Reviewerly/exHarmony huggingface dataset:
Path Finding Visualizer Application for Shortest Path Algorithm

Visualization is an efficient way of learning any concept faster than conventional methods. Modern technology allows creating e-Learning tools that also helps in improving computer science education very much. The goal of this project is to create a web based e-Learning tool, ‘PathFinding Visualizer’, which can be used to visualize shortest path algorithms. The conceptual application of the project is illustrated by implementation of algorithms like Dijkstra’s , A* and DFS. This project aims to complete all these tasks with some knowledge of HTML, CSS, JavaScript and React Framework. The end product is a web application so that any user can easily see and learn the working of the algorithms. User-friendliness of the project provides user with easy instructions on how to operate it. The initial results of using the application show promises of the benefits of this e-Learning tool towards students getting a good understanding of shortest paths algorithms.
From Coarse to Fine: Hierarchical Structure-aware Video Summarization

Hierarchical structure is a common characteristic for some kinds of videos (e.g., sports videos, game videos): The videos are composed of several actions hierarchically and there exist temporal dependencies among segments with different scales, where action labels can be enumerated. Our ideas are based on two observations: First, the actions are the fundamental units for people to understand these videos. Second, the humans summarize a video by iteratively observing and refining, i.e., observing segments in video and hierarchically refining the boundaries of important actions. Based on the above insights, we generate action proposals to construct the structure of the video and formulate the summarization process as a hierarchical refining process. We also train a hierarchical summarization network with deep Q-learning (HQSN) to achieve the refining process and explore temporal dependency. Besides, we collect a new dataset that consists of structured game videos with fine-grain actions and importance annotations. The experimental results demonstrate the effectiveness of the proposed method.
No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention

The rapid growth of video consumption and multimedia applications has increased the interest of the academia and industry in building tools that can evaluate perceptual video quality.Since videos might be distorted when they are captured or transmitted, it is imperative to develop reliable methods for no-reference video quality assessment (NR-VQA).To date, most NR-VQA models in prior art have been proposed for assessing a specific category of distortion, such as authentic distortions or traditional distortions.Moreover, those developed for both authentic and traditional distortions video databases have so far led to poor performances.This resulted in the reluctance of service providers to adopt multiple NR-VQA approaches, as they prefer a single algorithm capable of accurately estimating video quality in all situations.Furthermore, many existing NR-VQA methods are computationally complex and therefore impractical for various real-life applications.In this paper, we propose a novel deep learning method for NR-VQA based on multi-task learning where the distortion of individual frames in a video and the overall quality of the video are predicted by a single neural network.This enables to train the network with a greater amount and variety of data, thereby improving its performance in testing.Additionally, our method leverages temporal attention to select the frames of a video sequence which contribute the most to its perceived quality.The proposed algorithm is evaluated on five publicly-available video quality assessment (VQA) databases containing traditional and authentic distortions.Results show that our method outperforms the state-of-theart on traditional distortion databases such as LIVE VQA and CSIQ video, while also delivering competitive performance on databases containing authentic distortions such as KoNViD-1k, LIVE-Qualcomm and CVD2014.
Digging Into Self-Supervised Learning of Feature Descriptors

Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks. However, most of them require per-pixel ground-truth keypoint correspondence data which is difficult to acquire at scale. To address this challenge, recent weakly-and self-supervised methods can learn feature descriptors from relative camera poses or using only synthetic rigid transformations such as homographies. In this work, we focus on understanding the limitations of existing self-supervised approaches and propose a set of improvements that combined lead to powerful feature descriptors. We show that increasing the search space from in-pair to in-batch for hard negative mining brings consistent improvement. To enhance the discriminativeness of feature descriptors, we propose a coarse-to-fine method for mining local hard negatives from a wider search space by using global visual image descriptors. We demonstrate that a combination of synthetic homography transformation, color augmentation, and photorealistic image stylization produces useful representations that are viewpoint and illumination invariant. The feature descriptors learned by the proposed approach perform competitively and surpass their fully- and weakly-supervised counterparts on various geometric benchmarks such as image-based localization, sparse feature matching, and image retrieval.
Towards a systematic understanding of blockchain governance in proposal voting: A dash case study

The transparent and immutable nature of blockchain provides incentives for organizations wishing to create and implement an open, decentralized governance structure. As members exercise their voting rights, a fault-tolerant record accumulates on the blockchain that can be analyzed to diagnose and intercept potential threats to the governing body. To date, there has not been a systematic study of on-chain governance with respect to voting. In this paper, we provide an analysis of blockchain governance through a case study of the first cryptocurrency to adopt on-chain voting, Dash. Our analysis introduces the key characteristics of blockchain governance, steps through a data-driven exploration of Dash's on-chain voting system, and highlights exploitable attack vectors and vulnerabilities for the subversion of Dash's on-chain voting system via a novel network analysis methodology. We then conclude with guidelines for other organizations looking to implement similar blockchain governance solutions while maintaining integrity in their operations.
Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework

3D human pose estimation using monocular images is an important yet challenging task. Existing 3D pose detection methods exhibit excellent performance under normal conditions however their performance may degrade due to occlusion. Recently some occlusion aware methods have also been proposed, however, the occlusion handling capability of these networks has not yet been thoroughly investigated. In the current work, we propose an occlusion-guided 3D human pose estimation framework and quantify its occlusion handling capability by using different protocols. The proposed method estimates more accurate 3D human poses using 2D skeletons with missing joints as input. Missing joints are handled by introducing occlusion guidance that provides extra information about the absence or presence of a joint. Temporal information has also been exploited to better estimate the missing joints. A large number of experiments are performed for the quantification of occlusion handling capability of the proposed method on three publicly available datasets in various settings including random missing joints, fixed body parts missing, and complete frames missing, using mean per joint position error criterion. In addition to that, the quality of the predicted 3D poses is also evaluated using action classification performance as a criterion. 3D poses estimated by the proposed method achieved significantly improved action recognition performance in the presence of missing joints. Our experiments demonstrate the effectiveness of the proposed framework for handling the missing joints as well as quantification of the occlusion handling capability of the deep neural networks.
Target-biased informed trees: sampling-based method for optimal motion planning in complex environments

Abstract Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.
Credit Card Fraud Detection using Logistic Regression

Credit card fraud detection targets to find out credit card fraud by analyzing the features of transaction data, which can prevent huge financial losses and reduce the pressure on police. There are three challenges in credit card fraud detection, i.e., unbalanced data, excessive invisible features, and the choice of suitable threshold to make the models more reasonable. To overcome the above challenges, this paper uses a modified Logistic Regression (LR) model to identify credit card frauds. Firstly, we balance the data by adopting the SMOTE method. We also figure out features’ importance and remove unimportant features. Secondly, we train the Logistic Regression model, and find out the best parameters for the model by Grid CV Search and compare their performance. Finally, we select the best threshold of the model by comparing confusion matrixes. To verify the effectiveness of LR, we compare LR with Support Vector Machines (SVM), K-Nearest Neighbour (KNN) and Decision Tree Classifier (DT) with the model score and Area Under ROC Curve (AUC). The experimental results show that LR achieves a 0.94 accuracy score, while KNN, SVM and DT achieve 0.93, 0.93 and 0.91 with default parameters, respectively. LR gets the highest classifier score 0.9824 at the AUC score, which demonstrates LR’s effectiveness in credit card fraud prediction.
Software Project Failure Process Definition

Many researchers have attempted to identify the factors behind software project failures and their solutions from various perspectives.However, systematic and integrated process definitions of failure as process models for success are lacking.This study aims to build a process definition for software project failure as an anti-pattern by identifying the main phases and their relationships in terms of team behavior.We researched software engineering literature and case studies to gather information about critical incidents and repeating behaviors of teams in failed projects into a novel dataset.Grounded theory was employed to build a theoretical foundation for failure phase definitions from the collected data.The design structure matrix and Bayesian belief network were used for the quantitative assessment of the transitions between phases.The results revealed that common behavioral patterns occurred in approximately 89 percent of the case studies, supporting the decision to consider software project failure as a process.The proposed failure process definition has a simple structure that uses everyday concepts for phase names and reveals the critical behaviors leading a software project to failure Thus, it provides critical insights for software professionals, non-technical stakeholders, and managers to evaluate the progress of their projects and design strategies to avoid failure.
Quantitative analysis of nonlinear optical input/output of a quantum-dot network based on the echo state property

The echo state property, which is related to the dynamics of a neural network excited by input driving signals, is one of the well-known fundamental properties of recurrent neural networks. During the echo state, the neural network reveals an internal memory function that enables it to remember past inputs. Due to the echo state property, the neural network will asymptotically update its condition from the initial condition and is expected to exhibit temporally nonlinear input/output. As a physical neural network, we fabricated a quantum-dot network that is driven by sequential optical-pulse inputs and reveals corresponding outputs, by random dispersion of quantum-dots as its components. In the network, the localized optical energy of excited quantum-dots is allowed to transfer to neighboring quantum-dots, and its stagnation time due to multi-step transfers corresponds to the hold time of the echo state of the network. From the experimental results of photon counting of the fluorescence outputs, we observed nonlinear optical input/output of the quantum-dot network due to its echo state property. Its nonlinearity was quantitatively verified by a correlation analysis. As a result, the relation between the nonlinear input/outputs and the individual compositions of the quantum-dot network was clarified.
Quantum cost of dense coding and teleportation

The quantum cost is a key ingredient to evaluate the quality of quantum protocols from a practical viewpoint. We show that the quantum cost of d-dimensional dense coding protocol is equal to d+3 when transmitting the classical message (0,0), and that is equal to d+4 when transmitting other classical message. It appears linear growth with the dimension and thus makes sense for implementation. In contrast, the quantum cost of high-dimensional teleportation protocols is equal to 13, which is the maximum value of the cost for the two-dimensional case. As an application, we establish the relation between the quantum cost and fidelity of dense coding protocols in terms of four typical noise scenario.
A Likelihood Ratio Based Domain Adaptation Method for E2E Models

End-to-end (E2E) automatic speech recognition models like Recurrent Neural Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR applications like voice assistants. While E2E models are very effective at learning representation of the training data they are trained on, their accuracy on unseen domains remains a challenging problem. Additionally, these models require paired audio and text training data, are computationally expensive and are difficult to adapt towards the fast evolving nature of conversational speech. In this work, we explore a contextual biasing approach using likelihood-ratio that leverages text data sources to adapt RNN-T model to new domains and entities. We show that this method is effective in improving rare words recognition, and results in a relative improvement of 10% in 1-best word error rate (WER) and 10% in n-best Oracle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> WER (n=8) on multiple out-of-domain datasets without any degradation on a general dataset. We also show that complementing the contextual biasing adaptation with adaptation of a second-pass rescoring model gives additive WER improvements.
ACGDP: An Augmented Code Graph-Based System for Software Defect Prediction

Recognizing and repairing defects to enhance quality in software life circle has become a critical research topic. Unfortunately, it is difficult to guarantee the validity of the defect prediction method based on manually designed features proposed in previous studies. Numerous scholars have endeavored to use a single model to obtain prediction results for different types of fault, but this is difficult to perform. This article improves the defect representation and prediction model in software defect prediction, proposing Augmented-Code Property Graph (CPG) based defect prediction method (ACGDP). Augmented-CPG is a novel encoding graph format introduced in this article. Based on Augmented-CPG, we suggested defect region candidate extraction approach linked to the defect category. Graph neural networks are used for obtaining defect characteristics. Experiments on three distinct types of defects indicate that ACGDP can predict certain classed of defects effectively.
Timely and Accurate Bitrate Switching in HTTP Adaptive Streaming With Date-Driven I-Frame Prediction

In today's Internet, bandwidth dynamics are inevitable, and hence, the bitrate for live streaming applications should also be dynamically adjusted. However, in existing HTTP-based adaptive streaming (HAS), bitrate switching can only be performed at segment boundaries, making decisions unresponsive and often inaccurate. In this paper, we start from a close investigation on the impact of the segment length in HAS and accordingly present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VHAS</i> , an extension towards intelligent variable-length segmentation, which makes client-side decisions based on the massive amount of real-time information from the network and viewers. VHAS implements a smart trigger mechanism that balances accuracy and overhead for variable-length segmentation. We further develop an adaptive bitrate switching algorithm with data-driven I-frame prediction, which is tailored to individual viewers to minimize bitrate mismatches. We evaluate VHAS via extensive trace-driven simulations, and our results demonstrate that compared with state-of-the-art solutions, VHAS achieves 15%–49% gains in QoE, with a noticeable bandwidth reduction of 37%–57%.
Image Forgery Localization Using U-Net based Architecture and Error Level Analysis

Image forensics mainly deals with checking the authenticity and integrity of the digital images. In this digital era, with the advancements in image manipulation tools and software, the tampering of images for malicious purposes has increased and the detection of such manipulation is necessary. In this paper, an approach using U-Net is proposed to detect multiple types of forgeries and to localize the tampered region at the pixel level in the images. The forged images are processed with Error Level Analysis (ELA) which highlights the forged region having different compression levels and helps in improving the model performance. The processed images are used to train the U-Net based encoder-decoder architecture. A pre-trained Residual Network 50 (ResNet50 backbone network initialized with pre-trained weights is used in the encoder of the network to speed up the training process and enhance the performance of the model by transfer learning. The encoded features are concatenated with the low-level image features at the decoder to localize the tampered region accurately. The proposed approach can detect and localize different types of forgeries with very high precision and outperforms several existing state-of-the-art methods used for forged region localization.
<scp>ClioQuery</scp> : Interactive Query-oriented Text Analytics for Comprehensive Investigation of Historical News Archives

Historians and archivists often find and analyze the occurrences of query words in newspaper archives to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual units, such as events, clusters, ranked documents, entity relationships, or thematic hierarchies. Informed by a study into the needs of historians and archivists, we thus propose ClioQuery , a text analytics system uniquely organized around the analysis of query words in context. ClioQuery applies text simplification techniques from natural language processing to help historians quickly and comprehensively gather and analyze all occurrences of a query word across an archive. It also pairs these new NLP methods with more traditional features like linked views and in-text highlighting to help engender trust in summarization techniques. We evaluate ClioQuery with two separate user studies, in which historians explain how ClioQuery ’s novel text simplification features can help facilitate historical research. We also evaluate with a separate quantitative comparison study, which shows that ClioQuery helps crowdworkers find and remember historical information. Such results suggest possible new directions for text analytics in other query-oriented settings.
Exploiting ICD Hierarchy for Classification of EHRs in Spanish Through Multi-Task Transformers

Electronic Health Records (EHRs) convey valuable information. Experts in clinical documentation read the report, understand the prior work, procedures, tests carried out, and encode the EHRs according to the International Classification of Diseases (ICD). Assigning these codes to the EHRs helps to share information, and extract statistics. In this paper, we explore computer-aided multi-label classification approaches. While Natural Language Understanding has evolved for clinical text mining, there is still a gap for languages other than English. Language-modeling aware Transformers has demonstrated state of the art approaches through exploiting contextual dependencies. Here we focus on EHRs written in Spanish, and try to benefit from the Language Model itself, with unannotated corpus with less data but in-house, in-domain and closely-related EHRs to that of the downstream task. The International Classification of Diseases coding scheme is hierarchical, but its synergies among hierarchical levels are rarely exploited. In this work, we implement and release a hierarchical head for multi-label classification, which benefits from the hierarchy of the ICD via multi-task classification.
Sparse and Flexible Projections for Unsupervised Feature Selection

In recent decades, unsupervised feature selection methods have become increasingly popular. Nevertheless, most of the existing unsupervised feature selection methods suffer from two major problems that lead to suboptimal solutions. Many methods impose a hard linear projection constraint on original data, which is overly strict in nature and not suitable for dealing with data sampled from nonlinear manifolds. Second, most existing methods use <i>l</i><sub>2,p</sub>-norm (0<p<1) regularization on projection matrix to obtain row sparse matrix and then calculate scores of each feature, which would introduce extra parameter with a slight possibility to directly obtain indexes of discriminative features. To solve the above problems, we propose two novel unsupervised feature selection methods called SF<sup>2</sup>S and SF<sup>2</sup>SOG, which can simultaneously learn optimal flexible projections and obtain an orthogonal sparse projection to directly select discriminative features by applying <i>l</i><sub>2,0</sub>-norm constraint. Moreover, we propose to explore the local structure of flexible embedding through preserving the manifold structure of original data and adaptively constructing an optimal graph in subspace. Thirdly, the novel iterative optimization algorithms are presented to solve objective functions guaranteeing convergence theoretically. Various evaluation experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of our proposed methods.