Reviewerly/exHarmony huggingface dataset:
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection

Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community. Most existing approaches employ the Faster R-CNN as basic detection framework, yet, due to the lack of tailored considerations for data-scarce scenario, their performance is often not satisfactory. In this paper, we look closely into the conventional Faster R-CNN and analyze its contradictions from two orthogonal perspectives, namely multi-stage (RPN vs. RCNN) and multi-task (classification vs. localization). To resolve these issues, we propose a simple yet effective architecture, named Decoupled Faster R-CNN (DeFRCN). To be concrete, we extend Faster R-CNN by introducing Gradient Decoupled Layer for multistage decoupling and Prototypical Calibration Block for multi-task decoupling. The former is a novel deep layer with redefining the feature-forward operation and gradient-backward operation for decoupling its subsequent layer and preceding layer, and the latter is an offline prototype-based classification model with taking the proposals from detector as input and boosting the original classification scores with additional pairwise scores for calibration. Extensive experiments on multiple benchmarks show our framework is remarkably superior to other existing approaches and establishes a new state-of-the-art in few-shot literature <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Alexa, what do we know about conversational commerce? Insights from a systematic literature review

Abstract Conversational agents are systems with human‐like features that can be deployed in service settings and are fast emerging as the future of online commerce. Considering the importance and proliferation of conversational commerce, it is opportune to take stock of the field's research from a bird's eye view to guide understanding of its current and future progress. In this regard, the goal of this study is to review the performance and intellectual structure of conversational commerce. To do so, this study conducts a comprehensive and systematic review of 722 publications on conversational commerce using the Scientific Procedures and Rationales for Systematic Literature Reviews protocol and a collection of bibliometric analysis techniques consisting of performance analysis and science mapping (e.g., content analysis, keyword co‐occurrence analysis, and bibliographic coupling). In doing so, this study reveals the performance (e.g., publication and citation trends, and top sources, publications, and authors) and the major theories and themes in the intellectual structure of the field. The study concludes with a conceptual framework depicting the constructs (e.g., antecedents, mediators, moderators, consequences, and enablers) of employing conversational agents for commerce and service delivery, as well as suggestions for its future research.
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models

Recent advances in Large Language Models (LLM) have made automatic code generation possible for real-world programming tasks in general-purpose programming languages such as Python. However, there are few human studies on the usability of these tools and how they fit the programming workflow. In this work, we conducted a within-subjects user study with 24 participants to understand how programmers use and perceive Copilot, a LLM-based code generation tool. We found that, while Copilot did not necessarily improve the task completion time or success rate, most participants preferred to use Copilot in daily programming tasks, since Copilot often provided a useful starting point and saved the effort of searching online. However, participants did face difficulties in understanding, editing, and debugging code snippets generated by Copilot, which significantly hindered their task-solving effectiveness. Finally, we highlighted several promising directions for improving the design of Copilot based on our observations and participants’ feedback.
Measurement-induced quantum phases realized in a trapped-ion quantum computer

Many-body open quantum systems balance internal dynamics against decoherence from interactions with an environment. Here, we explore this balance via random quantum circuits implemented on a trapped ion quantum computer, where the system evolution is represented by unitary gates with interspersed projective measurements. As the measurement rate is varied, a purification phase transition is predicted to emerge at a critical point akin to a fault-tolerent threshold. We probe the "pure" phase, where the system is rapidly projected to a deterministic state conditioned on the measurement outcomes, and the "mixed" or "coding" phase, where the initial state becomes partially encoded into a quantum error correcting codespace. We find convincing evidence of the two phases and show numerically that, with modest system scaling, critical properties of the transition clearly emerge.
Parallel implementation for 3D medical volume fuzzy segmentation

In the past, 2D models were the main models for medical image processing applications, whereas the wide adoption of 3D models has appeared only in recent years. The 2D Fuzzy C-Means (FCM) algorithm has been extensively used for segmenting medical images due to its effectiveness. Various extensions of it were proposed throughout the years. In this work, we propose a modified version of FCM for segmenting 3D medical volumes, which has been rarely implemented for 3D medical image segmentation. We present a parallel implementation of the proposed algorithm using Graphics Processing Unit (GPU). Researchers state that efficiency is one of the main problems of using FCM for medical imaging when dealing with 3D models. Thus, a hybrid parallel implementation of FCM for extracting volume objects from medical files is proposed. The proposed algorithm has been validated using real medical data and simulated phantom data. Segmentation accuracy of predefined datasets and real patient datasets were the key factors for the system validation. The processing times of both the sequential and the parallel implementations are measured to illustrate the efficiency of each implementation. The acquired results conclude that the parallel implementation is 5X faster than the sequential version of the same operation.
Dense 3D Object Reconstruction from a Single Depth View

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 2563 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of 3D encoder-decoder and the conditional adversarial networks framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
Coarse-to-Fine UAV Target Tracking With Deep Reinforcement Learning

The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV) tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle from such a problem as they mainly focus on the scale variation issue by maintaining a certain aspect ratio. In this paper, we propose a coarse-to-fine deep scheme to address the aspect ratio variation in UAV tracking. The coarse-tracker first produces an initial estimate for the target object, then a sequence of actions are learned to fine-tune the four boundaries of the bounding box. The coarse-tracker and the fine-tracker are designed to have different action spaces and operating target. The former dominates the entire bounding box and the latter focuses on the refinement of each boundary. They are trained jointly by sharing the perception network with an end-to-end reinforcement learning architecture. Experimental results on benchmark aerial data set prove that the proposed approach outperforms existing trackers and produces significant accuracy gains in dealing with the aspect ratio variation in UAV tracking.
Fine-grained just-in-time defect prediction

Defect prediction models focus on identifying defect-prone code elements, for example to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews. While the research community has been highly active in proposing metrics and methods to predict defects on long-term periods (i.e.,at release time), a recent trend is represented by the so-called short-term defect prediction (i.e.,at commit-level). Indeed, this strategy represents an effective alternative in terms of effort required to inspect files likely affected by defects. Nevertheless, the granularity considered by such models might be still too coarse. Indeed, existing commit-level models highlight an entire commit as defective even in cases where only specific files actually contain defects. In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in a commit, that are defective. Finally, we evaluate our model in terms of (i) performance and (ii) the extent to which it decreases the effort required to diagnose a defect. Our study highlights that: (1) defective commits are frequently composed of a mixture of defective and non-defective files, (2) our fine-grained model can accurately predict defective files with an AUC-ROC up to 82% and (3) our model would allow practitioners to save inspection efforts with respect to standard just-in-time techniques.
Slice

This paper reformulates the problem of recommending related queries on a search engine as an extreme multi-label learning task. Extreme multi-label learning aims to annotate each data point with the most relevant subset of labels from an extremely large label set. Each of the top 100 million queries on Bing was treated as a separate label in the proposed reformulation and an extreme classifier was learnt which took the user's query as input and predicted the relevant subset of 100 million queries as output. Unfortunately, state-of-the-art extreme classifiers have not been shown to scale beyond 10 million labels and have poor prediction accuracies for queries. This paper therefore develops the Slice algorithm which can be accurately trained on low-dimensional, dense deep learning features popularly used to represent queries and which efficiently scales to 100 million labels and 240 million training points. Slice achieves this by reducing the training and prediction times from linear to logarithmic in the number of labels based on a novel negative sampling technique. This allows the proposed reformulation to address some of the limitations of traditional related searches approaches in terms of coverage, density and quality. Experiments on publically available extreme classification datasets with low-dimensional dense features as well as related searches datasets mined from the Bing logs revealed that slice could be more accurate than leading extreme classifiers while also scaling to 100 million labels. Furthermore, slice was found to improve the accuracy of recommendations by 10% as compared to state-of-the-art related searches techniques. Finally, when added to the ensemble in production in Bing, slice was found to increase the trigger coverage by 52%, the suggestion density by 33%, the overall success rate by 2.6% and the success rate for tail queries by 12.6%. Slice's source code can be downloaded from [21].
Methodology for replacing indirect measurements with direct measurements

This work investigates the mechanism by which indirect measurements can be replaced by direct ones in quantum algorithms. The authors propose a protocol to simplify the measurement of correlators in the quantum simulation and derivatives with respect to the parameters in the variational algorithms, by reducing the required number of gates.
Dual-Hand Detection for Human–Robot Interaction by a Parallel Network Based on Hand Detection and Body Pose Estimation

In this paper, a parallel network based on hand detection and body pose estimation is proposed to detect and distinguish human's right and left hands. The network is employed for human–robot interaction (HRI) based on hand gestures. This method fully uses hand feature information and hand information in the human body structure. One channel in the network uses a ResNet-Inception-Single Shot MultiBox Detector to extract hand feature information for human's hand detection. The other channel estimates human body pose first and then estimates the positions of the left and right hands using the forward kinematic tree of the human skeleton structure. Thereafter, the results of the two channels are fused. In the fusion module, the human body structure can be utilized to correct hand detection results and distinguish between the right and left hands. Experimental results verify that the parallel deep neural network can effectively improve the accuracy of hand detection and distinguish between the right and left hands effectively. This method is also used for the hand-gesture-based interaction between astronauts and an astronaut assistant robot. Our method can be suitably used in this HRI system.
Developing a UX KPI based on the user experience questionnaire

Decisions in Companies are made typically by using a number of entirely different key figures. A user experience key figure is one of many important key figures that represents one aspect of the success of the company or its products. What we aim in this article is to present to those responsible for a product a method of how a user experience key performance indicator (UX KPI) can be developed using a UX questionnaire. We have developed a UX KPI for use in organizations based on the User Experience Questionnaire (UEQ). To achieve this, we added six questions to the UEQ to measure the importance of the UEQ scales. Based on the UEQ scales and the scores given for importance, we then developed a User Experience Questionnaire KPI (UEQ KPI). In a first study with 882 participants, we calculated and discussed the UEQ KPI using Amazon and Skype. The results show that the six supplementary questions could be answered independently of the UEQ itself. In our opinion, the extension can be implemented without any problems. The resulting UEQ KPI can be used for communication within an organization as a key performance indicator.
Enhancing human learning via spaced repetition optimization

Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention. However, current spaced repetition algorithms are simple rule-based heuristics with a few hard-coded parameters. Here, we introduce a flexible representation of spaced repetition using the framework of marked temporal point processes and then address the design of spaced repetition algorithms with provable guarantees as an optimal control problem for stochastic differential equations with jumps. For two well-known human memory models, we show that, if the learner aims to maximize recall probability of the content to be learned subject to a cost on the reviewing frequency, the optimal reviewing schedule is given by the recall probability itself. As a result, we can then develop a simple, scalable online spaced repetition algorithm, MEMORIZE, to determine the optimal reviewing times. We perform a large-scale natural experiment using data from Duolingo, a popular language-learning online platform, and show that learners who follow a reviewing schedule determined by our algorithm memorize more effectively than learners who follow alternative schedules determined by several heuristics.
Cloud-DNN

The efficacy and effectiveness of Convolutional Neural Networks (CNNs) have been proven in a wide range of machine learning applications. However, the high computational complexity of CNNs presents a critical challenge towards their broader adoption in real-time and power-efficient scenarios. FPGAs are poised to take a significant role for high-performance and energy-efficient computation of CNNs for both mobile (e.g., UAVs, self-driving cars, and IoT devices) and cloud computing domains. However, implementing an effective CNN system onto FPGAs efficiently remains problematic. The current cloud-based FPGAs with unique design constraints and architectural characteristics further increase the challenges. To address these challenges, we propose a novel open-source automated tool chain called Cloud-DNN. Our tool chain takes trained CNN models specified in Caffe as input, performs a set of transformations, and maps the model to a cloud-based FPGA. Cloud-DNN can significantly improve the overall design productivity of CNNs on FPGAs while satisfying the emergent computational requirements. Our design provides an alternative solution compared to other cloud-based options (e.g., GPUs or TPUs) while offering flexible, and high performance DNN inferences. The unique features of Cloud-DNN include the optimizations with cloud-platform characteristics and the support of easier and streamlined implementation. Experimental results demonstrate up to 104.55x performance improvement when compared to CPU implementation and comparable usability, flexibility, and strong quality compared to other state-of-the-art DNN inference implementations on standalone FPGAs.
Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications

High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related industrial applications like recommender systems. Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from them. However, they mostly fail to fulfill the non-negativity constraints that describe the non-negative nature of many industrial data. Moreover, existing models suffer from slow convergence rate. An alternating-direction-method of multipliers-based non-negative LF (AMNLF) model decomposes the task of non-negative LF analysis on an HiDS matrix into small subtasks, where each task is solved based on the latest solutions to the previously solved ones, thereby achieving fast convergence and high prediction accuracy for its missing data. This paper theoretically analyzes the characteristics of an AMNLF model, and presents detailed empirical studies regarding its performance on nine HiDS matrices from industrial applications currently in use. Therefore, its capability of addressing HiDS matrices is justified in both theory and practice.
Aspect based fine-grained sentiment analysis for online reviews

Fine-grained sentiment analysis for online reviews plays more and more important role in many applications. The key techniques here are how to efficiently extract multi-grained aspects, identify associated opinions, and classify sentiment polarity. Although various topic models have been proposed to process some of these tasks in recent years, there was little work available for effective sentiment analysis. In this paper, we propose a joint aspect based sentiment topic (JABST) model that jointly extracts multi-grained aspects and opinions through modeling aspects, opinions, sentiment polarities and granularities simultaneously. Moreover, by means of the supervised learning, we then propose a maximum entropy based JABST model (MaxEnt–JABST) to improve accuracy and performance in extracting opinions and aspects. Comprehensive evaluation results on real-world reviews for electronic devices and restaurants demonstrate that our JABST and MaxEnt–JABST models significantly outperform related proposals.
Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems

To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, we propose RippleNet , an end-to-end framework that naturally incorporates the KG into recommender systems. RippleNet has two versions: (1) The outward propagation version, which is analogous to the actual ripples on water, stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user’s potential interests along links in the KG. The multiple “ripples” activated by a user’s historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item. (2) The inward aggregation version aggregates and incorporates the neighborhood information biasedly when computing the representation of a given entity. The neighborhood can be extended to multiple hops away to model high-order proximity and capture users’ long-distance interests. In addition, we intuitively demonstrate how a KG assists with recommender systems in RippleNet, and we also find that RippleNet provides a new perspective of explainability for the recommended results in terms of the KG. Through extensive experiments on real-world datasets, we demonstrate that both versions of RippleNet achieve substantial gains in a variety of scenarios, including movie, book, and news recommendations, over several state-of-the-art baselines.
An iterative wavelet threshold for signal denoising

This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest. The SpcShrink is algorithmically presented and numerically evaluated according to Monte Carlo simulations. Two empirical applications to real biomedical data filtering are also included and discussed. The SpcShrink shows superior performance when compared with competing algorithms.