Reviewerly/exHarmony huggingface dataset:
A Novel Gaussian Ant Colony Algorithm for Clustering Cell Tracking

Cell behavior analysis is a fundamental process in cell biology to obtain the correlation between many diseases and abnormal cell behavior. Moreover, accurate number estimation plays an important role for the construction of cell lineage trees. In this paper, a novel Gaussian ant colony algorithm, for clustering or spatial overlap cell state and number estimator, simultaneously, is proposed. We have introduced a novel definition of the Gaussian ant system borrowed from the concept of the multi-Bernoulli random finite set (RFS) in the way that it encourages ants searching for cell regions effectively. The existence probability of ant colonies is considered for the number and state estimation of cells. Through experiments on two real cell sequences, it is confirmed that our proposed algorithm could automatically track clustering cells in various scenarios and has enabled superior performance compared with other state-of-the-art approaches.
A Study on the Application of Technical Assessment Methodology (TAM) for CyberSecurity in Nuclear Power Plant

Recently 1, cyber attacks targeting Industrial Control Systems (ICS) have been increasing rapidly; accordingly, cyber security applications and security evaluations of ICS are becoming very important. Technical Assessment Methodology (TAM) is a method developed by the Electric Power Research Institute (EPRI) in the United States for assessing and applying security control methods for power plants. By applying TAM, limitations of cyber security application and security evaluation of existing nuclear facilities are able to address. In this study, a virtual test bed was composed for one of the safety systems of APR1400, and the TAM was applied and analyzed to derive two advantages and five features of TAM. Based on this, the rationale for using TAM for the application and assessment of security control methods in nuclear facilities was explained, including five considerations for the better application of TAM. Finally, we propose future work for applying TAM.
Business Model Canvas for Usability Testing of E-Commerce Website Design Using SEO On-Page Technique

Advances in information technology have had a tremendous impact on all of the professions today. All buying and selling activities, including selling baby products, have become easier with digital technology, namely the marketplace. Until now, the marketplace business only uses a physical storefront and marketplace to offer their wares. However, there are still obstacles such as the lack of product information obtained by customers due to decreased sales, lack of a wide range of promotions due to lower income, and discounts when using the marketplace due to lower marketplace revenue. Based on the problems mentioned above, an e-commerce website is needed to solve the problem in the Mimimama store. This e-commerce website is built with the WordPress Content Management System (CMS), and the Business Model Canvas (BMC) method is used to analyze the business model. Designs are built using the Unified Modeling Language (UML), and websites are tested using Usability Testing to ensure the functionality works properly. After the website is built, SEO is applied using On-Page SEO to optimize website pages. Google easily indexes them and is easy to find by customers using certain keywords. The development of an e-commerce website provides answers to make it easier for customers to access information, expand marketing reach, and allow Mimimama to focus more on building the Mimimama store brand.
Knowledge Representation in Artificial Intelligence: An Overview

In real world, knowledge plays a significant role in exhibiting intelligent behavior by artificial intelligence (AI) agents. In AI, an agent can act accurately if it has past experience related to input or have some knowledge of input. In terms of machines, all the things done by humans are termed as knowledge representation and reasoning (KRR). KRR is a segment of AI that deals with thinking of AI agents and how it contributes to agents’ intelligent behavior. This chapter undertakes a basic overview of knowledge representation and how it is helpful in demonstrating intelligent behavior in AI agents, different types of knowledge, and a relation between intelligence and knowledge. This chapter also provides a detailed overview of a life cycle of knowledge in AI, basic requirements for a knowledge representation (KR) system, and different techniques and challenging issues of knowledge representation.
Labels4Free: Unsupervised Segmentation using StyleGAN

We propose an unsupervised segmentation framework for StyleGAN generated objects. We build on two main observations. First, the features generated by StyleGAN hold valuable information that can be utilized towards training segmentation networks. Second, the foreground and background can often be treated to be largely independent and be composited in different ways. For our solution, we propose to augment the StyleGAN2 generator architecture with a segmentation branch and to split the generator into a foreground and background network. This enables us to generate soft segmentation masks for the foreground object in an unsupervised fashion. On multiple object classes, we report comparable results against state-of-the-art supervised segmentation networks, while against the best unsupervised segmentation approach we demonstrate a clear improvement, both in qualitative and quantitative metrics.
An Unsupervised-Learning Based Method for Detecting Groups of Malicious Web Crawlers in Internet

Malicious web crawler has been a serious threat to the security and performance of web servers in Internet. Generally, malicious web crawler systematically obtains massive web pages without approval, and may involve the theft of data assets. In this paper, we propose an unsupervised learning based method for detecting malicious web crawler. The method can be divided into three phases. Firstly, the method generates a representative vector for each client by combining the information of its visiting statistic behaviors and page request stream. Secondly, a new subspace clustering algorithm is developed to cluster the clients into groups. Finally, four metrics are designed to detect the groups of malicious web crawlers. The proposed method is validated based on a real data set consisting of 580 thousand accessing requests. Experimental results show that the proposed method can accurately detect malicious web crawlers with a high TPR (true positive rate) of 91.0% and a low FPR (false positive rate) of 1.3%.
Labeling Consecutive Search Query Pairs Using Siamese Networks

As internet users interact with search engines to meet their information needs, a huge amount of search queries are stored. Proper analysis of such query data enhances prediction and understanding of user tasks. User tasks can be used to increase the performance of search engines and recommendations. Query segmentation is an initial step that is commonly performed while analyzing user queries. It determines whether two consecutive query expressions belong to the same sub-task. Any deficits in query segmentation process is likely to affect all other advanced query based steps and activities like task identification and query suggestion. Recently, some researchers focused on application of algorithms including Recurrent Neural Networks (RNN) to seek for the semantics of queries, and attention based neural networks, but such methodologies are not task-specific. In this paper, we propose a Siamese Convolutional Neural Network (CNN) that models input queries into a more task-specific embedding and a decider network that does the labelling. The proposed method is compared with Context Attention Based Long Short Term Memory (CA-LSTM) and Bi-RNN Gated Retified Unit (GRU) models on Webis Search Mission Corpus 2012 (WSMC12) and Cross-Session Task Extraction (CSTE) datasets. The proposed model performs 95%, implying a 1% improvement over the already existing models and an accuracy of 81% on CSTE dataset implying an improvement classification accuracy of 6% over the previous best results.
Semantic Similarity and Difference in the Estimate of Verbal Designations of Basic Emotions by Young School Children and Adults

The study is devoted to the experimental study of semantic similarities and differences in the assessments of verbal designations of basic emotions in adult and child samples — the focus is on the regularities of assessing basic emotions by children and adults using the semantic differential in terms of affective polar concepts. A comparative analysis of assessments of verbal designations of basic emotions by children and adults was carried out, which showed their coincidence in 72% of cases. The differences between adults and children are that adults rate them more pronounced (with the exception of “surprise”). The semantic differential developed taking into account age characteristics can be used to verify emoji expressions of basic emotions.
Lossy compression of statistical data using quantum annealer

We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original data. The optimization for the basis vectors is performed classically, while binary coefficients are retrieved through both simulated and quantum annealing for comparison. A bias correction procedure is also presented to estimate and eliminate the error and bias introduced from the inexact reconstruction of the lossy compression for statistical data analyses. The compression algorithm is demonstrated on two different datasets of lattice quantum chromodynamics simulations. The results obtained using simulated annealing show 3-3.5 times better compression performance than the algorithm based on neural-network autoencoder. Calculations using quantum annealing also show promising results, but performance is limited by the integrated control error of the quantum processing unit, which yields large uncertainties in the biases and coupling parameters. Hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system. Our study shows that the Advantage system is more likely to obtain low-energy solutions for the problems than the 2000Q.
A Spatio-Frequency Domain Anisotropic Filtering for Contrast Enhancement of Histopathological Images

Medical images play a critical role in diagnosing of several symptomatic and asymptomatic disorders and medical conditions for early treatment to prevent and reduce fatalities. Medical image processing has played an indomitable role in processing of these medical images from age old times. Preprocessing and contrast enhancement is a critical and foremost stage in any medical image processing technology. This research article proposes a novel edge preserving spatio-frequency domain anisotropic filtering in a hybrid filtering approach to overcome the limitations of existing methods like lack of edge preserving features, significant loss of data during filtering process etc. In the proposed work, the process of filtering has been done in the frequency domain which least reflects any modifications in the time domain representation which forms the motivation behind the proposed utility. Histopathological images like red blood cells (RBC), white blood cells (WBC) and platelets whose analysis is of great significance in medical domain for detection of several abnormalities, is taken as the prime dataset for investigation in this research work. Three noise types involving Speckle, Impulse and Gaussian which normally tend to corrupt medical images has been taken for the filtering and enhancement process. Performances in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM) to analyze the edge structure deformity indicate superior performance of proposed hybrid filtering approach.
Analysis of Tiago Robot for Autonomous Navigation Applications

Autonomously Navigating robots are rapidly evolving, finding their space in a variety of different fields. There are also many controllers proposed to control the speed and braking of these vehicles in autonomous mode. Time taken in completing the task by these autonomous robots is of a major concern. Alongside path optimization techniques, efforts are made to implement velocity profiles in these controllers. Going this way increases the complexity of these controllers and requires a lot of computation which can cause time delay in the system. In this paper robot's behavior to several velocity profiles are studied and evaluated using Gazebo simulation. A comparative study between the velocity profiles are also made by comparing the velocity-time graph obtained from the simulations.
Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images.Generally, conventional stereo-aided depth completion methods have two limiations.(i) They assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice.(ii) They have limited accuracy in the long range because the depth is estimated by pixel disparity.To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework.This depth selection approach can handle any type of mis-projection.Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo.SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries.Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range.Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93times from previous depth completion methods.
Single Target Tracking Algorithm Based On Adaptive Fusion Of Multi-layer Convolution Features

Fast moving target and occlusion often result in poor tracking robustness and low tracking accuracy in complex video scenes. Aiming at such problem, this paper proposes a Siamese target tracking algorithm with adaptive fusion of multilayer convolutional feature tracking results. Firstly, the target is tracked using the Siamese tracking framework, which uses a wider and deeper backbone network, Resnet22. Then, the multi-layer convolution features of the frame image in the convolutional neural network are extracted and tracked. Finally, the forward tracking and backward tracking schemes are used to locate the target position by comparing the results of forward tracking and backward tracking, and the adaptive fusion prediction is performed on the hierarchical CNN feature tracking results. The algorithm solves the problem that the backbone network of Siamese tracker is shallow and the deep learning cannot be fully utilized, avoids the defect that the single-layer features of the network represent the target information incomprehensively, and enhances the generalization ability of the algorithm. The performance of the proposed algorithm is verified on the RGB public test set, and compared with the existing Siamese tracking algorithm. The experimental results show that the proposed algorithm performs better in accuracy and success rate, and has better robustness in complex cases.
Field of View Limitation-Driven Design of a Mixed Reality Game for Heritage Sites

In this work, we describe the design of a customized user interface (UI) for a mixed reality application in a heritage site. The visuals, widgets and spatial interaction techniques have been customized to improve the user experience without diverting the user’s attention by minimizing the effect of the limited field of view (FOV) of the see-through head-mounted display (HMD) used, Microsoft HoloLens v1. The approach consists in using diegetic elements that are coherent with the narrative of the heritage site, and widgets and augmented layers always entirely included in the FOV of the see-through display.
Discriminative non-negative representation based classifier for image recognition

During the past decade, representation based classification method has received considerable attention in the community of pattern recognition. The recently proposed non-negative representation based classifier achieved superb recognition results in diverse pattern classification tasks. Unfortunately, discriminative information of training data is not fully exploited in non-negative representation based classifier, which undermines its classification performance in practical applications. To address this problem, we introduce a decorrelation regularizer into the formulation of non-negative representation based classifier and propose a discriminative non-negative representation based classifier for pattern classification. The decorrelation regularizer is able to reduce the correlation of representation results of different classes, thus promoting the competition among them. Experimental results on benchmark datasets validate the efficacy of the proposed discriminative non-negative representation based classifier, and it can outperform some state-of-the-art deep learning based methods. The source code of our proposed discriminative non-negative representation based classifier is accessible at https://github.com/yinhefeng/DNRC .
HCI-E$$^2$$: HCI Engineering Education

This workshop aims at identifying, examining, structuring and sharing educational resources and approaches to support the process of teaching/learning Human-Computer Interaction (HCI) Engineering. The broadening of the range of available interaction technologies and their applications, many times in safety and mission critical areas, to novel and less understood application domains, brings the question of how to address this ever-changing nature in university curricula usually static. Beyond, as these technologies are taught in diverse curricula (ranging from Human Factors and psychology to hardcore computer science), we are interested in what the best approaches and best practices are to integrate HCI Engineering topics in the curricula of programs in software engineering, computer science, human-computer interaction, psychology, design, etc. The workshop is proposed on behalf of the IFIP Working Groups 2.7/13.4 on User Interface Engineering and 13.1 on Education in HCI and HCI Curricula.
ASSESSMENT OF EXTENSION COMMUNICATION CHANNELS AMONG AGROFORESTRY FARMERS IN EKITI STATE, NIGERIA

Communication channels are pathways through which information or messages are transmitted to an audience or receiver.The focus of this study was to assess extension communication channels among agro-forestry farmers in Ekiti State, Nigeria.Primary data were collected from 267 respondents using structured questionnaire.The data were analysed with descriptive statistics and 5-points likert scale.The result showed that (44.57%) of the farmers are within productive age bracket (21-40 years) with an average age of 38 years.Majorities (67.4%) of the farmers are male and 73.4% married.The result showed that the majority of the respondents are male (67.4%) with mean age of 38 years.They are married (73.4%) with average household size of 5, average farm size 2.8 ha and average farming experience of 11 years respectively.The result further showed that radio (79.78%) and television (70.79%) were rated high by the famers among the channel of communication used in the dissemination of agro-forestry technology.Also, radio (81.65%) and television (73.03%) are the major source of information to agro-forestry farmers.Tree and shrubs on pasture system (͞ =3.91; SD= 1.06) and integrated production system (͞ =3.90; SD= 1.03) are the most aware agro-forestry technologies by the farmers.Group discussion (79.78%)and visit (70.79%) were the most preferred extension communication channels.Inadequate extension agent (80.15%), incompetency of extension agents (62.55%) and respondent's poor attendance at meeting (52.43%) are the major constraint to effective extension communication channels.The study recommends the use of group discussion and visit as the most appropriate communication channels to the agro-forestry farmers in the study area.Also, more trained extension agents should be made available to farmers for effective and efficient agro-forestry technologies delivery.
Quantum Algorithms for Anomaly Detection Using Amplitude Estimation

Anomaly detection, as an important branch of machine learning, plays a critical role in fraud detection, health care, intrusiondetection, military surveillance, etc. An anomaly detection algorithm based on density estimation (called ADDE algorithm) is one of the widely used algorithms. However, the ADDE algorithm is computationally expensive when processing big datasets. To solve this problem, in this paper, we propose an efficient quantum ADDE algorithm based on amplitude estimation. It is shown that our algorithm achieves exponential speedup on the number of training data points M over the classical counterpart. Besides, the idea of our algorithm can be applied to accelerate the anomaly detection algorithm based on kernel principal component analysis (called ADKPCA algorithm), which also has a wide range of applications. Our algorithm shows exponential speedup on M compared with its classical counterpart.
SDR: Efficient Neural Re-ranking using Succinct Document Representation

BERT based ranking models have achieved superior performance on various information retrieval tasks.However, the large number of parameters and complex self-attention operations come at a significant latency overhead.To remedy this, recent works propose late-interaction architectures, which allow precomputation of intermediate document representations, thus reducing latency.Nonetheless, having solved the immediate latency issue, these methods now introduce storage costs and network fetching latency, which limit their adoption in real-life production systems.In this work, we propose the Succinct Document Representation (SDR) scheme that computes highly compressed intermediate document representations, mitigating the storage/network issue.Our approach first reduces the dimension of token representations by encoding them using a novel autoencoder architecture that uses the document's textual content in both the encoding and decoding phases.After this token encoding step, we further reduce the size of the document representations using modern quantization techniques.Evaluation on MSMARCO's passage rereranking task show that compared to existing approaches using compressed document representations, our method is highly efficient, achieving 4x-11.6xhigher compression rates for the same ranking quality.Similarly, on the TREC CAR dataset, we achieve 7.7x higher compression rate for the same ranking quality.