Reviewerly/exHarmony huggingface dataset:
Philosophy in a technical university: development strategies in the digital age

The relevance of research. The development of information technologies has led to a radical pace of transformation of the technological and, as a result, the socio-economic infrastructure of modern society. In these conditions, the question of a model of higher engineering education becomes especially relevant, which makes it possible to respond to new challenges associated with unpredictability in the field of employment, the formation of new information habits, and the environmental crisis. The modern professional must possess a wide range of knowledge, skills and abilities, including, but not limited to, competencies of an ethical and aesthetic nature. Purpose of the study. Revealing the role of the humanitarian component, and, first of all, the philosophy course, in the professional education of students in technical areas of training. Research methodology. The research was carried out using the methods of critical and comparative historical analysis, axiological and hermeneutic methods based on relevant foreign and domestic scientific literature. Results and discussion. It is demonstrated that a modern philosophy course should be focused not so much on the assimilation of knowledge, but on the development of the student's personality. A three-part model of the course content is proposed, implying the traditional level of knowledge of historical and philosophical material, the level of development of skills and competencies of critical thinking, argumentation and discussion, as well as the level of skills of personal development and self-care. For the content saturation of the third level, it is proposed to turn to modern philosophical and aesthetic concepts and practices, such as the somaesthetics of R. Shusterman and the everyday aesthetics of Yu. Saito. As a result, the growing importance of humanitarian disciplines in education for non-humanitarian specialties has been explicated.
Daily motionless activities: A dataset with accelerometer, magnetometer, gyroscope, environment, and GPS data

Abstract The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including the pants pockets, in a wristband, over the bedside table, on a table, inside the car, or on other furniture, for the acquisition of accelerometer, magnetometer, gyroscope, GPS, and microphone data. The data was collected by 25 individuals (15 men and 10 women) in different environments in Covilhã and Fundão municipalities (Portugal). The dataset includes the sensors’ captures related to a minimum of 2000 captures for each motionless activity, which corresponds to 2.8 h (approximately) for each one. This dataset includes 8.4 h (approximately) of captures for further analysis with data processing techniques, and machine learning methods. It will be useful for the complementary creation of a robust method for the identification of these type of activities.
Beauty and truth, truth and beauty: Chiastic structure increases the subjective accuracy of statements.

The Keats heuristic suggests that people find esthetically pleasing expressions more accurate than mundane expressions. We test this notion with chiastic statements. Chiasmus is a stylistic phenomenon in which at least two linguistic constituents are repeated in reverse order, conventionally represented by the formula A-B-B-A. Our study focuses on the specific form of chiasmus known as antimetabole, in which the reverse-repeated constituents are words (e.g., All for one and one for all; A = all, B = one). In three out of four experiments (N = 797), we find evidence that people judge antimetabolic statements (e.g., Success is getting what you want. Happiness is wanting what you get.) as more accurate than semantically equivalent nonantimetabolic statements (e.g., Success is getting what you wish. Happiness is wanting what you receive.). Furthermore, we evaluate fluency as a potential mechanism explaining the observed accuracy benefit afforded to antimetabolic statements, finding that the increased speed (i.e., fluency) with which antimetabolic statements were processed predicted judgments of accuracy. Overall, the present work is consistent with the growing literature on stylistic factors biasing assessments of truth, using the distinctive stylistic pattern of antimetabole. We find that information communicated using an antimetabolic structure is judged to be more accurate than nonantimetabolic paraphrases. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
An English Translation Quality Evaluation Model Integrating Knowledge Transfer and Wireless Network

Machine translation is to automatically translate one natural language text into another. It is one of the important applications of natural language processing. The research of machine translation is closely related to machine translation evaluation technology. Therefore, with the progress of machine translation technology in recent years, researchers are also committed to designing more reasonable evaluation methods. Manual evaluation is costly, time-consuming, and subjective, so it has been difficult to provide practical help for the development of machine translation systems. Finding a feasible automatic machine translation evaluation method has been a hot spot in machine translation evaluation research in recent years. Based on this, this paper proposes an English translation quality evaluation model integrating knowledge transfer and wireless network. Firstly, wireless network technology is used to coordinate multitasks, and then knowledge transfer optimization technology is used to construct an English translation quality evaluation model. In this paper, simulation experiments are designed to verify the effectiveness of the model. The experimental results show that the accuracy of the evaluation method based on fusion technology is significantly improved compared with the existing evaluation methods.
Classification of Paddy Growth Phase with Machine Learning Algorithms to Handle Imbalanced Multi-Class Big Data

The global Sustainable Development Goals (SDGs) adopted by countries in the world have significant implications for national development planning in Indonesia in the period 2015 to 2030. The Agricultural sector is one of the most important sectors in the world and has a very important contribution to achieving the goals. Availability of accurate paddy production data must be available to measure the level of food security. This can be done by monitoring the growth phase of paddy and predicting the classification of its growth phase accurately and precisely. The paddy growth phase has 6 classes with the number of class members usually not the same (imbalanced data). This study describes the results of the classification of paddy growth phases with imbalanced data in Bojonegoro Regency, East Java in 2019 using machine learning algorithms on the Google Earth Engine (GEE) platform. Classification is done by Classification and Regression Tree, Support Vector Machine, and Random Forest. Oversampling technique is used to deal the problem of imbalanced data. The Area Sampling Frame survey in 2019 conducted by BPS was used as a label for classification model training. The results showed that the overall accuracy (OA) using the Random Forest algorithm by modifying the dataset using oversampling was 82.30% and the kappa statistic was 0.76, outperforming the SVM and CART algorithms.
A Reflection on Corporate Social Responsibility in Africa contrasted with the UAE and some Asian Nations

Corporate social responsibility (CSR) is still a controversial theme in discussions about companies and what they do to benefit communities in which they operate and society at large, and many argue that it does not permit the maximisation shareholder value. CSR is nonetheless a significant matter to consider in any business conduct. A plethora of research proves that socially responsible companies benefit both themselves and society. A key benefit that emanates from CSR is ab enhanced relationship between a company and the community in which it operates. CSR must be leveraged by organisations who are committed to society and stakeholders. Only in this way can sustainable development be realised, and this must be done in a transparent and ethical fashion so that maximising profits do not remain the key drivers of CSR which is a mere façade as noted. Corporate social responsibility offers numerous advantages and strategic benefits to companies that adopt it and do so in a serious manner. This study aims to fill that gap in knowledge by providing a different perspective to the discussion on CSR in South Africa, using insights garnered from the UAE approach and that of some other nations. Keywords: Societal support, organisations, good governance, ethical practice, regulations.
A Large-scale TV Dataset for Partial Video Copy Detection

This paper is interested with the performance evaluation of the partial video copy detection. Several public datasets exist designed from web videos. The detection problem is inherent to the continuous video broadcasting. The alternative is then to process with TV datasets offering a deeper scalability and a control of degradations for a fine performance evaluation. We propose in this paper a TV dataset called STVD. It is designed with a protocol ensuring a scalable capture and robust groundtruthing. STVD is the largest public dataset on the task with a near 83k videos having a total duration of 10,660 h. Performance evaluation results of representative methods on the dataset are reported in the paper for a baseline comparison.
IS THE CRYPTOCURRENCY POLICY UNCERTAINTY A DETERMINANT OF BITCOIN’S PRICE?

This study attempts to answer the question: "Is the Cryptocurrency Policy Uncertainty (UCRY Policy) a determinant of the Bitcoin's price (BTC)?".Besides, this study uses these factors as explanatory variables for the BTC movements alongside the UCRY Policy and control variables such as the velocity of the Bitcoin in circulation (BC), the computational power of Bitcoin (HR), popularity (PO), and exchange rate (EX).In the study, December 30, 2013-February 21, 2021, was determined as the term and weekly data were investigated.The ARDL bounds testing method was used to determine the relationship between the variables.According to empirical findings, this study suggests that the UCRY Policy is essential to the BTC.There is a negative relationship between UCRY Policy and BTC.When UCRY Policy increases, BTC decreases, holding other variables constant.Besides, this study shows that control variables can be used as determinants of BTC.In long run, BC and HR have a significant, positive relationship with BTC.The EX has a significant, negative relationship with BTC.The PO has a significant, positive relationship with BTC in the short run.In addition, this study demonstrates that UCRY Policy can be used as a type of uncertainty index for Bitcoin.
A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.
Patient Acceptability of a Novel Technological Solution (Ambient Intelligent Geriatric Management System) to Prevent Falls in Geriatric and General Medicine Wards: A Mixed-Methods Study

As effective interventions to prevent inpatient falls are lacking, a novel technological intervention was trialed. The Ambient Intelligent Geriatric Management (AmbIGeM) system used wearable sensors that detected and alerted staff of patient movements requiring supervision. While the system did not reduce falls rate, it is important to evaluate the acceptability, usability, and safety of the AmbIGeM system, from the perspectives of patients and informal carers.We conducted a mixed-methods study using semistructured interviews, a pre-survey and post-survey. The AmbIGeM clinical trial was conducted in two geriatric evaluation and management units and a general medical ward, in two Australian hospitals, and a subset of participants were recruited. Within 3 days of being admitted to the study wards and enrolling in the trial, 31 participants completed the pre-survey. Prior to discharge (post-intervention), 30 participants completed the post-survey and 27 participants were interviewed. Interview data were thematically analyzed and survey data were descriptively analyzed.Survey and interview participants had an average age of 83 (SD 9) years, 65% were female, and 41% were admitted with a fall. Participants considered the AmbIGeM system a good idea. Most but not all thought the singlet and sensor component as acceptable and comfortable, with no privacy concerns. Participants felt reassured with extra monitoring, although sometimes misunderstood the purpose of AmbIGeM as detecting patient falls. Participants' acceptability was strongly positive, with median 8+ (0-10 scale) on pre- and post-surveys.Patients' acceptability is important to optimize outcomes. Overall older patients considered the AmbIGeM system as acceptable, usable, and improving safety. The findings will be important to guide refinement of this and other similar technology developments.
Development of CBT Integrated E-Module to Improve Student Literacy HOTS

Generations in the 21st century must be prepared to have HOTS literacy competencies and skills. The application of HOTS literacy in the learning process makes students more resilient and able to solve their own problems. This study aims to develop an integrated CBT e-module to improve student literacy HOTS. The resulting e-module has met the valid criteria and has proven to be effective in increasing student literacy HOTS. Student responses to the resulting e-modules are also very positive because the e-modules are designed using the Kvisof Flipbook Maker application which can be opened on laptops, computers or Android devices so that material can be repeated anytime and anywhere as well as HOTS materials and questions. compiled based on CBT to train and improve student literacy HOTS.
Coverless Video Steganography Based on Audio and Frame Features

The coverless steganography based on video has become a research hot spot recently. However, the existing schemes usually hide secret information based on the single-frame feature of video and do not take advantage of other rich features. In this work, we propose a novel coverless steganography, which makes full use of the audio and frame image features of the video. First, three features are extracted to obtain hash bit sequences, which include DWT (discrete wavelet transform) coefficients and short-term energy of audio and the SIFT (scale-invariant feature transformation) feature of frame images. Then, we build a retrieval database according to the relationship between the generated bit sequences and three features of the corresponding videos. The sender divides the secret information into segments and sends the corresponding retrieval information and carrier videos to the receiver. The receiver can use the retrieval information to recover the secret information from the carrier videos correspondingly. The experimental results show that the proposed method can achieve larger capacity, less time cost, higher hiding success rate, and stronger robustness compared with the existing coverless steganography schemes based on the video.
Advancing Multidisciplinary STEM Education with Mathematics for Future-Ready Quantum Algorithmic Literacy

The perception that mathematics is difficult has always persisted. Nevertheless, mathematics is such an essential component of STEM education. Quantum technologies are already having enormous effects on our society, with advantages seen across a broad variety of industries, including finance, aerospace, and energy. These innovations promise to transform our lives. Managers in the business and public sectors will need to learn quantum computing. Quantum algorithmic literacy may help increase mathematical understanding and enthusiasm. The current paper proposes that one possible approach is to present the information in a reasonably gentle but intelligible way, in order to excite individuals with the mathematics that they already know by extending them to acquiring quantum algorithmic literacy. A gentle introduction to the mathematics required to model quantum computing ideas, including linear transformations and matrix algebra, will be given. Quantum entanglement, linear transformations, quantum cryptography, and quantum teleportation will be used as examples to illustrate the usefulness of basic mathematical concepts in formulating quantum algorithms. These exemplars in quantum algorithmic literacy can help to invigorate people’s interest in mathematics. Additionally, a qualitative comparative analysis (QCA) framework is provided that teachers can utilize to determine which students to approach for remediation. This assists the teachers in dispelling any pupils’ uncertainty about mathematical concepts.
Intrinsic neural activity predisposes susceptibility to a body illusion

Abstract Susceptibility to the rubber hand illusion (RHI) varies. To date, however, there is no consensus explanation of this variability. Previous studies, focused on the role of multisensory integration, have searched for neural correlates of the illusion. But those studies have failed to identify a sufficient set of functionally specific neural correlates. Because some evidence suggests that frontal α power is one means of tracking neural instantiations of self, we hypothesized that the higher the frontal α power during the eyes-closed resting state, the more stable the self. As a corollary, we infer that the more stable the self, the less susceptible are participants to a blurring of boundaries—to feeling that the rubber hand belongs to them. Indeed, we found that frontal α amplitude oscillations negatively correlate with susceptibility. Moreover, since lower frequencies often modulate higher frequencies, we explored the possibility that this might be the case for the RHI. Indeed, some evidence suggests that high frontal α power observed in low-RHI participants is modulated by δ frequency oscillations. We conclude that while neural correlates of multisensory integration might be necessary for the RHI, sufficient explanation involves variable intrinsic neural activity that modulates how the brain responds to incompatible sensory stimuli.
Textual Adversarial Training of Machine Learning Model for Resistance to Adversarial Examples

Deep neural networks provide good performance for image recognition, speech recognition, text recognition, and pattern recognition. However, such networks are vulnerable to attack by adversarial examples. Adversarial examples are created by adding a small amount of noise to an original sample in such a way that no problem is perceptible to humans, yet the sample will be incorrectly recognized by a model. Adversarial examples have been studied mainly in the context of images, but research has expanded to include the text domain. In the textual context, an adversarial example is a sample of text in which certain important words have been changed so that the sample will be misclassified by a model even though to humans it is the same as the original text in terms of meaning and grammar. In the text domain, there have been relatively few studies on defenses against adversarial examples compared with the number of studies on adversarial example attacks. In this paper, we propose an adversarial training method to defend against adversarial examples that target the latest text model, bidirectional encoder representations from transformers (BERT). In the proposed method, adversarial examples are generated using various parameters and then are applied in additional training of the target model to instill robustness against unknown adversarial examples. Experiments were conducted using five datasets (AG’s News, a movie review dataset, the IMDB Large Movie Review Dataset (IMDB), the Stanford Natural Language Inference (SNLI) corpus, and the Multi-Genre Natural Language Inference (MultiNLI) corpus), with TensorFlow as the machine learning library. According to the experimental results, the baseline model had an accuracy of 88.1% on the original sentences and an accuracy of 9.2% on the adversarial sentences, whereas the model that underwent the proposed training method maintained an average accuracy of 87.2% on the original sentences and had an average accuracy of 22.5% on the adversarial sentences.
TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021

We present a dataset specifically designed to be used as a benchmark to compare vision systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection of images taken in various real-world locations as well as a collection of simulated images. It enables comparing vision approaches with a meaningful and expressive metric. The contributions of this paper consist of providing a comprehensive and annotated dataset, an overview of the recent approaches to vision in RoboCup, methods to generate vision training data in a simulated environment, and an approach to increase the variety of a dataset by automatically selecting a diverse set of images from a larger pool. Additionally, we provide a baseline of YOLOv4 and YOLOv4-tiny on this dataset.