Reviewerly/exHarmony huggingface dataset:
Context-Aware Model for Mining User Intentions from App Reviews

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users.To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective.Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost.In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically.We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism.The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall, and F1-score evaluation metrics, achieving state-of-the-art performance in this task.Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.
Increasing Representative Ability for Topic Representation

As for standard topic model, such as LDA (Latent Dirichlet Allocation), each topic is generally depicted by a weighted word set, where the high-ranked words are deemed more representative.Meanwhile, the probability of each word is considered as the ability to represent the semantic contribution for the topic.However, few efforts are focused on enhancing the representative ability of the topic to support fine grained topic representation.In this paper, we propose a Word Topic Ware (WTW) model to take word inherent diversity characteristic into consideration, in order to screen out and enhance the more representative words for topic representation.Experimental results on three large datasets show that our proposed method can increase the representative ability for topic representation.In addition, our work will positively affect improving the quality of topic content analysis.
Research on Identification and Refactoring Approach of Event-driven Architecture Based on Ontology

Event-driven architecture is one of the common software architecture patterns.In the process of software evolution, the deviation and corrosion often occur to architecture, which leads to larger deviation between actual software architecture and design architecture.Therefore, it is of great significance to study the approach of software architecture identification and refactoring.To solve this problem, we propose an identification and refactoring approach of event-driven based on ontology, i.e., IRABO.We evaluated IRABO on 50 open-source projects and the results show that it performs effectively and efficiently.
Boosting aspect category detection with inference heuristics and knowledge enhancement

Aspect category detection (ACD) aims to identify the aspect categories from reviewers’ expressed opinions in a given sentence, where one or multiple predefined aspect categories are mentioned explicitly or implicitly. With the boom of the pretrained language model, related studies have achieved significant improvements in ACD. However, the studies usually follow the canonical method of fine-tuning and neglect deeply mining internal knowledge or incorporating external knowledge, which could lead to suboptimal results. To address this issue, we propose a novel multilevel knowledge-aware ACD model by innovatively converting ACD to a binary sentence-pair classification task from the viewpoint of natural language inference, which is effective and consists of four key components. The model first expands the predefined aspect categories by introducing terms with high semantic similarity scores from commonsense knowledge bases. Next, the model generates synthetic premise-hypothesis sentence pairs based on the aspect categories and an inference heuristic template. Then, the training data are effectively augmented and used for fine-tuning the proposed model. Moreover, the model designs a pooling strategy to mine the rich syntactic and semantic knowledge encoded in the internal layers of BERT. Finally, the pooled low-dimensional representation is fed to a linear classifier to detect aspect categories. Experimental results on the SemEval-2014 and SemEval-2016 benchmark datasets achieve F1-scores of 92.75% and 83.58%, respectively, which demonstrate the superiority of our proposed model compared with some strong baselines.
R<sup>2</sup> P<sup>3</sup>: Renewal-Reward Process-Based Password Protection in Internet of Things

Given the eruption of Internet of Things (IoT) systems, it has become critical for connected devices to have persistent security. Password-based security measures provide an interface for users to effectively secure their personal data. Nonetheless, passwords are often stolen by cyber criminals due to inferior user security awareness. To address these issues, in this paper, we develop a renewal-reward process-based password protection <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(R^{2}P^{3})$</tex> scheme to prevent unauthorized users from breaching IoT devices even after they obtain user passwords. Specifically, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}P^{3}$</tex> utilizes the renewal-reward process to accu-mulate a tendency to trust <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(T^{3})$</tex> score for any <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{IoT}$</tex> device password attempt on the basis of keystroke dynamics (i.e., dwell time and flight time) and assures device authentication by comparing user attempt <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T^{3}$</tex> scores to a predefined threshold. We have validated the security efficacy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}P^{3}$</tex> . Our experimental results demonstrate that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}F^{3}$</tex> achieves superior security performance in terms of unauthorized user blocking ratio.
Evaluation of Spiral Pattern Watermarking Scheme for Common Attacks to Social Media Images

The 21st century might be considered the "boom" period for social networking due to the fast expansion of social media use.In terms of user privacy and security regulations, a plethora of new requirements, issues, and concerns have arisen due to the proliferation of social media.With the increase in social media use, images on social media are often modified or fabricated for certain purposes.Therefore, this work implements and evaluates the SPIRAL-LSB algorithm for common attacks for social media images.Image compression was also discussed as images published to social media platforms was often compressed.An analysis was performed to assess the algorithm's output on social media images.The experiments were carried out prior to and after uploading to the Instagram platform.The dataset was subjected to image splicing, copy-move, cut-andpaste, text insertion, and 3D-sticker insertion attacks.The outcome of SPIRAL-LSB was effective for text insertion attacks solely.Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were selected as the experiment's metrics.The average PSNR value is 63.25, and the SSIM value is 0.99964, both of which are regarded high.This indicates that the watermark has not degraded the quality of the images.This work was designed for usage on social media for intellectual property reasons and may be used to validate the validity of social media images and prevent issues with image integrity, such as image manipulation.
El placer de ser docente: notas para repensar la educación en contingencia

This article proposes reflections on the experiences, situations, events that evoke pleasure and bring joy to the teaching profession in order to empower them and get out of the logic of the deficit that has prevailed as a result of the temporary closure of schools caused by the COVID-19.&nbsp; In the first place, theoretical considerations on the teaching profession and pleasure are presented;&nbsp; the perspective adopted highlights that the affects and emotions in their political dimension impact on the collective experience.&nbsp; Secondly, the analysis of the teachers' testimonies obtained from the application of an online consultation is presented and finally, the relevance of the material dimension of the school and the daily life is highlighted, as well as the role of the presence and the suspended time offered by the school for the construction of the scenes of joy of the teaching profession
Al Engine Structures in LG TV Processor

Recently, artificial intelligence(AI) functions have been rapidly adopted to TV systems. We have developed two different kinds of AI picture processing engine structures. The first one is an optimized structure for pixel level processing, which requires large logic area in chip for highly complicated computations on huge amount of data. Specifically, the activation data can be transferred from the previous layer to the next layer without external data movement, which only requires minimum internal memory transactions. The second one is a more general structure which can be used for various applications. Most deep learning engines show lower MAC utilization ratio than expected, which may result from excessive external memory transactions. For high utilization, we adopted memory bandwidth reduction methods such as weight clustering, layer fusion, and multiple layer fusion. Our convolution engine with 8-bit integer precision also reduced both memory bandwidth and power consumption. In the result, LG TV processor can conduct both pixel level processing and recognition in a very efficient way and shows astonishing image quality.
Semantic-Driven Efficient Service Network Towards Smart Healthcare System in Intelligent Fabric

The advantages of intelligent fabrics enable health service systems more efficient and safe to perceive and transmit, where massive sensitive data related to health and behavior are collected through the multi-source flexible sensor network. However, due to its complex network structure, health service systems face the challenge of low-power data transmission and processing in intelligent fabric space. Recently, Deep learning-powered semantic communication (DeepSC) has emerged as a promising communication paradigm. To tackle the above problem, we introduce the concept of semantic cognition into the cyber-physical system of health service and present the DeepSC based network service framework for the healthcare system, where physical layer blocks are merged into the traditional communication system to jointly optimize the transceiver in communication. Since semantic encoding and decoding require additional knowledge, consider the introduction of the semantic knowledge base in the system to formulate the joint optimization problem of service offloading and bandwidth allocation in the proposed network service framework, with the goals of reliable semantic communication and efficient network transmission. Furthermore, an online algorithm that optimally adapts service offloading and subcarrier allocation decisions to the time-varying channel conditions is required. Finally, we use a reinforcement learning-based decision algorithm to solve combinatorial optimization problems, which further reduces the computing complexity. Experimental results show that the proposed framework achieves significant performance improvements in system energy consumption and reliable transmission, compared with traditional communication strategies.
Key-Policy ABE with Switchable Attributes

This paper revisits Key-Policy Attribute-Based Encryption (KP-ABE), allowing delegation of keys, traceability of compromised keys, and key anonymity, as additional properties. Whereas delegation of rights has been addressed in the seminal paper by Goyal et al. in 2006, introducing KP-ABE, this feature has almost been neglected in all subsequent works in favor of better security levels. However, in multi-device scenarios, this is quite important to allow users to independently authorize their own devices, and thus to delegate their initial rights with possibly more restrictions to their everyday-use devices. But then, one may also require tracing capabilities in case of corrupted devices and anonymity for the users and their devices. To this aim, we define a new variant of KP-ABE including delegation, with switchable attributes, in both the ciphertexts and the keys, and new indistinguishability properties. We then provide a concrete and efficient instantiation with adaptive security under the sole SXDH assumption in the standard model. We eventually explain how this new primitive can address all our initial goals.
THE ANOMALY DETECTION EFFICIENCY OF KERNEL DENSITY ESTIMATION FUNCTIONS ON UAV IMAGES

The anomalous pixel detection efficiency of algorithms on UAV images is represented by the two criteria: anomalous pixel detection effect (which uses the area under ROC curve for evaluation) and calculation time. A highly effective researcher-recommended technique for anomaly detection on UAV images is to apply Neyman-Pearson lemma by calculating Kernel Density Estimation (KDE) for background data and making decision therefrom. In this method, the selection of kernel function and bandwidth plays the determinant role in anomaly detection efficiency. However, there has not been any research that mentions this issue to date. Hereby, in this study, we evaluate anomaly detection efficiency on UAV images through a number of common kernel functions typically cited in researches on KDE, and follow up with making recommendations for appropriate uses of kernel functions. Experiments and evaluations are carried out on the sample data set photographed on varied terrain types and objects of interest.
Safe Design of Stable Neural Networks for Fault Detection in Small UAVs

Stability of a machine learning model is the extent to which a model can continue to operate correctly despite small perturbations in its inputs. A formal method to measure stability is the Lipschitz constant of the model which allows to evaluate how small perturbations in the inputs impact the output variations. Variations in the outputs may lead to high errors for regression tasks or unintended changes in the classes for classification tasks. Verification of the stability of ML models is crucial in many industrial domains such as aeronautics, space, automotive etc. It has been recognized that data-driven models are intrinsically extremely sensitive to small perturbation of the inputs. Therefore, the need to design methods for verifying the stability of ML models is of importance for manufacturers developing safety critical products. In this work, we focus on Small Unmanned Aerial Vehicles (UAVs) which are in the frontage of new technology solutions for intelligent systems. However, real-time fault detection/diagnosis in such UAVs remains a challenge from data collection to prediction tasks. This work presents application of neural networks to detect in real-time elevon positioning faults. We show the efficiency of a formal method based on the Lipschitz constant for quantifying the stability of neural network models. We also present how this method can be coupled with spectral normalization constraints at the design phase to control the internal parameters of the model and make it more stable while keeping a high level of performance (accuracy-stability trade-off).
Application of the PBL Model with Exe Learning Media on Student's Motivation and Learning Outcomes

This study aims to apply the exe learning computer media program with the PBL model as an effort to increase motivation and improve student learning outcomes.The subjects in this study were class XI students of SMA Negeri 1 Garawangi, namely class A consisting of 32 students and class B consisting of 31 students.The research method was carried out using an experimental method, with a research design of pretstposttest control group design.Hypothesis testing of learning motivation data and learning outcomes using Independent sample t-test.Based on the results of the study, it was concluded that the ability of students to use problem-based learning strategies integrated with the exe learning computer media program got higher motivation and learning outcomes.In practice, exe learning can present more interesting material, both audio and visual, according to the needs of students in the digital era.Based on the results of the study, that the increase in learning outcomes and student motivation with the application of the PBL model using the exe learning media was higher than the increase in the learning outcomes taught by the application of the PBL model without the exe learning media.This finding is relevant to the development of the industrial revolution 4.0 that in the world of education it is necessary to apply learning media that make it easier for teachers to deliver subject matter to technology-based students, in the world of education we usually know e-learning.Therefore, it is necessary to implement the use of other technology-based learning media in supporting a more varied and innovative learning process.
multiple secret image embedding in dynamic ROI keypoints based on hybrid Speeded Up Scale Invariant Robust Features (h-SUSIRF) algorithm

This paper presents a robust and high-capacity video steganography framework using a hybrid Speeded Up Scale Invariant Robust Features (h-SUSIRF) keypoints detection algorithm. There are two main objectives in this method: (1) determining the dynamic Region of Interest (ROI) keypoints in video scenes and (2) embedding the appropriate secret data into the identified regions. In this work, the h-SUSIRF keypoints detection scheme is proposed to find keypoints within the scenes. These identified keypoints are dilated to form the dynamic ROI keypoints. Finally, the secret images are embedded into the dynamic ROI keypoints’ locations of the scenes using the substitution method. The performance of the proposed method (PM) is evaluated using standard metrics Structural Similarity Index Measure (SSIM), Capacity (Cp), and Bit Error Rate (BER). The standard of the video is ensured by Video Quality Measure (VQM). To examine the efficacy of the PM some recent steganalysis schemes are applied to calculate the detection ratio and the Receiver Operating Characteristics (ROC) curve is analyzed. From the experimental analysis, it is deduced that the PM surpasses the contemporary methods by achieving significant results in terms of imperceptibility, capacity, robustness with lower computational complexity.
Meningkatan Minat Belajar Melalui Model Reciprocal Teaching pada Murid Kelas V SD Inpres Antang 1 Kota Makassar

This study examines Increasing Interest in Learning through the Reciprocal Teaching Model in Class V Students of SD Inpres Antang 1 Kota Makassar. The main problems in the research are; How to increase interest in learning through the Reciprocal Teaching model in fifth grade students at SD Inpres Antang 1, Makassar Regency. The main objective of this research is to increase students' interest in learning and apply the Reciprocal Teaching model to increase learning interest in fifth grade students of SD Inpres Antang 1, Makassar Regency. This research uses classroom action research. The research subjects were fifth grade students at SD Inpres Antang 1 which consisted of 10 boys and 14 girls. The data collection procedure used observation, student learning interest questionnaires, and documentation. Data analysis used descriptive analysis. The results showed that the student's interest in learning in the first cycle with the percentage of classical student interest in learning was 58.3% and there was an increase in learning interest in the second cycle with the percentage of classical student interest in learning 83.3%, so there was an increase in student interest in learning from cycle I to cycle II.
Computation community cloud on low power devices

This paper presents a model of distributed, community elastic computation cloud for running containerized applications on low power devices. Increasingly more applications use cloud computation they are even called cloud-native. However, only big cloud providers offer computational resources to the client applications. The major cloud providers use big centralized datacenters with huge power consumption and carbon footprint. Big tech companies hold a monopoly on data and resources. Many people and organizations do not want to be dependent on giant tech companies. Our solution offers a model for building a distributed community-driven cloud for running containerized applications on low power devices. Our solutions will allow ordinary people and communities to build low cost distributed clouds. It is a step in a direction that offers alternative and freedom from the big tech companies and has a small carbon footprint.
Pemerolehan bahasa pada anak usia pascapubertas berkepribadian introvert di SMP-IT

The title of this research is Language Acquisition in Postpubertal Age Children in Class IX. A SMP-IT Bunayya, Padangsidimpuan City. The purpose of the study is to obtain the results of the calculation of the Mean Length of Utterances (MLU) or the average length of utterances produced by postpubertal students with introverted personalities in writing. Explaining sentences produced by postpubertal students with introverted personalities in terms of simplicity and completeness of the basic sentences they have. Methods and Techniques used in this research: Listen (Sudaryanto 1993): SLC, SBLC, record, note, fishing, psychoneurolinguistic methods according to Nunan as well as formal and informal. The expected result of this research is that it can be used as a tool to find out how language is able to represent the personality possessed by humans. This research is also expected to be able to contribute to language teaching to postpubertal students with introverted personalities. It is hoped that this research will increase the insight of educators and the general public, especially about the language attitudes of post-pubertal students with introverted personalities and introverted groups in general.
Multi-source scheduling method on supply side of microgrid based on reinforcement learning algorithm

In order to improve the convergence and economy of resource scheduling on the supply side of microgrid, a multi-source scheduling method on the supply side of microgrid based on reinforcement learning algorithm is proposed. The structure of microgrid is analyzed, and the principle of reinforcement learning algorithm is given. Based on this principle, the objective function of multi-source optimal scheduling on the supply side of microgrid is designed, that is, the total operation cost of the system is the lowest, and the corresponding constraints are proposed, including the system power balance constraints and the operation constraints of energy storage battery. Based on the above established objective functions and constraints, a multi-source scheduling model on the supply side of the microgrid is constructed to maximize the operation income of the microgrid. The experimental results show that the convergence speed of this method is faster and the total operation cost of microgrid is reduced, which verifies its application value.