Reviewerly/exHarmony huggingface dataset:
Employment Recommendation System Based on User Portrait

With the opening of the era of big data, using big data technology to collect and analyze data, build models and systems according to the needs, has become a research hot spot in various fields. This paper is based on big data technology and user portrait, using 2557 user data and 99997 job data. In order to analysis of the user's target industry and realize the job recommendation, the characteristic engineering is introduced to build accurate user portrait and job information portrait. Otherwise, the paper also uses decision tree, BP neural network and other machine learning model for matching prediction, introducing the distance recommended algorithm framework. This system provides users with 10 high degree of relevant job information, from the technical means to provide the possibility to promote employment.
AN EXPERT SYSTEM FOR DIAGNOSING INFANTS’ DISEASES USING CLIPS.

This paper proposes a knowledge-based system to help parents diagnose some of their children's diseases at an early stage and to give them insight into whether they should see a doctor. It covers the most common diseases encountered by pediatric specialists, such as intestinal obstruction, dehydration, anemia, etc. An overview of these diseases is provided, the etiology of the disease is outlined, and treatment suggestions for the most common diseases are given when possible. CLIPS was used to develop the proposed expert system.
Utilization of Deep Learning for Mapping Land Use Change Base on Geographic Information System: A Case Study of Liquefaction

This study aims to extract buildings and roads and determine the extent of changes before and after the liquefaction disaster. The research method used is automatic extraction. The data used are Google Earth images for 2017 and 2018. The data analysis technique uses the Deep Learning Geography Information System. The results showed that the extraction results of the built-up area were 23.61 ha and the undeveloped area was 147.53 ha. The total length of the road before the liquefaction disaster occurred was 35.50 km. The extraction result after the liquefaction disaster was that the area built up was 1.20 ha, while the buildings lost due to the disaster were 22.41 ha. The total road length prior to the liquefaction disaster was 35.50 km, only 11.20 km of roads were lost, 24.30 km. Deep Learning in Geographic Information Systems (GIS) is proliferating and has many advantages in all aspects of life, including technology, geography, health, education, social life, and disasters.
A full-factorial analysis of combined vehicle motion profiles on cross-coupled coriolis effect, subjective vertical and motion sickness incidence

Motion sickness research has always been shaped by current events. With the advent of highly automated vehicles (HAVs), the topic is currently being revisited as 60% of users of HAV functions are expected to suffer from motion sickness. Failure to address this condition will jeopardize user acceptance of HAV functions. We investigated the vestibular mechanisms of motion misinterpretation and hypothesized that cross-coupled stimuli induce more sensory conflict and lead to higher motion sickness incidence compared to the non-coupled control condition. We conducted an experiment on a dynamic driving simulator with realistic motion profiles and analyzed the influence of cross-coupled motion on motion sickness incidence. Results show no significant difference in motion sickness incidence between cross-coupled and non-coupled motion profiles. Further research is needed to investigate the thresholds of the Coriolis effect and should include the measurement of compensatory or inertial head motion of participants.
Capturing Smart Contract Design with DCR Graphs

Smart contracts manage blockchain assets and embody business processes. However, mainstream smart contract programming languages such as Solidity lack explicit notions of roles, action dependencies, and time. Instead, these concepts are implemented in program code. This makes it very hard to design and analyze smart contracts. We argue that DCR graphs are a suitable formalization tool for smart contracts because they explicitly and visually capture the mentioned features. We utilize this expressiveness to show that many common high-level design patterns representing the underlying business processes in smart-contract applications can be naturally modeled this way. Applying these patterns shows that DCR graphs facilitate the development and analysis of correct and reliable smart contracts by providing a clear and easy-to-understand specification.
Cosmotechnic Encounters: Designing with foodwaste, landscapes, and livelihoods

Organic wastes are vital for farming, energy generation, and carbon capture—embodying naturally the ideal of circularity. However, due to their messiness and weight, organic matter arrives with biological and sociotechnical challenges. What kind of imaginaries, recovery practices, and contingencies are required to reclaim and revalue such lively material? Pursuing this, how can we mitigate the detriment of urban food waste, and, in turn, regenerate regions impacted by climatic and economic precarity? In response, we conducted a series of collaborative encounters with farmers, chefs, retailers, and biotech entrepreneurs in rural Hong Kong to explore what a reverse supply chain might involve that redirects organic wastes from the city to agricultural landscapes. We took insight from Yuk Hui’s cosmotechnics vision, design studies, and diverse economies for differentiating a broader spectrum of economic possibilities. Following this embeddedness with interdependent livelihoods enables us to live in fullness with the world, particularly with organic waste as the foundation for contributing to a circularity that tangibly interlinks humans, nonhumans, cities, and the countryside to different futures. Such constellations can manifest varied instances of economisation—the mutually regenerative and stabilising relationships which facilitate exchange. They also embody a cosmological imaginary that reconfigures local economies predicated on designing with the shapelessness of contingency: staying put with what easily is ignored while relinquishing determinist categories, fragmentation, and totalising systems.
Sensory System Based on Intelligent Sensors for Implementation of a Modern Weather Station

When we discuss the quick development of technology, the weather station has become an asset for those who want to enjoy a tool that can provide accurate data about temperature, pressure, and various weather data, but also an essential element in the successful operation and development of various economic branches. In this work, we have presented how to take information from the environment and use it to create predictions. Using a modular concept, we can easily connect several controllers without making significant changes to the original system, which is a great advantage over other products on the market.
LIVED EXPERIENCES OF ELEMENTARY TEACHERS ON CLASSROOM MANAGEMENT DURING IN-PERSON CLASSES: AN INQUIRY

The purpose of this phenomenological inquiry is to explore and describe the lived experiences of elementary teachers concerning classroom management during in-person classes in the division of Davao De Oro. This is a qualitative research design that utilizes a phenomenological approach. Purposive sampling was used to gather participants. In-depth interviews and focus group discussions were conducted in data collection. Thematic analysis and data coding was employed to analyze the gathered data. In the experiences of the elementary teachers, four themes emerged: employing varied teaching and learning strategies, employing varied classroom management strategies, handling students varied attitudes and behavior, and dealing with poor attention span of students. As to the coping mechanisms, seven themes emerged: involving parents in addressing issues and concerns of students, seeking advice from colleagues and superiors, having self-management in dealing with challenges, implementing classroom strategy based on students needs and style, applying a reward system, having patience in dealing with students varied classroom behavior, and sharing experiences with family and colleagues. With regards to the insights, five themes emerged: find strategies to address students abilities and behaviors, setting goals and limitations in the classroom, wide understanding of the students situation, teaching entails dedication and sincerity, and effective classroom management. KEYWORDS: classroom management, education, in-person classes, phenomenological approach
A Method for Adversarial Example Generation Using Wavelet Transformation

With the advance of Deep Neural Networks (DNN), the accuracy of various tasks in machine learning has dramatically improved. Image classification is one of the most typical tasks. However, various papers have pointed out the vulnerability of DNN.It is known that small changes to an image can easily makes the DNN model misclassify it. The images with such small changes are called adversarial examples. This vulnerability of DNN is a major problem in practical image recognition. There have been researches on the methods to generate adversarial examples and researches on the methods to defense DNN models not to be fooled by adversarial example. In addition, the transferability of the adversarial example can be used to easily attack a model in a black-box attack situation. Many of the attack methods used techniques to add perturbations to images in the spatial domain. However, we focus on the spatial frequency domain and propose a new attack method.Since the low-frequency component is responsible for the overall tendency of color distributions in the images, it is easy to see the change if modified. On the other hand, the high-frequency component of an image holds less information than the low-frequency component. Even if it is changed, the change is less apparent in the appearance of the image. Therefore, it is difficult to perceive an attack on the high-frequency component at a glance, which makes it easy to attack. Thus, by adding perturbation to the high-frequency components of the images, we can expect to generate adversarial examples that appear similar to the original image with human eyes.R. Duan et al. used a discrete cosine transformation for images when focusing on the spatial frequency domain. This was a method by use of quantization, which drops the information that DNN models would have extracted. However, this method has the disadvantage that block-like noise appears in a resultant image because the target image is separated by 8 × 8 to apply the discrete cosine transformation. In order to avoid such disadvantage, we propose a method which applies the wavelet transformation to target images. Reduction of the information in the high-frequency component changes the image with the perturbation that is not noticeable, which results in a smaller change of the image than previous studies. For experiments, the peak signal to noise ratio (PSNR) was used to quantify how much the image was degraded from the original image. In our experiments, we compared the results of our method with different learning rates used to generate perturbations with the previous study and found that the maximum learning rate of our method was about 43, compared to about 32 in the previous study. Unlike previous studies, the attached success rate was also improved without using quantization: our method improved attack accuracy by about 9% compared to the previous work.
A very brief guide to further reading

Drawing upon over a quarter of a century’s worth of research, The Urban Now illuminates our present urban condition. John Rennie Short captures the main features of this moment of urban significance, investigating the city as a crucial arena strategically located between global flows and national surfaces.
Pengukuran Prestasi Belajar Mahasiswa Berdasarkan Prediksi Nilai Menggunakan General Linear Model

The Covid-19 pandemic is an international disaster experienced by almost all countries in the world. Several research results reveal the special impact of the pandemic on the education sector. Not only lecturers and students, but higher education providers also experience the same thing. Various adjustments were made so that all parties involved were able to adapt properly. It's been two years since the pandemic among us and during that time the learning process has continued. Based on this, several institutions began to take steps that raised questions about whether the learning achievement targets in each subject could still be achieved. This study aims to predict student grades using several machine-learning algorithms. The prediction results are a measure to find out whether learning outcomes have been achieved or not, if not achieved then additional steps need to be taken to help students. The results of this research are expected to help UNIS to prepare appropriate learning models for its students and ensure that all learning achievement targets are achieved. The research method used is a technique of machine learning. The results of this study indicate that the General Linear Model is a classification model with the best accuracy, which can be used to predict student achievement in certain classes based on the evaluation scores of the first structured activity (EKT1), midterm exams, grades (UTS), and second structured activity evaluation scores (EKT2). And it turns out that the UTS score has the greatest influence between EKT1 and EKT.
Measuring the Impact of Picture-Based Explanations on the Acceptance of an AI System for Classifying Laundry

Artificial intelligence (AI) systems have increasingly been employed in various industries, including the laundry sector, e.g., to assist the employees sorting the laundry. This study aims to investigate the influence of image-based explanations on the acceptance of an AI system, by using CNNs that were trained to classify color and type of laundry items, with the explanations being generated through Deep Taylor Decomposition, a popular Explainable AI technique. We specifically examined how providing reasonable and unreasonable visual explanations affected the confidence levels of participating employees from laundries in their respective decisions. 32 participants were recruited from a diverse range of laundries, age, experience in this sector and prior experience with AI technologies and were invited to take part in this study. Each participant was presented with a set of 20 laundry classifications made by the AI system. They were then asked to indicate whether the accompanying image-based explanation strengthened or weakened their confidence in each decision. A five-level Likert scale was utilized to measure the impact, ranging from 1 (strongly weakens confidence) to 5 (strongly strengthens confidence). By providing visual cues and contextual information, the explanations are expected to enhance participants' understanding of the AI system's decision-making process. Consequently, we hypothesize that the image-based explanations will strengthen participants' confidence in the AI system's classifications, leading to increased acceptance and trust in its capabilities. The analysis of the results indicated significant main effects for both the quality of explanation and neural network certainties variables. Moreover, the interaction between explanation quality and neural network certainties also demonstrated a notable level of significance.The outcomes of this study hold substantial implications for the integration of AI systems within the laundry industry and other related domains. By identifying the influence of image-based explanations on acceptance, organizations can refine their AI implementations, ensuring effective utilization and positive user experiences. By fostering a better understanding of how image-based explanations influence AI acceptance, this study contributes to the ongoing development and improvement of AI systems across industries. Ultimately, this research seeks to pave the way for enhanced human-AI collaboration and more widespread adoption of AI technologies. Future research in this area could explore alternative forms of visual explanations, to further examine their impact on user acceptance and confidence in AI systems.
RustGraph: Robust Anomaly Detection in Dynamic Graphs by Jointly Learning Structural-Temporal Dependency

Dynamic graph-based data are ubiquitous in the real world, such as social networks, finance systems, and traffic flow. Fast and accurately detecting anomalies in these dynamic graphs is of vital importance. However, despite promising results the current anomaly detection methods have achieved, there are two major limitations when coping with dynamic graphs. The first limitation is that the topological structures and the temporal dynamics have been modeled separately, resulting in less expressive features for detection. The second limitation is that the models have been trained by unreliable noisy labels generated by random negative sampling, rendering it severely vulnerable to subtle perturbations. To overcome the above limitations, we propose RustGraph, a robust anomaly detection framework by jointly learning structural-temporal dependency in dynamic graphs. To this end, we design a variational graph auto-encoder with informative prior that simultaneously encodes both graph structural and temporal information. Then we introduce a fine-grained contrastive learning method to learn better node representations by utilizing the temporal consistency between two snapshots. Furthermore, we formulate the noisy label learning problem for anomaly detection in dynamic graph, and then propose a robust anomaly detector to improve the model performance by leveraging learned graph structure signal. Our extensive experiments on six real-world datasets demonstrate the proposed RustGraph method achieves state-of-the-art performance with an average of 3.64% improvement on AUC-ROC metric compared with all baselines. The codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/aubreygjh/RustGraph</uri> .
Acoustic Compensation System Against Adversarial Voice Recognition

Automatic speech recognition (ASR) provides diverse audio-to-text services for humans to communicate with machines. However, recent researches reveal ASR systems are vulnerable to various malicious audio attacks. In particular, by removing the non-essential frequency components for humans, a new spectrum reduction attack can generate attacked audios that can be perceived by humans but cannot be correctly interpreted by the ASR systems. It raises a new challenge for content moderation solutions to detect harmful content in audio and video available on social media platforms. In this chapter, we propose an acoustic compensation system named ACE to counter the spectrum reduction attacks over ASR systems. Our system design is based on two observations, namely, frequency component dependencies and perturbation sensitivity. First, since the Discrete Fourier Transform computation may inevitably introduce aliasing, spectral leakage, and picket fence effects to the audio frequency spectrum, the frequency components with similar frequencies will have a high correlation. Thus, considering the intrinsic dependencies between neighboring frequency components, it is possible to recover more of the original audio by compensating for the removed components based on the remaining ones. Second, since the removed components in the spectrum reduction attacks can be treated as an inverse of adversarial noise, the attack success rate will decrease when the adversarial audio is replayed in an over-the-air scenario. Hence, we can model the acoustic propagation process to add the over-the-air perturbations into attacked audio. We implement a prototype of ACE, and the experimental results show that ACE can effectively reduce up to 87.9% of ASR inference errors caused by spectrum reduction attacks. Furthermore, by analyzing the remaining errors on real audio samples, we summarize six general types of ASR inference errors and investigate the error causes and potential mitigation solutions.
Hybrid Autoregressive Integrated Moving Average-Support Vector Regression for Stock Price Forecasting

Stock investment provides high-profit opportunities but also has a high risk of loss. Investors use various decision-making methods to minimize this risk, such as stock price forecasting. This research aims to predict daily closing stock prices using a hybrid Autoregressive Integrated Moving Average (ARIMA)-Support Vector Regression (SVR) model and compare it with the single model of ARIMA and SVR, as well as compiling the R-shiny web for the hybrid ARIMA-SVR model which makes it easier for investors to use the model to support investment decision making. The hybrid ARIMA-SVR model is composed of two components: the linear component from the results of stock price forecasting using the Autoregressive Integrated Moving Average (ARIMA) model and the nonlinear components from the residual forecasting results of the ARIMA model using the Support Vector Regression (SVR) model. The data used was closing stock price data from April 1, 2019, to April 1, 2021, from PT Unilever Indonesia Tbk (UNVR.JK), PT Perusahaan Gas Negara Tbk (PGAS.JK), and PT Telekomunikasi Indonesia Tbk (TLKM.JK), from the Yahoo Finance website. The research results conclude that the hybrid ARIMA-SVR model has excellent capabilities in forecasting stock prices with the MAPE values ​​for UNVR, PGAS, and TLKM stocks, respectively of 0.797%, 2.213%, and 0.993%, which are lower than the MAPE values of ARIMA-GARCH and SVR models. The hybrid model can be an alternative model with excellent capabilities in forecasting stock prices.
Effectiveness of Flash Card Media-Oriented Group Investigation Learning Models on Learning Outcomes of Elementary Students

The purpose of this study was to determine the effectiveness of the flash card media-oriented group investigation (GI) learning model on the learning outcomes of elementary students. The approach used in this study is a quantitative approach with pre-experimental methods and the type of One Group Pretest-Posttest design. With the overall population of SD Inpres Oeba 2 class V students 22 people. Sampling using nonprobability sampling saturated sampling where all members of the population are sampled. The data collection technique was obtained from a multiple choice written test. The data analysis technique used descriptive analysis and inferential statistics with the help of the SPSS version 22 application. The results showed that independent t-test data processing obtained a sig value of 0.000 &lt;0.05, then Ha was accepted and H0 was rejected or in other words there was a significant difference between pretest and posttest classes in improving student learning outcomes in the material for the digestive tract of food in humans. Thus, the Flash Card Oriented Group Investigation (GI) learning model can improve the Learning Outcomes of Class V Students of SD Inpres Oeba 2 Kupang City.
STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds

This article proposes a means of autonomous mobile robot navigation in dense crowds based on predicting pedestrians’ future trajectories. The method includes a pedestrian trajectory prediction for a running mobile robot and spatiotemporal path planning for when the path crosses with pedestrians. The predicted trajectories are converted into a time series of cost maps, and the robot achieves smooth navigation without dodging to the right or left in crowds; the path planner does not require a long-term prediction. The results of an evaluation implementing this method in a real robot in a science museum show that the trajectory prediction works. Moreover, the proposed planning’s arrival times is 26.4% faster than conventional 2D path planning’s arrival time in a simulation of navigation in a crowd of 50 people.