Reviewerly/exHarmony huggingface dataset:
Cache side-channel attacks detection based on machine learning

Security has always been one of the main concerns in the field of computer architecture and cloud computing. Cache-based side-channel attacks pose a threat to almost all existing architectures and cloud computing. Especially in the public cloud, the cache is shared among multiple tenants, and cache attacks can make good use of this to extract information. Cache side-channel attacks are a problem to be solved for security, in which how to accurately detect cache side-channel attacks has been a research hotspot. Because the cache side-channel attack does not require the attacker to physically contact the target device and does not need additional devices to obtain the side channel information, the cache-side channel attack is efficient and hidden, which poses a great threat to the security of cryptographic algorithms. Based on the AES algorithm, this paper uses hardware performance counters to obtain the features of different cache events under Flush + Reload, Prime + Probe, and Flush + Flush attacks. Firstly, the random forest algorithm is used to filter the cache features, and then the support vector machine algorithm is used to model the system. Finally, high detection accuracy is achieved under different system loads. The detection accuracy of the system is 99.92% when there is no load, the detection accuracy is 99.85% under the average load, and the detection accuracy under full load is 96.57%.
Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined a priori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across different cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, the Bayesian mixture auto-regressive decomposition (BMARD) method is proposed, as a data-driven approach that identifies (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks). Using the BMARD method, the standardized SDF is represented as a Dirichlet process mixture based on a kernel derived from second-order auto-regressive processes which completely characterize the location (peak) and scale (bandwidth) parameters. A Metropolis-Hastings within the Gibbs algorithm is developed for sampling the posterior distribution of the mixture parameters. Simulations demonstrate the robust performance of the proposed method. Finally, the BMARD method is applied to analyze local field potential (LFP) activity from the hippocampus of laboratory rats across different conditions in a non-spatial sequence memory experiment, to identify the most prominent frequency bands and examine the link between specific patterns of brain oscillatory activity and trial-specific cognitive demands.
Hyperspectral Imager Suite (HISUI): Its Launch and Current Status

Hyperspectral Imager Suite (HISUI) was launched on Dec. 5, 2019 and successfully attached to Japan Experiment Module Exposed Facility, International Space Station. Currently, initial check out activities are ongoing, and the first light images will be released around August, 2020. The distribution of HISUI Level 1 products will be started in the first half of 2021.
Privacy-Preserving Distributed Data Access Control for CloudIoT

The Edge-Fog-Cloud interplay in the Internet-of-Things (IoT) leads to many exciting data-sharing applications that use access control systems as primary requirements. To ensure a fine-grained data access control for such data-sharings on untrusted storage (e.g., Cloud), Attribute-Based Encryption (ABE) is a promising tool. To address privacy concerns in such ABE-based access control systems, we propose a new Privacy-preserving Distributed data Access control (PDAC) in CloudIoT. Our PDAC improves the previous privacy-preserving distributed ABE systems in three aspects. The first introduces a new user's anonymity approach against the colluding untrusted (honest-but-curious) authorities. The second presents a novel policy-hiding mechanism that efficiently preserves the privacy of policy-forming attributes (metadata) against colluding parties. The third introduces an independent-authorities system for our privacy-preserving improvements, where an authority can join and leave the system without reinitializing other authorities. Moreover, our PDAC offloads the user's computations over the Cloud servers for efficiency enhancement. We prove the security of our PDAC through formal analysis. Then, we present empirical results on different classes of mobile devices, including a laptop and a smartphone.
Product Recommendation System for Supermarket

Customers who seek the services at supermarkets are subjected to inconsistencies & ambiguities over choosing their desired products from a wide range of products with the closest quality. Meanwhile, supermarkets find it very difficult to satiate the customers' demand. Therefore, proposing a method to analyze the customers' need plays an important role in attracting new and regular customers. The purpose of this study is to formulate a product recommendation system which analyze customers' needs and thus recommend the best products. This system recommends products to the regular customers and to the new customers as well. New customers mean obviously the customers with no purchasing history at the supermarket in question. The system referred to recommends the products to the new customers using up two methods. One method recommends the most popular products while the other method solely focuses on the product description for recommendation. The system recommends the products to the regular customers using up user-based collaborative filtering, item based collaborative filtering and association rule mining. It recommends products to regular customers based on purchasing history and priority ratings given by other users who bought the products. Initially, the recommendation algorithm finds a set of customers who purchased and rated the products that overlap with the user who purchased and rated the products. The algorithm aggregates products from the customers with similar preference and eliminates the products the user has already purchased or rated. The proposed methodology improves the shopping experience of customers by recommending accurately and efficiently the products that are personalized to the need of the customers.
Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization

Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement. In this work, a real-time UAV tracking algorithm with powerful size estimation ability is proposed. Specifically, the overall tracking task is allocated to two 2D filters: (i) translation filter for location prediction in the space domain, (ii) size filter for scale and aspect ratio optimization in the size domain. Besides, an efficient two-stage re-detection strategy is introduced for long-term UAV tracking tasks. Large-scale experiments on four UAV benchmarks demonstrate the superiority of the presented method which has computation feasibility on a low-cost CPU.
Point-of-Interest Recommendation for Users-Businesses With Uncertain Check-ins

Most existing studies on next point-of-interest (POI) recommendation assume that users deliver certain check-ins over individual POIs. In reality, we typically obtain uncertain check-ins due to the presence of collective POIs, which are gathering places of multiple individual POIs (e.g., shopping malls). On one hand, such uncertain check-ins over collective POIs hinder more accurate next POI recommendation for users due to the transition vanishing issue; on the other hand, the presence of collective POIs poses the challenge for businesses to select which collective POIs to locate in due to complicated competition and cooperation relations between businesses. As such, these collective POIs bring an unprecedented opportunity and necessity on recommendation for both users and businesses. Therefore, we propose novel solutions of location service beneficial for users-businesses. For users, we propose the STSP equipped with category- and location-aware encoders, to deliver more accurate next POI prediction by fusing rich context features. Regarding businesses, we explore their competition and cooperation relations from check-in records, based on which we derive the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">living environment</i> (LE) of a business. Insight on site selection for businesses is provided by exploiting the LE, aiming to bring in more profits. Extensive empirical studies demonstrate the efficiency of our solutions.
Intelligent Exploration and Autonomous Navigation in Confined Spaces

Autonomous navigation and exploration in confined spaces are currently setting new challenges for robots. The presence of narrow passages, flammable atmosphere, dust, smoke, and other hazards makes the mapping and navigation tasks extremely difficult. To tackle these challenges, robots need to make intelligent decisions, maximising information while maintaining the safety of the system and their surroundings. In this paper, we present a suite of reasoning mechanisms along with a software architecture for exploration tasks that can be used to underpin the behavior of a broad range of robots operating in confined spaces. We present an autonomous navigation module that allows the robot to safely traverse known areas of the environment and extract features of the unknown frontier regions. An exploration component, by reasoning about these frontiers, provides the robot with the ability to venture into new spaces. From low-level sensory input and contextual information, the robot incrementally builds a semantic network that represents known and unknown parts of the environment and then uses a logic-based, high-level reasoner to interrogate such a network and decide the best course of actions. We evaluate our approach against several mine-like challenging scenarios with different characteristics using a small drone. The experimental results indicate that our method allows the robot to make informed decisions on how to best explore the environment while preserving safety.
Understanding Compassion Cultivation for Design: Towards an Autoethnography of Tonglen

Compassion is a concept related to ethics and wellbeing and is an area of emerging interest in human-computer interaction. There are examples applying philosophy or the science of compassion to interaction design solutions, however, design for compassion has not been explored through a first-person approach. This late-breaking work presents our self-observation study of Tonglen, a Buddhist compassion cultivation technique. Our paper illustrates the authentic lived experience of compassion cultivation contributing to a growing body of first-person research in HCI. Our findings reveal the complexity of the contemplative practice of Tonglen, where thoughts, feelings and sensations embodying connection and acceptance arise. These are expressed and documented as metaphors of experiential qualities within the process. This initial study emphasises how deliberate first-person engagement and systematic self-enquiry can be used to explore contemplative and cultivation practices developing a richer lexicon for design researchers to navigate and practice more meaningfully in this area.
Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process

An End-Of-Turn Detection Module (EOTD-M) is an essential component of automatic Spoken Dialogue Systems. The capability of correctly detecting whether a user’s utterance has ended or not improves the accuracy in interpreting the meaning of the message and decreases the latency in the answer. Usually, in dialogue systems, an EOTD-M is coupled with an Automatic Speech Recognition Module (ASR-M) to transmit complete utterances to the Natural Language Understanding unit. Mistakes in the ASR-M transcription can have a strong effect on the performance of the EOTD-M. The actual extent of this effect depends on the particular combination of ASR-M transcription errors and the sentence featurization techniques implemented as part of the EOTD-M. In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). We introduce an Automatic Speech Recognition Simulator (ASR-SIM) that models different types of semantic mistakes in the ASR-M transcription as well as different speech profiles. We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. Our experiments reveal the different ways in which the performance of the model is influenced by the ASR-M errors. We corroborate that not only is the ASR-SIM useful to estimate the performance of an EOTD-M in customized noisy scenarios, but it can also be used to generate training datasets with the expected error rates of real working conditions, which leads to better performance.
Uncertainty in Gradient Boosting via Ensembles

For many practical, high-risk applications, it is essential to quantify uncertainty in a model's predictions to avoid costly mistakes. While predictive uncertainty is widely studied for neural networks, the topic seems to be under-explored for models based on gradient boosting. However, gradient boosting often achieves state-of-the-art results on tabular data. This work examines a probabilistic ensemble-based framework for deriving uncertainty estimates in the predictions of gradient boosting classification and regression models. We conducted experiments on a range of synthetic and real datasets and investigated the applicability of ensemble approaches to gradient boosting models that are themselves ensembles of decision trees. Our analysis shows that ensembles of gradient boosting models successfully detect anomaly inputs while having limited ability to improve the predicted total uncertainty. Importantly, we also propose a concept of a virtual ensemble to get the benefits of an ensemble via only one gradient boosting model, which significantly reduces complexity.
RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale. We demonstrate our method on synthetic multi-scale image datasets and the CelebA dataset, showing that the disentangled representations enable semantic manipulation and style mixing of the images at different scales. To visualize the latent representations, we introduce receptive fields for flow-based models and show that the receptive fields of RG-Flow are similar to those of convolutional neural networks. In addition, we replace the widely adopted isotropic Gaussian prior distribution by the sparse Laplacian distribution to further enhance the disentanglement of representations. From a theoretical perspective, our proposed method has $O(\log L)$ complexity for inpainting of an image with edge length $L$, compared to previous generative models with $O(L^2)$ complexity.
Utilizing Structure-Rich Features to Improve Clustering

For successful clustering, an algorithm needs to find the boundaries between clusters. While this is comparatively easy if the clusters are compact and non-overlapping and thus the boundaries clearly defined, features where the clusters blend into each other hinder clustering methods to correctly estimate these boundaries. Therefore, we aim to extract features showing clear cluster boundaries and thus enhance the cluster structure in the data. Our novel technique creates a condensed version of the data set containing the structure important for clustering, but without the noise-information. We demonstrate that this transformation of the data set is much easier to cluster for k-means, but also various other algorithms. Furthermore, we introduce a deterministic initialisation strategy for k-means based on these structure-rich features.
Motion Prediction in Visual Object Tracking

Visual object tracking (VOT) is an essential component for many applications, such as autonomous driving or assistive robotics. However, recent works tend to develop accurate systems based on more computationally expensive feature extractors for better instance matching. In contrast, this work addresses the importance of motion prediction in VOT. We use an off-the-shelf object detector to obtain instance bounding boxes. Then, a combination of camera motion decouple and Kalman filter is used for state estimation. Although our baseline system is a straightforward combination of standard methods, we obtain state-of-the-art results. Our method establishes new state-of-the-art performance on VOT (VOT-2016 and VOT-2018). Our proposed method improves the EAO on VOT-2016 from 0.472 of prior art to 0.505, from 0.410 to 0.431 on VOT-2018. To show the generalizability, we also test our method on video object segmentation (VOS: DAVIS-2016 and DAVIS-2017) and observe consistent improvement.
Estimation of global horizontal irradiance in China using a deep learning method

Quasi-real-time estimation of Global Horizontal Irradiance (GHI) is a key parameter for many solar energy applications. We propose the use of a deep belief network (DBN) to estimate GHI under all-sky conditions derived from Himawari-8 satellite images with a high accuracy and high efficiency, and a high spatial and time resolution for a large geographical area. The DBN solver for GHI (DBN-GHI) is based upon a radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to maintain the balance between computational efficiency and accuracy. The computational time of DBN-GHI for one satellite image with more than 400,000 pixels is around 9 seconds. Aerosol was considered as the main attenuation factor for clear skies, while cloud parameters were used for cloudy-sky GHI estimation. The main novelty of this research is that prior to it, there is a dearth of GHI estimations in China at minutely or hourly intervals in all sky conditions. The results of hourly comparison of this with ground-based observations gave a very good Pearson correlation coefficient (r), above 0.95, with a Root-Mean-Square-Error (RMSE) between about 30 to 80 w m−2.
Chapter 5 Robotics as an Enabler of Resiliency to Disasters: Promises and Pitfalls

The Covid-19 pandemic is a reminder that modern society is still susceptible to multiple types of natural or man-made disasters, which motivates the need to improve resiliency through technological advancement. This article focuses on robotics and the role it can play towards providing resiliency to disasters. The progress in this domain brings the promise of effectively deploying robots in response to life-threatening disasters, which includes highly unstructured setups and hazardous spaces inaccessible or harmful to humans. This article discusses the maturity of robotics technology and explores the needed advances that will allow robots to become more capable and robust in disaster response measures. It also explores how robots can help in making human and natural environments preemptively more resilient without compromising long-term prospects for economic development. Despite its promise, there are also concerns that arise from the deployment of robots. Those discussed relate to safety considerations, privacy infringement, cyber-security, and financial aspects, such as the cost of development and maintenance as well as impact on employment.
High-speed harvesting of random numbers

A broad-area multimode laser can be made into a powerful random number generator
Learning bilingual sentiment lexicon for online reviews

High-quality sentiment lexicons are the key to the success of lexicon-based sentiment analysis, because they allow the appropriate interpretation of the sentiment determination of unlabeled documents. However, most lexicons are general and available only in certain dominant languages such as English, and they are insufficient to help in detecting the sentiments in specific domains such as online reviews and/or different languages. Several recent studies have proposed producing a bilingual lexicon using the bilingual word vector space. Unfortunately, without proper adjustment, words with opposite sentiment polarities could share similar representations due to the similarity of their contexts, resulting in less-accurate sentiment lexicons. In this paper, we propose a multistep approach for bilingual sentiment lexicon induction (MS-BSLI), which incorporates lexical constraints into the word vector space in order to disambiguate words with opposite sentiment polarities. Since lexical resources, such as sentiment lexicons, synonyms, and antonyms, are scarce in most languages, MS-BSLI is designed to only require lexical resources of the dominant language and follows a deep feed-forward network to generalize the lexical transformation to the words of the other low-resource language. Experiments using two real-world online review data sets have demonstrated that the lexicon generated by MS-BSLI is more accurate and provides better coverage than two existing lexicons and three competing approaches.
gRemote: Cloud rendering on GPU resource pool based on API-forwarding

Traditional GPU resource allocation approaches, widely adopted in today's data centers as the rise of cloud gaming, only focus on server-side functions while ignoring the client-side, which wastes hardware resources. To solve this problem, cloud-edge integrated architectures are put forward, leaving some workloads to the client-side. However, many cloud-edge integrated frameworks suffer from one big issue: shared-resource interference, stemming from two reasons: (a) GPU resource racing caused by resources overuse for single client, and (b) CPU resource racing caused by resources shortage among clients. This paper presents gRemote, an open-source cloud-rendering system that can address these issues on a GPU resource pool. To mitigate the CPU resource shortage, gRemote improves CPU configuration by expanding CPU resources from server-side to both server- and client-side. To maintain the reasonable GPU usage for individual tasks, gRemote innovates a new resource-sharing mechanism called GPU throttle. Furthermore, remote API-Forwarding brings another type of network bandwidth consumption: command streaming. To optimize the network bandwidth for GPU command transmission, gRemote proposes two methods, command characteristics-oriented (CCO) compression algorithm and command-transmitted methodology. gRemote supports 1,228 OpenGL commands and provides cloud rendering for more than 40 clients with negligible shared-resource interference. With CCO algorithm and command-transmitted methodology, the network bandwidth is saved by more than 90%.