Reviewerly/exHarmony huggingface dataset:
Cervical Cell Nuclei Segmentation On Pap Smear Images Using Deep Learning Technique

The fourth most common type of cancer in women is cervical cancer. The cells lining the cervix, the lowest portion of the uterus, are where cervical cancer first develops. Cancer detection that is accurate and timely can save lives. The Pap smear experiment is a form of cervical screening that looks for probably malignant and cervical or colon cancer precursors. Automated and precise segmentation of the cervical nucleus is required as nuclei contain crucial diagnostic details for automated cervical cancer monitoring and computer detection techniques. The deep learning model used for image segmentation in this research is the EfficientDet. By undertaking a rigorous analysis of earlier modern detection prototypes, it uncovered methods to enhance computing efficiency, which led to the creation of EfficientDet. We evaluated our model using the Herlev Pap Smear data set and observed that the precision and recall values obtained were above 0.9, and the mean IOU score was 0.836.
Blockchain-based Power Digital Asset Security Management Framework

Due to the hierarchical management structure of electric power enterprises, data flow often needs to go through multiple layers of nodes for review, which makes data synchronization slow and distortion high and data reliability difficult to improve. The existing data asset management mode of electric power enterprises faces security problems such as node trustworthiness and data tampering in the process of identity mutual trust, transmission and storage, data collection and data usage. In view of this, this paper proposes a blockchain-based digital asset management framework using the decentralized, untrustworthy, and hard-to-tamper features of blockchain. In the proposed framework, we optimized security authentication mechanism, data recording and consensus process, and data storage and transmission strategy involved in blockchain data sharing applications. The framework can effectively ensure the security, integrity, operability and consistency of digital assets in the management process under the blockchain environment.
Ten Years of Living Machines Conferences: Transformers-Based Automated Topic Grouping

The overwhelming growth in the number of scientific publications has its drawbacks: it is difficult to cope with it. Researchers need to constantly process the information contained in a very large and increasing number of articles to be up-to-date with their research community. This is especially difficult when interdisciplinary research requires knowledge of the state of the art in different fields. High interdisciplinarity is at the heart of the Living Machines conferences, which bring together scientists from different fields of biology, neuroscience, physics, robotics, artificial intelligence and other engineering fields. Here, using algorithmic AI methods, we analyze the publications of the ten “Living Machines” conferences to automatically discover clusters of research topics. We show that neural networks for natural language processing can find a set of thematic clusters that correspond to different research topics within this community.
The Mining Selection in Multi-pools Under Block WithHolding Attack

Recently, Bitcoin, as a popular digital currency, is getting more and more attention. It uses the proof of work (PoW) consensus mechanism to generate blocks, thus creating a new type of mining method. In this mining mode, there is a network attack named block withholding(BWH) attack, where some pools attack others by infiltrating miners who only submit partial proof-of-work and discard full proof of work. Previously, many researchers have analyzed its impact. However, their researches are based on the case of two mining pools, which is too far from the actual number of mining pools and has no commonalities. Therefore, this paper proposes a novel model built by the evolutionary game theory for analysing BWH attack in multiple mining pools. Simulation results show that only launching a BWH attack may not necessarily help the mining pools attract miners, and the attack time also plays an vital role.
Reverse 3D Sound Flow Can Decrease VR Sickness?

One way to battle simulation sickness in virtual reality is to reduce the visual feedback of the apparent motion. In this vein, mixing in and overlaying motion trail in the reverse direction to the original motion in the virtual contents have been suggested as one such method. However, as such visual feedback can be intrusive and distractive to the original, its application must be restrained. In this poster, we consider supplementing the sickness reduction methods based on visual manipulation of the content with 3D sound effects that mimic the behavior of reverse motion flow. For instance, in a forward motion, the environment sound sources should be normally heard louder as one approaches them - here, we emulate the reverse, in hopes of eliciting and strengthening the vection nullifying effect. The pilot experiment tested of the lone effect of such “reverse” sound effect (or airflow) against the conditions of no sound or natural sound effects and revealed the positive results, indicating the potential when combined with the visual oriented approaches.
Augmented Reality Support for Performance and Decision-Making in Collaborative Time-Critical Tasks

This research explores augmented reality (AR) as a tool for supporting performance and decision-making in a collaborative time-critical task. To this end, we conducted a user study comparing traditional <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\mathrm{D}$</tex> displays to traditional 2D displays supplemented with AR. The study results indicate that AR is a promising tool for supporting collaborative time-critical tasks. The results revealed that AR enhanced primary task performance and indicates that traditional 2D displays supplemented with AR can be used as an alternative to standard 2D displays for collaborative time-critical tasks.
A scalable algorithm for homomorphic computing on multi-core clusters

Homomorphic encryption draws huge attention as it provides a way of privacy-preserving computations on encrypted data. But sadly, such computations are extremely expensive both in terms of calculation time and memory consumption and so much slower than the corresponding computations with unencrypted data. One solution is using parallelism and in this work, we investigate using distributed architectures of interconnected nodes (multi-core clusters) to execute homomorphic computations that have been programmed with the cingulata environment, a toolchain which is able to generate boolean circuits (where gates manipulate encrypted booleans) from homomorphic C++ codes. Such circuits are spliting into slices and we have used a bsp algorithm to executed each of them.
An Improved Squirrel Search Algorithm for Optimization

Squirrel search algorithm (SSA) is a novel meta-heuristic optimization algorithm which is more appropriate for solving unimodal, multimodal, and compound problems. However, SSA has shortcomings such as limited accuracy and slow convergence. In order to improve its global search ability and convergence speed, this paper proposes an improved squirrel search algorithm (ISSA). First, the social part of the particle swarm algorithm is introduced into the position update formula on the ordinary tree to improve the search range and convergence speed of SSA. Second, a new location update condition is added, which can balance between exploration and exploitation of ISSA. Third, in order to prevent falling into a local optimal solution, an adaptive strategy of the minimum seasonal constant is proposed. The proposed ISSA has been tested by 12 benchmark functions and compared with the results of six other intelligent optimization algorithms. The effectiveness and superiority of ISSA are shown in average convergence value and convergence speed.
Defining Requirements Strategies in Agile: A Design Science Research Study

Research shows that many of the challenges currently encountered with agile development are related to requirements engineering. Based on design science research, this paper investigates critical challenges that arise in agile development from an undefined requirements strategy. We explore potential ways to address these challenges and synthesize the key building blocks of requirements strategies. Our design science research rests on a multiple case study with three industrial cases in the domains of communication technology, security services, and automotive. We relied on a total of 20 interviews, two workshops, participant observation in two cases, and document analysis in each of the cases to understand concrete challenges and workflows. In each case, we define a requirements strategy in collaboration with process managers and experienced engineers. From this experience, we extract guidelines for defining requirements strategies in agile development.
A Multi-Objective Quantum-Inspired Seagull Optimization Algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.
News text named entity Recognition based on BI-LSTM-CRF model

In order to solve the problem of poor discrimination of entity words in Chinese news texts by traditional statistical methods, relying heavily on artificial embedding features and poor model generalization ability, this paper uses Bi-LSTM-CRF (Bi-directional-Long Short Term Memory) based named entity recognition model. The model does not rely too much on manual labeling and rule making of domain knowledge. The model uses bidirectional long sequence processing mechanism of BI-LSTM to understand the context semantics of news text, and uses sequence labeling method of BIO to use Conditional Random Field (CRF) module to constrain the output of text prediction sequence to ensure model recognition ability. In the demonstration experiment, the data set of People's Daily news in 1998 reached the F1 value of 94.53%, which is better than the traditional HMM(Hidden Markov Model) and Conditional Random Field (CRF) models.
Deep Learning in Automatic Math Word Problem Solvers

Abstract The design of an automatic solver for mathematical word problems (MWPs) dates back to the early 1960s and regained booming attention in recent years, owing to revolutionary advances in deep learning. Its objective is to parse the human-readable word problems into machine-understandable logical expressions. The problem is challenging due to the existence of a substantial semantic gap. To a certain extent, MWPs have been recognized as good test beds to evaluate the intelligence level of agents in terms of natural language understanding and automatic reasoning. The successful solving of MWPs can benefit online tutoring and constitute a milestone toward general AI. In this chapter, we present a general introduction to the technical evolution trend for MWP solvers in recent decades and pay particular attention to recent advancement with deep learning models. We also report their performances on public benchmark datasets, which can update readers’ understandings of the latest status of automatic math problem solvers.
Shadow Removal for Documents with Reflective Textured Surface

An increasing number of institutions are converting from traditional verification to online digital verification of user documents. In Sri Lanka, this requires clean digital images of documents such as the National Identity Card (NIC), driver’s license etc. which often have background textures and reflective surfaces. Due to human error, uneven natural light and reflectance properties, such document images contain cast shadows which pose a difficulty to further processing. The NIC dataset itself is unique in nature. It has some properties unique to a document image, i.e., dark letters on a light background, and some properties unique to a natural image, i.e., background object textures. Therefore, the target domain or nature of dataset itself is a novelty. For such domain, we propose a shadow removal mechanism based on Dual Hierarchical Aggregation Network (DHAN) and VGG-19 (Convolutional network by Visual Geometry Group) object detection deep learning model. Since previous research is not already done on this specific target area, we do not have a direct benchmark to compare our proposed methodology. Hence, we have experimented our dataset with already existing state of the art models in both shadow removal for document images and shadow removal for natural images. Our proposed model reflects the typical backbone architecture for shadow removal models for natural images when removing shadows while preserving background textures. Our architecture can be directly utilised or added to an already-existing image processing pipeline. Although our target domain is relatively new, for comparison purpose we went comparing our model with a close relative which is shadow removal on natural images. Our architecture results in an overall quality improvement of 12% and 63% improvement in output resolution when compared with the state-of-the-art architecture in shadow removal for natural images.
PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK

The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%.
Error Classification Using Automatic Measures Based on n-grams and Edit Distance

Machine translation (MT) evaluation plays an important task in the translation industry. The main issue in evaluating the MT quality is an unclear definition of translation quality. Several methods and techniques for measuring MT quality have been designed. Our study aims at interconnecting manual error classification with automatic metrics of MT evaluation. We attempt to determine the degrees of association between automatic MT metrics and error classes from English into inflectional Slovak. We created a corpus, which consists of English journalistic texts, taken from the British online newspaper The Guardian and their human and machine translations. The MT outputs, produced by Google translate, were manually annotated by three professionals using a categorical framework for error analysis and evaluated using reference proximity through the metrics of automated MT evaluation. The results showed that not all examined automatic metrics based on n-grams or edit distance should be implemented into a model for determining the MT quality. When determining the quality of machine translation in respect to syntactic-semantic correlativeness, it is sufficient to consider only the Recall, BLEU-4 or F-measure, ROUGE-L and NIST (based on n-grams) and the metric CharacTER, which is based on edit distance.
Early-Awareness Collision Avoidance in Optimal Multi-Agent Path Planning With Temporal Logic Specifications

Dear Editor, This letter investigates a multi-agent path planning problem in a road network with the requirement of avoiding collisions among all agents in the partitioned environment. We first abstract the agents to a set of transition systems, and construct a team transition system from these individual systems. A mechanism is designed for the team transition system to detect all collisions within the synthesized run. Then, “wait” and “go-back” transitions are added to the individual transition systems, while removing all possible collisions in the team transition system. Finally, two examples are given to illustrate the effectiveness of the proposed approach.
Detecting 3D Hand Pointing Direction from RGB-D Data in Wide-Ranging HRI Scenarios

This paper addresses the detection of 3D hand pointing direction from RGB-D data by a mobile robot. Considering ubiquitous forms of pointing gestures, the 3D pointing direction is assumed to be inferable from hand data only. First, a novel sequential network-based learning model is developed for the simultaneous detection of hands and humans in RGB data. Differing from previous work, its performance is shown to be both accurate and fast. Following, a new geometric method for estimating the 3D pointing direction from depth data of the detected hand is presented along with a mathematical analysis of sensor noise sensitivity. Two new data sets for pointing gesture classification and continuous 3D pointing direction with varying proximity, arm pose and background are presented. As there are no such data sets to the best of our knowledge, both will be publicly available. Differing from previous work, the robot is able to estimate the 3D hand direction both accurately and fast regardless of hand proximity, background variability or the detectability of specific human parts - as demonstrated by end-to-end experimental results.
Weakly supervised video anomaly detection with temporal attention module

Weakly supervised anomaly detection has become more attractive in terms of various application, such as surveillance video, and monitoring unacceptable video contents. A lot of existing weakly supervised learning methods depend on a Multiple Instance Learning (MIL) framework since MIL allows that a neural network yields temporal predictions when video-level annotations are only given. Although those methods have shown outstanding performance, it has still problems in terms of the fact that they do not consider temporal dependencies among instances. To tackle this issue, we propose Weakly supervised video anomaly detection with temporal attention module, which facilitate the model to learn the relations of consecutive snippets of videos. In addition, we select top-k features in both abnormal segments and normal segments to maximize the separability of abnormal and normal instances. With the help of the proposed method, we improve performance on anomaly detection in UCF Crime dataset.
Global Positional Self-Attention for Skeleton-Based Action Recognition

In this paper, we introduce a novel global positional self-attention network, which represents the spatially structured dependencies and the globally ordered semantic information for skeleton-based action recognition. The structured dependencies are learned through our spatial self-attention composed of the weighted sum of three correlations <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , each of which is a correlation between spatial elements, between sequential positions, and between structural geodesic positions. In addition, our channel self-attention with the global average pooling and channel-wise positional encoding operations allows capturing the globally ordered semantic and positional dependencies between channels. The self-attention modules are incorporated into standard CNNs, which is referred to as global positional self-attention network, GPS-Net. Our GPS-Net is a simple yet effective network, which leads to make accurate action predictions. We evaluate our GPS-Net on open large-scaled benchmark datasets NTU-RGB+D and DHG for 3D body action and hand gesture recognitions, respectively. Moreover, our self-converted NTU dataset for compatibility with OpenPose skeletons is used for the experiments. Numerical and visual comparisons with the existing state-of-the-art methods confirm the usefulness of the proposed network.
From Forgetting Signature Elements to Forgetting Formulas in Epistemic States

In this paper, we bring together marginalization and forgetting of signature elements in the framework of epistemic states. Marginalization of epistemic states is a specific approach to actively reduce signatures, also aiming at forgetting atoms which are very well known from probability theory. On the other hand, rooted in Boole's variable elimination, Delgrande's knowledge level account of forgetting provides a general approach to forgetting syntax elements from sets of formulas with links to many other forgetting operations. We generalize Delgrande's axioms to epistemic states and show that marginalization is the most specific and informative forgetting operator that satisfies these axioms. Moreover, we propose rephrasing Delgrande's postulates suitable for forgetting formulas by transferring the basic ideas of the axioms to forgetting formulas in epistemic states.
ARShopping: In-Store Shopping Decision Support Through Augmented Reality and Immersive Visualization

Online shopping gives customers boundless options to choose from, backed by extensive product details and customer reviews, all from the comfort of home; yet, no amount of detailed, online information can outweigh the instant gratification and hands-on understanding of a product that is provided by physical stores. However, making purchasing decisions in physical stores can be challenging due to a large number of similar alternatives and limited accessibility of the relevant product information (e.g., features, ratings, and reviews). In this work, we present ARShopping: a web-based prototype to visually communicate detailed product information from an online setting on portable smart devices (e.g., phones, tablets, glasses), within the physical space at the point of purchase. This prototype uses augmented reality (AR) to identify products and display detailed information to help consumers make purchasing decisions that fulfill their needs while decreasing the decision-making time. In particular, we use a data fusion algorithm to improve the precision of the product detection; we then integrate AR visualizations into the scene to facilitate comparisons across multiple products and features. We designed our prototype based on interviews with 14 participants to better understand the utility and ease of use of the prototype.