS-based service composition in Cloud system environment

The main challenges of Service Composition in environment like Cloud Computing are to ensure high quality execution. Nowadays, the Quality of Service (QoS) that should be offered by service composition becomes a priority for service providers and an exigence for customers. Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over Internet. In today’s IT field, where it’s already beyond human ability to analyze services and generate efficient composition plans manually, service composition are created and updated on the fly. One of the most important challenges in these service composition systems is to aggregate appropriate services while taking into account QoS. In this paper, we provide an extensive survey on QoS service composition research, while highlighting the specific concerns in Cloud system environment. We classify service composition approaches into four main classes: Heuristic, Framework, Agent and Artificial Intelligence based. We reviewed the major development in these four classes and outlined new challenges. This survey paper also provides a discussion of differences between considered approaches in terms of QoS attributes as well as directions for future research.
Dynamic Temporal–Spatial Regularization-Based Channel Weight Correlation Filter for Aerial Object Tracking

Correlation filter (CF) has drawn extensive interest in aerial object tracking due to its remarkable performance. Recently, the popular CF methods based on temporal–spatial regularization have been proved to be able to effectively improve the tracking results. However, the boundary effect and filter template degradation still influence the speed and accuracy of the trackers. To handle the two problems, a novel dynamic temporal–spatial regularization-based channel weighted tracking (DTSCT) method was proposed in this work. First, we attempted to employ the saliency detection technique to describe object variation for weakening the boundary effect. Then, the filter template was introduced to the temporal regularization to alleviate the template degradation. In addition, an adaptive weighting strategy was utilized to remove data redundancy in the feature channels. Experiments on three benchmark datasets showed the competitive performance of our DTSCT approach compared to the state-of-the-art methods.
The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs

Reproducibility is a crucial requirement in scientific research. When results of research studies and scientific papers have been found difficult or impossible to reproduce, we face a challenge which is called reproducibility crisis. Although the demand for reproducibility in Machine Learning (ML) is acknowledged in the literature, a main barrier is inherent non-determinism in ML training and inference. In this paper, we establish the fundamental factors that cause non-determinism in ML systems. A framework, ReproduceML, is then introduced for deterministic evaluation of ML experiments in a real, con-trolled environment. ReproduceML allows researchers to investigate software configuration effects on ML training and inference. Using ReproduceML, we run a case study: investigation of the impact of bugs inside ML libraries on performance of ML experiments. This study attempts to quantify the impact that the occurrence of bugs in a popular ML framework, PyTorch, has on the performance of trained models. To do so, a comprehensive methodology is proposed to collect buggy versions of ML libraries and run deterministic ML experiments using ReproduceML. Our initial finding is that there is no evidence based on our limited dataset to show that bugs which occurred in PyTorch do affect the performance of trained models. The proposed methodology as well as ReproduceML can be employed for further research on non-determinism and bugs.
Data Modeling and Performance Analysis Approach of Big Data

In this modern and information technology based digital world the user generate and create more than 1.5 Zettabytes of data over internet everyday intentionally or automatically. In various research fields including data analysis, cloud computing, engineering, artificial intelligence, and others, the big data plays very important role. This paper introduced multiple techniques of data modeling which could have one or more modeling tools, that helps to measure performance analysis. The key challenges in Big Data are when where and how to apply the data modeling and performance analysis approach of Big data in order to conclude the core aspect that used to obtain high value processing outcome in less computing time and expenditure. Sometimes, the available processing model is not able to believe in this requisite and try to focus on achieving high computation performance, which will enlarge the computing time and cost. The performance measurement analysis outcomes are very useful because they help to identify the sources of degradation of application as well as cloud. The main objective of this paper is to cover the drawbacks that exist between quantitative representations of quality concepts of software engineering and performance analysis approach.
SeqAD: An Unsupervised and Sequential Autoencoder Ensembles based Anomaly Detection Framework for KPI

Key Performance Indicator (KPI), a kind of time-series data, its anomalies are the most intuitive characteristics when failures occurred in IT systems. KPI anomaly detection is increasingly critical to provide reliable and stable services for IT systems. Unsupervised learning is a promising method because of lacking labels and the unbalance in KPI samples. However, existing unsupervised KPI anomaly detection methods suffer from high false alarm rates. They handle KPI sequence as non-sequential data and ignore the time information, which is an essential KPI character. To this end, in this paper, we propose an unsupervised and sequential autoencoder ensembles based anomaly detection framework called SeqAD. SeqAD inherits the advantages both from the sequence-to-sequence model and autoencoder ensembles. SeqAD reduces the KPI over-fitting problem effectively by introducing autoencoder ensembles. In order to better capture the time information of KPI, we propose a random step connection based recurrent neural network (RSC-RNN) to train the KPI sequence, which can provide random connections to construct autoencoders with different structures and retain time information to the most extent. Extensive experiments are conducted on two public KPI data-sets from real-world deployed systems to evaluate the efficiency and robustness of our proposed SeqAD framework. Results show that SeqAD is able to smoothly capture most of the characteristics in all KPI data-sets, as well as to achieve a high F1 score between 0.93 and 0.98, which is better than the state-of-art unsupervised KPI anomaly detection methods.
Resistance for Side-Channel Attack by Virtual Dual-Rail Effect

Side-channel attacks are a serious threat in the IoT era. Dual-rail digital circuit design has been proposed to mask side-channel information such as waveforms of power consumption and electromagnetic waves emitted from encryption chips. The dual-rail makes it difficult for the encryption key to leak from the power consumption by equilibrate bit transition of the inputs to the circuit. In this paper, we propose and evaluate novel design that has the similar effect as the dual-rail design, and with a more simple and reasonable circuit. The novel design is called W-FF design. The W-FF design can dramatically improve security performance and achieves all most all area overhead can be eliminated compared with dual-rail design.
Human resources optimization with MARS and ANN: Innovation geolocation model for generation Z

&lt;p style='text-indent:20px;'&gt;Human resources (HR) have a key impact on the creation and implementation of modern products, solutions and concepts. Relatively new and rarely undertaken research challenge in enterprise is optimization of HR in the context of their location and requirements for working conditions. A great challenge here is the transparency and reliability of the collected data. In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines optimizing the availability of HR with a high innovation rate, taking into account their availability time and location. This study was conducted on a group of 5095 young people from the Z generation. A total of 11 variables were analyzed in the context of innovation and presented in this article. The effect of research using machine learning methods is the analysis of the characteristics of generation Z representatives, whose desire is to work in innovative companies. Research results indicate that some regions offer candidates with a higher level and commitment to innovation, and thus make HR more available for the development of innovative products. Chosen models designed by using AI and Operational Research Analytics were presented in the graphic visualization, which is a novelty in the presentation of similar issues in relation to HR.&lt;/p&gt;
Counting irreducible polynomials with prescribed coefficients over a finite field

We continue our study on counting irreducible polynomials over a finite field with prescribed coefficients. We set up a general combinatorial framework using generating functions with coefficients from a group algebra which is generated by equivalence classes of polynomials with prescribed coefficients. Simplified expressions are derived for some special cases. Our results extend some earlier results.
FFT Program Generation for Ring LWE-Based Cryptography

Fast Fourier Transform (FFT) enables an efficient implementation of polynomial multiplication, which is at the core of any cryptographic constructions based on the hardness of the Ring learning with errors (RLWE) problem. Existing implementations of FFT for RLWE-based cryptography rely on hand-written assembly code for performance, making it difficult to understand, maintain, and extend for new architectures.We present a novel framework to implement FFT for RLWE-based cryptography, based on a principled program-generation approach. We start with a high-level, abstract definition of an FFT program, and generate low-level code by interpreting high-level primitives and delegating low-level details to an architecture-specific module. Since low-level details concerning modular arithmetic and vectorization are separated from high-level logic, we can easily generate both AVX2- and AVX512-optimized low-level code from the same high-level description of the FFT program. Our generated code is highly competitive compared to expert-written assembly code: For AVX2 (and AVX512, resp) it runs 1.13x (and 1.39x, resp) faster than the AVX2-optimized assembly implementation in the NewHope key-exchange protocol.
Compressed Positive Quadrature Filter

A compressed positive quadrature filter (CPQF) is proposed in this technical note. Accuracy, efficiency, and numerical stability are the main concerns for quadrature-based nonlinear Gaussian filtering, but desirable performance in all these three aspects can hardly be simultaneously achieved in previously proposed nonlinear Gaussian filters, such as unscented Kalman filter, Gauss–Hermite quadrature filter (GHQF), and sparse grid quadrature filter. To improve the overall capacity of nonlinear Gaussian filtering techniques, the CPQF is proposed by numerically solving the constrained moment matching equations. By enforcing the positivity of the quadrature weights, the CPQF is numerically as stable as the GHQF. Through exploiting the underlying sparsity while solving the moment matching equations, the CPQF can also achieve the same level of accuracy as the GHQF using far fewer quadrature points. Simulations are conducted to evaluate the effectiveness of the CPQF, and the numerical results demonstrate its superior performance over conventional Gaussian filters.
Accelerate Cooperative Deep Inference via Layer-wise Processing Schedule Optimization

Computation offloading is proposed to solve one obstacle of enabling high-accurate and real-time deep inference in resource-constrained Internet of Things (IoT) devices. Cooperative deep inference is proposed recently to further trade-off the introduced communication latency in computation offloading, which partitions a Deep Neural Network (DNN) model into two parts and utilizes the IoT end device and the server to process the DNN model cooperatively. We observe one important but ignored fact in all previous works: DNN computation and communication processing cbe conducted simultaneously in cooperative deep inference. As a result, the DNN layer-wise processing schedule has an impact on inference latency and it is non-trivial to find the optimal schedule in State-Of-The-Art (SOTA) DNNs with Directed Acyclic Graph (DAG) computational architectures. The contributions of this paper are as follows. (1) The proposed Deep Inference Optimization with Layer-wise Schedule, Deep-Inference-L, is a unique pipeline-based DAG schedule problem, which turns out to be NP-hard. (2) We categorize SOTA DNNs into three different categories and discuss the corresponding optimal processing schedule in special cases and efficient heuristic schedules in the general case. (3) The proposed solutions are extensively tested via a proof-of-concept prototype. (4) Results indicate that our algorithms can achieve an 8x speedup compared with local inference in the best case.
Robots for Foreign Language Learning: Speaking Style Influences Student Performance

Much previous research suggests that teachers’ individual characteristics may affect students’ performance; however, which factors are particularly helpful is as yet unclear and methodologically very difficult to assess. In this paper, we study the effects of robots’ speaking styles when instructing students on a task. 40 participants saw a brief video in which a robot presented its instructions either in a charismatic or a not so charismatic speaking style. Participants’ task was then to produce foreign language sentences on the basis of visualizations of the prosodic properties of these sentences. A subsequent analysis of participants’ productions shows that language learners’ performance was significantly better when the robot had delivered its instructions in a charismatic voice. The results suggest not only that a charismatic speaking style may be crucial for teachers in general and hence one of the factors causing the interpersonal variation between teachers, but also that students can benefit from instructions by robots delivered in a charismatic speaking style.
Blockchain Integrated ERP for a Better Supply Chain Management

This paper represents how supply chain will be more empowered when the ERP system will be integrated with Blockchain technology, it also details out how the two technologies will work together to provide transparency, and cost reduction to supply chain features.
Using time-correlated noise to encourage exploration and improve autonomous agents performance in Reinforcement Learning

Intelligent autonomous agents need to know how to carry out actions based on reasoning, perception, and analysis. Therefore, reinforcement learning algorithms guide the agent to reach this goal by performing steps that guarantee the most significant reward. The problem with this approach is that when the agent finds an optimal action with a considerable premium, it tends to stop exploring the environment to guarantee only that great reward. In this way, the agent stops making a great exploration to find new ways and learn alternatives that could generate a bigger bonus in the face of a change in its context. To alleviate this problem, some techniques, such as Soft Actor-Critic (SAC) and Asynchronous Advantage Actor-Critic (A3C), use entropy regulation to improve policy optimization in reinforcement learning. Thus, the greater the entropy, the lower the probability of the agent’s certainty in a given action. Despite entropy, these algorithms are not immune to local optimal and require additional exploration mechanisms. Therefore, this work proposes an approach that helps the agent’s exploitation policy during reinforcement learning training and improves its performance during the test time. We use the latest state-of-the-art (SOTA) approaches, that is, Asynchronous Advantage Actor-Critic (A3C), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) in this work. We created a time-correlated noise in the agent’s policy network from the use of a random disturbance generated with the Ornstein-Uhlenbeck process. According to the experiments carried out and the results obtained, we can see that our proposal allowed the agent to explore the environment more during training and improve its performance during the testing time, increasing the reward received in different learning contexts.
Service-Oriented Architecture for Data-Driven Fault Detection

Predictive maintenance approaches result in shorter interventions, less downtime and less maintenance and production costs, leading to more sustainable manufacturing and increasing earnings. Machine learning and data mining approaches are now popular in this field, producing data-driven models that can provide insights regarding equipment behavior, identify anomalies, and generate predictions that can be used to support decision-making processes. In this paper, we propose a service-oriented architecture that adapts a predictive maintenance reference architecture to the domain of the flexible packaging market. Focusing on the data analytics services required for predictive maintenance, the proposed architecture was assessed by conducting a case-study using real-world data. To detect faults in the company’s coextrusion film blowing machines, we devised a predictive maintenance methodology whereby the isolation forest algorithm is used to detect potentially anomalous data points and the anomaly threshold is defined by computing the interquartile range of anomaly scores. To compensate for the fact that single anomalies are unlikely to be of significance, when deploying the isolation forest model in real time the percentage of anomalies detected within a given time interval is monitored. An alarm is issued by the system should that percentage exceed a specific cutoff. Experimental results show the described approach is successful in distinguishing anomalous data from normal data in non-stationary time-series.