Reviewerly/exHarmony huggingface dataset:
FPGA-based Object Detection Acceleration Architecture Design

Abstract To solve the problems of insufficient acceleration capability and high power consumption of the existing embedded terminal target detection, a multi-strategy optimization hardware accelerator based on the YOLOv2 model is designed by taking advantage of the high concurrency characteristics of the CPU+FPGA structure. The accelerator improves its parallelism and real-time performance while reducing power consumption and resource consumption through floating-point quantization, adder optimization, and various HLS optimization strategies. Tested on Xilinx’s PYNQ-Z2 platform, the overall computing power of the accelerator reaches 27.1GOP/s, the average detection accuracy is 80.6%, and the overall power consumption is 2.609W. Compared with CPU and GPU, the obtained accuracy only loses 2%, but the power consumption is greatly reduced. It has strong practical significance in edge target detection that requires high real-time performance and low power consumption.
Human Pose Estimation Based on Light-Weight High-Resolution Network with Polarized Self-Attention

With the development of computer vision technology, human pose estimation as an indispensable part of human-computer interaction has gradually attracted the attention of researchers. Unlike most existing high-to-low resolution networks in human pose estimation networks, HRNet can maintain high-resolution representations throughout the process, resulting in reliable output results. However, HRNet has the problem of large amount of operation parameters and loss of feature information when multi-resolution feature fusion occurs. Therefore, based on HRNet-32, this paper makes improvements and proposes Human Pose Estimation Based on Light-Weight High-Resolution Network with Polarized Self- Attention. By introducing the improved Gaff module of GhostNet into the first stage of HRNet-32, the network is successfully lightened, and the spatial and channel feature information is extracted more carefully through the Polarized Self-Attention mechanism, and a more accurate human pose estimation effect is obtained.
Qubit Bias using a CMOS DAC at mK Temperatures

Scaling up a quantum processor to tackle real-world problems requires qubit numbers in the millions. Scalable semiconductor-based architectures have been proposed, many of them relying on integrated control instead of room-temperature electronics. However, it has not yet been shown that this can be achieved. For developing a high-density, low-cost wiring solution, it is highly advantageous for the electronics to be placed at the same temperature as the qubit chip. Therefore, tight integration of the qubit chip with ultra low power complemen-tary metal-oxide-semiconductor (CMOS) electronics presents a promising route. We demonstrate DC biasing qubit electrodes using a custom-designed 65nm CMOS capacitive digital-to-analog converter (DAC) operating on the mixing chamber of a dilution refrigerator below 45 mK. Our chip features a complete proof of principle solution including interface, DAC memory and logic, the capacitive DAC, and sample-and-hold structures to provide voltages for multiple qubit gates. The bias- DAC is combined with the qubit using a silicon interposer chip, enabling flexible routing and tight integration. Voltage stability, noise performance, and temperature are benchmarked using the qubit chip. Our results indicate that qubit bias at cryogenic temperatures with a power consumption of 4 n W /ch is feasible with this approach. They validate the potential of very low power qubit biasing using highly integrated circuits whose connectivity requirements do not increase with the number of qubits.
Obstacle avoidance path planning of mobile robot based on improved DWA

A new dynamic window algorithm is proposed to solve the problem that the optimal trajectory is difficult to plan and the trajectory is not smooth due to the dense obstacles in the complex workshop path planning task. The algorithm sets the initial direction angle, and then improves the evaluation function through the direction angle change factor to improve the efficiency and flexibility of the algorithm; The fuzzy controller is used to adjust the coefficient of the evaluation function, which improves the adaptability of the mobile robot to the complex workshop environment. The MATLAB simulation results show that the improved DWA can plan a more reasonable and smooth running path in the area with dense obstacles, and has better obstacle avoidance effect while ensuring the safety of the robot.
Indoor Top Mobile Service Robot Based on Improved RRT Algorithm

An indoor top mobile service robot based on the improved RRT algorithm is proposed. The robot is mainly composed of a lifting truss platform and a manipulator, which avoids the mapping and positioning of complex ground and obstacle avoidance planning. At the same time, the kinematics analysis of the manipulator is carried out. In view of the function of obstacle avoidance planning when the manipulator completes the task, the traditional RRT algorithm is improved to overcome the shortcomings of the low efficiency of path planning in the traditional RRT algorithm, and the planned path quality is uncertain and not smooth enough. The simulation environment is built based on ROS platform to verify the feasibility of the algorithm. To a certain extent, it improves the autonomy of robots to complete tasks.
Traffic Signal Recognition Using End-to-End Deep Learning

Traffic signal recognition is a core task in autonomous driving. In this paper, an end-to-end, real-time deep learning object detection algorithm called YOLO (you only look once) is adopted for traffic signal recognition applications. Two separate open-source image data sets were used for training signal detection models for day and night-time traffic scenarios, respectively. Since the default YOLO framework offers real-time detection, focus was given on achieving acceptable levels of detection accuracy through improving data quality for training. First, the training data sets were cleansed of existing ground truth annotation errors and irregularities. Second, by applying mosaic image augmentation, stitching four randomly upscaled images together, twice during the model training, overall detection accuracy was further improved.
Demystifying Map Space Exploration for NPUs

Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model on an accelerator. It is known to be extremely computationally expensive, and there has been active research looking at both heuristics and learning-based methods to make the problem computationally tractable. However, while there are dozens of mappers out there (all empirically claiming to find better mappings than others), the research community lacks systematic insights on how different search techniques navigate the map-space and how different mapping axes contribute to the accelerator’s performance and efficiency. Such insights are crucial to developing mapping frameworks for emerging DNNs that are increasingly irregular (due to neural architecture search) and sparse, making the corresponding map spaces much more complex. In this work, rather than proposing yet another mapper, we do a first-of-its-kind apples-to-apples comparison of search techniques leveraged by different mappers. Next, we extract the learnings from our study and propose two new techniques that can augment existing mappers — warm-start and sparsity-aware — that demonstrate speedups, scalability, and robustness across diverse DNN models <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.</sup> Code avaliable at https://github.com/maestro-project/gamma-timeloop.
Fan Speed Control Based Defence for Thermal Covert Channel Attacks in Multi-Core Systems

Multicore systems are vulnerable to thermal covert channel attacks as the increased power consumption may remain undetected, while occurring alongside normal, CPU-intensive applications. The purpose of this work is to study thermal effects on modern multi-core systems and decrease the possible risk of attacks. In this research, by controlling the fan speed, we have tried to decrease the temperature of the system to an appropriate level to fight these kinds of attacks. The results indicate that when the temperature decreases at a considerable level, lower power consumption is achieved.
Distribution of controlled unitary quantum gates towards factoring large numbers on today’s small-register devices

Factoring a 2048-bit number using Shor's algorithm, when accounting for error correction, reportedly requires 400,000 qubits. However, it is well known that there is yet much time before we will have this many qubits in the same local system. This is why we propose a protocol for distributed quantum computation applicable to small register devices, specifically for the distribution of controlled unitary gates, the key element in the construction of every quantum computation algorithm. We leverage quantum sharing of partial results to obtain a parallel processing scheme, allowing for the first time the quantum distribution of very large gates with thousands of inputs using only small register devices with tens of qubits. In this way, we improve all previous controlled unitary gate distribution approaches, obtaining surprising results. The impact is quantified for recent milestone hardware realizations of quantum processors.
A novel transfer-learning based physician-level general and subtype classifier for non-small cell lung cancer

Confirming histological patterns of lung carcinoma is important for determining the prognosis and the next steps of treatment for a patient. Confirming the histologic patterns (subtype) of lung adenocarcinoma is important for determining the prognosis and treatment options for a patient. The task is challenging, and often requires the input of experienced pathologists, who by themselves lack interobserver concordance. A computer-aided diagnosis holds the potential to accelerate the time to diagnosis. As many adenocarcinoma tissue samples contain multiple histologic patterns, accurate computer-aided diagnosis requires annotations manually labeled by pathologists. We propose a method that merges weak supervised learning and Integrated Learning using Transfer Learning using two datasets: The Cancer Genome Atlas (TCGA), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) to reduce the need for manual annotation by a pathologist while maintaining accuracy. Whole-slide images (WSI) are first determined to be either adenocarcinoma or squamous cell carcinoma, then further identify the subtypes by generating weak classifiers for each subtype, then using integrated learning to create a strong classifier. Our model was evaluated with independent datasets from the CPTAC dataset and a dataset from a private hospital. It can achieve AUC values of 0.86, 0.91, 0.82, 0.77, 0.96, 0.98 in Acinar, LPA, Micropapillary, Papillary, Solid, and Normal, respectively.
SSVS-SSVD Based Desynchronization Attacks Resilient Watermarking Method for Stereo Signals

Most of the audio signals in real-world applications are stereo signals. However, the previous desynchronization attacks resilient watermarking methods cannot preserve perceptual quality or achieve robustness when constrained by high embedding rates and stereo host. In this paper, based on two novel features segmental singular values summation (SSVS) and segmental singular values difference (SSVD) that are generated using discrete cosine transform (DCT) and singular value decomposition (SVD), we present a robust watermarking method for stereo signals that not only is robust to desynchronization attacks and common signal processing attacks but also has a larger embedding rate compared with the previous methods. In the proposed method, we first apply DCT and SVD on each segment of the host signal to extract the SSVS feature and the SSVD feature. Then we generate the adaptive embedding parameters and embed watermark bits via optimized embedding strategies based on these features. Due to the use of the adaptive embedding parameters and the optimized embedding strategies, the proposed method significantly increases the embedding rate without compromising the robustness and perceptual quality. Analysis results show our proposed method outperforms the state-of-the-art methods by a large margin, where the perceptual quality improvement is over 14%, and the robustness against desynchronization attacks is improved by more than 49% when the embedding rate is 70 bps.
Technology and control of corruption in Africa

Abstract The study assesses the impact of Information and Communication Technologies (ICT) on the control of corruption in Africa from 2002 to 2020. The fixed‐ and the random‐effects models are employed as strategies of estimation. Due to the weaknesses of the fixed‐ and the random‐effects models, the two‐stage least square (2SLS) is adopted as a strategy that addresses the problem of potential endogeneity. For robustness checks, we adopt Lewbel's technique as an alternative instrumental technique to address the concern of endogeneity. The findings reveal that mobile phones penetration, internet penetration and the composite indicator of ICT contribute positively to the enhancement of corruption control in Africa. Though, other ICT indicators such as fixed telephone and fixed broadband negatively affect control of corruption. The composite indicator that encompasses four proxies of ICT shows a significant positive effect on the control of corruption. From the findings of the study, growth in technology infrastructures can help to build a surveillance system that can help to monitor and control corruption. We recommend that African economies accelerate e‐government to ensure transparency in public sectors.
Driving risk-aversive motion planning in off-road environment

Incremental sampling-based motion-planning algorithms, such as the rapidly-exploring random tree (RRT), are popular in motion planning because of their ability to proffer rapid solutions. Driving safety is the ability of a vehicle to prevent overturning and collision. However, the RRT algorithms neither involve any safety mechanism, nor consider any terrain measurement information of the environment, there is no assurance on the driving safety of the solutions. Therefore, obtaining an efficient trajectory while considering driving safety is a dilemma for intelligent vehicles in unstructured off-road environments. This study presents a potential-field based RRT* motion-planning algorithm for vehicle risk aversion. The algorithm constructs a configuration space using the potential field, which identifies risk range around obstacles and off-road terrains. A random-exploring tree grows through sampling inside the potential-field space with the consideration of nonholonomic constraints of the vehicles. A cost function that trades off driving safety and efficiency is used as a priority-sequence mechanism to get an optimized planning solution. A heuristic sampling method is employed to obtain a fast solution to the initial trajectory, and the trajectory can be optimized by decreasing the cost in the following sampling process thereafter. We tested the algorithm on scenarios to verify its effectiveness. Simulation results, under different scenarios, demonstrate that the proposed algorithm improves vehicles' motion planning performance.
Hardware-Software Co-Design of BIKE with HLS-Generated Accelerators

In order to mitigate the security threat of quantum computers, NIST is undertaking a process to standardize postquantum cryptosystems, aiming to assess their security and speed up their adoption in production scenarios.Several hardware and software implementations have been proposed for each candidate, while only a few target heterogeneous platforms featuring CPUs and FPGAs.This work presents a HW/SW co-design of BIKE for embedded platforms featuring both CPUs and small FPGAs and employs high-level synthesis (HLS) to timely deliver the hardware accelerators.In contrast to state-of-the-art solutions targeting performance-optimized HLS accelerators, the proposed solution targets the small FPGAs implemented in the heterogeneous platforms for embedded systems.Compared to the softwareonly execution of BIKE, the experimental results collected on the systems-on-chip of the entire Xilinx Zynq-7000 family highlight a performance speedup ranging from 1.37×, on Z-7010, to 2.78×, on Z-7020.
Applications of Deep Learning in Endocrine Neoplasms

Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.
A framework for digital forensics of encrypted real-time network traffic, instant messaging, and VoIP application case study

Digital forensics is one of the prime professional fields for law enforcement forces. It is also a major active research topic in the cybersecurity field. Internet traffic and content analysis are leading tasks within this research area. Most of the internet traffic is now encrypted, making the traditional analysis of contents impossible. In this paper, we proposed a novel framework and methodology to extract a valuable set of information from encrypted traffic of Instant Messages and Voice Over IP applications. The presented framework enables the analysts to detect, classify and analyze encrypted traffic (typing, chatting, media transmission of audio and video calls, etc.). The provided framework was tested by taking over 30 trace files of these activities and looking at some specific payload patterns. The proposed methodology's results enable investigators to detect and extract application user behavior that can be used as evidence for a forensics investigation. Also, it shows that a valuable set of information can be extracted from encrypted WhatsApp and Telegram traffic.
Setting Up a Dedicated Virtual Reality Application for Learning Critical Thinking and Problem-Solving Skills

The following paper propounds the framework and steps followed to create a game for children in Middle school using Immersive virtual reality (IVR) technologies to practice and improve two high-demand skills in 21 century, Critical thinking and problem-solving skills. We start by modeling some three-dimensional objects that represent the world we created such as platforms, River, Boat, Wolf, Sheep, Cabbage, Framer, and every element related to the environment. Our progress uses the Unity game engine to launch the interactive game for IVR environments to reinforce students' soft skills, several challenges faced while creating IVR scenes, Solutions are proposed where possible. The overall build of this IVR world is described with some models added to the game engine for a better experience.
Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing

AbstractAbstractOver the years, Internet of Things (IoT) devices have become more powerful. This sets forth a unique opportunity to exploit local computing resources to distribute model learning and circumvent the need to share raw data. The underlying distributed and privacy-preserving data analytics approach is often termed federated learning (FL). A key challenge in FL is the heterogeneity across local datasets. In this article, we propose a new personalized FL model, PFL-DA, by adopting the philosophy of domain adaptation. PFL-DA tackles two sources of data heterogeneity at the same time: a covariate and concept shift across local devices. We show, both theoretically and empirically, that PFL-DA overcomes intrinsic shortcomings in state of the art FL approaches and is able to borrow strength across devices while allowing them to retain their own personalized model. As a case study, we apply PFL-DA to distributed desktop 3D printing where we obtain more accurate predictions of printing speed, which can help improve the efficiency of the printers.KEYWORDS: Concept shiftCovariate shiftFederated inferenceInternet of Things3D Printing Supplementary MaterialsThe code for numerical experiments in this article are available in the GitHub repository. In the supplementary material, we will show the proof of Theorem 3.1. We will also present additional experiments and illustrations, mathematical derivations of the proof of concept experiment, and details of our case study implementations.
Path planning algorithm based on improved Bidirectional RRT

Abstract This paper studies the autonomous path planning of unmanned vehicles in a known driving environment. The path planning algorithm used is a hybrid algorithm combining bidirectional fast random search tree and artificial potential field method. On the basis of introducing the principle of the two algorithms, an improved algorithm combining Bi-RRT and artificial potential field is proposed. In order to reflect the advantages of the improved algorithm, the traditional RRT and Bi-RRT algorithms are simulated and compared in Matlab.
A Differential Privacy Protection Protocol Based on Location Entropy

A Location-Based Service (LBS) refers to geolocation-based services that bring both convenience and vulnerability.With an increase in the scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility.To solve the revealing problems in LBS, we propose a differential privacy protection protocol based on location entropy.First, we design an algorithm of the best-assisted user selection for constructing anonymity sets.Second, we employ smart contracts to evaluate the credibility of participants, which ensures the honesty of participants.Moreover, we provide a comprehensive experiment; the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge attacks.Generally, our protocol improves data availability.Particularly, it realizes user-controllable privacy protection, which improves privacy protection and strengthens security.
Panoramic annular image restoration algorithm by prediction based on the lens design characteristics

A panoramic annular lens optical system is important in circumferential detection. However, a panoramic annular image (PAI) has severe tangential and radial distortion. This study proposes a 3σ guideline-based method, which determines the unwrapping center point of the PAI, and a prediction method based on the lens design characteristics according to the intrinsic distortion of the PAL to correct, respectively, the tangential distortion and the radial distortion. The proposed methods can ensure the restoration ability and simplify the dependence on experimental conditions. The experimental results confirm the effectiveness of the proposed method, thus enabling future panoramic optical image detection.