Reviewerly/exHarmony huggingface dataset:
Structured Attention Knowledge Distillation for Lightweight Networks

Knowledge distillation is to transfer the effective knowledge learned by the teacher networks to the student networks through the designed loss function, which helps the student network to get better performance with less calculation cost. However, when there is a great difference between the student network and the teacher network in terms of structure and calculation amount, the previous knowledge distillation methods can hardly improve the student network performance effectively. In order to improve this situation, this paper proposes structured attention distillation for lightweight networks. Structured attention distillation groups the features from models by channels, which helps student networks learn the feature extraction ability of teacher networks by refining the spatial attention maps. Our proposed method is evaluated on CIFAR100 and large-scale face recognition validation set (LFW, CFP-FP, Age-DB). Compared with other distillation methods, our proposed method achieves better accuracy on CIFAR100 and face recognition validation set.
A High Throughput and Energy Efficient Lepton Hardware Encoder With Hash-Based Memory Optimization

Although it has been surpassed by many subsequent coding standards, JPEG occupies a large storage share of the current data hosting service. To reduce the storage costs, DropBox proposed a secondary lossless compression algorithm, Lepton, to further improve the compression rate of JPEG images. However, the bloated probability models defined by Lepton severely restrict its throughput and energy efficiency. To solve this problem, we construct an access probability-based hash function for the probability models, and then propose a hardware-friendly memory optimization method by combining the proposed hash function with N-way Set-Associative unit. Besides, we also propose a synchronization mechanism for the serial accessing probability model, so that the syntax elements can be processed in parallel without changing the resulting bitstream. After that, we implement a high throughput, high energy efficiency, and low-cost Lepton hardware encoder. To the best of our knowledge, this is the first hardware implementation of transparent image recompression. The synthesis result shows that the proposed hardware structure reduces the total area of the probability models by 70.97%. Compared with DropBox’s software solution, the throughput and the energy efficiency of the proposed Lepton hardware encoder are increased by 63 and 1398 times on average. In terms of manufacturing cost, the proposed Lepton hardware encoder is also lower than the general-purpose CPU used by DropBox.
The case for wearable proximity devices to inform physical distancing among healthcare workers

Despite the importance of physical distancing in reducing SARS-CoV-2 transmission, this practice is challenging in healthcare. We piloted use of wearable proximity beacons among healthcare workers (HCWs) in an inpatient unit to highlight considerations for future use of trackable technologies in healthcare settings.We performed a feasibility pilot study in a non-COVID adult medical unit from September 28 to October 28, 2020. HCWs wore wearable proximity beacons, and interactions defined as <6 feet for ≥5 s were recorded. Validation was performed using direct observations.A total of 6172 close proximity interactions were recorded, and with the removal of 2033 false-positive interactions, 4139 remained. The highest proportion of interactions occurred between 7:00 Am-9:00 Am. Direct observations of HCWs substantiated these findings.This pilot study showed that wearable beacons can be used to monitor and quantify HCW interactions in inpatient settings.Technology can be used to track HCW physical distancing.
Research on the Construction of Smart Teaching in College English with the Assistance of Artificial Intelligence

Artificial intelligence has changed the way of education because it can provide abundant teaching resources, diversified teaching models and effective teaching management. College English teaching should face up to the challenge to integrate classroom teaching with online teaching so as to promote the efficiency of English teaching. This thesis will make a comprehensive analysis on the characteristic of AI technology, put forward a systematic way to construct smart teaching in college English teaching, and provide some constructive suggestions for further development of English teaching.
Effectiveness of Cup Sealer Based Smart Augmented Reality (SAR) Module for Citrus Farmers During The Pandemic

The Cup Sealer Innovation based on Smart Augmented Reality (SAR) is a research project aimed at assisting citrus producers in the event of a pandemic. The major purpose is to create a Smart Augmented Reality (SAR) device that will make it easier for farmers to utilize cup sealers. As the lockout mechanism is known all over the world, food distribution has a vulnerability, Growers, in particular fruit farmers, benefit from the invention of this technique because fruit cannot be preserved for lengthy periods of time. Farmers can now simply pack Citrus Fruit into Cups that may last up to two months. The Cup Sealer SAR is a completely automatic cup sealer machine with a SAR mechanism that also includes a Bluetooth remote and AR-based instructions. Smart AR used in this study is the development of AR without markers, or AR without marker objects, the smart AR concept used is to detect objects that match the Cup Sealer then provide interaction with the object through the appearance of tutorials on using objects, design cup selaer and notifications if cup sealer machine is ready to use. The SAR Sealer Cup has also met the eligibility criteria for producing this product at an advanced stage, as evidenced by the degree of readability and product efficiency of 83.45 percent of 140 respondents, indicating that this product is very suited for usage. The results of the expert test were 87.25 percent, this is categorized as good when the test is tested by experts, you will find that the average results acquired from all testing carried out are 81 percent in terms of design and also the appearance that makes it easy for the user. The data for the Usability test meets the Level of Eligibility Criteria (Sudjana, 1992) criteria, which is based on the UI and UX percentages in the 88.70 percent group (good category), The Cup Sealer development has good usability and efficiency levels, with a total average of 87.82 percent, and the machine feedback aspect with app also has good conditions, namely 87.40 percent with a total average of 87.82 percent, proving that the Cup Sealer development has good usability and efficiency levels and can be used to help citrus farmers boost the economy during a pandemic.
A Study on New Product Recommendation Using Multi-Label CVAE for Fresh Flowers

In recent years, it has become very popular to use purchase history data on e-commerce sites for marketing measures to increase sales. Under such a situation, this paper considers measures using the purchase history data of a company providing delivering services of fresh flower products through an e-commerce site. This site deals mainly with fresh flowers, and the majority of items are purchased for gifts. The demands of flower gifts are usually strongly related with certain events, such as birthday, Mother’s day, opening celebration, etc. Since each customer often makes purchase only at certain event when purchasing a flower gift, and it is important to encourage them to make purchases at other events from marketing viewpoint. In addition, the appearance of fresh flowers is important, so product recommendation with product images is necessary. It is relatively easy to develop floral gifts because they consist of certain patterns such as types of fresh flowers and shapes such as bouquets. However, there is no development of product which quantitatively uses purchase history information, The purpose of this research is, therefore, to generate product images that are preferred by customers in another event, considering the characteristics of product images purchased in individual event, where it is also possible to create new product images that are not contained in existing items. The proposed model is based on Conditional Variational Auto Encoader (CVAE) and can generate image outputs by inputting product images as multi-labels of events and attributes such as age and gender of customers that greatly affect product selection. Then, after learning a generator model, we consider to analyze what kinds of new products a customer with certain attributes who purchased at certain event would newly prefer at other events by changing the labels. Furthermore, in this study, we demonstrate the validity of the model by analyzing an actual data set.
FCOS-Lite: An Efficient Anchor-free Network for Real-time Object Detection

Fully Convolutional One-Stage detector(FCOS) is a simple and strong anchor-free detector. However, the outstanding performance comes at the cost of long inference time. In this paper, a more efficient anchor-free detector FCOS-Lite is proposed to speed up the inference time of FCOS. FCOS-Lite improves the three components of FCOS, namely the backbone, neck and head. We first apply an efficient attention module in the backbone to extract more important semantic information. Then, we propose an adaptive feature-fusion module in the neck to detect small objects accurately. Finally, we use some strategies in the head of FCOS-Lite to reduce the computation. With these methods on the FCOS-Lite, we achieve a speed-accuracy balance on the MS COCO dataset. FCOS-Lite's inference time is almost halved based on the FCOS with a slight accuracy drop. (33.2% mAP at 24 ms for FCOS-Lite compared to 37.4% mAP at 44ms for FCOS on COCO). FCOS-Lite suits for the real-time object detection, improving both accuracy and efficiency of the real-time detector YOLOv3 and FCOS-mobileNet(31.0% mAP at 29ms for YOLOv3 and 30.9% mAP at 27ms for FCOS-mobileNet on COCO).
Design and Implementation of Advanced Encryption Standard Using Verilog HDL

Encryption plays an important role in data security against third-party attacks and it is significant to safeguard sensitive data and personal information for the community. Within this era of technology, privacy and confidentiality are the essential considerations to be addressed as a result of the exponential development of the Internet. One of the main concerns involving software implementation of encryption algorithm is the possibility of slower processing when transmitting and receiving data which consequently will encounter low security level during process of encryption for real-time application. The focus of this paper is to match with the existing cryptography algorithm, 128-bit Advanced Encryption Algorithm and improving the processing speed for the design with hardware implementation. Real-time application is essential for today's modern world and Field Programmable Gate Array approach is applied for this purpose. The optimization approaches include loop release, pipeline architecture and Look-Up-Table (LUT) which allow for exact synchronization in order to meet applications' requirements in real time. The design is coded using the Verilog HDL and the hardware design is analyzed and tested with Altera Cyclone II-V in Quartus II and ModelSim. Through comparative analysis with previous implementation, the maximum throughput for this design is 31.37 Gbit/s for the encryption process can operate at 244.89 MHz. The complete 128-bit AES encryption cycle requires only 41 clock cycles to get the encrypted data.
Optimized Two-Dimensional Chaotic Mapping for Enhanced Image Security Using Sea Lion Algorithm

With the rapid progression of digital image processing and network communication, information security issues have become ever more outstanding. Image encryption has to turn out to be an imperative research direction. Till now, countless scholars have projected copious image encryption algorithms based on chaos systems. However, the low-dimensional chaotic sequences have the troubles of shortcode period and low accuracy, which cannot assure the algorithm security. Thus, this paper intends to propose an optimized two-dimensional (2D) chaotic mapping (O2DCM) for image encryption. Here, the initial chaotic system parameters are fine-tuned with a new optimization algorithm referred as the average fitness-based Sea Lion optimization algorithm (AF-SLnO), which is an improved version of standard Sea Lion optimization algorithm (SLnO). The chaotic key generation system is nothing but the proposed AF-SLnO attempts to maximize the information entropy model. As a result, optimal initial parameters for the chaotic system can be determined. The security improvement is demonstrated using standard security analysis such as key sensitivity analysis, histogram analysis and adjacent pixel autocorrelation tests.
CryptoGram: Fast Private Calculations of Histograms over Multiple Users’ Inputs

Histograms have a large variety of useful applications in data analysis, e.g., tracking the spread of diseases and analyzing public health issues. However, most data analysis techniques used in practice operate over plaintext data, putting the privacy of users’ data at risk. We consider the problem of allowing an untrusted aggregator to privately compute a histogram over multiple users’ private inputs (e.g., number of contacts at a place) without learning anything other than the final histogram. This is a challenging problem to solve when the aggregators and the users may be malicious and collude with each other to infer others’ private inputs, as existing black box techniques incur high communication and computational overhead that limit scalability. We address these concerns by building a novel, efficient, and scalable protocol that intelligently combines a Trusted Execution Environment (TEE) and the Durstenfeld-Knuth uniformly random shuffling algorithm to update a mapping between buckets and keys by using a deterministic cryptographically secure pseudorandom number generator. In addition to being provably secure, experimental evaluations of our technique indicate that it generally outperforms existing work by several orders of magnitude, and can achieve performance that is within one order of magnitude of protocols operating over plaintexts that do not offer any security.
A Dataset Generation Tool for Deep learning-based Motion Planning in Complex Environments

Learning-based approaches for motion planning have gained much attention. These approaches show better performance in terms of computational efficiency as compared to the tradition approaches, such as sampling-based motion planning. One of the key challenges for learning-based approaches is to generate training datasets. This work-in-progress proposes a generalized dataset generation approach for motion planning that allows to generate simulation data for motion planning under geometric, kinodynamic and physics-based constraints. The generated dataset can easily be encoded in any required format for training. This tool will be used at the Institute of Industrial and Control Engineering (IOC-UPC) to test learning-based motion planning strategies for dual arm mobile manipulators under physics-based constraints. As a first step, in this study the developed approach is tested by generating datasets for mobile robots. The generated datasets are used to train motion planning networks, an approach for deep learning-based motion planning.
Efficient Hand Gesture Recognition System based on Computer Vision: An Overview

Gesture recognition has been applied in many fields such as human-machine interface (HCI), home automation, robot control, etc. Hand gestures can be considered as a form of nonverbal communication. Various techniques including software and hardware are used to improve hand gesture recognition. Unlike the use of sensors for gesture detection, the recognition algorithm can be as simple as conventional image processing methodology based on rule-based classification algorithms, or complicated as deep learning identification that uses large datasets and computational resources to achieve dynamic gesture recognition. This paper provides a brief review of the approaches for gesture recognition and applications in various fields on computer vision.
Multi-target Track-to-Track Association Based on Relative Coordinate Assignment Matrix

Multi-sensor data fusion has been widely recognized to improve the performance of multi-target tracking. Track-to-track association (TTTA) must be completed before performing track-to-track fusion. However, the TTTA problem will be complicated by the systematic sensor biases which are different from random error. This article focuses on the TTTA problem in the presence of sensor biases and dense false tracks. A novel multi-target TTTA method based on the relative coordinate assignment matrix (RCAM) is proposed. More specifically, the RCAM leverages the relative coordinate information between two targets instead of the absolute position information of each target. We theoretically analyze the influence of systematic azimuth and range biases on target measurement information and RCAM of target, and test the proposed method in different challenging scenarios. Simulation results demonstrate better performance of the new method against systematic sensor biases and false tracks compared with other competing methods.
Data-driven networking research

We in Google's various networking teams would like to increase our collaborations with academic researchers related to data-driven networking research. There are some significant constraints on our ability to directly share data, which are not always widely-understood in the academic community; this document provides a brief summary. We describe some models which can work - primarily, interns and visiting scientists working temporarily as employees, which simplifies the handling of some confidentiality and privacy issues. We describe some specific areas where we would welcome proposals to work within those models.
Measuring the balance of the world's largest machine

The electrical power grid has been described as the largest machine in the world. Although we customarily assume our electrical outlets provide a steady sinusoidal voltage with a frequency of 60 Hz (in North America), the variations from that ideal are easy to observe using the methods described in this article. These frequency variations are important since, as can be shown with introductory-level physics concepts, they arise when power generation and usage are not balanced on the electrical grid. The challenge of balancing the electrical grid will increase over the coming years as we adopt more sustainable but less predictable energy sources, and this connection to one of our current societal challenges makes this project interesting to students.
MASS

Convolutional neural network (CNN)-based object detection is a key technology to enable autonomous mobile vision applications on mobile end devices such as smart phones and drones. With the advance of edge computing technology, a prevalent solution is to offload the computation-intensive CNN inference tasks to edge networks for fast and accurate object detection. However, a single edge server may not be powerful enough to ensure fast and accurate object detection due to its resource constraints. In this paper, we propose a multi-edge assisted fast object detection framework, MASS, to further reduce the object detection latency while maintaining the detection accuracy. In MASS, the CNN model is divided into two parts, namely the Head part and the Tail part. The Head part is executed locally and the Tail part is further split into multiple subtasks, which can be offloaded to multiple edge servers for parallel execution. First, we propose a method to select the optimal parallel entry point to separate the Head part from the Tail part. Then, an adaptive subtask generation and offloading strategy is proposed to divide the Tail part into multiple subtasks and offload these subtasks to multiple heterogeneous edge servers. Besides, we also propose a uniformly sampled zero-padding scheme to reduce the communication cost among edge servers when executing these subtasks in parallel. We implement MASS in a testbed with four edge servers and evaluate its performance, where the experimental results show that MASS can reduce object detection latency by up to $64.83%$, while the detection accuracy degradation is less than $3%$.
High-speed 3D shape measurement from noisy fringe images using deep learning

High-speed three-dimensional (3D) shape measurement has become a very important technology in industrial manufacturing, motion detection and other scientific research. Although there are some methods to measure 3D surface patterns, it is still difficult to accurately measure the rapidly changing 3D high-speed scenes. Multi-frequency phase unwrapping usually uses a combination of noisy fringe images with different fringe frequencies for phase unwrapping, which has high accuracy and reliability. Benefiting from the success of deep learning in the field of computer vision in recent years, we combine multi-frequency phase-shifting and phase unwrapping with deep learning, and propose the high-speed 3D shape measurement from noisy fringe images using deep learning. Compared with traditional methods, this method can achieve more convenient and robust phase retrieval at high speed. Based on a good training model, the deep learning neural network can directly achieve the corresponding high-quality phase results after extensive learning of the data set collected at high speed. The experimental results demonstrate that this method can achieve 3D shape of the measured object with an accuracy of about 51μm at the camera frame rate of 700 frames per second.
A Usability Testing Approach on A Boarding House Search Website in Kupang City Using The Nielsen Heuristic Method

Mamikos.com is a very popular boarding house or apartment search website in Indonesia. The number of students or people who want to find a place to live is the cause of one of the factors the website is created. but today, there are still many people who do not know about the website or application and its use in their lives. So based on this, the researchers tried to make a social research experience about the usefulness of the Mamikos.com website. The purpose of this study is to determine the usability of this website in accordance with the criteria of usability. The sample used in this study was 88 respondents who were randomly selected using the lameshow formula. This study uses a usability approach using the Nielsen heuristic method in finding the usability of the website. The results of research using Nielsen Heuristics can show that the Mamikos.com website has a very good usability level with a percentage of 46% from 50%.