Reviewerly/exHarmony huggingface dataset:
Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning

Computer-vision methods have been extensively used in intelligent transportation systems for vehicle detection. However, the detection of severely occluded or partially observed vehicles due to the limited camera fields of view remains a challenge. This paper presents a multi-camera vehicle detection system that significantly improves the detection performance under occlusion conditions. The key elements of the proposed method include a novel multi-view region proposal network that localizes the candidate vehicles on the ground plane. We also infer the vehicle position on the ground plane by leveraging multi-view cross-camera context. Experiments are conducted on dataset captured from a roadway in Richardson, TX, USA, and the system attains 0.7849 Average Precision and 0.7089 Multi Object Detection Precision. The proposed system results in an approximately 31.2% increase in AP and 8.6% in MODP than the single-camera methods.
Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.
An efficient method for allocating resources in a cloud computing environment with a load balancing approach

Summary Cloud computing technology forms a computational ensemble of large computing services and systems. Recently, it has been the focus of research on resource management, task scheduling, and effective resource sharing among users. Given the computational and resource management challenges in cloud computing, an improved method is required to approach the optimal allocation of resources. The current research combines evolutionary algorithms, fuzzy logic and task scheduling techniques to improve computational cloud resource allocation with the aim of maintaining load balance in cloud providers. Simulation of the proposed model reveals that the response time, task execution time, and energy consumption of the proposed method are better than for those of other methods.
Agricultural Service Mobile Robot Modeling and Control Using Artificial Fuzzy Logic and Machine Vision

This paper represents modeling and control of an agricultural service skid steering mobile robot for the purposes of grass cutting using Proportional-Integral-Derivative (PID) controller and Fuzzy Logic techniques and feedback signals from sensors as IMU, encoders, and Machine Vision. The paper deals with the system modeling into two methods: The first is using Fuzzy modeling as a modeling tool for complex nonlinear system, the second is using MATLAB software system Identification Tool. The study Uses PID, Fuzzy logic controller and fuzzy self-tuning of PID controller to control the path tracking of the skid steering mobile robot and improve its response. Utilizing Machine vision provides better results than that of the IMU sensor and the encoders. The fuzzy self-tuning of PID controller shows better dynamic results than the fuzzy logic and PID controllers.
CRF-based Single-stage Acoustic Modeling with CTC Topology

In this paper, we develop conditional random field (CRF) based single-stage (SS) acoustic modeling with connectionist temporal classification (CTC) inspired state topology, which is called CTC-CRF for short. CTC-CRF is conceptually simple, which basically implements a CRF layer on top of features generated by the bottom neural network with the special state topology. Like SS-LF-MMI (lattice-free maximum-mutual-information), CTC-CRFs can be trained from scratch (flat-start), eliminating GMM-HMM pre-training and tree-building. Evaluation experiments are conducted on the WSJ, Switchboard and Librispeech datasets. In a head-to-head comparison, the CTC-CRF model using simple Bidirectional LSTMs consistently outperforms the strong SS-LF-MMI, across all the three benchmarking datasets and in both cases of mono-phones and mono-chars. Additionally, CTC-CRFs avoid some ad-hoc operation in SS-LF-MMI.
Recipes for Programmable Money

This paper presents a qualitative study of the recent integration of a UK-based, digital-first mobile banking app - Monzo - with the web automation service IFTTT (If This Then That). Through analysis of 113 unique IFTTT 'recipes' shared by Monzo users on public community forums, we illustrate the potentially diverse functions of these recipes, and how they are achieved through different kinds of automation. Beyond achieving more convenient and efficient financial management, we note many playful and expressive applications of conditionality and automation that far extend traditional functions of banking applications and infrastructure. We use these findings to map opportunities, challenges and areas of future research in the development of 'programmable money' and related financial technologies. Specifically, we present design implications for the extension of native digital banking applications; novel uses of banking data; the applicability of blockchains and smart contracts; and future forms of financial autonomy.
Speech Super Resolution Generative Adversarial Network

The goal of speech super-resolution (SSR) or speech bandwidth expansion is to generate the missing high-frequency components for a given low-resolution speech signal. It has the potential to improve the quality of telecommunications. We propose a new method for SSR that leverages the generative adversarial networks (GANs) and a regularization method for stabilizing the GAN training. The generator network is a convolutional autoencoder with 1D convolution kernels, operating along time-axis and generating the high-frequency log-power spectra from the low-frequency log-power spectra input. We employ two recent deep neural network (DNN) based approaches to compare them with our proposed method, including both objective speech quality metrics and subjective perceptual tests. We show that our proposed method outperforms the baseline methods in terms of both objective and subjective evaluations.
Multi-speaker Sequence-to-sequence Speech Synthesis for Data Augmentation in Acoustic-to-word Speech Recognition

The acoustic-to-word (A2W) automatic speech recognition (ASR) realizes very fast decoding with a simple architecture and achieves state-of-the-art performance. However, the A2W model suffers from the out-of-vocabulary (OOV) word problem and cannot use text-only data to improve the language modeling capability. Meanwhile, sequence-to-sequence neural speech synthesis has also been developed and achieved naturalness comparable to human speech. We investigate leveraging sequence-to-sequence neural speech synthesis to augment training data for the ASR system in a target domain. While speech synthesis model is usually trained with single speaker data, ASR needs to cover a variety of speakers. In this work, we extend the speech synthesizer so that it can output speech of many speakers. The multi-speaker speech synthesizer is trained with a large corpus in the source domain, then used to generate acoustic features from texts of the target domain. These synthesized speech features are combined with real speech features of the source domain to train an attention-based A2W model. Experimental results show that the A2W model trained with the multi-speaker model achieved a significant improvement over the baseline and the single speaker model.
Social Reflections on Fitness Tracking Data

Wearable activity trackers can encourage physical activity (PA)-a behavior critical for preventing obesity and reducing the risks of chronic diseases. However, prior work has rarely explored how these tools can leverage family support or help people think about strategies for being active-wo factors necessary for achieving regular PA. In this 2-month qualitative study, we investigated PA tracking practices amongst 14 families living in low-income neighborhoods, where obesity is prevalent. We characterize how social discussions of PA data rarely extended beyond the early stages of experiential learning, thus limiting the utility of PA trackers. Caregivers and children rarely analyzed their experiences to derive insights about the meaning of their PA data for their wellbeing. Those who engaged in these higher-order learning processes were often influenced by parenting beliefs shaped by personal health experiences. We contribute recommendations for how technology can more effectively support family experiential learning using PA tracking data.
An innovative hybrid approach for extracting named entities from unstructured text data

Abstract Named entity recognition (NER) is the core part of information extraction that facilitates the automatic detection and classification of entities in natural language text into predefined categories, such as the names of persons, organizations, locations, and so on. The output of the NER task is crucial for many applications, including relation extraction, textual entailment, machine translation, information retrieval, etc. Literature shows that machine learning and deep learning approaches are the most widely used techniques for NER. However, for entity extraction, the abovementioned approaches demand the availability of a domain‐specific annotated data set. Our goal is to develop a hybrid NER system composed of rule‐based deep learning as well as clustering‐based approaches, which facilitates the extraction of generic entities (such as person, location, and organization) out of natural language texts of domains that lack generic named entities labeled domain data sets. The proposed approach takes the advantages of both deep learning and clustering approaches but separately, in combination with a knowledge‐based approach by using a postprocessing module. We evaluated the proposed methodology on court cases (judgments) as a use case since it contains generic named entities of different forms that are poorly or not present in open‐source NER data sets. We also evaluated our hybrid models on two benchmark data sets, namely, Computational Natural Language Learning (CoNLL) 2003 and Open Knowledge Extraction (OKE) 2016. The experimental results obtained from benchmark data sets show that our hybrid models achieved substantially better performance in terms of the F‐score in comparison to other competitive systems.
Comments on “SEPDP: Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage”

Provable Data Possession is viewed as an important technique to check the integrity of the data stored in remote servers. Recently, a new provable data possession scheme [Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage, IEEE Transactions on Services Computing, (2018) DOI: 10.1109/TSC.2018.2820713] was proposed. The authors claimed this scheme can guarantee the storage correction. In this paper, we show this scheme cannot satisfy this fundamental security. Specifically, we demonstrate the malicious cloud can generate a proof to pass the third party auditor's verification even if it does not store the user's whole file.
Underwater salient object detection by combining 2D and 3D visual features

Automatic salient object detection over images is an important step for object detection and recognition. Many existing salient object detection methods are performed excellently on ground, but, it remains a challenge to detect salient objects in water. Different from common scenes on ground, challenges to underwater salient object detection are posed by poor underwater image quality, uncontrolled underwater objects and environments. Till now, most of existing methods are hindered from adaptability to difficult underwater environments, due to the strong light attenuation and scattering effects. To this end, we propose a novel underwater salient object detection method by combining 2D and 3D visual features. For feature combination, a novel detection model is established by mathematically stimulating the biological vision mechanism of aquatic animals. This model, apart from the 2D visual features i.e. the color and intensity, extracts 3D depth features to arouse the depth sensitivity in the three-dimensional space. These 2D and 3D visual features are combined by our biologically inspirited model, generating comprehensive salient object detection results. Here, aiming to correctly estimate 3D depth features from underwater images, a regional method is used to respectively extract 3D depth features in two regions, namely the artificial light and natural light regions. Evaluations show the diverse and comprehensive performances of various features for underwater salient object detection. High accuracy of our proposed method is demonstrated by comparing to state-of-the-art saliency detection methods on public underwater benchmarks acquired in diverse underwater environments.
Developing enhanced conversational agents for social virtual worlds

In this paper, we present a methodology for the development of embodied conversational agents for social virtual worlds. The agents provide multimodal communication with their users in which speech interaction is included. Our proposal combines different techniques related to Artificial Intelligence, Natural Language Processing, Affective Computing, and User Modeling. A statistical methodology has been developed to model the system conversational behavior, which is learned from an initial corpus and improved with the knowledge acquired from the successive interactions. In addition, the selection of the next system response is adapted considering information stored into user's profiles and also the emotional contents detected in the user's utterances. Our proposal has been evaluated with the successful development of an embodied conversational agent which has been placed in the Second Life social virtual world. The avatar includes the different models and interacts with the users who inhabit the virtual world in order to provide academic information. The experimental results show that the agent's conversational behavior adapts successfully to the specific characteristics of users interacting in such environments.
Evaluating Pan and Zoom Timelines and Sliders

Pan and zoom timelines and sliders help us navigate large time series data. However, designing efficient interactions can be difficult. We study pan and zoom methods via crowd-sourced experiments on mobile and computer devices, asking which designs and interactions provide faster target acquisition. We find that visual context should be limited for low-distance navigation, but added for far-distance navigation; that timelines should be oriented along the longer axis, especially on mobile; and that, as compared to default techniques, double click, hold, and rub zoom appear to scale worse with task difficulty, whereas brush and especially ortho zoom seem to scale better. Software and data used in this research are available as open source.
Online Optimization in Cloud Resource Provisioning

Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning decisions, there has been a great deal of literature on prediction workload demand. However, even with all of this literature on workload predictions and their utilization in control algorithms, the understanding of prediction error and how to handle it remains an important open issue and research challenge.
Hybrid learning environments by data-driven augmented reality

Augmented Reality (AR) offers the potential to integrate physical, digital and social learning experiences in hybrid learning environments and thereby to achieve learning gains, higher motivation or improved interaction and collaboration. Moreover, by means of AR, theory- or calculus-based learning and experimental exploration in the lab can be brought closer together. Here we present a data-driven AR enhancement of experiments in an electricity and magnetism lab course, where measurement data such as actual current and voltage, are transmitted to a head-mounted semi-transparent display (HMD). In addition, preliminary evaluation results are presented. An improved technical understanding of the subject and especially more dense interrelations between individual learners conceptions are observed.
Demystifying Crypto-Mining: Analysis and Optimizations of Memory-Hard PoW Algorithms

Blockchain technology has become extremely popular, during the last decade, mainly due to the successful application in the cryptocurrency domain. Following the explosion of Bitcoin and other cryptocurrencies, blockchain solutions are being deployed in almost every aspect of transactional operations as a means to safely exchange digital assets between non-trusted parties. At the heart of every blockchain deployment is the consensus protocol, which maintains the consistency of the blockchain upon satisfying incoming transactions. Although many consensus protocols have been recently introduced, the most prevalent is Proof-of- Work, which scales the blockchain globally by converting the consensus problem to a competition based on cryptographic hash functions; a process called “mining”. The Proof-of- Work consensus protocol employs memory-hard algorithms in order to counteract ASIC or FPGA mining that may compromise the decentralization and democratization of the blockchain. Unfortunately, this leads to increased power consumption and scalability challenges since numerous processing units such as GPUs, FPGAs, and ASICs, are required to satisfy the ever-increasing operational requirements of blockchain deployments. In this paper, we perform an in-depth performance analysis and characterization of the most common memory-hard PoW algorithms running on NVIDIA GPUs. Motivated by our experimental findings, we apply a series of optimizations on Ethash algorithm, the consensus protocol of the Ethereum blockchain. The implemented optimizations accelerate performance by 14% and improve energy efficiency by 10% when executing on three NVIDIA GPUs. As a result, the optimized Ethash algorithm outperformed its fastest commercial implementation.
Resolving Target Ambiguity in 3D Gaze Interaction through VOR Depth Estimation

Target disambiguation is a common problem in gaze interfaces, as eye tracking has accuracy and precision limitations. In 3D environments this is compounded by objects overlapping in the field of view, as a result of their positioning at different depth with partial occlusion. We introduce VOR depth estimation, a method based on the Vestibulo-ocular reflex of the eyes in compensation of head movement, and explore its application to resolve target ambiguity. The method estimates gaze depth by comparing the rotations of the eye and the head when the users look at a target and deliberately rotate their head. We show that VOR eye movement presents an alternative to vergence for gaze depth estimation, that is feasible also with monocular tracking. In an evaluation of its use for target disambiguation, our method outperforms vergence for targets presented at greater depth.
Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images

An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images. In the adaptive ELM network, one hidden layer is used for the source data to provide the random features, whereas two hidden layers are set for target data to produce the random features as well as a transformation matrix. DA is achieved by constraining both the source data and the transformed target data to share the same output weights. Moreover, manifold regularization is adopted to preserve the local geometry of unlabeled target data. The proposed ELM-based HDA (EHDA) method is applied to cross-domain classification of remote sensing images, and the experimental results using multisensor remote sensing images demonstrate the effectiveness of the proposed approach.
Transformation through Provocation?

Can a chatbot enable us to change our conceptions, to be critically reflective? To what extent can interaction with a technologically 'minimal' medium such as a chatbot evoke emotional engagement in ways that can challenge us to act on the world? In this paper, we discuss the design of a provocative bot, a 'bot of conviction', aimed at triggering conversations on complex topics (e.g. death, wealth distribution, gender equality, privacy) and, ultimately, soliciting specific actions from the user it converses with. We instantiate our design with a use case in the cultural sector, specifically a Neolithic archaeological site that acts as a stage of conversation on such hard themes. Our larger contributions include an interaction framework for bots of conviction, insights gained from an iterative process of participatory design and evaluation, and a vision for bot interaction mechanisms that can apply to the HCI community more widely.
Nonlinear Control for Autonomous Trajectory Tracking while Considering Collision Avoidance of UAVs Based on Geometric Relations

Trajectory tracking with collision avoidance for a multicopter is solved based on geometrical relations. In this paper, a new method is proposed for a multicopter to move from the start position to a desired destination and track a pre-planned trajectory, while avoiding collisions with obstacles. The controller consists of two parts: First, a tracking control is introduced based on the errors between the relative position of the multicopter and the reference path. Second, once the obstacles with a high possibility of collision are detected, a boundary sphere/cylinder of the obstacle is generated by the dimensions of the vehicle and the obstacles, so as to define the safety and risk areas. Afterwards, from the relation between the vehicle’s motion direction, and the tangential lines from the vehicle’s current position to the sphere/cylinder of the obstacle, a collision detection angle is computed to decide the fastest direction to take in order to avoid a collision. The obstacle/collision avoidance control is activated locally when an object is close, and null if the vehicle moves away from the obstacles. The velocity control law and the guidance law are obtained from the Lyapunov stability. In addition, a proportional controller is used at the end of vehicle’s journey to ensure the vehicle stops at the target position. A numerical simulation in different scenarios was performed to prove the effectiveness of the proposed algorithm.