Reviewerly/exHarmony huggingface dataset:
Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems

The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.
"I run the world's largest historical outreach project and it's on a cesspool of a website." Moderating a Public Scholarship Site on Reddit: A Case Study of r/AskHistorians

Online communities provide important functions in their participants' lives, from providing spaces to discuss topics of interest to supporting the development of close, personal relationships. Volunteer moderators play key roles in maintaining these spaces, such as creating and enforcing rules and modeling normative behavior. While these users play important governance roles in online spaces, less is known about how the work they do is impacted by platform design and culture. r/AskHistorians, a Reddit-based question and answer forum dedicated to providing users with academic-level answers to questions about history, provides an interesting case study on the impact of design and culture because of its unique rules and their strict enforcement by moderators. In this article I use interviews with r/AskHistorians moderators and community members, observation, and the full comment log of a highly upvoted thread to describe the impact of Reddit's design and culture on moderation work. Results show that visible moderation work that is often interpreted as censorship, and the default masculine whiteness of Reddit create challenges for moderators who use the subreddit as a public history site. Nonetheless, r/AskHistorians moderators have carved a space on Reddit where, through their public scholarship work, the community serves as a model for combating misinformation by building trust in academic processes.
Leveraging Filter Correlations for Deep Model Compression

We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair naïvely, the model is re-optimized to make the filters in these pairs maximally correlated, so that discarding one of the filters from the pair results in minimal information loss. Moreover, after discarding the filters in each round, we further finetune the model to recover from the potential small loss incurred by the compression. We evaluate our proposed approach using a comprehensive set of experiments and ablation studies. Our compression method yields state-of-the-art FLOPs compression rates on various benchmarks, such as LeNet-5, VGG-16, and ResNet-50,56, while still achieving excellent predictive performance for tasks such as object detection on benchmark datasets.
The Lao Text Classification Method Based on KNN

Text categorization is a common application scenario in the NLP field, and has many applications in public opinion monitoring and news classification. At present, there are few classifications for Lao text, but classification-oriented methods are widely used in other languages. Faced with the situation, this paper proposes a KNN-based classification method of Lao news text. First of all, preprocessing and feature extraction of Lao news text, then adjusting the parameters through KNN classifier, finally processing in data normalization and data dimensionality reduction, thus improving the classification effect.
Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method.
Detection of malicious social bots: A survey and a refined taxonomy

Social bots represent a new generation of bots that make use of online social networks (OSNs) as command and control (C&C) channels. Malicious social bots have been used as tools for launching large-scale spam campaigns, promoting low-cap stocks, manipulating users’ digital influence, and conducting political astroturfing. Recent studies in this area either focus only on general security issues related to social networks or on coarse-grained categorization to support detection approaches. This survey aims to provide a comprehensive analysis from a social network perspective. To this end, we first categorize social bot attacks at different stages, then provide an overview of different types of social bots. Next, we propose a refined taxonomy that shows how different techniques within a category are related or differ from each other, followed by a detailed discussion of the strengths and limitations of each method. Following this, we review the existing datasets and summarize the results of empirical investigations. Finally, we highlight the limitations of existing detection approaches and suggest future directions for further improvement. Our study should help OSN administrators and researchers understand the destructive potential of malicious social bots and improve upon the current defensive strategies.
Quantum Communications in Future Networks and Services

Over the last few years, we have witnessed an impressive growth of data traffic and a progressive Digital Transformation of Industry and Society: the deployment of the ultra-broadband and low latency network infrastructures (e.g., 5G) are leading to a global digitalization of several domains. These techno-economic trends are expected to continue and even accelerate in the next decade, at end of which, 6G and smart networks and services will be exploited. Innovation will continue to drive the global economy into the next decade. This paper draws some technology trends and applications scenarios for this horizon, where Quantum Optical Communications are likely to disrupt Information and Communications Technology (ICT) and Telecommunications. Among the enabling technologies and solutions moving in this direction, this paper briefly addresses: quantum optical switching and computing, THz-to-optical conversions and advanced metamaterials for smart radio-optical programmable environments and Artificial Intelligence. The paper concludes with the description of a future application scenario, called Quantum Optical Twin, where the above Quantum Optical Communications technologies are exploited to provide services such as: ultra-massive scale communications for connected spaces and ambient intelligence, holographic telepresence, tactile Internet, new paradigms of brain computer interactions, innovative forms of communications.
Binary atom search optimisation approaches for feature selection

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Flexible scheme for the implementation of nonadiabatic geometric quantum computation

In this paper, a flexible and effective scheme is proposed to realize nonadiabatic geometric quantum gates with the invariant based reverse engineering and the nonadiabatic holonomic quantum computation ($\mathrm{NHQC}+$) presented in recent work [B.-J. Liu et al., Phys. Rev. Lett. 123, 100501 (2019)] for extensible geometric quantum computation. The scheme provides variabilities for most of control parameters, and can build up multiple evolution paths with different geometric phases acquired in a cycling evolution. As the computational basis can be covered by the evolution paths, the realization of the nonadiabatic geometric quantum computation is possible without auxiliary levels. Moreover, multiple types of nonadiabatic geometric quantum gates can also be easily constructed by only adjusting boundary conditions of control parameters with the method. To show the applications of the scheme, we discuss the implementations of nonadiabatic geometric quantum gates of spin qubits in a double-quantum-dot system with numerical simulations. The results indicate that the scheme possesses robustness against the errors and noise. Therefore, the scheme may offer some interesting perspectives for the realization of the nonadiabatic geometric quantum computation.
Joint Learning of Geometric and Probabilistic Constellation Shaping

The choice of constellations largely affects the performance of communication systems. When designing constellations, both the locations and probability of occurrence of the points can be optimized. These approaches are referred to as geometric and probabilistic shaping, respectively. Usually, the geometry of the constellation is fixed, e.g., quadrature amplitude modulation (QAM) is used. In such cases, the achievable information rate can still be improved by probabilistic shaping. In this work, we show how autoencoders can be leveraged to perform probabilistic shaping of constellations. We devise an information theoretical description of autoencoders, which allows learning of capacity-achieving symbol distributions and constellations. Recently, machine learning techniques to perform geometric shaping were proposed. However, probabilistic shaping is more challenging as it requires the optimization of discrete distributions. Furthermore, the proposed method enables joint probabilistic and geometric shaping of constellations over any channel model. Simulation results show that the learned constellations achieve information rates very close to capacity on an additive white Gaussian noise (AWGN) channel and outperform existing approaches on both AWGN and fading channels.
SPRNet: Single-Pixel Reconstruction for One-Stage Instance Segmentation

Object instance segmentation is one of the most fundamental but challenging tasks in computer vision, and it requires the pixel-level image understanding. Most existing approaches address this problem by adding a mask prediction branch to a two-stage object detector with the region proposal network (RPN). Although producing good segmentation results, the efficiency of these two-stage approaches is far from satisfactory, restricting their applicability in practice. In this article, we propose a one-stage framework, single-pixel reconstruction net (SPRNet), which performs efficient instance segmentation by introducing a single-pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors. The added SPR branch reconstructs the pixel-level mask from every single pixel in the convolution feature map directly. Using the same ResNet-50 backbone, SPRNet achieves comparable mask AP with Mask R-CNN at a higher inference speed and gains all-round improvements on box AP at every scale compared with RetinaNet.
Facial Expression Recognition with Convolutional Neural Networks

Emotions are a powerful tool in communication and one way that humans show their emotions is through their facial expressions. One of the challenging and powerful tasks in social communications is facial expression recognition, as in non-verbal communication, facial expressions are key. In the field of Artificial Intelligence, Facial Expression Recognition (FER) is an active research area, with several recent studies using Convolutional Neural Networks (CNNs). In this paper, we demonstrate the classification of FER based on static images, using CNNs, without requiring any pre-processing or feature extraction tasks. The paper also illustrates techniques to improve future accuracy in this area by using pre-processing, which includes face detection and illumination correction. Feature extraction is used to extract the most prominent parts of the face, including the jaw, mouth, eyes, nose, and eyebrows. Furthermore, we also discuss the literature review and present our CNN architecture, and the challenges of using max-pooling and dropout, which eventually aided in better performance. We obtained a test accuracy of 61.7% on FER2013 in a seven-classes classification task compared to 75.2% in state-of-the-art classification.
mmASL

Home assistant devices such as Amazon Echo and Google Home have become tremendously popular in the last couple of years. However, due to their voice-controlled functionality, these devices are not accessible to Deaf and Hard-of-Hearing (DHH) people. Given that over half a million people in the United States communicate using American Sign Language (ASL), there is a need of a home assistant system that can recognize ASL. The objective of this work is to design a home assistant system for DHH users (referred to as mmASL) that can perform ASL recognition using 60 GHz millimeter-wave wireless signals. mmASL has two important components. First, it can perform reliable wake-word detection using spatial spectrograms. Second, using a scalable and extensible multi-task deep learning model, mmASL can learn the phonological properties of ASL signs and use them to accurately recognize the ASL signs. We implement mmASL on 60 GHz software radio platform with phased array, and evaluate it using a large-scale data collection from 15 signers, 50 ASL signs and over 12K sign instances. We show that mmASL is tolerant to the presence of other interfering users and their activities, change of environment and different user positions. We compare mmASL with a well-studied Kinect and RGB camera based ASL recognition systems, and find that it can achieve a comparable performance (87% average accuracy of sign recognition), validating the feasibility of using 60 GHz mmWave system for ASL sign recognition.
Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network

Recently, graph neural networks (GNNs) have been shown to be an effective tool for learning the node representations of the networks and have achieved good performance on the semi-supervised node classification task. However, most existing GNNs methods fail to take networks’ temporal information into account, therefore, cannot be well applied to dynamic network applications such as the continuous-time link prediction task. To address this problem, we propose a Temporal Dependent Graph Neural Network (TDGNN), a simple yet effective dynamic network representation learning framework which incorporates the network temporal information into GNNs. TDGNN introduces a novel Temporal Aggregator (TDAgg) to aggregate the neighbor nodes’ features and edges’ temporal information to obtain the target node representations. Specifically, it assigns the neighbor nodes aggregation weights using an exponential distribution to bias different edges’ temporal information. The performance of the proposed method has been validated on six real-world dynamic network datasets for the continuous-time link prediction task. The experimental results show that the proposed method outperforms several state-of-the-art baselines.
SARA: Secure Asynchronous Remote Attestation for IoT Systems

Remote attestation has emerged as a valuable security mechanism which aims to verify remotely whether or not a potentially untrusted device has been compromised. The protocols of Remote attestation are particularly important for securing Internet of Things (IoT) systems which, due to the large number of interconnected devices and limited security protections, are susceptible to a wide variety of cyber attacks. To guarantee the integrity of a software running on a single device, remote attestation is usually executed as an uninterrupted procedure: at the attestation time, a device stops the normal operation and executes the attestation of the entire device without interruption. The remote attestation protocols that aim to attest a large number of devices also follow the assumption on uninterrupted execution: when a device attests its network neighbours, each device verified in the neighborhood suspends its normal operation until the attestation protocol is completed. To avoid unnecessary suspension of the normal operation of the devices, this paper proposes a novel Secure Asynchronous Remote Attestation (SARA) protocol that releases the constraint of synchronous interaction among devices. In particular, SARA is an attestation protocol that exploits asynchronous communication capabilities among IoT devices in order to attest a distributed IoT service executed by them. SARA verifies both that each IoT device is not compromised (device trustworthiness), and that the exchanged communication data have not maliciously influence the communicating devices (legitimate operations). By tracing the execution order of each service invocation of an asynchronous distributed service, SARA allows each service to collect accurately historical data of its interactions, and transmits asynchronously such historical data to other interacting services. We have implemented and validated SARA through a realistic simulation on the Contiki emulator that demonstrates the functionality and efficiency of our protocol. The results confirm the suitability of SARA for low-end devices.
Improved K2 algorithm for Bayesian network structure learning

In this paper, we study the problem of learning the structure of Bayesian networks from data, which takes a dataset and outputs a directed acyclic graph. This problem is known to be NP-hard. Almost most of the existing algorithms for structure learning can be classified into three categories: constraint-based, score-based, and hybrid methods. The K2 algorithm, as a score-based algorithm, takes a random order of variables as input and its efficiency is strongly dependent on this ordering. Incorrect order of variables can lead to learning an incorrect structure. Therefore, the main challenge of this algorithm is strongly dependency of output quality on the initial order of variables. The main contribution of this paper is to derive a significant order of variables from the given dataset. Also, one of the significant challenges of structure learning is to find a practical structure learning approach to learn an optimal structure from complex and high-dimensional datasets in a reasonable time. We propose a new fast and straightforward algorithm for addressing this problem in a reasonable time. The proposed algorithm is based on an ordering by extracting strongly connected components of the graph built from data. We reduce the super-exponential search space of structures to the smaller space of nodes ordering. We evaluated the proposed algorithm using some standard benchmark datasets and compare the results with the results obtained from some state of the art algorithms. Finally, we show that the proposed algorithm is competitive with some algorithms for structure learning.
Keyphrase Extraction as Sequence Labeling Using Contextualized Embeddings

In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architecture using both contextualized and fixed word embedding models on three different benchmark datasets, and compare with existing popular unsupervised and supervised techniques. Our results quantify the benefits of: (a) using contextualized embeddings over fixed word embeddings; (b) using a BiLSTM-CRF architecture with contextualized word embeddings over fine-tuning the contextualized embedding model directly; and (c) using domain-specific contextualized embeddings (SciBERT). Through error analysis, we also provide some insights into why particular models work better than the others. Lastly, we present a case study where we analyze different self-attention layers of the two best models (BERT and SciBERT) to better understand their predictions.
SSTNet: Detecting Manipulated Faces Through Spatial, Steganalysis and Temporal Features

Compared to conventional object detection which focuses on high-level image content, face manipulation detection pays more attention to low-level artifacts and temporal discrepancies. However, there are few methods considering both of these two characteristics. In this work, we propose a novel manipulation detection framework, named SSTNet, which detects tampered faces through Spatial, Steganalysis and Temporal features. Spatial features are extracted by a deep neural network for finding visible tampering traces like unnatural color, shape and texture. We propose a constraint on convolutional filters to extract steganalysis features for detecting hidden tampering artifacts like abnormal statistical characteristics of image pixels. Temporal features are extracted by a recurrent network for discovering inconsistency between consecutive frames. Experimental results on Face-Forensics++ dataset demonstrate that SSTNet outperforms other methods and achieves state-of-the-art performance on accuracy and robustness to compression. Furthermore, the generalization capability of SSTNet is verified on the GAN-based DeepFakes dataset.