Reviewerly/exHarmony huggingface dataset:
On the ability of virtual agents to decrease cognitive load: an experimental study

Abstract When attempting to solve a problem, humans call upon cognitive resources. These resources are limited, and the degree of their utilisation is described as cognitive load. While the number of parameters to be taken into account and to be processed by modern-day knowledge workers increases, their cognitive resources do not. Research shows that too high a load can increase stress and failure rates and decrease the work satisfaction and performance of employees. It is thus in the interest of organisations to reduce the cognitive load of their employees and keep it at a moderate level. One way to achieve this may be the application of virtual assistants (VAs), software programs, that can be addressed via voice or text commands and respond to the users’ input. This study uses a laboratory experiment with N = 91 participants comparing two groups in their ability to solve a task. One group was able to make use of a VA while the other could not. Besides task performance, the cognitive load of the participants was measured. Results show that (a) cognitive load is negatively related to task performance, (b) the group using the VA performed better at the task and (c) the group using the VA had a lower cognitive load. These findings show that VAs are a viable way to support employees and can increase their performance. It adds to the growing field of IS research on VAs by expanding the field for the concept of cognitive load.
A two-step hybrid unsupervised model with attention mechanism for aspect extraction

Social networking sites have a wealth of user-generated unstructured text for fine-grained sentiment analysis regarding the changing dynamics in the marketplace. In aspect-level sentiment analysis, aspect term extraction (ATE) task identifies the targets of user opinions in the sentence. In the last few years, deep learning approaches significantly improved the performance of aspect extraction. However, the performance of recent models relies on the accuracy of dependency parser and part-of-speech (POS) tagger, which degrades the performance of the system if the sentence doesn't follow the language constraints and the text contains a variety of multi-word aspect-terms. Furthermore, lack of domain and contextual information is again an issue to extract domain-specific, most relevant aspect terms. The existing approaches are not capable of capturing long term dependencies for noun phrases, which in turn fails to extract some valid aspect terms. Therefore, this paper proposes a two-step mixed unsupervised model by combining linguistic patterns with deep learning techniques to improve the ATE task. The first step uses rules-based methods to extract the single word and multi-word aspects, which further prune domain-specific relevant aspects using fine-tuned word embedding. In the second step, the extracted aspects in the first step are used as label data to train the attention-based deep learning model for aspect-term extraction. The experimental evaluation on the SemEval-16 dataset validates our approach as compared to the most recent and baseline techniques.
Research on 3D face recognition method based on LBP and SVM

In order to improve the accuracy and speed of three-dimensional face recognition, this paper proposes a three-dimensional face recognition method combining LBP and SVM. First, the LBP algorithm is used to extract the feature information of the three-dimensional face depth image, then the SVM algorithm is used to classify the feature information. By selecting samples in the Texas 3DFRD three-dimensional face depth database and the self-made depth database, the experiment proves that the algorithm has a better recognition rate and shorter time consumption.
A Vision-Based Pipeline for Vehicle Counting, Speed Estimation, and Classification

Cameras have been widely used in traffic operations. While many technologically smart camera solutions in the market can be integrated into Intelligent Transport Systems (ITS) for automated detection, monitoring and data generation, many Network Operations (a.k.a Traffic Control) Centres still use legacy camera systems as manual surveillance devices. In this paper, we demonstrate effective use of these older assets by applying computer vision techniques to extract traffic data from videos captured by legacy cameras. In our proposed vision-based pipeline, we adopt recent state-of-the-art object detectors and transfer-learning to detect vehicles, pedestrians, and cyclists from monocular videos. By weakly calibrating the camera, we demonstrate a novel application of the image-to-world homography which gives our monocular vision system the efficacy of counting vehicles by lane and estimating vehicle length and speed in real-world units. Our pipeline also includes a module which combines a convolutional neural network (CNN) classifier with projective geometry information to classify vehicles. We have tested it on videos captured at several sites with different traffic flow conditions and compared the results with the data collected by piezoelectric sensors. Our experimental results show that the proposed pipeline can process 60 frames per second for pre-recorded videos and yield high-quality metadata for further traffic analysis.
Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.
Interpretable clinical prediction via attention-based neural network

The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in the last decade, which accumulated abundant electronic patient data, neural networks or deep learning techniques are gradually being applied to clinical tasks by utilizing the huge potential of EHR data. However, typical deep learning models are black-boxes, which are not transparent and the prediction outcomes of which are difficult to interpret.To remedy this limitation, we propose an attention neural network model for interpretable clinical prediction. In detail, the proposed model employs an attention mechanism to capture critical/essential features with their attention signals on the prediction results, such that the predictions generated by the neural network model can be interpretable.We evaluate our proposed model on a real-world clinical dataset consisting of 736 samples to predict readmissions for heart failure patients. The performance of the proposed model achieved 66.7 and 69.1% in terms of accuracy and AUC, respectively, and outperformed the baseline models. Besides, we displayed patient-specific attention weights, which can not only help clinicians understand the prediction outcomes, but also assist them to select individualized treatment strategies or intervention plans.The experimental results demonstrate that the proposed model can improve both the prediction performance and interpretability by equipping the model with an attention mechanism.
Invariant Feature-Based Darknet Architecture for Moving Object Classification

Object detection and classification is important for video surveillance applications. Counting vehicles like cars, truck and vans is useful for intelligent transportation systems to identify dense and sparse roads, track loaded vehicles at the country borders. Even though many solutions such as appearance-based (Multi-block Local Binary Pattern) and model-based ((DATMO) algorithm) are proposed to classify the moving objects within the satellite images using machine learning and deep learning techniques, they either have over fitting problems or low performance. Hence these challenges have to be addressed during detecting and classifying the objects. Instead of training the classifiers with hand-crafted features, this paper uses neural network based object detection and classification to achieve promising accuracy better than the humans. Invariant feature concept is added to the existing Darknet Architecture of You Only Look Once (YOLO) and is combined with Faster Region-Based Convolutional Neural Networks (Faster R-CNN) to count the number of vehicles with different spatial locations. This combined model improves feature extraction step and vehicle classification process. The proposed system is tested on two benchmark datasets Cars Overhead with Context (COWC) and Vehicle Detection in Aerial Imagery (VEDAI) for counting the cars and trucks. Experimental results prove that the proposed system is better by 9% in detecting smaller objects than existing works.
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning

Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where data is collected from end-users or IoT devices, so that it can discover insights behind real-world data, may not be feasible for many data-driven industry applications in light of such regulations. A new machine learning method, coined by Google as Federated Learning (FL) enables multiple participants to train a machine learning model collectively without directly exchanging data. However, recent studies have shown that there is still a possibility to exploit the shared models to extract personal or confidential data. In this paper, we propose to adopt Multi-Party Computation (MPC) to achieve privacy-preserving model aggregation for FL. The MPC-enabled model aggregation in a peer-to-peer manner incurs high communication overhead with low scalability. To address this problem, the authors proposed to develop a two-phase mechanism by 1) electing a small committee and 2) providing MPC-enabled model aggregation service to a larger number of participants through the committee. The MPC-enabled FL framework has been integrated in an IoT platform for smart manufacturing. It enables a set of companies to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy, model accuracy vis-a`-vis traditional machine learning methods and execution efficiency in terms of communication cost and execution time.
Our digital footprint under Covid-19: should we fear the UK digital contact tracing app?

With the objective of controlling the spread of the coronavirus, the UK has decided to create and, since 5 May 2020, is live testing a digital contact tracing app, under the direction of NHS X, a branch of NHS Digital, and with the help of the private sector.Given the lack of details as to what the app will exactly do or not do, there are fears that the project will increase government surveillance beyond the pandemic.While I share these concerns, I argue that we need to simultaneously tackle one of the most significant, yet overlooked, contributors to the problem of government surveillance: our inflated digital stems from the non-compliance with data protection laws.A systematic enforcement of the General Data Protection Regulation (GDPR) on the private sector would disrupt the current dynamics of surveillance which are hidden in plain sight.I share those concerns and agree with the various recommendations put forward but I argue that we need to simultaneously tackle one of the most significant, yet overlooked, 1The Guardian, 14 April 2020. 2 considerations and societal implications of using technology to transition from the COVID-April 2020.
Semi-Quantum Key Distribution with Single Photons in both Polarization and Spatial-Mode Degrees of Freedom

In this paper, a novel semi-quantum key distribution (SQKD) protocol is designed based on single photons in both polarization and spatial-mode degrees of freedom, which allows to establish a raw key between one quantum communicant and one classical communicant. The proposed SQKD protocol only adopts one kind of quantum state as the initial quantum resource. The detailed security analysis shows that it can resist Eve’s active attacks, such as the intercept-resend attack, the measure-resend attack, the Trojan horse attack and the entangle-measure attack. The proposed SQKD protocol only needs single photons in both polarization and spatial-mode degrees of freedom as quantum resource and employs single-photon measurements. Thus, it has excellent feasibility, since the preparation and the measurement of a single photon in both polarization and spatial-mode degrees of freedom can be easily acheived with present quantum technologies.
Decentralized Cloud Storage Using Blockchain

Cloud storage is one of the leading options to store massive data, however, the centralized storage approach of cloud computing is not secure. On the other hand, Blockchain is a decentralized cloud storage system that ensures data security. Any computing node connected to the internet can join and form peers network thereby maximizing resource utilization. Blockchain is a distributed peer to peer system where each node in the network stores a copy of blockchain thus making it immutable. In the proposed system, the user's file is encrypted and stored across multiple peers in the network using the IPFS (InterPlanetary File System) protocol. IPFS creates hash value. The hash value indicates the path of the file and is stored in the blockchain. This paper focuses on decentralized secure data storage, high availability of data, and efficient utilization of storage resources.
Selfish Mining Re-Examined

Six years after the introduction of selfish mining, its counterintuitive findings continue to create confusion. In this paper, we comprehensively address one particular source of misunderstandings, related to difficulty adjustments. We first present a novel, modified selfish mining strategy, called intermittent selfish mining, that, perplexingly, is more profitable than honest mining even when the attacker performs no selfish mining after a difficulty adjustment. Simulations show that even in the most conservative scenario ( $$\gamma =0$$ ), an intermittent selfish miner above 37% hash power earns more coins per time unit than their fair share. We then broadly examine the profitability of selfish mining under several difficulty adjustment algorithms (DAAs) used in popular cryptocurrencies. We present a taxonomy of popular difficulty adjustment algorithms, quantify the effects of algorithmic choices on hash fluctuations, and show how resistant different DAA families are to selfish mining.
CAsT-19: A Dataset for Conversational Information Seeking

CAsT-19 is a new dataset that supports research on conversational information seeking. The corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eighty information seeking dialogues (30 train, 50 test) are an average of 9 to 10 questions long. A dialogue may explore a topic broadly or drill down into subtopics. Questions contain ellipsis, implied context, mild topic shifts, and other characteristics of human conversation that may prevent them from being understood in isolation. Relevance assessments are provided for 30 training topics and 20 test topics.
Health Care Insurance Fraud Detection Using Blockchain

The health care industry is one of the important service providers that improves people lives. As the cost of the healthcare service increases, health insurance becomes the only way to get quality service in case of an accident or a major illness. As health insurance will reduces the costs and provides financial and economic stability for an individual. One of the main tasks of healthcare insurance providers is to monitor and manage the data and to provide support to customers. Due to regulations and business secrecy, insurance companies do not share the patient's data but since the data are not integrated and not in sync between insurance providers, there has been an increase in the number of fraud's occurring in healthcare. Often times ambiguous or false information is provided to health insurance companies in order to make them pay for some false claims to the policy holders. The individual policyholder may also claim benefits from multiple insurance providers. There is a financial loss of billions of dollars each year as estimated by the National Health Care Anti-Fraud Association (NHCAA). In order to prevent health insurance fraud, it is necessary to build a system to securely manage and monitor insurance activities by integrating data from all the insurance companies. As blockchain provides an immutable data maintaining and sharing, we propose a blockchain based solution for health insurance fraud detection.
Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.
Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images

Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation.Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue.We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images.We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2-) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2-) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2-) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability.We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic.Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0.65-0.98) on held-out cases and an AUC of 0.76 (CI: 0.61-0.89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections.Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability.
Blockchain technology applications in power distribution systems

Blockchain technologies have attracted the attention of multiple control, operation, and planning entities in the energy industry. Blockchain technologies, which allow a shared and distributed database, allow for secure, transparent, economic, and automated operations in power distribution systems. In this paper, we introduce how blockchains work and can be utilized to enhance the operation of power distribution systems. We discuss multiple scenarios in which blockchain technologies have been implemented in power distribution systems. Also, we discuss pertinent challenges for applying blockchain technologies to power distribution systems and provide some measures to manage these challenges.
Toward Secure and Privacy-Preserving Distributed Deep Learning in Fog-Cloud Computing

Fog-cloud computing promises many new vertical service areas beyond simple data communication, storing, and processing. Among them, distributed deep learning (DDL) across fog-cloud computing environment is one of the most popular applications due to its high efficiency and scalability. Compared with the centralized deep learning, DDL can provide better privacy protection with training only on sharing parameters. Nevertheless, when DDL meets fog-cloud computing, it still faces two major security challenges: 1) how to protect users' privacy from being leaked to other internal participants in the training process and 2) how to guarantee users' identities from being forged by external adversaries. To combat them, several approaches have been proposed via various technologies. Nevertheless, those approaches suffer from drawbacks in terms of security, efficiency, and functionality, and cannot guarantee the legitimacy of participants' identities during training. In this article, we propose a secure and privacy-preserving DDL (SPDDL) for fog-cloud computing. Compared with the state-of-the-art works, our proposal achieves a better tradeoff between security, efficiency, and functionality. In addition, our SPDDL can guarantee the unforgeability of users' identities against external adversaries. Extensive experimental results indicate the practical feasibility and high efficiency of our SPDDL.