Reviewerly/exHarmony huggingface dataset:
How Subtle Can It Get?

Flying drones have become common objects in our daily lives, serving a multitude of purposes. Many of these purposes involve outdoor scenarios where the user combines drone control with another activity. Traditional interaction methods rely on physical or virtual joysticks that occupy both hands, thus restricting drone usability. In this paper, we investigate one-handed human-to-drone-interaction by leveraging three modalities: force, touch, and IMU. After prototyping three different combinations of these modalities on a smartphone, we evaluate them against the current commercial standard through two user experiments. These experiments help us to find the combination of modalities that strikes a compromise between user performance, perceived task load, wrist rotation, and interaction area size. Accordingly, we select a method that achieves faster task completion times than the two-handed commercial baseline by 16.54% with the merits of subtle user behaviours inside a small-size ring-form device and implements this method within the ring-form device. The last experiment involving 12 participants shows that thanks to its small size and weight, the ring device displays better performance than the same method implemented on a mobile phone. Furthermore, users unanimously found the device useful for controlling a drone in mobile scenarios (AVG = 3.92/5), easy to use (AVG = 3.58/5) and easy to learn (AVG = 3.58/5). Our findings give significant design clues in search of subtle and effective interaction through finger augmentation devices with drone control. The users with our prototypical system and a multi-modal on-finger device can control a drone with subtle wrist rotation (pitch gestures: 43.24° amplitude and roll gestures: 46.35° amplitude) and unnoticeable thumb presses within a miniature-sized area of (1.08 * 0.61 cm2).
GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management

The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.
Domain Ontology Construction and Evaluation for the Entire Process of Software Testing

As an important part of software engineering, software testing is a knowledge-intensive work. In the process of software testing, inconsistent knowledge expression, diverse knowledge carriers, and a few experienced people have mastered most of the knowledge, which hinders the transfer and sharing of domain knowledge. Ontology is widely used in various stages of software engineering to define the semantic relationship between relevant information and knowledge. To solve the problem of knowledge silo in the process of software testing, this article forms an Entire Process Ontology on Software Testing (EPOST). EPOST covers the concepts and relationships of software testing process information, software test object information, and software defect information. The concepts and terms in the ontology are extracted from ISTQB, SWEBOK, IEEE std.829-2008 standard, and IEEE std.610.12-1990 standard. We adopt a comprehensive ontology construction method based on Dev. 101 method and Methontology method. The developed ontology is successfully evaluated by using validation and verification tests. Ontology verification uses an improved FOCA evaluation method by adding a cohesion metric. The evaluation result infers that EPOST has a high quality of ontology and good domain coverage, and achieves the purpose of ontology construction. Finally, we make a case study on the role of EPOST in software testing process. The results show that ontology-based application in the software testing process can promote the sharing and transmission of domain knowledge, and improve the testing process and testing quality.
FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.
Gradient boosting learning for fraudulent publisher detection in online advertising

Purpose Analysis of the publisher's behavior plays a vital role in identifying fraudulent publishers in the pay-per-click model of online advertising. However, the vast amount of raw user click data with missing values pose a challenge in analyzing the conduct of publishers. The presence of high cardinality in categorical attributes with multiple possible values has further aggrieved the issue. Design/methodology/approach In this paper, gradient tree boosting (GTB) learning is used to address the challenges encountered in learning the publishers' behavior from raw user click data and effectively classifying fraudulent publishers. Findings The results demonstrate that the GTB effectively classified fraudulent publishers and exhibited significantly improved performance as compared to other learning methods in terms of average precision (60.5 %), recall (57.8 %) and f-measure (59.1%). Originality/value The experiments were conducted using publicly available multiclass raw user click dataset and eight other imbalanced datasets to test the GTB's generalizing behavior, while training and testing were done using 10-fold cross-validation. The performance of GTB was evaluated using average precision, recall and f-measure. The performance of GTB learning was also compared with eleven other state-of-the-art individual and ensemble classification models.
Deep Learning Based Motion Tracking of Ultrasound Image Sequences

Conventional motion estimation techniques in ultrasound images such as exhaustive search-based block matching (ES-BM) have been studied exhaustively and are known to be computationally expensive and slow. Consequently, they are not feasible for real-time processing. On the other hand, several deep learning-based techniques are being developed for realtime motion estimation of day-to-day objects. In this paper, we attempt to bridge the gap between tracking techniques being used for ultrasound images and recent deep learning-based techniques used for non-medical real-world objects. We propose to adopt the deep neural network-based Fully-Convolutional Siamese tracker (SiamFC) to track regions of interest (ROI) in ultrasound images. We prove that siamese architecture-based tracker is feasible for motion tracking in ultrasound images and performs better than conventional ES-BM technique. We applied SiamFC and ES-BM on 10 different image sequences to track the motion of the transverse section of the carotid artery. Our experiments showed that SiamFC was almost six times faster with slightly better performance compared to ES-BM in most of the cases.
Distributed Kalman Consensus Filter for Estimation With Moving Targets

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.
Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest

One major challenge in medical imaging analysis is the lack of label and annotation which usually requires medical knowledge and training. This issue is particularly serious in the brain image analysis such as the analysis of retinal vasculature, which directly reflects the vascular condition of Central Nervous System (CNS). In this paper, we present a novel semi-supervised learning algorithm to boost the performance of random forest under limited labeled data by exploiting the local structure of unlabeled data. We identify the key bottleneck of random forest to be the information gain calculation and replace it with a graph-embedded entropy which is more reliable for insufficient labeled data scenario. By properly modifying the training process of standard random forest, our algorithm significantly improves the performance while preserving the virtue of random forest such as low computational burden and robustness over over-fitting. Our method has shown a superior performance on both medical imaging analysis and machine learning benchmarks.
Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition

Human activity recognition (HAR) works have mostly focused on the activities of adults. However, HAR is typically beneficial to the safety and wellness of newborn or infants because they have difficulties in verbal communication. The activities of infants are different from those of adults in terms of its types and intensity. Hence, it is necessary to study the behavior of infants separately. We study newborn and infant activity recognition by analyzing accelerometer data from the sensors attached to body. We aim to classify four types of activities: sleeping, moving in agony, moving in normal condition, and movement by external force. For this work, we collected 11 h videos and corresponding sensor data from 10 infant subjects. For recognition, we propose an end-to-end deep model using autoencoder and k-means clustering, which is trained in an unsupervised way. From a set of performance tests, our model can achieve 0.96 in balanced accuracy and F-1 score of 0.95.
A survey of deep network techniques all classifiers can adopt

Deep neural networks (DNNs) have introduced novel and useful tools to the machine learning community. Other types of classifiers can potentially make use of these tools as well to improve their performance and generality. This paper reviews the current state of the art for deep learning classifier technologies that are being used outside of deep neural networks. Non-neural network classifiers can employ many components found in DNN architectures. In this paper, we review the feature learning, optimization, and regularization methods that form a core of deep network technologies. We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification performance. Because many opportunities and challenges still exist, we discuss directions that can be pursued to expand the area of deep learning for a variety of classification algorithms.
Channel Selection Using Gumbel Softmax

Important applications such as mobile computing require reducing the computational costs of neural network inference. Ideally, applications would specify their preferred tradeoff between accuracy and speed, and the network would optimize this end-to-end, using classification error to remove parts of the network. Increasing speed can be done either during training – e.g., pruning filters – or during inference – e.g., conditionally executing a subset of the layers. We propose a single end-to-end framework that can improve inference efficiency in both settings. We use a combination of batch activation loss and classification loss, and Gumbel reparameterization to learn network structure. We train end-to-end, and the same technique supports pruning as well as conditional computation. We obtain promising experimental results for ImageNet classification with ResNet (45–52% less computation).
Multi-layer Perceptron based Photovoltaic Forecasting for Rooftop PV Applications in Smart Grid

In the smart grid, a consumer can choose either to expend energy from the grid or vend energy back to the grid. On this principle, for profit maximization based on the current day's electricity selling price, a smart home with a rooftop photo voltaic (PV) system can determine whether the yielded energy during the day should be expended by the consumer at night or stored in a storage cell for the purpose of selling on the following days. For this, the smart home system needs to predict the electricity generation of the PV system for making a better decision. In this paper, we propose a Multi-layer Perceptron based PV forecasting method for the PV systems. The system is trained with the historical data of irradiance, temperature, solar zenith angle, wind speed, and relative humidity. The performance is evaluated by the cross validation and testing with the help of real world PV data. Being light weight and high accuracy, our proposed algorithm can be a potential method for the roof top PV forecasting in the smart home applications.
The relation between color and spatial structure for interpreting colormap data visualizations

Interpreting colormap visualizations requires determining how dimensions of color in visualizations map onto quantities in data. People have color-based biases that influence their interpretations of colormaps, such as a dark-is-more bias—darker colors map to larger quantities. Previous studies of color-based biases focused on colormaps with weak data spatial structure, but color-based biases may not generalize to colormaps with strong data spatial structure, like “hotspots” typically found in weather maps and neuroimaging brain maps. There may be a hotspot-is-more bias to infer that colors within hotspots represent larger quantities, which may override the dark-is-more bias. We tested this possibility in four experiments. Participants saw colormaps with hotspots and a legend that specified the color-quantity mapping. Their task was to indicate which side of the colormap depicted larger quantities (left/right). We varied whether the legend specified dark-more mapping or light-more mapping across trials and operationalized a dark-is-more bias as faster response time (RT) when the legend specified dark-more mapping. Experiment 1 demonstrated robust evidence for the dark-is-more bias, without evidence for a hotspot-is-more bias. Experiments 2 to 4 suggest that a hotspot-is-more bias becomes relevant when hotspots are a statistically reliable cue to “more” (i.e., the locus of larger quantities) and when hotspots are more perceptually pronounced. Yet, comparing conditions in which the hotspots were “more,” RTs were always faster for dark hotspots than light hotspots. Thus, in the presence of strong spatial cues to the locus of larger quantities, color-based biases still influenced interpretations of colormap data visualizations.
Coherence Depletion in Quantum Algorithms

Besides the superior efficiency compared to their classical counterparts, quantum algorithms known so far are basically task-dependent, and scarcely any common features are shared between them. In this work, however, we show that the depletion of quantum coherence turns out to be a common phenomenon in these algorithms. For all the quantum algorithms that we investigated, including Grover’s algorithm, Deutsch–Jozsa algorithm, and Shor’s algorithm, quantum coherence of the system states reduces to the minimum along with the successful execution of the respective processes. Notably, a similar conclusion cannot be drawn using other quantitative measures such as quantum entanglement. Thus, we expect that coherence depletion as a common feature can be useful for devising new quantum algorithms in the future.
Sketch-Driven Regular Expression Generation from Natural Language and Examples

Recent systems for converting natural language descriptions into regular expressions (regexes) have achieved some success, but typically deal with short, formulaic text and can only produce simple regexes. Real-world regexes are complex, hard to describe with brief sentences, and sometimes require examples to fully convey the user’s intent. We present a framework for regex synthesis in this setting where both natural language (NL) and examples are available. First, a semantic parser (either grammar-based or neural) maps the natural language description into an intermediate sketch, which is an incomplete regex containing holes to denote missing components. Then a program synthesizer searches over the regex space defined by the sketch and finds a regex that is consistent with the given string examples. Our semantic parser can be trained purely from weak supervision based on correctness of the synthesized regex, or it can leverage heuristically derived sketches. We evaluate on two prior datasets (Kushman and Barzilay 2013 ; Locascio et al. 2016 ) and a real-world dataset from Stack Overflow. Our system achieves state-of-the-art performance on the prior datasets and solves 57% of the real-world dataset, which existing neural systems completely fail on. 1
Parallel multipopulation optimization for belief rule base learning

As a rule-based expert system, the belief rule base (BRB) exhibits tremendous advantages in modeling nonlinearity for complex systems. Present BRB learning studies can be classified into three categories: BRB structure learning, BRB parameter learning, and BRB joint optimization but only in an iterative and separate fashion. In this study, a novel Parallel Multipopulation optimization approach for BRB, i.e., PMP-BRB, is proposed that simultaneously optimizes the structure and parameters of a BRB. In the optimization model of PMP-BRB, the structure of BRB, i.e., the number of rules, is used as another decisive variable. In the optimization algorithm of PMP-BRB, multiple populations are initialized and subsequently optimized, with different populations representing BRBs of varied sizes. Furthermore, a "completion and deletion" strategy is proposed, wherein individual BRBs in different populations are completed with additional genes that only engage in the optimization operations but not in fitness calculations. Moreover, a trade-off analysis is conducted for decision-makers to identify the final optimal configuration of a BRB based on their preference. The proposed PMP-BRB approach is validated by four cases, namely a numerical case, two practical cases, and a case study with five classification benchmarks. Four evolutionary algorithms are tested and compared. With the structure and parameters optimized simultaneously, all the four cases yield competitive results in comparison with those of previous studies.
Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review

Solar photovoltaic (PV) power generation has strong intermittency and volatility due to its high dependence on solar radiation and other meteorological factors. Therefore, the negative impact of grid-connected PV on power systems has become one of the constraints in the development of large scale PV systems. Accurate forecasting of solar power generation and flexible planning and operational measures are of great significance to ensure safe, stable, and economical operation of a system with high penetration of solar generation at transmission and distribution levels. In this paper, studies on the following aspects are reviewed: (1) this paper comprehensively expounds the research on forecasting techniques of PV power generation output. (2) In view of the new challenge brought by the integration of high proportion solar generation to the frequency stability of power grid, this paper analyzes the mechanisms of influence between them and introduces the current technical route of PV power generation participating in system frequency regulation. (3) This section reviews the feasible measures that facilitate the inter-regional and wide-area consumption of intermittent solar power generation. At the end of this paper, combined with the actual demand of the development of power grid and PV power generation, the problems that need further attention in the future are prospected.
Minor class-based status detection for pipeline network using enhanced generative adversarial networks

For the operational status detection of a pipeline network, it is essential to obtain enough samples of each class during the network operation. However, the phenomenon of few actual leak samples give rise to the imbalanced dataset problem. To address this issue, this paper proposes a minor class-based status detection method using enhanced generative adversarial networks (enhanced-GANs). First, a generative model with a U-net structure is established to generate the required samples with the modified normal samples, and the L1 loss and L2 loss functions are utilized to update the network parameters. Then, output results and extracted features regarding different layers of discriminative network are added in the generative network loss function to improve the quality of the generated samples. Furthermore, based on the hidden features extracted by the trained discriminative network, an enhanced dual judgment scheme is proposed to improve the status detection performance. Finally, extensive experiments are carried out to evaluate the proposed method with the dataset collected from a practical pipeline network. The experiment results show that the proposed method can not only provide enough leak samples but also effectively improves the status detection accuracy for a pipeline network.