Reviewerly/exHarmony huggingface dataset:
Features of responsibility of future specialists of the socionomic professions as an indicator of their digital competence

The article dwells on to the study of the responsibility of future specialists of socionomic professions as an important indicator of their digital competence. The role of responsibility as a vital indicator of digital competence of future specialists of socionomic professions was determined, which determines their conscious and responsible activities in the context of obtaining and disseminating information in the digital space, promoting both their own psychological safety alongside psychological safety of other members of the digital community. The results of an empirical study were highlighted, which revealed an insufficient level of both responsibility and cognitive-operational components of digital competence for a significant number of future specialists in socionomic professions. Gender differences in the manifestations of responsibility of future specialists depending on the gender are characterized according to which the female respondents were slightly more responsible for the consequences of dissemination of information than male specialists. The expediency of promoting the development of responsibility of future specialists of socionomic professions as an indicator of their digital competence is stated, which can be provided in a specially organized psychological training.
Real noise image adjustment networks for saliency-aware stylistic color retouch

Automatic Image Adjustment (AIA) mainly aims to realize stylistic color retouch in images. Recent years have witnessed unprecedented success in learning-based AIA methods, especially convolutional neural networks (CNNs). However, existing AIA methods usually handle images without real noise from ideal scenarios, resulting in poor retouch performance when processing real noise images. Furthermore, these AIA methods lack attentive capability when learning salient areas to perform stylistic color retouch as human artists do. To address these problems, we first remodel the adjustment task for real noise images to remove the real noise. Then, we further propose the Real Noise Image Adjustment Networks (RNIA-Nets) using saliency-aware stylistic color retouch and adaptive denoising methods. Specifically, the saliency-aware stylistic color retouch predicts visual salient areas to learn stylistic color mapping using a proposed multifaceted attention (MFA) module. The adaptive denoising mechanism effectively predicts the denoising kernel for various real noise images. Eventually, to equitably verify the effectiveness of the proposed RNIA-Nets, a new challenging benchmark dataset collected from real noise images is established. Extensive experimental results demonstrate that the proposed method can achieve favorable results on real noise image adjustment, providing a highly effective solution to practical AIA applications. The code and datasets will be released at https://github.com/JiangBoCS/RNIA-Nets.
Response Generation by Jointly Modeling Personalized Linguistic Styles and Emotions

Natural language generation (NLG) has been an essential technique for various applications, like XiaoIce and Siri, and engaged increasing attention recently. To improve the user experience, several emotion-aware NLG methods have been developed to generate responses coherent with a pre-designated emotion (e.g., the positive or negative). Nevertheless, existing methods cannot generate personalized responses as they frequently overlook the personalized linguistic style. Apparently, different human responsers tend to have different linguistic styles. Inspired by this, in this work, we focus on a novel research theme of personalized emotion-aware NLG ( PENLG ), whereby the generated responses should be coherent with the linguistic style of a pre-designated responser and emotion. In particular, we study PENLG under a scenario of generating personalized emotion-aware response for social media post. Yet it faces certain research challenges: (1) the user linguistic styles are implicit and complex by nature, and hence it is hard to learn their representations; and (2) linguistic styles and emotions are usually expressed in different manners in a response, and thus how to convey them properly in the generated responses is not easy. Toward this end, we present a novel scheme of PENLG, named CRobot, which consists of a personalized emotion-aware response generator and two discriminators, i.e., general discriminator and personalized emotion-aware discriminator . To be more specific, the post-based and avatar-based user linguistic style modeling methods are incorporated into the encoder-decoder–based generator, while the discriminators are devised to ensure that the generated response is fluent and consistent with both the emotion and the linguistic style of the user. Different from the traditional adversarial networks, we embed adversarial learning under the umbrella of reinforcement learning. In this way, the response generation problem can be tackled by the generator taking a sequence of actions on selecting the proper word of each timestep for output. To justify our model, we construct a large-scale response generation dataset based on Twitter, consisting of 6,763 tweets with a corresponding 1,461,713 response created by 153,664 users. Extensive experiments demonstrate that CRobot surpasses the state-of-the-art baselines regarding both subjective and objective evaluation.
Knowledge Enhanced Sports Game Summarization

Sports game summarization aims at generating sports news from live commentaries. However, existing datasets are all constructed through automated collection and cleaning processes, resulting in a lot of noise. Besides, current works neglect the knowledge gap between live commentaries and sports news, which limits the performance of sports game summarization. In this paper, we introduce K-SportsSum, a new dataset with two characteristics: (1) K-SportsSum collects a large amount of data from massive games. It has 7,854 commentary-news pairs. To improve the quality, K-SportsSum employs a manual cleaning process; (2) Different from existing datasets, to narrow the knowledge gap, K-SportsSum further provides a large-scale knowledge corpus that contains the information of 523 sports teams and 14,724 sports players. Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news. Extensive experiments on K-SportsSum and SportsSum datasets show that our model achieves new state-of-the-art performances. Qualitative analysis and human study further verify that our model generates more informative sports news.
Unified Operations for Variability in Space and Time

Software and systems engineering is challenged by variability in space (concurrent variations at a single point in time) and time (sequential variations due to evolution). Managing both dimensions of variability independently is cumbersome and error-prone. A common foundation for operations on these dimensions is still missing, hampering the comparison and integration of existing techniques coping with variability in space and time as well as the design of new ones. In this paper, we address this problem by systematically identifying, categorizing, and unifying operations from contemporary tools and extending them to cope with both variability dimensions. Based on our gained insights, we identify gaps and trade-offs in current tools for managing variability in space and time, and discuss open challenges. The unified operations establish a common foundation that helps researchers and practitioners to gain a deeper understanding of existing techniques and tools for managing variability in space and/or time, analyze and compare them, and design new ones.
Improving the Applicability of Knowledge-Enhanced Dialogue Generation Systems by Using Heterogeneous Knowledge from Multiple Sources

Traditional conversational systems can only access the given query during the response generation, leading to meaningless responses. To this end, researchers proposed to enhance dialogue generation by integrating external knowledge. Although such methods have achieved remarkable gains, the use of only single-source knowledge often makes existing knowledge-enhanced methods degenerate into traditional models in real scenarios because of the insufficient knowledge coverage of single-source knowledge. To improve the applicability of knowledge-enhanced methods, we propose two novel frameworks to use heterogeneous knowledge from multiple sources. We first propose an MHKD-Seq2Seq framework, which can use different heterogeneous knowledge by identifying abstract-level knowledge behaviors; meanwhile, a Diffuse-Aggregate scheme is used to process multiple knowledge simultaneously and produce a unified result. The next framework MHKD-ARPLM can leverage the advantages of pretrained language models with Knowledge Linearization techniques. In experiments, we collected dialogues from previously open-released datasets and built a multi-source knowledge-aligned dataset TriKE-Weibo, which involves three knowledge sources: commonsense, texts, and infobox tables. Extensive evaluations demonstrate the performance leadership of our approaches against competitive baseline models.
A fuzzy ISM analysis of factors in growth of initial coin offerings (ICOs)

In the previous few years, the value of cryptocurrencies and their market capitalization have grown at an incredibly rapid pace. There are currently over 1500 distinct trading currencies with a total market valuation of over 384 billion US dollars. It is the purpose of this study to examine the elements that have helped the ICO industry flourish over the past several years. Findings from previous studies and recommendations from industry professionals lead to the selection of 16 variables. ISM (Interpretive Structural Modeling) analysis was utilised in order to better comprehend the influence and interrelationships across seven different Fuzzy levels of identified obstacles. Based on the drive and reliance power of the Fuzzy matrix utilising Fuzzy MICMAC, the factors are further divided into four primary groups. High reliance and driving power can be found in all but one of the selected fifteen criteria, except for no or low regulation. The storm's expansion in the field is directly linked to the relationship between all factors. This revolutionary fundraising approach is changing and accepted by common investors because of "no or low regulation," but it cannot be managed directly by the ICOs industry's connected participants.
A point tracking method of TDDM for vibration measurement and large-scale rotational motion tracking

To simultaneously balance the high accuracy of small displacement vibration measurement and the tracking robustness of large-scale rotational motion in structural dynamic testing, a tracking-detecting-deformable matching (TDDM) point tracking method is proposed in this paper. In this new method, the movement range of the target point is estimated through tracking, and then the drift and accumulated error of the tracker is corrected by real-time detecting. The tracking and detecting decision result is taken as the initial guess for matching, and the optimal local deformation correction is finally solved. The proposed method is first tested in the numerical experimental images with different interference terms to demonstrate its performance in challenging application scenarios. In addition, low amplitude vibration testing and large-scale rotational motion tracking cases are tested. The TDDM shows a much higher measurement accuracy and tracking success rate than the existing point-tracking-based vision measurement methods.
LigMSANet: Lightweight multi-scale adaptive convolutional neural network for dense crowd counting

Scale variation and real-time counting are challenging problems for crowd counting in highly congested scenes. To remedy these issues, we proposed a Lightweight Multi-Scale Adaptive Network (LigMSANet). There are two strong points in our method. First, the scale limitation is broken and the proportion of neurons with different receptive field sizes are adjusted spontaneously according to input images through a novel multi-scale adaptation module (MSAM). Second, the model performance is significantly improved at a little cost of parameter by replacing the standard convolution with the depthwise separable convolution and a tailored MobileNetV2 with 5 bottleneck blocks (here, the step size of the fourth bottleneck block is 1). To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three major crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCSD) and our method achieves superior performance to state-of-the-art methods while with much less parameters and runtimes.
Penalty based robust learning with noisy labels

In general, deep neural network is vulnerable to noisy labels also known as erroneous labels. As a main solution to mitigate this problem, sample selection techniques have been actively studied. However, if the labels are dominantly corrupted by some classes (these noisy samples are called dominant noisy labeled samples), the network also learns dominant noisy labeled samples rapidly via content-aware optimization. This can cause memorization (reduce generalization) in the deep neural network. In this study, we propose a compelling criteria to penalize dominant-noisy-labeled samples intensively through class-wise penalty labels. By averaging prediction confidences for the each observed label, we obtain suitable penalty labels that have high values if the labels are largely corrupted by some classes. Additionally, to enhance the accuracy of penalty labels, temporal ensembling and weight are exploited. Experiments were performed using benchmarks (CIFAR-10, CIFAR-100, Tiny-ImageNet) and real-world datasets (ANIMAL-10 N, Clothing1M) to evaluate the proposed criteria in various scenarios with different noise rates. Using the proposed sample selection, the learning process of the network becomes significantly robust to noisy labels compared to existing methods in several noise types. Moreover, the proposed criteria can be easily combined with the algorithms of loss correction and hybrid categories through a simple modification to improve learning performance.
Multi-Leak Deep-Learning Side-Channel Analysis

Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES).By utilizing deeplearning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic algorithm.However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a appropriate preprocess step for each leakage interval.This degenerates the quality of profiling traces due to the noise and nonprimary components.In this paper, we first divide the multi-leaky traces into leakage intervals and train models on different intervals separately.Afterwards, we concatenate these neural networks to build the final network, which is called multi-input model.We test the proposed multi-input model on traces captured from STM32F3 microcontroller implementations of AES-128 and show a 2-fold improvement over the previous single-input attacks.
Teachers’ understanding of the concept of ‘embedding’ digital technology in education

(2022). Teachers' understanding of the concept of 'embedding' digital technology in education. Irish Educational Studies: Vol. 41, Digital Education Futures: Design for Doing Education Differently, Guest Editors: Tony Hall, Rupert Wegerif, Suzanna Loper, Déirdre Ní Chróinín and Emma O'Brien, pp. 27-39.
Image Quality Assessment using Synthetic Images

Training deep models using contrastive learning has achieved impressive performances on various computer vision tasks. Since training is done in a self-supervised manner on unlabeled data, contrastive learning is an attractive candidate for applications for which large labeled datasets are hard/expensive to obtain. In this work we investigate the outcomes of using contrastive learning on synthetically generated images for the Image Quality Assessment (IQA) problem. The training data consists of computer generated images corrupted with predetermined distortion types. Predicting distortion type and degree is used as an auxiliary task to learn image quality features. The learned representations are then used to predict quality in a No-Reference (NR) setting on real-world images. We show through extensive experiments that this model achieves comparable performance to state-of-the-art NR image quality models when evaluated on real images afflicted with synthetic distortions, even without using any real images during training. Our results indicate that training with synthetically generated images can also lead to effective, and perceptually relevant representations.
Scene Text Recognition with Orientation Rectification via IC-STN

Text recognition from natural scene images is an arduous task due to non-horizontal shaped text caused by perspective distortion and low camera angle. This work presents a framework for natural scene text recognition that includes a text orientation correction module based on Inverse Compositional - Spatial Transformer Network (IC-STN) [1]. The text recognition module is made up of an encoder-decoder attentional sequential network. The IC-STN is a differentiable module which can be trained using the error that is backpropagated by the text recognition network. The input image's orientation is corrected during testing using the IC-STN module that determines the parameters of image transformation techniques such as affine and homographic transformation. This, in turn, produces a more easily recognizable form of the text to be used by the sequence recognition network. As per our knowledge, this is the first time where IC-STN has been incorporated in the scene text recognition pipeline. Experimental analysis conducted on the benchmark datasets, ICDAR 2013 and CUTE80, proved that the proposed work performs better than the existing state-of-the-art text recognition systems.
DIPPAS: a deep image prior PRNU anonymization scheme

Abstract Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counterpart is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the photo response non-uniformity (PRNU), a noise pattern left by the device on the acquired images. In this paper, we devise a methodology for suppressing such a trace from natural images without a significant impact on image quality. Expressly, we turn PRNU anonymization into the combination of a global optimization problem in a deep image prior (DIP) framework followed by local post-processing operations. In a nutshell, a convolutional neural network (CNN) acts as a generator and iteratively returns several images with attenuated PRNU traces. By exploiting straightforward local post-processing and assembly on these images, we produce a final image that is anonymized with respect to the source PRNU, still maintaining high visual quality. With respect to widely adopted deep learning paradigms, the used CNN is not trained on a set of input-target pairs of images. Instead, it is optimized to reconstruct output images from the original image under analysis itself. This makes the approach particularly suitable in scenarios where large heterogeneous databases are analyzed. Moreover, it prevents any problem due to the lack of generalization. Through numerical examples on publicly available datasets, we prove our methodology to be effective compared to state-of-the-art techniques.
Monocular Depth Estimation with Adaptive Geometric Attention

ingle image depth estimation is an ill-posed problem. That is, it is not mathematically possible to uniquely estimate the 3rd dimension (or depth) from a single 2D image. Hence, additional constraints need to be incorporated in order to regulate the solution space. In this paper, we explore the idea of constraining the model by taking advantage of the similarity between the RGB image and the corresponding depth map at the geometric edges of the 3D scene for more accurate depth estimation. We propose a general light-weight adaptive geometric attention module that uses the cross-correlation between the encoder and the decoder as a measure of this similarity. More precisely, we use the cosine similarity between the local embedded features in the encoder and the decoder at each spatial point. The proposed module along with the encoder-decoder network is trained in an end-to-end fashion and achieves superior and competitive performance in comparison with other state-of-the-art methods. In addition, adding our module to the base encoder-decoder model adds only an additional 0.03% (or 0.0003) parameters. Therefore, this module can be added to any base encoder-decoder network without changing its structure to address any task at hand.
Super-Resolution Network for Remote Sensing Images via Preclassification and Deep–Shallow Features Fusion

A novel super-resolution (SR) method is proposed in this paper to reconstruct high-resolution (HR) remote sensing images. Different scenes of remote sensing images have great disparities in structural complexity. Nevertheless, most existing SR methods ignore these differences, which increases the difficulty to train an SR network. Therefore, we first propose a preclassification strategy and adopt different SR networks to process the remote sensing images with different structural complexity. Furthermore, the main edge of low-resolution images are extracted as the shallow features and fused with the deep features extracted by the network to solve the blurry edge problem in remote sensing images. Finally, an edge loss function and a cycle consistent loss function are added to guide the training process to keep the edge details and main structures in a reconstructed image. A large number of comparative experiments on two typical remote sensing images datasets (WHURS and AID) illustrate that our approach achieves better performance than state-of-the-art approaches in both quantitative indicators and visual qualities. The peak signal-to-noise ratio (PSNR) value and the structural similarity (SSIM) value using the proposed method are improved by 0.5353 dB and 0.0262, respectively, over the average values of five typical deep learning methods on the ×4 AID testing set. Our method obtains satisfactory reconstructed images for the subsequent applications based on HR remote sensing images.
A Heuristic Exploration of Retraining-free Weight-Sharing for CNN Compression

The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. The scientific literature provides a large number of approximation techniques to address this problem. Among them, the Weight-Sharing (WS) technique gives promising results, but it requires carefully determining the shared values for each layer of a given CNN. As the number of possible solutions grows exponentially with the number of layers, the WS Design Space Exploration (DSE) time can easily explode for state-of-the-art CNNs. In this paper, we propose a new heuristic approach to drastically reduce the exploration time without sacrificing the quality of the output. The results carried out on recent CNNs (GoogleNet [1], ResNet50V2 [2], MobileNetV2 [3], InceptionV3 [4], and EfficientNet [5]), trained with the ImageNet [6] dataset, show over 5× memory compression at an acceptable accuracy loss (complying with the MLPerf [7] quality target) without any retraining step and in less than 10 hours. Our code is publicly available on GitHub [8].