Reviewerly/exHarmony huggingface dataset:
CELS: Counterfactual Explanations for Time Series Data via Learned Saliency Maps

As the demand for interpretable machine learning approaches increases, there is an increasing need for human involvement to provide diverse explanations for model decisions. This is crucial for enhancing trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. In this paper, we design a novel counterfactual explanation model, CELS, which learns a saliency map for the interest of an instance and generates a counterfactual explanation guided by the learned saliency map. CELS adopts a gradient-based approach composed of three interdependent modules that combine to generate sparse counterfactual explanations that are easily understood by end users. To the best of our knowledge, this is the first attempt to guide the perturbation to generate a counterfactual explanation via a learned saliency map. To validate our model, we conducted experiments using five popular real-world time-series datasets obtained from the UCR repository. The experimental results demonstrate the superiority of our model in achieving higher sparsity, proximity, and interpretability of counterfactual explanations when compared to other state-of-the-art baselines.
Technical Analysis of Data-Centric and Model-Centric Artificial Intelligence

The artificial intelligence (AI) field is going through a dramatic revolution in terms of new horizons for research and real-world applications, but some research trajectories in AI are becoming detrimental over time. Recently, there has been a growing call in the AI community to combat a dominant research trend named model-centric AI (MC-AI), which only fiddles with complex AI codes/algorithms. MC-AI may not yield desirable results when applied to real-life problems like predictive maintenance due to limited or poor-quality data. In contrast, a relatively new paradigm named data-centric (DC-AI) is becoming more popular in the AI community. In this article, we discuss and compare MC-AI and DC-AI in terms of basic concepts, working mechanisms, and technical differences. Then, we highlight the potential benefits of the DC-AI approach to foster further research on this recent paradigm. This pioneering work on DC-AI and MC-AI can pave the way to understand the fundamentals and significance of these two paradigms from a broader perspective.
SOCIAL MEDIA AS MALAY AND CULTURAL ART PRESERVATION: GOOGLE SCHOLAR AS REFERENCE TOOL

In Malaysia, a diverse nation encompassing multiple ethnicities and languages, each community holds unique cultural traditions and beliefs. As responsible Malaysian citizens, it is imperative that we foster mutual respect among these various races. Malaysia places a significant emphasis on its Malay cultural heritage, featuring traditional games like Gasing, Congkak, and Wau, as well as entertainment forms such as Wayang Kuli, Dikir Barat, and Menora. These cultural elements play a crucial role in shaping the identity of the Malay youth. In the contemporary era dominated by digital technology, our generation increasingly relies on social media for various purposes. Within this context, this study focuses on exploring literature that examines the intersection of social media and the preservation of Malay cultural art. Leveraging Google Scholar as a reference tool, we aim to delve into past research that sheds light on how social media platforms contribute to the conservation of Malay cultural heritage. The study employs a methodological approach that involves identifying relevant literature from year 2017 until 2022 through Google Scholar, using keywords such as "Malay cultural art" and "social media." By undertaking this research, we seek to advance our understanding of past literature discussing the use of social media for the preservation of Malay cultural art. The findings are expected to contribute significantly to the current body of literature, serving as a valuable resource for scholars and researchers interested in this field. Ultimately, this study aims to foster awareness and appreciation for the rich cultural tapestry of Malaysia, encouraging future endeavours in preserving and promoting its diverse heritage.
Sentiment Analysis and Machine Learning Algorithm Implementation on Flipkart Product Customer Reviews

Sentiment analysis is a vital aspect of data mining that aims to prize private information from textual data. With the adding fashionability of online shopping platforms like Flipkart, assaying client feedback through product reviews have become a pivotal task for businesses to ameliorate their products and services. In recent times, researchers have explored the operation of machine knowledge algorithms for sentiment analysis of Flipkart product reviews. The results of this approach have demonstrated great promisein directly classifying the opposition of reviews as positive, negative, or neutral. colorful ways similar as Naive Bayes, SVM, Random Forest, Decision Trees, deep literacy, and rule-grounded approaches have been used for sentiment analysis on Flipkart reviews. also, preprocessing ways like Bag of Words, TF-IDF, Word Embeddings, and N-grams have been employed for point birth. This literature check highlights ten exploration papers published from 2017 to 2022, which bandy the perpetration of sentiment analysis and machine literacy algorithms on Flipkart product reviews. The findings of these studies give perceptivity into the effectiveness of different approaches and ways for sentiment analysis on Flipkart product reviews, and can guide unborn exploration in this field. We proposed a model called “GRUUSE” (Gated Recurrent Unit - Universal Sentence Encoder), which can directly classify the product reviews into any one of the three orders positive, negative or neutral. It is a neural network model that converts variable-length words into numerical representations of a fixed length using GRU cells. Tasks like phrase similarity or grouping are made possible by these representations, which capture semantic meaning.
Development and Situation Analysis of Knowledge Graph

Taking technology patents related to knowledge graph included in PSS as research samples, this paper conducts quantitative analysis on the trend of annual application, distribution of IPC, distribution of key technologies, and distribution of application fields. The results show that the number of technology patents related to knowledge graph will continue to increase, and a large number of related technologies have been applied to data processing systems. Also, the technical hotspots and hot fields of knowledge graph are clarified, and the technology frontiers of knowledge graph in the future are pointed out.
Personalized Hotel Recommendation Algorithms Based on Online Reviews

Online users’ review information can be an objective reflection of product features and values. An increasing number of consumers rely on online review information to make purchase decisions. Therefore, it is worth investigating how to extract effective information from a large number of online reviews to improve the personalization and accuracy of the recommendation systems. Due to the ambiguity of expressions, there is a high degree of uncertainty in users’ online reviews. In this essay, we introduce the theory of Probabilistic Language Term Set (PLTS) to describe the meaning of different terms and solve the ambiguity in online review information and propose a hotel recommendation algorithm. First, we use Jieba, Boson sentiment dictionary and manual annotation methods to perform sentiment analysis on online review sentences. Second, we use the PLTS tool to describe and statistically process the review information and generate a rating matrix. Third, we use the maximum deviation method to calculate the weights of different hotel attributes. Finally, we use the PLTS-corrected cosine similarity formula to calculate the similarity between hotels and generate hotel recommendation rankings based on user history. We selected 10 hotels in Zhuhai City as case studies and compared the results with other recommendation algorithms to verify the effectiveness of the proposed algorithm.
Path planning for multiple UAVs using RRT*FND in a dynamic environment

Adiyatov et al. proposed RRT*FN-Dynamic, an extension of Rapidly-exploring random trees (RRT) that can be applied in dynamic environments. This method re-plans the path if an obstacle intrudes on the path while moving along the obtained path. The purpose of this study is to guide multiple UAVs from each initial state to each target state while avoiding static obstacles and other airframes. In this paper, we applied RRT*FN-Dynamic(RRT*FND) to multiple UAVs in dynamic environments. The behavior of the airframes was simulated by using MATLAB, and it was confirmed that by considering other airframes as obstacles, multiple airframes can reach each target state while avoiding static obstacles and other airframes.
五指を有する表皮骨格一体型ロボットハンドの製作

In recent years, soft robotic hands that mimic the functions of human hands have been studied. While grasping performance can be improved by adding the characteristics of human skin, the number of parts increases when trying to reproduce multiple degrees of freedom, causing problems in assembly and maintenance. Therefore, we aimed to create a robot hand that has the advantage of reproducing the human body and is easy to assemble by integrally molding the skin and skeleton into a single part. In this study, we describe the design and fabrication methods, and show that a 15-DOF robot hand can be fabricated in less than one hour.
Development of a passive stabilizer for underwater camera systems

To develop a passive stabilizer for an underwater camera system to generate undersea 3D map data using a towing underwater robot. A prototype gimbal mechanism with hydraulic components was fabricated and evaluated to reduce image blurring.
双腕ロボットを用いた紙包装行動実現のための合わせ包みの構造に基づく動作戦略とハンド設計条件に関する研究

Paper wrapping behavior is performed in various situations in daily life, and automation is required due to increased demand for parcel delivery services and labor shortages. We take up caramel wrapping(Awase-Tsutsumi) to realize wrapping with dual-arm robots. We organize the structure of paper wrapping behavior based on the paper’s condition for each caramel wrapping action procedure to realize caramel wrapping. Based on the structure, we define the necessary motions and propose movement strategies and hand design conditions for each motion. In addition, the effectiveness of these methods is demonstrated through experiments.
Achieving obstacle avoidance behavior through a transferable learning policy adaptable to various types of multirotors

Quadrotor navigation for obstacle avoidance is an indispensable aspect of autonomous flight. Policy-based approaches have been extensively studied to address this problem. However, these approaches have limitations in terms of hyperparameter specificity for a given navigation system, as the learned policy can only be utilized in the same scale and dynamics as in the learning phase. To overcome this issue, we propose a transferable policy method that can be implemented across different dynamics and controller systems. This can be accomplished by (1) training a high-gain and agile quadrotor to achieve a broad range of velocities, without sacrificing performance for agile multirotor, (2) during execution, adjusting the velocity by the ratio of the maximum gain of the quadrotor to avoid high-speed collisions, and (3) randomizing only the size of the multirotor used to investigate collisions during the learning process to reduce the domain to randomize and achieve the desired policy in smaller timesteps. Our evaluation demonstrates that a universal policy, without prior knowledge of obstacles, can evade obstacles for multirotors ranging from 0.5 meters to 1.0 meter in size and across various fields. To the best of our knowledge, this is the first study to demonstrate the use of a universal policy for maneuvering tasks in various types of multirotors.
Realization of flexible manipulation of environmental objects using detachable wire modules

A wire module is coarsely attached to a moving object in the environment, such as a ceiling rail or a crane. Then, various sensor data are acquired while operating the wire module to move the environmental motion target. Focusing on the correlation between the control target and these data, the system solves an inverse problem through machine learning to achieve the environmental state desired by the user.
音声駆動型身体的傾聴システムにおける瞳孔反応のミラーリング効果

It is known that human pupil is related to own affects or interests and becomes more attractive when it is expanded. Focusing on effects of the dilated pupil in human communication and interaction, we analyzed the relationship between the speaker’s speech and his/her own pupil response and confirmed that the pupil dilates in synchronization with the speech. Based on the analysis, we developed the pupil response interface that expresses various pupil responses such as dilated or contracted pupil by speech input. In addition, we developed the speech-driven embodied listening system by mirroring of pupil response in synchronization with the speaker’s speech. This system expresses a positive listening attitude by the pupil response and the nodding response. In this study, we evaluated impressions that the system mirroring the pupillary response acted as a listener. The effectiveness of the system by mirroring the pupil response was demonstrated by sensory evaluations.
Evaluation of Remote Operation Display Layout to Improve Remote Operability for Autonomous Car

Currently, there is active development of autonomous driving around the world. Remote control technology is a necessary technology to realize autonomous car. The purpose of this study is to improve the operability of autonomous car during remote control. Driving experiments were conducted using a driving simulator while changing the in-vehicle camera placement. Based on the results of subjective evaluation by the experiment collaborators, we examined what kind of image presentation would improve the teleoperability of the system. As a result, camera placement patterns that improve remote operability could be identified according to driving conditions.
Evaluation System of Substation Based on AHP-EWM and Matter-element Extension Theory

As an important part of power transmission and conversion, it is important to establish a scientific and comprehensive evaluation system for substations. Firstly, a comprehensive evaluation system model of substation containing safety, energy efficiency and environmental protection indexes is established, then the index weights are determined based on AHP-EWM, and the material element topology method is introduced to rank the comprehensive level of substations in order of merits and demerits, and the actual substation is simulated and analyzed, and the results show that the comprehensive evaluation indexes and model of substation can reflect the comprehensive level of substation.
ONEWO: A Dataset of Anomaly and Object Detection for Community Surveillance

In the era of smart cities, the role of surveillance systems in ensuring community safety and environmental sustainability has become increasingly significant. Traditional surveillance methods, reliant on human monitoring, often exhibit deficiencies in terms of speed, accuracy, and scalability. However, to the best of our knowledge, there is no related dataset on this research field. This paper introduces a novel approach to address these challenges, leveraging the power of computer vision technology to enhance surveillance capabilities. To bridge the gap between algorithms research and application, in this paper, we propose the task and dataset, and highlight the necessity of camera grading. ONEWO comprises 18,307 images from 2,863 cameras, which covers 20 main objects of interest for city management, providing annotation of bounding boxes and segmentation masks of 121,342 instances. Experiments demonstrate that our best baseline model achieves mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> of 77.5 for the entire test set. This work aims at contributing to the development of more efficient, accurate, and scalable surveillance systems, thereby promoting community safety and environmental sustainability in smart cities.
THE RELATIONSHIP BETWEEN THE APPLICATION OF THE PROBLEM METHOD BASED LEARNING WITH PARTICIPANT LEARNING OUTCOMES COMPUTER COURSE AT SMART COLLEGE PADANG

This research was motivated by the low learning outcomes of computer course participants at LKP Smart College Padang. This is thought to be due to the poor application of problem based learning methods so that the learning outcomes of course participants are low. The research objectives are: 1) To find out the description of the application of the Problem Based Learning method to the learning outcomes of computer course participants at LKP Smart College Padang, 2) To find out the description of the learning outcomes of computer course participants at LKPSmart College Padang, 3) To find out the relationship between the application of the Problem Based Learning method to learning outcomes of computer course participants at LKP Smart College Padang.This type of research is correlational, with a quantitative approach. The population in this study was 30 people and a sample of 21 people whose sample was determined using the Simple Random Sampling method. Then a questionnaire is used to collect data. As well as data analysis techniques using percentage and rank order formulas. The results of the research show that: 1) The description of the application of the Problem Based Learning method among computer course participants at Smart College Padang is categorized as low; 2) A description of the learning outcomes of computer course participants at LKP Smart College Padang is categorized as low. 3) A description of the significant relationship between the application of the problem based learning method and the learning outcomes of computer course participants at LKP Smart College Padang.The research suggestions are: 1) It is hoped that instructors can innovate regarding learning methods so that they can become good and appropriate learning methods for course participants to learn. 2) It is hoped that course participants will be able to learn and adapt learning methods so that they can maximize learning outcomes. 3) It is hoped that future researchers will innovate and research new variables to add variables and complete them.Keywords: Problem Based Learning, Learning Outcomes.
Footprinting the Behaviour of Particle Swarm Optimization with Increasing Dimensionality

It is well documented that the performance of Particle Swarm optimization changes (deteriorates) with increasing dimensionality of the search space. It is less well documented that the operational behaviour of Particle Swarm optimization (PSO) can also change with increasing dimensionality. The current study documents these changes by using Self-Organizing Maps to “footprint” the operation of PSO. Increasing dimensionality produces key changes to the footprints in a multi-modal search space, but these changes do not occur in a unimodal search space. A deeper analysis is then conducted to connect the observed changes in footprints in multi-modal search spaces to changes in the operational behaviour of PSO caused by the effects of increasing dimensionality. The collected data indicate a correlation between the performance degradation of PSO and the decreased rates of success of exploratory moves, and this trend can be isolated from the effects of the exponentially increasing search space volumes that are produced in higher dimensions for continuous domain search spaces.
Towards a Disaster Preparedness School: Mapping of Disaster Mitigation Evacuation Routes at SMP Muhammadiyah Sewon Bantul, Special Region of Yogyakarta

Abstract. Indonesia is a country with a large population, with geographical conditions traversed by confluence of earth's plates that are actively moving, so that the area is prone to disasters. With these natural disasters, residents must be prepared for various kinds of disasters in order to save lives and minimize material losses and casualties. The level of preparedness for natural disasters should be instilled from the earliest possible age. One of the Muhammadiyah schools that is being reviewed in this service proposal is Muhammadiyah Middle School Sewon Bantul. This junior high school is located in Bandung Kulon, Pendowoharjo, Sewon. Bantul, Special Region of Yogyakarta. Looking historically, the Special Province of Yogyakarta is one of the areas in Indonesia which is prone to natural disasters such as earthquakes, exposure to volcanic ash from Mount Merapi, and cold lava. This school has general junior high school grade levels, namely grades seven, eight, and nine. From preliminary surveys and personal communications made to school administrators, it is known that there is no information and planning regarding natural disaster preparedness, and infrastructure related to evacuation routes and gathering points in the event of a natural disaster such as an earthquake. Departing from the problems found and described above, it is deemed necessary to carry out community service at the school in relation to mapping the existing conditions of spatial functions, planning evacuation routes and assembly points, as well as outreach regarding disaster preparedness. It is hoped that with this community service, students can better understand and be aware of the importance of being prepared for natural disasters such as earthquakes, not causing panic, and reducing the risks of losses that can result from natural disasters. In the long term, it is hoped that with optimal assistance, teachers can guide students so they can be more prepared for natural disasters such as earthquakes and furthermore, insert in the subjects taught to junior high school students related to natural disaster preparedness.
Frame-Level Smooth Motion Learning for Human Mesh Recovery

Reconstructing accurate and smooth 3D human mesh from a monocular video is still a challenge, due to the temporal consistency requirement of body movements. A frame-level smooth motion learning approach called SLMR model was proposed in this work. Specifically, we first design the temporal encoding with a multi-headed attention mechanism, which captures the global and local temporal context relations of motion to retain more dynamic motion features for improving mesh recovery accuracy. We also propose a probabilistic generative model consisting of a conditional variational autoencoder, which learns the distribution of pose changes in each frame, and solves temporal inconsistency of the body movement. Compared with existing approaches, SLMR can take full advantage of inter-frame motion contexts. Experiments validated the effectiveness and smoothness of the proposed approach for human mesh recovery in the wild.