Reviewerly/exHarmony huggingface dataset:
Robust in practice: Adversarial attacks on quantum machine learning

State-of-the-art classical neural networks are observed to be vulnerable to small crafted adversarial perturbations. A more severe vulnerability has been noted for quantum machine learning (QML) models classifying Haar-random pure states. This stems from the concentration of measure phenomenon, a property of the metric space when sampled probabilistically, and is independent of the classification protocol. In order to provide insights into the adversarial robustness of a quantum classifier on real-world classification tasks, we focus on the adversarial robustness in classifying a subset of encoded states that are smoothly generated from a Gaussian latent space. We show that the vulnerability of this task is considerably weaker than that of classifying Haar-random pure states. In particular, we find only mildly polynomially decreasing robustness in the number of qubits, in contrast to the exponentially decreasing robustness when classifying Haar-random pure states and suggesting that QML models can be useful for real-world classification tasks.
Sentiments Detection for Amazon Product Review

In this paper, we evaluated the sentiments in the present technological age over the reviews of online products, online products are used by the majority of people. They provide their feedback and then products are recommended for purchase and sale on that factor too. The large e-commerce platforms such as Flipkart, Myntra, Amazon, and many others enable their users to review the Products. To buy a commodity, the consumer will examine to have a better-quality understanding of the product and product work. The interpretation will be a really simple product polarized into positive, neutral, and negative Product checks. We may use machine learning methods to perform this experiment. Sentiment Analysis is research in which consumers are conscious of a product reaction. A Kaggle of amazon product reviews gathers the data collection used. We use various Logistic Regression, Naive Bayes, and Random Forest methodology for classifying feedback and achieving the best of precision. Among all the algorithms used we find that the Random forest machine-learning algorithm to be the most accurate.
TEA-fed

Federated learning (FL) has attracted more and more attention recently. The integration of FL and edge computing makes the edge system more efficient and intelligent. FL usually uses the server to actively select certain edge devices to participate in the global model training. However, the selected edge devices may be stragglers, or even crash during training. Meanwhile, the unselected idle edge devices cannot be fully utilized for training. Therefore, besides the widely studied communication efficiency and data heterogeneity issues in FL, we also take the above time efficiency into consideration, and propose a time-efficient asynchronous federated learning protocol, TEA-Fed, to solve these problems. With TEA-Fed, idle edge devices actively apply for training tasks and participate in model training asynchronously once assigned tasks. Considering that there may be a huge number of edge devices in edge computing, we introduce control parameters to limit the number of devices participating in training the identical model at the same time. Meanwhile, we also introduce caching mechanism and weighted averaging with respect to model staleness in the model aggregation step to reduce the adverse effects of model staleness and further improve the accuracy of the global model. Finally, the experimental results show that the protocol can accelerate the convergence of model training, improve the accuracy, and has robustness to heterogeneous data.
Educational Landscape of Virtual Reality in Higher Education: Bibliometric Evidences of Publishing Patterns and Emerging Trends

This bibliometric study examines the overall research trends and productivity in the field of virtual reality (VR) in higher education. Bibliometric data were retrieved from Scopus databases. The findings suggest a rising trend in terms of citations and publications showing increased interest in the VR domain have been seen during the last few decades. The year in which the most citations of this type occurred was 2009, in which 1913 citations were recorded, whereas 2019 was the most productive year, as 127 documents on this subject were published in that year. The data analysis revealed that all the top ten researchers belong to Australia. Further, the top three researchers (Gregory S., Lee, M.J.W., and Wood, D.), countries (United States, the United Kingdom, and Australia), organizations (Charles Sturt University, Queensland University of Technology, and University of New England, Australia), journals (Computers and Education, International Journal of Emerging Technologies in Learning, and Journal of Surgical Education) and collaborations (Australia and New Zealand, United States and the United Kingdom, and Australia and the United Kingdom) belong to developed countries. Virtual reality, virtual worlds, augmented reality, e-learning, and simulations are the top keywords used in the VR domain. The thematic evolution of the keyword shows the importance of “Virtual Reality” as a keyword throughout the 27 years of its existence (1994–2020). Furthermore, the main finding of the study is the interdisciplinary nature of the VR domain, which extends from the field of computer sciences to other disciplines.
Mining guidelines for architecting robotics software

The Robot Operating System (ROS) is the de-facto standard for robotics software. However, ROS-based systems are getting larger and more complex and could benefit from good software architecture practices. We aim at (i) unveiling the state-of-the-practice in terms of targeted quality attributes and architecture documentation in ROS-based systems, and (ii) providing empirically-grounded guidance to roboticists about how to properly architect ROS-based systems. We designed and conducted an observational study where we (i) built a dataset of 335 GitHub repositories containing real open-source ROS-based systems, and (ii) mined the repositories to extract and synthesize quantitative and qualitative findings about how roboticists are architecting ROS-based systems. First, we extracted an empirically-grounded overview of the state of the practice for architecting and documenting ROS-based systems. Second, we synthesized a catalog of 47 architecting guidelines for ROS-based systems. Third, the extracted guidelines were validated by 119 roboticists working on real-world open-source ROS-based systems. Roboticists can use our architecting guidelines for applying good design principles to develop robots that meet quality requirements, and researchers can use our results as evidence-based indications about how real-world ROS systems are architected today, thus inspiring future research contributions.
Prediction of time series using an analysis filter bank of LSTM units

Time series emerge in various applications such as financial data and production data, however, most of the generated data exhibit nonlinear inter-dependency between samples and noise, making necessary the development of methods capable of handling such nonlinearities and other abnormalities. In this paper we present an architecture for prediction of time series embedded in noise. The proposed architecture combines a convolutional and long short term memory (LSTM) layers into a structure similar to an analysis filterbank of two channels. The first element of each channel is a convolutional layer followed by a LSTM, which is able to find temporal dependencies of the signal. Finally the channels are summed to obtain a prediction. We found that the frequency response of the filters resemble a complementary filter bank response, with each channel having a maximum at different bands which could suggest that it characterizes the incoming signal in frequency. Comparisons with other methods demonstrate that the proposed method offer much better results in terms of different error measures.
Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks

Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns associated with incorrect responses can, in part, be understood, supporting insights into the different sources of failure on a task. To this end, we make use of sequence mining techniques that leverage the information contained in time-stamped action sequences commonly logged in assessments with interactive tasks for (a) investigating what distinguishes incorrect behavioral patterns from correct ones and (b) identifying subgroups of examinees with similar incorrect behavioral patterns. Analyzing a task from the Programme for the International Assessment of Adult Competencies 2012 assessment, we find incorrect behavioral patterns to be more heterogeneous than correct ones. We identify multiple subgroups of incorrect behavioral patterns, which point toward different levels of effort and lack of different subskills needed for solving the task. Albeit focusing on a single task, meaningful patterns of major differences in how examinees approach a given task that generalize across multiple tasks are uncovered. Implications for the construction and analysis of interactive tasks as well as the design of interventions for complex problem-solving skills are derived.
Study on strategy of CT image sequence segmentation for liver and tumor based on U-Net and Bi-ConvLSTM

Accurate segmentation of the liver and tumors in computed tomography (CT) images is critical for intelligent computer-aided diagnosis (CAD). The commonly used segmentation methods based on fully convolutional networks (FCN) only take a single image into consideration but do not make good use of sequence information. In this paper, two more feasible sequence segmentation strategies than 3D U-net which can utilize inter-slice and intra-slice features simultaneously at the lower hardware and time cost are studied to improve the segmentation result. U-net serves as the backbone model of segmentation and Bi-directional convolutional long short-term memory (Bi-ConvLSTM) is chosen to extract and fuse the inter-slice feature. Strategy A corrects the pre-segmented results of U-net in the fusion of sequence information as a post-processing, where Mod-1, Mod-2 and Mod-3 models are built to compare the effects of width, depth, and residual structure on the modified model of sequence segmentation. Strategy B directly integrates the fusion of sequence information into the feature extraction of U-net, and then an end-to-end model called W-net is built based on it. The experiment results show that both strategies improve the liver and tumor segmentation performance in various aspects. The results based on strategy A are closer to the ground truth with less misdiagnose region: Mod-1 achieves better accuracy on liver contour segmentation because of the largest model width; Mod-2 can obtain more accurate tumor contour since the greatest depth of feature extraction process; and Mod-3 is at the average segmentation performance. Therefore, strategy A is recommended in the application of surgery planning of tumors. Strategy B achieves better space coincidence degree and less training time cost, which is more suitable for the early screening of liver cancer.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking

Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks. Only using a few AI component benchmarks like MLPerf alone in the other stages may lead to misleading conclusions. Moreover, the learning dynamics are not well understood, and the benchmarks' shelf-life is short. This paper proposes a balanced benchmarking methodology. We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent. After performing an exhaustive survey on Internet service AI domains, we identify and implement nineteen representative AI tasks with state-of-the-art models. For repeatable performance ranking (RPR subset) and workload characterization (WC subset), we keep two subsets to a minimum for affordability. We contribute by far the most comprehensive AI training benchmark suite. The evaluations show: (1) AIBench Training (v1.1) outperforms MLPerf Training (v0.7) in terms of diversity and representativeness of model complexity, computational cost, convergent rate, computation, and memory access patterns, and hotspot functions; (2) Against the AIBench full benchmarks, its RPR subset shortens the benchmarking cost by 64%, while maintaining the primary workload characteristics; (3) The performance ranking shows the single-purpose AI accelerator like TPU with the optimized TensorFlow framework performs better than that of GPUs while losing the latter's general support for various AI models. The specification, source code, and performance numbers are available from the AIBench homepage https://www.benchcouncil.org/aibench-training/index.html.
Dual-Hiding Side-Channel-Attack Resistant FPGA-Based Asynchronous-Logic AES: Design, Countermeasures and Evaluation

We present a side-channel-attack (SCA) resistant asynchronous-logic (async-logic) Advanced Encryption Standard (AES) accelerator with dual-hiding SCA countermeasures, i.e. the amplitude moderation (vertical dimension) and the time moderation (horizontal dimension). There are five contributions in this paper. First, we propose an async-logic design flow with relative timing to simplify the AES realization in Field-Programmable-Gate-Array (FPGA). Second, we optimize completion detection circuits therein to achieve a low power/overhead solution. Third, we propose a randomized delay-line control and a data-propagation control to amplify the dual-hiding SCA countermeasures for our async-logic AES accelerator. Fourth, we validate the async-logic design flow based on two commercially-available Sakura-X and Arty-A7 FPGA boards. Fifth, we comprehensively evaluate 74 SCA attacking models for our async-logic AES accelerator on these two boards, and compare the results against a benchmarking AES based on synchronous-logic (sync-logic). We show that our async-logic AES accelerator is unbreakable within 1 million electromagnetic (EM) traces where the sync-logic counterpart is breakable within <; 30K EM traces. To our best knowledge, our async-logic AES accelerator is the first async-logic AES design evaluated comprehensively at the first/last round, at various attacking locations (i.e. before/after Substitute-Box), and with various Hamming weight/distance, bit model, and zero-model of SCAs.
Towards Novel Target Discovery Through Open-Set Domain Adaptation

Open-set domain adaptation (OSDA) considers that the target domain contains samples from novel categories unobserved in external source domain. Unfortunately, existing OSDA methods always ignore the demand for the information of unseen categories and simply recognize them as "un-known" set without further explanation. This motivates us to understand the unknown categories more specifically by exploring the underlying structures and recovering their interpretable semantic attributes. In this paper, we propose a novel framework to accurately identify the seen categories in target domain, and effectively recover the semantic attributes for unseen categories. Specifically, structure preserving partial alignment is developed to recognize the seen categories through domain-invariant feature learning. Attribute propagation over visual graph is designed to smoothly transit attributes from seen to unseen categories via visual-semantic mapping. Moreover, two new cross-domain benchmarks are constructed to evaluate the proposed framework in the novel and practical challenge. Experimental results on open-set recognition and semantic recovery demonstrate the superiority of the proposed method over other compared baselines.
Multirobot coordination with deep reinforcement learning in complex environments

In the multiple autonomous robot system, it is very important to complete path planning coordinately and effectively in the processes of interference avoidance, resource allocation and information sharing. In traditional multirobot coordination algorithms, most of the solutions are in known environments, the target position that each robot needs to move to and the robot priority are set, which limits the autonomy of the robot. Only using visual information to solve the problem of multirobot coordination is still less. This paper proposes a multi-robot cooperative algorithm based on deep reinforcement learning to make the robot more autonomous in the process of selecting target positions and moving. We use the end-to-end approach, using only the top view, that is, a robot-centered top view, and the first-person view, that is, the image information collected from the first-person perspective of the robot, as input. The proposed algorithm, which includes a dueling neural network structure, can solve task allocation and path planning; we call the algorithm TFDueling. Through its perception and understanding of the environment, the robot can reach the target position without collision, and the robot can move to any target position. We compare the proposed algorithm, TFDueling, with different input structure algorithms, TDueling and FDueling, and with different neural network structures, TFDQN and TFDDQN. Experiments show that the proposed TFDueling algorithm has the highest accuracy and robustness.
Overview of Touché 2021: Argument Retrieval

This paper is a condensed report on the second year of the Touché shared task on argument retrieval held at CLEF 2021. With the goal to provide a collaborative platform for researchers, we organized two tasks: (1) supporting individuals in finding arguments on controversial topics of social importance and (2) supporting individuals with arguments in personal everyday comparison situations.
Comparison of neutrosophic approach to various deep learning models for sentiment analysis

Deep learning has been widely used in numerous real-world engineering applications and for classification problems. Real-world data is present with neutrality and indeterminacy, which neutrosophic theory captures clearly. Though both are currently developing research areas, there has been little study on their interlinking. We have proposed a novel framework to implement neutrosophy in deep learning models. Instead of just predicting a single class as output, we have quantified the sentiments using three membership functions to understand them better. Our proposed model consists of two blocks, feature extraction, and feature classification. Having a separate feature extraction block enables us to use any model as a feature extractor. We experimented with BiLSTM using GloVe (Global Vectors for word representation), BERT (Bidirectional Encoder Representations from Transformers), ALBERT (A Lite BERT), RoBERTa (Robustly optimized BERT approach), MPNet, and stacked ensemble models. Feature classification performs prediction and dimensionality reduction of features. Experimental analysis was done on the SemEval 2017 Task 4 dataset (Subtask A). We used the intermediate layer features to define membership functions of Single Valued Neutrosophic Sets (SVNS). We used these membership functions for prediction as well. We have compared our models with the top five teams of the task and recent state-of-the-art systems. Our proposed stacked ensemble model achieved the best recall (0.733) score.
Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning

Prevailing deep methods for image recognition require massive labeled samples in each visual category for training. However, large amounts of data annotations are timeconsuming, and some uncommon categories only have rare samples available. For this issue, we focus on more challenging few-shot learning (FSL) task, where just few labeled images are used in the training stage. Existing FSL models are constructed with various convolutional neural networks (CNNs), which are trained on an auxiliary base dataset and evaluated for new fewshot predictions on a novel dataset. The performance of these models is difficult to break through because of the domain shift between base and novel datasets and the monotonous network architectures. Considering that, we propose a novel automatic attribute consistent network called Auto-ACNet to overcome the above problems. On one hand, Auto-ACNet utilizes the attribute information about base and novel categories to guide the procedure of representation learning. It introduces the consistent and non-consistent subnets to capture the common and different attributes of image pair, which helps to mitigate the domain shift problem. On the other hand, the architecture of Auto-ACNet is searched with the popular neural architecture search (NAS) technique DARTS, for obtaining a superior FSL network automatically. And the DARTS’s search space is improved by adding the position-aware module to extract the attribute characteristics better. Extensive experimental results on two datasets indicate that the proposed Auto-ACNet achieves significant improvement over the state-of-the-art competitors in this literature.
Interactive Learning of Signal Processing Through Music: Making Fourier Analysis Concrete for Students

In this artaicle, we illustrate how music may serve as a vehicle to support education in signal processing. Using Fourier analysis as a concrete example, we demonstrate how the music domain provides motivating and tangible applications that make learning signal processing an interactive pursuit. Furthermore, we indicate how software tools, originally developed for music analysis, provide students multiple entry points to delve deeper into classical signal processing techniques while bridging the gap between education and cutting-edge research.
M-YOLO: A Nighttime Vehicle Detection Method Combining Mobilenet v2 and YOLO v3

Abstract Vehicle detection at nighttime plays a vital role in reducing the incidence of night traffic accidents. In order to further improve the accuracy of nighttime vehicle detection, and to be suitable for constrained environments (such as: embedded devices in vehicles), this study proposes a deep neural network model called M-YOLO. First, M-YOLO’s feature extraction backbone network used the lightweight network MobileNet v2. Second, the K-means algorithm is reused to cluster the dataset to obtain the anchor boxes which are suitable for this paper. Third, M-YOLO uses the EIoU loss function to continuously optimize the model. The experiments showed that the average precision (AP) of proposed M-YOLO can reach to 94.96%. And ten frames per second (FPS) were processed in a constrained environment. Compared with YOLO v3, the proposed model performs better in detection accuracy and real-time performance.
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training

Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performance recommendation inference have recently been the target of several prior literatures. Unfortunately, little have been explored and understood regarding the training side of this emerging ML workload. In this paper, we first perform a detailed workload characterization study on training recommendations, root-causing sparse embedding layer training as one of the most significant performance bottlenecks. We then propose our algorithm-architecture co-design called Tensor Casting, which enables the development of a generic accelerator architecture for tensor gather-scatter that encompasses all the key primitives of training embedding layers. When prototyped on a real CPUGPU system, Tensor Casting provides 1.9-21× improvements in training throughput compared to state-of-the-art approaches.