Reviewerly/exHarmony huggingface dataset:
CAPEF: Context-Aware Policy Enforcement Framework for Android Applications

The notion of Context-Awareness of mobile applications is drawing more attention, where many applications need to adapt to physical environments of users and devices, such as location, time, connectivity, resources, etc.While these adaptive features can facilitate better communication and help users to access their information anywhere at any time, this however bring risks caused by the potential loss, misuse, or leak of users' confidential information.Therefore, a flexible policy-based access control system is needed to monitor critical functions executed by Android applications, especially, those requiring access to user's sensitive and crucial information.This paper introduces CAPEF, which is a policy specification framework that enforces context-aware inter-app security policies to mitigate privacy leakage across different Android applications.It also, provides an instrumentation framework to effectively enforce different behaviors based on automated context-aware policies to each Android application individually without modifying the underlying platform.Accordingly, the modified applications will be forced to communicate with our centralized policy engine to avoid any malware collusion that occur without the users' awareness.Experiments conducted on CAPEF shows an effective performance on the size of the enforced application after the instrumentation.The average size added was 705 bytes, which is about 0.063% of the size of the original applications, which is significantly small compared to other existing enforcement approaches.Also, we have denoted that the size and the execution time of the policy increases whenever the policies become more complex.
PELAYANAN DAN INFORMASI CUSTOMER SERVICE BERBASIS BOT TELEGRAM DENGAN ALGORITMA FORWARD CHAINING PADA CV.PRIMGUARD INDONESIA

CV Primguard Indonesia is a company engaged in the service of managing vehicle documents or documents. Information related to vehicle management is currently not optimal because it only uses customer service as the information provider, so that shorter information-related inquiries will take a lot of customer service time to explain to the customer. The purpose of this research is to provide convenience for customer service in providing an explanation of simple information that can be transferred by bot service. Meanwhile, customer service will explain information to customers if they need more detailed information. The method used is prototyping. The algorithm used is forward chaining so that it can be display the response according to the existing data. In obtaining data, using interviews, observation, and bibliography methods to produce more accurate data. The results of this research are automated bot-based information services using the telegram application. Telegram provides an API that can be used to develop bots. The bot is made using the PHP programming language, MySQL Server database, and Telegram Bot. The conclusion of this study is the use of telegram bot makes it easy for customers to get information quickly without the need to contact customer service directly if the information is only simple.
Continual relation extraction via linear mode connectivity and interval cross training

Continual relation extraction techniques aim to meet the requirements of real-world applications, in which new data and relations emerge constantly. To strike a balance between continual learning ability and computational cost, many previous works on replay-based methods emphasized on employing different strategies to use a smaller number of stored replay samples to maintain the continual ability of the model. However, catastrophic forgetting is prone to occur if a limited number of previously stored replay samples have been used. In this paper, we proposed a continual learning method which contains three learning phases, namely preliminary learning, memory retention, and memory reconsolidation. In more detail, the model is fine-tuned on the model of the last previous task firstly in the preliminary learning stage. During the memory retention stage, the method utilizes the characteristics of the model’s linear mode connectivity between the multi-task learning model and the continual learning model to retain the knowledge of previous tasks as much as possible. At the memory reconsolidation stage, it uses the previously stored samples as well as the ones of the current task for interval cross training to reconsolidate the learned knowledge. In order to evaluate the continual learning ability of the model learning by the proposed method, we tested it on three well-studied benchmarks, and the experimental results show that our algorithm performs better than the other compared algorithms in terms of the average accuracy as well as overall accuracy.
Self-Care Behavior of Type 2 Diabetes Patients with Symptoms of Peripheral Neuropathy during the Covid-19 Pandemic: A Qualitative Study

Purpose: This study aims to explore the experiences and perceptions of type 2 diabetes patients with symptoms of Diabetic Peripheral Neuropathy (DPN) in managing their self-care behavior during the COVID-19 pandemic. Methods: Qualitative research with a descriptive phenomenological approach reveals diabetes self-care behavior during the COVID-19 pandemic. Individual interviews were conducted at the participants’ homes with a semi-structured interview guide on twelve type 2 diabetes patients. All interviews were transcribed verbatim and analyzed using thematic content analysis. Results: A total of five themes were identified from the interview analysis, namely: 1) Perception of diabetes self-management; 2) Efforts to control blood sugar; 3) Barriers to blood sugar control; 4) Perceived disturbance; 5) Social support. Since its emergence, the Covid-19 pandemic has become a new obstacle in controlling blood glucose in diabetic patients. In addition to the covid-19 pandemic, negative spiritual coping, low self-motivation, and perceived physical effects are inhibiting factors for blood glucose control in type 2 diabetes patients with symptoms of DPN. Conclusions: Obstacles in efforts to control blood glucose in type 2 diabetes patients with symptoms of DPN include physical, psychological, social, and spiritual aspects. The suggestion in this study is that during the COVID-19 pandemic, it is necessary to adjust and modify diabetes self-management for patients with DPN symptoms. The interventions provided should not only focus on the physical aspect but also need holistic attention to the psychosocial and spiritual aspects simultaneously by involving family participation in the management of the patient’s diabetes.
Two-level quantum walkers on directed graphs. II. Application to quantum random access memory

This is the second paper in a series of two. Using a multiparticle continuous-time quantum walk with two internal states, which was formulated in the first paper [R. Asaka et al., preceding paper, Phys. Rev. A 107, 022415 (2023)], we physically implement a quantum random access memory (QRAM). Data with address information are dual-rail encoded into quantum walkers. The walkers pass through perfect binary trees to access the designated memory cells and copy the data stored in the cells. A roundabout gate allocated at each node serves as a router to move the walker from the parent node to one of two child nodes, depending on the internal state of the walker. In this process, the address information is sequentially encoded into the internal states so that the walkers are adequately delivered to the target cells. The present QRAM, which processes ${2}^{n}\phantom{\rule{4pt}{0ex}}m$-qubit data, is implemented in a quantum circuit of depth $O(nlog(n+m))$ and requires $O(n+m)$ qubit resources. This is more efficient than the conventional bucket-brigade QRAM that requires $O({n}^{2}+nm)$ steps and $O({2}^{n}+m)$ qubit resources for processing. Moreover, since the walkers are not entangled with any device on the binary trees, the cost of maintaining coherence can be reduced. Notably, by simply passing quantum walkers through binary trees, data can be automatically extracted in a quantum superposition state. In other words, any time-dependent control is not required.
Impact of Anti-Fraud Awareness, Fraud Detection Procedures, and Technology to Fraud Detection Skill

Public complaints regarding indications of corruption in government institutions have increased.These complaints responded by increasing fraud red flag knowledge.Hopefully, understanding procedures and technology to trace fraud and growing skepticism in internal auditors will enhance fraud detection skills.This study aims to determine the factors influencing fraud detection skills, anti-fraud awareness, fraud detection procedures, and technology with professional skepticism as moderating variables.This study uses questionnaires distributed to the structural ranks, Head of Balai Besar, head of BMKG Station, head of division/field, sub-section/sub-sector, and BMKG internal auditors.The results showed that anti-fraud awareness, detection procedures and technology, and fraud prevention positively affected fraud detection skills.Professional skepticism is proven only to strengthen the influence of fraud detection and prevention technology on fraud detection skills.This research was used as a reference to identify factors affecting the fraud detection skills of internal auditors and leaders in the future.This research also serves as a reference a better anti-fraud awareness culture policy, improve fraud detection skills by updating procedures and technology used in the Meteorology, Climatology and Geophysics Agency environment (BMKG).
"Applying Graph Theory and Multi-Variables G-Function to Solved the Problem Related to Cooling a Sphere "

We have observed that the separation of variables method and graph theory in mathematics can be applied to several types of problems on bounded three-dimensional domains.The problem must be linear and have homogeneous boundary-conditions.In this paper, we present the result of classical problem in three dimensions by applying modified integral form, of Newton's law of cooling for sphere, which is encountered in ordinary differential equations texts.Also show that, the rate at which a body cools is proportional to the difference of its temperature and the temperature of the environment.The assumed symmetries in the problem will permit us to reduce the dimension of the problem to spatial dimension and time with the help of Bipartite Graphs in Graph theory.
Emotion Recognition Based on the Structure of Narratives

One important application of natural language processing (NLP) is the recognition of emotions in text. Most current emotion analyzers use a set of linguistic features such as emotion lexicons, n-grams, word embeddings, and emoticons. This study proposes a new strategy to perform emotion recognition, which is based on the homologous structure of emotions and narratives. It is argued that emotions and narratives share both a goal-based structure and an evaluation structure. The new strategy was tested in an empirical study with 117 participants who recounted two narratives about their past emotional experiences, including one positive and one negative episode. Immediately after narrating each episode, the participants reported their current affective state using the Affect Grid. The goal-based structure and evaluation structure of the narratives were analyzed with a hybrid method. First, a linguistic analysis of the texts was carried out, including tokenization, lemmatization, part-of-speech tagging, and morphological analysis. Second, an extensive set of rule-based algorithms was used to analyze the goal-based structure of, and evaluations in, the narratives. Third, the output was fed into machine learning classifiers of narrative structural features that previously proved to be effective predictors of the narrator’s current affective state. This hybrid procedure yielded a high average F1 score (0.72). The results are discussed in terms of the benefits of employing narrative structure analysis in NLP-based emotion recognition.
Efficient image compression method using image super-resolution residual learning network

With the rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1).
BlindSpotNet: Seeing Where We Cannot See

We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual driver or a self-driving system to potential causes of accidents (e.g., draw attention to a road region from which a child may spring out). Detecting blind spots in full 3D would be challenging, as 3D reasoning on the fly even if the car is equipped with LiDAR would be prohibitively expensive and error prone. We instead propose to learn to estimate blind spots in 2D, just from a monocular camera. We achieve this in two steps. We first introduce an automatic method for generating "ground-truth" blind spot training data for arbitrary driving videos by leveraging monocular depth estimation, semantic segmentation, and SLAM. The key idea is to reason in 3D but from 2D images by defining blind spots as those road regions that are currently invisible but become visible in the near future. We construct a large-scale dataset with this automatic offline blind spot estimation, which we refer to as Road Blind Spot (RBS) dataset. Next, we introduce BlindSpotNet (BSN), a simple network that fully leverages this dataset for fully automatic estimation of frame-wise blind spot probability maps for arbitrary driving videos. Extensive experimental results demonstrate the validity of our RBS Dataset and the effectiveness of our BSN.
Hemisphere Tabulation Method: An Ingenious Approach for Pose Evaluation of Instruments Using the Electromagnetic-Based Stereo Imaging Method

Drilling of a bone surface often occurs in clinical orthopaedic surgery. The position and orientation of the instrument are the most important factors in this process. Theoretically, some mechanical components may assist in orienting an instrument to certain bone shapes, such as the knee joint and caput femoris. However, the mechanical assisting component does not seem to work in some confined spaces where the bone shape is a free-form surface. In this paper, we propose an ingenious hemisphere tabulation method (HTM) for assessing the pose accuracy of an instrument. The acquisition and assessment of HTM is conducted based on an electromagnetic-based stereo imaging method using a custom-made optical measurement unit, and the operation steps of HTM are described in detail. Experimental results based on 50 tests show that the HTM can identify ideal poses and the evaluated pose of an instrument location on a hemisphere model. The mean error of pose localisation is 7.24 deg, with a range of 1.35 to 15.84 and a standard of 3.66 deg, which is more accurate than our previous method.
An Overview of International Research on Language Attrition

This study used co-citation analysis in CiteSpace to identify trends and patterns in international research on language attrition. The analysis was based on journal articles from the Web of Science Core Collection, covering 2010 to 2020. The research showed that international studies on language attrition increased significantly over the past decade. Additionally, the citation frequency of these studies has also grown substantially. The research also showed that international language attrition studies have primarily been distributed across multidisciplinary fields such as linguistics, psychology, and education. These studies also seem to be merging with disciplines like pathology and neurology. Another research finding was that topics related to language attrition covered four main areas: first language attrition, foreign language attrition, the factors influencing language attrition, and the linguistic areas impacted by language attrition. Additionally, this study evaluates the research trend of language attrition by analyzing burst keywords. The research concludes with a summary of current international studies on language attrition and offers insights into prospects in this field.
Research on methods of personalized English teaching promoted by Big Data teaching platform

In the reform and development of modern education, the integration and use of big data education platform to build and promote personalized English teaching system can not only reflect the teaching concept of individualized teaching, but also fully meet the learning needs of different groups of students at different stages. On the basis of understanding the development of big data technology and the research status of personalized English teaching, this paper uses fuzzy control centralized algorithm to improve the recommendation mechanism of English teaching system, so that students can choose suitable professional teachers according to their own needs. At the same time, after drawing students' learning portraits, the portrait technology is used to evaluate students' passing rate. This provides an effective basis for the practice of personalized English teaching guidance. The final experimental results prove that the design of the whole system meets the personalized needs of the student users, and in the future technology research and development, we should pay attention to the design of functional services from the perspective of professional teachers.
The Quantum-House Effect and Its Demonstration on SpinQ Gemini

We introduce the quantum-house effect, a non-local quantum phenomenon which goes against classical intuition. We show how the effect can be achieved with any bipartite quantum state where neither subsystem is in a pure state. Besides its theoretical description, the quantum-house effect is also demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.
Design and Implementation of the Development of a Corpus-based Loose-leaf Textbook for Higher Vocational English Learners: Using the Cross-border E-Commerce Operations English as an Example

Current higher vocational English textbooks have limitations in providing timely work-related information, representing the nature of vocational education, preserving authenticity of text materials, and using technology to enhance textbook interactivity. To address the limitations of current higher vocational English textbooks, this study adopts corpus-based approaches to developing a loose-leaf textbook for higher vocational English, a new form of textbook that delivers timely, authentic, job-oriented content enhanced by digitalization. Upon an on-the-job investigation, this study builds CECLOTED, a corpus congruent with the typical job tasks for cross-border E-commerce operations. Based on corpus data analysis upon metrics of frequency, keyness, dispersion, collocation and colligation, this study strikes a balance between authentic text feature and linguistic complexity appropriate for higher vocational English learners. This study also develops an online learning platform coherent with the printed textbook with B/S architecture so as to present information in a ubiquitous, dynamic and interactive manner. Compared with the conventional textbook, the loose-leaf textbook which comprises learning texts, vocabulary lists, learning tasks and an online learning platform is more conducive to enhancing language proficiency and job-related skills.
Word Usage Change and the Pandemic: A Computational Analysis of Short-Term Usage Change in the Italian Reddit Community

The COVID-19 pandemic has affected every aspect of our lives.Our work assesses whether it has also impacted the usage of the Italian language, particularly its lexicon.We create a new corpus of Italian texts taken from Reddit and apply a recent unsupervised usage change detection method on two sub-corpora, one with data from 2019 and one with data from 2020.The focus of our investigation is short-term usage change.The results for the first 10-top candidates and for a selection of candidates among the top-100 are analyzed, to show that usage change has indeed happened.
Policies for Blockchain Adoption in Education

The Encyclopedia of Services is a ground-breaking resource that offers a unique overview of what constitutes the main source of wealth and employment in our contemporary economies, namely services. This title contains one or more Open Access chapters.
A modified asynchronous sequential Kalman track fusion based on sound propagation time

Abstract This paper presents a sequential Kalman asynchronous track fusion algorithm based on the effective sound velocity method proposed to deal with the problem of random asynchrony of multi-node measurement information in the distributed underwater multi-target detection system due to the propagation effect of the sound channel. This algorithm updates the time stamp of target information reported by each sensor by using the effective sound velocity method, so as to obtain the actual time when the target state information appears in the underwater acoustic channel. Then, according to the actual asynchronous situation of the multi-sensor, it uses the sequential filter algorithm to fuse the asynchronous sensors. The simulation results show that the algorithm can improve the positioning accuracy of the original algorithm, has a strong adaptability to sensor target loss and accuracy loss, and has a certain application value.