Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis with Various Deep Learning Models on Movie Reviews

Social media have led to the development of artificial intelligence tasks such as sentiment analysis to see whether people’s posts have a positive or negative effect on other people. Ideas that affect society directly or indirectly about various domains, such as a movie or a meal, are very important for many business operations. This paper presents a sentiment analysis study which was carried out with 7 models based on various methods of deep learning algorithms on IMDB dataset. The best result was obtained with the model consisting of 2 Bi-LSTM and 2 dropout layers with 80%–20% train-test separation and an accuracy value of 88.21%.
Human Pose Estimation: Benchmarking Deep Learning-based Methods

Human posture estimation (HPE) aims to detect human body parts and generate a demonstration of the human body with incoming data such as images and video. HPE has lately gained much attention as shown numerous applications in the real world. State-of-the-art human posture estimation methods may benefit from deep learning. In this view, this study aims to examine and assess deep learning-based human posture estimation algorithms. To describe and analyze recent research, multiple deep learning-based human posture estimation approaches (OpenPose, ViTPose-B, HRNet, AlphaPose, DenseNet, EfficientPose, DensePose, Hourglass, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4^{*}\text{RSN}-50$</tex> ) deployed on the COCO and MPII datasets. This paper examined human posture using evaluation measures like average precision (AP) and probability of correct key points (PCK). Therefore, ViTPose-B methods outperform AP on the COCO dataset & Hourglass achieves better PCK on MPII. The comparative analysis helps to understand the applicability of different techniques for human pose estimation. Furthermore, unresolved issues and future research challenges of the human pose are mentioned.
Simulated Autonomous Driving Using Reinforcement Learning: A Comparative Study on Unity’s ML-Agents Framework

Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results. In this paper, we explore the use of the Unity ML-Agents toolkit to train intelligent agents to navigate a racing track in a simulated environment using RL algorithms. The paper compares the performance of several different RL algorithms and configurations on the task of training kart agents to successfully traverse a racing track and identifies the most effective approach for training kart agents to navigate a racing track and avoid obstacles in that track. The best results, value loss of 0.0013 and a cumulative reward of 0.761, were yielded using the Proximal Policy Optimization algorithm. After successfully choosing a model and algorithm that can traverse the track with ease, different objects were added to the track and another model (which used behavioral cloning as a pre-training option) was trained to avoid such obstacles. The aforementioned model resulted in a value loss of 0.001 and a cumulative reward of 0.068, proving that behavioral cloning can help achieve satisfactory results where the in game agents are able to avoid obstacles more efficiently and complete the track with human-like performance, allowing for a deployment of intelligent agents in racing simulators.
Affective Corners as a Problematic for Design Interactions

Domestic robots are already commonplace in many homes, while humanoid companion robots like Pepper are increasingly becoming part of different kinds of care work. Drawing on fieldwork at a robotics lab, as well as our personal encounters with domestic robots, we use here the metaphor of “hard-to-reach corners” to explore the socio-technical limitations of companion robots and our differing abilities to respond to these limitations. This article presents “hard-to-reach-corners” as a problematic for design interaction, offering them as an opportunity for thinking about context and intersectional aspects of adaptation.
Accurate Open-Set Recognition for Memory Workload

How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the process is open-set recognition which aims to detect new workloads not seen in the training phase. Despite its importance, however, existing open-set recognition methods are unsatisfactory in terms of accuracy since they fail to exploit the characteristics of workload sequences. In this article, we propose Acorn , an accurate open-set recognition method capturing the characteristics of workload sequences. Acorn extracts two types of feature vectors to capture sequential patterns and spatial locality patterns in memory access. Acorn then uses the feature vectors to accurately classify a subsequence into one of the known classes or identify it as the unknown class. Experiments show that Acorn achieves state-of-the-art accuracy, giving up to 37% points higher unknown class detection accuracy while achieving comparable known class classification accuracy than existing methods.
Comparing the intensity of variability changes in software product line evolution

The evolution of a Software Product Line (SPL) typically affects multiple kinds of artifacts. The intensity (frequency and amount) in which developers change variability information in them was unknown, until we introduced a fine-grained approach for the variability-centric extraction and analysis of changes to code, build, and variability model artifacts. Its application to the commits of the Linux kernel revealed that changes to variability information occur infrequently and only affect small parts of the analyzed artifacts. Further, we outlined how these results may improve certain analysis and verification tasks during SPL evolution. However, the sole analysis of a single SPL did not allow for generic conclusions. In this paper, we extend our previous work to a comparative evolution analysis of four SPLs. We provide a detailed analysis of the individual intensities of variability changes by applying our updated approach to each SPL. A comparison of these results confirms our findings of infrequent and small changes to variability information in our previous study. However, differences in the details of these changes exist, which we cannot explain solely by the characteristics of the SPLs or their development processes. We discuss their implications on supporting SPL evolution and on our previous optimization proposals.
Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations

Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract-concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb-noun thematic role relations and noun-noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations.
Boundary regression model for joint entity and relation extraction

Joint extraction of named entities and their relations has the advantage of avoiding cascading failures caused by falsely recognized named entities. Recent studies have focused on span classification modes to support end-to-end multiobjective learning. However, the enumeration of a large number of inaccurate entity spans creates a serious data imbalance and incurs high computational complexity. In this study, we propose a boundary regression model for joint entity and relation extraction, where a boundary regression mechanism is adopted to learn the offset of a possible named entity relevant to a true named entity. Instead of exhaustively enumerating all possible entity spans, this model receives only a small number of coarse entities with inaccurate boundaries as inputs. It can locate named entities and extract relations between them simultaneously. Experiments demonstrated that our boundary regression model outperforms state-of-the-art models in terms of the F1 score by +2.5%, +0.4%, +2.1%, and +1.3% on ADE, ACE05, ACE04, and CoNLL04 benchmark datasets respectively. Analytical experiments further confirmed the effectiveness of our model for refining entity boundaries and learning accurate span representations.
Task-oriented contrastive learning for unsupervised domain adaptation

Unsupervised domain adaptation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, thereby improving the classification performance of the target domain. Recent methods use contrastive learning to optimize this task, however, these methods only focus on contrastive learning for aspects of domain alignment, which do not actively serve the classification task, resulting in suboptimal solution. To this end, we propose Task-oriented Contrastive Learning for Unsupervised Domain Adaptation (TOCL), which addresses the inconsistency problem of optimizing contrastive learning and classification tasks. We perform feature weighting on the source domain images and extract the task-related features containing semantic information after data augmentation as the source domain positive and negative sample pairs for contrastive learning. At the same time, a delimitation discriminator is introduced to maximize the output difference between two types of random data-augmented samples of the target domain to detect data-augmented samples that are not conducive to classification, and then minimize the difference through the feature generator to generate effective data-augmented features (good for classification). Our extensive experiments on several public datasets show that TOCL has good adaptability and effectiveness, which improves the accuracy of classification results.
Automatic Feature Engineering for Learning Compact Decision Trees

Decision forests are known to excel in tabular data once their hyperparameters are well-tuned. In addition to being accurate and robust classifiers, these models can be easily converted into a collection of if-else rules that clearly describe the model's decision-making process. However, in practice, decision trees may reach enormous depth and size, and as a result, the collection of rules is vast and complicated. Furthermore, the model may consume a significant amount of memory space on the machine. By creating compact models, it is possible to establish a modest set of rules that may be easier to understand, require less memory, and by nature, may increase the decision-making ability and avoid overfitting. Previous studies attempted to reduce the size of the trees by altering their structure, but this affects both their advantages and simplicity because their structure is much more complicated. (e.g. oblique trees). In this research, we present FACET, a novel algorithm for retaining the compactness of trees while referring to the model as a black box. FACET addresses this by utilizing automated feature engineering methods, which generate a new feature set from the data set by manipulating the current feature set, resulting in a drastic reduction in the size of the decision trees while preserving and even improving the model's accuracy. Our algorithm, FACET, has been extensively tested on multiple datasets, models, and operators to demonstrate its effectiveness. On average, FACET achieves a 24% reduction in the size criteria of the tree-based model without sacrificing accuracy. This reduction in size leads to an average memory reduction of 44% on the dataset required for learning. These statistically significant results demonstrate the potential of FACET to enable more efficient and interpretable tree-based models, without compromising their accuracy, in practical applications.
Technological trends &amp; software engineering education: a systematic review study

The quality of software engineering education is influenced by the consideration of modern industrial trends, IT trends and software development methodologies. The transition to a new level of industry - “Industry 4.0”, characterized by robotics and cyber-socialization, brings a wave of new, never-before-seen challenges to the field of IT and programming. Under such conditions, it is impos- sible to achieve the desired synergistic effect without the formation of an appropriate informational and educational environment in institutions of higher education. One of the sources of the content of such an environment can be the publications of the world scientific community registered in international scientometric databases. On this basis, it becomes possible not only to provide effective digital educational content, but also to choose and use the latest technological means of learning support. A targeted review of primary sources in the Web of Science Core Collection database made it possible to identify, select, synthesize and evaluate research evidence relevant to the research questions: (i) what technological trends are analyzed in research, (ii) what information technolo- gies are considered in research, (iii) what strategies are reflected in publications to overcome the above challenges of SE teaching and learning. According to the results of the study, we offer to improve the educational and methodological support of software engineering education based on ontological modeling of the integration of the requirements of modern trends in the development of the industry and the IT sector, international recommendations and the capabilities of technological teaching aids. This approach is a good basis for the processes of intellectualization of information systems of the educational environment and the construction of appropriate technological educational tools on their basis.
Reinforcement Learning for Mobile Robot Navigation: An overview

For several years, research shows that interest in autonomous mobile robots is increasing and it has more and more grown. Autonomous mobile robots is an object of discussion but nowadays it's an emerging topic due to the all progress related to field like autonomous driving and UAV (drones). Integrating intelligence into robotic systems requires solving various research problems, including one of the most important problems of mobile robotic systems: navigation. Find the answers to the following three questions: What is the localisation of the robot? Where are the robot going? How can it get there? presenting the solution of mobile robot navigation problem. These questions are answered by basic navigation parts which are localization, mapping and path planning. The paper present an overview of research on autonomous mobile robot navigation. First, a quick introduction to the various features of navigation. We also discuss machine learning and reinforcement learning in mobile robotics. Furthermore, we will discuss some path planning techniques. Some future directions are also suggested.
YOLOv4 enhancement with efficient channel recalibration approach in CSPdarknet53

In recent years, Attention Mechanisms in the computer vision field have been gaining a significant level of importance, thanks to their great performance and adaptability with convolutional neural networks. In this paper, we propose a new model YOLOv4-SE that outperforms YOLOv4 thanks to the integration of channel attention. We enable the network CSPdarknet53 to model interdependencies between channels and adaptively recalibrate the channels' weights. Furthermore, we optimize and enhance the squeeze and excitation block by thoroughly studying mixed pooling. Experiments show that our model achieves a 1.6% higher mAP than that of YOLOv4 on Pascal VOC 2007 test dataset with marginal computational overhead. YOLOv4-SE is a real time object detector that runs at 63.3 FPS and reaches a mAP equal to 80%.
Controller Design for Unmanned Quadrotors Behavior with PD-PID Controllers

An increasing number of applications are being developed for Unmanned Aerial Vehicles (UAV) and quadrators. Forest fire detection and management are continuously improved by including economical new technologies to preserve ecosystem degradation and disasters. This paper deals with an inner outer loop of an economical design of attitude and altitude controller of a quadrotor. Quadrotor dynamics model is highly nonlinear system that could simplified with several assumptions. Non linear feedback linearization associated with (LQR), (SMC), (PD) or (PID) techniques are used to develop controllers. These techniques are often associated for control improvement and disturbances rejection. The PD-PID designed controller is used in an intelligent smoke or fire algorithm tracking or surveillance. An evaluation of the proposed approach is conducted based on computational simulation results.
An Analysis on the Adoption of Chatbots in the Banking Sector of Hong Kong

In view of the fierce competition and rapidly changing business environment in the banking and finance industries, many banks have been progressively shifting their retail banking services online. With the aim of streamlining operations and improving customer service, the burgeoning use of artificial intelligence (AI) in the banking industry accelerates the adoption of various AI-based technologies. This chapter conducted a case study on Fubon Bank, researching the adoption of chatbots in the Hong Kong banking industry. A chatbot development framework for e-banking is being developed. Lastly, a comparison of chatbot services among three local banks in Hong Kong is provided.
Target detection for remote sensing based on the enhanced YOLOv4 with improved BiFPN

To solve problems for false detection, inadequate regression performance of anchor frames, and the inability to detect small targets in traditional multiscale target detection methods based on YOLOv4, we propose a novel target detection framework named as Enhanced YOLOv4. Firstly, our improved BiFPN replaced the original PANet as the feature fusion module, which can achieve multi-scale feature fusion by way of shared weights. Secondly, the channel attention mechanism (CAM) was embedded before the detection head to highlight the correlation between channels so that small targets can be get more attention. At last, to improve the anchor box regression effect and accelerate the training speed of YOLOv4, we improved the net training loss function, in which the original CIoU was replaced by CDIoU. The experimental results on the DOTA dataset validate our improvement. The mAP of our method is 90.88%, and the frame rate reached 58.76 FPS, at the same time, the speed of detection is not affected significantly.
Group Target Tracking for Highly Maneuverable Unmanned Aerial Vehicles Swarms: A Perspective

Group target tracking (GTT) is a promising approach for countering unmanned aerial vehicles (UAVs). However, the complex distribution and high mobility of UAV swarms may limit GTTs performance. To enhance GTT performance for UAV swarms, this paper proposes potential solutions. An automatic measurement partitioning method based on ordering points to identify the clustering structure (OPTICS) is suggested to handle non-uniform measurements with arbitrary contour distribution. Maneuver modeling of UAV swarms using deep learning methods is proposed to improve centroid tracking precision. Furthermore, the group's three-dimensional (3D) shape can be estimated more accurately by applying key point extraction and preset geometric models. Finally, optimized criteria are proposed to improve the spawning or combination of tracking groups. In the future, the proposed solutions will undergo rigorous derivations and be evaluated under harsh simulation conditions to assess their effectiveness.
An efficient quantum partial differential equation solver with chebyshev points

Differential equations are the foundation of mathematical models representing the universe's physics. Hence, it is significant to solve partial and ordinary differential equations, such as Navier-Stokes, heat transfer, convection-diffusion, and wave equations, to model, calculate and simulate the underlying complex physical processes. However, it is challenging to solve coupled nonlinear high dimensional partial differential equations in classical computers because of the vast amount of required resources and time. Quantum computation is one of the most promising methods that enable simulations of more complex problems. One solver developed for quantum computers is the quantum partial differential equation (PDE) solver, which uses the quantum amplitude estimation algorithm (QAEA). This paper proposes an efficient implementation of the QAEA by utilizing Chebyshev points for numerical integration to design robust quantum PDE solvers. A generic ordinary differential equation, a heat equation, and a convection-diffusion equation are solved. The solutions are compared with the available data to demonstrate the effectiveness of the proposed approach. We show that the proposed implementation provides a two-order accuracy increase with a significant reduction in solution time.
Robot Navigation in Complex Workspaces Employing Harmonic Maps and Adaptive Artificial Potential Fields

In this work, we address the single robot navigation problem within a planar and arbitrarily connected workspace. In particular, we present an algorithm that transforms any static, compact, planar workspace of arbitrary connectedness and shape to a disk, where the navigation problem can be easily solved. Our solution benefits from the fact that it only requires a fine representation of the workspace boundary (i.e., a set of points), which is easily obtained in practice via SLAM. The proposed transformation, combined with a workspace decomposition strategy that reduces the computational complexity, has been exhaustively tested and has shown excellent performance in complex workspaces. A motion control scheme is also provided for the class of non-holonomic robots with unicycle kinematics, which are commonly used in most industrial applications. Moreover, the tuning of the underlying control parameters is rather straightforward as it affects only the shape of the resulted trajectories and not the critical specifications of collision avoidance and convergence to the goal position. Finally, we validate the efficacy of the proposed navigation strategy via extensive simulations and experimental studies.