Reviewerly/exHarmony huggingface dataset:
A Quantitative Evaluation Method of Aero-engine Blade Defects Based on Ultrasonic C-Scan

Given the unintuitive and nonquantitative analysis of traditional artificial detection results of aero-engine blades, a characterization evaluation technique has been carried out based on ultrasonic defect point cloud. 3-D reconstruction display model of ultrasonic point cloud was completed by ultrasonic C-Scan imaging principle, spatial surface display technology, and 3-D visualization technology. A hybrid reconstruction method based on Delaunay triangulation and mesh growth algorithm was proposed, which achieved the 3-D reconstruction of a defect point cloud of regional discretization, the obtained results exhibited the spatial distribution of defect area, which intuitively provided the comprehensive view of 3-D defect model. Furthermore, the defect area was calculated via the vertex coordinates of each triangle in the reconstruction mesh. Taking flat-bottom holes as an example, an experimental model for quantitative evaluation was established based on the method. The quantization error of defect size is less than ±10% with comparison of the prefabricated defect size, so the feasibility and accuracy of the method are verified, which provides a convenient and effective way to quantitatively characterize the defects of curved components with immersion ultrasonic testing.
On the Comparison of Classifiers' Construction over Private Inputs

Classifiers are often trained over data collected from different sources. Sharing their data with other entities, however, can raise privacy concerns for data owners. To protect data confidentiality while being able to train a classifier, effective solutions have been proposed in the literature to construct various types of classifiers over private data. However, to date an analysis and comparison of the computation and communication costs for the construction of classifiers over private data is missing, making it difficult to determine which classifier can be used in a given application domain. In this work, we show how two well-known classifiers (Naive Bayes and SVM classifiers) can be securely build over private inputs, and evaluate their construction costs. We assess the computation and communication costs for training the classifiers both theoretically and empirically for different benchmark datasets.
Temporal and spatial solar resource variation by analysis of measured irradiance in Geba catchment, North Ethiopia

The availability of solar energy resource information in Ethiopia is limited due to the lack of ground measurement stations. The aim of this work was, therefore, to estimate the temporal variation and spatial distribution of solar radiation for Geba catchment located in the Northern part of Ethiopia based on four selected measurement sites. To achieve this, ground measurement data at ten-minute interval that has been recorded for five years was employed. Time series data and temporal variation analysis were done for all the sites. The spatial distribution analysis was carried out by interpolation of the data from the sites across the catchment using Inverse Distance Weight (IDW) method of the ArcGIS software. The temporal data analysis indicated higher solar radiation of greater than 5 kWh/m2/day during the dry months of September to May and in the range of 3–4 kWh/m2/day during the rainy season of June to August. The result of the analysis on spatial distribution of solar radiation showed 5%–7.5% variation with daily average solar radiation of 5.6 kWh/m2/day across the catchment. Areas in central part around Mekelle and southern part around Mayderhu site have relatively higher solar radiation.
Decentralized Data Aggregation: A New Secure Framework based on Lightweight Cryptographic Algorithms

Blockchain has become very popular and suitable to the Internet of Things (IoT) field due to its non-tamperabaility and decentralization properties. The number of IoT devices and leaders (who own IoT devices) is increased exponentially, and thus data privacy and security are undoubtedly significant concerns. In this paper, we summarize some issues for the BeeKeeper system, a blockchain-based IoT system, proposed by Zhou et al., and then aim for presenting an improved solution for dencentralized data aggregation (DDA) on IoT. We propose our DDA system by using secret sharing to improve its efficiency, and smart contracts as the computing processors. In order to achieves data sharing (e.g., a leader to access data of others' devices), we use local differential privacy and cryptographic primitives such as token-based encryption. Finally, to show the feasibility, we provide some implementations and experiments for the DDA systems.
GPU Parallelization of Policy Iteration RRT#

Sampling-based planning has become a de facto standard for complex robots given its superior ability to rapidly explore high-dimensional configuration spaces. Most existing optimal sampling-based planning algorithms are sequential in nature and cannot take advantage of wide parallelism available on modern computer hardware. Further, tight synchronization of exploration and exploitation phases in these algorithms limits sample throughput and planner performance. Policy Iteration RRT# (PI-RRT#) exposes fine-grained parallelism during the exploitation phase, but this parallelism has not yet been evaluated using a concrete implementation. We first present a novel GPU implementation of PI-RRT#'s exploitation phase and discuss data structure considerations to maximize parallel performance. Our implementation achieves 3-4× speedup over a serial PI-RRT# implementation for a 77.9% decrease in overall planning time on average. As a second contribution, we introduce the Batched-Extension RRT# algorithm, which loosens the synchronization present in PI-RRT# to realize independent 12.97× and 12.54× speedups under serial and parallel exploitation, respectively.
A Taxonomy for Distributed Ledger Analytics

Over the past decade, blockchains and distributed ledger technologies have rapidly evolved. With increasing transaction volumes and the proliferation of decentralized applications based on smart contracts, a need for a deeper understanding arises. We structure the field that we term distributed ledger analytics.
Early and Late Fusion of Multiple Modalities in Sentinel Imagery and Social Media Retrieval

Discovering potential concepts and events by analyzing Earth Observation (EO) data may be supported by fusing other distributed data sources such as non-EO data, for instance, in-situ citizen observations from social media. The retrieval of relevant information based on a target query or event is critical for operational purposes, for example, to monitor flood events in urban areas, and crop monitoring for food security scenarios. To that end, we propose an early-fusion (low-level features) and late-fusion (high-level concepts) mechanism that combines the results of two EU-funded projects for information retrieval in Sentinel imagery and social media data sources. In the early fusion part, the model is based on active learning that effectively merges Sentinel-1 and Sentinel-2 bands, and assists users to extract patterns. On the other hand, the late fusion mechanism exploits the context of other geo-referenced data such as social media retrieval, to further enrich the list of retrieved Sentinel image patches. Quantitative and qualitative results show the effectiveness of our proposed approach.
How Would Image Down-Sampling and Compression Impact Object Detection in the Context of Self-driving Vehicles ?

Accurate and real-time detection of road objects is a necessary component that self-driving cars must be equipped with to drive as safely as humans do. This task is critical since it is essential for more crucial tasks such as tracking, free slot identification, road-risk assessment and path planning. In this work, we provide an analysis of some image preprocessing techniques for real-time object detection in the context of autonomous vehicles. Image resolution and quality are often overlooked in the design of deep neural networks. To this end, we first examine the impact of image resolution on road object detection accuracy. Then, we examine the impact of image compression on object detection. To this end, several experiments have been carried out on the state-of-the-art YOLOv3 detector. The experimental results show that the network is resilient to compression given that its level is sufficient. The inference time is reduced without affecting considerably the other metrics. However, downsampling affects adversely the overall accuracy.
airFinger: Micro Finger Gesture Recognition via NIR Light Sensing for Smart Devices

Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10, 000 gesture samples collected from 10 volunteers.
Drift-Proof Tracking With Deep Reinforcement Learning

Object tracking is an essential and challenging sub-domain in the field of computer vision owing to its wide range of applications and complexities of real-life situations. It has been studied extensively over the last decade, leading to the proposal of several tracking frameworks and approaches. Recently, the introduction of reinforcement learning and the ‘Actor-Critic’ framework has effectively improved the tracking speed of deep learning trackers. However, most existing deep reinforcement learning trackers experience a slight performance degradation mainly owing to the drift issues. Drifts pose a threat to the tracking performance, which may lead to losing the tracked target. Herein, we propose a drift-proof tracker with deep reinforcement learning that aims to improve the tracking performance by counteracting drifts while maintaining its real-time advantage. We utilize a reward function with the Distance-IoU (DIoU) metric to guide the reinforcement learning to alleviate the drifts caused by the trained model. Furthermore, double negative samples (hard negative and drift samples) are constructed in tracking for network initialization, which is followed by calculating the loss by a small error-friendly loss function. Therefore, our tracker can better discriminate between the positive and negative samples and correct the predicted bounding boxes when the drift occurs. Meanwhile, a generative adversarial network is adopted for positive sample augmentation. Extensive experimental results on multiple popular benchmarks show that our algorithm effectively reduces the occurrences of drift and boosts the tracking performance, compared to those of other state-of-the-art trackers.
Memory and Energy Efficient Method Toward Sparse Neural Network Using LFSR Indexing

Deep Neural Networks (DNNs) require enormous computational power and storage memory. This impose a critical challenge to their efficient deployment on resource-constrained computing platforms such as edge devices. In this paper, we present a novel pruning algorithm and its hardware implementation to reduce the required memory-footprint and power usage of DNNs to enable them to be deployable on edge and mobile devices. we demonstrated a hardware-friendly pruning method where the locations of non-zero weights are derived from a Linear Feedback Shift Registers (LFSRs) in real-time. The results show a total power and area savings up to 49.97 % and 50.20 % for VGG-16 network on down-sampled ImageNet, respectively.
A Deep Reinforcement Learning Framework for Optimal Trade Execution

In this article, we propose a deep reinforcement learning based framework to learn to minimize trade execution costs by splitting a sell order into child orders and execute them sequentially over a fixed period. The framework is based on a variant of the Deep Q-Network (DQN) algorithm that integrates the Double DQN, Dueling Network, and Noisy Nets. In contrast to previous research work, which uses implementation shortfall as the immediate rewards, we use a shaped reward structure, and we also incorporate the zero-ending inventory constraint into the DQN algorithm by slightly modifying the Q-function updates relative to standard Q-learning at the final step.
Features Selection Algorithms for Classification of Voice Signals

In data mining problems, the high dimensionality of the input features can affect the performance of the process. In this way, the features selection methods appear as a solution to the problems encountered when analyzing databases with large dimensions. This article presents the implementation of the Pearson’s linear correlation, ReliefF, Welch’s t-test and multilinear regression based algorithms with forwards selection and backward elimination direction for the selection of acoustic features for the task of voice pathologies identification. The best set of selected features improved the accuracy and F1-score from 83% to 92% (9 points of percentage), using the ReliefF algorithm.
Supervised Autoencoder Variants for End to End Anomaly Detection

Despite the success of deep learning in various domains such as natural language processing, speech recognition, and computer vision, learning from a limited amount of samples and generalizing to unseen data still pose challenges. Notably, in the tasks of outlier detection and imbalanced dataset classification, the label of interest is either scarce or its distribution is skewed, causing aggravated generalization problems. In this work, we pursue the direction of multi-task learning, specifically the idea of using supervised autoencoders (SAE), which allows us to combine unsupervised and supervised objectives in an end to end fashion. We extend this approach by introducing an adversarial supervised objective to enrich the representations which are learned for the classification task. We conduct thorough experiments on a broad range of tasks, including outlier detection, novelty detection, and imbalanced classification, and study the efficacy of our method against standard baselines using autoencoders. Our work empirically shows that the SAE methods outperform one class autoencoders, adversarially trained autoencoders and multi layer perceptrons in terms of AUPR score comparison. Additionally, our analysis of the obtained representations suggests that the adversarial reconstruction loss functions enforce the encodings to separate into class-specific clusters, which was not observed for non-adversarial reconstruction loss functions.
Accordia: Adaptive Cloud Configuration Optimization for Recurring Data-Intensive Applications

Recognizing the diversity of big data analytic jobs, cloud providers offer a wide range of virtual machine (VM) instances or even clusters to cater for different use cases. The choice of cloud configurations can have a significant impact on the response time and running cost of data-intensive, production batch-jobs, which need to be re-run regularly using cloud-scale resources. However, identifying the best cloud configuration with a low search cost is quite challenging due to i) the large and high-dimensional configuration-parameters space, ii) the time-varying price of some instance types (e.g. spot-price ones), iii) job execution-time variation even given the same configuration, and iv) gradual drifts / unexpected changes of the characteristics of a recurring job. To tackle these challenges, we have designed and implemented Accordia, a system that enables Adaptive Cloud Configuration Optimization for Recurring Data-Intensive Applications. By leveraging recent algorithmic advances in Gaussian Process UCB (Upper Confidence Bound) techniques, the design of Accordia can handle time-varying instance pricing while providing a performance guarantee of sub-linearly increasing regret when comparing with the static, offline optimal solution. Using extensive trace-driven simulations and empirical measurements of our Kubernetes-based implementation, we demonstrate that Accordia can dynamically learn a near-cost-optimal cloud configuration (i.e. within 10% of the optimum) after fewer than 20 runs from over 7000 candidate choices within a 5-dimension search space, which translates to a 2X-speedup and 17.9% cost-savings, when comparing to CherryPick.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2557" altimg="si112.svg"><mml:mrow><mml:mi mathvariant="script">G</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:math>: A distributed engine for scalable path queries over temporal property graphs

Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and their properties and structure vary over time. These are seen in social, telecom, transit and epidemic networks. However, current graph databases and query engines have limited support for temporal relations among graph entities, no support for time-varying entities and/or do not scale on distributed resources. We address this gap by extending a linear path query model over property graphs to include intuitive temporal predicates and aggregation operators over temporal graphs. We design a distributed execution model for these temporal path queries using the interval-centric computing model, and develop a novel cost model to select an efficient execution plan from several. We perform detailed experiments of our Granite distributed query engine using both static and dynamic temporal property graphs as large as 52M vertices, 218M edges and 325M properties, and a 1600-query workload, derived from the LDBC benchmark. We frequently offer sub-second query latencies on a commodity cluster, which is 149×–1140× faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph/Spark distributed graph query engine. Granite also completes 100% of the queries for all graphs, compared to only 32–92% workload completion by the baseline systems. Further, our cost model selects a query plan that is within 10% of the optimal execution time in 90% of the cases. Despite the irregular nature of graph processing, we exhibit a weak-scaling efficiency of ≥60% on 8 nodes and ≥40% on 16 nodes, for most query workloads.
Communicating Service Offers in a Conversational User Interface

The increased interest in chatbots accentuates the importance of conversational design. A key conversational design challenge concerns how to communicate available service offers to users. We present an exploratory study, conducted in the context of financial service provision. Here, we first detailed four alternative approaches to communicate available service offers, reflecting different levels of proactivity. We then gathered feedback on user preference through interviews with 17 users following their interactions with prototypes representing the four approaches. Proactivity in the communication of service offers was found to be potentially valuable, provided that the offer is relevant to the conversation, do not compromise conversational efficiency, and is easy to discard. However, proactive communication of service offers may also entail challenges concerning perceptions of privacy and invasiveness, and, hence, needs to be designed with great care. Based on our findings, we summarize implications for theory and practice and propose directions for future research.
A composite framework for supporting user emotion detection based on intelligent taxonomy handling

Abstract One of the most relevant issues of a social robot is its capability of catching the attention of a new acquaintance and empathize with her. The first steps towards a system which can be used by a social robot in order to be empathetic are illustrated in this paper. The system can analyze the Twitter ID of the new acquaintance, trying to detect the IAB (Interactive Advertising Bureau) Tier 1 categories that possibly can let arise in him/her a joyful feeling. Furthermore, it can retrieve news about that category and report them to the user, hopefully increasing his/her curiosity towards the system, improving the naturalness of the interaction. Moreover, the system is capable of querying Wikipedia in order to clarify any doubts that may arise in the user. A sample of a possible interaction is reported at the end of the paper.
A Public-key Encryption with Multi-keyword Search Scheme for Cloud-based Smart Grids

The smart grid is viewed as the next generation power system due to its reliability, resiliency, sustainability and energy efficiency. The smart grid technology has aroused widespread attention in communities of academia and industry over the years. Rapid development of smart grid enables more data to be collected and power to be efficiently distributed in real-time. With the increasing of power data, conventional smart grid cannnot satisfy the growing storage and data management demand. Cloud computing exactly handles these issues and is integrated with smart grid perfectly for more efficient operation of system. However, cloud-based smart grid system is vulnerable for various security flaws. Data confidentiality is one indispensable method of protecting users' privacy. Public-key Encryption with Multi-keyword Search (PEMKS) realizes the cloud server search encrypted files with users' multi-keyword trapdoor without revealing any information of retrival contents. In this paper, we propose a PEMKS scheme and apply it to cloud-based smart grid. Moreover, the proposed scheme is proved to be SS-sCKA secure and meets security requirements. Performance analysis indicates that the proposed scheme has better time complexity and lower storage cost compared with two recent PEMKS schemes.
When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous

We aim to enable robots to visually localize a target person through the aid of an additional sensing modality - the target person's 3D inertial measurements. The need for such technology may arise when a robot is to meet a person in a crowd for the first time or when an autonomous vehicle must rendezvous with a rider amongst a crowd without knowing the appearance of the person in advance. A person's inertial information can be measured with a wearable device such as a smart-phone and can be shared selectively with an autonomous system during the rendezvous. We propose a method to learn a visual-inertial feature space in which the motion of a person in video can be easily matched to the motion measured by a wearable inertial measurement unit (IMU). The transformation of the two modalities into the joint feature space is learned through the use of a triplet loss which forces inertial motion features and video motion features generated by the same person to lie close in the joint feature space. To validate our approach, we compose a dataset of over 3,000 video segments of moving people along with wearable IMU data. We show that our method is able to localize a target person with 80.7% accuracy averaged over testing data with various number of candidates using only 5 seconds of IMU data and video.