Reviewerly/exHarmony huggingface dataset:
Finger Gesture Recognition Based on Wireless Coupling Resonance

This article proposes a finger state capture and recognition system based on near-field coil coupling and multifrequency resonance. Finger state capture is realized by load coils mounted on the fingers that form coupling resonance at multiple frequencies under sweep frequency excitations. The motions of the finger joints lead to changes of the distance and angle between the load coils, which further affect the amplitude and frequency of the resonance through mutual inductances. These features are extracted from the measurements of reflected signals for finger state recognition. The process of a finger from full flexion to full extension is approximately evenly divided into 20 states. A weighted parallel amplitude and phase (WPAP) classification method is proposed based on the support vector machine (SVM) algorithm. This method processes the amplitude and phase of the complex reflected signal separately in parallel, and then combines their classification results by assigning weights to the output. The experimental results verify the effectiveness of finger state recognition from the reflected signals. The classification result and performance evaluation demonstrate that the proposed method outperforms the two SVM classifiers trained on amplitude data and phase individually. The classification accuracies of the 20 states of the index finger and the thumb are 96.92% and 83.58%, respectively. Moreover, four hand gestures are selected and tested using the measurements of the thumb and the index finger. All gestures are correctly recognized. This work achieves accurate finger state recognition without using additional active sensors on the fingers, which largely preserves the finger flexibility. It has potential applications in finger tracking and manipulator monitoring.
Contrastive R-CNN for Incremental Learning in Object Detection

Incremental learning for image classification has been widely studied in the past few years, but few works explored incremental learning for object detection. Most existing incremental object detectors deploy knowledge distillation to constrain the model to retain old knowledge and avoid the catastrophic forgetting phenomenon. Nonetheless, this common practice results in strong constraints adhering to the old knowledge, therefore deteriorating the learning ability to new knowledge. In this work, we propose a new framework named Contrastive R-CNN for incremental learning of object detection to balance the retaining of the old knowledge and the learning of the new knowledge. The proposed framework is mainly composed of two modules, data distillation and temporal contrast. Data distillation presents a median entropy filter strategy to generate the annotations for the RoIs of the old objects, while temporal contrast designs an RoI contrast mechanism to minimize the ambiguity between old and new instances for better incremental learning. Extensive experiments on the PASCAL VOC dataset demonstrate the effectiveness of our proposed approach.
Multimodal Sarcasm Detection Based on Multimodal Sentiment Co-training

Sarcasm detection is a difficult task in sentiment analysis because sarcasm often includes both positive and negative sentiments, making it difficult to identify. In recent years, visual information has been used to study sarcasm in social media data. Based on sentiment contrast in image and text, this paper proposes a Multimodal Sentiment and Sarcasm Gradient Co-training (MSSGC) model. The model uses text and image feature sharing networks to explicitly learn image and text sentimental features from image and text sentiment datasets and integrates a cross-modal fusion module for Multimodal Sarcasm Detection (MSD). The training algorithm uses the sentimental features for sarcasm detection by weighting the sentiment and sarcasm classification gradients. Extensive experiments, including case studies, are performed to evaluate the MSSGC model. The results illustrate that the proposed model outperforms recent MSD models. The code is available at: https://github.com/vantree/MSSGC.
Assessing Video Game Balance using Autonomous Agents

As the complexity and scope of games increase, game testing, also called playtesting, becomes an essential activity to ensure the quality of video games. Yet, the manual, ad-hoc nature of game testing leaves space for automation. In this paper, we research, design, and implement an approach to supplement game testing to balance video games with autonomous agents. We evaluate our approach with two platform games. We bring a systematic way to assess if a game is balanced by (1) comparing the difficulty levels between game versions and issues with the game design, and (2) the game demands for skill or luck.
Feature-based Data Augmentation Approach for Sequential Recommendation

Sequential recommendation aims to predict the next item of interest based on a user’s historical behavior. Most existing recommendation methods tend to focus on designing complex deep neural network models to solve some problems but ignore the feature selection process, which also has a great impact on the recommendation performance. Recently, many data augmentation methods have been used to improve the data sparsity problem and improve recommendation performance. However, data augmentation methods inevitably introduce noisy augmented data, which compromises the true representation of unaugmented data during subsequent model inference training. To alleviate this problem, in this paper, we propose an adaptive data augmentation method (FDARec) for sequential recommendation, which preserves as much important information of the data as possible. The core idea is to use the distribution of items in a high-dimensional space to determine the important features of each item and then retain these important features during the data augmentation process. This information retention strategy makes the generated training samples a more accurate representation of the essential nature of the data. In addition, if we use only the augmented data for unsupervised contrastive learning, then the sequences that are semantically similar are classified as negative samples, so we design a new sampling strategy to distinguish semantic similarity between sequences. Experiments on four public datasets show that our method offers superior performance compared to the baseline method.
V-TransTDN: Visual Transformer Network for Target-Driven Navigation using Meta-reinforcement Learning

Target-driven navigation (TDN) aims to navigate an agent toward a target object specified by a semantic label, only through the first-person view. In this task, the agent is required to learn highly nonlinear mappings from images and semantic labels to actions. To this end, it is pivotal for agents to get a powerful model for feature extraction and a fast-trained navigation policy. Recent fruitful approaches that rely on deep reinforcement learning frameworks prove very promising for this purpose. In this work, we propose VTransTDN, an adaptive TDN model for strong generalization in unseen scenes and novel target objects. We design several techniques in V-TransTDN to address the two challenges in TDN: 1) To handle different target object features and environmental features, we introduce a visual transformer to obtain better visual representations. 2) To better trade-off the adaptation parameters and action quality, we introduce meta-learning to learn good initialization parameters to avoid train from scratch. In a nutshell, V-TransTDN organically combines transformers’ advantages with the advantages of meta-reinforcement learning to achieve informative representation and a strong navigation policy. Experiment results show that V-TransTDN achieves much better adaptation quality than baseline approaches. When combined, the techniques bring significant improvement over baseline methods in navigation effectiveness and efficiency in unseen environments. We report a 22.8% and 23.5% increase in success rate and Success weighted by Path Length (SPL), respectively.
Efficient 3D Object Detection Models and Evaluation Method for Autonomous Driving

Recently, due to the increasing interest in autonomous driving, many researchers have been focused on Open Source Software (OSS) such as the Autoware platform, which enables full-stack autonomous driving. As a research group dedicated to the development of autonomous driving software platforms, we are particularly focused on the development of 3D object detection models that take into account real-time performance as well as accuracy. In this paper, we propose a VariFocal-based CenterPoint model and an adaptive shape estimation method combined with Apollo segmentation to improve the performance of the 3D object detection module included in the Autoware platform. Additionally, we present a Bird’s Eye View (BEV) metric-based quantitative evaluation method to compare the performance of different models according to the dataset. By means of the evaluation method, we demonstrate the experimental results of the performance between the 3D object detection modules of the Autoware platform and our models on both our own dataset and a public dataset. Furthermore, since these models will be used for autonomous driving platforms where real-time processing is a crucial factor, we also perform experimental results on the processing time by converting the models to TensorRT. As shown in the experimental results, the improved models trained on a large amount of self-constructed datasets resolve issues such as missed detection of large vehicles and small objects like motorcycles and pedestrians compared to the previous models. As a result, VF-CenterPoint performed 13.76 mAP better than the previous model, and A-Shape Estimation method achieved 9.7 AP higher performance than the previous method.
IMPLEMENTATION OF NETWORK WEBSITE TECHNOLOGY IN COMPANY BRANDING PROFILE

A website is a network-based information medium that can be accessed anywhere and anytime using a computer. It is relatively inexpensive compared to conventional information media such as newspapers. We can do many things by using a website, one of which is to inform about a company, also known as a company profile. The research method used is WDLC. In the testing phase, three different web browsers were used. When tested on these three browsers, there were no bugs at all.
Leadership, Commitment, and the Failure of Trust – What Companies Must Do to Thrive

Employees limit their commitment and organizations fail to be competitive for predictable reasons that have been well-documented (Nobel, 2011). According to the extensive research of the Gallup corporation, much of that failure is attributed to ineffective leadership (Clifton &amp; Harter, 2019). The primary failure of those who lead is their inability to be perceived as being worthy of their employees’ trust – a problem that organization leaders have faced for decades (Barnard, 1938). Trust, the willing followership of a leader in the expectant hope that the leader will honor implied promises(Caldwell, 2019), has been elusive in organizations of all types, according to research reported in the Harvard Business Review (Bingham, 2017).
The Case of English for Specific Purposes on Academic Writing Difficulties of Hospitality and Tourism Management Students

This study assessed the case of English for Specific Purposes (ESP) on academic writing difficulties of hospitality and tourism management students. This study used a qualitative approach upon a case study design because it deals with the study of an individual language learner. This study was conducted in the second semester of the academic year 2021-2022. The respondents of this study were ten hospitality management students and ten tourism students from the College of Business and Management. The researcher used an interview guide as the main data-gathering instrument supported by documentary analysis. Content analysis was used in interpreting the gathered data. Results indicated that hospitality and tourism management students performed poorly in writing because they had low self-efficacy toward writing. The majority of the students had little courage to spell words correctly, they had little confidence to put thoughts into writing and had much fear in writing a composition. Moreover, based on documentary analysis, students struggle to organize and use writing mechanics. Students encounter numerous writing problems regarding the correct use of grammatical rules, and they find writing in a second language quite tricky.
R. BRADBURY'S NOVEL "FAHRENHEIT 451˚" AND ITS FILM ADAPTATION: AN ATTEMPT AT COMPARATIVE ANALYSIS

The article is devoted to the comparative research in the sphere of literature and cinematography. The question of the proximity between the film adaption and literary work, which it is based on, is considered. There is an attempt of comparative analysis of R. Bradbury’s novel «Fahrenheit 451˚» and its film adaption of 2018. The visual means of implementing the author’s concept in the film are analyzed. The plot, figurative system and ideological orientation of the literary work and its film version are subjected to comparison. Conclusions are drawn about a significant departure in the film from the literary source, which contributed to the weakening of the problem.
Adaptive multiparameter estimation at the quantum bound on chip

The achievement of quantum-enhanced estimation precision is a fundamental challenge. Here, we prove experimentally the saturation of the ultimate quantum limits employing an integrated photonic sensor and developing a fully artificial intelligence-based adaptive protocol.
Experimental Validation of Deep Learning-based Models for Optical Time Domain Analysis

Optical constellations generated by Deep Learning models trained with datasets generated through simulation are compared to experimentally collected ones. The obtained high accuracy enables its application for optical time domain analysis in complex network scenarios.
Black-box optimization in quantum information protocols

We used a black-box approach in quantum information protocols. This allows us to optimize an engineering protocol compensating for experimental imperfections and to optimize the nonclassicality of an unknown system reinforcing the device-independent paradigm.
Sub-standard quantum limit estimation precision for a wide resources range

Quantum metrology protocols need to face several issues when implemented in realistic scenarios. Here, we experimentally demonstrate quantum-enhanced estiamtion precisions in both a single and multiparameter framework maintained for an exceptionally large range of resources.
Non-Gaussian state generation by multi-photon subtraction at the telecommunication wavelength

Non-Gaussian quantum states were generated by photon subtraction of up to four photons from a squeezed light at 1545.32 nm. The Wigner negativity and the parity change with the subtracted photon number were directly observed.
Planning an ESP Course Design in SMK Context Teachers’ Roles and Problems

ESP or English for Specific Purposes is distinctive in that it considers students' learning needs because ESP equips students with the necessary skills to compete in the workplace. In other words, based on the student's major, the teacher must tailor English instruction to be fit for employment. How the teachers preparing English lessons for vocational students have not been fully disclosed in prior study, despite some of the issues teachers confront when teaching English at vocational schools. Therefore, the purpose of this study is to analyze the teachers’ roles and also problems in planning the instructions for their students. In answering the released research questions, qualitative research using the interview method and also an open-ended questionnaire were conducted on 3 English teachers from 3 different SMKs with different majors. These three English teachers have a combined teaching experience of almost exactly ten years. Online interviews and questionnaires with questions about learning preparation based on the Dudley-Evans hypothesis were completed via phone and email. The results of the surveys and interviews were then examined by classifying the data according to theory. The study's findings reveal that these teachers encounter similar challenges when putting together course plans that are appropriate for their students' majors, selecting subject matter that is relevant to their line of work, and selecting learning resources that will enable them to help their students meet set learning objectives.
Static and dynamic human activity recognition with VGG-16 pre-trained CNN model

Human Activity Recognition has been widely studied using the Convolutional Neural Network (CNN) algorithm to classify a person's movements by utilizing data from devices that record movements such as cameras. The benefits generated by this technology are useful for modern devices such as Virtual Reality and Smart Home technology with CCTV cameras. The VGG-16 (Visual Geometric Group with 16 Layers) pre-trained model is one of the models used for transfer learning and has won the Image Net competition. In this study, the authors tested the performance of the VGG-16 model to identify two types of human activity, namely Static and Dynamic. This study uses 1,680 public datasets which are divided into 80% Data Train, 10% Data Validation, and 10% Data Test I. In addition, there are also 100 local datasets as Data Test II. There is no overfitting issue in the training and testing process. The accuracy of the Testing process with public and local images dataset produces a high accuracy of 98.8% and 97% respectively.
When Do Developers Not Have to Talk to Stakeholders?

An ontologist can bridge1 domain expertise and software development via production of a semi-informal so-called intermediate representation2 that can be understood by domain experts, and a formal ontology / knowledge graph that represents the domain in a machine-actionable way. When you do software development, you want to take the human out of the loop as much as possible – really automate it, least amount of manual effort, just minimize that…When I explain to [developers]...
Measure term similarity using a semantic network approach

Computing semantic similarity between two words comes with a variety of approaches. This is mainly essential for applications such as text analysis and text understanding. In traditional systems, search engines are used to compute the similarity between words. In that sense, search engines are keyword based. There is one drawback that users should know what exactly they are looking for. There are mainly two main approaches for computation, namely knowledge-based and corpus-based approaches. However, there is one drawback that these two approaches are not suitable for computing similarity between multiword expressions. This system provides an efficient and effective approach for computing term similarity using a semantic network approach. A clustering approach is used in order to improve the accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. This technique can also be applied to large-scale datasets to compute term similarity.
A Study on the Unification Movement and Unification Network of the Religious Community as Social Capital

The purpose of this study is to examine how the religious community has formed and developed the unification movement and unification network in Korean society as social capital. To this end, looking at the preceding studies, it was found that research on the unification movement of domestic religious organizations was insufficient. This study examined the view of unification and the theory of unification in the religious community using social capital and social trust as a theoretical framework.&#x0D; As a result of this study, the tasks necessary for the religious community to fulfill its role are as follows. First, it is the revitalization of unification education in the religious community. Second, it is the establishment of a cooperative network with the religious community and the government. Third, it is necessary to increase the unification solidarity of the religious community and the spread and utilization of international networks. Through this, it is possible to expect unification public diplomacy in which the domestic religious community can promote cooperation and support with religious organizations.
ANALISIS SALURAN DRAINASE AKIBAT GENANGAN DI KECAMATAN ILIR TIMUR II KOTA PALEMBANG

Changes in land use in an area greatly affect water absorption. Inundation occurred in Ilir Timur II Subdistrict, Palembang City due to lack of maintenance of drainage channels and changes in land conversion which was originally land into a water catchment area, now it is a concrete or residential area. During the rainy season, water will fill residents' spaces in the form of puddles and floods, which will continue to increase surface runoff in the area. The impact of the inundation is very disturbing and detrimental to various parties. The purpose of this study is to determine the peak discharge, analyze existing drainage, and plan a new secondary drainage design. The data used are primary data, namely channel length measurement data, channel slope, and existing channel dimension measurement data. While the secondary data used is in the form of 10-year rainfall data obtained from two rain posts, namely the rain post in Sako District and the rain post in Ilir Barat I District, as well as land use data in the Fruit Watershed of Palembang City covering an area of 1222.89 hectares. Then carry out an analysis of changes in land use currently and calculation of peak discharge to plan the dimensions of the new secondary drainage channel. The current peak discharge (Qp) calculation results in the studied area are 24.4 m3/s. For the calculation of channel dimensions, it is planned to have a rectangular cross-section with channel dimensions at sta 0.00 - 672.80 m, namely the channel dimension height is 4.5 m, the channel width is 4.5 m, and the guard height (f) is 0.30 m. From the results of analysis and calculations it is known that with the dimensions of the new drainage design there will be no runoff along the secondary canal. It is hoped that this can be a reference in planning the drainage rearrangement in Ilir Timur II District to reduce frequent inundation.
Investigation of the Legislation of Crimes Concerning Endangering Drug Safety in China

The protection and improvement of drug safety is a major and realistic issue of concern to Chinese society and the public at present. Thus, the crimes concerning endangering drug safety are one of the key areas of Chinese criminal law. The Criminal Law of the People's Republic of China has been regulating crimes concerning endangering drug safety since 1979, and special activities have been carried out in practice to combat crimes concerning endangering drug safety. Although the criminal legislation on crimes concerning endangering drug safety changes frequently in China, its spirit of legislation and the developmental sequence is clear. Firstly, the main legislative form is the criminal code, supported by administrative laws and regulations. Secondly, the scope of criminal law shows a trend of gradual expansion, which is mainly reflected in the expansion of the object of crime and the scope of regulatory act. Thirdly, the time-point of intervention has been brought forward by the criminal law. Fourthly, the punishment tends to be more severe. In criminal legislation of China, the specific content of the crimes concerning endangering drug safety is mainly concentrated on the core crimes related to the act of producing or selling of counterfeit and substandard drugs. This article carried out a detailed analysis of the constituent elements and the controversial issues about these core crimes such as the crime of producing, selling or providing counterfeit drugs, the crime of producing, selling or providing substandard drugs, the crime of disrupting drug administration, the crime of neglecting duties in drug regulation, and other relevant crimes.