Reviewerly/exHarmony huggingface dataset:
Towards identifying and linking data silos along the software life cycle

Software is of increasing importance in all industries and it&#x2019;s efficient creation an important factor in the success of corporations. Using the data generated during the entire software life cycle to create understanding and derive actionable insights, decisions can be made on a factual basis. One of the key requirements to making these objective decisions possible is the collection, composition, and communication of data to the correct stakeholders. To further these goals, the most complete collection of data artifacts available in the software life cycle is presented. These are abstracted to be independent of programming language, development process and toolset. As value is derived from the connection of entities, a set of possible connections is introduced as well as challenges and solutions in their creation discussed. The theoretical observations, and results are verified in the context of a large development organization with more than a thousand developers working from multiple global locations. Our results show that the combination of multiple data sources and their systematic composition are paramount to deriving value from life cycle data in large corporations.
No-reference stereoscopic image quality assessment on both complex contourlet and spatial domain via Kernel ELM

Stereoscopic imaging is widely used in many fields. To guarantee the best quality of experience, it is necessary to design a robust and accurate quality assessment model for stereoscopic content. In this paper, we proposed a no-reference stereoscopic image quality assessment (NR-SIQA) model using both complex contourlet and spatial domain features of monocular and binocular images. Monocular features extracted from the CIELAB color space are exploited to characterize quality degradation, including the across-scale and across-orientation correlation in complex contourlet domain and natural scene statistics-based (NSS) features in spatial domain. Then, the binocular features consist of energy along with energy difference and structural correlation in complex contourlet domain, and statistics distribution in spatial domain extracted from the synthesized cyclopean image and the 3D visual discomfort measure based on the statistics of disparity map. Finally, the above features are mapped into the predicted quality scores by the Kernel ELM (KELM) regression model. Experimental results on four public datasets show that the proposed model is highly consistent with human subjective perception in terms of accuracy and generalization.
Neural Network-Assisted Robust Symbol Detection Under Intersymbol Interference

In recent years, the machine learning assisted communication system design has drawn a lot of attentions. As a remarkable progress, a recent work proposed to incorporate a neural network (NN) into the traditional algorithms for symbol detection under intersymbol interference (ISI), e.g. the Viterbi algorithm and the BCJR algorithm, to achieve robustness against channel estimation errors. This paper presents an improved design over the state-of-the-art by using a neural network to approximate the likelihood of the received sample given different state transitions of the trellis diagram. The simulation results show that the proposed method performs similarly to the conventional methods in the channel model-matched scenarios, but is significantly more robust against channel estimation errors. Our design is superior to the state-of-art NN -assisted methods in two aspects: it requires significantly smaller training overhead and is robust against non-Gaussian noise.
A Study on Interaction Prediction for Reducing Interaction Latency in Remote Mixed Reality Collaboration

Various studies on latency in remote mixed reality collaborations (remote MR collaboration) have been conducted, but studies related to interaction latency are scarce. Interaction latency in a remote MR collaboration occurs because action detection (such as contact or collision) between a human and a virtual object is required for finding the interaction performed. Therefore, in this paper, we propose a method based on interaction prediction to reduce the time for detecting the action between humans and virtual objects. The proposed method predicts an interaction based on consecutive joint angles. To examine the effectiveness of the proposed method, an experiment was conducted and the results were given. From the experimental results, it was confirmed that the proposed method could reduce the interaction latency compared to the one obtained by conventional methods.
Autonomous Systems Design: Charting a New Discipline

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> Autonomous systems are becoming pervasive in many domains. This article presents a survey regarding various emerging research directions centered around the broad topic of autonomous systems. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Partha Pratim Pande, Washington State University</i>
AIM: Automatic Interaction Machine for Click-Through Rate Prediction

Feature embedding learning and feature interaction modeling are two crucial components of deep models for Click-Through Rate (CTR) prediction in recommender systems. Most existing deep CTR models suffer from the following three problems. First, feature interactions are either manually designed or simply enumerated. However, not all the feature interactions are useful for the prediction task and useless feature interactions may introduce noisy signals thus causing overfitting. Second, all the feature interactions are modeled with an identical interaction function, whereas different interaction functions introduce different inductive biases to better capture various feature interaction patterns. Third, in most existing models, different features share the same embedding size. However, model size can be further optimized without sacrificing performance by differentiating embedding sizes for individual features, as the amount of information contained in each feature varies much. To address the three issues mentioned above, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Automatic Interaction Machine (AIM)</i> with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), respectively. To tackle the first problem, FIS component automatically identifies different orders of essential feature interactions with useless ones pruned. Taking care of the second problem, IFS component selects appropriate interaction functions for each individual feature interaction in a learnable way. Moreover, to avoid learning conflict among different interaction functions, IFS proposes function-wise embeddings via performing multiple embeddings for each feature, where each feature embedding corresponds to one possible interaction function. However, utilizing multiple embeddings for each feature may make the model size affordably large if we keep the same embedding size as utilizing shared embedding (i.e., each feature shares the same embedding for different interaction functions). To solve this third problem, EDS automatically selects proper embedding size for each feature. Such a flexible embedding size adaptation is able to reduce the large amount of embedding parameters introduced by function-wise embeddings. Offline experiments on three large-scale datasets (two public benchmarks, one private dataset) validate that AIM can significantly improve various FM-based models. AIM has been deployed in the recommendation service of a mainstream app market, where a three-week online A/B test demonstrated the superiority of AIM, improving DeepFM model by 4.4% in terms of CTR.
Generalized Net Model of Forest Zone Monitoring by UAVs

The paper presents a generalized net (GN) model of the process of terrain observation with the help of unmanned aerial vehicles (UAVs) for the prevention and rapid detection of wildfires. Using a GN, the process of monitoring a zone (through a UAV, which is further called a reconnaissance drone) and the localization of forest fires is described. For a more indepth study of the terrain, the reconnaissance drone needs to coordinate with a second UAV, called a specialized drone, so that video and sensory information is provided to the supervising fire command operational center. The proposed GN model was developed to assist in the decision-making process related to the coordination of the operation of both UAVs under dynamically changing terrain circumstances, such as those related to preventing or quickly containing wildfires. It describes the stages (transitions), logical determinants (transition predicate matrices), and directions of information flow (token characteristics) within the process of localization of fires using the pair of reconnaissance and specialized drones.
Fast Identification of Volcanic Tremor and Lahar Signals during the 2009 Redoubt Eruption Using Permutation Entropy and Supervised Machine Learning

Abstract Despite their usefulness for volcano monitoring, emergent seismic signals, such as volcanic tremor or signals generated by lahars, are difficult to identify with confidence in a timely fashion. Machine-learning algorithms offer an objective alternative to traditional methods of identifying such volcanoseismic signals, because they are able to handle quickly large amounts of data, while requiring little input from the user. In this work, we combine permutation entropy and centroid as well as dominant frequency with supervised machine learning to evaluate their potential in identifying volcanic tremor and lahar signals recorded during the 2009 Redoubt volcano eruption. The particular dataset was chosen for the reason that the properties and occurrence times of the volcanoseismic signals during the eruption are well known from previous studies. We find that the selected features can effectively discriminate both types of signals against the seismic background, especially for stations that are near the source. Results show that the identification success rate for volcanic tremor reaches up to 96%, whereas this rate becomes up to 91% for lahar signals. The calculation of the features as well as the application of the machine-learning algorithms is fast, allowing their implementation in the operational environment of a volcano observatory during a volcanic crisis. Finally, the proposed methodology can potentially be used to objectively identify other emergent seismic signals such as tectonic tremor along subduction zones, glacial tremor, or seismic signals generated during landslides.
Conceptual Designing of a Virtual Receptionist: Remote Desktop Walkthrough and Bodystorming in VR

Early user involvement in the design of intelligent virtual agents (IVAs) is fundamental for usability and otherwise good user experience. In this paper, we present a combination of methods used in the remote conceptual design of a virtual receptionist for a university department. The study builds on two workshops with potential users. The first was a bodystorming workshop in virtual reality (VR) with four researchers at the department, and the second was a desktop walkthrough workshop on an online whiteboard with five students at the department. Proposed solutions from the workshops were deconstructed using a morphological chart into a pentad of parameters: agent, act, scene, agency, and purpose. New design concepts were then composed by combining solutions. Sketching was used to further detail and present the generated concepts. Our analysis of the workshops indicates that the bodystorming workshop had an aesthetic perspective on embodied interaction while the desktop walkthrough workshop had a more instrumental perspective on usability. The combination of embodied but remote ideation methods with morphological chart structured by the pentad is novel to not only the IVA field, but also to interaction design in general. Finally, the conceptual design of a novel cross-platform IVA is proposed.
Privacy-Preserving and Sustainable Contact Tracing Using Batteryless Bluetooth Low-Energy Beacons

Contact tracing is the techno-choice of reference to address the COVID-19 pandemic. Many of the current approaches have severe privacy and security issues and fail to offer a sustainable contact tracing infrastructure. We address these issues introducing an innovative, privacy-preserving, sustainable, and experimentally tested architecture that leverages batteryless BLE beacons.
On the Life Aesthetics of Packaging Design in the Context of Digital Economy

All the beautiful satisfying things in life have their own internal aesthetic mechanism. Digital economy promotes the new pattern of digital economy integration and transformation of industrial digitalization and digital industrialization. In digital economy, the life aesthetics of the integration of science and art advocates judging and optimizing the things in life through the organic unity of science and aesthetics. First of all, through the literature reading and data collection analysis of creative packaging works and smart packaging cases, to explore the differences between smart packaging and traditional packaging, summed up for the packaging design of the new economic model under the innovative path. Secondly, the semantic difference method is used to integrate and filter the emotional vocabulary of the public in the new economy, to enhance the public’s sense of experience, and to analyze the process of beauty in the literary and creative packaging design from the perspective of life aesthetics, this paper discusses the integration of functional beauty and formal beauty. From the perspective of life aesthetics and technology aesthetics, this paper examines the innovative design of all elements of packaging, making it possible to reflect on the connotation and explicit innovation of life aesthetics in packaging design under the background of digital economy, to achieve the original intention and vision of making people’s lives better. The theory and method are proved to be accurate and applicable by taking the packaging design of time-honored cakes as an example.
A Lightweight Smart Meter Framework using a Scalable Blockchain for Smart Cities

The role of smart meters in the smart cities is becoming increasingly important. These smart meters are always connected to the network and this connectivity may leads to security risks. And, blockchain (BC) technology has recently received a lot of attention due to its data security stability and a distributed platform. BC has been applied to a plethora of different applications. However, BC requires high performance to be applied directly to a smart meter, which is an Internet of Things (IoT) device with low memory and slow CPU computation power. And, it cannot be scale up easily due to the nature of the distributed system. In order to compensate for this shortcoming, we propose a lightweight smart meter framework using BC technology, which is scalable and requires low computation power. In this proposed framework, data blockchain (dBC) and meta blockchain (mBC) are divided, and smart meters are divided into small groups to support scalability. The two proposed grouping algorithms are a delay-aware grouping algorithm that considers network delay and a random grouping algorithm that can improve security with a randomness. We also propose a new Enhanced Privacy Piggy Bank Protocol (EPPB) using bilinear pairing of cryptographic keys to authenticate the communicating parties for smart meters. We presented simulation results and security analysis to verify our proposed framework. From this simulation results, we can see that even if the number of smart meters increases, the performance is not affected.
Research on Classification and Recognition of Emotional EEG Signal Based on Extreme Learning Machine

There are many research methods of emotion recognition, and the emotion recognition of EEG signals is one of the important researches in the field of emotion computing. Extreme learning machine can recognize more generalized features with fewer training parameters. Therefore, this paper constructs a power spectral density-extreme learning machine classification model and applies it to the classification and recognition of emotional EEG signals. The experimental paradigm was designed and the emotion-related EEG signals of 10 subjects were collected, and the PSD characteristics of the corresponding frequency bands of their EEG signals in the three emotions were extracted, and the model was used to achieve specific and cross-subject evaluation of emotional EEG signals. Positive, neutral, and negative three categories are distinguished. Among them, the accuracy of single body recognition is up to 96.17%, and the average accuracy is 81.72%; the cross-subject recognition accuracy is 76.82%, which verifies the combination of this feature and the classification method. Effectiveness in the field of emotional EEG signal recognition.
A QKeras Neural Network Zoo for Deeply Quantized Imaging

Neural network zoos are quite common in the literature and are particularly useful for demonstrating the potential of any deep learning framework by providing examples of its use to the Artificial Intelligence community. Unfortunately most of them uses FP32 (32bits floating point) or INT8 (8bits integer) precision for activation and weights. Communities such as TinyML are paying more and more attention to memory and energy-saving to achieve mW and below power consumptions and therefore to Deeply Quantized Neural Networks (DQNNs). Two frameworks: QKeras and Larq, are gaining momentum for defining and training DQNNs. To best of our knowledge, the only available zoo for DQNN is the Larq framework. In this work we developed a new QKeras zoo and comparing the accuracy with the available Larq zoo. To avoid costly re-training, we show how to re-use the weights from Larq zoo. We also developed the zoo with ten networks and matched the performance of the Larq zoo for seven out of ten networks. Our work will be made publicly available through a GitHub repository.
Device Scheduling and Resource Allocation for Federated Learning under Delay and Energy Constraints

Federated Learning (FL) is an emerging technique to enhance edge intelligence, where mobile devices train machine learning models collaboratively with their local data. Limited energy on devices and scarce wireless bandwidth can notably impact the convergence of FL over wireless networks, and thus device scheduling and resource allocation are critical. In this paper, we propose a joint device scheduling and resource allocation scheme to maximize the model accuracy under total training delay and device energy budgets. Since FL consists of multiple training rounds, there is an inherent trade-off between per-round delay, per-round energy consumption, and the total number of rounds. To find solution, we decouple the accuracy maximization problem into two sub-problems. First, given a scheduling policy, the bandwidth allocation and local computing frequency are jointly optimized to maximize the number of rounds that can be conducted. Then, a device scheduling policy is proposed to balance the trade-off between the per-round energy and delay cost and the number of rounds, with the ultimate goal of accuracy optimization. Experiments on various learning tasks and datasets show that the proposed scheme can greatly improve the convergence rate of resource-constrained FL.
Quantum Circuit Design of RECTANGLE Lightweight Cipher

Quantum computers will have a significant impact on the security aspects of many conventional cryptographic algorithms as the quantum circuit implementation of the cryptographic algorithms can be attacked with the help of Grover’s key search algorithm. This work proposes quantum circuit design of RECTANGLE lightweight cipher for 80-bit and 128-bit key variants. All the individual modules of the cipher are designed with minimal quantum resources and finally they are integrated together to build the complete quantum circuit. The quantum circuit synthesis is performed with Toffoli family of quantum gates. The proposed design outperforms the existing design by achieving low-latency. The quantum resources are also estimated to mount the quantum attack by using Grover’s key search algorithm.
A Blockchain-Powered Data Market for Multi-User Cooperative Search

Cloud computing provides a feasible solution to data outsourcing, and hence forming a cloud-based data market, where data users buy data from owners through querying cloud servers. However, it also incurs new privacy and security problems, as data is under a centralized third-party instead of the data owner’s direct control. Existing data markets are also questioned on their inflexible and opaque pricing, where the value of data ownership and the cost of query searches are mixed. In this paper, we consider blockchain-based storage as a better choice to ensure safe data outsourcing since data is spread out across many data points. We propose an Ethereum-based data market that provides distributed storage and correct remote data search. We design a new pricing model, where each query will be charged by two parties: owner (paid for providing his data) and miner (rewarded by performing query searches). We study a new cooperative search scheme through a proxy to reduce cost on the user side. Given that each user query is charged based on its number of keywords, then a cooperative search can reduce user-side cost by combining multiple queries into a group so that overlapped keywords will only be charged for one time. To ensure user QoE, a combined query should not be significantly larger than any of its original queries in terms of the number of keywords. The total price is based on the total number of keywords in all groups. Since it is a cooperative model with shared resources, we also study various incentive properties on the user side, yielding a cost sharing mechanism to split joint cost in a truth-revealing and fair manner. We further extend our market with a set of substitute data owners and propose a double auction mechanism to match users and owners based on their requirements. Experiments have been conducted on real query trace to demonstrate the effectiveness of our proposed scheme.
Neural Capacity Estimators: How Reliable Are They?

Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks and without the knowledge of closed form distribution of the data. This class of estimators is referred to as neural mutual information estimators. Although very promising, such techniques have yet to be rigorously bench-marked so as to establish their efficacy, ease of implementation, and stability for capacity estimation which is joint maximization frame-work. In this paper, we compare the different techniques proposed in the literature for estimating capacity and provide a practitioner perspective on their effectiveness. In particular, we study the performance of mutual information neural estimator (MINE), smoothed mutual information lower-bound estimator (SMILE), and directed information neural estimator (DINE) and provide insights on InfoNCE. We evaluated these algorithms in terms of their ability to learn the input distributions that are capacity-approaching for the AWGN channel, the optical intensity channel, and peak power-constrained AWGN channel. For both scenarios, we provide insightful comments on various aspects of the training process, such as accuracy, stability, and sensitivity to initialization.