Reviewerly/exHarmony huggingface dataset:
New Security Mechanisms of High-Reliability IoT Communication Based on Radio Frequency Fingerprint

Nowadays, the serious security threat of industrial control system and sensors has become a major challenge with the rapid development of Industrial Internet of Things (IIoT). Man-in-the-middle (MITM) attack is a very common intrusion method, which will make a great security threat in the application of IIoT. In IIoT scenario, the lightweight safety certification can play a very important role in the development of data-intensive and decentralized applications running on billions of sensors and devices, preserving their security. Therefore, in this paper, a low-latency high-reliability security mechanism is proposed to avoid the MITM attack in IIoT scenario. First, combining the radio frequency fingerprint (RFF) technology with IIoT applications, a lightweight IIoT security architecture is proposed. Based on the proposed IIoT security architecture, the process of device access authentication and communication service is illustrated. Second, according to the requirement of IIoT identification method, an access authentication method of the device is proposed based on the RFF. The method of feature extraction, classifier designing, and the access authentication process is discussed in detail. Finally, the simulation results show that the identification rate of devices can reach 95% under SNR = 6 dB, and can nearly reach 100% under SNR = 15 dB. Through the new process of access authentication, the access authentication rate can reach 95% under SNR = 15 dB. Therefore, according to the simulation results, the new security mechanisms based on RFF can be used to avoid the MITM attack in IIoT scenario.
Continuous Alertness Assessments

As the day progresses, cognitive functions are subject to fluctuations. While the circadian process results in diurnal peaks and drops, the homeostatic process manifests itself in a steady decline of alertness across the day. Awareness of these changes allows the design of proactive recommender and warning systems, which encourage demanding tasks during periods of high alertness and flag accident-prone activities in low alertness states. In contrast to conventional alertness assessments, which are often limited to lab conditions, bulky hardware, or interruptive self-assessments, we base our approach on eye blink frequency data known to directly relate to fatigue levels. Using electrooculography sensors integrated into regular glasses' frames, we recorded the eye movements of 16 participants over the course of two weeks in-the-wild and built a robust model of diurnal alertness changes. Our proposed method allows for unobtrusive and continuous monitoring of alertness levels throughout the day.
Patching Gender

Non-binary people are rarely considered by technologies or technologists, and often subsumed under binary trans experiences on the rare occasions when we are discussed. In this paper we share our own experiences and explore potential alternatives - utopias, impossible places, as our lived experience of technologies' obsessive gender binarism seems near-insurmountable. Our suggestions on how to patch these gender bugs appear trivial while at the same time revealing seemingly insurmountable barriers. We illustrate the casual violence technologies present to non-binary people, as well as the on-going marginalisations we experience as HCI researchers. We write this paper primarily as an expression of self-empowerment that can function as a first step towards raising awareness towards the complexities at stake.
Privacy-preserving and high-accurate outsourced disease predictor on random forest

Training data distributed across multiple different institutions is ubiquitous in disease prediction applications. Data collection may involve multiple data sources who are willing to contribute their datasets to train a more precise classifier with a larger training set. Nevertheless, integrating multiple-source datasets will leak sensitive information to untrusted data sources. Hence, it is imperative to protect multiple-source data privacy during the predictor construction process. Besides, since disease diagnosis is strongly associated with health and life, it is vital to guarantee prediction accuracy. In this paper, we propose a privacy-preserving and high-accurate outsourced disease predictor on random forest, called PHPR. PHPR system can perform secure training with medical information which belongs to different data owners, and make accurate prediction. Besides, the original data and computed results in the rational field can be securely processed and stored in cloud without privacy leakage. Specifically, we first design privacy-preserving computation protocols over rational numbers to guarantee computation accuracy and handle outsourced operations on-the-fly. Then, we demonstrate that PHPR system achieves secure disease predictor. Finally, the experimental results using real-world datasets demonstrate that PHPR system not only provides secure disease predictor over ciphertexts, but also maintains the prediction accuracy as the original classifier.
Visual-textual sentiment classification with bi-directional multi-level attention networks

Social network has become an inseparable part of our daily lives and thus the automatic sentiment analysis on social media content is of great significance to identify people’s viewpoints, attitudes, and emotions on the social websites. Most existing works have concentrated on the sentiment analysis of single modality such as image or text, which cannot handle the social media content with multiple modalities including both image and text. Although some works tried to conduct multi-modal sentiment analysis, the complicated correlations between the two modalities have not been fully explored. In this paper, we propose a novel Bi-Directional Multi-Level Attention (BDMLA) model to exploit the complementary and comprehensive information between the image modality and text modality for joint visual-textual sentiment classification. Specifically, to highlight the emotional regions and words in the image–text pair, visual attention network and semantic attention network are proposed respectively. The visual attention network makes region features of the image interact with multiple semantic levels of text (word, phrase, and sentence) to obtain the attended visual features. The semantic attention network makes semantic features of the text interact with multiple visual levels of image (global and local) to obtain the attended semantic features. Then, the attended visual and semantic features from the two attention networks are unified into a holistic framework to conduct visual-textual sentiment classification. Proof-of-concept experiments conducted on three real-world datasets verify the effectiveness of our model.
Foreign Direct Investment, Sectoral Effects and Economic Growth in Africa

Earlier studies on the impact of Foreign Direct Investment (FDI) on economic growth have not been instructive largely on their failure to examine the sectoral transmission channels through which FDI affects growth. We re-examine the impact of FDI on economic growth in Africa using the system generalized method of moments. The results reveal that, while FDI positively and unconditionally spurs economic growth, its growth-enhancing effect is imaginary when the conditional sectoral effects are introduced. On the channels of manifestation, we notice that the pass-through impact of FDI is only significant for the agricultural and service sectors.
Towards Automatically-Tuned Deep Neural Networks

Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any human intervention. The first version, Auto-Net 1.0, builds upon ideas from the competition-winning system Auto-sklearn by using the Bayesian Optimization method SMAC and uses Lasagne as the underlying deep learning (DL) library. The more recent Auto-Net 2.0 builds upon a recent combination of Bayesian Optimization and HyperBand, called BOHB, and uses PyTorch as DL library. To the best of our knowledge, Auto-Net 1.0 was the first automatically-tuned neural network to win competition datasets against human experts (as part of the first AutoML challenge). Further empirical results show that ensembling Auto-Net 1.0 with Auto-sklearn can perform better than either approach alone, and that Auto-Net 2.0 can perform better yet.
Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants, etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (unpopular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may lead the RS to make increasingly biased recommendations over time. In this paper, we view the RS environment as a chain of events that are the result of interactions between users and the RS. Based on that, we propose several debiasing algorithms during this chain of events, and evaluate how these algorithms impact the predictive behavior of the RS, as well as trends in the popularity distribution of items over time. We also propose a novel blind-spot-aware matrix factorization (MF) algorithm to debias the RS. Results show that propensity matrix factorization achieved a certain level of debiasing of the RS while active learning combined with the propensity MF achieved a higher debiasing effect on recommendations.
SpectreGuard

Speculative execution is an essential performance enhancing technique in modern processors, but it has been shown to be insecure. In this paper, we propose SpectreGuard, a novel defense mechanism against Spectre attacks. In our approach, sensitive memory blocks (e.g., secret keys) are marked using simple OS/library API, which are then selectively protected by hardware from Spectre attacks via low-cost micro-architecture extension. This technique allows microprocessors to maintain high performance, while restoring the control to software developers to make security and performance trade-offs.
The Role of Smart Contracts in Sustainability: Worldwide Research Trends

The advent and development of digital technologies has had a significant impact on the establishment of contracts. Smart contracts are designed as computer code containing instructions for executing user agreements, offering a technologically secure solution with numerous advantages and applications. However, smart contracts are not without their problems when we try to fit them into the traditional system of contract law, and their presumed benefits can become shortcomings. Bibliometric studies can help to assess the current state of science in a specific subject and support decision making and research direction. Here, this bibliometric study is used to analyze global trend research in relation to this novel contractual methodology, the smart contract, which seems to have experienced exponential growth since 2014. Specially, this analysis was focused on the main countries involved and the institutions that lead this research worldwide. On the other hand, the indexations of these works are analyzed according to major scientific areas and the keywords of all the works, to detect the subjects to which they are grouped. Community detection has been used to establish the relationship between countries researching in this area, and six clusters have been identified, around which all the work related to this topic is grouped. This work shows the temporal evolution of research related to smart contracts, highlighting that there are two trends—e-commerce and smart power grids. From the perspective of driving sustainability, smart contracts could provide a contribution in the near future.
Multisensor-Based Navigation and Control of a Mobile Service Robot

Service robot navigation must take the humans into account explicitly so as to produce motion behaviors that reflect its social awareness. Generally, the navigation problems of mobile service robot can be summarized to three aspects: 1) human detection; 2) robot real-time localization; and 3) robot motion planning. The purpose of this paper is to provide a feasible strategy to integrate these three aspects to achieve a conscious, safe, accurate, robust, and efficient navigation. We first introduce the human detection system for recognition of human gesture using a weighted dynamic time warping (DTW) with kinematic constraints. Thus, by interpreting the human body language through gesture recognition, robot motion behaviors like heading to the assigned position or following people can be activated. Then, for the robot localization, a simultaneous localization and mapping (SLAM) method based on artificial and natural landmark recognition is employed to provide absolute position feedback in real time. For the motion planning, a novel quadrupole potential field (QPF) method is proposed to plan collision-free trajectories, adequately considering the nonholomic constraint of the mobile robot system. Then, a robust kinematic controller is designed for trajectory tracking to account for slip disturbances. Such a design automatically merges path finding, trajectory generation, and trajectory tracking in a closed-loop fashion, achieving simultaneous motion planning for obstacle avoidance and feedback stabilization to a desired position and orientation even in the presence of slippage. Finally, experiments prove the effectiveness and feasibility of the proposed strategy, showing a good navigation performance on mobile service robot.
Summarizing vulnerabilities’ descriptions to support experts during vulnerability assessment activities

Vulnerabilities affecting software and systems have to be promptly fixed, to prevent violations to integrity, availability and confidentiality policies of targeted organizations. Once a vulnerability is discovered, it is published on the Common Vulnerabilities and Exposures (CVE) database, freely available on the web. However, vulnerabilities are described using natural language, which makes them hard to be automatically interpreted by machines. As a consequence, vulnerability assessment activities tend to be time-consuming and imprecise, as the assessors must manually read the majority of the vulnerabilities concerning the perimeter to be protected, to make a decision on which vulnerabilities have the highest priority for patching. In this paper we present CVErizer, an approach able to automatically generate summaries of daily posted vulnerabilities and categorize them according to a taxonomy modeled for industry. We empirically assess the classification capabilities of the approach on a set of 3369 pre-labeled CVE records and perform an end-to-end evaluation of CVErizer summaries involving 15 cybersecurity master students and 4 professional security experts. Our study demonstrates the high performance of the proposed approach in correctly extracting and classifying information from CVE descriptions. Summaries are also considered highly useful for helping analysts during the vulnerability assessment processes.
Waste Classification System Using Image Processing and Convolutional Neural Networks

Image segmentation and classification is more and more being of interest for computer vision and machine learning researchers. Many systems on the rise need accurate and efficient segmentation and recognition mechanisms. This demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. The use of convolutional neural networks for the image classification and recognition allows building systems that enable automation in many industries. This article presents a system for classifying plastic waste, using convolutional neural networks. The problem of segregation of renewable waste is a big challenge for many countries around the world. Apart from segregating waste using human hands, there are several methods for automatic segregation. The article proposes a system for classifying waste with the following classes: polyethylene terephthalate, high-density polyethylene, polypropylene and polystyrene. The obtained results show that automatic waste classification, using image processing and artificial intelligence methods, allows building effective systems that operate in the real world.
BranchGAN: Unsupervised Mutual Image-to-Image Transfer With A Single Encoder and Dual Decoders

Image-to-image translation is a fundamental task for a wide range of applications, such as image style transfer, video effect generation, cross-domain retrieval, etc. Due to the limited number of labeled data, complex scenes, abstract semantics and various involved domains, image translation remains a challenging task. Compared to the supervised approaches for image translation that need a large collection of paired images for training, the unsupervised methods can significantly reduce the training cost. In this paper, an unsupervised end-to-end generative adversarial network is proposed, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BranchGAN</i> , for mutual image-to-image transfer between two domains. A structure with one single encoder and dual decoders is novelly proposed to capture the cross-domain distributions and generate the images in both domains. Three factors, that is, pixel-level overall style, region semantics, and domain distinguishability are comprehensively considered to constrain the training process of the proposed model, corresponding to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reconstruction loss</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoding loss</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial loss</i> , respectively. Experiments conducted on three benchmark datasets demonstrate the effectiveness of the proposed method that outperforms the unsupervised state-of-the-art approaches and has the competitive performance as the supervised method.
Mobile VR on edge cloud

In this paper we design and implement MEC-VR, a mobile VR system that uses a Mobile Edge Cloud (MEC) to deliver high quality VR content to today's mobile devices using 4G/LTE cellular networks. Our main contribution is in realizing a low latency control loop that streams VR scenes containing only the user's Field of View (FoV) and a latency-adaptive margin area around the FoV. This allows the clients to render locally at a high refresh rate to accommodate and compensate for the head movements before the next motion update arrives. Compared with prior approaches, our MEC-VR design requires no viewpoint prediction, supports dynamic and live VR content, and adapts to the real-world latency experienced in cellular networks between the MEC and mobile devices. We implement a prototype of MEC-VR and evaluate its performance on a MEC node connected to an LTE testbed. We demonstrate that MEC-VR can effectively stream live VR content up to 8K resolution over 4G/LTE networks and achieve more than 80% of bandwidth savings.
Engerer2: Global re-parameterisation, update, and validation of an irradiance separation model at different temporal resolutions

The Engerer2 separation model estimates the diffuse fraction Kd from inputs of global horizontal irradiance, UTC time, latitude, and longitude. The model was initially parameterized and validated on 1-min resolution data for Australia and performed best out of the 140 models in global validation studies. This research reparameterizes Engerer2 on a global training dataset and at many common temporal resolutions (1-min, 5-min, 10-min, 15-min, 30-min, 1-h, and 1-day), so that it may be more easily implemented in the future; the need for the user to perform prerequisite calculations of solar angles and clear-sky irradiance has also been removed for ease of use. Comparing the results of the new 1-min parameterization against the original Engerer2 parameterization on a global testing dataset, the root mean squared error (RMSE) improves from 0.168 to 0.138, the relative RMSE from 30.4% to 25.1%, the mean bias error from 8.01% to –0.30%, and the coefficient of determination (R2) from 0.80 to 0.86; hence, there is a significant improvement to the model. Engerer2 was unsuited to 1-day averages; however, it performed remarkably well at all other averaging periods. A climate specific analysis found poor suitability of Engerer2 in polar climates; however, improvement and suitability were found for all other climates and temporal averaging periods. Code for the model are provided as supplementary material in languages R, Python, and Matlab®—selected for their wide-adoption in academia and industry—and they can also be found in the Github repository: Engerer2-separation-model.
Non-Rigid HCI

Deformable interfaces are emerging in HCI and prototypes show potential for non-rigid interactions. Previous reviews looked at deformation as a material property of shape-changing interfaces and concentrated on output. As such,deformable input was under-discussed. We distinguish deformable from shape-changing interfaces to concentrate on input. We survey 131 papers on deformable interfaces and review their key design elements (e.g., shape, material) based on how they support input. Our survey shows that deformable input was often used to augment or replace rigid input, particularly on elastic and flexible displays. However, when shapes and materials guide interactions, deformable input was used to explore new HCI paradigms, where gestures are potentially endless, and input become analogy to sculpting, metaphor to non-verbal communication, and expressive controls are enhanced. Our review provides designers and practitioners with a baseline for designing deformable interfaces and input methodically. We conclude by highlighting under-explored areas and identify research goals to tackle in future work with deformable interfaces.
Comparative evaluation of region query strategies for DBSCAN clustering

Clustering is a technique that allows data to be organized into groups of similar objects. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) constitutes a popular clustering algorithm that relies on a density-based notion of cluster and is designed to discover clusters of arbitrary shape. The computational complexity of DBSCAN is dominated by the calculation of the ϵ-neighborhood for every object in the dataset. Thus, the efficiency of DBSCAN can be improved in two different ways: (1) by reducing the overall number of ϵ-neighborhood queries (also known as region queries), or (2) by reducing the complexity of the nearest neighbor search conducted for each region query. This paper deals with the first issue by considering the most relevant region query strategies for DBSCAN, all of them characterized by inspecting the neighborhoods of only a subset of the objects in the dataset. We comparatively evaluate these region query strategies (or DBSCAN variants) in terms of clustering effectiveness and efficiency; additionally, a novel region query strategy is introduced in this work. The results show that some DBSCAN variants are only slightly inferior to DBSCAN in terms of effectiveness, while greatly improving its efficiency. Among these variants, the novel one outperforms the rest.