Reviewerly/exHarmony huggingface dataset:
Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation

Abstract Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningful non -differences between systems’ performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts.
Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix

User–item bipartite networks consist of users and items, where edges indicate the interactions of user–item pairs. We propose a Bayesian generative model for the user–item bipartite network that can measure the two types of rich-get-richer biases: item popularity and user influence biases. Furthermore, the model contains a novel measure of an item, namely the item quality that can be used in the item recommender system. The item quality represents the genuine worth of an item when the biases are removed. The Gibbs sampling algorithm alongside the adaptive rejection sampling is presented to obtain the posterior samples to perform the inference on the parameters. Monte Carlo simulations are performed to validate the presented algorithm. We apply the proposed model to Flickr user-tag and Netflix user–movie networks to yield remarkable interpretations of the rich-get-richer biases. We further discuss genuine item quality using Flickr tags and Netflix movies, considering the importance of bias elimination.
An initial theory to understand and manage requirements engineering debt in practice

Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED). We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.
CASSED: Context-based Approach for Structured Sensitive Data Detection

The need for sensitive data detection and identification has increased in recent years. Sensitive data detection and identification are necessary steps for privacy protection. The focus in this field has been on unstructured data detection using natural language processing (NLP) approaches, while there has been little progress in the field of structured data. Most of the structured data approaches consider independent feature representations of cells, without taking potentially relevant context into account. In this work, we introduce a novel context-based approach named CASSED, which stands for Context-based Approach for Structured SEnsitive Data Detection. CASSED addresses the problem of sensitive data detection in structured data through the lens of NLP, using the transformer-based BERT method. Our approach aims to actively capture relations both within and between cells in the same column as the assumption is that the data present in the same column in a table are mostly very similar. CASSED works as a classifier for columns in database tables with the task of predicting a label or multiple labels for different types of sensitive data that a column may represent. Since there is no officially recognized dataset for the task, we compared CASSED on datasets used for similar tasks from related work. Furthermore, we created our own dataset focused on sensitive data to evaluate CASSED. Our method outperformed methods from related work both on their datasets and achieved significantly better results on our own dataset compared to our baseline model as well as models from related work. Our research suggests that treating structured data as context-rich is a viable strategy for sensitive data detection and identification.
Surrogate approach to uncertainty quantification of neural networks for regression

Uncertainty quantification is essential in preventing inaccurate predictions of neural networks. A vanilla neural network for regression does not intrinsically provide explicit information about prediction uncertainty. To quantify the prediction uncertainty for regression problems, we can build an alternative prediction model specialized for uncertainty quantification. However, this requires the use of training data, which are inaccessible in many real-world situations. To address such situations, this study presents a surrogate approach to quantify the prediction uncertainty of a regression network without using training data. A regression network tends to have high prediction uncertainty when its output is sensitive to its input. Based on this intuition, we quantify the sensitivity and use it as a surrogate measure of the prediction uncertainty. To do so, we introduce four surrogate measures that capture the sensitivity in different ways: Input perturbation, Gradient norm, MC-dropout, and Knowledge distillation. For a query instance, each surrogate measure can be calculated by using the regression network only to estimate the prediction uncertainty. We demonstrate the respective effectiveness of the proposed surrogate measures on nine regression datasets.
Investigation Of Data Augmentation Techniques For Bi-LSTM Based Direct Speech To Speech Translation

Direct speech-to-speech translation (DS2ST) system translates the speech in the source language directly to the speech in the target language. It has been shown in the literature that deep learning systems trained using parallel datasets have given a good translation of the speech. However, getting large datasets to train deep learning networks for DS2ST tasks extensively is not easy. Also, the parallel data might not capture the variabilities like session, gender, speaker, and domain variation that might be present in the real-world dataset. In this work, we explore the data-augmentation techniques such that the pool of the training data can be increased and the DS2ST task can be generalized for all the variations. This work uses noise injection, speed perturbation, pitch perturbation, and vocal tract modification-based data-augmentation approaches as an initial attempt. From the experimental results, it has been found that these augmentation approaches improve the performance of the DS2ST system when compared with the clean/original data. Mel-cepstral distortion (MCD) and intelligibility score (IS) are used as metrics to compare the translated speech with the target language speech. Among the augmentation approaches explored, speed perturbation provides the best improvement of 6.125 in terms of MCD. Vocal tract modification improves the performance of the speaker variability in the dataset. This study shows the robustness of the DS2ST system trained on augmented data.
Uncertainty estimation in the classification of histopathological images with HER2 overexpression using Monte Carlo Dropout

Classification of breast cancer tissues with HER2 overexpression into 0, 1+, 2+, or 3+ categories is a crucial clinical task for determining HER2 positivity, and hence, prescribing the adjuvant HER2-targeted therapy. Deep learning-based methods have been fundamental for creating support tools that improve decision-making, and they have provided outstanding performance in the classification of histopathological images. However, the quantification of the uncertainty has been traditionally neglected when deep learning-based methods are used, despite its critical importance in healthcare applications. In this work we propose a new method, using deep learning and the Monte Carlo Dropout, to measure the uncertainty when classifying breast cancer tissue images with HER2 overexpression. The proposed method has four main stages including (1) WSI pre-processing, (2) patch dataset generation, (3) Bayesian deep learning-based classification, (4) and prediction and uncertainty estimation. For efficient computation of the predictive distribution, we propose a 2-step predictive distribution method that decreases significantly the execution time. Furthermore, the method achieves tissue-level classification by training the classifier on a patch-level and using aggregation techniques. The proposed method achieved on average 0.89 accuracy, 0.81 precision, and 0.74 recall for classification on a whole slide tissue-level. The method is also capable of characterizing the higher and lower uncertainty regions in the whole slide tissue images by estimating the predictive entropy and mutual information. This additional piece of information can be very important for practitioners to take decisions in the healthcare domain.
Pseudo-Retinex decomposition-based unsupervised underwater image enhancement and beyond

Underwater images suffer from color casts and low contrast degraded due to wavelength-dependent light scatter and abortion of the underwater environment. To effectively improve the quality of the underwater images, deep learning-based underwater image enhancement methods have been widely proposed. However, most deep learning-based underwater image enhancement methods rely heavily on paired datasets. Actually, obtaining distortion-free images as reference images is difficult in underwater imaging. To address this problem, a fully Unsupervised convolution neural network-based Underwater Image Enhancement (UUIE) is proposed by pseudo-Retinex decomposition. The innovation of the proposed UUIE is to establish a relationship between the underwater imaging model and the Retinex model, then use terrestrial images to replace underwater images for training and estimate pseudo-illumination and pseudo-reflection maps through self-supervision using the pseudo-Retinex decomposition. The pseudo-reflection image and pseudo-illumination image are reconstructed by the pseudo-Retinex decomposition to obtain the enhanced image. Additionally, the proposed UUIE can also be extended to image dehazing and low-light enhancement with only one trained model. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed UUIE quantitatively and qualitatively.
The effects of augmented reality shopping experiences: immersion, presence and satisfaction

Purpose Augmented reality (AR) is transforming the business and interactive marketing landscape. This research aims to investigate consumers' degree of involvement and if a feeling of immersion and presence influences AR shopping satisfaction, comparing high- and low-immersive AR experiences. Design/methodology/approach This paper uses a quantitative approach. Two studies were carried out: a high-immersive AR experiment with 173 participants and a low-immersive AR experience with 222 participants. Findings were analyzed using partial least square structural equation modeling with SmartPLS. Findings Results indicate the antecedents of immersion and presence differ when it comes to different immersive AR levels. In a high-immersive AR experience, flow, information seeking and novelty are attributes related to immersion, while enjoyment and personalization are related to presence. Contrastingly, in a low-immersive AR experience, only flow is related to immersion, while information seeking, novelty and personalization are related to presence. These results highlight the role of immersion and presence as mediators for AR shopping satisfaction experience. Originality/value This study's originality lies in the use of a rival model for analysis. Findings suggest a contingent perspective of AR experience, depending on high- or low-immersion experience, so companies must pay attention for how to measure AR experiences to increase involvement and satisfaction.
Regret and Hope on Transformers: An Analysis of Transformers on Regret and Hope Speech Detection Datasets

In this paper, we analyzed the performance of different transformer models for regret and hope speech detection on two novel datasets. For the regret detection task, we compared the averaged macro-scores of the transformer models to the previous state-of-the-art results. We found that the transformer models outperformed the previous approaches. Specifically, the roberta-based model achieved the highest averaged macro F1-score of 0.83, beating the previous state-of-the-art score of 0.76. For the hope speech detection task, the bert-based, uncased model achieved the highest averaged-macro F1-score of 0.72 among the transformer models. However, the specific performance of each model varied slightly depending on the task and dataset. Our findings highlight the effectiveness of transformer models for hope speech and regret detection tasks, and the importance of considering the effects of context, specific transformer architectures, and pre-training on their performance.
Anchor-free infrared pedestrian detection based on cross-scale feature fusion and hierarchical attention mechanism

An anchor-free infrared pedestrian detection algorithm is proposed to address the problems of multiscale, partial occlusion and environmental interference in infrared images, which make it difficult for traditional algorithms to perform accurate detection. First, a cross-scale feature fusion module is devised to improve the detection performance of multiscale and partially occluded objects. This module fuses the global features of different residual layers and multiscale local region features, thereby improving the multiscale feature fusion capability of the model while expanding the feature receptive field of the backbone network. Second, the object texture features of infrared images are weak owing to the imaging mechanism, which affects the accuracy of detection model. Thus, we construct a hierarchical attention mapping module to enhance the significance of pedestrian features in complex environments while suppressing the background information. Moreover, to improve the detection performance further, a novel dual-branch head detector is designed to decouple the input features from the channel dimension, which can be used for classification and location. Finally, by introducing the anchor-free concept into the network prediction mechanism, the prediction box can learn ground truth regression parameters by itself, thereby simplifying the network structure and improving the model generalization ability. The experimental results demonstrate that the proposed algorithm can accurately detect multiscale infrared pedestrian objects in complex environments and exhibits better objective evaluation indices compared to other pedestrian detection algorithms. The average precision and recall reached 98.78% and 98.67%, respectively.
Evaluation of regression models' accuracy for estimating soil heavy metals content from topographic rectified satellite imagery (Case study: Irankouh from Iran)

Although the traditional approach is vital in environmental studies, satellite images have recently become one of the main tools for estimating soil pollution due to their advantages. Among sources of soil pollution, heavy metals (HMs) play a crucial role; in this regard, evaluating the soil contamination anomaly is indispensable due to an enormous number of HMs mines and agricultural land irrigation by industrial sewage in developing countries. Therefore, the main goals of this study are: to evaluate Landsat-8 image capability for estimating HMs content (Cr, Cd, Co, and AS) of the soil, performance evaluation of linear and nonlinear regression models, and topographic influence on the estimation accuracy of heavy metals. To do so, we applied multivariate-linear, partial least square, exponential regression, and neural network models to achieve a relation between the HMs content and soil spectral reflectance. Regarding the R2 value of models, the neural network demonstrated a more acceptable performance than other models. The results show that topographic correction improves the accuracy of HMs estimation; for example, the Minnaert model obtained the highest score (equal to 30.02), which is sufficient to support the preceding sentence. Furthermore, the neural network and Minnaert (with a score of 17.56) would produce the best results for estimating HMs content.
A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)

As an industrial infrastructure, the safety and reliability of the Cyber-Physical System requires the effective anomaly detection. However, the existing detection methods have bottleneck in the face of insufficient training datasets. This work proposed and a novel anomaly detection approach based on ensemble semi-supervised active learning, which can effectively detect anomalous traffic when there is few labeled samples and the dataset is unbalanced. Specifically, this work proposed balanced sampling strategy, which combines the margin sampling and the democratic co-learning techniques, to construct a balanced training set that consists of manually labeled high-information samples and automatically labeled high-confidence samples, to effectively train the detection model on a limited budget. We also found adding correctly labeled high-confidence samples into training set improves the performance of detection model when the training samples are few and the label budget is limited. This work achieves a good balance between the effectiveness of model training and the cost of sample querying when the traffic data in CPS is rare labeled and imbalanced. In addition, we designed five pairs of experiments with NSL-KDD and SWaT dataset, and the results demonstrate the capability and advancement of proposed approach.
Stealing and evading malware classifiers and antivirus at low false positive conditions

Model stealing attacks have been successfully used in many machine learning domains, but there is little understanding of how these attacks work against models that perform malware detection. Malware detection and, in general, security domains have unique conditions. In particular, there are very strong requirements for low false positive rates (FPR). Antivirus products (AVs) that use machine learning are very complex systems to steal, malware binaries continually change, and the whole environment is adversarial by nature. This study evaluates active learning model stealing attacks against publicly available stand-alone machine learning malware classifiers and also against antivirus products. The study proposes a new neural network architecture for surrogate models (dualFFNN) and a new model stealing attack that combines transfer and active learning for surrogate creation (FFNN-TL). We achieved good surrogates of the stand-alone classifiers with up to 99\% agreement with the target models, using less than 4% of the original training dataset. Good surrogates of AV systems were also trained with up to 99% agreement and less than 4,000 queries. The study uses the best surrogates to generate adversarial malware to evade the target models, both stand-alone and AVs (with and without an internet connection). Results show that surrogate models can generate adversarial malware that evades the targets but with a lower success rate than directly using the target models to generate adversarial malware. Using surrogates, however, is still a good option since using the AVs for malware generation is highly time-consuming and easily detected when the AVs are connected to the internet.
Online Platforms for Remote Immersive Virtual Reality Testing: An Emerging Tool for Experimental Behavioral Research

Virtual Reality (VR) technology is gaining in popularity as a research tool for studying human behavior. However, the use of VR technology for remote testing is still an emerging field. This study aimed to evaluate the feasibility of conducting remote VR behavioral experiments that require millisecond timing. Participants were recruited via an online crowdsourcing platform and accessed a task on the classic cognitive phenomenon “Inhibition of Return” through a web browser using their own VR headset or desktop computer (68 participants in each group). The results confirm previous research that remote participants using desktop computers can be used effectively for conducting time-critical cognitive experiments. However, inhibition of return was only partially replicated for the VR headset group. Exploratory analyses revealed that technical factors, such as headset type, were likely to significantly impact variability and must be mitigated to obtain accurate results. This study demonstrates the potential for remote VR testing to broaden the research scope and reach a larger participant population. Crowdsourcing services appear to be an efficient and effective way to recruit participants for remote behavioral testing using high-end VR headsets.
Parameter Optimization in a Leaky Integrator Echo State Network with an Improved Gravitational Search Algorithm

In the prediction of a nonlinear time series based on a leaky integrator echo state network (leaky-ESN), building a reservoir related to the specific problem is a key step. For problems such as poor performance of randomly generated reservoirs, it is tough to determine the parameter values of the reservoirs. The work in this paper uses the gravitational search algorithm (GSA) to optimize the global parameters of a leaky-ESN, such as the leaking rate, the spectral radius, and the input scaling factor. The basic GSA has some problems, such as slow convergence and poor balance between exploration and exploitation, and it cannot solve some complex optimization problems well. To solve these problems, an improved gravitational search algorithm (IGSA) is proposed in this paper. First, the best agent and elite agents were archived and utilized to accelerate the exploration phase and improve the convergence rate in the exploitation phase. Second, to improve the effect of the poor fitness agents on the optimization result, a differential mutation strategy was proposed, which generated new individuals to replace original agents with worse fitness, increasing the diversity of the population and improving the global optimization ability of the algorithm. Finally, two simulation experiments showed that the leaky-ESN optimized by the IGSA had better prediction accuracy.