Reviewerly/exHarmony huggingface dataset:
How Do Python Framework APIs Evolve? An Exploratory Study

Python is a popular dynamic programming language. In recent years, many frameworks implemented in Python have been widely used for data science and web development. Similar to frameworks in other languages, the APIs provided by Python frameworks often evolve, which would inevitably induce compatibility issues in client applications. While existing work has studied the evolution of frameworks in static programming languages such as Java, little is known on how Python framework APIs evolve and the characteristics of the compatibility issues induced by such evolution. To bridge this gap, we take a first look at the evolution of Python framework APIs and the resulting compatibility issues in client applications. We analyzed 288 releases of six popular Python frameworks from three different domains and 5,538 open-source projects built on these frameworks. We investigated the evolution patterns of Python framework APIs and found that they largely differ from those of Java framework APIs. We also investigated the compatibility issues in client applications and identified common strategies that developers adopt to fix these issues. Based on the empirical findings, we designed and implemented a tool, P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</inf> C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OMPAT</inf> , to automatically detect compatibility issues caused by misusing evolved framework APIs in Python applications. Experiments on 10 real-world projects show that our tool can effectively detect compatibility issues of developers' concern.
Named Entity Recognition for Sensitive Data Discovery in Portuguese

The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, two statistical models were tested—Conditional Random Fields and Random Forest and, finally, a Bidirectional-LSTM approach as experimented. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus, and DataSense NER Corpus.
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid

Proposal-free instance segmentation methods mainly generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostly employ separate modules for these two sub-tasks and require multiple passes for inference. In addition to the lack of efficiency, previous methods also failed to perform as well as proposal-based approaches. To this end, this work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction. Our method is based on learning an affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner. Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition (CGP) module is presented to fuse the two predictions and segment instances efficiently. As an additional contribution, we conduct an experiment to demonstrate the benefits of proposal-free methods in capturing detailed structures from finely annotated training examples. Our approach is evaluated on the Cityscapes and COCO datasets and achieves state-of-the-art performance.
A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model

For high-frequency digital signals to be transmitted over long distances, the basic digital signal needs to be modulated with a high-frequency carrier. In this case, the baseband digital signal is called the passband signal. In particular, in wireless communication systems or ultraviolet or infrared communications, transitional band digital modulations are used. The most commonly used transition band modulations are ASK (Amplitude Shift Keying), FSK (Frequency Shift Keying) and PSK (Phase Shift Keying) modulations. In this study, 8 bits of all digital baseband data were obtained from the transition band modulations in ASK, FSK and PSK modulations in MATLAB. Also, the noises ranging from 5 dB to 25 dB were added to these ASK, FSK, and PSK modulations. The originality of this paper is to a single deep learning model to demodulate the ASK, FSK, and PSK modulations by using a data-driven approach. The main aim is to demodulate the baseband numerical data from the transition band noised modulation signals instead of the hardware demodulator circuits. For this aim, the noised modulated signals were applied to deep LSTM (Long short-term memory) model without feature extraction. The performance measures to evaluate the proposed deep learning-based demodulator method have been used, and they are MAPE, MSE, R2, RMSE, and NRMSE. The obtained MAPE demodulation results for the worst case of ASK, FSK, and PSK (added 5 dB to these modulations) are 4.392, 5.60, and 3.166, respectively. The experimental results have demonstrated that the proposed LSTM demodulator model could be used safely in the demodulation of ASK, FSK, and PSK modulations in the real world.
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, such as speaker information, is required. Existing unsupervised methods, during training, generate speech by computing style from the corresponding ground truth sample and use a decoder to combine the style vector with the input text. Training the model in such a way leaks content information into the style vector. The decoder can use the leaked content and ignore some of the input text to minimize the reconstruction loss. At inference time, when the reference speech does not match the content input, the output may not contain all of the content of the input text. We refer to this problem as "content leakage", which we address by explicitly estimating and minimizing the mutual information between the style and the content through an adversarial training formulation. We call our method MIST - Mutual Information based Style Content Separation. The main goal of the method is to preserve the input content in the synthesized speech signal, which we measure by the word error rate (WER) and show substantial improvements over state-of-the-art unsupervised speech synthesis methods.
Improving Deep CNN Networks with Long Temporal Context for Text-Independent Speaker Verification

Deep CNN networks have shown great success in various tasks for text-independent speaker recognition. In this paper, we explore two approaches for modeling long temporal contexts to improve the performance of the ResNet networks. The first approach is simply integrating the utterance-level mean and variance normalization into the ResNet architecture. Secondly, we combine the BLSTM and ResNet into one unified architecture. The BLSTM layers model long range, supposedly phonetically aware, context information, which could facilitate the ResNet to learn the optimal attention weight and suppress the environmental variations. The BLSTM outputs are projected into multiple-channel feature maps and fed into the ResNet network. Experiments on the VoxCeleb1 and the internal MS-SV tasks show that with attentive pooling, the proposed approaches achieve up to 23-28% relative improvement in EER over a well-trained ResNet.
Masked GAN for Unsupervised Depth and Pose Prediction With Scale Consistency

Previous work has shown that adversarial learning can be used for unsupervised monocular depth and visual odometry (VO) estimation, in which the adversarial loss and the geometric image reconstruction loss are utilized as the mainly supervisory signals to train the whole unsupervised framework. However, the performance of the adversarial framework and image reconstruction is usually limited by occlusions and the visual field changes between frames. This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation.The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively. Furthermore, we also consider the scale consistency of our pose network by utilizing a new scale-consistency loss, and therefore, our pose network is capable of providing the full camera trajectory over a long monocular sequence. Extensive experiments on the KITTI dataset show that each component proposed in this paper contributes to the performance, and both our depth and trajectory predictions achieve competitive performance on the KITTI and Make3D datasets.
Tweet recommender model using adaptive neuro-fuzzy inference system

Twitter is a ubiquitous, socially engaging and a rapid communication medium. To filter the relevant information like news, hashtags, links, followers, retweets for better user experience recommender systems have been extensively used on Twitter. Uncertainty in user preference, fuzziness in the rating process and the imprecision associated with the voluminous and varied Twitter data are some of the difficulties associated which impede enhanced recommendations. This research put forwards an Adaptive Neuro-Fuzzy Inference System (ANFIS) based tweet recommender model to handle the uncertainty, impreciseness and vagueness in item features and user’s behavior. The proposed hybrid content-based and collaborative filtering based recommended model learns the interests of source and target users to categorize tweets. The users are characterized as source user and target user to whom the tweet is to be recommended. The interests of the source and target user are extracted and the correlation between user interests is established which along with the category of the target tweet are then used to build the neuro-fuzzy model. The results show that the proposed model predicts the recommendation score correctly most of the time with the satisfactory Root Mean Square Error (RMSE) value indicating the fitness of the designed ANFIS model.
Study of Formant Modification for Children ASR

The performance of automatic speech recognition systems for children’s speech is known to suffer from the large variation and mismatch in the acoustic and linguistic attributes between children’s and adults’ speech. One of the various identified sources of mismatch is the difference in formant frequencies between adults and children. In this paper, we propose a formant modification method to mitigate differences between adults’ and children’s speech and to improve the performance of ASR for children. The explored technique gives a relative 27% improvement in system performance compared to a hybrid DNN-HMM baseline. We also compare the system performance with related speaker adaptation methods like vocal tract length normalization (VTLN) and speaking rate adaptation (SRA) and find that the proposed method gives improvements over them, as well. Combining the proposed method with VTLN and SRA results in a further reduction of WER. We also found that the proposed method performs well even for noisy speech.
End-end Speech-to-Text Translation with Modality Agnostic Meta-Learning

Collecting large amounts of data to train end-to-end Speech Translation (ST) models is more difficult compared to the ASR and MT tasks. Previous studies have proposed the use of transfer learning approaches to overcome the above difficulty. These approaches benefit from weakly supervised training data, such as ASR speech-to-transcript or MT text-to-text translation pairs. However, the parameters in these models are updated independently of each task, which may lead to sub-optimal solutions. In this work, we adopt a meta-learning algorithm to train a modality agnostic multi-task model that transfers knowledge from source tasks=ASR+MT to target task=ST where the ST task severely lacks data. In the meta-learning phase, parameters are updated in such a way that they act as a good ini-tialization point for the target ST task. We evaluate the proposed meta-learning approach for ST tasks on English-German (En-De) and English-French (En-Fr) language pairs from the Multilingual Speech Translation Corpus (MuST-C). Our method outperforms the previous transfer learning approaches and sets new state-of-the-art results for En-De and En-Fr ST tasks by obtaining 9.18, and 11.76 BLEU point improvements, respectively.
Intelligent Student Behavior Analysis System for Real Classrooms

In this paper, we design an intelligent student behavior analysis system for recorded classrooms, which automatically detects hand-raising, standing, and sleeping behaviors of students. Detecting these behaviors is quite challenging mainly due to various scale behaviors, low resolution, and imbalanced behavior samples. To overcome the above-mentioned challenges, we first build a large-scale student behavior corpus from thirty schools, labeling these behaviors using bounding boxes frame-by-frame, which changes the behavior recognition problem into object detections. Then, we propose an improved Faster R-CNN, a classical object detection model, for student behavior analysis. Specifically, we first present a novel scale-aware detection head to overcome scale variations. Secondly, we propose a new feature fusion strategy to detect low-resolution behaviors while introduces little computation overhead. Thirdly, we utilize OHEM (Online Hard Example Mining) to alleviate severe class imbalances. Experiment results on our real corpus are increased by 3.4% mAP while maintaining a fast speed.
Medical Image Denoising with Recurrent Residual U-Net (R2U-Net) base Auto-Encoder

Deep learning (DL) approaches have been applied in different sectors of medical imaging applications, i.e. classification, segmentation and detection tasks and shown superior performance. The DL based generative methods are used for image denoising, enhancement and restoration task. In case of image analysis, image denoising is one of the most crucial preprocessing steps. Recently, there are various DL approaches are applied in image denoising problems and achieved state-of-the-art performance. In this work, we apply recurrent residual U-Net (R2U-Net) based autoencoder model for medical image denoising which is applied for digital pathology, dermoscopy, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images denoising tasks. The performance of R2U-Net based autoencoder model is also evaluated for Transfer domain (TD) between MRI and CT scan images. The experiments have conducted on different publicly available medical image datasets and shows promising denoising results which can be applied in different medical imaging applications.
End-To-End Voice Conversion Via Cross-Modal Knowledge Distillation for Dysarthric Speech Reconstruction

Dysarthric speech reconstruction (DSR) is a challenging task due to difficulties in repairing unstable prosody and correcting imprecise articulation. Inspired by the success of sequence-to-sequence (seq2seq) based text-to-speech (TTS) synthesis and knowledge distillation (KD) techniques, this paper proposes a novel end-to-end voice conversion (VC) method to tackle the reconstruction task. The proposed approach contains three components. First, a seq2seq based TTS is first trained with the transcribed normal speech. Second, with the text-encoder of this trained TTS system as "teacher", a teacher-student framework is proposed for cross-modal KD by training a speech-encoder to extract appropriate linguistic representations from the transcribed dysarthric speech. Third, the speech-encoder of the previous component is concatenated with the attention and decoder of the first component (TTS) to perform the DSR task, by directly mapping the dysarthric speech to its normal version. Experiments demonstrate that the proposed method can generate the speech with high naturalness and intelligibility, where the comparisons of human speech recognition between the reconstructed speech and the original dysarthric speech show that 35.4% and 48.7% absolute word error rate (WER) reduction can be achieved for dysarthric speakers with low and very low speech intelligibility, respectively.
Backdoor Attacks and Defenses for Deep Neural Networks in Outsourced Cloud Environments

Deep neural networks have achieved tremendous success in various fields, especially in recognition and classification applications. However, faced with the difficulty of training millions of parameters of such networks, many users outsource the training procedure of a specific prediction work to the powerful cloud servers that own abundant computation and storage resources. Although such outsourced training can significantly simplify and expedite the development circles, it also introduces many security risks. In recent years, a new type of attack, the so-called backdoor attack, has attracted much attention, where the attacker's goal is to create a maliciously deep neural network to make misclassification on the special inputs with the backdoor trigger. For its concealment, such attacks can potentially cause disastrous consequences. Subsequently, many defense mechanisms against this attack are also appearing. In this article, we conduct a retrospective review on the existing schemes of the backdoor attacks and defenses in outsourced cloud environments. According to the resources the adversary has, and whether the detection time is during run-time or not, we classify the attack and defense approaches into multiple categories. We present a detailed overview of each category, and we provide a comparison of these approaches and evaluate part of the attack schemes by the experiments. We also highlight various future research directions in this field. These views shed light on possible avenues for future research.
Every Feature Counts: An Improved One-Stage Detector in Thermal Imagery

Detecting objects such as pedestrians and cars plays an indispensable role in autonomous driving. To be robust to the low-illumination situation such as nighttime, thermal cameras have become increasingly popular in autonomous driving system, and it is highly desirable to develop an accurate, robust algorithm concentrated on object detection in thermal imagery. In this work, we propose an DNN-based, one-stage detector namely ThermalDet. The main idea of ThermalDet is that since there doesn't exist lots of detailed visual characteristics (e.g. color and texture) in thermal images, features from low levels and high levels are equally important when performing detection task on them. The proposed detector inherits the architecture of RefineDet and further improves it. First, we design a dual-pass fusion block(DFB) to directly fuse features from all different levels. Then we add a channel-wise enhance module(CEM) to adaptively assign weights to different channels of the feature maps to achieve the best use of them. Experimental results on FLIR ADAS Dataset demonstrate that ThermalDet performs better than the state-of-art methods such as MMTOD-UNIT, MMTOD-CG.
MAMNet: Multi-path adaptive modulation network for image super-resolution

In recent years, single image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) have made significant progress. However, due to the non-adaptive nature of the convolution operation, they cannot adapt to various characteristics of images, which limits their representational capability and, consequently, results in unnecessarily large model sizes. To address this issue, we propose a novel multi-path adaptive modulation network (MAMNet). Specifically, we propose a multi-path adaptive modulation block (MAMB), which is a lightweight yet effective residual block that adaptively modulates residual feature responses by fully exploiting their information via three paths. The three paths model three types of information suitable for SR: 1) channel-specific information (CSI) using global variance pooling, 2) inter-channel dependencies (ICD) based on the CSI, 3) and channel-specific spatial dependencies (CSD) via depth-wise convolution. We demonstrate that the proposed MAMB is effective and parameter-efficient for image SR than other feature modulation methods. In addition, experimental results show that our MAMNet outperforms most of the state-of-the-art methods with a relatively small number of parameters.
Quantum speed limit via the trajectory ensemble

In this paper, we present the theoretical framework of quantum speed limits (QSLs) in terms of trajectory ensembles in phase space. This indicates that the QSL can be thought of as the summation of the connecting harmonic oscillators: the connections between the points of the system in phase space and the trajectory ensemble. Two typical models, the time-dependent harmonic oscillator and the undriven harmonic oscillator coupled to a thermal bath, are investigated by employing the theoretical framework. Our results from this perspective are in agreement with previous treatments.
Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.