Reviewerly/exHarmony huggingface dataset:
Adversarial Attack and Defence on Handwritten Chinese Character Recognition

Abstract Deep Neural Networks (DNNs) have shown their powerful performance in classification; however, the robustness issue of DNNs has arisen as one primary concern, e.g., adversarial attack. So far as we know, there is not any reported work about the adversarial attack on handwritten Chinese character recognition (HCCR). To this end, the classical adversarial attack method (i.e., Projection Gradient Descent: PGD) is adopted to generate adversarial examples to evaluate the robustness of the HCCR model. Furthermore, in the training process, we use adversarial examples to improve the robustness of the HCCR model. In the experiments, we utilize a frequently-used DNN model on HCCR and evaluate its robustness on the benchmark dataset CASIA-HWDB. The experimental results show that its recognition accuracy is decreased severely on the adversarial examples, demonstrating the vulnerability of the current HCCR model. In addition, we can improve the recognition accuracy significantly after the adversarial training, demonstrating its effectiveness.
Modified Deterministic Approach for Size Invariant Visual Secret Sharing with Improved Quality of Recovered Secret

In the digital world, securing data is very significant. Digital data can focus either on content secrecy or the quality of recovered secret content. Visual Secret Sharing (VSS) becomes vital when content secrecy is essential over quality. VSS encrypts the secret into “n” share. The individual share cannot reveal any information; the secret gets revealed only when a predefined number of shares come together. Earlier attempted probabilistic and random grid approaches of size invariant VSS compromise in quality of recovered secret. Paper presents a method as a modified deterministic approach for size invariant VSS with improved quality of recovered secret; giving minimum Mean Squared Error (MSE), maximum Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) close to “1” as compared to other existing methods.
Evapotranspiration Computation for Irrigation using Mamdani Fuzzy Inference System

Agriculture is a sector that can get easily affected by various factors like climatic conditional changes, availability of water, attributes of soil, etc. The data mining techniques can be used predict the crop yield and its quantity in advance before harvesting. The expert involvement to predict crop yield may fail due to lack of knowledge in natural events, fatigue and etc. An efficient method for irrigation is proposed in this system for Evapotranspiration (ET) calculation using Mamdani fuzzy inference system. This will help the former to plan irrigation based on the crops in various seasons, and its requirement according to the change of climatological parameters. This system avoids the excessive or insufficient irrigation that significantly affects quality of the crop and yields. The outcomes prove that the fuzzy model is a fast and exact tool to calculate the evapotranspiration required in net irrigation. Using this system farmer can indenture before their harvest and they can expect the quantity of yield in advance to ensure a more competitive price than wait until after the harvest. The obtained results shows that the system have a coefficient of correlation is more than 0.9.
Computer Simulation Algorithm of Team Gymnastics Formation Change Path under Artificial Intelligence and Network Big Data

With the development of economy, all world countries are entering the ranks of competitive sports. The Olympic Games or large-scale gymnastics performances are of great significance to the publicity and implementation of the national health plan. This paper aims to study the computer simulation algorithm of group gymnastics formation change path under artificial intelligence and network big data, so as to find out the path change algorithm of each team member, so that members in group gymnastics can clearly see their own moving route, and finally achieve the purpose of reducing the difficulty and cost of rehearsal. In this paper, the ellipse search algorithm and the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>D</mi> <mi>i</mi> <mi>j</mi> <mi>k</mi> <mi>s</mi> <mi>t</mi> <mi>r</mi> <mi>a</mi> </math> algorithm, the rectangle search algorithm, and the traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>D</mi> <mi>i</mi> <mi>j</mi> <mi>k</mi> <mi>s</mi> <mi>t</mi> <mi>r</mi> <mi>a</mi> </math> algorithm are deeply compared and analyzed, and then, the experimental comparison between the ellipse search algorithm and the rectangle search algorithm is carried out. It is concluded that when finding the shortest path and when the straight-line distance between two nodes is 4.0 km, the time used by the rectangle search algorithm is 2.1 s, while the ellipse search algorithm needs 3.98 s. This shows that the rectangle search algorithm greatly saves the operation time and has superior performance.
Cooperative Multi-Agent Nash Q-Learning (CMNQL) for Decision Building in Retail Shop

Based on the mentioned cooperative Nash Q-learning framework, the study presents a novel cooperative multi-agent Nash Q-learning approach for building decisions for the retail shop. The technique provides three retail stores throughout the market. Dealers can assist one another and profit from cooperative knowledge by building their own strategies that are solely focused on their goals and benefits. Assuming key hypotheses about the dealer’s stock policy, reload duration, and consumer coming method, the technique is created as a Markov decision process model to build the learning algorithms. Furthermore, the research shows the results of three shop agents’ cooperative reinforcement learning algorithms using Nash Q-learning over a one-year sale period. The findings of two methodologies, Nash Q-learning and joint action learning, are compared in the paper. When compared to a single-agent Q-learning approach, agents with Nash Q are to arrive at a joint optimal path. If at least one agent accepts N, the effect of combined agents is superior to single Q-learning.
SCAF: Skip-Connections in Auto-encoder for Face Alignment with Few Annotated Data

Supervised face alignment methods need large amounts of training data to achieve good performance in terms of accuracy and generalization. However face alignment datasets rarely exceed a few thousand samples making these methods prone to overfitting on the specific training dataset. Semi-supervised methods like $$\mathrm {TS^3}$$ or 3FabRec have emerged to alleviate this issue by using labeled and unlabeled data during the training. In this paper we propose Skip-Connections in Auto-encoder for Face alignment (SCAF), we build on 3FabRec by adding skip-connections between the encoder and the decoder. These skip-connections lead to better landmark predictions, especially on challenging examples. We also apply for the first time active learning to the face alignment task and introduce a new acquisition function, the Negative Neighborhood Magnitude, specially designed to assess the quality of heatmaps. These two proposals show their effectiveness on several face alignment datasets when training with limited data.
What we achieve on text extractive summarization based on graph?

Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired from the survey, our work could provide more clues for future research on extractive summarization.
An Efficient Underwater Image Restoration Model for Digital Image Processing

Digital image processing (DIP) is showing a massive growth in today’s trending world particularly, in the field of biological research. Underwater image analysis plays a vital role, where the images are easily prone to attenuation and haziness. Capturing underwater images has always been a challenging job due to dispersion and scattering of light inside water on a high scale. Several image enhancement and restoration methodologies are currently available to address these issues, where hazing and color diffusion are viewed as a common phenomenon in it. Such procedures normally includes two basic methodologies in it, namely dehazing and contrast or color enhancement, which improves the overall output of the degraded image. However, the quality and processing time of the images can still be enhanced with additional techniques incorporated to it. This work is intended toward proposing one such channel called improvised bright channel prior for dehazing the underwater images. The technique further improves on the existing methodologies by estimating the atmospheric light and refining the transmittance of the image along with image restoration. The experimental results show that the improvised bright channel prior methodology is found to perform better in dehazing underwater images with a balanced intensity in terms of dark and white patches obtained from it. When comparing and contrasting the processing time of the proposed methodology with the existing techniques, it is found that improvised bright channel prior performs better. Also, the quality of the dehazed underwater image obtained from the proposed channel is found to be effective when compared with the existing channels.
Designing for Bouldering: Supporting Kinesthetic Learning of Action Sport Enthusiasts

In recent years, there have been increased attention from HCI research towards action sports. Results of these studies are mainly artifacts with a focused view on performance and technology. It is argued that an approach focusing on the unique interaction possibilities between users’ physicality, users’ cognitive processes and environment is still missing in the field. Whilst some previous work outlined these unique interaction possibilities, such as mental and emotional load from various action sports and how these translate to users’ learning processes, dedicated works for understanding user needs and addressing them are lacking. During my PhD, I'm focus on bouldering, a branch of rock climbing. Using a RtD approach, I'll be looking into learning processes of novices. User insights and I aim to address cases of bodily learning in action sports, which is very important in terms of understanding the users and having long-term impact on their practices.
EyeLikert: Eye-based Interactions for Answering Surveys

Surveys are a widely used method for data collection from participants. However, responding to surveys is a time consuming task and requires cognitive and physical efforts of the participants. Eye-based interactions offer the advantage of high speed pointing, low physical effort and implicitness. These advantages are already successfully leveraged in different domains, but so far not investigated in supporting participants in responding to surveys. In this paper, we present EyeLikert, a tool that enables users to answer Likert-scale questions in surveys with their eyes. EyeLikert integrates three different eye-based interactions considering the Midas Touch problem. We hypothesize that enabling eye-based interactions to fill out surveys offers the potential to reduce the physical effort, increase the speed of responding questions, and thereby reduce drop-out rates.
Anticipating the Dynamic Effects of the Learning Smart Home

Everyday domestic life will inevitably change in the future, as smart homes–alongside their inhabitants–will increasingly be able to learn over time. The purpose of my PhD research is to explore what it could look like if learning smart home technologies (SHTs) become integrated into the future domestic everyday. More specifically, I seek to study what role these technologies and their designers could, would, and should play in affecting households’ domestic lives. Existing literature on this topic focuses on making algorithms understandable (XAI) and offering users more control over how their homes learn. So far, most of these efforts have been reactive, analyzing and responding after these technologies have already entered everyday domestic life. This project, however, adopts a more dynamic and anticipatory approach. This means investigating how these learning technologies interact with the dynamics of the domestic–often unpredictable–everyday.
Segmentation and Classification Methods Of Mammograms: A Case Study

Now-a-days most of the women are suffering with various types of cancers, among those Breast cancer is the major one with high mortality rate.This abnormality intends accurate and timely diagnosis so that the possibility of survival of the patient increases. Mammogram images are one of the key tools in visualizing various types of breast cancers. Automatic detection of mass regions and classification of these regions is still a challenging task in research domain which plays a vital role in predicting the severity of the disease and options for better treatment. Several Computer Aided diagnostic (CAD) systems deploying computer vision algorithms were proposed to perform this activity however the advent of deep Neural networks in recent times have increased the pave for conducting research in this domain. This work also presents a case study on classification methods that are used for classifying the type and severity of the disease as one of the major cause for high mortality rate in women in recent times.
Utilization of pre-trained models of CNN in mammograms processing for the diagnosis of breast cancer

In this work, Pre-trained models of CNN on ImageNet were used and improved to design a perfect system with a lower error rate. The suggested system is essentially composed of two modules: (i) CADe for anomaly detection, that classifies images into normal or abnormal cases. (ii) CADx for identification, that classifies abnormal (cancerous) cases as malignant or benign. The proposed system was evaluated on two augmented datasets taken respectively from Digital Database for Screening Mammography (DDSM) and Invasive Ductal Carcinoma (IDC) databases.
Accelerating federated learning based on grouping aggregation in heterogeneous edge computing

Recently, edge devices such as mobile phones and smartwatches have become part of modern distributed systems, federated learning is an effectively distributed learning paradigm that can leverage these edge devices to collaboratively train models without sharing raw data. In federated learning, the device periodically downloads the model from the server, uses the local data for training, and uploads it to the server, while the servers aggregates params uploaded to update the global model. However, different devices are located in different network environments and have different communication and computation capability. Therefore, the model training speed depends on the slowest device, and the system between devices is heterogeneous. To effectively address these problems, we propose to group the devices, firstly use the synchronous method to aggregate model updates within a group, then aggregate updates between groups in an asynchronous way, and propose an algorithm based on weight update to aggregate models. We conduct extensive simulations on our proposed algorithms, and the results show that they can dramatically accelerate model training while achieving high accuracy.
A Recommender Algorithm to Automatically Generate Metrics for GQM Models in Software Development

Software development is a wicked problem that has no formal criteria of solution correctness. For this reason, developers use goal-focused software metrics to estimate and improve the quality of the result. However, it is not an easy task to determine the minimal set of metrics that fits the goals. That is why this research presents the recommender system that automatically generates metrics for each question from the GQM model using multi-label classification.
Collision Avoidance Concept of UAV During Fire Related Incident for SAR Missions

Current trend in Search and Rescue mission begins to incorporate the use of Unmanned Aerial Vehicle or drone which means that for each type of catastrophes, a specific payload is required. The main target for this project is to develop a new payload which is an active solution for flame-based collision avoidance system (CAS) concept so that a drone can manoeuvre very closely in a flammable related area without harming itself. A hex-copter drone type will be used as a platform for this proposed system. The proposed payload is developed using off-the-shelf sensors, integrated using Arduino with Pixhawk Cube is used as the flight control system (FCS) and remotely control by using 8-channels remote controller (RC). Static test (propeller is not connected) was performed to evaluate the response of all 6 motors in order to see it responses when the payload is being triggered. The preliminary result shows that the ground control RC command can be bypass successfully by maintaining some level of control of the motor. Extensive study will be carried out later in the phase to ensure the correct response before actual flight test can be carry out. In conclusion, this flame-CAS payload shows a promising result that can be implemented as an alternative manoeuvring aid to ensure safety of the drone deployed during fire related SAR operation provided an efficient control scheme can be developed.
On the Identification of Third-Party Library Usage Patterns for Android Applications

The rapid growth of mobile applications development and usage raises several new challenges to developers as they need to respond quickly to the users’ needs in a world of continuous changes. Developers often use third-party libraries to add functionality, which significantly improves developers productivity, and reduces time-to-market. In this paper, we present an approach for the visualization and recommendation of libraries for Android apps. Our approach, named LibScanDroid, is based on how libraries are used within existing Android applications. LibScanDroid groups together libraries based on their history of joint and separate usage in existing Android applications available in Google Play Store. The library groups, i.e., usage patterns, are presented in several layers to visualize and navigate through the patterns. These groupings are performed using the ϵ-DBSCAN hierarchical clustering algorithm.We implement our approach in the form of an interactive tool and evaluate it on a database that covers 1,458 libraries that are used by over 1,000 Android applications. Our experiments have shown that our approach can detect library patterns with high co-usage cohesion. The results from the cross-validation, allows us to affirm the generalizability of the detected patterns.
Memory and Energy Efficient Memory Model and Instruction Set Architectures for Tree Data Structures

In general, data structures incorporating significant numbers of memory address pointers needlessly occupy large areas of dynamic memory. Moreover, packed binary data structures suffer from slow access time caused by unpacking and repacking. To address these issues and improve power and energy efficiency, we have designed a special memory model and instruction set architectures (ISA) for it. Our Random-Bit-Vector-Access Memory (RBVAM) together with Flexible-Width Data Computer (FWDC) ISA family enable access to and computation on data whose bitwidth is software specified, effectively eliminating packed binary data structure access overhead. The FWDC ISA family is inspired by the RISC-V’s RV32 ISA but tailored for RBVAM. We investigate a 32-bit and a 24-bit FWDC and propose a microarchitecture that implements them on an FPGA. For voxelized 3D point-cloud indexing, a RBVAM in combination with a FWDC ISA can achieve about 5 × energy savings merely from the reduced physical memory space requirements, and about 5 × energy savings compared to conventional memory models used with a RISC-V core.
Perceived Dominating Colors Optimization for Camouflage Texture Generation &amp; ITS Effect Evaluation

Camouflaging is the common military technique of concealing something from an opponent on the battlefield or elsewhere. The main aim is to enhance the degree of adaptability by adapting environmental features like perceptual colors, luminance, and texture. Colors, among other characteristics, play the most important role in camouflage texture. The study is geared on the optimization and standardization of colors for camouflage textures designed for a certain terrain. This article discusses a multi-step procedure for automatically developing pixelated camouflage patterns that are appropriate for a specific geographical region. The ability of a camouflage texture to conceal objects or people in an environment determines its performance. Experimentation was carried out in order to assess the effect of camouflage texture generated with perceived and optimized dominant colors.
Requirements and Challenges for Digital Forensic Readiness in Industrial Automation and Control Systems

Industrial automation and control systems (IACS) are vital components in various industries. Incidents in the past, highlighted the magnitude of impacts arising from attacks, such as disruptions of power supply or in manufacturing plants. In order to adequately react on incidents and analyze deliberate attacks on IACS, it is indispensable to have appropriate forensics capabilities in this area. Based on an analysis of existing approaches and recent research results, this paper provides an overview of significant challenges that need to be overcome as prerequisites for building effective and efficient forensic capabilities for IACS. The classification and typology of Digital Forensic Readiness (DFR) in IACS requirements presented in this paper, pave the way to better integrate forensic capabilities into future IACS systems. As smart manufacturing is now a focus of designing and implementing the next generation of IACS systems, a unique opportunity arises for introducing forensic capabilities at the system design level without having to re-engineer whole infrastructures. At the same time the much higher dependence on IT infrastructures and the resulting temptation for criminals are creating an environment in which such forensic capabilities become indispensable. This paper can therefore be considered as an initial step, laying the foundations for the development of a generic forensic analysis model in smart manufacturing environments.
Material Matters: Exploring Materiality in Digital Musical Instruments Design

Research on the design of Digital Musical Instruments (DMIs) has highlighted the importance of musical gestures and embodied interaction in DMI design. However, this research often focuses on technical and sonic factors of design, with less attention on how materials influence the design process and DMI design idea generation. Thus, this paper explores materiality in DMIs design through a material probe approach with deformable materials. This paper reports on a study with fifteen DMI designers investigating the evoked meaning of material properties in a musical context beyond their digital interactivity. Results suggest that material properties inspired participants' design thinking, and there was a strong connection between tactility and imagined sound production. We also reported the patterns of gestural interaction of deformable materials in DMIs. We reflect on these results to report lessons learned that could inform interactive systems' material design within and beyond the musical domain.