Reviewerly/exHarmony huggingface dataset:
Joint Program and Layout Transformations to Enable Convolutional Operators on Specialized Hardware Based on Constraint Programming

The success of Deep Artificial Neural Networks (DNNs) in many domains created a rich body of research concerned with hardware accelerators for compute-intensive DNN operators. However, implementing such operators efficiently with complex hardware intrinsics such as matrix multiply is a task not yet automated gracefully. Solving this task often requires joint program and data layout transformations. First solutions to this problem have been proposed, such as TVM, UNIT, or ISAMIR, which work on a loop-level representation of operators and specify data layout and possible program transformations before the embedding into the operator is performed. This top-down approach creates a tension between exploration range and search space complexity, especially when also exploring data layout transformations such as im2col, channel packing, or padding. In this work, we propose a new approach to this problem. We created a bottom-up method that allows the joint transformation of both computation and data layout based on the found embedding. By formulating the embedding as a constraint satisfaction problem over the scalar dataflow, every possible embedding solution is contained in the search space. Adding additional constraints and optimization targets to the solver generates the subset of preferable solutions. An evaluation using the VTA hardware accelerator with the Baidu DeepBench inference benchmark shows that our approach can automatically generate code competitive to reference implementations. Further, we show that dynamically determining the data layout based on intrinsic and workload is beneficial for hardware utilization and performance. In cases where the reference implementation has low hardware utilization due to its fixed deployment strategy, we achieve a geomean speedup of up to × 2.813, while individual operators can improve as much as × 170.
Implementation of Rehabilitation Platform based on Augmented Reality Technology

Stroke is now one of the leading causes of death and disability in both motor and cognitive functions. In addition, rehabilitation for stroke survivors also faces many physical and human difficulties. To address this issue, we studied the use of Augmented Reality (AR) in rehabilitation using a system called RARS. This system creates limbs rehabilitation exercises in the form of games using an AR interface. The goal of the RARS is to increase patient's positive emotions, which motivates them to enjoy rehabilitation exercises. As a result, recovery efficiency will be improved, and the burden on physiotherapists will be reduced. The RARS is evaluated through a research about the effectiveness of this system on rehabilitation for patients after stroke (n=10). Reported results showed that the RARS produced significant improvement in the patient's indicators of functional status. From that, this system shows that it not only creates great benefits for personnel and economic but also bring about huge potential for future growth.
Point Cloud-based 3D Underwater Pose Estimation Using RANSAC and VFH Descriptors

Underwater pose estimation plays an important role in the process of underwater positioning and operation. In this paper, the point cloud data are collected by a depth camera, and the obtained point cloud data are clustered by RanSanc algorithm to accurately identify the 3D point cloud data of the target. By extracting the view feature histogram(VFH) of the target 3D point cloud data for subsequent pose estimation research, the time-consuming and labor-consuming caused by the large amount of overall point cloud data is avoided. Then, the VFH descriptors in different pose are trained and calibrated by the two-dimensional code truth measurement system, and the training set is saved by using the kd-tree neighbor search structure. Finally, the accuracy and feasibility of the proposed pose estimation algorithm are verified in a water tank experiments.
3D image compression during ultrasound phased diagnostics based on wavelet subband coding planar scans

Abstract The proposed compression method is based on the application of a two-dimensional discrete fast wavelet transform (FWT) to planar scans of 3D ultrasound images in order to simultaneously reduce redundancy and suppress speckle at a fixed quota of bits. The main idea of the method is to fuse three rules for threshold processing the wavelet coefficients of the scans, uniform and non-uniform quantizers, and bit quota distributions over subbands of the scan FWT based on the proposed cost function. The simulation results have shown that at the encoding rate of up to 1 bit/pixel, the quantity of artefacts were decreased up to 5-7 % of the original quantity under a signal-to-speckle ratio more than 16 dB, and the structural similarity index (SSIM) increased to 0.94-0.97 for defects of rectangular, triangular and oval shapes. The paper also presents the results proving the effectiveness of the proposed method in comparison with some variants of the solution according to the scheme “pre-filtering + codec”.
A Case Study: Characterization of Performance Inconsistency for Reinforcement Learning on Flappy Bird Game

One of the serious problems in Reinforcement Learning (RL) algorithms is that their performance usually varies when the same experiment is repeated or reproduced. Although RL results are hard to reproduce due to algorithms' intrinsic variance, which was not investigated systematically. Through this case study on Flappy Bird environment, we introduce and characterize four important factors on performance inconsistency of RL algorithms: 1) level of environment randomness, 2) order of action-value updates process, 3) exploration rate strategy, and 4) selection between on- and off-policy algorithms. Using a quantitative metric (coefficient of variation), we compare, analyze and investigate the results and the effects of each factor on the performance inconsistency/variance in RL. We believe our experimental results and analysis will provide opportunities to obtain an efficient agent that repeats/reproduces more consistent performance results.
Variant Graph Convolutional Networks for Skeleton-Based Hand Action Recognition

Graph convolutional network is widely used in skeleton-based applications such as action recognition. In this paper, a Variant Graph Convolutional Network (VGCN) is proposed to learn not to be constrained of the physical connections of hand structure since a predefined fixed graph structure lacks of flexibility to capture variance and different actions. With experiment on our hand actions skeleton dataset, the proposed method outperform with significance accuracy to the conventional ones.
DRESS: Data-Repository Enhancer through Semantic Sources

In recent years, there has been a huge research effort in the field of Knowledge Base Population (KBP). General approaches based on statistical techniques have been applied to popular resources on the Web (e.g., Wikipedia) with successful results. However, when it comes to small and private digital libraries, where the stored data is scarce and the existing entities might not be so popular, such approaches are not usually enough: many of the common techniques may lack specific tools to disambiguate entities that operate in a local environment. In this paper, we propose an approach to deal with private and isolated digital collections. Our proposed system (named DRESS, with the idea of “dressing” the digital library) builds a domain Knowledge Base (KB) from scratch, leveraging the available local knowledge. Then, DRESS enriches the KB integrating the local data with external knowledge obtained from the Semantic Web. Enhancing the digital repository in this way allows to build high value services for the user, recommending contents and improving the presentation of results (i.e., via infoboxes). Preliminary evaluations of the system have been carried out with promising results.
Impact on Inference Model Performance for ML Tasks Using Real-Life Training Data and Synthetic Training Data from GANs

Collecting and labeling of good balanced training data are usually very difficult and challenging under real conditions. In addition to classic modeling methods, Generative Adversarial Networks (GANs) offer a powerful possibility to generate synthetic training data. In this paper, we evaluate the hybrid usage of real-life and generated synthetic training data in different fractions and the effect on model performance. We found that a usage of up to 75% synthetic training data can compensate for both time-consuming and costly manual annotation while the model performance in our Deep Learning (DL) use case stays in the same range compared to a 100% share in hand-annotated real images. Using synthetic training data specifically tailored to induce a balanced dataset, special care can be taken concerning events that happen only on rare occasions and a prompt industrial application of ML models can be executed without too much delay, making these feasible and economically attractive for a wide scope of industrial applications in process and manufacturing industries. Hence, the main outcome of this paper is that our methodology can help to leverage the implementation of many different industrial Machine Learning and Computer Vision applications by making them economically maintainable. It can be concluded that a multitude of industrial ML use cases that require large and balanced training data containing all information that is relevant for the target model can be solved in the future following the findings that are presented in this study.
A Robust Training Method for Pathological Cellular Detector via Spatial Loss Calibration

Computer-aided diagnosis of pathological images usually requires detecting and examining all positive cells for accurate diagnosis. However, cellular datasets tend to be sparsely annotated due to the challenge of annotating all the cells. However, training detectors on sparse annotations may be misled by miscalculated losses, limiting the detection performance. Thus, efficient and reliable methods for training cellular detectors on sparse annotations are in higher demand than ever. In this study, we propose a training method that utilizes regression boxes' spatial information to conduct loss calibration to reduce the miscalculated loss. Extensive experimental results show that our method can significantly boost detectors' performance trained on datasets with varying degrees of sparse annotations. Even if 90% of the annotations are missing, the performance of our method is barely affected. Furthermore, we find that the middle layers of the detector are closely related to the generalization performance. More generally, this study could elucidate the link between layers and generalization performance, provide enlightenment for future research, such as designing and applying constraint rules to specific layers according to gradient analysis to achieve "scalpel-level" model training.
A Practical Parallel Computation in a Scalable Multiparty Private Set Intersection

Medical data is often managed individually by multiple medical institutions, and it is used to analyze patients’ diseases. However, for more detailed analysis, it is necessary to integrate such scattered data without violating the privacy of patients and companies. Multiparty Private Set Intersection (MPSI) is a kind of privacy protection protocol that can integrate the same person’s data managed by several independent institutions without information leakage. MPSI is an indispensable technology for the use of big data containing sensitive information. However, usability and performance improvements of MPSI have been a problem for its practical use. In this paper, we implemented a parallelization of the existing MPSI protocol and discussed the characteristics of the parallelization through experiments.
Energy-Efficient Deep Neural Networks Implementation on a Scalable Heterogeneous FPGA Cluster

In recent years, with the rapid development of DNN, the algorithm complexity in a series of fields such as computer vision and natural language processing is increasing rapidly. FPGA-based DNN accelerators have demonstrated superior flexibility and performance, with higher energy efficiency compared to high-performance devices such as GPU. However, the computing resources of a single FPGA are limited and it is difficult to flexibly meet the requirements of high throughput and high energy efficiency of different computing scales. Therefore, this paper proposes a DNN implementation method based on the scalable heterogeneous FPGA cluster to adapt to different tasks and achieve high throughput and energy efficiency. Firstly, the method divides a single enormous task into multiple modules and running each module on different FPGA as the pipeline structure between multiple boards. Secondly, a task deployment method based on dichotomy is proposed to maximize the balance of task execution time of different pipeline stages to improve throughput and energy efficiency. Thirdly, optimize DNN computing module according to the relationship between computing power and bandwidth, and improve energy efficiency by reducing waste of ineffective resources and improving resource utilization. The experiment results on Alexnet and VGG-16 demonstrate that we use Zynq 7035 cluster can at most achieves ×25.23 energy efficiency of optimized AMD AIO processor. Compared with previous works of single FPGA and FPGA cluster, the energy efficiency is improved by 59.5% and 18.8%, respectively.
Experimental Verification of EMPA Fault Mechanism

The efficient fault injection attack (FIA) technique, electromagnetic pulse attack (EMPA), is the serious threat to the security of integrated circuits (ICs). To protect ICs against EMPA, it is necessary to understand EMPA fault mechanism. In this paper, a widely accepted hypothesis suggests that the faults are induced by the disturbances, caused by the EMPA, on the power/ground grids in ICs. The goal of this work is to verify the hypothesis by experiments. A coupling model of EMPA is first presented based on the Faraday’s law and the hypothesis. The influence of EMPA on the power/ground grids is predicted with the model. Then an experiment is designed, where inverters are used to monitor the disturbances on the power/ground grids when EMPA with various parameters is performed. A physical EMPA platform is built and a circuit with inverter chain is fabricated in 0.11um technology. The experimental results show that the disturbances on the power/ground grids are closely related to the EMPA pulses, in accordance to the prediction. The results, on the one hand, demonstrate the reasonability of the hypothesis and on the other hand reveal that the influence of EMPA on the carriers of CMOS gates in the attacked circuit should be also considered for the precise analysis about the EMPA fault mechanism.
Design and Research of Non-contact Temperature Measurement and Identity Recognition System

Abstract With the rapid spread of the new crown epidemic, prevention of the epidemic has become an important task in daily life. As of now, the cumulative number of confirmed cases in the world has exceeded 150 million, and the epidemic prevention situation is very serious. In the face of the spread of the epidemic, effective temperature measurement, mask recognition, and face recognition are necessary for mobile personnel. Traditional methods such as the deployment of Raspberry Pi, windows platform, etc. can better meet the above requirements, but there are disadvantages such as low number of recognition frames, low accuracy, and bulky equipment. Due to the large market demand, the cost of the chip has also risen. If the above-mentioned functions are deployed according to the traditional method, the cost is relatively high, which increases the economic pressure on small and medium-sized enterprises. The main work of this research is to solve related functions through a processor Kendryte K210 based on the open source instruction set RISC-V. So as to realize a portable and low-cost non-contact temperature measuring device. The paper introduces a low-cost solution based on K210 on the basis of comprehensive discussion of multi-party implementation methods. Based on this processor, the face recognition and mask recognition functions based on YOLOv2 training are deployed through its machine vision and machine hearing capabilities. The temperature is measured by an external MLX90614 sensor; the serial port screen and the voice module JQ8400 are used for user interaction, which meets the guidance of users to the greatest extent. In the design of the hardware circuit, a lithium battery charging and boosting mobile power board based on MH-CD42 is designed. The user can supply power through an external lithium battery to realize the portability of the system.
NL-CALIC Soft Decoding Using Strict Constrained Wide-Activated Recurrent Residual Network

In this work, we propose a normalized Tanh activate strategy and a lightweight wide-activate recurrent structure to solve three key challenges of the soft-decoding of near-lossless codes: 1. How to add an effective strict constrained peak absolute error (PAE) boundary to the network; 2. An end-to-end solution that is suitable for different quantization steps (compression ratios). 3. Simple structure that favors the GPU and FPGA implementation. To this end, we propose a Wide-activated Recurrent structure with a normalized Tanh activate strategy for Soft-Decoding (WRSD). Experiments demonstrate the effectiveness of the proposed WRSD technique that WRSD outperforms better than the state-of-the-art soft decoders with less than 5% number of parameters, and every computation node of WRSD requires less than 64KB storage for the parameters which can be easily cached by most of the current consumer-level GPUs. Source code is available at https://github.com/dota-109/WRSD.
Analytics Platform for Morphometric Grow out and Production Condition of Mud Crabs of the Genus Scylla with K-Means

The traditional use of an electronic spreadsheet is poorly suited to operationalize and manage the visualization and analysis of datasets related to mud crab production in the Philippines because of the long process of constructing equations to produce the desired diagram. A mud crab-specific analytic platform is needed to accurately visualize and analyze the datasets that will eventually result in better and data-driven decision-making on the part of farmers and stakeholders. This paper introduces the design and development of a state-of-the-art Mud crab Analytics Platform (MAP) utilizing the K-means algorithm. A holistic system development approach was applied which consists of three phases namely; 1) capture, 2) consumption, and 3) integration and transformation. As a result, MAP was able to provide significant insights in terms of descriptive, predictive, and prescriptive analytics. This real-time and dynamic analytic platform may provide farmers and stakeholders a powerful means to conduct data analysis on a community level to obtain actionable insights.
Improving Graph Neural Network by Filter Preprocessing

For graph-based semi-supervised learning, Graph Convolutional Networks (GCNs) and their variants has shown outstanding results and gained wide attention. Several works on analyzing GCNs from the perspective of spectral graph theory shows that GCNs has the function of low-pass filtering on certain learning tasks, which enables us to have a deeper understanding of GCN. However, GCN achieves the filtering effect through layers of matrix multiplication, so one cannot flexibly control the filtering process. In this paper, we propose to preprocess the graph-structured data by using low-pass filters explicitly before network training and prediction. We conduct experiments on the citation network datasets. Our preliminary results show that, the filter preprocessing step can effectively improve the predicative accuracy of common neural networks or graph neural networks in graph-based semi-supervised learning.
Multi-angle Facial Expression Image Generation Method Based on Mask Vector Guided Generation Adversarial Network

In recent studies, the method of generative adversarial network to augment facial expression images has been widely used. However, the existing methods ignore the influence of face angle on facial expression image generation. To solve this problem, this paper proposes a mask vector - guided generative adversarial network to generate multi-angle expression images. Mask vectors enable a single model architecture to train multiple facial expression datasets from different angles simultaneously. Experimental results on the KDEF dataset show that, compared with the IcGAN, CycleGAN and StarGAN benchmark models, the proposed method uses fewer parameters and the classification accuracy of the generated facial expression images is improved by 1%-4%, indicating that the facial expression images generated by the proposed method are more realistic.
Optimization Classification of Diseases Which is Dominant Suffered by Coastal Areas Using Neural Network

This study focuses on the health problems of coastal communities that are very important to pay attention to. Indonesia has 8,090 coastal villages, currently 89% of coastal communities do not understand the meaning of health, healthy lifestyles and environmental hygiene so that coastal communities have dominant diseases that are often suffered by the community. Therefore, an accurate study model is needed regarding the classification of the dominant diseases suffered by coastal communities in order to improve the health quality of the coastal communities of North Sumatra. The purpose of this research is to classify the dominant diseases of coastal communities using the Artificial Neural Network method. The stages of the research method are literature study, problem identification and data collection based on the variables studied, namely economic, environmental, social, health factors and the types of diseases classified are diarrhea, dengue fever and hypertension. The data sample used is 100 data. From the results of the study, it can be stated that the dominant coastal community disease is diarrhea with a percentage of 40%, then hypertension is 26% and dengue fever is 25%. Furthermore, the best method in classifying the dominant diseases of coastal communities is Neural Network Multilayer Perceptron at 89%, then Naïve Bayes is 54% and Decicion tree is 50%.
Research on Highly Efficient Deep Learning Engineering Teaching for Information Management and Information System Undergraduates

The education of deep learning engineering is an important direction for Information Management and Information Systems (IMIS) undergraduate education in universities in recent years. This paper investigates the specific methods to realize the education of deep learning engineers based on the existing teaching plan for Information Management and Information Systems undergraduate students through teaching cases. The methods and means of constructing deep learning knowledge system and engineering practice experience at each stage of undergraduate education are specifically analyzed. The results of the study show that deep learning engineers can be effectively trained by introducing targeted teaching contents and engineering practice cases into the existing undergraduate teaching plan of Information Management and Information Systems. The study is expected to provide a reference for the education of deep learning engineers in related teaching plan.