Reviewerly/exHarmony huggingface dataset:
Improved artificial bee colony algorithm based on damping motion and artificial fish swarm algorithm

Abstract The basic artificial bee colony (ABC) algorithm is easy to fall into local optimum, and it has poor exploitation ability and slow convergence speed. According to the defects, the improved algorithm is proposed, which is based on damping motion and artificial fish swarm(AFS) algorithm. In the employed bees phase, the swarm behavior of AFS algorithm which can avoid sinking into local optimum is introduced. At the same time, considering the slow convergence speed, employed bees adopt the multidimensional updating process in the early stage of iteration. In the onlooker bees phase, an adaptive step size based on damping motion is designed to replace the random step size, which can balance the global exploration and the local exploitation. Through the simulation of six test functions, we get the average convergence algebra, the mean, the best and the variance of the optimal solution through 30 experiments. Simulation shows that the improved algorithm has a better performance than the basic one.
Privacy-Preserving near Neighbor Search via Sparse Coding with Ambiguation

In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding with ambiguation (SCA) mechanism that introduces the notion of inherent shared secrecy based on the support intersection of sparse codes. This approach is ‘fairness-aware’, in the sense that any point in the neighborhood has an equiprobable chance to be chosen. Our approach can be applied to raw data, latent representation of autoencoders, and aggregated local descriptors. The proposed method is tested on both synthetic i.i.d data and real image databases.
Gender Differences of Cognitive Loads in Augmented Reality-based Warehouse

The rapid emergence of augmented reality (AR) has brought considerable advantages to warehouse workers. However, due to inherent biological and cognitive differences, the male and female workers perceive cognitive loads differently. Understanding the differences is essential to improve the workers' productivity and well-being. Therefore, we developed the AR headset that helped participants facilitate parcel scanning and evaluated the gender differences in the context of long-lasting repetitive parcel scanning. The results show that the female workers had significantly lower operational efficiency, higher visual attention, and higher memory loads than the male, but they quickly gained advantages in these aspects.
Investigating into Minimum Detectable Displacement Signal in Image-Based Vibration Measurement

Image-based vibration measurement is becoming more and more popular due to its critical advantages of non-contact and full-field measurement. However, the minimum detectable displacement of a measurement system still lacks a theoretical study and experimental verification. When measuring vibration displacement of micron scale, it becomes difficult to distinguish the signal from noises. Therefore, this paper puts forward an explicit equation to calculate the minimum detectable signal (MDS) of displacement in vibration measurement using high-speed camera. The influence factors including blurring, color depth, image noise, and resolution are all included in the equation, which presents how these factors impact on the MDS. A simulation test is conducted on synthetic image and results show a great agreement with theoretical prediction. Furthermore, the equation of MDS is validated by an experimental study on modal analysis.
Feature-aware unsupervised learning with joint variational attention and automatic clustering

Deep clustering aims to cluster unlabeled real-world samples by mining deep feature representation. Most of existing methods remain challenging when handling high -dimensional data and simultaneously exploring the complementarity of deep feature representation and clustering. In this paper, we propose a novel Deep Variational Attention Encoder-decoder for Clustering (DVAEC). Our DVAEC improves the representation learning ability by fusing variational attention. Specifically, we design a feature-aware automatic clustering module to mitigate the unreliability of similarity calculation and guide network learning. Besides, to further boost the performance of deep clustering from a global perspective, we define a joint optimization objective to promote feature representation learning and automatic clustering synergistically. Extensive experimental results show the promising performance achieved by our DVAEC on six datasets comparing with several popular baseline clustering methods.
Adaptive Accessible AR/VR Systems

Augmented, virtual and mixed reality technologies offer new ways of interacting with digital media. However, such technologies are not well explored for people with different ranges of abilities beyond a few specific navigation and gaming applications. While new standardization activities are investigating accessibility issues with existing AR/VR systems, commercial systems are still confined to specialized hardware and software limiting their widespread adoption among people with disabilities as well as seniors. This proposal takes a novel approach by exploring the application of user model-based personalization for AR/VR systems to improve accessibility. The workshop will be organized by experienced researchers in the field of human computer interaction, robotics control, assistive technology, and AR/VR systems, and will consist of peer reviewed papers and hands-on demonstrations. Keynote speeches and demonstrations will cover latest accessibility research at Microsoft, Google, Verizon and leading universities.
Realistic face generation using a textual description

A lot of research is going on in the field of GANs and their ability to generate photorealistic images from their textual descriptions. Still the images generated by existing tasks focus primarily on either generating birds or flowers from their descriptions. This paper has successfully implemented a sketch-refinement technique for human face creation from their text descriptions. This problem has a wide variety of industrial applications as well such as creating comics automatically, assisting a movie creator to generate frames, art creation assisting an artist to generate sketches and even for educational purposes. Thus, keeping the need for such a system in mind, a two-staged StackGAN architecture obtained from deep convolutional neural networks is proposed in this paper. The features of faces like blonde hair, arched eyebrows are converted into an image of an actual person with these features. Not limiting to this, facial expressions like a wide s mile, happy face are converted from its textual form to the corresponding image of the person with the same expressions. The generation of a high resolution 256 × 256 image using captions provided in CelebA dataset makes it a valuable contribution in the field of research. Further, the proposed research work has successfully obtained an inception score of over 4.04 ± 0.05 10 iterations of evaluation and have shown promising results.
Role of MOOCs in teaching Mathematics to students majoring in Engineering

Abstract The research is dedicated to the features of providing mathematical training to would-be engineers through using the potential of mass open online courses. The article reveals the importance of mathematical training as the basis for forming the professional competence of engineers. A sociological poll among the participants of the educational process at Russian technical universities revealed high awareness of the professors about MOOCs (93.3%), while only 45.4% of students knew about such platforms. The undisputed leader by popularity among both professors (53.3%) and students (34.5%) is the project “National platform of open education”. The reasons impeding the use of mass open online courses are: the lack of spare time, the lack of skills of working in such format, and the lack of regulation for introducing the results of studying at MOOC into the formal educational process. At the same time, analysis of Coursera, EdX, Open Education, Intuit, Lektorium and Stepik platforms showed that, as of January 2021, 150 courses were available in the broad and specific mathematical domains. Intensification of using MOOCs in preparing for academic sessions may increase the efficiency of mastering knowledge and skills in solving practical mathematical tasks, which would improve training of the would-be engineers.
Secure Deterministic Wallet and Stealth Address: Key-Insulated and Privacy-Preserving Signature Scheme With Publicly Derived Public Key

Deterministic Wallet (DW) and Stealth Address (SA) mechanisms have been widely adopted in the cryptocurrency community, due to their virtues on functionality and privacy protection, which come from a key derivation mechanism that allows an arbitrary number of derived keys to be generated from a master key. However, these algorithms suffer a vulnerability that, when one derived key is compromised somehow, the damage is not limited to the leaked derived key only, but to the master key and in consequence all derived keys are compromised. In this article, we introduce and formalize a new signature variant, called Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key (PDPKS), which fully captures and improves the functionality, security, and privacy requirements of DW and SA. We propose a PDPKS construction and prove its security and privacy in the random oracle model. Furthermore, we implement the construction with parameters for 128-bit security, and the results show that it is practically efficient for the setting of cryptocurrencies. With its solid guarantee on functionality, security and privacy, as well as its practical efficiency, our PDPKS construction provides a practical cryptographic tool that refines DW and SA, without security vulnerability.
Transformation of Cybersecurity Posture in IT Telecommunication: A Case Study of a Telecom Operator

Organisations are facing sophisticated and advanced persistent threats (APT) that are targeting sensitive information assets. Any form of cyber-presence can be typically attacked by adversaries, and the motives of such attacks are context dependent. Besides, users and organisations are prone to software vulnerabilities, misconfigurations, outdated systems and several other systemic deficiencies which can be leveraged to compromise enterprise assets and gain an initial foothold within an organisation network. The aim of the paper is to develop a flexible and generally comprehensive organisational strategy to defend against the massive increase in cyberattacks, in order to protect the strategic business objectives of an organisation and keep an alignment between business objectives and security. Moreover, this paper reflects on the work undertaken by multiple teams within the chosen case study organisation to enhance the cybersecurity.
Nonasymptotic Bounds for Stochastic Optimization With Biased Noisy Gradient Oracles

We introduce biased gradient oracles to capture a setting where the function measurements have an estimation error that can be controlled through a batch size parameter. Our proposed oracles are appealing in several practical contexts, for instance, risk measure estimation from a batch of independent and identically distributed samples, or simulation optimization, where the function measurements are “biased” due to computational constraints. In either case, increasing the batch size reduces the estimation error. We highlight the applicability of our biased gradient oracles in a risk-sensitive reinforcement learning setting. In the stochastic nonconvex optimization context, we analyze a variant of the randomized stochastic gradient algorithm with a biased gradient oracle. We quantify the convergence rate of this algorithm by deriving nonasymptotic bounds on its performance. Next, in the stochastic convex optimization setting, we derive nonasymptotic bounds for the last iterate of a stochastic gradient descent algorithm with a biased gradient oracle.
Optimization-based User Support for Cinematographic Quadrotor Camera Target Framing

To create aesthetically pleasing aerial footage, the correct framing of camera targets is crucial. However, current quadrotor camera tools do not consider the 3D extent of actual camera targets in their optimization schemes and simply interpolate between keyframes when generating a trajectory. This can yield videos with aesthetically unpleasing target framing. In this paper, we propose a target framing algorithm that optimizes the quadrotor camera pose such that targets are positioned at desirable screen locations according to videographic compositional rules and entirely visible throughout a shot. Camera targets are identified using a semi-automatic pipeline which leverages a deep-learning-based visual saliency model. A large-scale perceptual study (N ≈ 500) shows that our method enables users to produce shots with a target framing that is closer to what they intended to create and more or as aesthetically pleasing than with the previous state of the art.
A Novel Study of Application of Information &amp;amp; Communication Technology in Library Classification

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Development and Psychometric Properties of the Hate-Motivated Behavior Checklist (HMBC)

Hate-motivated behavior (HMB) in criminal and noncriminal forms is a public health dilemma. This study is an advanced measurement of the perpetration of HMB through development of the Hate-Motivated Behavior Checklist (HMBC). Through a cross-sectional online-administered survey of adults ( N = 289), we examined: (a) factor structure and internal consistency of HMBC behaviors, (b) descriptive patterns of targeted groups and motivations for HMB, and (c) correlates of HMBC behaviors. Findings support a single-factor HMBC behaviors score with high internal consistency. HMB based on race, ethnicity, sexual orientation, and sex were the most commonly endorsed. The most highly endorsed motivations for HMB were perceived intrusion and perceived threat. Antigay prejudice, antilesbian prejudice, positive views of the Ku Klux Klan (KKK) and Nazis were among significant correlates of HMB perpetration. The HMBC represents a preliminary research measurement tool for the assessment of HMB in need of further psychometric study.
Design in Action: Unpacking the Artists’ Role in Performance-Led Research

This paper illustrates design work carried out to develop an interactive theater performance. HCI has started to address the challenges of designing interactive performances, as both audience and performers' experiences are considered and a variety of professional expertise involved. Nevertheless, research has overlooked how such design unfolds in practice, and what role artists play in exploring both the creative opportunities and the challenges associated with interweaving digital technologies. A two-day workshop was conducted to tailor the use of the ChameleonMask, a telepresence technology, within a performance. The analysis highlights the artists' work to make the mask work while framing, exploring and conceptualizing its use. The discussion outlines the artists' skills and design expertise, and how they redefine the role of HCI in performance-led research.
Design and Implementation of a Secure QR Payment System Based on Visual Cryptography

In this paper, we will describe the design and implementation of a secure payment system based on QR codes. These QR codes have been extensively used in recent years since they speed up the payment process and provide users with ultimate convenience. However, as convenient as they may sound, QR-based online payment systems are vulnerable to different types of attacks. Therefore, transaction processing needs to be secure enough to protect the integrity and confidentiality of every payment process. Moreover, the online payment system must provide authenticity for both the sender and receiver of each transaction. In this paper, the security of the proposed QR-based system is provided using visual cryptography. The proposed system consists of a mobile application and a payment gateway server that implements visual cryptography. The application provides a simple and user-friendly interface for users to carry out payment transactions in user-friendly secure environment.
Senone-Aware Adversarial Multi-Task Training for Unsupervised Child to Adult Speech Adaptation

Acoustic modeling for child speech is challenging due to the high acoustic variability caused by physiological differences in the vocal tract. The dearth of publicly available datasets makes the task more challenging. In this work, we propose a feature adaptation approach by exploiting adversarial multi-task training to minimize acoustic mismatch at the senone (tied triphone states) level between adult and child speech and leverage large amounts of transcribed adult speech. We validate the proposed method on three tasks: child speech recognition, child pronunciation assessment and child fluency score prediction. Empirical results indicate that our proposed approach consistently outperforms competitive baselines, achieving 7.7% relative error reduction on speech recognition and up to 25.2% relative gains on the evaluation tasks.
Large Margin Training Improves Language Models for ASR

Language models (LM) have been widely deployed in modern ASR systems. The LM is often trained by minimizing its perplexity on speech transcript. However, few studies try to discriminate a "gold" reference against inferior hypotheses. In this work, we propose a large margin language model (LMLM). LMLM is a general framework that enforces an LM to assign a higher score to the "gold" reference, and a lower one to the inferior hypothesis. The general framework is applied to three pretrained LM architectures: left-to-right LSTM, transformer encoder, and transformer decoder. Results show that LMLM can significantly outperform traditional LMs that are trained by minimizing perplexity. Especially for challenging noisy cases. Finally, among the three architectures, transformer encoder achieves the best performance.
A smart learning assistance tool for inclusive education

The government has established special schools to cater to the needs of children with disabilities but they are often segregated rather than receiving equitable opportunities. Artificial Intelligence has opened new ways to promote special education with advanced learning tools. These tools enable to adapt to a typical classroom set up for all the students with or without disabilities. To ensure social equity and the same classroom experience, a coherent solution is envisioned for inclusive education. This paper aims to propose a cost-effective and integrated Smart Learning Assistance (SLA) tool for Inclusive Education using Deep Learning and Computer Vision techniques. It comprises speech to text and sign language conversion for hearing impaired students, sign language to text conversion for speech impaired students, and Braille to text for communicating with visually impaired students. The tool assists differently-abled students to make use of various teaching-learning opportunities conferred to them and ensures convenient two-way communication with the instructor and peers in the classroom thus makes learning easier.
Combining Touchscreens with Passive Rich-ID Building Blocks to Support Context Construction in Touchscreen Interactions

This research investigates the design space of combining touchscreens with passive rich-ID building block systems to support the physical construction of contexts in touchscreen interactions. With two proof-of-concept systems, RFIPillars and RFITiles, we explore various schemes for using tangible inputs for context enrichment in touchscreen interactions. Instead of incorporating an electronic touchscreen module that requires per-module maintenance, this work intentionally makes each tangible object passive. We explore rear-projection solutions to integrate touchscreen interactions into these passive building blocks with capacitive touch sensing techniques and deliberate physical forgiving to retain the merits of being both batteryless and wireless. The presented research artifacts embody the interaction designs and elucidate scalability challenges in integrating touchscreen interactions into this emerging tangible user interface.
When Ellipsis Can Save Defectiveness and When It Can’t

We discuss cases of salvation and non-salvation by deletion in the domain of lexical gaps, and distinguish two types of defectiveness: (a) defectiveness that can be saved by PF deletion, which we take to signal the lack of an eligible allomorph for certain environments within a language, and (b) defectiveness that cannot be saved by PF deletion, which we take to signal the lack of a proper alloseme for a given environment. With ellipsis modeled as an instruction for nonpronunciation on the PF branch of the grammar, only gaps on the Exponent List can be saved by it.
Simulation and Assessment of Safety Procedure in an Immersive Virtual Reality (IVR) Laboratory

This paper presents the early research finding of our approach to utilize an immersive virtual reality (IVR) environment as an educational tool in Science, Technology, Engineering, and Math (STEM). The proposed approach is demonstrated for the science laboratory. Realistic environment and interactions, immersive presence, automatic (in-app) data collection, and the possibility of following different educational theories such as experiential learning and use of the actual course content are among features that make this approach novel and help address existing shortcomings in STEM education, especially during COVID-19 restrictions. An initial usability study and VR presence survey were completed. Finding suggests that the majority of users found the environment realistic, memorable, and easy to use and learn. A full study is underway to compare the same application on Desktop VR (DVR) and text-based/2D safety training.
The Research and Implementation of Human Posture Recognition Algorithm Via OpenPose

The single-person pose estimation algorithm based on OpenPose and the multi-person pose detection algorithm are used to process the action image of the person collected by the camera to realize the positioning of the key points of the human face, the key points of the human hand, and the various joints of the human body positioning of points; Bresenham algorithm is used to draw lines of the key points of the human body. The accuracy of the traditional human posture recognition algorithm will be reduced when it recognizes the occluded body parts. This paper improves the problem based on OpenPose algorithm. The experimental results show that the method adopted in this paper can adapt to the complex background, and has higher recognition accuracy and operation rate.