Reviewerly/exHarmony huggingface dataset:
A Collaborators Recommendation Method Based on Multi-feature Fusion

This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation.
Estimation of Gaze-Following Based on Transformer and the Guiding Offset

Gaze-following is a challenging task in computer vision. With the help of gaze-following, we can understand what other people are looking and predict what they might do. We propose a two-stage solution for the gaze point prediction of the target person. In the first stage, the head image and head position are fed into the gaze pathway to predict the guiding offset, then we generate the multi-scale gaze fields with the guiding offset. In the second stage, we concatenate the multi-scale gaze fields with full image and feed them into the heatmap pathway to predict a heatmap. We leverage the guiding offset to facilitate the training of gaze pathway and we add the channel attention module. We use Transformer to capture the relationship between the person and the predicted target in the heatmap pathway. Experimental results have demonstrated the effectiveness of our solution on GazeFollow dataset and DL Gaze dataset.
Causal Transfer Evidential Clustering

Classical prototype-based clustering algorithms usually cannot achieve satisfactory results when the data is insufficient. Transfer learning can be adopted to address this problem. For instance, in the recently proposed transfer clustering methods Transfer Evidential C-Means (TECM), the prototypes of data in the source domain are transferred to the target domain to help improve the clustering performance. However, in TECM the prototypes are calculated based on all the features of samples in the clusters in source data sets. Due to distribution shift in two domains, sometimes the prototypes obtained from all the features of samples in the source may not be a good representation for clusters in the target domain. In this paper, we propose an approach for solving this problem by exploiting causal inference, and introduce a new prototype-based causal transfer evidential clustering algorithm. The experimental results demonstrate the effectiveness of the proposed clustering approach.
Chinese textual entailment recognition model based on contextual feature extraction

Textual entailment recognition is widely used in machine translation, reading comprehension and other fields. Aiming at the problem that the existing textual entailment recognition models fail to make full use of textual contextual semantic information, this paper constructs a Chinese textual entailment recognition model based on contextual feature extraction. The model consists of input layer, embedding layer, encoding layer, interaction layer, prediction layer and output layer. Among them, the encoding layer adopts a dual-encoding network for text. Firstly, Sentence-State LSTM is used to encode the text to obtain rich word-level contextual features, and then self-attention mechanism is added to enhance the ability of text to obtain textual contextual features from a long distance; the interaction layer designs an enhanced contextual cross-attention mechanism, which takes the hidden states of the text sequence encoded by Sentence-State LSTM as the contextual representation and connects it with the text sequence output by the encoding layer, so as to make full use of the contextual information of the text and achieve better text alignment. This paper evaluates the model on two datasets, CNLI and RITE, and achieves competitive results, which shows that the proposed model can effectively improve the accuracy of Chinese textual entailment recognition.
DenSE SwinHDR: SDRTV to HDRTV Conversion Using Densely Connected Swin Transformer With Squeeze and Excitation Module

Modern displays have the capability of rendering video contents encoded with high dynamic range (HDR) standards. HDR contents deliver more realistic visual experiences by wider color gamut and luminance compared to standard dynamic range (SDR) contents. However, most of the available contents are encoded with SDR standards. To provide HDR contents, the technology converts existing SDR contents to HDR contents, what we call SDRTV-to-HDRTV, is highly demanded by providers such as IPTV or broadcasting services. In this paper, we divide SDRTV-to-HDRTV conversion problem into global and local mapping problems. Transformers recently achieve significant performance and are known for conducting global mapping effectively. Convolution Neural Networks (CNN) are specialized for extracting and converting local features. In this regard, we introduce a combined model with a transformer for global mapping and CNN for local mapping, which solves the SDRTD-to-HDRTV problem in a complementary manner. We intensively explore the best combination strategy for transformers and CNNs. Through comprehensive objective/subjective experiments, we verified that the proposed method achieves the highest performance compared to the existing models in both fidelity and visual quality perspectives. To the best of our knowledge, we are the first to utilize Vision Transformer for the SDRTV-to-HDRTV conversion problem. To boost the performance, we combined Vision Transformer with architectural strategies which are previously applied on convolutional neural networks such as residual connection, dense connection, and squeeze-and-excitation module. We introduce a new Vision Transformer architecture denoted as DenSE-SwinHDR. Our method outperforms in terms of objective scores and visual quality compared to the state-of-the-art methods. Specifically, DenSE-SwinHDR achieved 0.79 dB PSNR, 0.93 dB PU-PSNR gain over HDRTVNet. Also, our proposed method achieve best performance on subjective quality assessment.
Some automatically generated patches are more likely to be correct than others

Defects4J is a popular dataset against which many Java Automatic Program Repair (APR) tools benchmark their performance. However, recent evidence suggests that some APR tools overfit to Defects4J, producing plausible patches which are incorrect. What we do not currently know is whether there is any commonality in the features of these plausible patches that turn out not to be correct. We compare the features of Defects4J's human written patches in terms of those correctly patched by existing APR tools and those incorrectly patched. We found that 48.4% of Defects4J v1.5 have been automatically patched by existing APR tools; of which only 28.9% have been correctly patched leaving 19.5% incorrectly patched. We found patches of defects that added a method call, added a variable, or wrapped existing code with new code, such as a try/catch block were significantly associated with incorrect patches. Editing only a single line was significantly associated with correct patches. Our results suggest that current tools are weak at generating multiline patches and synthesising new code especially when wrapping existing code. Our results highlight potential future areas of development for new APR approaches, such as developing a tool that effectively repairs defects that require a try/catch block.
Spatial-Spectral Convolutional Sparse Neural Network for Hyperspectral Image Denoising

Sparse representation (SR) is a widely accepted hyper-spectral image (HSI) denoising model. Because of the curse of dimensionality and the desire to better fit the data, the SR models are typically deployed on small and fully overlapping blocks whose results are averaged to produce the global de-noised HSI. This “local-global” denoising mechanism ignores the dependencies between blocks, resulting in visual artifacts. This paper describes the underlying clean HSI with a 3D con-volutional sparse coding (CSC) model, representing the HSI with a linear combination of few shift-invariant 3D spatial-spectral filters in a global dictionary. Instead of operating on patches, the CSC model sees the clean HSI is generated from a sum of local atoms that appear in a small number of locations throughout the image, naturally retaining the relationship between pixels. Moreover, we unfold the optimization process of the model into a spatial-spectral convolutional sparse neural network which absorbs the interpretation ability of the model while supporting discriminative learning from data. Experimental results on both synthetic and real-world datasets show that our network achieves competitive denoising performances, qualitatively and quantitatively.
Reordering Based Unsupervised Neural Machine Translation system for English To Telugu

Neural Machine Translation (NMT) is a progressing field of research among many Machine Translation (MT) systems. Many proposed works in machine translation have made standard benchmarks, and they differ from each other based on the model’s approach and type of learning. In this project, we aim to improve the performance of an unsupervised neural machine translation (UNMT) to give us better results by working only on the monolingual corpus. The unsupervised approach helps remove the barrier of translating languages with low-resource in a parallel corpus. Our model tries to extend the recent work on UNMT with reordering techniques to achieve better results.
Novel Innovations for Improving the Quality of Weak PRNGs

Pseudo-random Number Generators are used in an enormous number of computer applications, from video games and wireless network collision avoidance protocols to cryptographic security and Monte Carlo simulations. For most applications, certain requirements regarding speed and statistical quality must be met. This is typically a trade-off. Some modern generators add additional steps to fast, weak generators, to improve statistical quality. The improvement comes at the cost of speed but allows developers to compromise for an acceptable balance of speed and quality. This paper presents novel techniques that can be used to improve the statistical quality of weak PRNGs and examines how existing techniques can be generalized so they can be applied to any PRNG.
The Importance of Multiple Temporal Scales in Motion Recognition: when Shallow Model can Support Deep Multi Scale Models

The execution of a human movement involves different muscles that are activated and coordinated by the brain at different temporal scales in a complex cognitive process. For this reason, studying human motion requires to properly model multiple temporal scales that fully describe its complexity. Current approaches are not able to address this requirement properly or are based on oversimplified models with obvious limitations. Data-driven models represent research frontiers able to provide new insights. In this work we will investigate different data-driven approaches. The first one is based on shallow models that, while achieving reasonably good recognition performance, require to handcraft features according to the domain knowledge The second one is based on deep models that can be extended to manage multiple temporal scales but they are hard to exploit as too many architecture configurations exist. For this reason, we will propose a new deep multiple temporal scale data-driven model, based on Temporal Convolutional Network, capable of learning features from the data at different temporal scales, of outperforming state of the art deep and shallow models, and of exploiting shallow models to tune the architecture configuration. We designed, collected data and tested our proposal in a specially devised experiment, to prove the validity of our approach. In particular, we collected motion capture data about dyad actions where two people exchange a ball. As the weight of the ball and the throwing intentions change, we will show how it is possible to automatically detect either the weight of the ball or the intention behind the throw just based on motion data. Data regarding our experiment and code of the methods proposed in this work are also made freely available to the research community. Results support both the proposal and the need for the use of deep multi scale models as a tool to better understand human movement and its multiple time scale nature.
Performance analysis of Face Expression Recognition using Feature level Fusion

Face Expression Recognition (FER) is gaining more attention in social communication. It is noted that, face expressions help humans to communicate strongly with emotions. The FER models include preprocessing, feature extraction and classification stages. In this paper, the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques forms a feature vector. Then, the fusion of all the features is carried out at the feature level. The multiclass SVM and KNN classifiers are used to recognize the facial expressions effectively. The publicly available JAFFE database is used to evaluate the performance of the face expression recognition models. The accuracy of 98.26% and 95.21% is recorded for SVM and KNN classifier.
KC2UM: Knowledge-Conversation Cyclic Utilization Mechanism for Knowledge-Grounded Dialogue Generation

End-to-End open-domain dialogue systems suffer from the issues of generating inconsistent and repetitive responses. Existing dialogue models pay attention to unilaterally incorporating personalized knowledge into the dialogue to enhance the quality of generated response. However, they ignore that incorporating the personality-related information from dialogue history into personalized knowledge can boost the subsequent dialogue quality. In this paper, A Knowledge-Conversation Cyclic Utilization Mechanism (KC2UM) is proposed to enhance the dialogue quality. Specifically, A novel cyclic interaction module is designed to iteratively incorporate personalized knowledge into each turn conversation and capture the personality-related conversation information to enhance personalized knowledge semantic representation. We represent the knowledge with semantic and utilization representations to keep track of the personalized knowledge utilization. Experiments on two knowledge-grounded dialogue datasets show that our approach manages to select knowledge more accurately and generates more informative responses.
First-person Activity Recognition by Modelling Subject - Action Relevance

The efficacy of Action Recognition methods depends upon relevance of the action (Verb) with respect to the subject (Noun). Existing methods overlook the suitability of Noun-Verb combination in defining an action. In this work, we propose an algorithm called Reduced Verb Set Generator (RVSGen) to reduce the number of possible verbs related to the actions, based upon the relevance of noun-verb combination. A dual modal fusion model for egocentric activity recognition is proposed here to combine the features extracted from the RGB channels and the Optical flow vectors for an egocentric video to recognize the human activity. Unlike state-of-the-art methods, where the key objects and temporal cues are extracted simultaneously, the proposed model first extracts the spatial features i.e., object information (Noun) from the RGB channels and then relates the Noun with the suitable action (Verb) obtained from motion information (Optical Flow). The verbs are predicted by an ConvLSTM architecture with the help of a modified softmax. The notion behind the modified softmax is to estimate the probability distribution with the reduced verb set obtained from RVSGen and the feature vector obtained from the ConvLSTM. With the help of an end-to-end trained architecture, the noun and verb are predicted which are then concatenated together constituting an action. The experiments are performed on a benchmark dataset. The results show the efficacy of the proposed method compared to the state-of-the-art. The codes related to this work can be found at: https://github.com/mpLogics/EgoAR-RVSGen.
Real-World Video Anomaly Detection by Extracting Salient Features in Videos

We propose a lightweight and accurate method for detecting anomalies in videos.Existing methods used multiple-instance learning (MIL) to determine the normal/abnormal status of each segment of the video.Recent successful researches argue that it is important to learn the temporal relationships among segments to achieve high accuracy, instead of focusing on only a single segment.Therefore we analyzed the existing methods that have been successful in recent years, and found that while it is indeed important to learn all segments together, the temporal orders among them are irrelevant to achieving high accuracy.Based on this finding, we do not use the MIL framework, but instead propose a lightweight model with a selfattention mechanism to automatically extract features that are important for determining normal/abnormal from all input segments.As a result, our neural network model has 1.3% of the number of parameters of the existing method.We evaluated the frame-level detection accuracy of our method on three benchmark datasets (UCF-Crime, ShanghaiTech, and XD-Violence) and demonstrate that our method can achieve the comparable or better accuracy than state-of-the-art methods.
Investigation of Explainability Techniques for Multimodal Transformers

Multimodal transformers such as CLIP and ViLBERT have become increasingly popular for visiolinguistic tasks as they have an efficient and generalizable understanding of visual features and labels. Notable examples of visiolinguistic models include OpenAI's CLIP by Radford et al. and VilBERT by Lu et al. One of the gaps in current multimodal transformers is that there are no unified explainability frameworks to compare attention interactions meaningfully between models. To address the comparability concern, we investigate two different explainability frameworks. Specifically, Label Attribution and Optimal Transport of Vision-Language semantic spaces with the VisualBERT multimodal transformer model provide an interpretability process towards understanding attention interactions in multimodal transformers. We provide a case study of the Visual Genome and Question Answer 2 Datasets trained using VisualBERT.
A taxi detour trajectory detection model based on iBAT and DTW algorithm

&lt;abstract&gt; &lt;p&gt;Taxi detour is a chronic problem in urban transport systems, which largely undermines passengers' riding experience and the city's image while unnecessarily worsening traffic congestion. Tourists unfamiliar with city roads often encounter detour problems. Therefore, it is important for regulatory authorities to develop a tool for detour behavior detection in order to discover or identify detours. This study proposes a detour trajectory detection model framework based on the trajectory data of taxis that can identify taxi driving detour fraud at the microscopic level and analyze the characteristics of detouring trajectories from the perspective of microscopic motion traits. The deviation from normal driving trajectories provides a framework for the automatic detection of detour trajectories for the off-site supervision platform of the taxis. Considering drawbacks of the isolation-Based Anomalous Trajectory (iBAT) algorithm, this paper made further improvements in trajectory anomaly detection. In this study, three methods including the iBAT, iBAT + Dynamic Time Warping (DTW), and iBAT + DTW algorithms considering the driving distance and time are compared using the relevant experimental data. The case studies verify that the proposed method outperforms the other methods. Verified by the experiments based on the trajectory data coming from Nanjing, the false positive rate of this framework is only 1.64%.&lt;/p&gt; &lt;/abstract&gt;
Team Creativity in a Hybrid Software Development World: Eight Approaches

Software development is inherently a creative endeavor. With hybrid software development here to stay, this article documents eight approaches we have found that hybrid software teams use to preserve, structure, and promote creativity as part of their day-to-day work.
Improved sparrow search algorithm based on normal cloud model and niche recombination strategy

To overcome the drawback of optimizing process with biased, slower convergence speed and easily falling into local optimum on high-dimensional optimization problems of Sparrow Search Algorithm (SSA), an improved Sparrow Search Algorithm (CRSSA) based on the Normal Cloud Model (NCM) and niche recombination strategy is proposed. Firstly, the position update strategy of the producers with alarm value less than safe value based on the NCM is proposed for avoiding the original update strategy to converge to the center of domain gradually; Secondly, the adaptive parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$En$</tex-math></inline-formula> in the NCM can better balance the exploitation and exploration capabilities of the optimization process; Then, the position update strategy of the followers with poor fitness value based on the NCM is presented for enhancing the diversity of population at the end of iteration and avoiding the algorithm to fall into local optimum; Finally, when the algorithm is stagnating, a niche-based recombination strategy is used to further avoid falling into the local optimum, and accelerating the convergence speed. Simulation results show that the CRSSA can not only avoid optimizing with biased of SSA, but keep better accuracy and convergence speed for solving high-dimensional complex optimization problems.
A Prediction for the Cluster Centers in Unlabeled Data

This article proposes an algorithm to predict the cluster centers and their locations in unlabeled data in which we do not know how many clusters in advance. The proposed method is a recursive algorithm and has good performance to deal with the clustering problem in data with or without noise.
Getting Saturated with Induction

Induction in saturation-based first-order theorem proving is a new exciting direction in the automation of inductive reasoning. In this paper we survey our work on integrating induction directly into the saturation-based proof search framework of first-order theorem proving. We describe our induction inference rules proving properties with inductively defined datatypes and integers. We also present additional reasoning heuristics for strengthening inductive reasoning, as well as for using induction hypotheses and recursive function definitions for guiding induction. We present exhaustive experimental results demonstrating the practical impact of our approach as implemented within Vampire.