Reviewerly/exHarmony huggingface dataset:
Data Association for Multi-Object Tracking Using Assignment Algorithms

Multi-object tracking (MOT) plays a very important role in advanced applications such as driver assistance systems, autonomous vehicles, aircraft monitoring systems. One of the fundamental problems of multi-object tracking is the unknown data association. At each time step, we get multiple sensor measurements. However, we don't know where these sensor measurements come from. Each measurement can come from one of the objects detected in the previous time step, from a new object entering the field of view (FOV), or from clutter. As the number of objects and measurements increases, the combinatorial nature causes the number of data association hypotheses to increase rapidly, making it impossible to consider all data associations [1,2]. One approach to solving the data association problem is to transform it into an optimal assignment problem. One approach to solving the data association problem is to use algorithms of the optimal assignment problem. In this paper, we will introduce the method of using optimal assignment algorithms to solve the data association problem in MOT. At the same time, we also compare the performance of these algorithms in terms of data association accuracy and computational complexity. The results showed that using assignment algorithms like Gibbs sampling brings high performance in data association.
Overcoming Unemployment of Computer Science Alumni through Mobile Apps Utilization

Mobile phones are considered extremely crucial for their daily usage due to their unique features such as mobility, availability, and compatibility. Moreover, the need for mobile apps periodically increases in terms of the variety of end-users as well as mobile platforms. Iraq is an emerging country concerning the development and requirements of mobile apps. There are numerous challenges faced by this trend on mobile phone app requirements, development, and usage. Consequently, the major consideration is how to build a suitable mobile app that can be adopted, adapted, and customized according to Iraqi market requirements. An essential fact worth mentioning is the increase in unemployed computer departments alumni in Iraq. Those who can develop mobile apps and software need to recruit suitable candidates. The purpose of this article is to develop a customized flight booking mobile application to partially reduce and solve the problem of unemployed computer alumni. The target of this application is to reduce time and effort for passengers and offer unique features. The developed application is the adoption of previous applications and the improvement of their inadequacies. By following a six-phase methodology to set a procedural technique for optimizing the use of the mobile application as a source of revenue for unemployed alumni as well as being beneficial for the customer. The validation and verification of the proposed application of this current research are done by evaluating and executing the mobile application. The results of this paper meet the main objective, where, the special features have been implemented and tested. In addition, the application has been installed on mobile phones and tested. Furthermore, the income from this application is obtained through purchasing it or subscribing to it for a specific duration. We encourage researchers as well as alumni who are targeted in this field to be attentive to cutting-edge technology and advancement and the concept of this paper.
A Prior Probability of Speaker Information and Emojis Embedding Approach to Sarcasm Detection

Sarcasm language is widely presented in social media and student chats. This is not only a difficult sentiment analysis task, but also the basis for completing other follow-up tasks. However, most of the previous methods are based on complex network model design, which leads to poor traceability of inference results. Or based on the non-corresponding pre-training model, which is not taking advantage of the area where each model can do better. We propose a DialoGPT model based on pre-training on the sarcasm task, combined with the information of the sarcasm probability about the speaker, and embedded emojis in sentences to enhance recognition. The algorithm we designed is tested on the dataset named sarcasm on reddit and compared with other related methods and systems. The results show that the proposed method achieves the most advanced performance in the four indicators in basically the same time. F1-score has achieved 77, which is better than other methods, and it uses the advantages of new information content. In the field of education, the method we proposed can be used to help understand and judge students' intention and personality.
Isolation Kernel Density Estimation

This paper shows that adaptive kernel density estimator (KDE) can be derived effectively from Isolation Kernel. Existing adaptive KDEs often employ a data independent kernel such as Gaussian kernel. Therefore, it requires an additional means to adapt its bandwidth locally in a given dataset. Because Isolation Kernel is a data dependent kernel which is derived directly from data, no additional adaptive operation is required. The resultant estimator called IKDE is the only KDE that is fast and adaptive. Existing KDEs are either fast but non-adaptive or adaptive but slow. In addition, using IKDE for anomaly detection, we identify two advantages of IKDE over LOF (Local Outlier Factor), contributing to significantly faster runtime.
Virtual Reality-Based Immersive Digital Learning Resources: Initial Study of the Design Features Perception from Future Architects

Increasing demands and needs of utilizing Virtual Reality (VR) technologies have pushed many commercial and non-commercial industries and institutions to innovate, including in the education sector. This paper presents a preliminary study of understanding the perception of undergraduate Architecture students in the first Sino-Foreign University in China of various design features. These features were selected on the basis of their functionalities in the VR environment development context, e.g. a VR scene or site that can be visited as part of the learning process within the discipline. Hence, the participants were asked to evaluate the importance of each design feature, after experiencing a VR scene created under a university-scale project, namely V-ROOM. Results obtained from this study are hoped to provide future recommendations for numerous stakeholders working in or pursuing the architecture field, including students, educators, and architects. It is interesting to find that amongst fourteen design features investigated in this work, collaborative real-time editing, object/model scaling, and texture modification appeared to receive the highest ratings from the participants, and hence are the most crucial features to be considered in the design process. Furthermore, correlation matrix is also produced to investigate the relationship between these features.
Compositional Linear Regression on Interval-valued Data

Uncertainty is pervasive in data and decision making, manifesting in various forms, from lack-of-information to vagueness. Interval-valued data provide an efficient and effective way to capture this uncertainty not afforded by using point-values such as numbers. Once interval-valued data have been captured, insights pertaining to their inherent range can be explored and communicated. Approaches to analysis span from exploring summary statistics, such as the average range/uncertainty, to inferential statistics such as regression and more complex machine learning techniques. In recent years, approaches in particular to regression for interval-valued data have attracted increasing interest, with advances affording improved regression model accuracy and increased resilience to parameter flipping, i.e. ‘loss of mathematical coherence’. This paper explores the application of a branch of statistics-compositional data analysis, for the computation of inferential statistics and specifically regression for interval-valued data. We articulate how, why and when a compositional representation of interval-valued data may be appropriate, and how intervals on a fixed domain can be easily converted to a compositional representation. Finally, we show how regression for compositional (interval) data can be conducted, and how this can provide an elegant means to mitigating some of the challenges faced by traditional approaches, including preserving mathematical coherence by virtue of the compositional representation.
Intelligent multiframe detection aided by Doppler information and a deep neural network

Multiframe detection can be effective for the detection of targets with low signal-to-noise ratios by integrating the target information in several consecutive frames. Traditional methods usually suffer from high computational complexity and insufficient use of available information. In this paper, we propose solving the multiframe detection problem through two sequential steps: Doppler-aided track formation (DTF) and data-driven track detection (DTD). By using the DTF, which considers the coupling relationship between the location measurements and Doppler measurements, the potential tracks can be extracted without introducing too many false tracks. Using DTD, which fuses multiple features along the extracted tracks through a deep neural network, the detection can be sequentially declared. Simulation results show that the detection performance of the proposed method is better than that of the traditional methods for weak targets, and that false alarms can be handled well. Both theoretical analysis and experimental studies corroborate the computational efficiency of the proposed method in real-time implementation.
A Survey of Entity Alignment of Knowledge Graph Based on Embedded Representation

Abstract This paper summarizes the main methods of knowledge representation learning. Representation learning represents the entity information of the knowledge graph as a low dimensional vector. Its vector dimension is low, which helps to improve the computational efficiency and make full use of the semantic information between entities. In order to embed two KGs into a unified semantic space, joint embedding is used to achieve this goal. With the development of research, there are many improved embedding methods, such as iteration, multi view embedding, knowledge graph entity alignment based on graph neural network and so on.
Deep Adversarial Image Synthesis for Nuclei Segmentation of Histopathology Image

Nuclei segmentation is a fundamental upstream task of digital pathology image analysis. Existing nuclei segmentation methods usually require pixel-level labeled images from experienced pathologists. In this paper, we proposed an innovative data augmentation workflow for histopathology images: a) generates a set of initial central points randomly with existing human-annotated histopathology image datasets; b) generates nuclei segmentation masks based on the generated centroid points of step a); c) generates Haematoxylin and Eosin (H&E)-stained histopathology images corresponding to the generated nuclei masks. In addition, we proposed a deep attention feature fusion generative adversarial network (DAFF -GAN) to improve the image quality and the photorealism of the generated image. We conducted extensive experiments on several existing nuclei segmentation methods, comparing using raw data with the augmented data by our strategy. Extensive experiments proved the effectiveness of our proposed strategy.
Human Resources Factors that Impact Front-line Employees Performance in the Hotel Industry in Bahrain

In this paper we are identifying the main factor affecting front-line employees' performance in Bahrain's hotels and tourism industry. The techniques of the focus group and the analytic hierarchy process (AHP) were utilized to collect and analyze the data from the front-line employees, various HR managers in Bahrain hotels, and customers. Besides the guests from three hotels: Ritz Gourmet Lounge, Jumeirah Royal Saray, and Four Seasons Hotels, which are the most famous hotels in Bahrain. The results reveal that training is the most important factor in enhancing front-line employee's performance in Bahrain hotels and tourism industry. The Work Result is the least critical criterion through assessing the employee's performance. The Incentives and Reward are the least attended and practiced factors by hotels in Bahrain.
Development of Teaching-Learning Contents for AI Core Principles at the Elementary School Level: With a Focus on Convolutional Neural Network

With the rapid development and multifarious application of artificial intelligence technology, elementary learners are demanded to learn about AI. At the same time, due to the pandemic, classes without face-to-face contact have been increasing. This study aims to develop teaching-learning contents instructing core principles of artificial intelligence, respectively in face-to-face and non-face-to-face learning environments. Proposed teaching-learning contents mainly target elementary learner and provide simplified experience for convolutional neural network(CNN) algorithm. Learners accomplish convolution operation with simple numbers then extract feature map. Students experience CNN process with 5x5 virtual input image consists of numbers. 2x2 filter is given for convolution operation, and students fill 4x4 feature map with the results. These same teaching-learning activities can be also offered with online interactive worksheets, using ‘LIVEWORKSHEETS’ website. By transforming printed worksheets into online ones, learners in distance learning classes can accomplish identical coursework clicking and typing then score themselves. Teachers and learners can choose appropriate teaching-learning way or mix both of them flexibly based on their situation. Subsequent studies are expected to revise the contents after sufficient pilot classes with diverse learner groups and devise additional physical teaching-learning activity suitable for elementary learners. This study proposes the importance of continuous research on artificial intelligence education for young learners and versatile teaching-learning contents for various learning environment.
A Shape-Based Method for Concept Drift Detection and Signal Denoising

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. Many unsupervised approaches in this context rely on measuring the discrepancy between the sample distributions of two time windows. However, due to the small number of samples that is used for the estimation, the results are usually rather noisy, which makes it hard to distinguish the actual, drift induced differences from the noisy ones induced by inner distribution variances. In this paper we analyze the structural properties of the drift induced signals. We derive a shape based filter mechanism, derive an efficient algorithmic solution, and demonstrate its usefulness in several experiments.
Swarm Reinforcement Learning Method Based on Hierarchical Q-Learning

In last decades the reinforcement learning method has attracted a great deal of attention and many studies have been done. However, this method is basically a trial-and-error scheme and it takes much computational time to acquire optimal strategies. Furthermore, optimal strategies may not be obtained for large and complicated problems with many states. To resolve these problems we have proposed the swarm reinforcement learning method, which is developed inspired by the multi-point search optimization methods. The Swarm reinforcement learning method has been extensively studied and its effectiveness has been confirmed for several problems, especially for Markov decision processes where the agents can fully observe the states of environments. In many real-world problems, however, the agents cannot fully observe the environments and they are usually partially observable Markov decision processes (POMDPs). The purpose of this paper is to develop a swarm reinforcement learning method which can deal with POMDPs. We propose a swarm reinforcement learning method based on HQ-learning, which is a hierarchical extension of Q-learning. It is shown through experiments that the proposed method can handle POMDPs and possesses higher performance than that of the original HQ-learning.
Arithmetic Problem Solver Based on BERT Model and Mathematical Cognitive Pattern

Problem solving is a hot research area in educational technology. Arithmetic problem is the basis of many other problems thus it is important to develop algorithms to solve arithmetic problems. This paper proposes an arithmetic problem solver based on BERT model and mathematical cognitive pattern for solving the simple arithmetic word problems. Firstly, BERT model was used to obtain the semantic representation of the problem text. Then two different neural networks were adopted to identify the cognitive pattern and the variable contained in it successively. Finally, for each problem, all the variables are mapped to the corresponding cognitive pattern so as to instantiate an equation, and the program directly calculated the equation and got the answer. The experiment used 392 AddSub type problems from previous research and 474 Mix type problems collected in this paper. The result shows the solver achieved the 91.1% problems on Add-Sub type dataset and 75.5% problems on Mix-type dataset.
Predictive Modeling

Predictive modeling approaches and techniques in archaeological research and cultural heritage management have experienced continuous developments. In this Chapter, a more specific prospect of past and present modeling approaches in archaeology is provided. Recent and concrete applications and case studies applying statistical learning are introduced, ranging from a global to a local (Swiss) perspective. The incorporation of uncertainty and vagueness inherent to the data and its delivering in Archaeological Predictive Maps is further tackled and summarized. Finally, more complex, non-linear machine learning and data mining approaches are described, with particular emphasis on the Random Forest algorithm as fundamental part of the methodological procedure developed in this study.
The Impact of Artificial Intelligence Applications on the Digital Marketing Development on the Telecommunications Companies in Jordan

This research mainly aims to analyze the main effect of the Artificial Intelligence applications; neural networks, expert systems and evolutionary computation, on digital marketing development. Specifically, the aim is to evaluate the new artificial intelligence applications in Jordan, especially those which can affect digital marketing development. Besides, the researcher designed questionnaires which were given on the basis of a technique of simple sampling. Moreover, they were applied on the Jordanian telecommunications companies. A number of 375 questionnaires were distributed. Furthermore, a number of 320 samples were gathered. The result was 85% response rate concerning all the respondents’ responses. The researcher relied on reliability test, descriptive analysis, and multiple regression tests, which were applied to achieve the objectives of this research. Furthermore, this study’s results reflected a positive effect of Artificial Intelligence applications; namely, expert systems, evolutionary computation as well as neural networks, on digital marketing development. However, Jordanian employees are concerned with Artificial Intelligence applications; expert systems, evolutionary computation as well as neural networks, on the basis of their perception’s analysis. Such applications can develop the digital marketing in the Jordanian telecommunications companies. Finally, this study’s results make it clear that companies have to improve those applications to enhance digital marketing world.
Particle Filter Based Range Search Approach for Localization of Radioactive Materials

Wireless Sensor Networks (WSNs) have recently become crucial in monitoring operations. The development of a Data Fusion Algorithm for radioactive source localization utilizing WSN based on the Particle Filter (PF) technique is presented. The localization of an unknown-intensity point radioactive source using sensor nodes measured intensities in Count per Minute (CPM) is considered. The surveillance area is covered by several sensors (radiation detectors) n . Instead of using four sensors, as described in previous research, two consecutive sensors are used in sequence (S1, S2), (S2, S3)……, (Sn-1, Sn) till reaching the last sensor available Sn. Apollonius circle calculated range guides a particle filter for estimating the source location using actual measurements. Compared with other approaches such as the Iterative Pruning Clustering algorithm, more accurate estimates in terms of the error between the estimated source position and ground truth are obtained. The comparison is conducted using the same real measurements data. The Particle Filter based algorithm is implemented in a Xilinx FPGA chip. The architecture is a two sequential steps implementation, where particle generation, weight calculation, and normalization are carried out in parallel during the first step, followed by a sequential or parallelized resampling in the second step. This architecture targets a balance between hardware resources and speed of operation. The future work plan includes security-related studies and complete WSN implementation using μC/FPGA devices.
The Context-Oriented System Based on ELECTRA for Solving Math Word Problem

This paper proposed a context-oriented system based on ELECTRA for solving a math word problem. The system contains two patterns for problem text matching and context graph matching, and the output is the equation groups. In the first step, we build a semantic text graph of the problem context. Then, the pattern based on word relationships is obtained from the problem text, and another pattern is obtained by matching the context to a mathematical equation. Our proposed system succeeded in solving textbook problems show that it has more than good potential.
Utilizing a Telegram Quiz Bot to Promote Retrieval Practice

This study aims to support learning by encouraging the application of retrieval practice by students. This usage of retrieval practice aims to tackle pedagogical inadequacies such as social isolation and poor peer-to-peer communication. In this study, retrieval practice is incorporated into social media usage via a Telegram Quiz Bot. In addition to gamifying a student's learning experience, the Quiz Bot incorporated relevant topics taught in an environmental chemistry module. Students were required to compete in groups for accuracy and speed in answering questions. To evaluate the effectiveness of this Telegram quiz bot, a survey was conducted with 29 students enrolled in an environmental chemistry module at the National University of Singapore. Overall, positive responses were obtained, and students would recommend its incorporation in other modules as well.