Reviewerly/exHarmony huggingface dataset:
Design of quantum filter for hybrid quantum-classical convolutional neural networks

Convolutional neural networks are widely used in image recognition problems as they have many advantages compared to other techniques. Parametrized quantum circuit, at the same time, is widely used for supervised learning task like images classification. Here we design two quantum filters for hybrid quantum-classical convolutional neural network for image recognition tasks using parametrized quantum circuit. We demonstrate the potential of these quantum filters by applying them to UCI digits dataset, and show that the hybrid quantum-classical convolutional neural network can accomplish classification tasks with a high learning accuracy. To explore how the quantum filter enhances the feature mapping process, we carry out a series of simulation experiments on different parametrized quantum circuit structure, quantum filter depth, and tasks of varying difficulty. The proposed quantum filters achieve excellent classification accuracy under a limited number of qubits available in current hardware, indicating the promising application potential of it in image recognition.
Usability Heuristics and Explicit Analysis of UI Design for Optimum User Experience: The Case of Pili (Canarium ovatum Engl.) Information System and Marketplace

The intricateness and dynamism of any software development project are viewed as prevailing attributes that affect its outcome. However, explicit utilization of recognized software development principles and standards ensures a higher possibility of achieving greater success. This paper presents the results of the efforts in the design and development of Comprehensive Pili (Canarium ovatum Engl.) Information System and Marketplace particularly the user experiences. This project was funded by the Department of Science and Technology (DOST) with the intention of establishing a dynamic web of knowledge for the Pili industry, thus addressing the dearth of accessible Pili information. The development process follows strict adherence to PiliIS and Marketplace Software Development Paradigm - researchers made software development procedures adopting the Agile software development concepts. This paradigm ensures explicit compliance to ISO/IEC 25010 standards. An internal evaluation team was employed to examine the user interface judging its compliance with the recognized usability principles. A diverse group of stakeholders evaluated the system following Jakob Nielsen&#x2019;s principles for interaction design. Complying to these highly recognized software quality standards and adhering to the researchers&#x2019; paradigm, the developed software was marked by stakeholders as &#x201C;highly usable&#x201D;, &#x201C;highly acceptable&#x201D; and &#x201C;highly reliable&#x201D; with average ratings of 94.65%, 93.71%, and 93.53%, respectively. Additionally, Philippine Pili Industry League, Inc. (PhilPili) rated the system as &#x201C;excellent&#x201D;. Given the limited study on the development and evaluation of information systems designed to support the marginalized sectors in the country, this study provides significant insights on the software quality principles and competencies as universal standards that boost favorable customer satisfaction and optimum user experience.
Scale Adaptive Enhance Network for Crowd Counting

Crowd counting is a fundamental computer vision task and plays a critical role in video structure analysis and potential down-stream applications, e.g., accident forecasting and urban traffic analysis. The main challenges of crowd counting lie in the scale variation caused by disorderly distributed “person-camera” distances, as well as the interference of complex backgrounds. To address these issues, we propose a scale adaptive enhance network (SAENet) based on the encoder-decoder U-Net architecture. We employ Res2Net as the encoder backbone for extracting multi-scale head information to relieve the scale variation problem. The decoder consists of two branches, i.e., Attention Estimation Network (AENet) to provide attention maps and Density Estimation Network (DENet) to generate density maps. In order to fully leverage the complementary concepts between AENet and DENet, we craft to propose two modules to enhance feature transfer: i) a lightweight plug-and-play interactive attention module (IA-block) is deployed to multiple levels of the decoder to refine the feature map; ii) we propose a global scale adaptive fusion strategy (GSAFS) to adaptively model diverse scale cues to obtain the weighted density map. Extensive experiments show that the proposed method outperforms the existing competitive method and establishes the state-of-the-art results on ShanghaiTech Part A and B, and UCF-QNRF. Our model can achieve 53.56 and 5.95 MAE in ShanghaiTech Part A and B, with obtains performance improvement of 6.0 % and 13.13%, respectively.
How Can a Chatbot Support Human Resource Management? Exploring the Operational Interplay

Chatbots are increasingly taken up to support organizational functions and processes, particularly in support of the Human Resource Management (HRM) function. However, there is a lack of knowledge on the organizational implications of this support – in particular the operational interplay between the chatbot, the HRM function, and the organization at large. In this study, we contribute knowledge to the fields of chatbot research and digital HRM support through interviews with 13 HRM practitioners in organizations that had implemented chatbots to support their function. The findings show that a chatbot may support the HRM function through handling of repetitive inquiries and tailoring of HRM support in response to insights from analysis of chatbot use. At the same time, the chatbot impacts the HRM function in terms of new tasks and competence requirements. The findings also provide insight into characteristics of the organization and the chatbot which may impact uptake and effective use. Based on the findings, we suggest implications for theory and practice and point out future research needs.
Remotely Vital Signs Capturer for Older Adults Applied in Residential Zones

Assisted technology combined with sensor networks is an emerging technology for better homecare of people requiring specific support. In this work, we focus our attention on monitoring vital signs and accident prevention of older adults in residential areas without invasive and uncomfortable medical devices. We prototyped two vital signal monitoring devices integrated in structures deployed in common areas such as an arch and a bench. In addition, an alarm system was implemented to monitor residents in common areas and prevent vehicle accidents. The system was tested using Zigbee and LoRa wireless technologies and the network's overall performance was compared. In addition, the proposal was compared to commercially available medical equipment and results show a 96% similarity in vital signs monitoring. The system offers a silent noninvasive operation together with a friendly user interaction. It allows caregivers and family members to monitor vital signs of the residents remotely without the need of wearable specialized devices.
Size vs. Stereo in Illusory Depth Inversion

OPINION article Front. Psychol., 07 February 2022Sec. Perception Science Volume 12 - 2021 | https://doi.org/10.3389/fpsyg.2021.817745
Integrated Design Framework for Facilitating Systems-Theoretic Process Analysis

Systems-Theoretic Process Analysis (STPA) helps mitigate identified safety hazards leading to unfortunate situations. Usually, a systematic step-by-step approach is followed by safety experts irrespective of any software based tool-support, but identified hazards should be associated with security risks and human factors issues. In this paper, a design framework using Integrating Requirements and Information Security (IRIS) and open source Computer Aided Integration of Requirements and Information Security (CAIRIS) tool-support is used to facilitate the application of STPA. Our design framework lays the foundation for resolving safety, security and human factors issues for critical infrastructures. We have illustrated this approach with a case study based on real life Cambrian Coast Line Railway incident.
Target Tracking Research Hotspots and Frontier Trends Based on Citespace

With the continuous development of artificial intelligence technology, target tracking is a hot problem in the field of computer vision, which has a wide range of application prospects in industrial, military, transportation, medical and other fields. In this paper, Citespace software is used to conduct descriptive statistical analysis and knowledge mapping analysis of target tracking based on domestic CNKI database literature, and to explore the development status and frontier trends in the field of target tracking in China. On this basis, point out three shortcomings of current research: low accuracy of target tracking in complex environments, poor real-time target tracking, and few application directions, and give suggestions to improve algorithm robustness, real-time, accelerate engineering implementation, and focus on future research trends.
DLNet: Domain-specific Lightweight Network for On-Device Object Detection

A deep neural network (DNN) is one of the key technique in many intelligent applications. Since DNN models have many parameters, they require machines to perform computation-intensive tasks. Therefore, it is difficult to apply the DNN model to resource-constrained environments. In this paper, we propose the domain-specific lightweight network (DLNet) for on-device object detection. Every domain has the classes group that contains objects with a high frequency of appearance in domains. Since the DLNet is trained to detect the classes group of each domains, it becomes more shallower and narrower than existing detection models. Therefore, we can reduce the number of parameters and runtime of object detection. To evaluate the performance of the proposed model, we conduct comparative experiments with YOLOv3 and Tiny-YOLO. The precision and recall are 9.9 % and 14 % higher than those of YOLOv3 and Tiny-YOLO, respectively. The results shows that the DLNet achieve higher efficiency and better performance on non-GPU devices.
ConfusionTree-Pattern: A Hierarchical Design for an Efficient and Performant Multi-Class Pattern

Developing neural networks for supervised multi-class classification has become important for theory and practice. An essential point is the design of the underlying network. Beside single-network approaches there are several multi-class patterns which decompose a classification problem into multiple sub-problems and derive systems of neural networks. We show that existing multi-class patterns can be improved by a new and simple labeling scheme for the training of the sub-problems. We efficiently derive a class hierarchy which is optimized for our labeling scheme and, unlike most of existing works, has no schematic restrictions. Based on that we introduce a hierarchical multi-class pattern, called ConfusionTree-pattern, which is able to reach high classification accuracies. Our experiments show that our multi-class ConfusionTree-pattern reaches state-of-the-art results regarding performance and efficiency.
Does Keypoint Estimation Benefit Object Detection? An Empirical Study of One-stage and Two-stage Detectors

This paper studies the benefit of keypoint estimation on object detection. In particular, we focus on the paradigmatic one-stage and two-stage methods, two main categories in the object detection community. We note that while there has been remarkable progress on object detection and keypoint detection, insights on how the latter would benefit the former are somehow lacking. In this paper, we make two contributions. As a major contribution, we point out that one-stage and two-stage detectors have different abilities in accommodating keypoint description. The difference is clearly shown in our experiment where multiple detectors are compared in various detection tasks. Our essential observation is that one-stage detectors benefit consistently from the inclusion of a keypoint detection branch, while for two-stage detectors such benefit is obscure. As a minor contribution, we make several variant designs to improve the trade-off between efficiency and accuracy of the one-stage CenterNet [39] on multiple detection tasks.
Image Splicing Detection and Localisation using EfficientNet and Modified U-Net Architecture

The process of preserving, identifying, extracting, and documenting computer evidence that can be utilized in a court of law is known as digital forensics. It is the science of finding evidence from digital media like a computer, mobile phone, server, or network. It equips the forensic team with the most up-to-date techniques and tools for resolving complex digital cases.. Among many of them, the major problem which is addressed in this project is Image Forgery Detection. More precisely we deal here with the “splicing attack detection and localization”. Even though there are numerous techniques to conquer the problem, still there are loopholes in them like the accuracy of the forged region localization. As a part of this project, we propose a Convolutional Neural Network (CNN) based Deep Learning architecture, which is capable of detecting this forgery with state-of-the-art performance. We propose a transfer learning (EfficientNet) based image classification algorithm for the classification of forged Images and EfficientNet based U-Net Architecture for localization. In the classification part, we extract the image patches from the forged regions and non-forged regions in an image and classify them. The classification fetched us state of the art result. In the localization part, we have built a U-Net architecture by replacing the U-Net Encoder part with EfficientNet layers and keeping the decoder part as usual. We then pass the forged images through the network to train the model. The localization experiment also fetched state-of-the-art results.
Self-Adaptive Visual Background Extraction with Ghost Regions Elimination

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results.
Object Tracking Glove

Hand gestures are a type of nonverbal communication that can be applied to a variety of sectors, including deaf-mute communication, human–computer interaction (HCI), medical applications, robot control and home automation. Various strategies have been used in hand gesture research articles, including those based on instrumented sensor technology and computer vision. In this paper, we have proposed an object tracking glove that uses simple hand motions and sensor technology to operate computer systems. With the assistance of the webcam the colour of the object over the glove is detected and therefore the color are going to be reflected on the PC screen, this will be accomplished with the utilization of IDE processing referred to as calibration. With an easy movement of the hand, the pointer are often moved to the acceptable spot on the PC during this application the hall sensor acts as a switch or button, which helps to open any menu or software application when the cursor is moved over the specified menu. Whenever the hall sensor is messed with the magnet then the data from the sensor passes to the microcontroller. This microcontroller sends data to the Bluetooth module, and the signals are then passed on to the system, allowing it to perform the actions. Our application controls the lights/fans of a room controlled using the glove.
Incorporating Social Practices in Dialogue Systems

Current dialogue management systems do not take social concepts such as norms, conventions, roles etc. into account when managing dialogues. Neither do they keep track of the personal (mental) state such as goals, needs, etc. While the data-driven approaches work quite well in some cases, they are usually domain/user dependent and not transparent. On the other hand, the rule-based methods can only work on the predefined scenarios and are not flexible in that sense. In addition, these approaches are limited to modeling only the dialogue system and do not include the human participant as part of the overall dialogue. This makes the current dialogue systems not well suited for complex and natural dialogues. In this paper, we present a dialogue management system framework that incorporates the notion of social practices as a first step to extend the type of dialogues that can be supported. The use of social practices is meant to give structure to the dialogue without restricting it to a fixed protocol. We demonstrate the use of the proposed system on a scenario between the doctor and patient roles where the doctor is a medical student and the patient is simulated by the dialogue management system.
Unsupervised Parallel Sentences of Machine Translation for Asian Language Pairs

Parallel sentence pairs play a very important role in many natural language processing tasks, especially cross-lingual tasks such as machine translation. So far, many Asian language pairs lack bilingual parallel sentences. As collecting bilingual parallel data is very time-consuming and difficult, it is very important for many low-resource Asian language pairs. While existing methods have shown encouraging results, they rely on bilingual data seriously or have some drawbacks in an unsupervised situation. To address these issues, we propose a new unsupervised similarity calculation and dynamic selection metric to obtain parallel sentence pairs in an unsupervised situation. First, our method maps bilingual word embedding by postdoc adversarial training, which rotates the source space to match the target without parallel data. Then, we introduce a new cross-domain similarity adaption to obtain parallel sentence pairs. Experimental results on real-world datasets show that our model can obtain better accuracy and recall on mining parallel sentence pairs. We also show that the extracted bilingual sentence corpora can significantly improve the performance of neural machine translation.
Microscopic handcrafted features selection from computed tomography scans for <scp>early stage</scp> lungs cancer diagnosis using hybrid classifiers

Lung's cancer is the leading cause of cancer-related deaths worldwide. Recently cancer mortality rate and incidence increased exponentially. Many patients with lung cancer are diagnosed late, so the survival rate is shallow. Machine learning approaches have been widely used to increase the effectiveness of cancer detection at an early stage. Even while these methods are efficient in detecting specific forms of cancer, there is no known technique that could be used universally and consistently to identify new malignancies. As a result, cancer diagnosis via machine learning algorithms is still fresh area of research. Computed tomography (CT) images are frequently employed for early cancer detection and diagnosis because they contain significant information. In this research, an automated lung cancer detection and classification framework is proposed which consists of preprocessing, three patches local binary pattern feature encoding, local binary pattern, histogram of oriented gradients features are extracted and fused. The fast learning network (FLN) is a novel machine-learning technique that is fast to train and economical in terms of processing resources. However, the FLN's internal power parameters (weight and basis) are randomly initialized, resulting it an unstable algorithm. Therefore, to enhance accuracy, FLN is hybrid with K-nearest neighbors to classify texture and appearance-based features of lung chest CT scans from Kaggle dataset into cancerous and non-cancerous images. The proposed model performance is evaluated using accuracy, sensitivity, specificity on the Kaggle benchmark dataset that is found comparable in state of the art using simple machine learning strategies. RESEARCH HIGHLIGHTS: Fast learning network and K-nearest neighbor hybrid classifier proposed first time for lung cancer classification using handcrafted features including three patches local binary pattern, local binary pattern, and histogram of oriented gradients. Promising results obtained from novel simple combination.
The Mellin Transform to Manage Quadratic Forms in Normal Random Variables

The problem of computing the distribution of quadratic forms in normal variables has a long tradition in the statistical literature.Well-established numerical algorithms that deal with this task rely on the inversion of Fourier transforms or series representations.In this paper, the Mellin transform is proposed as a tool to compute both the density and the cumulative distribution functions of a positive definite quadratic form: an outline of the numerical algorithm is presented, providing details on the error analysis.The algorithm's characteristics allow us to propose an efficient way to compute the random variables' quantiles.From the theoretical point of view, the analytic properties of the Mellin transform are exploited to provide a novel representation of the distribution of the ratio of independent quadratic forms as a mixture of beta random variables of the second kind.Moreover, algorithms are proposed for computations related to ratios of both independent and dependent quadratic forms.The methods are tested and compared to popular numerical algorithms in terms of computational times and accuracy.The R package QF implementing all the proposed algorithms is also made available.Supplementary information is available online.