Reviewerly/exHarmony huggingface dataset:
A cross-layer approach for supporting real-time multi-user video streaming over WLANs*

MU-MIMO is a high-speed technique in IEEE 802.11ac and upcoming 802.11ax technologies that improves spectral efficiency by allowing concurrent communication between one Access Point and multiple users. In this paper, we present MuVIS, a novel framework that proposes MU-MIMO-aware optimization for multi-user multimedia applications over IEEE 802.11ac/ax. Taking a cross-layer approach, MuVIS first optimizes the MU-MIMO user group selection for the users with the same characteristics in the PHY/MAC layer. It then optimizes the video bitrate for each group accordingly. We present our design and its evaluation on smartphones and laptops over 802.11ac WiFi.
CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

Constant bit rate (CBR) videos are widely used in streaming playback applications.However, the image quality of the CBR video is often unstable, especially for scenes with large motion.To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN).We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain.To address the degradation resulting from compression, we propose a twostep quantization degradation estimation strategy.The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to finetune the quantization table according to the content of each frame.In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network.Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video.Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension

Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text. To support discrete reasoning, evidence, typically the concise textual fragments that describe question-related facts, including topic entities and attribute values, are crucial clues from question to answer. However, previous end-to-end methods that achieve state-of-the-art performance rarely solve the problem by paying enough emphasis on the modeling of evidence, missing the opportunity to further improve the model’s reasoning ability for R-MRC. To alleviate the above issue, in this paper, we propose an Evidence-emphasized Discrete Reasoning approach (EviDR), in which sentence and clause level evidence is first detected based on distant supervision, and then used to drive a reasoning module implemented with a relational heterogeneous graph convolutional network to derive answers. Extensive experiments are conducted on DROP (discrete reasoning over paragraphs) dataset, and the results demonstrate the effectiveness of our proposed approach. In addition, qualitative analysis verifies the capability of the proposed evidence-emphasized discrete reasoning for R-MRC (Code is released at https://github.com/JD-AI-Research-NLP/EviDR).
Leveraging Structure in Software Merge: An Empirical Study

Large-scale software development today relies heavily on version control systems facilitating distributed development of software projects. For the purpose of merging diverging versions of the code base, version control systems employ line-based merge algorithms, which are applicable to all text files. Structured merge algorithms have been proposed as an alternative to unstructured, line-based merging, with the goal of reducing the number of merge conflicts that have to be manually resolved by the developer. By leveraging the structure inherent in source code (i.e., by representing source code files in terms of abstract syntax trees instead of sequences of text lines), these algorithms are able to merge revisions in various situations (e.g., reordering of methods) that would cause conflicts when merged using an unstructured approach. However, merging abstract syntax trees is inherently more complex than merging sequences of text lines, which makes structured merge algorithms computationally more expensive than an unstructured merge. To reduce the runtime cost of structured merge algorithms, semistructured merge as well as combinations of different merge strategies were proposed. As such, we observe a range of increasingly structured merge algorithms, which feature different characteristics in terms of conflict resolution and runtime. The progressively increasing use of structure to avoid merge conflicts or to automate conflict resolution raises a number of questions: How is the correctness of the code resulting from a merge affected when employing structured merge algorithms? Which algorithm strikes the best balance between runtime, conflict resolution potential, and correctness of the merge result? For the first time, we evaluate a whole range of merge algorithms (from unstructured over semistructured to structured as well as combinations) by replaying merge commits in a controlled setting. We employ the test suite of the projects in question as an oracle for the correctness of the resulting code, triangulated by a thorough manual analysis. Using 7727 merge commits from 10 open-source projects, we find that combined strategies appear to be the best of both worlds: They resolve as many conflicts as structured merge at a significantly lower runtime per merge commit. Notably, structured merge strategies do cause more test failures, however, the increase is small.
Towards Explaining Epsilon: A Worst-Case Study of Differential Privacy Risks

Differential privacy is a concept to quantity the disclosure of private information that is controlled by the privacy parameter $\varepsilon$. However, an intuitive interpretation of $\varepsilon$ is needed to explain the privacy loss to data engineers and data subjects. In this paper, we conduct a worst-case study of differential privacy risks. We generalize an existing model and reduce complexity to provide more understandable statements on the privacy loss. To this end, we analyze the impact of parameters and introduce the notion of a global privacy risk and global privacy leak.
Cluster-Based Memetic Approach of Image Alignment

The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels belonging to the target image and to the sensed image respectively, the boundaries of the search space are computed. Next, the registration mechanism residing in a hybridization between a version of firefly population-based search procedure and the two membered evolutionary strategy computed on clustered data is applied. In addition, a procedure designed to deal with the premature convergence problem is embedded. The fitness to be maximized by the memetic algorithm is defined by the Dice coefficient, a function implemented to evaluate the similarity between pairs of binary images. The proposed methodology is applied on both binary and monochrome images. In case of monochrome images, a preprocessing step aiming the binarization of the inputs is considered before the registration. The quality of the proposed approach is measured in terms of accuracy and efficiency. The success rate based on Dice coefficient, normalized mutual information measures, and signal-to-noise ratio are used to establish the accuracy of the obtained algorithm, while the efficiency is evaluated by the run time function.
MGNETS

Table search aims to retrieve a list of tables given a user's query. Previous methods only consider the textual information of tables and the structural information is rarely used. In this paper, we propose to model the complex relations in the table corpus as one or more graphs and then utilize graph neural networks to learn representations of queries and tables. We show that the text-based table retrieval methods can be further improved by graph-based predictions which fuse multiple field-level information.
Automatic Speaker Verification using Gammatone Frequency Cepstral Coefficients

Biometric based technology is often used for identification in access control systems. One of the most important behavioral biometrics methods is speech verification, wherein audio cues are utilized for decision making. Since there is an increase in audio verification systems nowadays, several attackers have developed novel spoofing techniques to capture the data. To resolve this problem, an automatic speaker verification system (ASV) is used. The ASV system authenticates speakers by analyzing speech utterances. This paper presents a probabilistic verification model built on machine learning techniques for accurately detecting and reporting such attacks. We have utilized Gammatone frequency cepstral coefficients (GFCCs) as a prominent feature along-with the existing features like Mel frequency cepstral coefficients (MFCCs) and pitch for improving the ASV system accuracy. These features are subsequently passed through the Gaussian mixture model (GMM) and K nearest neighbor (KNN) classifiers for making the final decision. Among all the results, the lowest EER of 0.07 is obtained when GFCC is used with GMM as the baseline classifier.
Quantifying identifiability to choose and audit ϵ in differentially private deep learning

Differential privacy allows bounding the influence that training data records have on a machine learning model. To use differential privacy in machine learning, data scientists must choose privacy parameters (ϵ, δ ). Choosing meaningful privacy parameters is key, since models trained with weak privacy parameters might result in excessive privacy leakage, while strong privacy parameters might overly degrade model utility. However, privacy parameter values are difficult to choose for two main reasons. First, the theoretical upper bound on privacy loss (ϵ, δ) might be loose, depending on the chosen sensitivity and data distribution of practical datasets. Second, legal requirements and societal norms for anonymization often refer to individual identifiability, to which (ϵ, δ ) are only indirectly related. We transform (ϵ, δ ) to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset. The bound holds for multidimensional queries under composition, and we show that it can be tight in practice. Furthermore, we derive an identifiability bound, which relates the adversary assumed in differential privacy to previous work on membership inference adversaries. We formulate an implementation of this differential privacy adversary that allows data scientists to audit model training and compute empirical identifiability scores and empirical (ϵ, δ ).
Absolute and Relative Pose Estimation in Refractive Multi View

This paper investigates absolute and relative pose estimation under refraction, which are essential problems for refractive structure from motion. We first present an absolute pose estimation algorithm by leveraging an efficient iterative refinement. Then, we derive a novel refractive epipolar constraint for relative pose estimation. The epipolar constraint is established based on the virtual camera transformation, making it in a succinct form and can be efficiently optimized. Evaluations of the proposed algorithms on synthetic data show superior accuracy and computational efficiency to state-of-the-art methods. For further validation, we demonstrate the performance on real data and show the application in 3D reconstruction of objects under refraction.
Image forgery detection using Deep Neural Network

Due to the availability of deep networks, progress has been made in the field of image recognition. Images and videos are spreading very conveniently and with the availability of strong editing tools the tampering of digital content become easy. To detect such scams, we proposed techniques. In our paper, we proposed two important aspects of employing deep convolutional neural networks to image forgery detection. We first explore and examine different pre-processing method along with convolutional neural networks (CNN) architecture. Later we evaluated the different transfer learning for pre-trained ImageNet(via-fine-tuning) and implement it over our dataset CASIA V2.0. So, it covers the pre-processing techniques with basic CNN model and later see the powerful effect of the transfer learning models.
Multi-modal Sarcasm Detection Based on Contrastive Attention Mechanism

In the past decade, sarcasm detection has been intensively conducted in a textual scenario. With the popularization of video communication, the analysis in multi-modal scenarios has received much attention in recent years. Therefore, multi-modal sarcasm detection, which aims at detecting sarcasm in video conversations, becomes increasingly hot in both the natural language processing community and the multi-modal analysis community. In this paper, considering that sarcasm is often conveyed through incongruity between modalities (e.g., text expressing a compliment while acoustic tone indicating a grumble), we construct a Contrastive-Attention-based Sarcasm Detection (ConAttSD) model, which uses an inter-modality contrastive attention mechanism to extract several contrastive features for an utterance. A contrastive feature represents the incongruity of information between two modalities. Our experiments on MUStARD, a benchmark multi-modal sarcasm dataset, demonstrate the effectiveness of the proposed ConAttSD model.
Sequence Transduction with Graph-Based Supervision

The recurrent neural network transducer (RNN-T) objective plays a major role in building today’s best automatic speech recognition (ASR) systems for production. Similarly to the connectionist temporal classification (CTC) objective, the RNN-T loss uses specific rules that define how a set of alignments is generated to form a lattice for the full-sum training. However, it is yet largely unknown if these rules are optimal and do lead to the best possible ASR results. In this work, we present a new transducer objective function that generalizes the RNN-T loss to accept a graph representation of the labels, thus providing a flexible and efficient framework to manipulate training lattices, e.g., for studying different transition rules, implementing different transducer losses, or restricting alignments. We demonstrate that transducer-based ASR with CTC-like lattice achieves better results compared to standard RNN-T, while also ensuring a strictly monotonic alignment, which will allow better optimization of the decoding procedure. For example, the proposed CTC-like transducer achieves an improvement of 4.8% on the test-other condition of LibriSpeech relative to an equivalent RNN-T based system.
A Normalizing Flow-Based Co-Embedding Model for Attributed Networks

Network embedding is a technique that aims at inferring the low-dimensional representations of nodes in a semantic space. In this article, we study the problem of inferring the low-dimensional representations of both nodes and attributes for attributed networks in the same semantic space such that the affinity between a node and an attribute can be effectively measured. Intuitively, this problem can be addressed by simply utilizing existing variational auto-encoder (VAE) based network embedding algorithms. However, the variational posterior distribution in previous VAE based network embedding algorithms is often assumed and restricted to be a mean-field Gaussian distribution or other simple distribution families, which results in poor inference of the embeddings. To alleviate the above defect, we propose a novel VAE-based co-embedding method for attributed network, F-CAN, where posterior distributions are flexible, complex, and scalable distributions constructed through the normalizing flow. We evaluate our proposed models on a number of network tasks with several benchmark datasets. Experimental results demonstrate that there are clear improvements in the qualities of embeddings generated by our model to the state-of-the-art attributed network embedding methods.
Towards an Ontology-Based Cotton Phytosanitary Surveillance System: A Case Study in Côte D’Ivoire

Cotton cultivation in the Côte d'Ivoire is subject to enormous pressure from pests. This situation has important consequences on the yield of this crop. To improve yield, a strategy based on phytosanitary surveillance has been elaborated by the National Agronomic Research Center (CNRA). In this paper, we propose a digital solution for the implementation of this strategy. This solution is founded on an ontology (ontoSySParCotCI). It includes two main modules. The first module is a data collection and an alert system. It has a mobile interface for data collection and a web interface for access to data by researchers. The second module is a semantic wiki which is an interface for sharing and co-constructing knowledge on cotton cultivation. A prototype of the first module was presented as well as a satisfaction survey for its use.
Adaptive Posterior Knowledge Selection for Improving Knowledge-Grounded Dialogue Generation

In open-domain dialogue systems, knowledge information such as unstructured persona profiles, text descriptions and structured knowledge graph can help incorporate abundant background facts for delivering more engaging and informative responses. Existing studies attempted to model a general posterior distribution over candidate knowledge by considering the entire response utterance as a whole at the beginning of decoding process for knowledge selection. However, a single smooth distribution could fail to model the variability of knowledge selection patterns over different decoding steps, and make the knowledge expression less consistent. To remedy this issue, we propose an adaptive posterior knowledge selection framework, which sequentially introduces a series of discriminative distributions to dynamically control when and what knowledge should be used in specific decoding steps. The adaptive distributions can also capture knowledge-relevant semantic dependencies between adjacent words to refine response generation. In particular, for knowledge graph-grounded dialogue generation, we further incorporate the adaptive distributions into generative word distributions to help express the knowledge entity words. The experimental results show that our developed methods outperform strong baseline systems by large margins.
Exponential-Distance Weights for Reducing Grid-like Artifacts in Patch-Based Medical Image Registration

Patch-based medical image registration has been well explored in recent decades. However, the patch fusion process can generate grid-like artifacts along the edge of patches for the following two reasons: firstly, in order to ensure the same size of input and output, zero-padding is used, which causes uncertainty in the edges of the output feature map during the feature extraction process; secondly, the sliding window extraction patch with different strides will result in different degrees of grid-like artifacts. In this paper, we propose an exponential-distance-weighted (EDW) method to remove grid-like artifacts. To consider the uncertainty of predictions near patch edges, we used an exponential function to convert the distance from the point in the overlapping regions to the center point of the patch into a weighting coefficient. This gave lower weights to areas near the patch edges, to decrease the uncertainty predictions. Finally, the dense displacement field was obtained by this EDW weighting method. We used the OASIS-3 dataset to evaluate the performance of our method. The experimental results show that the proposed EDW patch fusion method removed grid-like artifacts and improved the dice similarity coefficient superior to those of several state-of-the-art methods. The proposed fusion method can be used together with any patch-based registration model.
Augmenting Knowledge Distillation with Peer-to-Peer Mutual Learning for Model Compression

Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network. In contrast, Mutual Learning (ML) provides an alternative strategy where multiple simple student networks benefit from sharing knowledge, even in the absence of a powerful but static teacher network. Motivated by these findings, we propose a single-teacher, multi-student framework that leverages both KD and ML to achieve better performance. Furthermore, an online distillation strategy is utilized to train the teacher and students simultaneously. To evaluate the performance of the proposed approach, extensive experiments were conducted using three different versions of teacher-student networks on benchmark biomedical classification (MSI vs. MSS) and object detection (Polyp Detection) tasks. Ensemble of student networks trained in the proposed manner achieved better results than the ensemble of students trained using KD or ML individually, establishing the benefit of augmenting knowledge transfer from teacher to students with peer-to-peer learning between students.
Prediction of Adverse Drug Reaction using Machine Learning and Deep Learning Based on an Imbalanced Electronic Medical Records Dataset

Early prediction of adverse drug reaction (ADR) is crucial in clinical research. The development of electronic medical record (EMR) provides an excellent resource for retrospective studies to extract samples and establish models that can be used for prediction of clinical deterioration. However, classical statistical models like multivariate logistic regression (LR) may result in unreliable predictions when handling unbalanced datasets. To develop a trustworthy model on unbalanced ADR data, we first transformed the EMR including medical notes into numeric variables. Then we introduced support vector machine (SVM), random forest (RF), AdaBoost, XGBoost, and artificial neural network (ANN) to deal with the challenge of high dimensionality. Furthermore, we utilized the ensembling approach to tackle data imbalance. Finally, we analyzed potential model mechanisms to provide interpretability and compared methods from the perspective of procedure elapsed time. The results showed ensembling contributed considerable improvement in prediction ability of various machine intelligence models. Compared with the baseline, RF, AdaBoost and XGBoost presented superiority, and ANN without fine-tuning showed similar competence. The results of this study demonstrated the great potential of machine learning models in medical domain.