Reviewerly/exHarmony huggingface dataset:
Counting With Focus for Free

This paper aims to count arbitrary objects in images. The leading counting approaches start from point annotations per object from which they construct density maps. Then, their training objective transforms input images to density maps through deep convolutional networks. We posit that the point annotations serve more supervision purposes than just constructing density maps. We introduce ways to repurpose the points for free. First, we propose supervised focus from segmentation, where points are converted into binary maps. The binary maps are combined with a network branch and accompanying loss function to focus on areas of interest. Second, we propose supervised focus from global density, where the ratio of point annotations to image pixels is used in another branch to regularize the overall density estimation. To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations. Experiments on six datasets show that all our contributions reduce the counting error, regardless of the base network, resulting in state-of-the-art accuracy using only a single network. Finally, we are the first to count on WIDER FACE, allowing us to show the benefits of our approach in handling varying object scales and crowding levels. Code is available at https://github.com/shizenglin/Counting-with-Focus-for-Free.
Does Transparency in Moderation Really Matter?

When posts are removed on a social media platform, users may or may not receive an explanation. What kinds of explanations are provided? Do those explanations matter? Using a sample of 32 million Reddit posts, we characterize the removal explanations that are provided to Redditors, and link them to measures of subsequent user behaviors---including future post submissions and future post removals. Adopting a topic modeling approach, we show that removal explanations often provide information that educate users about the social norms of the community, thereby (theoretically) preparing them to become a productive member. We build regression models that show evidence of removal explanations playing a role in future user activity. Most importantly, we show that offering explanations for content moderation reduces the odds of future post removals. Additionally, explanations provided by human moderators did not have a significant advantage over explanations provided by bots for reducing future post removals. We propose design solutions that can promote the efficient use of explanation mechanisms, reflecting on how automated moderation tools can contribute to this space. Overall, our findings suggest that removal explanations may be under-utilized in moderation practices, and it is potentially worthwhile for community managers to invest time and resources into providing them.
(((They))) rule: Memetic antagonism and nebulous othering on 4chan

Previously theorised as vehicles for expressing progressive dissent, this article considers how political memes have become entangled in the recent reactionary turn of Web subcultures. Drawing on Chantal Mouffe’s work on political affect, this article examines how online anonymous communities use memetic literacy, memetic abstraction, and memetic antagonism to constitute themselves as political collectives. Specifically, it focuses on how the subcultural and highly reactionary milieu of 4chan’s /pol/ board does so through an anti-Semitic meme called triple parentheses. In aggregating the contents of this peculiar meme from a large dataset of /pol/ comments, the article finds that /pol/ users, or anons, tend to use the meme to formulate a nebulous out-group resonant with populist demagoguery.
Perceiving a Mind in a Chatbot: Effect of Mind Perception and Social Cues on Co-presence, Closeness, and Intention to Use

A chatbot equipped with a conversational user interface often allows its users to feel as if they are conversing with a human being. The current study examined whether users’ perception of a mind within a chatbot is associated with their feeling of co-presence, closeness, and intention to use and whether the influence of mind perception is reinforced when the chatbot presents social cues in its language. A laboratory experiment (N = 64) revealed that the more participants perceived a mind behind a chatbot, the more co-presence and interpersonal closeness they experienced with the chatbot. The associations with co-presence and closeness became stronger when the chatbot used social cues. Furthermore, mind perception had an indirect effect on intention to use via closeness when social cues were presented. These findings imply the importance of mind perception and social cues in a chatbot’s language in creating a positive chatbot experience.
Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles

The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.
Fractional discrete Tchebyshev moments and their applications in image encryption and watermarking

Discrete Tchebyshev moments (DTMs), as a kind of typical discrete orthogonal moments, have been widely used in image analysis. However, the order of DTMs is restricted to an integer, and the fractional versions of DTMs have not been investigated. A novel framework for deriving fractional order DTMs (FrDTMs) by the eigen-decomposition of kernel matrices is proposed in this paper, and some properties of the proposed FrDTMs are analyzed. Two applications, i.e., image encryption and image watermarking, are investigated to validate the superiorities of the proposed FrDTMs.
Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification

Existing person re-identification has achieved great progress in the visible domain, capturing all the person images with visible cameras. However, in a 24-hour intelligent surveillance system, the visible cameras may be noneffective at night. In this situation, thermal cameras are the best supplemental components, which capture images without depending on visible light. Therefore, in this paper, we investigate the visible-thermal cross-modality person re-identification (VT Re-ID) problem. In VT Re-ID, there are two knotty problems should be well handled, cross-modality discrepancy and intra-modality variations. To address these two issues, we propose focusing on enhancing the discriminative feature learning (EDFL) with two extremely simple means from two core aspects, (1) skip-connection for mid-level features incorporation to improve the person features with more discriminability and robustness, and (2) dual-modality triplet loss to guide the training procedures by simultaneously considering the cross-modality discrepancy and intra-modality variations. Additionally, the two-stream CNN structure is adopted to learn the multi-modality sharable person features. The experimental results on two datasets show that our proposed EDFL approach distinctly outperforms state-of-the-art methods by large margins, demonstrating the effectiveness of our EDFL to enhance the discriminative feature learning for VT Re-ID.
Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.
Human staff vs. service robot vs. fellow customer: Does it matter who helps your customer following a service failure incident?

The key to excellent service delivery is to ensure harmonious interactions between service actors. Therefore, in the event of service failure incidents, an understanding of the roles and interactions of these service actors is critical to achieving positive service outcomes. This research addresses the question: How do interactions between customers and these service actors (human staff/automated technological interface/fellow customer) influence their service experience following a service failure? We draw on role theory and answer this question via three experimental studies. Findings show that customers evaluate their service experience less favourably when receiving service recovery from fellow customers rather than firms (human staff and service robots). Furthermore, a firm’s instrumental recovery, rather than informational recovery, leads to a more favourable service evaluation, although this effect is absent when service recovery was given by fellow customers. Findings provide insights into the role of each actor in determining customer service evaluation.
Zero-noise extrapolation for quantum-gate error mitigation with identity insertions

In addition to readout errors, two-qubit gate noise is the main challenge for complex quantum algorithms on noisy intermediate-scale quantum (NISQ) computers. These errors are a significant challenge for making accurate calculations for quantum chemistry, nuclear physics, high energy physics, and other emerging scientific and industrial applications. There are two proposals for mitigating two-qubit gate errors: error-correcting codes and zero-noise extrapolation. This paper focuses on the latter, studying it in detail and proposing modifications to existing approaches. In particular, we propose a random identity insertion method (RIIM) that can achieve competitive asymptotic accuracy with far fewer gates than the traditional fixed identity insertion method (FIIM). For example, correcting the leading order depolarizing gate noise requires $n_\text{CNOT}+2$ gates for RIIM instead of $3n_\text{CNOT}$ gates for FIIM. This significant resource saving may enable more accurate results for state-of-the-art calculations on near term quantum hardware.
PSI from PaXoS: Fast, Malicious Private Set Intersection

We present a 2-party private set intersection (PSI) protocol which provides security against malicious participants, yet is almost as fast as the fastest known semi-honest PSI protocol of Kolesnikov et al. (CCS 2016). Our protocol is based on a new approach for two-party PSI, which can be instantiated to provide security against either malicious or semi-honest adversaries. The protocol is unique in that the only difference between the semi-honest and malicious versions is an instantiation with different parameters for a linear error-correction code. It is also the first PSI protocol which is concretely efficient while having linear communication and security against malicious adversaries, while running in the OT-hybrid model (assuming a non-programmable random oracle). State of the art semi-honest PSI protocols take advantage of cuckoo hashing, but it has proven a challenge to use cuckoo hashing for malicious security. Our protocol is the first to use cuckoo hashing for malicious-secure PSI. We do so via a new data structure, called a probe-and-XOR of strings ( $$\mathsf{PaXoS}$$ ), which may be of independent interest. This abstraction captures important properties of previous data structures, most notably garbled Bloom filters. While an encoding by a garbled Bloom filter is larger by a factor of $$\varOmega (\lambda )$$ than the original data, we describe a significantly improved $$\mathsf{PaXoS}$$ based on cuckoo hashing that achieves constant rate while being no worse in other relevant efficiency measures.
Blockchain energy: Blockchain in future energy systems

The ongoing, in-depth transformation of the electricity sector towards increased use of alternative, renewable energy sources extends beyond a simple decentralisation drive in the electricity market. The transformation process is characterised by the interplay of old and new technologies from the energy sector as well as structural coupling with other sectors, such as the information and communications technology (ICT), enabling the technology transfer as well as market entry by information technology (IT) actors. Blockchain-based technologies have the potential to play a key role in this transition by offering decentralised interfaces and systems as well as an alternative approach to the current organisation form of the energy market. This paper discusses the applicability and prospects for blockchain-based technologies in the energy sector, which are described using the term “blockchain energy”. For the purposes of this study, blockchain energy encompasses all socio-technical and organisational configurations in the energy sector based on the utilisation of the blockchain principle for energy trading, information storage, and/or increased transparency of energy flows and energy services. In the following chapters, the authors present and discuss the current transformation in the electricity market, followed by a review of the different utilisation possibilities for blockchain technologies in the energy sector and a discussion of the barriers and potential for blockchain energy using a transition studies perspective. Finally, the opportunities and risks of blockchain energy are discussed.
CN-Celeb: A Challenging Chinese Speaker Recognition Dataset

Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. However, most publicly available datasets are collected under constrained environments, i.e., with little noise and limited channel variation. These datasets tend to deliver over-optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.In this paper, we present CN-Celeb, a large-scale speaker recognition dataset collected ‘in the wild’. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world. Experiments conducted with two state-of-the-art speaker recognition approaches (i-vector and x-vector) show that the performance on CN-Celeb is far inferior to the one obtained on Vox-Celeb, a widely used speaker recognition dataset. This result demonstrates that in real-life conditions, the performance of existing techniques might be much worse than it was thought. Our database is free for researchers and can be downloaded from http://project.cslt.org.
Street Scene: A new dataset and evaluation protocol for video anomaly detection

Progress in video anomaly detection research is currently slowed by small datasets that lack a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move this research effort forward by introducing a large and varied new dataset called Street Scene, as well as two new evaluation criteria that provide a better estimate of how an algorithm will perform in practice. In addition to the new dataset and evaluation criteria, we present two variations of a novel baseline video anomaly detection algorithm and show they are much more accurate on Street Scene than two well known algorithms from the literature.
Identifying high potential zones of gold mineralization in a sub-tropical region using Landsat-8 and ASTER remote sensing data: A case study of the Ngoura-Colomines goldfield, eastern Cameroon

Climatic conditions and vegetation constrain the use of optical satellite imagery as an exploration tool for hydrothermal ore mineralization in tropical and subtropical regions. In this investigation, Landsat-8 and ASTER satellite imagery were used to detect hydrothermal alteration zones associated with gold mineralization in the Ngoura-Colomines region, Eastern Cameroon. The study area contains several gold-bearing quartz veins associated with zones of pyritization, muscovite/sericite, iron oxides, and silicification. Principal Component Analysis (PCA), Independent Component Analysis (ICA), and specialized spectral band ratios were used to extract spectral information related to vegetation, iron oxide/hydroxide minerals, Al–OH, Fe-Mg–OH, carbonate group minerals, and silicification using Landsat-8 data at regional scale. Linear Spectral Unmixing (LSU) algorithm was implemented to ASTER VNIR + SWIR bands for detailed discrimination of hematite, jarosite, kaolinite, muscovite, chlorite and epidote at district scale. The Automated Spectral Hourglass (ASH) technique was employed to extract reference spectra directly from the ASTER bands for producing fraction images of end-members using the LSU. A comprehensive field survey was used to verify the remote sensing results. Petrographic study, X-ray diffraction analysis and reflectance spectroscopy indicated the presence of quartz, goethite and sericite, as well as the absorption features of Fe3+/Fe2+, Al–OH, OH/H2O and SiO2 in the alteration zones. Several hydrothermal alteration zones of iron oxide/hydroxide, clay, carbonate minerals and silicification zones were identified, which are spatially associated with known mining areas and gold occurrences in the study area. High potential prospects were also delineated, including the Ngoura-Colomines prospects and the newly discovered Yangamo-Ndatanga and Taparé-Tapondo prospects in the southwestern and southeastern parts of the study area. Consequently, satellite-based mineral prospectivity maps at regional and district scales were generated for the study area by implementing the fuzzy logic model to the most informative thematic layers derived from image processing results. The satellite-based prospectivity maps are reliable for exploration of new gold prospective zones in the Ngoura-Colomines goldfield.
Hybrid Autoregressive Transducer (HAT)

This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoder-decoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the quality of the internal language model that can be used to decide whether inference with an external language model is beneficial or not. We evaluate our proposed model on a large-scale voice search task. Our experiments show significant improvements in WER compared to the state-of-the-art approaches <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Deep Learning Based Multistep Solar Forecasting for PV Ramp-Rate Control Using Sky Images

Solar forecasting is one of the most promising approaches to address the intermittent photovoltaic (PV) power generation by providing predictions before upcoming ramp events. In this article, a novel multistep forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time series models (e.g., recurrent neural network (RNN) or long short-term memory) and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting-based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach.
XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum

Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permission-less blockchains. The analysis of massive blockchain data can bring huge business values. However, the absence of well-processed up-to-date blockchain datasets impedes big data analytics of blockchain data. To fill this gap, we collect and process the up-to-date on-chain data from Ethereum, which is one of the most popular permission-less blockchains. We name such well-processed Ethereum data as XBlock-ETH, which consists of transactions, smart contracts, and cryptocurrencies (i.e., tokens). However, it is non-trivial to partition and categorize the collected raw Ethereum data to the well-processed datasets since the whole processing procedure requires sophisticated knowledge on software engineering as well as big data analytics. Moreover, we also present basic statistics and exploration for each of the well-processed datasets. Furthermore, we also outline the possible research opportunities based on XBlock-ETH, with the data and code released online.
An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values ​​in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.