Reviewerly/exHarmony huggingface dataset:
Ocupado: Visualizing Location‐Based Counts Over Time Across Buildings

Abstract Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi‐year design study that resulted in Ocupado, a set of visual decision‐support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location‐based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location‐based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real‐world data refined through extensive stakeholder feedback, and discussion of its take‐up by our industry partner.
Subjective and Objective Quality Assessment for Stereoscopic Image Retargeting

Binocular stereoscopic image retargeting (SIR) aims to adjust 3D images into target aspect ratios. In recent years, various SIR methods have been proposed, but there are few researches on visual quality assessment. As a consequence, we construct a benchmark stereoscopic image retargeting quality assessment database (NBU-SIRQA), which contains 720 stereoscopic retargeted images generated by eight representative SIR operators. Subjective test is conducted to obtain the mean opinion score (MOS) for each stereoscopic retargeted image. Additionally, we propose an objective SIRQA metric based on grid deformation and information loss (GDIL). The main idea of GDIL is to decompose the SIR operator into two transformations: monocular image retargeting transformation and viewpoint transformation. In each transformation, grid deformation and information loss are extracted simultaneously to represent image quality and 3D perception quality. Experimental results validated on our established NBU-SIRQA database show the superiority of our metric in measuring the quality of stereoscopic retargeted images over the existing approaches.
DAG-Aware Joint Task Scheduling and Cache Management in Spark Clusters

Data dependency, often presented as directed acyclic graph (DAG), is a crucial application semantics for the performance of data analytic platforms such as Spark. Spark comes with two built-in schedulers, namely FIFO and Fair scheduler, which do not take advantage of data dependency structures. Recently proposed DAG-aware task scheduling approaches, notably GRAPHENE, have achieved significant performance improvements but paid little attention to cache management. The resulted data access patterns interact poorly with the built-in LRU caching, leading to significant cache misses and performance degradation. On the other hand, DAG-aware caching schemes, such as Most Reference Distance (MRD), are designed for FIFO scheduler instead of DAG-aware task schedulers.In this paper, we propose and develop a middleware Dagon, which leverages the complexity and heterogeneity of DAGs to jointly execute task scheduling and cache management. Dagon relies on three key mechanisms: DAG-aware task assignment that considers dependency structure and heterogeneous resource demands to reduce potential resource fragmentation, sensitivity-aware delay scheduling that prevents executors from long waiting for tasks insensitive to locality, and priority-aware caching that makes the cache eviction and prefetching decisions based on the stage priority determined by DAG-aware task assignment. We have implemented Dagon in Apache Spark. Evaluation on a testbed shows that Dagon improves the job completion time by up to 42% and CPU utilization by up to 46% respectively, compared to GRAPHENE plus MRD.
Fast algorithm with theoretical guarantees for constrained low-tubal-rank tensor recovery in hyperspectral images denoising

Hyperspectral images (HSIs) are unavoidably degraded by mixed noise, including Gaussian noise and sparse noise. In this paper, we consider a constrained tubal rank and sparsity model (CTSD) to tackle the HSIs mixed noise removal, which characterizes the clean HSIs via the low-tubal-rank constraint and the sparse noise via the l0 and l∞ norm constraints, respectively. Due to the strong non-convexity, the CTSD model is challenging to solve. To tackle the CTSD, we develop the proximal alternating minimization (PAM) algorithm via the exact tensor singular value decomposition (t-SVD) and establish the global convergence under mild assumptions. Since the t-SVD is computationally expensive, especially for large scale images, we further design an efficient inexact PAM algorithm via an inexact t-SVD. The inexact PAM enjoys two advantages: (1) The computational complexity for SVDs of the inexact PAM (O(rn1n2n3)) is about twofold faster than that of the exact PAM (O(min(n1,n2)n1n2n3)) for r≪min(n1,n2); (2) The accuracy of the inexact PAM is theoretically guaranteed. Extensive experiments on HSIs denoising demonstrate that the exact and inexact methods both outperform comparing methods in quantitative evaluation metrics and visual effects, and the inexact PAM can compromise between the accuracy and efficiency for large scale HSIs.
A Comparative Analysis of Task Scheduling Approaches in Cloud Computing

Recently, cloud computing has emerged as a primary enabling technology to provide compute, storage, platform, and analytics services to end-users and organizations based on pay-as-you-use. In essence, cloud provides agility, availability, scalability, and resiliency. However, increased number of users leads to issues such as scheduling of requests, demands, and work-load efficiency over the available cloud resources. Similarly, since the inception of cloud computing, task scheduling is reckoned as an essential ingredient in the commercial value of this technology. Task scheduling is considered as an NP-hard problem in cloud computing and different solutions exist in the literature to address this issue. In this paper, we investigate and empirically compare some of the recent state-of-the-art scheduling mechanisms in cloud computing with respect to Makespan (the time difference between the start and finish of a sequence of jobs or tasks) and throughput (number of tasks successfully executed per unit time (Makespan)). We then extend the comparison by evaluating the considered approaches with respect to Average Resource Utilization Ratio (ARUR). We also recommend and identify factors that can improve resource utilization and maximize revenue-generation for cloud service providers.
Speech acts redux

We communicate to (1) express how we feel, (2) share observations about the world, (3) commit to future acts, (4) request others to do things, and (5) change the state of the world according to pragmatics. Of these categories, today's conversational interfaces like Siri and Alexa are mainly designed to fulfill our imperatives, i.e., to respond to our requests on command. Yet, could future conversational interfaces go beyond request-response interactions? One way forward is to consider what conversational interactions allow us to do with language. Not only can we send request to CUIs, but we can also share our emotions, attitudes, beliefs, and promises as speech acts---acts we regularly perform with other humans. To open up pragmatics as an under-investigated design space for conversational technologies, I elaborate on what pragmatics and affective pragmatics are and give examples involving conversational agents. As a theoretical contribution, I provide a taxonomy from pragmatics and affective pragmatics to move beyond request-response interactions. The aim is to extend our conversational experiences with technology to cover the full spectrum of everyday speech acts. Our words can change the world; expressions to CUIs can also do so.
Temporal Heterogeneous Interaction Graph Embedding for Next-Item Recommendation

In the scenario of next-item recommendation, previous methods attempt to model user preferences by capturing the evolution of sequential interactions. However, their sequential expression is often limited, without modeling complex dynamics that short-term demands can often be influenced by long-term habits. Moreover, few of them take into account the heterogeneous types of interaction between users and items. In this paper, we model such complex data as a Temporal Heterogeneous Interaction Graph (THIG) and learn both user and item embeddings on THIGs to address next-item recommendation. The main challenges involve two aspects: the complex dynamics and rich heterogeneity of interactions. We propose THIG Embedding (THIGE) which models the complex dynamics so that evolving short-term demands are guided by long-term historical habits, and leverages the rich heterogeneity to express the latent relevance of different-typed preferences. Extensive experiments on real-world datasets demonstrate that THIGE consistently outperforms the state-of-the-art methods.
Astute Video Transmission for Geographically Dispersed Devices in Visual IoT Systems

Visual IoT (VIoT) is a promising IoT paradigm that visualizes sensing data from massive numbers of dispersed devices. A key objective in VIoT is to efficiently manage the devices to perform complex task-related visual data processing. Prior multimedia IoT systems have mainly focused on the delivery of captured video to remote servers, without considering the video tasks’ characteristics and the devices’ heterogeneous capabilities. In this work, we propose an astute video transmission framework for such a VIoT system composed of heterogeneous visual devices. First, we formulate the problem of joint video task allocation and heterogeneous device management by constructing a device hypergraph (DH) structure, which enables devices with different capabilities to perform complex video tasks cooperatively. Second, we model the video transmission within a VIoT system by applying fractal theory considering the NP-hardness of the optimization. In particular, we construct a comprehensive fractal submodular optimization framework through a DH and explore the inner submodular property to effectively leverage both video-task complexity and device heterogeneity. Third, we consider the geographically dispersed characteristic of massive numbers of VIoT devices and propose a multi-hop dispersed transmission mechanism for achieving globally cooperative optimality. The proposed architecture has been evaluated under diverse parameter settings. Numerical results are provided to validate the proposed algorithm in terms of delay, computational efficiency, and bandwidth utilization. Simulation results confirm the effectiveness and superiority of the proposed method.
Methods and Tools for Prototyping Voice Interfaces

Voice technology has experienced tremendous growth in both sophistication and popularity in recent years. This paper considers how researchers and designers might build novel and intuitive voice interfaces. We begin by briefly describing two of the key design techniques for voice design---elicitation methods and Wizard of Oz. We then describe the available tools for prototyping and implementing voice interfaces, and attempt to categorize these according to their function, ease of use, and the fidelity of prototypes they can produce. Finally, we conclude by describing three major aspects of voice communication that are not yet supported by existing prototyping tools, and motivate the need for new design tools in future work.
Identification of Dynamical Systems Using a Broad Neural Network and Particle Swarm Optimization

System identification plays an important role in improving the structure and parameters of a system, but there are many problems encountered in actual operation.The identification of dynamic systems is not as simple as it is for static systems; thus, choosing effective model structures and parameters is the key to solving this problem.This paper proposes a novel algorithm based on a combination of a broad learning system (BLS) and particle swarm optimization (PSO) to identify nonlinear dynamical systems.The proposed method first uses the dimension expansion of the data set as the input of the BLS and then optimizes the model weight by the PSO algorithm.To verify the effectiveness of our proposal, we use four second-order systems for simulation experiments.The simulation results clearly show the efficiency and anti-interference ability of the proposed method.
Auto-3P: An autonomous VNF performance prediction &amp; placement framework based on machine learning

We propose Auto-3P, an Autonomous module for Virtual Network Functions Performance Prediction and Placement at network cloud and edge facilities based on Machine Learning (ML). Auto-3P augments the autonomous placement capabilities of MANagement and Orchestration frameworks (MANOs) by considering both resource availability at hosting nodes and the implied impact of a VNF node placement decisions on the whole service level end-to-end performance. Unlike that, most existing placement methods take a rather myopic approach after manual rule-based decisions, and/or based exclusively on a host-centric view that focuses merely on node-local resource availability and network metrics. We evaluate and validate Auto-3P with real-field trials in the context of a well-defined Smart City Safety use case using a real end-to-end application over a real city-based testbed. We meticulously conduct repeated tests to assess (i) the accuracy of our adopted prediction models; and (ii) their placement performance against three other existing MANO approaches, namely, a "Traditional", a "Latency-aware" and a "Random" one, as well as against a collection of well-known Time Series Forecasting (TSF) methods. Our results show that the accuracy of our ML models outperforms the one by TSF models, with the most prominent accuracy performances being exhibited by models such as K-Nearest Neighbors Regression (K-NNR), Decision Tree (DT), and Support Vector Regression (SVR). What is more, the resulted end-to-end service level performance of our approach outperforms "Traditional", "Latency-aware", and Random MANO placement. Last, Auto-3P achieves load balancing at selected VNF hosts without degrading end-to-end service level delay, and without a need for a (fixed) overload threshold check, unlike what is suggested by other works in the literature for coping with heavy system-wide load conditions.
Follow the Blue Bird: A Study on Threat Data Published on Twitter

Open Source Intelligence (OSINT) has taken the interest of cybersecurity practitioners due to its completeness and timeliness. In particular, Twitter has proven to be a discussion hub regarding the latest vulnerabilities and exploits. In this paper, we present a study comparing vulnerability databases between themselves and against Twitter. Although there is evidence of OSINT advantages, no methodological studies have addressed the quality and benefits of the sources available. We compare the publishing dates of more than nine-thousand vulnerabilities in the sources considered. We show that NVD is not the most timely or the most complete vulnerability database, that Twitter provides timely and impactful security alerts, that using diverse OSINT sources provides better completeness and timeliness of vulnerabilities, and provide insights on how to capture cybersecurity-relevant tweets.
A Decentralized Voting Mechanism: Engaging ERC-20 token holders in decision-making

This paper presents a Blockchain-based voting mechanism that has the potential to disrupt current voting systems and fuel the development of decentralized governance. More specifically, the paper presents a study on the design and deployment of a Solidity smart contract that can interact with any Ethereum-based token (ERC-20) in order to help decentralized organizations to run public voting campaigns while at the same time engaging tokens holders in decision making. The use case scenarios outlined in the paper demonstrate with success the effectiveness of the proposed approach.
Safe sample screening for regularized multi-task learning

As a machine learning paradigm, multi-task learning (MTL) attracts increasing attention recently. It can improve the overall performance by exploiting the correlation among different tasks. It is especially helpful in dealing with small sample learning problems. As a classic multi-task learner, regularized multi-task learning (RMTL) inspired lots of multi-task learning researches in the past. Massive researches have shown the performance of RMTL when compared to single-task learners, i.e., support vector machine. However, the training complexity will be considerably large when training large datasets. To tackle such a problem, we propose safe screening rules for an improved regularized multi-task support vector machine (IRMTL). By statically detecting and removing inactive samples from multiple tasks simultaneously before solving the reduced optimization problem, both rules reduce the training time significantly without incurring performance degradation of the proposed method. The experimental results on 13 benchmark datasets and an image dataset also clearly demonstrate the effectiveness of safe screening rules for IRMTL.
Digital Pen Features Predict Task Difficulty and User Performance of Cognitive Tests

Digital pen signals were shown to be predictive for cognitive states, cognitive load and emotion in educational settings. We investigate whether low-level pen-based features can predict the difficulty of tasks in a cognitive test and the learner's performance in these tasks, which is inherently related to cognitive load, without a semantic content analysis. We record data for tasks of varying difficulty in a controlled study with children from elementary school. We include two versions of the Trail Making Test (TMT) and six drawing patterns from the Snijders-Oomen Non-verbal intelligence test (SON) as tasks that feature increasing levels of difficulty. We examine how accurately we can predict the task difficulty and the user performance as a measure for cognitive load using support vector machines and gradient boosted decision trees with different feature selection strategies. The results show that our correlation-based feature selection is beneficial for model training, in particular when samples from TMT and SON are concatenated for joint modelling of difficulty and time. Our findings open up opportunities for technology-enhanced adaptive learning.
Effects of Argumentative Explanation Types on the Perception of Review-Based Recommendations

Recommender systems have achieved considerable maturity and accuracy in recent years. However, the rationale behind recommendations mostly remains opaque. Providing textual explanations based on user reviews may increase users' perception of transparency and, by that, overall system satisfaction. However, little is known about how these explanations can be effectively and efficiently presented to the user. In the following paper, we present an empirical study conducted in the domain of hotels to investigate the effect of different textual explanation types on, among others, perceived system transparency and trustworthiness, as well as the overall assessment of explanation quality. The explanations presented to participants follow an argument-based design, which we propose to provide a rationale to support a recommendation in a structured way. Our results show that people prefer explanations that include an aggregation using percentages of other users' opinions, over explanations that only include a brief summary of opinions. The results additionally indicate that user characteristics such as social awareness may influence the perception of explanation quality.
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This paper addresses the problem of square-rooting in the Unscented Kalman Filtering (UKF) methods rooted in the Ito^-Taylor approximation of the strong order 1.5. Since its discovery the UKF has become one of the most powerful state estimation means because of its outstanding performance in numerous stochastic systems of practical value, including continuous-discrete ones. Besides, the main shortcoming of this technique is the need for the Cholesky decomposition of covariance matrices derived in its time and measurement update steps. Such a factorization is time-consuming and highly sensitive to round-off and other errors committed in the course of computation, which can result in losing the covariance's positivity and, hence, failing the Cholesky decomposition. The latter problem is usually overcome by means of square-root filter implementations, which propagate not the covariance itself but its square root (Cholesky factor), only. Unfortunately, negative weights arising in applications of the UKF to high-dimensional stochastic systems preclude from designing conventional square-root UKF methods. We resolve it with low-rank Cholesky factor update procedures or with hyperbolic QR transforms used for yielding J-orthogonal square roots. Our novel square-root filters are justified theoretically and examined and compared numerically to the existing UKF in a flight control scenario.
Secure content‐based image retrieval using modified Euclidean distance for encrypted features

Abstract In this article, a secure image retrieval scheme is proposed, which focuses on providing satisfactory retrieval results, and the framework searches relevant images even in an encrypted domain without compromising the performance of the retrieval process. Initially, bit‐level features have been endeavored from the luminance component of the image, from which statistical parameters are computed to generate more intrinsic values. These values are subsequently divided into bins to configure two histograms, which effectively reduce the length of the feature vector. These histograms are then eventually combined with quantized chrominance features to enhance the discriminative property of the feature vector. Since the proposed scheme is in the encrypted domain, conventional similarity measure distance for the image is not well suited. So, a modified Euclidean distance is incorporated, which is modeled to work with encrypted features. To comprehend the security, a piecewise logistic map sequence is considered, where seed values are assimilated to generate two secret keys. As a result, not only the system provides an efficient, secure retrieval system but also cryptographic components have no impact on its retrieval efficiency, and satisfactory results are obtained. Experimental results on Corel‐1K and GHIM‐10K illustrate decent performance in retrieval as compared to existing work in the retrieval domain.