Reviewerly/exHarmony huggingface dataset:
Case Study Comparison of Computational Notebook Platforms for Interactive Visual Analytics

The way of using computational notebooks is quite different between data science and visual analytics. Data scientists focus on data exploration with the code, while visual analytics users are interested in engaging with interactive visual interfaces to facilitate analytical reasoning. Such a difference leads to design contradictions while merging visual analytics tools with data science tools in computational notebooks. In this work, we investigated the problem using an example called “Andromeda,” which is an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python code in a Jupyter Notebook, 2) JavaScript code in an Observable Notebook, and 3) embedding both Python (data science use) and JavaScript (visual analytics use) in a Jupyter Notebook. Advantages and disadvantages are concluded for each platform by making comparisons based on various aspects, such as design logic, coding differences, performance, and usability. Laying the groundwork for data scientists, advice and recommendations are made on architecting similar notebooks and which platform to choose in various situations.
An Assessment Method for Equipment Battle Damage in a Single Operation

Information-based joint operations reflect the confrontation between the networks based on information system, making the conventional modeling theories unsuitable for equipment battle damage assessment. In order to accurately identify the influence of information elements on equipment battle damage, a method is proposed in this study to assess equipment battle damage in a single operation based on information effect mechanism. The proposed method realizes the accurate calculation of information-based equipment battle damage in three steps, that is, modeling for operations network information effect mechanism, modeling for equipment firepower damage mechanism, and modeling for influence of equipment damage on operations network. This study provides reference for information-based equipment management and joint operations.
Learning to Predict Keypoints and Structure of Articulated Objects without Supervision

Reasoning about the structure and motion of novel object classes is a core ability in human cognition, crucial for manipulating objects and predicting their possible motion. We present a method that learns to infer the skeleton structure of a novel articulated object from a single image, in terms of joints and rigid links connecting them. The model learns without supervision from a dataset of objects having diverse structures, in different poses and states of articulation. To achieve this, it is trained to explain the differences between pairs of images in terms of a latent skeleton that defines how to transform one into the other. Experiments on several datasets show that our model predicts joint locations significantly more accurately than prior works on unsupervised keypoint discovery; moreover, unlike existing methods, it can predict varying numbers of joints depending on the observed object. It also successfully predicts the connections between joints, even for structures not seen during training.
Deep Neural Network Interlayer Feature Map Compression Based on Least-Squares Fitting

Deep convolutional neural networks (CNNs) have brought a significant amount of interlayer data during computation, resulting in a large data-exchange delay and power consumption. This paper proposes a Least-Squares Fitting Compression (LSFC) method to compress the interlayer data to resolve the above problem. In LSFC, the feature maps are firstly divided into block groups; then, two base blocks are selected for each block group. Finally, the LSFC core is applied to get the fitting parameters, and the fitting parameters are selectively stored in the on-chip memory according to the mean-squared error (MSE) results. The proposed compression method is hardware-implemented and integrated into an AI accelerator to support the on-the-fly compression process with a slight hardware overhead and latency. Experiments show that the LSFC can reduce the required on-chip storage space by 21.9% $\sim$ 33.6% during CNN computation without loss of network prediction.
Underwater Object Tracking Based on Error Self-correction

Underwater object tracking is more challenging than tracking on land due to the image distortion. Underwater illumination changes, motion blur and other problems will greatly degrade the performance of various tracking methods, especially when using underwater cameras. To overcome these limitations, we proposed an underwater tracking method based on error self-correction. We combine the SiamFC tracker and correlation filter trackers into one framework. In this framework, the correlation filter tracker is used to guide the update of SiamFC tracker. In addition, this framework contains a multi-peak suppression module, which comprehensively improves the tracking accuracy of our tracker in underwater scenarios. Experimental results on the underwater dataset we established demonstrate that the success rate of the AUC criterion is 68.2%, which has a 11.4% improvement over the baseline tracker. Also, our proposed algorithm achieves a 12.6% improvement over the conventional correlation filter tracker BACF, especially in challenging scenarios.
A Simple Structure for Building a Robust Model

As deep learning applications, especially programs of computer vision, are increasingly deployed in our lives, we have to think more urgently about the security of these applications. One effective way to improve the security of deep learning models is to perform adversarial training, which allows the model to be compatible with samples that are deliberately created for use in attacking the model. Based on this, we propose a simple architecture to build a model with a certain degree of robustness, which improves the robustness of the trained network by adding an adversarial sample detection network for cooperative training. At the same time, we design a new data sampling strategy that incorporates multiple existing attacks, allowing the model to adapt to many different adversarial attacks with a single training. We conducted some experiments to test the effectiveness of this design based on Cifar10 dataset, and the results indicate that it has some degree of positive effect on the robustness of the model. Our code could be found at https://github.com/dowdyboy/simple_structure_for_robust_model .
Intent-Aware Graph Neural Networks for Session-based Recommendation

With anonymous sessions, session-based recommendation aims to forecast user's next action. It has been a difficult endeavor due to the limited information and lack of user profiles. Recent advances have demonstrated that graph structure is more suited to model complex item transitions than chronological order alone. Most existing GNN-based models mainly concentrate on the current session, mining more intra-session sequential pattern data. Other models that leverage neighbor session information or item co-occurrence to obtain global collaborative signals are too sensitive to noise and are insufficient to infer user preference. In this paper, we propose a novel Intent-Aware Graph Neural Networks (IA-GNN) for session-based recommendation. In IA-GNN, we leverage two encoders to learn item embeddings:(1) Local Transition Encoder (LTE) based on session graph to learn complicated sequence dependencies, and (2) Intent Match Encoder (IME) with the help of intent-aware graph to obtain collaborative signals from the perspective of user intent. Furthermore, a tailored position enhanced soft attention mechanism joins the two levels of item representations to generate user preference. Extensive experiments on three real-world benchmark datasets demonstrate that our model is superior to the state-of-the-art models.
Comprehensive Information Security Risk Assessment Model for Transportation Industry

In the context of the new round of technological revolution and industrial change, information technology and transportation are deeply integrated, the level of informatization in the industry is continuously improved, and the information security risk problem needs to be dealt with effectively. In order to improve the accuracy and assessment efficiency of information security risk assessment in transportation industry, this paper constructs a multi-level risk assessment index system, combines entropy weight method, TOPSIS assessment method and BP neural network to determine the weight of influencing factors and predict risk values, also proposes a comprehensive information security risk assessment model for transportation industry. The simulation results show that the prediction accuracy of the comprehensive assessment model of information security risk in transportation industry is high, and it can evaluate the risk factors in the information system of transportation industry scientifically and reasonably, and has good practicality.
Efficient Uncertainty Quantification for Multilabel Text Classification

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty quantification methods. Existing approaches to uncertainty quantification in natural language processing (NLP) take a Bayesian Deep Learning approach. However, the latter is known to not be computationally efficient in testing time, thus hindering its applicability in real-life scenarios. This paper proposes a new focus on the efficiency of uncertainty quantification methods, evaluating them on four multi-label text classification tasks. Our novel methods of representing epistemic and aleatoric uncertainties enable efficient uncertainty quantification (around 13 to 45 times faster than existing approaches, depending on architecture) with posterior analysis in the (approximated) latent- and data space. We conduct extensive experiments and studies on diverse neural network architectures (LSTM, CNN and Transformer) to analyse their power. Our results prove the benefits of explicitly modelling uncertainty in neural networks.
TSFed: A Two-Stage Federated Learning Framework via Cloud-Edge Collaboration for Services QoS Prediction

Federated learning-based quality of service (QoS) prediction methods are regularly used to protect user privacy in smart cities. However, federated learning (FL) is fragile for heterogeneous QoS data, and these FL methods usually update a single global model by aggregating diverging gradients, which cannot effectively capture the heterogeneous data features of different users, resulting in less than optimal model convergence speed. Moreover, the existing FL methods do not pay attention to the positive effect of regional similarity of QoS data on model convergence. To address these issues, we propose a two-stage federated learning QoS prediction framework (TSFed) based on cloud-edge collaboration. In the first stage, the cloud server coordinates the user to train a partially optimized pre-training model. In the second stage, the edge server coordinates users to fine-tune the pre-training model. Experiments on real-world datasets show that TSFed can achieve a 21.54%–46.73% reduction in the number of communication rounds and a 29.83%–50.73% reduction in communication delay required to achieve the target prediction accuracy compared to existing approaches.
An Experiment to Evaluate IoT Application Security Scheme (IoTGazePass)

IoTGazePass is a new password scheme that has been designed to tackle the weaknesses of existing types of passwords. It is intended to be suitable for IoT applications. An experiment was conducted to evaluate the strengths and weaknesses of the IoTGazePass scheme (e.g., security, usability and memorability). The evaluation aspects include a comparison with traditional textual and graphical based passwords in terms of memorability and security of the passwords. The results of the evaluation indicate that the IoTGazePass scheme is easy to use, memorable and flexible, as it can generate strong, secure and scalable passwords. The experiment results have also showed that the IoTGazePass scheme have good ability to deal with shoulder surfing attacks. In fact, the experiment results lead to some recommendations and suggestions to improve the planned performance.
Crafting Textual Adversarial Examples through Second-Order Enhanced Word Saliency

Textual adversarial examples crafted with well-designed perturbation can mislead state-of-the-art natural language models. Most previous works on word-level black-box attacks propose different text substituting strategies based on the word saliency determined by Leave One Out (LOO) methods, while the attack effectiveness is actually limited due to the model pathology. The word saliency determined by LOO methods can be severely affected by the model pathology, and unconscious bias is introduced. In this paper, we propose a word saliency method called Second-Order Enhanced Word Saliency (SOEWS), which considers the overfitting information obtained from the second-order model pathological behavior that helps to reduce the bias of LOO methods. Extensive experiments show that our method outperforms current black-box word saliency methods and can boost the effectiveness of previous attack frameworks. Performing adversarial training utilizing our method best improves model robustness, and human evaluation shows that the adversarial examples crafted by our method are almost imperceptible to humans. We also conduct case study to intuitively demonstrate the superiority of our method.
Unsupervised Learning-based Early Anomaly Detection in AMS Circuits of Automotive SoCs

With the proliferation of safety-critical applications in the automotive domain, it is imperative to guarantee the functional safety of circuits and components constituting automotive systems, e.g., the electrical and/or electronic subsystems in automotive vehicles. Analog and Mixed-Signal (AMS) circuits, prevalent in such systems, are more susceptible to faults than their digital counterparts, due to advanced manufacturing nodes, parametric perturbations, environmental stress, etc. However, their continuous signal characteristics provide an opportunity for early anomaly detection, which in turn, facilitates the deployment of safety mechanisms to prevent eventual system failure. Towards this end, we propose a novel unsupervised machine learning-based framework to perform early anomaly detection in AMS circuits. Our approach involves anomaly injection in various circuit locations and individual components to develop a training dataset encompassing a wide range of possible anomalous scenarios, feature extraction from observation signals, and clustering algorithms to facilitate anomaly detection. To this end, we propose a novel centroid selection technique for the unsupervised learning algorithms, which is tailored for detecting anomalies in AMS circuits. This approach furnishes high fidelity anomaly detection by identifying the ideal cluster centers corresponding to anomalous and non-anomalous signals. Furthermore, time series-based analysis is proposed to improve and expedite the anomaly detection performance. We evaluated our solution using a case study of two AMS circuits commonly present in automotive systems-on-chips. Our experimental results exhibit that the proposed approach furnishes up to 100% accuracy. Additionally, the time series-based technique reduces the anomaly detection latency by 5×, thereby demonstrating the efficacy of our solution.
BSAM: Bidirectional Scene-Aware Mixup for Unsupervised Domain Adaptation in Semantic Segmentation

Unsupervised domain adaptation for semantic segmentation aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing mixup methods usually paste parts of the source domain images onto the target domain images. However, they often neglect the scene consistency of the generated images, which will result in wrong semantic relationships. To address this issue, we propose a Bidirectional Scene-Aware Mixup (BSAM) method in this paper. BSAM adopts a bi-directional pasting strategy to ensure scene awareness between the two domains, and takes the correctness of semantic relationships into account. Specifically, BSAM selects some contextually related classes from each domain to another domain, and generates bidirectional fused images for training. BSAM ensures the correct scene layout, facilitating the model to adapt to the different scenario characteristics. Extensive experiments on two benchmarks (GTA5 to Cityscapes and SYNTHIA to Cityscapes) demonstrate that BSAM achieves state-of-the-art performance.
Active Exploration for Robotic Manipulation

Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation is the exploration of the dynamics of the environment when there is continuous contact between the objects being manipulated. This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks. The proposed method estimates an information gain objective using an ensemble of probabilistic models and deploys model predictive control (MPC) to plan actions online that maximize the expected reward while also performing directed exploration. We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method, on a challenging ball pushing task on tilted tables, where the target ball position is not known to the agent a-priori. Our real-world robot experiment serves as a fundamental application of active exploration in model-based reinforcement learning of complex robotic manipulation tasks. Project page https://sites.google.com/view/aerm.
Hybrid Uncalibrated Near-light Photometric Stereo in Realistic Environment

Photometric stereo aims at recovering the surface and shape of an object from a set of observations under different light conditions. Deep learning based methods have made substantial contributions to the surface normal estimation under complex surface reflections, various materials, near-light settings, etc. These deep learning based methods learn the mapping from observed images to the surface normal of an object directly via training deep neural networks on large labeled datasets. However, the shapes, materials, and reflectance properties of objects in these datasets are limited, leading to abridged performances in realistic environments. In this paper, we introduce a work-piece dataset for near-light photometric stereo under industrial application scenarios, which consists of observed images taken under at most 40 light conditions, and the ground truth surface normals. Based on this datasets, we propose the Hybrid Near-light Uncalibrated Photometric Stereo (HNUPS) for both unsupervised light calibration and surface normal estimation. Experimental results on the work-piece dataset demonstrate that HNUPS can obtain the least mean angular error when compared to recent photometric stereo methods, which have verified the effectiveness of the proposed HNUPS.
Design of a Web3D Serious Game for Human Anatomy Education

The use of 3D web-based models allows researchers to explore interesting characteristics searching for the increase of quality in the anatomy education. This chapter aims to present the design of a serious game for human anatomy education that can assist the students in the understanding of the anatomical structures as well as the relationship between them in a virtual environment. The digital game design and development was carried out in partnership with specialists in human anatomy for the study of the lower limb bones (skeletal system), vessels and arteries (cardiovascular system), and lower limb skeletal muscles (muscular system). A case study was performed with students of physiotherapy undergraduate program in the course of anatomy through usability assessment techniques. Based on the results obtained it was possible to identify fundamental and innovative features that have to be present in the design of serious games for the teaching of health sciences.
A Real-time Face Detector on CPU Using Efficient Transformer

Face detection is a basic vision method to find the fa-ciallocation. It is usually used in the initial step of advanced facial analysis. Therefore, this approach is required to work quickly, especially on low-cost devices to support practical applications. A deep learning architecture can robustly extract the distinctive feature by employing a lot of weighted filters. However, the model produces heavy parameters and computational complexity. A transformer is a deep learning architecture that can capture the feature position relationship, which increases the detector performance. This work in this paper proposes a new efficient transformer architecture that is implemented to face detection. It can highlight the spatial information from a similarity map by utilizing a 2D-convolutional filter. This architecture generates low computation and lightweight trainable parameters that serve the proposed face detector to run fast on an inexpensive device. As a result, this proposed network achieves high performance and competitive precision with the low-cost model. Additionally, the proposed transformer module does not significantly add computation and parameters that can run fast at 95 frames per second on a Core is CPU.
Online Corrective Feedback to explore EFL Learners: A Case Study of Higher Education Students from North Sumatera Indonesia

Providing feedback on student writing remains an important research topic. Due to the numerous numbers of student assignments papers, technology is a solution for teachers overwhelmed by providing feedback and correction by handwriting. Few studies have been conducted to investigate the students’ linguistic problems through the online application platform to provide feedback especially in Indonesia. This research, therefore, aimed to explore the Indonesian EFL students’ linguistic problems using “Grammarly” as the online application. This study adopted the ex-post facto design to investigate the assignment written by 54 fourth-semester students of the English department in the Scientific Writing course. Participants’ linguistic problems were classified, identified, and categorized by submitting the soft copy to the application. The Statistical Package for Social Sciences was used to obtain the average, standard deviation, and significant differences in accordance to the linguistic problems between male and female students. The result stated that grammar is the major problem possessed by students. Furthermore, it is expected to provide feedback for students and lecturers in the learning process of Scientific Writing.
Delay-Based TCP with Pacing and ECN for Solving Incast Problem in Data Center Networks

This paper proposes a delay-based TCP variant, TCP-VPECN, which aims to solve the Incast problem in data center networks by using pacing and Explicit Congestion Notification (ECN). As observed by NS-2 simulations, TCPVPECN can utilize idle bandwidth more rapidly than DCTCP, Vegas and IA-TCP.
Supersynthesis: A Communal Synthesis

This pictorial presents the journey of a light and sound installation called Supersynthesis, which collects data from its users through an interactive digital interface and expresses it through the physical installation. It begins by going over historical works and methodologies that align with this project, goes over the design decisions behind the sculptural form and its software architecture, and finally analyzes its function through the lens of a “performative object” to draw connections with the theme of Mingling Spaces.