Reviewerly/exHarmony huggingface dataset:
Content Analyses of User Comments in Journalism: A Systematic Literature Review Spanning Communication Studies and Computer Science

Different disciplines have studied the content of online user comments in various contexts, using manual qualitative/quantitative or (semi-)automated approaches. The broad spectrum and disciplinary divides make it difficult to grasp an overview of those aspects which have already been examined, e.g. to identify findings related to one’s own research, recommendable methodological approaches, and under-researched topics. We introduce a systematic literature review concerning content analyses of user comments in a journalistic context. Our review covers 192 papers identified through a systematic search focussing on communication studies and computer science. We find that research predominantly concentrates on the comment sections of Anglo-American newspaper brands and on aspects like hate speech, general incivility, or users’ opinions on specific issues, while disregarding media from other parts of the world, comments in social media, propaganda, and constructive comments. From our results we derive a research agenda that addresses research gaps and also highlights potentials for automating analyses as well as for cooperation across disciplines.
HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.
Identity-and-pose-guided generative adversarial network for face rotation

Face rotation has been a challenging task due to the variation of head poses, illumination, and occlusions, etc. This work provides a novel Identity-and-Pose-Guided Generative Adversarial Network (IPG-GAN) to generate faces with arbitrary head poses. Besides the proposed architecture, an adversarial head pose estimation loss is newly introduced to make IPG-GAN learn the precise head pose representation, and further improve the face synthesis with the employed adversarial identity classification loss. A dual training strategy is adopted to learn the mutual transformation of identity and head pose representation between two source images in one iteration. Under the supervision of such training, the pose-robust identity representation is learned, and the domain knowledge of the learned representation of identity and head pose is properly distinguished. Additionally, based on the two synthesis approaches, IPG-GAN proposes two strategies of frontalizing the profile probe faces and rotating the faces to profile views for both recognition and verification, which differs from most previous face rotation methods. Quantitative and qualitative experiments on Multi-PIE, LFW, and CFP databases for face synthesis, recognition, and verification show that the proposed method achieves state-of-the-art performance and yields substantial improvements.
KLL <sup>±</sup> approximate quantile sketches over dynamic datasets

Recently the long standing problem of optimal construction of quantile sketches was resolved by K arnin, L ang, and L iberty using the KLL sketch (FOCS 2016). The algorithm for KLL is restricted to online insert operations and no delete operations. For many real-world applications, it is necessary to support delete operations. When the data set is updated dynamically, i.e., when data elements are inserted and deleted, the quantile sketch should reflect the changes. In this paper, we propose KLL ± , the first quantile approximation algorithm to operate in the bounded deletion model to account for both inserts and deletes in a given data stream. KLL ± extends the functionality of KLL sketches to support arbitrary updates with small space overhead. The space bound for KLL ± is [EQUATION], where ∈ and δ are constants that determine precision and failure probability, and α bounds the number of deletions with respect to insert operations. The experimental evaluation of KLL ± highlights that with minimal space overhead, KLL ± achieves comparable accuracy in quantile approximation to KLL.
ViFin

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.
Together Recognizing, Localizing and Summarizing Actions in Egocentric Videos

Analysis of egocentric video has recently drawn attention of researchers in the computer vision as well as multimedia communities. In this paper, we propose a weakly supervised superpixel level joint framework for localization, recognition and summarization of actions in an egocentric video. We first recognize and localize single as well as multiple action(s) in each frame of an egocentric video and then construct a summary of these detected actions. The superpixel level solution helps in precise localization of actions in addition to improving the recognition accuracy. Superpixels are extracted within the central regions of the egocentric video frames; these central regions being determined through a previously developed center-surround model. A sparse spatio-temporal video representation graph is constructed in the deep feature space with the superpixels as nodes. A weakly supervised solution using random walks yields action labels for each superpixel. After determining action label(s) for each frame from its constituent superpixels, we apply a fractional knapsack type formulation for obtaining a summary (of actions). Experimental comparisons on publicly available ADL, GTEA, EGTEA Gaze+, EgoGesture, and EPIC-Kitchens datasets show the effectiveness of the proposed solution.
Acoustic Word Embeddings for Zero-Resource Languages Using Self-Supervised Contrastive Learning and Multilingual Adaptation

Acoustic word embeddings (AWEs) are fixed-dimensional representations of variable-length speech segments. For zero-resource languages where labelled data is not available, one AWE approach is to use unsupervised autoencoder-based re-current models. Another recent approach is to use multilingual transfer: a supervised AWE model is trained on several well-resourced languages and then applied to an unseen zero-resource language. We consider how a recent contrastive learning loss can be used in both the purely unsupervised and multilingual transfer settings. Firstly, we show that terms from an unsupervised term discovery system can be used for contrastive self-supervision, resulting in improvements over previous unsupervised monolingual AWE models. Secondly, we consider how multilingual AWE models can be adapted to a specific zero-resource language using discovered terms. We find that self-supervised contrastive adaptation outperforms adapted multilingual correspondence autoencoder and Siamese AWE models, giving the best overall results in a word discrimination task on six zero-resource languages.
Detection and Evaluation of Human and Machine Generated Speech in Spoofing Attacks on Automatic Speaker Verification Systems

Automatic speaker verification (ASV) systems utilize the biometric information in human speech to verify the speaker's identity. The techniques used for performing speaker verification are often vulnerable to malicious attacks that attempt to induce the ASV system to return wrong results, allowing an impostor to bypass the system and gain access. Attackers use a multitude of spoofing techniques for this, such as voice conversion, audio replay, speech synthesis, etc. In recent years, easily available tools to generate deepfaked audio have increased the potential threat to ASV systems. In this paper, we compare the potential of human impersonation (voice disguise) based attacks with attacks based on machinegenerated speech, on black-box and white-box ASV systems. We also study countermeasures by using features that capture the unique aspects of human speech production, under the hypothesis that machines cannot emulate many of the finelevel intricacies of the human speech production mechanism. We show that fundamental frequency sequence-related entropy, spectral envelope, and aperiodic parameters are promising candidates for robust detection of deepfaked speech generated by unknown methods.
On The Security of Block Permutation and Co-XOR in Reversible Data Hiding

Block permutation and Co-XOR (BPCX) image encryption is a commonly used encryption method for reversible data hiding in the encryption domain, which can effectively improve the embedded capacity and the ability resisting the existing attacks including ciphertext-only attack and known plaintext attack (KPA). This paper proposes a KPA based on bit-block inversion and mean equivalent division (MED) to estimate the block permutation key of BPCX image encryption. Firstly, we divide an image block into the bit-block and point out that the maximum of the numbers of 0 bit and 1 bit of a bit-block before and after the Co-XOR encryption remains unchanged. And then two inversion rules of bit-block are defined to construct pseudo plain-ciphertext images to eliminate the influence of pixel value changes caused by Co-XOR encryption. Finally, the MED based KPA is designed to estimate the block permutation key sequence according to the pseudo plain-ciphertext images. The relationship between the key estimation accuracy and the number of known plain-ciphertext pairs, block size, and pseudo ciphertext are discussed. Experimental results show that even in the minimum block size (<inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula>), the average estimated correct rate of the block permutation sequence exceeds 40&#x0025;. The block permutation key estimation accuracy is more than 50&#x0025; when the block size is greater than <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>. Some improved encryption methods against the proposed KPA are also given.
Learned Multi-Resolution Variable-Rate Image Compression With Octave-Based Residual Blocks

Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rates, which increase the implementation complexity. In this paper, we propose a new variable-rate image compression framework, which employs generalized octave convolutions (GoConv) and generalized octave transposed-convolutions (GoTConv) with built-in generalized divisive normalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- and GoTConv-based residual blocks are also developed in the encoder and decoder networks. Our scheme also uses a stochastic rounding-based scalar quantization. To further improve the performance, we encode the residual between the input and the reconstructed image from the decoder network as an enhancement layer. To enable a single model to operate with different bit rates and to learn multi-rate image features, a new objective function is introduced. Experimental results show that the proposed framework trained with variable-rate objective function outperforms the standard codecs such as H.265/HEVC-based BPG and state-of-the-art learning-based variable-rate methods.
Neural MOS Prediction for Synthesized Speech Using Multi-Task Learning with Spoofing Detection and Spoofing Type Classification

Several studies have proposed deep-learning-based models to predict the mean opinion score (MOS) of synthesized speech, showing the possibility of replacing human raters. However, inter- and intra-rater variability in MOSs makes it hard to en-sure the high performance of the models. In this paper, we propose a multi-task learning (MTL) method to improve the performance of a MOS prediction model using the following two auxiliary tasks: spoofing detection (SD) and spoofing type classification (STC). Besides, we use the focal loss to maximize the synergy between SD and STC for MOS pre-diction. Experiments using the MOS evaluation results of the Voice Conversion Challenge 2018 show that proposed MTL with two auxiliary tasks improves MOS prediction. Our proposed model achieves up to 11.6% relative improvement in performance over the baseline model.
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, integrating Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obstacle avoidance with conventional global planning methods using waypoint generation. Therefore, we integrate different waypoint generators into existing navigation systems and compare the joint system against traditional ones. We found an increased performance in terms of safety, efficiency and path smoothness, especially in highly dynamic environments.
An attention enhanced sentence feature network for subtitle extraction and summarization

An automatic subtitle summarization of videos not only aims to tackle the problem of content overloading but can also improve the performance of video retrieval, allowing viewers to efficiently access and understand the main content of a video. However, subtitle summarization is a challenging task due to documents being composed of incomplete sentences, meaningless phrases, and informal language. In this paper, we introduce a novel multiple attention mechanism for subtitle summarization to address such issues. We take advantage of both Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) Networks to capture the critical information of the sentence that is used to identify the importance of the sentence. Based on the salient sentence score, we introduce the summary generation method to produce a summary of the video. The experiments are conducted on both subtitle documents from educational videos and text documents. To the best of our knowledge, no previous studies have applied multiple-attention mechanisms for summarizing educational videos. Besides, we experiment on two well-known text document datasets, DUC2002, and CNN/Daily Mail, to test the performance of our model. We utilize ROUGE measures for evaluating the generated summaries at 95% confidence intervals. The experimental results demonstrated that our model outperforms the baseline and state-of-the-art models on the ROUGE-1, ROUGE-2, and ROUGE-L scores.
Auriferous pyrite formed by episodic fluid inputs in the Akeshi and Kasuga high-sulfidation deposits, Southern Kyushu, Japan

Abstract Pyrite geochemistry has proven useful for tracking changes in the composition and physico-chemical conditions of hydrothermal fluids in ore-forming environments. Here, we investigated the microtextural features and chemical composition of pyrite, a main Au-bearing phase in the Akeshi and Kasuga deposits (Southern Kyushu, Japan), to better constrain the ore-forming processes in these high-sulfidation epithermal Au deposits. Despite the widespread distribution of Au-bearing pyrite in both deposits, no visible Au minerals coexist with pyrite. However, in situ laser ablation inductively coupled plasma mass spectrometry results show that Au concentrations in pyrite vary from below the detection limit to 41 ppm and are positively correlated with Cu ( r = 0.4; up to 7400 ppm) and Bi concentrations ( r = 0.44; up to 640 ppm). In both deposits, high Cu and Au concentrations occur in small (&lt; 25 μm) anhedral grains of pyrite, which are interpreted to have rapidly crystallized from the ore-forming hydrothermal fluid. In addition, dissolution–reprecipitation textures and thin, concentric, Cu-rich overgrowths were identified in a number of larger (&gt; 25 μm) pyrite grains and aggregates. These abrupt changes in the trace element compositions of pyrite grains likely record episodic metal-rich fluid inputs. We also propose that gold adsorption onto growing pyrite surfaces played a key role in the mineralization of these deposits.
One-Class Double Compression Detection of Advanced Videos Based on Simple Gaussian Distribution Model

Passive video forensics has become an active topic in recent years. Generally, a pristine video obtained from surveillance cameras or other video recording devices is single compressed. At the same time, double lossy compression will certainly be introduced in tampered videos since it needs to go through recompression to perform tampering on a commonly used compressed video. In the video’s double compression, some traces are left due to the intrinsic effects of recompression. Traditional supervised learning is inefficient because it requires two classes (the pristine and the manipulated) that occur in the video to be exhaustively assigned labels. Actually, compared with pristine videos, manipulated videos with labels are more difficult to obtain. To address this problem, in this paper, one-class classification, which is often used for anomaly detection and only needs the target class, is introduced. We first treat all decompressed video frames as still images and extract subtractive pixel adjacency matrix (SPAM) steganalysis features to detect traces left in the double compression process. Then we adopt a Gaussian density-based one-class classifier since SPAM features extracted from pristine video frames approximately subject to the Gaussian distribution. Furthermore, we improve the robustness of the classifier by using ensemble strategy. Experimental results indicate that our proposed method exceeds other more complex one-class classification methods, and outperforms fully-supervised learning methods only by feeding features from single compressed video frames to our one-class classifier.
Differential spectra of a class of power permutations with characteristic 5

Let n be a positive integer and $$F(x)=x^d$$ over $${\mathbb {F}}_{5^n}$$ , where $$d=\frac{5^n-3}{2}$$ . In this paper, we study the differential properties of the power permutation F(x). It is shown that F(x) is differentially 4-uniform when n is even, and differentially 5-uniform when n is odd. Based on some knowledge on elliptic curves over finite fields, the differential spectrum of F(x) is also determined.
A Study on the Application of Blended Teaching to English Reading Course under the Background of Artificial Intelligence

Abstract The development of artificial intelligence technology brings a lot of benefits to various fields, including education. This paper discusses how artificial intelligence leads to the opportunity to improve learning processes and develop innovative approaches to education like blended teaching. Based on that, the author tries to provide tentative suggestions on the application of blended teaching to English reading courses with the help of artificial intelligence technology, in the hope of improving teaching and learning quality of this course, benefiting both teachers and students.
Software Defect Prediction using Dimensionality Reduction and Deep Learning

Software Defect Prediction is a significant angle to guarantee programming quality. Deep Learning methods can likewise be utilized for the same. In this paper, we propose to extract a bunch of expressive features from an underlying arrangement of essential change measures using different dimensionality techniques namely PCA and Kernel PCA, and then train a classifier based on the extracted features using Decision tree and Artificial Neural Networks. We utilize five open source datasets from NASA Promise Data Repository to play out this comparative study. For evaluation, we have used three widely used metrics: Accuracy, F1 scores and Areas under Receiver Operating Characteristic curve. It is found that Kernel PCA along with Artificial Neural Network as a classifier outperformed all the other techniques.
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment

The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high correlation numbers on existing IQA datasets, DNN-based models may be easily falsified in the group maximum differentiation (gMAD) competition. Here we show that gMAD examples can be used to improve blind IQA (BIQA) methods. Specifically, we first pre-train a DNN-based BIQA model using multiple noisy annotators, and fine-tune it on multiple synthetically distorted images, resulting in a "top-performing" baseline model. We then seek pairs of images by comparing the baseline model with a set of full-reference IQA methods in gMAD. The spotted gMAD examples are most likely to reveal the weaknesses of the baseline, and suggest potential ways for refinement. We query human quality annotations for the selected images in a well-controlled laboratory environment, and further fine-tune the baseline on the combination of human-rated images from gMAD and existing databases. This process may be iterated, enabling active fine-tuning from gMAD examples for BIQA. We demonstrate the feasibility of our active learning scheme on a large-scale unlabeled image set, and show that the fine-tuned quality model achieves improved generalizability in gMAD, without destroying performance on previously seen databases.