Reviewerly/exHarmony huggingface dataset:
DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition

We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks. Nonlinear least squares are commonly solved with the Levenberg-Marquardt (LM) algorithm for fast convergence. We implement a general and efficient LM solver on a deep learning framework by designing a new backward jacobian network to enable automatic sparse jacobian matrix computation. Furthermore, we introduce a stochastic domain decomposition approach that enables batched optimization and preserves convergence for large problems. We evaluate our method by solving bundle adjustment as a fundamental problem. Experiments show that our optimizer significantly outperforms the state-of-the-art solutions and existing deep learning solvers considering quality, efficiency, and memory. Our stochastic domain decomposition enables distributed optimization, consumes little memory and time, and achieves similar quality compared to a global solver. As a result, our solver effectively solves nonlinear least squares on an extremely large scale. Our code will be available based on Pytorch <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and Mindspore <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Hybrid Embroidery Games: Playing with Materials, Machines, and People

Our work centers on aspects of crafting creativity that are often overlooked in digital fabrication: playful- ness, and possibilities for social engagement. We draw from precedents in both crafting (e.g. quilting bees) and gameplay (e.g. "Exquisite Corpse") to inform the design of a set of turn-based collaborative games which center a computer-controlled embroidery machine as a "player" in games for one or more crafters. We proto- type these games using our own computational input/ output embroidery pipeline and observe how they can guide crafter-players to engage with physical, digital, and social affordances. We summarize our findings on how creative focus can shift over a playful experience of fabrication and how technology can mediate social crafting.
Designing a Collaborative Construction-Project Platform on Blockchain Technology for Transparency, Traceability, and Information Symmetry

The construction industry is a $6 trillion industry worldwide with a prediction to grow towards $10,3 trillion by 2023 and constitutes an essential part of the global economy. Nevertheless, the management of the construction effort is still very manual. The construction process from design, to sourcing of material, contract management, and so on, is a convoluted and intransparent process filled with risks the collaborating parties are exposed to. A need exists for management platforms that streamline and automate collaborative construction processes, establish transparency, traceability, and information symmetry between business parties. This paper presents the Construction Project Management (CoPM) platform that is based on blockchain- and smart-contract technologies for enabling peer-to-peer collaboration between construction parties that enhances the flow of information for reducing cost- and time expenditures while improving the quality of service. The CoPM system is based on diligent up-front requirement studies from which we derive a coherent system architecture and set of cooperation protocols. Thereby, the CoPM system overcomes the currently existing fractured value propositions for construction-management systems.
Activity Tracking of Employees in Industries using Computer Vision

Human resource management is integral to every organization. Its employees are managed based on various aspects such as time management and efficiency monitoring which never goes out of scope at every level in an organization. Many strategies and methods have been proposed and used by the human resource department as a routine to monitor employees to effectively utilize the employee's man-hours and properly train those who lack certain efficient working skills. This paper discusses effective monitoring of employees in the workplace through computer vision technology using CCTV cameras in an organization. The employees are detected within their work region and the effective time spent within are mapped against their efficiency of work completed within the stipulated time. In this proposed method, the effective working hours of an employee are consolidated where the cost of implementation is much simpler than existing strategies and methods used in employee monitoring.
A Blind Signature-Aided Privacy-Preserving Power Request Scheme for Smart Grid

Smart grid is an emerging power system capable of providing appropriate electricity generation and distribution adjustments in the two-way communication mode. However, privacy preservation is a critical issue in the power request system since malicious adversaries could obtain users’ daily schedule through power transmission channel. Blind signature is an effective method of hiding users’ private information. In this paper, we propose an untraceable blind signature scheme under the reputable modification digital signature algorithm (MDSA). Moreover, we put forward an improved credential-based power request system architecture integrated with the proposed blind signature. In addition, we prove our blind signature’s blindness and unforgeability under the assumption of Elliptic Curve Discrete Logarithm Problem (ECDLP). Meanwhile, we analyze privacy preservation, unforgeability, untraceability, and verifiability of the proposed scheme. Computational cost analysis demonstrates that our scheme has better efficiency compared with other two blind signatures.
Explorative data analysis of time series based algorithm features of CMA-ES variants

In this study, we analyze behaviours of the well-known CMA-ES by extracting the time-series features on its dynamic strategy parameters. An extensive experiment was conducted on twelve CMA-ES variants and 24 test problems taken from the BBOB (Black-Box Optimization Bench-marking) testbed, where we used two different cutoff times to stop those variants. We utilized the tsfresh package for extracting the features and performed the feature selection procedure using the Boruta algorithm, resulting in 32 features to distinguish either CMA-ES variants or the problems. After measuring the number of predefined targets reached by those variants, we contrive to predict those measured values on each test problem using the feature. From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants. We conducted a hierarchical clustering analysis on the test problems and noticed a drastic change in the clustering outcome when comparing the longer cutoff time to the shorter one, indicating a huge change in search behaviour of the algorithm. In general, we found that with longer time series, the predictive power of the time series features increase.
KoRASA: Pipeline Optimization for Open-Source Korean Natural Language Understanding Framework Based on Deep Learning

Since the emergence of deep learning-based chatbots for knowledge services, numerous research and development projects have been conducted in various industries. A high demand for chatbots has drastically increased the global market size; however, the limited functional scalability of open-domain chatbots is a challenge to their application to industries. Moreover, as most chatbot frameworks employ English, it is necessary to create chatbots customized for other languages. To address this problem, this paper proposes KoRASA as a pipeline-optimization method, which uses a deep learning-based open-source chatbot framework to understand the Korean language. KoRASA is a closed-domain chatbot that is applicable across a wide range of industries in Korea. KoRASA’s operation consists of four stages: tokenization, featurization, intent classification, and entity extraction. The accuracy and F1-score of KoRASA were measured based on datasets taken from common tasks carried out in most industrial fields. The algorithm for intent classification and entity extraction was optimized. The accuracy and F1-score were 98.2% and 98.4% for intent classification and 97.4% and 94.7% for entity extraction, respectively. Furthermore, these results are better than those achieved by existing models. Accordingly, KoRASA can be applied to various industries, including mobile services based on closed-domain chatbots using Korean, robotic process automation (RPA), edge computing, and Internet of Energy (IoE) services.
Difficulty-Aware Machine Translation Evaluation

The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation.Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings.In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration.A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely.Experimental results on the WMT19 English↔German Metrics shared tasks show that our proposed method outperforms commonly-used MT metrics in terms of human correlation.In particular, our proposed method performs well even when all the MT systems are very competitive, which is when most existing metrics fail to distinguish between them.
Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users' privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor's latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.
PQFabric: A Permissioned Blockchain Secure from Both Classical and Quantum Attacks

Hyperledger Fabric is a prominent and flexible solution for building permissioned distributed ledger platforms. Access control and identity management relies on a Membership Service Provider (MSP) whose cryptographic interface only handles standard PKI methods for authentication: RSA and ECDSA classical signatures. Also, MSP-issued credentials may use only one signature scheme, tying the credential-related functions to classical single-signature primitives. RSA and ECDSA are vulnerable to quantum attacks, with an ongoing post-quantum standardization process to identify quantum-safe drop-in replacements. In this paper, we propose a redesign of Fabric's credential-management procedures and related specifications in order to incorporate hybrid digital signatures, protecting against both classical and quantum attacks using one classical and one quantum-safe signature. We create PQFabric, an implementation of Fabric with hybrid signatures that integrates with the Open Quantum Safe (OQS) library. Our implementation offers complete crypto-agility, with the ability to perform live migration to a hybrid quantum-safe blockchain and select any existing OQS signature algorithm for each node. We perform comparative benchmarks of PQFabric with each of the NIST candidates and alternates, revealing that long public keys and signatures lead to an increase in hashing time that is sometimes comparable to the time spent signing or verifying messages itself. This is a new and potentially significant issue in the migration of blockchains to post-quantum signatures.
Efficient Zero-Activation-Skipping for On-Chip Low-Energy CNN Acceleration

A new convolution paradigm is proposed for convolutional neural networks (CNNs) in this paper, which can efficiently skip the storage and computation of zeros in the input feature maps, by exploring the position information of the activations. Furthermore, by reusing the activations as much as possible, load balance is achieved among different processing elements without hardware cost. With the proposed sparse convolution technique, the calculation speed is enhanced by 7.29x for convolutional layers with a sparsity of 90% and by 2.59x for running VGG16 with ImageNet2012 dataset, compared to the traditional convolution method. Implemented in a UMC 55-nm low power CMOS technology, a CNN accelerator with the proposed technique achieves an effective energy efficiency of 1.94 TOPS/W while running at 100 MHz and 1.08 V supply voltage.
Multi object tracking: a survey

Multiple target tracking (MTT) or multiple object tracking (MOT), which is a key stage operation for many computer vision applications, has relied and it relies on detecting and identifying targets within videos. The objects of these videos may be, for instance, pedestrians, vehicles or animals regardless of the number of targets or their appearance. The main goal of multi object tracking is to follow paths or trajectories of multiple targets in a sequence. Multiple object tracking is a field of computer vision that has a wide range of applications, starting from video surveillance and human computer interaction to robotics. The aim of visual object tracking is to maintain objects’ identities. Developing an effective and accurate visual tracking system is very challenging due to the enormous number of problems, such as Illumination variation or background, the famous Clutters meaning the overlap between objects. Low resolution may be due to cameras problems, scale variation, occlusion, and change of targets’ positions within video. These challenges have a wide range of approaches and methods in this evolving research area with several algorithms in each subfield. This paper covers the crucial research area for Multiple Object Tracking (MOT) and this study will help researchers accomplish their scientific projects relying on the wide range of algorithms mentioned on this review.
Augmented Reality Anatomy Visualization for Surgery Assistance with HoloLens

Immediate care for trauma wounded patients in austere or remote settings makes medical knowledge, skills, and efficiency of the on-duty medical professional paramount. For wounds that extend deep into internal anatomy, proper visualization of internal anatomy can enable more efficient and effective evaluation when presented to medical providers positioned close to the point of injury (POI). In this paper, a conceptual Augmented Reality (AR) surgical tool is presented to provide visualization of internal human anatomy, superimposed on the view of a patient, to assist medical providers for immediate casualty care. This AR surgical tool can play a role in 3D surgery or treatment planning as a navigational aid in preparing medical interventions and enhancing surgery or treatment procedures by displaying otherwise obscured anatomy and nearby vessels. Critical software and hardware components are integrated to construct a prototype AR system for the portable AR surgical visualization tool. The system uses a Microsoft HoloLens 1 and an Azure Kinect camera for simultaneous body-tracking and anatomy overlay to demonstrate the overall concept. Future extension of this work will aim to create a more accurate and compact prototype system that utilizes HoloLens 2 with an embedded Kinect camera for laboratory and field tests of its use in surgery assistance. Such an AR tool can also serve as a training tool for medical caregivers, applied with a human subject or a medical manikin.
A Novel Modified Sparrow Search Algorithm Based on Adaptive Weight and Improved Boundary Constraints

A novel modified sparrow search algorithm based on adaptive weight and improved boundary constraints is proposed to tackle disadvantages of sparrow search algorithm, which tends to fall into local optimum and has limited convergence speed. The convergence speed of algorithm is improved by adaptive weight, and the improved boundary handling strategy improves the convergence accuracy of algorithm to a certain extent. In order to verify the effectiveness of improved algorithm, a total of nine benchmark test functions of three types were calculated, and the ant lion optimizer, seagull optimization algorithm, tunicate swarm algorithm and standard sparrow search algorithm were compared and analyzed statistically. The simulation results indicate that the improved algorithm can overcome precocious convergence problem effectively, and is superior to the other four algorithms in terms of convergence speed and precision.
Green technology innovation path based on blockchain algorithm

• This paper mainly studies the green technology innovation path analysis based on block chain algorithm. • Aiming at the high delay and low efficiency of PBFT algorithm in green technology innovation, a low delay consensus algorithm ipBFT based on alternative voting mechanism was proposed. • This algorithm adds a group of candidate nodes to support the dynamic change of nodes.Optimize view switching protocol, do not save unfinished suggestion messages when switching views, initialize view number after switching to reduce the delay of reaching agreement. The core of green technological innovation is to protect the ecology. The innovative introduction of ecological concepts into the existing ecosystem can ensure the sustainable development and progress of society. This paper mainly studies the path analysis of green technology innovation based on blockchain algorithm. Aiming at the high latency and low efficiency of pbft algorithm in green technology innovation, a low latency consensus algorithm ipbft based on an alternative voting mechanism is proposed. Explore the influencing factors of domestic realization of green technology innovation, clarify the reasons that hinder green technology innovation and give reasonable strategies to help domestic green technology innovation work to be carried out faster and better. This article compares the ipbft algorithm with the improved consensus algorithms cpbft, vpbft and pbft in recent years. Experimental results show that the consensus delay of the ipbft algorithm is much lower than that of the other three algorithms, and the average delay of the ipbft single consensus is only about 370ms. In different transaction volume and different node number throughput performance tests, the ipbft throughput in these two tests is better than the other three algorithms. In the view switching phase, the ipbft algorithm also takes less time than the other three algorithms. The above results show that the ipbft algorithm has lower consensus delay and higher throughput, which can reduce network communication overhead and save system transmission energy consumption. It can ensure the security and authenticity of information in the green technology innovation based on blockchain, and reduce system overhead.
Inclusive VR through Inclusive Co-Design with Neurodiverse Learners

This paper reports on a fully inclusive co-design process of an informal VR science game intended to be accessible to a broad range of learners. The co-design embraces the `nothing about us without' us movement by ensuring that stakeholder voices have a prominent role throughout the design process. In the project described in this paper, professional designers and researchers work with a team of neurodiverse stakeholders as peer members of the design team. The design process is described, and the findings based on feedback from all co-design participants is reported. Recommendations for others in the field are provided to help guide those interested in implementing an effective and inclusive co-design process.
BE-ACGAN: Photo-realistic residual bit-depth enhancement by advanced conditional GAN

• An advanced conditional generative adversarial network is proposed for BE task. • The BE-ACGAN is trained to reconstruct residual images rather than HBD ones. • The discriminator is improved by adopting ZP images as additional inputs. • A more reliable loss function is involved to stabilize the adversarial training. Since more demands for high quality visualization have been raised in various fields, monitors with higher bit-depth (HBD) become popular in recent years. However, most digital images are at low bit-depth (LBD) and usually of low visual quality with annoying false contours when displayed on HBD monitors directly. To reconstruct visually pleasant HBD images, many bit-depth enhancement (BE) algorithms have been proposed from various aspects, but the recovered HBD images are usually unsatisfactory with conspicuous false contours or over-blurred textures. Inspired by discriminative learning, we propose a residual BE algorithm based on advanced conditional generative adversarial network (BE-ACGAN), in which the discriminator adversarially helps assess image quality and train the generator to achieve more photo-realistic recovery performance. Besides, since it is hard to distinguish between the reconstructed and real HBD images with similar structures, the discriminator takes residual images as input and further takes LBD images as conditions to achieve more reliable performance. In addition, we present a novel loss function to deal with the difficulty of unstable adversarial training. The proposed algorithm outperforms the state-of-the-art methods on large-scale benchmark datasets. Source codes are available at https://github.com/TJUMMG/BE- ACGAN/.