Reviewerly/exHarmony huggingface dataset:
GPU Accelerated Boosted Trees and Deep Neural Networks for Better Recommender Systems

In this paper we present our 1st place solution of the ACM RecSys 2021 challenge. Twitter provided a dataset of around 1 billion tweets-user pairs to develop models predicting user engagements. The challenge simulates a realistic production environment by introducing latency constraints of an average of 6ms per example on a single CPU core with 64GB memory. Our solution ranked first having the highest score in each of the eight performance metrics to calculate the final position. Our final submission is an ensemble of stacked models, using in total 5 XGBoost models and 3 neural networks. Although ensembles are rarely used in production environments, we demonstrate their superiority and feasibility to inference the test dataset given the time limitation and computational resources. As an alternative to retraining, continuous training or fine-tuning models when new data is available, we stacked trained models to leverage additional data. Stacking requires less data to calibrate the trained models. Finally, we analyze the benefits of a GPU-accelerated production environment. Using open source libraries, such as Forrest Inference Library, NVTabular, RAPIDS cuDF, PyTorch and TensorFlow, we are able to accelerate our final solution by 257x from 23 hours and 40 minutes to 5.5 minutes and reduce total cost by 88% using a single NVIDIA A100 GPU with 40GB memory, enabling opportunities for significant cost savings and larger models with higher accuracies. We published our solution on github.com1
Efficient Computation of Depthwise Separable Convolution in MoblieNet Deep Neural Network Models

MobileNets are light-weight deep neural network (DNN) models with fewer parameters and less computation compared with some popular models such as AlexNet and VGG. MobileNets adopt depthwise separable convolution (DSC) composed of 1x1 pointwise convolution (PWC) and depthwise convolution (DWC) to reduce model complexity. In this paper, we present a DNN accelerator design which can perform the DSC more efficiently by combining the PWC and DWC with fewer external memory accesses. Experimental results show that the proposed design can reduce more than 50% DRAM accesses with increased speed.
Lens less Cameras for Face Detection and Verification

Face detection and verification based on the camera have progressed to such an extent that they may be integrated into several applications. Traditional cameras are shrinking and driven, adopted by innumerable applications like from household to IOT’s in drones. Some of these applications inflicted by some limitations like form factor, weight and cost. But they require lens. Flat cams have no lens and these can be fabricated like microchips and thinner than dime. However, lens less detectors cannot at yet give their optics equivalents the same spatial resolution and clarity. With almost acceptable same resolution, speed and much lesser cost these flat cams are available. These are even flexible, foldable, wearable and even disposable and can find myriad applications in security. We suggest the use of certain approaches to detect and verify the lens less cameras nowadays for resolution, noise and artifacts. The images acquired from lens less cameras, which pave the door to their integration into new applications, can be conducted with great exactitude both facial detection and verification.
Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation

Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet-a custom dataset of images of labeled caregiver's feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver's distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver's feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.
Augmented Reality children's book for intangible cultural heritage through participatory content creation and promotion. Case study: the pastoral life of Psiloritis as a UNESCO World Geopark

This paper explores a digital approach to reintroduce the pastoral life of Psiloritis to children using the technology of Augmented Reality (AR) books. Pastoral life's existence as intangible cultural heritage is declining nowadays because children prefer popular activities with exciting visuals and presentations. At the same time, AR technology assimilation to conventional media has started to emerge. Previous studies show the great potential of AR-based books for children and indicate that this technology enhances their learning experience. This paper adapts participatory content creation, including data collection, analysis, synthesis, development, and communication within an AR book. The AR book is expected to provide a tangible experience for children to enjoy and preserve traditions and life scenes from the mountain of Psiloriti in Crete.
SDN-Based Application-Aware Segment Routing for Large-Scale Network

With the rapid growth of fifth-generation (5G), network operators have met with difficulties in ensuring user satisfaction due to diverse service-level agreement (SLA) requirements. Application-aware routing is able to potentially address this issue in implementing differentiated routing policies corresponding to various applications. To facilitate the application-aware routing, software-defined networking (SDN) and segment routing (SR) are potential solutions with programmability property to steer network traffic into appropriate routing paths. However, application-aware SR encounters two main problems, including application identification for encrypted traffic and path identification with human intervention. Therefore, this article proposes a new SDN-based SR mechanism that could help network operators overcome these problems. This approach implements reinforcement learning to adapt to dynamic networks in order to optimize the quality of experience (QoE) that network operators must guarantee. The proposal also considers the application class to implement corresponding routing policies for various applications to meet the stringent SLA requirements. Identifying the applications is sometimes complicated due to encrypted network traffic. Hence, our proposal implements a traffic classification approach to classify encrypted traffic into different kinds of applications. Obtained results under considered conditions illustrate that our proposal outperforms the standard SR mechanism related to QoE and decreases up to 64.39% of overhead compared to several benchmarks.
ChartReader: Automatic Parsing of Bar-Plots

Scientific figures such as bar graphs are a critical part of scientific research and a predominant method used to represent trends and relationships in data. However, manually interpreting and extracting information from graphs is often tedious. Since data consumption has exponentially evolved over the past few decades, there is a need for automated data inference from these bar graphs. ChartReader presents a fully automated end-to-end framework that extracts data from bar graphs in scientific research papers focusing on process engineering and environmental science journals. ChartReader uses a deep learning-based classifier to determine the chart type of a given chart image. We then develop novel heuristic methods for analyzing scientific figures (text detection, pixel grouping, object detection) and address prime challenges like axis detection, legend parsing, and label detection. Our framework achieves 98% and 68% accuracy in parsing x-axis and y-axis ticks, respectively. It achieves 83% accuracy in parsing legends and 42% accuracy in parsing data values in the testing corpus. We compare the proposed method with state-of-the-art methods and address its limitations.
Simple Baseline for Single Human Motion Forecasting

Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction. Visual and social information are often used to boost model performance, however, they may consume too much computational resources. In this paper, we establish a simple but effective baseline for single human motion forecasting without visual and social information, equipped with useful training tricks. Our method "futuremotion_ICCV21" outperforms existing methods by a large margin on SoMoF benchmark <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We hope our work provide new ideas for future research.
Multi-task manifold learning for small sample size datasets

In this study, we develop a method for multi-task manifold learning. The method aims to improve the performance of manifold learning for multiple tasks, particularly when each task has a small number of samples. Furthermore, the method also aims to generate new samples for new tasks, in addition to new samples for existing tasks. In the proposed method, we use two different types of information transfer: instance transfer and model transfer. For instance transfer, datasets are merged among similar tasks, whereas for model transfer, the manifold models are averaged among similar tasks. For this purpose, the proposed method consists of a set of generative manifold models corresponding to the tasks, which are integrated into a general model of a fiber bundle. We applied the proposed method to artificial datasets and face image sets, and the results showed that the method was able to estimate the manifolds, even for a tiny number of samples.
When should we (not) use the mean magnitude of relative error (MMRE) as an error measure in software development effort estimation?

The mean magnitude of relative error (MMRE) is an error measure frequently used to evaluate and compare the estimation performance of prediction models and software professionals. This paper examines conditions for proper use of MMRE in effort estimation contexts. We apply research on scoring functions to identify the type of estimates that minimizes the expected value of the MMRE. We show that the MMRE is a proper error measure for estimates of the most likely (mode) effort, but not for estimates of the median or mean effort, provided that the effort usage is approximately log-normally distributed, which we argue is a reasonable assumption in many software development contexts. The relevance of the findings is demonstrated on real-world software development data. MMRE is not a proper measure of the accuracy of estimates of the median or mean effort, but may be used for the accuracy evaluation of estimates of most likely effort.
The Smallest Perfect Quantum Accumulate Codes

Quantum has many unique characteristics for future applications in the sixth telecommunication generation (6G) 2030 based on entanglement and quantum teleportation. However, quantum communications are still vulnerable to errors and require quantum error corrections (QECC). This paper proposes quantum accumulate codes, where idea is coming from the classical accumulate codes that can provide good performances to remove or minimize the error-floor in classical communications. The proposed quantum codes can exactly satisfy both Hamming and Singleton bounds resulting in one of the smallest perfect quantum codes. This paper explains in details the steps to construct the codewords, syndrome extraction, error correction, and the quantum circuits. We perform a series of computer simulations to evaluate the quantum word error rate (QWR) and quantum fidelity performances under the depolarizing channels. Our results confirm that the stabilizers of the proposed codes can extract the syndrome and distinguish all error patterns of any single qubit errors of Pauli X, Y, and Z providing performances confirmed by the theoretical QWER curves. The results are expected to provide contributions to the development of quantum communications and future security issues.
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for face hallucination

The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attributes associated with the low-resolution images to improve performance is unsatisfactory. In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process on various facial features in the image. Stacking multiple AGA modules enables the recovery of both high and low-level facial structures. We design the discriminator to learn discriminative features by exploiting the relationship between the high-resolution image and their corresponding facial attribute annotations. We then explore the use of U-Net based architecture to refine existing predictions and synthesize further facial details. Extensive experiments across several metrics show that our AGA-GAN and AGA-GAN + U-Net framework outperforms several other cutting-edge face hallucination state-of-the-art methods. We also demonstrate the viability of our method when every attribute descriptor is not known and thus, establishing its application in real-world scenarios. Our code is available at https://github.com/NoviceMAn-prog/AGA-GAN.
Post-Quantum Zero Knowledge, Revisited or: How to Do Quantum Rewinding Undetectably

When do classical zero-knowledge protocols remain secure against quantum attacks? In this work, we develop the techniques, tools, and abstractions necessary to answer this question for foundational protocols:1)We prove that the Goldreich-Micali-Wigderson protocol for graph non-isomorphism and the Feige-Shamir protocol for NP remain zero-knowledge against quantum adversaries. At the heart of our proof is a new quantum rewinding technique that enables extracting information from multiple invocations of a quantum adversary without disturbing its state.2)We prove that the Goldreich-Kahan protocol for NP is post-quantum zero knowledge using a simulator that can be seen as a natural quantum extension of the classical simulator.Our results achieve negligible simulation error, appearing to contradict a recent impossibility result due to Chia-Chung-Liu-Yamakawa (FOCS 2021). This brings us to our final contribution:3.We introduce coherent-runtime expected quantum polynomial time, a simulation notion that (a) precisely captures all of our zero-knowledge simulators, (b) cannot break any polynomial hardness assumptions, (c) implies strict polynomial-time ε-simulation and (d) is not subject to the CCLY impossibility. In light of our positive results and the CCLY negative results, we propose coherent-runtime simulation to be the appropriate quantum analogue of classical expected polynomial-time simulation.
A Water Behavior Dataset for an Image-Based Drowning Solution

Drowning is responsible for an estimated of 320,000 deaths annually worldwide, roughly 25% of those deaths are in swimming pools. This is probably due to the fact that a drowning person, to the untrained eye, will appear to be normally playing or floating in the water. While drowning, a person is unable to call for help, as the nervous system focuses on gathering oxygen for the lungs. To assist the lifeguards with their rescue mission, we propose a water behavior dataset curated to support the design of image-based methods for drowning detection. The dataset includes three major water activity behaviors (swim, drown, idle) that have been captured by overhead and underwater cameras. Moreover, we develop and test two methods to detect and recognize the drowning behavior using the proposed dataset. Both methods use deep learning and aim to support a fast and smart pool rescue system by watching for the early signs of drowning rather than looking for a drowned person. The results show a high performance of the presented methods validating our dataset, which is the first public water behavior dataset and the main contribution of the work.
Double cross-modality progressively guided network for RGB-D salient object detection

With the advent of depth sensors, the use of RGB and depth(D) information for salient object detection (SOD) has been explored extensively in recent years. However, the depth quality from the different scenes usually varies, leading to fusing and achieving complementary between RGB and low-quality depth is still a challenging problem. In this paper, we first design a Double Dilated Merge Module (DDMM) to extract comprehensive and beneficial high-level cross-modality features and explore further global context information at multi-scales to obtain a coarse saliency map. Then, we propose a Cross-Modality Enhance Module (CMEM) to enhance cross-modality features compatibility and fuse them with the previous predicted coarse saliency map to generate a more accurate saliency map. Furthermore, we introduce a Distribution-Region Combination Loss (DRcom Loss) to optimize our proposed Double Cross-Modality Progressively Guided Network (DCPGNet) in a coarse-to-fine manner. DCPGNet achieves satisfactory performance on five public benchmarks compared with recent state-of-the-art algorithms.
A Toolkit for Generating Code Knowledge Graphs

Knowledge graphs have been proven extremely useful in powering diverse applications in semantic search and natural language understanding. In this work, we present GraphGen4Code, a toolkit to build code knowledge graphs that can similarly power various applications such as program search, code understanding, bug detection, and code automation. GraphGen4Code uses generic techniques to capture code semantics with the key nodes in the graph representing classes, functions and methods. Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow). Our toolkit uses named graphs in RDF to model graphs per program, or can output graphs as JSON. We show the scalability of the toolkit by applying it to 1.3 million Python files drawn from GitHub, 2,300 Python modules, and 47 million forum posts. This results in an integrated code graph with over 2 billion triples. We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
Materialising Data Feminism – How Textile Designers Are Using Materials to Explore Data Experience

In our increasingly technology driven world, many human experiences are at risk of falling into the hands of Big Tech who trade, manipulate and design with our data. Catherine Ignacio and Lauren F Klein (2020 BBC News 2020. “Coronavirus: Ethnic Minorities ‘are a Third’ of Patients.” https://www.bbc.co.uk/news/uk-52255863 (accessed 2021). [Google Scholar]) propose data feminism as a set of considerations to challenge power and injustices using data science practices. The work of textile designers can also support critical thinking by acknowledging alternative forms of knowledge, experience and activism. Textile designers collect, organise, analyse and present data in formats and contexts which engage and elevate people and stories from different communities. This paper explores the ways that textile designers experimenting with data experience embody the proposed suggestions to tackle issues and challenge systems of oppression.Textile design artefacts can inspire and provoke considerations for data ethics by enabling engagement with information which supports critical thinking. This paper demonstrates the principles of data feminism using examples of contemporary textile practitioners to illustrate how data representation is being designed to evoke emotional response, to communicate meaning and to consider alternative forms of knowledge production. Engaging material artefacts to explore ethical implications of data products is proposed as an application for textile design viewed through the lens of data feminism. This paper argues from the perspective of textile designers, for textile and material thinking be considered as valid methodologies to support dialogue used to challenge injustices and oppression perpetuated through data experience. This paper contributes to textile design research and practice which is expanding the traditional space for textile and material thinking to engage science and technology. Using textile practice to enable a connection and through intersectional feminist framing, a position is established for textile designers to challenge issues found in data science.This paper contributes to the interpretations of soft systems by introducing ways that designers are using materials to reconceptualise and question the status quo of engagement with the digital world. Engaging textile design practices can support critical thinking on data ethics; this paper contributes to research which strengthens the case and scope for textile design and explores relevant and timely issues facing society today.
Accurate fringe projection profilometry using instable projection light source

Fringe projection profilometry (FPP) usually uses a projector with an instable light source due to the unsatisfactory driving power. As a result of the fluctuations of the projection light source, the fringe captured by a camera deviates from its ideal distribution, from which, the calculated phase has a random phase error, thus reducing the 3-D measurement accuracy. We found that the random phase error influences both the system calibration and the 3-D reconstruction, where the influence on the system calibration has not been previously noticed. In this paper, we formulate the phase error model of the phase-shifting algorithm caused by the fluctuations of the projection light source, and analyze its interference on calibration and reconstruction, respectively. From which, an accurate FPP using a combination of accurate calibration and accurate 3-D reconstruction is proposed, which are performed by using a deep learning-based phase calculation method. The provided experimental results verify that the proposed FPP can obviously improve the measurement accuracy.