Reviewerly/exHarmony huggingface dataset:
Academic Performance before and during the State of Emergency due to Covid-19: Analysis from the Perspective of Distance Education

Faced with Covid-19, and the need to adapt to environments that guarantee continuity of educational service in the context of social distancing, many universities did not initially plan the mechanisms for adapting to the virtual modality adequately. Therefore, this period of transition to e-learning was characterised by a decrease in academic performance . This article reports on a study that focused on determining whether the transition from a classroom to a virtual teaching–learning model had an effect or influence on the academic performance of university students in mechanical and electrical engineering at a public university in Peru during the period 2018 to 2021. The purpose of the study was to ensure the quality of the education system in the face of the implementation of a hybrid mode of teaching. Methodologically, a descriptive type of investigation and longitudinal non-experimental design were undertaken. The research methodology followed a hypothetical-deductive approach. The number of participants was 157 and a registration form was used to collect data on the indicators that made up the academic performance variable. The results reveal that the switch to a virtual teaching–learning modality significantly influenced the academic performance of the students. Student’s t-test found a significance equal to 0.000. Passing grades were achieved by 98.57% of students under the virtual teaching–learning modality, compared to 68.4% under classroom learning.
Incremental user embedding modeling for personalized text classification

Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning by utilizing user personalized information has become increasingly challenging due to ever-growing history data. In this work, we propose an incremental user embedding modeling approach, in which embeddings of user's recent interaction histories are dynamically integrated into the accumulated history vectors via a transformer encoder. This modeling paradigm allows us to create generalized user representations in a consecutive manner and also alleviate the challenges of data management. We demonstrate the effectiveness of this approach by applying it to a personalized multi-class classification task based on the Reddit dataset, and achieve 9% and 30% relative improvement on prediction accuracy over a baseline system for two experiment settings through appropriate comment history encoding and task modeling.
An Improved Receptive Fields Network for Matching Remote Sensing Images

We present a new network combining Residual Network (ResNet) and Receptive Fields Network (RF-Net) for matching remote sensing images.Firstly, a new remote sensing image datasets are setup, which consist of images and homograph matrices.The images are obtained by cropping, illumination changing and affine transforming of the original remote sensing images.The matrices are obtained by calculating the homograph between different images of one sequence.Next, a dual-channel network structure is proposed for keypoints detection.The network consists of Receptive Fields Detection (RF-Det) and ResNet for extracting receptive feature maps with detail information and the deep layer maps with semantic information.Then descriptors of these keypoints are generated using a L2-Net.Finally, the strategies of the nearest neighbor, nearest neighbor with a threshold and nearest neighbor distance ratio are used for matching descriptors.Experimental results demonstrate its superior matching performance with respect to the original RF-Net.
Confident AI

In this paper, we propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results. The 4 basic tenets of Confident AI are Repeatability, Believability, Sufficiency, and Adaptability. Each of the tenets is used to explore fundamental issues in current AI/ML systems and together provide an overall approach to Confident AI.
Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do

Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, its variants, GPT-2/3, and others. Using them as pre-trained models and fine-tuning them for specific tasks, researchers have extended state of the art for many NLP tasks and shown that they capture not only linguistic knowledge but also retain general knowledge implicitly present in the data. Unfortunately, LMs trained on unfiltered text corpora suffer from degenerated and biased behaviour. While this is well established, we show that recent LMs also contain human-like biases of what is right and wrong to do, some form of ethical and moral norms of the society -- they bring a "moral direction" to surface. That is, we show that these norms can be captured geometrically by a direction, which can be computed, e.g., by a PCA, in the embedding space, reflecting well the agreement of phrases to social norms implicitly expressed in the training texts and providing a path for attenuating or even preventing toxic degeneration in LMs. Being able to rate the (non-)normativity of arbitrary phrases without explicitly training the LM for this task, we demonstrate the capabilities of the "moral direction" for guiding (even other) LMs towards producing normative text and showcase it on RealToxicityPrompts testbed, preventing the neural toxic degeneration in GPT-2.
Globally Optimal Joint Search of Topology and Trajectory for Planar Linkages

We present a method to find globally optimal topology and trajectory jointly for planar linkages. Planar linkage structures can generate complex end-effector trajectories using only a single rotational actuator, which is very useful in building low-cost robots. We address the problem of searching for the optimal topology and geometry of these structures. However, since topology changes are non-smooth and non-differentiable, conventional gradient-based searches cannot be used. We formulate this problem as a mixed-integer convex programming (MICP) problem, for which a global optimum can be found using the branch-and-bound (BB) algorithm. Compared to existing methods, our experiments show that the proposed approach finds complex linkage structures more efficiently and generates end-effector trajectories more accurately.
A Transitivity Analysis of Discourses in George W. Bush and Scott Morrison’s Speeches on ‘War on Terror’

This article is a continuation of the article entitled Comparing Discourses in George W. Bush and Scott Morrison’s Speeches on ‘war on terror’ about the transitivity systems, including types of processes, participant functions, and circumstance elements, are used in these two speech discourses by George W. Bush and Scott Morrison. This research is qualitative research with a descriptive statistical method that presents linguistic analysis findings in speech discourses about the war on terror. This article aims to identify and explain how George W. Bush and Scott Morrison construct the image of terrorism through the language used in their speech discourses. The analysis uses Systemic Functional Linguistics (SFL) and utilizes the framework of transitivity analysis, which identifies the ideational meaning embodied by choice of grammar. This study found that George W. Bush uses material processing to show a negative image of terrorism. In addition to the material process, Scott Morrison also uses a verbal process to convey the image of terrorists. The linguistic features in both speech discourses both depict a negative image of terrorists. The findings also prove that the choice of grammar in the two discourses indirectly uses the speaker’s perspective on terrorists, which influences listeners' opinion.
Post-Quantum Cryptographic Hardware Primitives

The development and implementation of post-quantum cryptosystems have become a pressing issue in the design of secure computing systems, as general quantum computers have become more feasible in the last two years. In this work, we introduce a set of hardware post-quantum cryptographic primitives (PCPs) consisting of four frequently used security components, i.e., public-key cryptosystem (PKC), key exchange (KEX), oblivious transfer (OT), and zero-knowledge proof (ZKP). In addition, we design a high speed polynomial multiplier to accelerate these primitives. These primitives will aid researchers and designers in constructing quantum-proof secure computing systems in the post-quantum era.
Machine Learning Approaches to Detect Unauthorized IoT Devices

Machine Learning Approaches to Detect UnauthorizedIoT Devices
A Novel Stream Mining Approach as Stream-Cluster Feature Tree Algorithm: A Case Study in Turkish Job Postings

Large numbers of job postings with complex content can be found on the Internet at present. Therefore, analysis through natural language processing and machine learning techniques plays an important role in the evaluation of job postings. In this study, we propose a novel data structure and a novel algorithm whose aims are effective storage and analysis in data warehouses of big and complex data such as job postings. State-of-the-art approaches in the literature, such as database queries, semantic networking, and clustering algorithms, were tested in this study to compare their results with those of the proposed approach using 100,000 Kariyer.net job postings in Turkish, which can be considered to have an agglutinative language with a grammatical structure differing from that of other languages. The algorithm proposed in this study also utilizes stream logic. Considering the growth potential of job postings, this study aimed to recommend new sub-qualifications to advertisers for new job postings through the analysis of similar postings stored in the system. Finally, complexity and accuracy analyses demonstrate that the proposed approach, using the Cluster Feature approach, can obtain state-of-the-art results on Turkish job posting texts.
Automated Electronic System for Census Process

Present-day censuses are generally completed in physical modes wherein the authorities need to go from door to door to gather the necessary information. All the Censuses to date have been directed similarly, and in this way require a ton of on-ground authorities for the same. Likewise, a ton of desk work is needed to have been kept up within this interaction making it a bulky interaction. There have been ongoing advancements in having an application for completing the Census, yet even that has numerous limits. Since for this situation individuals can get to the application and give the responses all alone, it can prompt off-base or deceiving information bringing about the failure of the reviews. Likewise, not all individuals can utilize portable applications with no help. Also, it has been referenced in the Lok Sabha that over 25000 villages in India still lack mobile and internet coverage. Because of this absence of web entrance in distant regions, the past conventional techniques for taking overviews are still in practice. Hence, in this paper, we introduce a new approach, an Automated survey system for the census process. The proposed system is faster and more authentic than the traditional process of pen and paper. All the data is collected digitally and stored directly into datasets, therefore data storage and retrieval are no longer cumbersome. This collected data is far easier to analyze. The authenticity is provided by Biometric sensors.
Comparison of Happiness Index in Northeast-Asian 4 Countries

This paper analyzes the change of happiness level in Northeast Asian four countries, Korea, Japan, China and Russia from 1990 to 2020. On the base of previous research, to estimate the national level happiness index, this paper modified the previous happiness index. This happiness index simultaneously considers the subject aspect and the object aspect. In particular, we mixed the subjective index and the objective index in three level index, and improved the methodology. This paper uses the WVS data to estimate the subjective happiness index. The result finds that Korea and Japan more higher than China and Russia in happiness index. Especially, from the dynamic change of the happiness index, Korea and Japan reveals the inversed U type, China and Russia reveals U type. This result explain that the happiness level of Korea and Japan gradually decreased in recent, but China and Russia gradually increased. This trend more remarkable in subjective happiness index. In wave 7 period, the subjective happiness index of China most high. But the objective happiness index of Korea and China decreased, Japan and Russia on the contrary increased. The result of this paper similar the Easterlin paradox, because the PPP per capita GDP of Korea and China already exceeded forty thousand dollars in wave 7 period, the income level is high, and the income level of China and Russia relatively low, be the left side of the inversed U type, Easterlin paradox.
Detection of Uranium Anomalies and Alteration Zones Using Airborne Gamma-Ray Spectrometry at Gabal Attala and Its Surrounding Area, Eastern Desert, Egypt

Gamma-ray spectrometry method is widely used in various fields.The study area is located in the Central Eastern Desert of Egypt and covers about 1200 square kilometers.The rock units in the study area can be organized in ages from the Precambrian (mainly granite) to Quaternary.Airborne gamma-ray spectrometry data were collected by Aero-Service, 1984.The main objectives of this study are to detect hydrothermal alteration zones and the uranium anomalies in the study area.The processing of the data was conducted to generate radioactive element concentration maps (K, eU and eTh).Hydrothermal alteration zones such as potassic and phyllic altered areas were detected using the Potassium Ternary Composite Image map and the eTh/K map.The F-parameter technique and K ideal method were used to determine the locations of the potassium-enrichment.These locations are characteristically associated with the orogenic gold mineralization in the study area.Fourteen radioactivity zones were classified based on the Interpreted Radio-Spectrometric Zonation (IRSZ) Map.These zones were divided into high, medium and low radioactive response where zones of high radioactive response were revealed as zones 1, 6, 9 and 13.Uranium anomalies have been identified as these anomalies are abundant in eU values and also associated with higher values of eU/eTh and eU/K, where the maximum value of eU is associated with the Younger Granite with a value of 18.831 ppm.These uranium anomalies are associated with Taref Formation, Post-Hammamat felsite, Quaternary deposits and Younger Granite.The locations of uranium anomalies are considered important exploration targets, as these locations are promising and have priority for ground geophysical and geological follow-up.
Unequally Interval Data Processing Across Fault Deformation Measurement

According to different regions, conditions and requirements, the cross-fault measurement specifications is allowed to measure at different resurvey periods, and resulted in unequal interval observation data.The unequal interval observation data is a common phenomenon data, the difference on both sides of the fault is observed by geological investigation, historical record, artificial observation, simulated record, digital sampling, encrypted observation before and after the event, change of observation equipment, change of observation environment, human factors, etc, and the unequal interval observation data is obtained.The characteristics of the unequal interval observation data is not only shown in time, but also in space.The unequal interval observation data is usually preprocessed into equal interval data by some kind of algorithm chosen before the subsequent complex calculation.In the data processing of cross-fault measurement, the unequal interval observation data is usually preprocessed into equal interval data, and then calculated, which leads to a series of new problems, such as time calculation, synchronization, master-slave relationship, comparability and so on.In view of unequal interval observation data in cross-fault measurement, some new problems are tried to solve in unequal interval data matching calculation by using conventional methods combined with some algorithm requirements, data characteristics and practical experience, and their adaptability in various algorithms is investigated in this paper.These works contribute to the improvement and development of cross-fault survey data processing methods, and enhance the role of cross-fault survey data in earthquake protection and disaster reduction.
Research on Data Sharing Access Control Based on Blockchain Technology

Blockchain is now widely used in various industries due to its openness, transparency, autonomy, imtamability, decentralization, and traceability.With the wide application of blockchain technology, its security problems have also brought significant challenges, seriously affecting the implementation of blockchain applications.Data on the traditional blockchain is open to the entire network node.The update operation of data records is also transparent, accompanied by the leakage of user information.Although blockchain has the characteristics of anonymity and privacy, with its development, it has not been easy to meet users' needs.How to effectively protect user data privacy, realize data sharing on the blockchain, and ensure the security of data integrity, transmission efficiency, storage efficiency, and application is very important to break through the bottleneck of the development of blockchain technology and promote its application.This paper proposes a traffic data resource access control scheme based on blockchain technology by combining the attribute encryption access control system based on ciphertext strategy based on blockchain's distributed ledger technology and the openness and imtamability it brings.In this scheme, the task of attribute encryption is to ensure the security involved in the process of plaintext and ciphertext conversion and the efficiency of data sharing, and to implement a one-to-many access sharing policy, to enhance the safety of the access sharing system and improve the efficiency of the access control module.This paper introduces the access control mechanism from two aspects of system architecture and the access sharing process.Then the attribute encryption mechanism is designed.The design process includes symbol description, primary structure, algorithm design idea and algorithm construction.Finally, the functionality and security of the access control mechanism are analysed.
Rebellion In The Poem Howl By Allen Ginsberg

This research aims to analyze the types of rebellion principle used and how the rebellion portrayed used in the poem Howl by Allen Gisnberg. The data source used in this research is the poem Howl. In analyzing the data, the writer used a qualitative descriptive research method through a sociological approach. The data were obtained by using reading, collecting, and analyzing techniques and to classify the types of rebellion principle in the poem by Jhon Lewis Gillin and Jhon Philip Gillin’s theory. The result of this research were 26 data that contain types of rebellion principle where 7 data of assimilation, 2 data of accommodation, 2 data of acculturation, 8 data of contravention, 4 data of competition and 3 data of conflict. There are 6 types with 26 data and an explanation of rebellion portrayed in this research poem, but the types and portrayed that are more prominent are negative because the researcher finds a lot of war, capitalism, consumerism, deceit, torture, and murder physically and mentally. Evil America has turned the lives of the lower classes into a terrible and nightmare.
Depictions of Violence Racism in Ron Stallworth‘s Novel Black Klansman

The objectives of this study were to identify the depictions of violence racism and identify the impact of violence racism on African-America society in“Black Klansman” novel by Rons Stallworth.The data source used in this research is Black Klansman novel. In analyzing the data, the writer used a qualitative descriptive research method through sociological approach. The data were obtained by using reading, identified, noting and quoting, and collecting data techniques to classify the racism and the impact of racism in the novel Black Klansman by Swingewood and Setiadi theories with his classification of violence in narrow and broad sense. The result of this research there were 13 data that depicted of violence racism in “Black Klansman” novel by Ron Stallworth. 5 data contained the violence racism in narrow sense, 8 data of the violence racism in broad sense, and there were 9 data which include the impact of violence racism.
Reforming Intellectual Property Right Based On Public Preferences Towards Artificial Intelligence Generated Music

&lt;p&gt;This study examines how legal frameworks should set a clear definition for Artificial Intelligence’s (AI) role in creative work through the perceptions of the younger generation on AI-generated music. Using a survey composed of 6 components (Perceived Quality, Perceived Imaginativeness, Spatial Presence, Empathy, and Musician’s Competence), this study sampled the technology and art related intended majors sampling groups to determine whether the computational music passes the Turing test. After listening to two samples with hidden artist identities (Human vs AI), the participants (n = 35) were asked to evaluate the samples presented. Then, artist identities are revealed and participants were asked to make changes to their initial responses. This study found that participants have similar appraisals towards human-composed music and AI generated music while there is a significant difference between the initial attitudes towards AI for the two sampling groups. For participants who changed their responses, there are no significant changes in attitude but an overall negative shift in responses is observed. This study concluded that the possibility for the younger generation to recognize AI as actual artists and thus supporting AI IP rights is low. Further implications of the findings and future directions for research are discussed. &lt;/p&gt;
Reinforcement Learning: Playing Tic-Tac-Toe

&lt;p&gt;Machine learning constructs computer systems that develop through experience. Applications surround disciplines in daily life ranging from malware filtering to image recognition. Recent research has shifted towards maximizing efficiency in decision-making, creating algorithms that quickly and accurately process patterns to generate insight. This research focuses on reinforcement learning, a paradigm of machine learning that makes decisions through maximizing reward. Specifically, we use Q-learning – a model-free reinforcement learning algorithm – to assign scores for different decisions given the unique states of the problem. Widyantoro et al. (2009) has studied the effect of Q-learning on learning to play Tic-Tac-Toe. However, the study yielded a win/tie rate of less than 50 percent. We believe that does not represent an effective algorithm to fully exploit the benefits of Q-learning. In the same environment, this research aims to close the gaps in the effectiveness of Q-learning while minimizing human input. Data were processed by setting the epsilon value as 0.9 to ensure randomness, then consecutively decrease with a constant rate as possible states increase. The program played 300,000 games against its previous version, eventually securing a win/tie rate of approximately 90 percent. Future directions include improving the efficiency of Q-learning algorithms and applying the research in practical fields.&lt;/p&gt;