Reviewerly/exHarmony huggingface dataset:
High Utilization Energy-Aware Real-Time Inference Deep Convolutional Neural Network Accelerator

Deep convolution Neural Network (DCNN) has been widely used in computer vision tasks. However, for edge device, even then inference has too large computational complexity and data access amount. Due to the mentioned shortcomings, the inference latency of state-of-the-art models are still impractical for real-world applications. In this paper, we proposed a high utilization energy-aware real-time inference deep convolutional neural network accelerator, which outperforms the current accelerators. First, we use 1x1 size convolution kernels as the smallest unit of the computing unit. And we design suitable computing unit for different models based on the requirement of each model. Second, we use Reuse Feature SRAM to store the output of current layer in the chip and use as the input of the next layer. Moreover, we import Output Reuse Strategy and Ring Stream Data flow not only to expand the reuse rate of data in the chip but to reduce the amount of data exchange between chips and DRAM. Finally, we present On-fly Pooling Module to let the calculation of the Pooling layer to be completed directly in the chip. With the aid of the proposed method in this paper, the implemented CNN acceleration chip has extreme high hardware utilization rate. We reduce a generous amount of data transfer on the specific module, ECNN [1]. Compared to the methods without reuse strategy, we can reduce 533 times of data access amount. At the same time, we have enough computing power to perform real-time execution of the existing image classification model, VGG16 [2] and MobileNet [3]. Compared with the design in [4], we can speed up 7.52 times and have 1.92x energy efficiency.
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing

To perform temporal and sequential machine learning tasks, the use of conventional Recurrent Neural Networks (RNNs) has been dwindling due to the training complexities of RNNs. To this end, accelerators for delayed feedback reservoir computing (DFRC) have attracted attention in lieu of RNNs, due to their simple hardware implementations. A typical implementation of a DFRC accelerator consists of a delay loop and a single nonlinear neuron, together acting as multiple virtual nodes for computing. In prior work, photonic DFRC accelerators have shown an undisputed advantage of fast computation over their electronic counterparts. In this paper, we propose a more energy-efficient chip-scale DFRC accelerator that employs a silicon photonic microring (MR) based nonlinear neuron along with on-chip photonic waveguides-based delayed feedback loop. Our evaluations show that, compared to a well-known photonic DFRC accelerator from prior work, our proposed MR-based DFRC accelerator achieves 35% and 98.7% lower normalized root mean square error (NRMSE), respectively, for the prediction tasks of NARMA10 and Santa Fe time series. In addition, our MR-based DFRC accelerator achieves 58.8% lower symbol error rate (SER) for the Non-Linear Channel Equalization task. Moreover, our MR-based DFRC accelerator has 98× and 93× faster training time, respectively, compared to an electronic and a photonic DFRC accelerators from prior work.
Characterizing, exploiting, and detecting DMA code injection vulnerabilities in the presence of an IOMMU

Direct memory access (DMA) renders a system vulnerable to DMA attacks, in which I/O devices access memory regions not intended for their use. Hardware input-output memory management units (IOMMU) can be used to provide protection. However, an IOMMU cannot prevent all DMA attacks because it only restricts DMA at page-level granularity, leading to sub-page vulnerabilities.
IIT-GAT: Instance-level image transformation via unsupervised generative attention networks with disentangled representations

Image-to-image translation is an important research field in computer vision, which is widely associated with Generative Adversarial Networks (GANs) and dual learning. However, the existing methods mainly translate the global image of the source domain to the target domain, which fails to implement instance-level image-to-image translation, and the translation results in the target domain cannot be controlled. In this paper, an instance-level image-to-image translation network (IIT-GAT) is proposed, which includes attention module and feature-encoder module. The attention module is used to guide our model to focus on more interesting instance to generate instance masks, which helps to separate instance and background of an image. The feature-encoder module is used to embed the images into two different spaces: domain-invariant content space and domain-specific attribute space. The content features and attribute features of different images are used as input to generator simultaneously to improve the controllability of image-to-image translation. To this end, we introduce a local self-reconstruction loss that encourages the network to learn the style feature of target instances. Generally, our method not only improves the quality of instance-level image-to-image translation, but also increases controllability on this basis. Extensive experiments are conducted on multiple datasets to validate the effectiveness of the proposed framework, and the results show our method has better performance than previous methods.
A Double Channel CNN-LSTM Model for Text Classification

The CNN-LSTM model has the advantages of combining Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). It can perform timing analysis while extracting abstract features. It is widely used in Computer Vision and Natural Language Processing (NLP) fields and has achieved satisfactory results. However, for a large number of samples of complex text data, especially for words with ambiguous meanings, the word-level CNN-LSTM model is insufficient. Therefore, in order to solve this issue, this paper presents an improved Double Channel (DC) mechanism as a significant enhancement to CNN-LSTM. In this DC mechanism, two channels are used to receive word-level and char-level embedding respectively, at the same time. Hybrid Attention is proposed to combine the current time output with the current time unit state, and then using Attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed, and the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. After experimental comparison, the DC CNN-LSTM model proposed in this paper has significantly superior accuracy and F1-score compared with the basic CNN-LSTM model.
Two Cases for Traces

In this paper we activate theoretical principles for HCI and CSCW in analysis and design of collaborative technologies. We critically evaluate theoretical principles about how technologies mediate collaborative activities through two empirical cases regarding smart speakers and collaborative writing, respectively. Common to them is that both common objects and mediating artifacts are set in a mixed virtual and physical setting. Principles from activity theory are examined, specifically with a focus on how people use artifacts and objects as mediators to collaborate in joint activity. As part of questioning the cases, we also position collaborative affordances in relation to the principles. We engage with the cases by using the theoretical principles both analytically and constructively. Our analysis specifically targets traces as a possible construct that has potential to constructively aid collaboration across the two cases. We explore what seems to be a lack of support, and in particular theoretical framing, of traces of past activities, connected objects, other users, etc. With an initial hypothesis that traces could be a mechanism that would support these two kinds of collaborative activities better in a future redesign, we set out to further explore the cases and their future possibilities, and how they could be conceptually grounded and supported. We present a set of analytically and constructively oriented questions to help researchers and designers of artifacts identify mediators for and instances of collaboration.
Pattern and content controlled response generation

Controllable response generation is an attractive and valuable task to the success of conversational systems. However, controlling both pattern and content of the response has not been well studied in existing models since they are mainly based on matching mechanisms. To tackle the problem, we first design a pattern model to automatically learn and extract speech patterns from words. The pattern is then integrated into the encoder–decoder model to control the response pattern. Second, a sentence sampling algorithm is built to directly insert or delete words in the generated response, so that the content is controlled. In this two-stage framework, the response could be explicitly controlled by the pattern and content, without any human annotation of the post-response dataset. Experiments show the proposed framework achieves better performance in response controllability than the state-of-the-art. • We develop a framework that tackles the task of controllable response generation, where diverse patterns and content are considered in the response generation. • A pattern model is devised to automatically analyze the sets of keywords of all the patterns and to capture a pattern distribution for each pattern. • To control content of the response, we employ a sentence sampling algorithm to directly perform local operations (word insertion and deletion) on the response.
Skills for Future Work (H2030): Multigroup Analysis in Professional and Baccalaureate Training

Purpose: This study sought to validate the Skills for Future Work Scale for its use with professional/vocational training (PT) and baccalaureate students. Methods: A total of 1,159 students were recruited. Structural equation analysis was performed using IBM Amos Graphics®. Results: Excellent fit indices were obtained, forming a final scale composed of nine indicators and two factors. The first dimension was associated with cognitive skills and adaptability, grouping together the skills of finding meaning, adaptable thinking, and understanding management. The second dimension comprises collaborative skills, as social intelligence and virtual collaboration. Discussion: Collaboration skills were more developed within those undertaking PT, while baccalaureate students had more strongly developed mastery over new media. This scale allows to know effectively the development of the work skills set by the European Union in the 2030 horizon. These findings contribute interesting data regarding the development of training strategies to support insertion into the job market.
Assessment of the professional competencies formation in students in the course of implementing online digital learning technologies

The article substantiates the relevance of research on the assessment of students’ professional competencies in the course of implementing online digital learning technologies at universities, as well as unpacks the features of the flipped learning system as a means of determining the assimilation of competencies by students in the studied disciplines. The authors consider the flipped learning implementation methods, such as information collection, analysis, systematization, understanding, application, and competencies. It is shown that digital technologies in education, being a complicated multifunctional complex of influence on the quality of education, have a very ambiguous effect on the formation of professional competencies. Revealed specifics of using high-quality resources when creating digital technologies include special illustrations, a large number of diagrams, animations, audio fragments, web-3.0, video fragments, interactive tasks, animated presentation forms, maps, analytical tables, digital content designed according to the rules of digital communications, its presence in social networks, and much more. The sociological study was conducted involving groups of intramural students of one of the leading universities of the Russian Federation. The professional competencies that students receive in the context of the implementation of online technologies have been assessed. The results of sociological study on students’ readiness for flipped learning have been presented in graphical form. The authors have analyzed the effectiveness of different forms of classes, such as lectures, practical classes, and control, as well as have systematized the trends of educational processes digitalization, and their role in the development of economic processes.
A Bayesian nonparametric mixture model for grouping dependence structures and selecting copula functions

The demand for advanced dependence modeling arises in a variety of fields, including finance, insurance and health science. When analyzing dependent data, it is important but challenging to properly model the dependence structure in order to carry out valid and efficient inferences. Grouping the data according to the similarity in the dependence structure is necessary, especially for data of a small size. A copula-based model, indexed by copula selection indicators and dependence parameters, is introduced to delineate dependent data and group similar dependence structures. To conduct inference, a Bayesian nonparametric method with the prior distributions specified as a Dirichlet Process is proposed as a mixture of Dirichlet process mixture copula model (M-DPM-CM). Extensive simulation studies have been conducted to evaluate the performance of the proposed procedure, and the results show that the proposed M-DPM-CM can recover the true grouping structure and achieve high accuracy in copula model selection under various finite sample settings. The M-DPM-CM is applied to analyze the Vertebral Column dataset from UCI Machine Learning Repository.
Augmentation of Small Training Data Using GANs for Enhancing the Performance of Image Classification

It is difficult to achieve high performance without sufficient training data for deep convolutional neural networks (DCNNs) to learn. Data augmentation plays an important role in improving robustness and preventing overfitting in machine learning for many applications such as image classification. In this paper, a novel method for data augmentation is proposed to solve the problem of machine learning with small training datasets. The proposed method can synthesise similar images with rich diversity from only a single original training sample to increase the number of training data by using generative adversarial networks (GANs). It is expected that the synthesised images possess class-informative features, which may be in the validation or testing data but not in the training data due to that the training dataset is small, and thus they can be effective as augmented training data to improve the classification accuracy of DCNNs. The experimental results have demonstrated that the proposed method with a novel GAN framework for image training data augmentation can significantly enhance the classification performance of DCNNs for applications where original training data is limited.
Research on multi UAV target detection algorithm in the air based on improved CenterNet

In recent years, with the rapid development of UAV technology, the following problems and hidden dangers are increasingly prominent: “unauthorized flight” incidents occur frequently, security risks are serious, and social security is threatened. In order to quickly find UAV targets in these events and take relevant measures, this paper proposes a fast UAV target detection algorithm based on improved CenterNet, which can extract the depth features of the collected images, and use Gaussian kernel function to generate keypoint feature map, and output multiple target location information and category information. By modifying the structure and parameters of feature extraction network, the size of the model is greatly reduced and the running speed of the model is accelerated. At the same time, the detection effect of small target is improved obviously after adding DIoU loss. We train and test the algorithm on our own UAV dataset, and the experimental results achieve high accuracy (48.9 AP) and real-time detection speed (143fps). The improved algorithm is easy to be integrated into the mobile terminal or embedded system, providing support for the next step of alarming, jamming and drive away actions.
Identifying Topics of Scientific Articles with BERT-Based Approaches and Topic Modeling

This paper describes neural models developed for the First Workshop on Scope Detection of the Peer Review Articles shared task collocated with PAKDD 2021. The aim of the task is to identify topics or category of scientific abstracts. We investigate the use of several fine-tuned language representation models pretrained on different large-scale corpora. In addition, we conduct experiments on combining BERT-based models and document topic vectors for scientific text classification. The topic vectors are obtained using LDA topic modeling. The topic-informed soft voting ensemble of neural networks achieved F1-score of 93.82%.
Enhancing instance-level constrained clustering through differential evolution

Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it received renewed attention when it was shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information encoded by constraints. Constraints can be given in the form of instance-level must-link and cannot-link constraints, which this paper focuses on. We propose the first application of Differential Evolution to the constrained clustering problem, which has proven to produce a better exploration–exploitation trade-off when comparing with previous approaches. We will compare the results obtained by this proposal to those obtained by previous nature-inspired techniques and by some of the state-of-the-art algorithms on 25 datasets with incremental levels of constraint-based information, supporting our conclusions with the aid of Bayesian statistical tests.
Day-Ahead Photovoltaic Power Forcasting Using Convolutional-LSTM Networks

Efficient and reliable forcasting of power generation from PV plants is important for grid management optimization and power dispatch allocation. In this paper, we propose a hybrid model for short-term photovoltaic power forcasting (PVPF), Convolutional-LSTM (Conv-LSTM), and extend its attention mechanism architecture and shortcut connection architecture, called Conv-LSTM-A and Conv-LSTM-S, respectively. The input data used are hourly data of commercial PV plants for one year and contain only four features: historical active power, solar radiation, panel temperature and current hour. The effects of different historical data lengths on the forcasting results are compared. The hyperparameters of the model were adjusted using bayesian optimization to find the model with the best hyperparameter configuration. The experimental results show that the RMSE of the Conv-LSTM-A model is 11.13% and the MAPE of the Conv-LSTM-S model is 7.0030 using 7 days of historical data for day-ahead PV power forcasting.
FuseKNA: Fused Kernel Convolution based Accelerator for Deep Neural Networks

Bit-serial computation has been a prevailing convolution method to accelerate varying-precision DNNs by slicing a multi-bit data into multiple 1-bit data and transforming a multiplication into multiple additions, where additions of zero bits are ineffectual, while additions of non-zero bits are repetitive since multiple kernels are quite possible to possess non-zero bits at the same kernel positions. Previous bit-serial accelerators only remove ineffectual additions by skipping computation of zero bits, however, repetitive additions are unable to be eliminated since they compute convolution of each kernel independently. In this work, we propose fused kernel convolution algorithm to eliminate both ineffectual and repetitive additions in bit-serial computation by exploiting bit repetition and bit sparsity in weights, for both convolutional and fully-connected layers. It unifies convolutions of multiple kernels into convolution of one fused kernel by firstly grouping additions into different patterns and secondly reconstructing convolution results, minimizing addition count. Meantime, the memory accesses of activations and partial sums are decreased due to less convolution count. Then a fused kernel convolution based accelerator, FuseKNA, is designed with compact compute logic, which fully exploits value sparsity of activations and bit sparsity of weights. Benchmarked with a set of mainstream DNNs, FuseKNA improves performance by $4.47 \times$, $2.31 \times$ and $1.81 \times$, energy efficiency by $4.13 \times$, $3.06 \times$ and $2.53 \times$ over state-of-the-art Stripes, Pragmatic and Bit-Tactical.
An anchor-free object detector with novel corner matching method

Recently, remarkable object detection performance has been made by keypoint-based detectors with different keypoint matching strategies. However, the factors considered in most of keypoint matching strategies are not comprehensive, which greatly affects the detection results. In this paper, based on CornerNet we propose a novel anchor-free detector with a brand new keypoint matching method. In our approach, instead of using embedding of each corner to achieve keypoint matching like CornerNet, we predict a matching degree score for each predicted bounding box formed by corners. In order to train model to get accurate matching degree prediction results, we fully consider the location and geometric information of targets, calculating the distance between centers of predicted and ground truth bounding boxes as well as IoU value to decide which pair of top-left and bottom-right corners can form the right bounding box. Besides that, to relieve the problem that categorical predictions associated with corners are not always reliable, we set an additional classification score for each predicted bounding boxes to further promote final detection results. We apply our model on MS-COCO test−dev set for evaluation and the result shows that our method achieves an AP of 46.3 % at single-scale and 47.9% at multi-scale, which are competitive with other state-of-the-art models such as CentripetalNet and Corner Proposal Net (CPN).
Research on UAV Path Planning Algorithms

Abstract UAVs have received extensive attention and research due to their superior mobility and flexibility, and they replace manned aircraft to perform tasks in some specific complex environments. In order to deal with the complex and changeable environment and various obstacles, it is necessary to use appropriate algorithms to plan the UAV path. This paper divides the existing UAV path planning algorithm research into three categories: traditional algorithm, intelligent algorithm and fusion algorithm. The basic principles, advantages and disadvantages of various algorithms are analyzed, and the future research and development are prospected based on the actual operation of UAV.