Reviewerly/exHarmony huggingface dataset:
Nonmaleficence in Shaming: The Ethical Dilemma Underlying Participation in Online Public Shaming

We show that a decision of potential shamers to take part in (“share” and “retweet”) an online shaming campaign against alleged wrongdoers is shaped by two factors: the potential shamer’s level of adherence to the nonmaleficence principle (i.e., do no harm) and the wrongdoer identifiability (the extent to which a wrongdoer’s details are exposed). Each shaming campaign may promote social norms and prevention of similar harm (i.e., positive consequences) and yet at the same time create harm for the individual wrongdoer being shamed (i.e., negative consequences). We suggest that potential shamers with high adherence to the nonmaleficence principle are more likely to join a shaming campaign with a low‐identifiability wrongdoer compared to potential shamers with a low endorsement of the nonmaleficence principle. We term this phenomenon the Nonmaleficence in Shaming effect. Five studies consistently demonstrate this effect and its attenuation in the case of a shaming campaign with a high‐identifiability wrongdoer. We further show that what drives the effect is advancing the positive over the negative consequences of a shaming campaign. Our findings contribute to a better understanding of norm‐enforcement behavior in digital communications and the social media space.
Children's Perspective on How Emojis Help Them to Recognise Relevant Results

We discuss the exploratory study we conducted to better understand children's ability to recognise relevant results when searching in the classroom. Teachers in two European schools sharing the same language assigned their students (ages 10 and 11) an online information discovery exercise about a history topic covered in class. For this, children used a classic search interface and two novel ones enriched with emojis associated to relevant vs. irrelevant results. At the end of the exercise, children filled out a post-task questionnaire meant to elicit their perception on usability of the interfaces. Guided by four lenses, we analyse our findings and discuss whether (i) emoji-enriched interfaces lead to better performance for children using a search engine in the classroom and (ii) "actions speak louder than words'' when looking at children's search experience. We learned various lessons from our examination of children's search behaviour that will guide the design of future interfaces, including the fact that emoji-enriched interfaces result in significant improvement in terms of children identifying relevant resources.
Application of human motion recognition technology in extreme learning machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.
Public Opinion of Transportation integrated (Jak Lingko), in DKI Jakarta, Indonesia

The Government of DKI Jakarta published transportation integrated program named Jak Lingko to improve public transportation system in Jakarta. This program covers several types of mass transportation in Jakarta such as Mass Rapid Transit (MRT), Light Rail Transit (LRT), Electric Train (KRL Commuter Line), and Bus Transjakarta (TJ). In hope, the people will be interested to switch their modes of transportation to using public transportation considering the congestion and air pollution in Jakarta which is horrible. By utilizing data from twitter, this study tries to analyze public opinion on public transportation by classifying theirs opinions into positive & negative. Furthermore, topic modeling activites will be conducted using the Latent Dirichlet Allocation (LDA) method to find out topics that are often discussed publicly for each public transportation. It was found that the KRL is the only type of public transportation with the number of negative responses exceeding the number of positive responses. It was found that in the MRT & LRT, the community tended to mention words that had positive sentiments, whereas in KRL & TJ people tended to mention words that had negative sentiments. In the grouping of public opinion, the LDA model produced a topic of public discussion on public transportation that was different for each type of public transportation. The model can also capture public communication with the official account administration of public transportation. Prediction models obtained the lowest error with a Mean Average Percentage Error of 8.79%. By adding the public sentiment variable, it can improve the accuracy of the prediction model, but the tweets sentiment variable has no direct relationship to the variable number of public transportation users.
Robust Audio Watermarking Using Graph-based Transform and Singular Value Decomposition

Graph-based Transform (GT) has been recently leveraged successfully in signal processing domain, specifically for compression purposes. In this paper, we employ the GBT, as well as the Singular Value Decomposition (SVD) with the goal to improve the robustness of audio watermarking against different attacks on the audio signals, such as noise and compression. Experimental results on the NOIZEUS speech database and MIR-1k music database clearly certify that the proposed GBT-SVD-based method is robust against the attacks. Moreover, the results exhibit a good quality after the embedding based on PSNR, PESQ and STOI measures. In addition, the payload for the proposed method is 800 and 1600 for speech and music signals, respectively which is higher than some robust watermarking methods such as DWT-SVD and DWT-DCT.
Extending collaborative filtering recommendation using word embedding: A hybrid approach

Summary Collaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems.
NISQ circuit compilation is the travelling salesman problem on a torus

Abstract Noisy, intermediate-scale quantum (NISQ) computers are expected to execute quantum circuits of up to a few hundred qubits. The circuits have to conform to NISQ architectural constraints regarding qubit allocation and the execution of multi-qubit gates. Quantum circuit compilation (QCC) takes a nonconforming circuit and outputs a compatible circuit. Can classical optimisation methods be used for QCC? Compilation is a known combinatorial problem shown to be solvable by two types of operations: (1) qubit allocation, and (2) gate scheduling. We show informally that the two operations form a discrete ring. The search landscape of QCC is a two dimensional discrete torus where vertices represent configurations of how circuit qubits are allocated to NISQ registers. Torus edges are weighted by the cost of scheduling circuit gates. The novelty of our approach uses the fact that a circuit’s gate list is circular: compilation can start from any gate as long as all the gates will be processed, and the compiled circuit has the correct gate order. Our work bridges a theoretical and practical gap between classical circuit design automation and the emerging field of quantum circuit optimisation.
The Benefits and Costs of Cybersecurity Risk Reduction: A Dynamic Extension of the Gordon and Loeb Model

This article develops a dynamic extension of the classic model of cybersecurity investment formulated by Gordon and Loeb. In this dynamic model, results are influenced by the rate at which cybersecurity assets depreciate and the rate of return on investment. Depreciation costs are lower in the dynamic model than is implicitly assumed in the classic model, while the rate-of-return threshold is higher. On balance, the user cost of cybersecurity assets is lower in the dynamic model than is implicitly assumed in the classic model. This difference increases the economically efficient size of the cybersecurity system in value terms, increasing the efficient level of risk reduction.
A Stochastic Logic-Based Fuzzy Logic Controller: First Experimental Results of a Novel Architecture

In stochastic computing (SC) systems numbers are represented with mean values of random binary sequences.This paper introduces a novel fuzzy inference architecture, in which the computational mechanism is based on stochastic logic (SL).First, the basic concept of SL is described, then the architecture of the SL-based fuzzy logic controller (SFLC) is built up systematically using the derived stochastic elements.The second part of the paper demonstrates the application of the proposed techniques, where the SFLC-based control performance is evaluated on a real mechatronic system.The results show that the SL-based approach provides effective and robust control performance, simple architecture and high noise tolerance.The proposed method is also benchmarked against conventional FLCs indicating that the robustness of the stochastic architecture allowed to outperform the benchmark controllers in noisy environments.
Solar-city plans with large-scale energy storage: Metrics to assess the ability to replace fossil-fuel based power

It’s important to have objective metrics for assessing the value of solar-city plans in terms of their ability to avoid fossil-fuel based generation. The need is further augmented by the prospect of integration of large-scale energy storage options with the solar-city plans. This paper addresses this need by defining a novel metric, energy-credit, along with the established metric, peak capacity-credit, and its slightly modified variant, the 90th percentile of peak capacity-credit (90PCC). The methodology has been illustrated by application to a solar-city plan for Mumbai city in India, along with two custom energy storage scenarios, ES20 and ES40. The ES20 scenario corresponds to shifting 20% of the solar-city generation during the slot 12:00 hrs – 15:00 hrs to the slot 18:00 hrs – 21:00 hrs; for the ES40 scenario, the corresponding value is 40%. The results show an energy-credit of 29.7% and 21.2% for P50 and P5 levels of solar-city generation, respectively. The metric 90PCC is found to vary from 12.78% for base case to 15.05% for ES20 to 15.45% for ES40, for P5 level of solar-city generation. The corresponding values of 90PCC for P50 level of solar-city generation are 13.72%, 18.82% and 22.60%, respectively.
Minimizing GPU Kernel Launch Overhead in Deep Learning Inference on Mobile GPUs

The need for on-device real-time Deep Learning inference is increasing as deep learning on edge devices such as smartphones and robots are becoming popular. Although hardware acceleration on NPU is attracting more attention, the recent mobile GPUs are fast enough to provide the potential to achieve real-time inference of many CNNs. In this paper, we first analyze the inference time of the widely used CNNs on the recent mobile GPUs and reveal that significant overhead exists for the GPU kernel launches. Then, we identify various factors that cause the kernel launch overhead, from which we formulate a performance model that can predict the optimal period for the kernel flush that can lead to the minimal overhead. Our experimental results show that we could achieve up to 64% and 31% of speedups in the inference of various CNNs with TensorFlow Lite and ARM Compute Library on Adreno 650 GPU and Mali G76 GPU.
Personalized Movie Recommendation Method Based on Deep Learning

With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.
Review of Target Detection Technology based on Deep Learning

Target detection is one of the most important contents in computer vision, which has been widely and effectively applied in production, daily life, and military. The target detection technology based on deep learning has gone far beyond the traditional target detection technology with high autonomy, accuracy and sensitivity. Based on convolutional neural network, it mainly develops two-stage detection algorithm and one-stage detection algorithm. The two-stage detection algorithms mainly include RCNN, SPP-Net, Fast-RCNN, Faster-RCNN, etc., and the one-stage detection algorithms mainly include Yolo, SSD, RetinaNet, etc. Deep learning framework is an important tool to implements target detection algorithm. Current mainstream deep learning frameworks include PyTorch, TensorFlow, PaddlePaddle, keras, etc.
Learning for Unconstrained Space-Time Video Super-Resolution

Recent years have seen considerable research activities devoted to video enhancement that simultaneously increases temporal frame rate and spatial resolution. However, the existing methods either fail to explore the intrinsic relationship between temporal and spatial information or lack flexibility in the choice of final temporal/spatial resolution. In this work, we propose an unconstrained space-time video super-resolution network, which can effectively exploit space-time correlation to boost performance. Moreover, it has complete freedom in adjusting the temporal frame rate and spatial resolution through the use of the optical flow technique and a generalized pixelshuffle operation. Our extensive experiments demonstrate that the proposed method not only outperforms the state-of-the-art, but also requires far fewer parameters and less running time.
New families of self-dual codes

Recently, the author has constructed families of MDS Euclidean self-dual codes from genus zero algebraic geometry (AG) codes. In the present correspondence, more families of optimal Euclidean self-dual codes from AG codes are explored. New families of MDS Euclidean self-dual codes of odd characteristic and those of almost MDS Euclidean self-dual codes are constructed explicitly from genus zero and genus one curves, respectively. More families of Euclidean self-dual codes are constructed from algebraic curves of higher genus.
Activity Recognition and Anomaly Detection in E-Health Applications Using Color-Coded Representation and Lightweight CNN Architectures

E-Health is becoming a vital industry and human activity recognition (HAR) is one of the most popular research areas of its scope. Although there are various studies on HAR, most of them come up with complex models that are not compatible with portable and wearable devices due to their limited computing capabilities. In this study, a new approach to data representation is presented with convolutional neural network architectures for high accuracy and lightweight activity detection. An anomaly detection framework is presented, which uses ECG data for the prediction of cardiac stress activities. The novel approach to data representation and the proposed deep learning model are tested on the MHEALTH dataset with two different validation techniques for accuracy and three different complexity metrics. The experimental results show that the proposed approaches can achieve up to 96.92% and 97.06% accuracy for the HAR and cardiac stress level, respectively. In addition, the models proposed for inertial data and ECG-based prediction are lighter than the existing approaches in the literature with sizes of 0.89 MB and 1.97 MB and complexities of 0.06 and 1.04 Giga FLOPS, respectively.
Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression

We develop an online probabilistic metric-semantic mapping approach for autonomous robots relying on streaming RGB-D observations. We cast this problem as a Bayesian inference task, requiring encoding both the geometric surfaces and semantic labels (e.g., chair, table, wall) of the unknown environment. We propose an online Gaussian Process (GP) training and inference approach, which avoids the complexity of GP classification by regressing a truncated signed distance function representation of the regions occupied by different semantic classes. Online regression is enabled through sparse GP approximation, compressing the training data to a finite set of inducing points, and through spatial domain partitioning into an Octree data structure with overlapping leaves. Our experiments demonstrate the effectiveness of this technique for large-scale probabilistic metric-semantic mapping of 3D environments. A distinguishing feature of our approach is that the generated maps contain full continuous distributional information about the geometric surfaces and semantic labels, making them appropriate for uncertainty-aware planning.
Flight Control of Sliding Arm Quadcopter with Dynamic Structural Parameters

The conceptual design and flight controller of a novel kind of quadcopter are presented. This design is capable of morphing the shape of the UAV during flight to achieve position and attitude control. We consider a dynamic center of gravity (CoG) which causes continuous variation in a moment of inertia (MoI) parameters of the UAV. These dynamic structural parameters play a vital role in the stability and control of the system. The length of quadcopter arms is a variable parameter, and it is actuated using attitude feedback-based control law. The MoI parameters are computed in real-time and incorporated in the equations of motion of the system. The UAV utilizes the angular motion of propellers and variable quadcopter arm lengths for position and navigation control. The movement space of the CoG is a design parameter and it is bounded by actuator limitations and stability requirements of the system. A detailed information on equations of motion, flight controller design and possible applications of this system are provided. Further, the proposed shape-changing UAV system is evaluated by comparative numerical simulations for way point navigation mission and complex trajectory tracking.
Modular control under privacy protection: Fundamental trade-offs

In privacy-preserving controller design, there is usually a trade-off between the privacy level and control performances, and we show in this paper that this trade-off in particular determines a lower bound on the differential privacy level of the closed-loop system. The control task we consider is reference tracking in a plug-and-play setting, and the plant under control is a networked system of modules, each of which has no access to the models of the others. For a module, we first identify the whole set of tracking local controllers based on the Youla parametrization. At the same time, each module, to protect its own privacy, tries to prevent the other interconnected modules to identify its private information; in this context, for example, the tracking reference signal (say, the target production amount if each module is a workshop in a factory) can be viewed as a piece of private information. Each module can tune the parameters of its local controller to increase the privacy level of its reference signal. However, if the distribution of Laplace (resp. uniform) noise is fixed, the differential privacy level of a Laplace (resp. uniform) mechanism cannot be further improved from a ceiling value no matter how one tunes parameters. In other words, for modular systems under local reference tracking control, there is a lower bound on the differential privacy level.
Probabilistic, Recurrent, Fuzzy Neural Network for Processing Noisy Time-Series Data

The rapidly increasing volumes of data and the need for big data analytics have emphasized the need for algorithms that can accommodate incomplete or noisy data. The concept of recurrency is an important aspect of signal processing, providing greater robustness and accuracy in many situations, such as biological signal processing. Probabilistic fuzzy neural networks (PFNN) have shown potential in dealing with uncertainties associated with both stochastic and nonstochastic noise simultaneously. Previous research work on this topic has addressed either the fuzzy-neural aspects or alternatively the probabilistic aspects, but currently a probabilistic fuzzy neural algorithm with recurrent feedback does not exist. In this article, a PFNN with a recurrent probabilistic generation module (designated PFNN-R) is proposed to enhance and extend the ability of the PFNN to accommodate noisy data. A back-propagation-based mechanism, which is used to shape the distribution of the probabilistic density function of the fuzzy membership, is also developed. The objective of the work was to develop an approach that provides an enhanced capability to accommodate various types of noisy data. We apply the algorithm to a number of benchmark problems and demonstrate through simulation results that the proposed technique incorporating recurrency advances the ability of PFNNs to model time-series data with high intensity, random noise.