Reviewerly/exHarmony huggingface dataset:
Hybrid Multi-Objective Genetic Programming for Parameterized Quantum Operator Discovery

The processing of quantum information is defined by quantum circuits. For applications on current quantum devices, these are usually parameterized, i.e., they contain operations with variable parameters. The design of such quantum circuits and aggregated higher-level quantum operators is a challenging task which requires significant knowledge in quantum information theory, provided a polynomial-sized solution can be found analytically at all. Moreover, finding an accurate solution with low computational cost represents a significant trade-off, particularly for the current generation of quantum computers. To tackle these challenges, we propose a multi-objective genetic programming approach---hybridized with a numerical parameter optimizer---to automate the synthesis of parameterized quantum operators. To demonstrate the benefits of the proposed approach, it is applied to a quantum circuit of a hybrid quantum-classical algorithm, and then compared to an analytical solution as well as a non-hybrid version. The results show that, compared to the non-hybrid version, our method produces more diverse solutions and more accurate quantum operators which even reach the quality of the analytical baseline.
The Effectiveness of Niching on OpenAI-Evolution Strategies in the Evolution of Robotic Behavior

In Evolutionary Robotics, techniques inspired by biological evolution are used to evolve robotic morphology and behaviors. Evolution Strategies is a popular algorithm in this field due to its capability to perform continuous optimization. In evolutionary robotics, the use of approaches as, for instance, niching techniques and environmental variation, has shown promising results in order to develop more effective adaptive behaviors or even improve the convergence time. The objective of this work is to analyze how a recent adaptation of Evolution Strategies developed by OpenAI, which we call OpenAI-ES, behaves when working alongside environmental variation and niching techniques. The results showed that this approach applied to the double pole balancing benchmark can improve performance by 9% for OpenAI-ES.
Human-Driven Genetic Programming for Program Synthesis: A Prototype

End users can benefit from automatic program synthesis in a variety of applications, many of which require the user to specify the program they would like to generate. Recent advances in genetic programming allow it to generate general purpose programs similar to those humans write, but require specifications in the form of extensive, labeled training data, a barrier to using it for user-driven synthesis. Here we describe the prototype of a human-driven genetic programming system that can be used to synthesize programs from scratch. In order to address the issue of extensive training data, we draw inspiration from counterexample-driven genetic programming, allowing the user to initially provide only a few training cases and asking the user to verify the correctness of potential solutions on automatically generated potential counterexample cases. We present anecdotal experiments showing that our prototype can solve a variety of easy program synthesis problems entirely based on user input.
Dashboard and Report Sharing

Power BI provides multiple ways to share content with users. Each sharing method has its pros and cons and can be used for specific scenarios. Some sharing methods can be used together to build a framework for sharing. This chapter discusses the most basic way to share Power BI content. This method is called dashboard (or report) sharing. Dashboard sharing is the easiest way to share; however, it may not always be the best way. In this chapter, you’ll learn how this method works, its pros and cons, and the scenarios for using it.
CryptoDataMR: Enhancing the Data Protection Using Cryptographic Hash and Encryption/Decryption Through MapReduce Programming Model

Cloud computing has become the most significant technology in today’s world as it provides several services including physical resources and platforms for their users through distributed and parallel computing. Due to the usage of technology and its facilities, large data has been stored and retrieved by their users. Thus, processing a huge volume of data has become progressively essential to satisfy their business and other private users effectively. A tool that is most popularly used for processing this big data is MapReduce. However, providing security for the private data stored by the users is the most challenging task. Despite using several security measures such as authentication, providing complete security for private data is almost critical as there is a possibility of compromising the security policies. Thus, there is an exigent need to protect data from leakage and other security breaches. This paper presents a solution CryptoDataMR model to enhance data protection by implementing cryptographic hash and encryption/decryption algorithms in the MapReduce programming model. The proposed model uses a chain rolling double hash algorithm at the map layer and position-based shift hill cipher at the reduce layer for retaining data privacy. This model secures the data effectively through simple cryptographic algorithms with intricate results. Experimental analysis has been made for the proposed model to verify the performance in enhancing data security. The results show that the proposed model provides better performance with minimum execution time and ensures the privacy of the data.
Comparative Effectiveness of Virtual Field Trip Strategy and Traditional Fieldtrip on Academic Performance and Motivation of Students in Conservation Concepts of Biology in Northwest, Nigeria

This study compares the effectiveness of virtual and traditional field trip strategies on academic performance and motivation of students in conservation concepts of biology in North-West Nigeria. The study developed 2 research objectives one of which is to: compare the effectiveness of virtual and traditional field trip strategies on academic performance of students in conservation concept. The study was guided by 2 research questions and 2 null hypotheses. A Quasi experimental research using test-retest method of pretest and posttest was adopted for the study. A sample of 120 N.C.E II biology students comprising of 81 males and 39 females were randomly selected from the population of 357 students of which 213 are males and 114 are females. The study developed 2 instruments namely: Conservation Concepts Performance Test (CCPT) and Conservation Concepts Motivation Questionnaire (CCMQ) which are validated and found reliable with r=0.65. At the end of the treatment data was collected and analyzed using Means and standard deviations to answer the research question while t-test and Mann- Whitney Statistics were used to answer the null hypotheses. The results obtained showed that: significant differences exist between the performance of students taught using virtual field trip strategy and those exposed to traditional field trip strategy. From the findings recommendations were made one of which is: the Ministry of Education/ colleges should provide a biology Virtual environment to serve each school in the region as technology is seen to help improve the performance and raise the level of motivation in biology concepts, moreover it will help to overcome problems faced by teachers from a lack of real field trips which leads to ignorance of some abstract concepts such as conservation concepts biology.
Competencia digital docente para la inclusión y la cohesión social en los procesos de enseñanza: experiencia chilena durante el COVID-19

This study examined teacher digital competence training for inclusion and social cohesion in Chile during the Covid-19 confinement. The objective was to analyze how teachers are trained in ICT and how they use these tools to develop distance education processes. The methodology used was a quantitative approach through a descriptive design of a multiple-choice questionnaire. The findings indicate that ICT training occurs mainly as a spontaneous process of self-learning by the teacher and that their motivations for training in ICT respond mainly to the desire to improve their professional skills. Teachers highly value training processes to develop distance education processes around the use of technologies. The teachers who adapted their planning for the new distance education are those who carried out self-learning processes and have training in technological tools. Most teachers opted for a mixed approach in content delivery and complemented classes through video calling platforms with tools for asynchronous work. Regarding evaluation processes, teachers certainly know that clear and precise instructions are relevant in distance education.
Computational analysis of Finnish nonfinite clauses

Abstract Finnish nonfinite clauses constitute a complex grammatical class with a seemingly chaotic mix of verbal and nominal properties. Thirteen nonfinite constructions, their selection, control, thematic role assignment, nonfinite agreement, embedded subjects, and syntactic status were targeted for analysis. An analysis is proposed which derives their syntactic and semantic properties by relying on a computational model of human information processing. The model analyzes Finnish nonfinite constructions as truncated clauses with one functional layer above the verb phrase. Research methods from naturalistic cognitive science and computational linguistics are considered as potentially useful tools for linguistics.
A Survey on Encryption Framework Against Insider Keyword-Guessing Attack in Cloud Storage

A key component of data security in cloud computing systems is encryption. Encryption might not be enough, though, to shield sensitive data from hostile intrusions. One such attack is the keyword guessing attack, in which an attacker uses a variety of techniques to attempt to decipher a term from the encrypted data. In this research, we suggest a system for encryption against a cloud computing keyword guessing attack. To achieve improved data security, the framework combines encryption methods with a safe keyword retrieval system. The framework that is being developed takes into account the difficulties that come with storing and retrieving encrypted data in cloud environments. Our test findings demonstrate that the suggested framework successfully thwarts keyword guessing attacks while preserving the data's confidentiality and integrity.
Límites y posibilidades en el proceso de virtualización tecno-pedagógica de universidades peruanas

The UTEC-UNED-TECSUP University Consortium took part in the eighth edition of the Support programme for the design and implementation of strategies for continuity of the Higher Education service in public universities of the Peruvian Ministry of Education (PMESUT), devised to promote the continuity of non-classroom-based educational service in public universities. This article presents the assessment carried out in four universities, with the aim of ensuring the ongoing digitization of education in the aftermath of the pandemic. Once the diagnosis had been made, the techno-pedagogical virtualization of 138 model courses was carried out in three phases: planning; curriculum design and teaching support; and implementation of the courses in the virtual classroom. Limitations were related to existing digital divides: time constraints, personal self-regulation and limited training in pedagogical and digital competences of teachers. Individualised support and tutoring measures were sought, increasing the number of synchronous sessions to facilitate assessment. The feedback focused on information, interaction and accountability on what has been done and the improvement of the didactic act in virtual mode. In conclusion, the complexity of the process was notable, as well as the high level of demand to achieve results which, although positive, encountered several obstacles arising from the different agents and elements involved.
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: 1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; 2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. 3) FL models significantly outperformed large language models using zero-/one-shot learning and offered lightning inference speed.
BUILDING A NEW CROWD-COUNTING ARCHITECTURE

&lt;p&gt;Crowd counting has become necessary in the age of information technology and network development; with many different goals, people have needed more services to meet their work. Therefore, crowd counting is also necessary for daily life, such as solid classrooms, vigorous meetings, conferences, etc. Based on practical needs, we propose counting the number of people in an image, known as crowd counting. Using several software techniques, we found that DM-Count outperformed previous advanced methods on two large-scale counting datasets by a wide margin in Mean Absolute Error. Furthermore, the VGG16 model for the algorithm does not certainly use the current&lt;/p&gt; &lt;p&gt;VGG19 and still shows good higher performance. We analyze multiple people marked with dots in each training image. Existing crowd-counting methods must either smooth each annotated drop with a Gaussian distribution or estimate the likelihood of each pixel given the annotated point. This paper proposes a crowd-counting model using deep learning and Computer Vision to improve performance by handling perspective distortion by intelligently using many features generated during coding. The use of Deep Learning and Computer Vision experimentally shows that our method can improve up to 34.91% Mean Absolute Error and 26.25% of Root Mean Squared Error compared to the referenced methods.&lt;/p&gt;
Leveraging Ensemble Method with Transformer for Robust Drug Use Detection on Twitter

Social media platforms are increasingly enabling the propagation of content from groups related to drug use, thus posing risks for the wider population and, in particular, individuals who are amenable to drug use and drug addiction. The detection of drug use content on social media platforms is a priority for governments, technology companies, and drug law enforcement organizations. To counter this issue, various techniques have been developed to identify and promptly remove drug use content, while also blocking its creators from network access. In this paper, we introduce a manually annotated Twitter dataset, comprising 156,521 tweets published between 2008 and 2022, specifically compiled for the purpose of drug use detection. The dataset underwent annotation by several group of expert annotators who classified the tweets as either drug use or non-drug use. Exploratory data analysis was conducted to comprehend the dataset&amp;#039;s characteristics. Various classification algorithms, including SVM, XGBoost, RF, NB, LSTM, and BERT were employed using the dataset. Among the traditional machine learning models, SVM utilizing term frequency-inverse document frequency features achieved the highest F1-Score (0.9017). However, BERT with textual features concatenated with numerical and categorical features in ensemble method surpassed the performance of traditional models, attaining F1-Score of 0.9112. To facilitate future research and enhance English online drug use classification accuracy, the dataset will be made publicly available.
Name Entity Recognition for Transliterated Bengali Text Using Machine Learning Algorithm

The task of named entity recognition (NER) for transliterated Bengali text is a challenging job due to the unavailability of adequate resources and standard tools. The ubiquity of transliterated Bengali comments in social media makes the job more difficult as state-of-the-art approaches cannot perform well. Named entities are the nouns in a text which represent the name of a person, location, organization, time, date, etc. This paper introduces an annotated corpus of transliterated Bengali name entities from social media comments. This paper also proposes an approach to recognize them and reports commendable results through extensive experiments.
Wavelet Based Adaptive Threshold Approach for Image Noise Removal

Visual information communicated in computerized pictures is a common kind of communications nowadays, but the image that is produced as a result of the connection is typically corrupted by noise. It is necessary to process the incoming picture before using it in apps. Denoising is the technique of modifying image data to produce an obviously superior image. Wavelet transformations allow us to express information with high levels of scarcity. A signal estimation technique called wavelet filters takes use of the Fourier transform’s capacity to eliminate noise in broadcasts.Diagnostic imaging noise reduction is an extremely sensitive and difficult endeavor. It is necessary to modify the tradeoff among de - noising and picture characteristic retention such that the image content that is diagnostically valuable is improved. Medical picture de-noising techniques have been extensively used, including wavelet thresholding. The objective is to create a wavelet basis from the picture data, where signal is represented by the bigger factors, and the disturbance by the smaller correlation coefficient. By correctly changing these coefficients, the data’s noise may be decreased. This thesis compares the effectiveness of several thresholding techniques, including Sure Reduce, Visual Shrink, and Bayes Shrink.
A Survey on Human Behavioral Cybersecurity Risk During and Post Pandemic World

COVID-19 pandemic also gave rise to cyber pandemic when the incidence of cybercrimes increased manifold all over the world. As the existing health infrastructure crumbled in the face of the severity of the COVID-19, so did the prevalent technical architecture, procedures, policies and practices of cybersecurity at various enterprises in the face of vigorous cyber-attacks. The strict lockdown(s) and quarantine protocols enforced during the pandemic affected the mental condition and human behavior adversely. Loss of lives and livelihoods further aggravated the mental condition causing so many people to become angry, anxious and paranoid. At the same time, the need for remote working increased the dependence on technology networks which proliferated immensely without due regard to cybersecurity architecture. Rapid pace of adoption of emerging technologies necessitated by lockdowns and the enhanced metal stress due to pandemic considerably increased the attack surface for social engineering-based attacks. In this paper we have carried out a systematic literature survey of the threat landscape both, during and post pandemic world in view of human behavioral changes. We have then recommended a mitigation framework incorporating necessary regulations at national level, policies and security culture at organizational level, procedures at operational level and training at individual levels to mitigate the risks associated with the changed threat landscape.
Penerapan Media Goggle Sites Dalam Pembelajaran Menulis Teks Deskriptif Tentang Upacara Adat Pada Siswa Kelas IX A SMP Negeri 2 Bantarkawung Tahun Ajaran 2022/2023

The background of this research is the low ability of class IX A student at SMP Negeri 02 Bantarkawung in writing descriptive text aboaut traditional ceremonies and delivering material that is still conventional and monotonous, so that learning becomes passive and boring. Seeing such conditions, it is necessary to strive for the application of interesting and appropriate learning media so that it can facilitate students in expressing their ideas, and feelings, especially in writing descriptive texts. Thus the formulation of the problem that can be taken in this study is how to apply Google Sites media in learning to write descriptive texts about traditional caremonies in class IX A students of SMP Negeri 02 Bantarkawung. In addition, there is a research objective is to describe the application of Google Sites media in learning to write descriptive texts about traditional ceremonies in class IX A students of SMP Negeri 02 Bantarkawung.This research is included in the descriptive qualitative research. Data collection techniques in this study used test techniques in the form of knowledge and skills aspect questions as well as non-test techniques in the form of questionnaires.The results of this study are that Google Sites media can be applied in learning to write descriptive texts about traditional ceremonies in class IX A students of SMP Negeri 02 Bantarkawung. These results can be proven by the average value of students who achieve 92 in terms of knowledge aspects with a total of 19 students who complete. Meanwhile, from the aspect of skills, an average score of 87 was obtained with a total of 19 students who passed.
Long-time-constant leaky-integrating oxygen-vacancy drift-diffusion FET for human-interactive spiking reservoir computing

Reservoir computing is one of the best feasible means of information processing that does not require substantial computation, such as backpropagation. Implementation using spiking neural networks is promising for real-time and low-energy computation that can be completed by edge devices alone. The key parameter is a time constant $\tau_{\mathrm{n}}$ of the neurons, which should be close to that of external stimuli and can be on the order of milliseconds for human-interactive reservoir computing. Here, we present a slow spiking neuron based on an oxygen-vacancy drift-diffusion field-effect transistor (ODD-FET). In these transistors, the slow migration of oxygen vacancies mimics a leaky integration with $\tau_{\mathrm{n}}$ on the order of 10 to 100 ms. The feasibility of the proposed ODDFET on the human-interactive application is demonstrated by a spiking reservoir composed of these neurons, which can accurately detect handwriting anomalies.
Automatic Extrinsic Calibration of a Camera and a 2D LiDAR With Point-Line Correspondences

Extrinsic calibration of a 2D camera and a 2D LiDAR is necessary to fuse information from two sensors by representing the information under the same frame. Various geometric constraints such as point-plane, point-line, and point-point are used for the extrinsic calibration. Usually, these require a manual step, including control points selection for camera calibration and LiDAR points. We propose a new algorithm for automatic extrinsic calibration with point-line correspondences. A calibration structure with two perpendicular planes having a chessboard on both sides is used for the extrinsic calibration. First, we use predefined colors at specific locations on a chessboard to quickly find the origin of the coordinate system. Second, we robustly detect three control points on LiDAR raw data using a geometric constraint that two end points among three control points should lie on the same line. The initial linear solution is obtained by using a point-line constraint. Finally, it is refined by nonlinear minimization, which gives a 15.3% improvement compared to the linear solution. Experimental results show the feasibility of the proposed algorithm.