Reviewerly/exHarmony huggingface dataset:
Intrinsic Image Decomposition via Ordinal Shading

Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in various inverse rendering and computational photography pipelines. Generating highly accurate intrinsic decompositions is an inherently under-constrained task that requires precisely estimating continuous-valued shading and albedo. In this work, we achieve high-resolution intrinsic decomposition by breaking the problem into two parts. First, we present a dense ordinal shading formulation using a shift- and scale-invariant loss in order to estimate ordinal shading cues without restricting the predictions to obey the intrinsic model. We then combine low- and high-resolution ordinal estimations using a second network to generate a shading estimate with both global coherency and local details. We encourage the model to learn an accurate decomposition by computing losses on the estimated shading as well as the albedo implied by the intrinsic model. We develop a straightforward method for generating dense pseudo ground truth using our model’s predictions and multi-illumination data, enabling generalization to in-the-wild imagery. We present exhaustive qualitative and quantitative analysis of our predicted intrinsic components against state-of-the-art methods. Finally, we demonstrate the real-world applicability of our estimations by performing otherwise difficult editing tasks such as recoloring and relighting.
Configuring Timing Parameters to Ensure Execution-Time Opacity in Timed Automata

Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as events with timestamps. Timed automata are an extension of finite-state automata with a set of clocks evolving linearly and that can be tested or reset, making this formalism able to reason on systems involving concurrency and timing constraints. In this paper, we summarize a recent line of works using timed automata as the input formalism, in which we assume that the attacker has access (only) to the system execution time. First, we address the following execution-time opacity problem: given a timed system modeled by a timed automaton, given a secret location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the secret location was visited. This means that for any such execution time, the system is opaque: either the final location is not reachable, or it is reachable with that execution time for both a run visiting and a run not visiting the secret location. We also address the full execution-time opacity problem, asking whether the system is opaque for all execution times; we also study a weak counterpart. Second, we add timing parameters, which are a way to configure a system: we identify a subclass of parametric timed automata with some decidability results. In addition, we devise a semi-algorithm for synthesizing timing parameter valuations guaranteeing that the resulting system is opaque. Third, we report on problems when the secret has itself an expiration date, thus defining expiring execution-time opacity problems. We finally show that our method can also apply to program analysis with configurable internal timings.
Languages with more speakers tend to be harder to (machine-)learn

Abstract Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study different aspects of human language. Here, we use LMs to test the hypothesis that languages with more speakers tend to be easier to learn. In two experiments, we train several LMs—ranging from very simple n-gram models to state-of-the-art deep neural networks—on written cross-linguistic corpus data covering 1293 different languages and statistically estimate learning difficulty. Using a variety of quantitative methods and machine learning techniques to account for phylogenetic relatedness and geographical proximity of languages, we show that there is robust evidence for a relationship between learning difficulty and speaker population size. However, contrary to expectations derived from previous research, our results suggest that languages with more speakers tend to be harder to learn.
Theoretical and Methodological Analysis of the Formation of “Soft-skills” in Higher Education Students of Pedagogical Specialties of Higher Education Institutions of Ukraine

The purpose of the study is to carry out a theoretical and methodological analysis of the formation of “soft-skills” in higher education students of pedagogical specialties of higher education institutions of Ukraine. The study is based on system analysis, forecasting methods, comparative analysis, specification, and the study of modern legislative materials. The results highlight the interpretation and differences in the theoretical foundations of hard skills and soft skills. Attention is also drawn to the peculiarities of soft skills of applicants for higher pedagogical education in Ukraine. It is crucial to incorporate the "Six Thinking Hats" methodology, which encourages the development of independent problem-solving skills by approaching challenges through the lens of one mental action at a time. Implementing this approach necessitates significant student engagement. In Ukraine, teacher motivation represents a multifaceted element within the teacher training system. Additionally, outdated training methods pose another challenge that needs to be addressed. The conclusions emphasize innovative approaches to understanding the conditions for the development of soft skills, as well as the need to take into account the materials of state control bodies as sources for an objective analysis of the situation.
Revelation of Significant Fake Rhetorical in Wrapping Bygone Utilizing Significant Learning Procedures

The developing computation control has made the profound learning calculations so powerful that making an unclear human synthesized video famously called a profound fake has got to be exceptionally straightforward. Scenarios where these practical confront swapped profound fakes are utilized to form political trouble, fake psychological warfare occasions, vindicate porn, and shakedown people groups are effortlessly imagined. In this work, we depict a modern profound learning-based strategy that can viably recognize AI-generated fake recordings from genuine videos. Our strategy can naturally be recognizing the substitution and reenactment of deep fakes. We are attempting to utilize Manufactured Intelligence (AI) to battle Fake Intelligence(AI). Our framework uses a res-next neural convolution system to extract frame-level highlights and promote the use of these highlights to prepare the long-term memory (LSTM)-based repetitive neural network (RNN) to classify whether the video is subject to art. control or not , i.e whether the video is profoundly fake or genuine. To imitate the genuine time scenarios and make the show perform way better on genuine time information, we assess our strategy on an expansive sum of adjusted and blended data-set arranged by blending the different accessible data-set like Face-Forensic, Deep Fake location challenge, and Celeb-DF. We moreover focus on how our framework can accomplish competitive results utilizing exceptionally straightforward and strong approaches.
Impact Of Transformational Leadership on Work Motivation: Mediating Role Of Procedural Justice

Transformational leadership, work motivation and procedural justice are getting an increased attention in the area of business and economics because of their impact on business in terms of high productivity, job satisfaction, and organizational commitment, higher performance and many other related outcomes. In this study we examined the direct and indirect impact of transformational leadership on work motivation. Procedural justice was used as a mediator to examine the indirect impact of transformational leadership on work motivation. Data was collected from University of Peshawar with 42 respondents. Results indicate that transformational leadership has a positive influence on work motivation and procedural justice partially mediate the relationship between transformational leadership and work motivation. Limitation of the study and future directions were also given. Study recommends that transformational leadership style should be adopted by the management to enhance work motivation and get economic benefits.
E-Öğrenme Sistemi Seçiminde Etkili Kriterlerin Bulanık AHP (F-AHP) Yöntemiyle Sıralanması

E-learning systems are one of the effective methods used for education. It is obvious that both during the Pandemic period when distance education is actively used and in normal life, participants apply to e-learning systems to follow lessons or improve themselves. Computer and internet applications are getting into education more and more day by day. Education through e-learning, which can work online or offline, is more and more effective every day. Thanks to these systems, education becomes more transparent, accessible and fairly distributed. Since many criteria will have an impact on the selection of a suitable e-learning system, these criteria were determined in the study and presented to expert opinions. In the selection of e-learning systems, 10 criteria were selected by literature review and the criteria were conveyed to the experts. The criteria were listed using the fuzzy AHP method. The most effective criterion in the study was found to be interaction. This criterion is followed by ease of use, content and reliability criteria.
The Correlation Between Students’ Interest In Speaking And Students Speaking Achievement At XITH Grade Of MAN 2 Bukittinggi

This study aims to determine the relationship between student interest and students' speaking scores in class XI MAN 2 Bukittinggi in the 2020/2021 academic year. This study uses a quantitative approach to the type of correlation design. The population in this study were all class XI at MAN 2 Bukittinggi consisting of 10 classes with a sample consisting of 71 students taken through a purposive technique. Data collection is done through questionnaires and tests. Students were asked to fill out a questionnaire related to their interest in speaking. The speaking data was taken from tests conducted in class. Researchers used SPSS for 20 windows to manage the data. The researcher used the Person Product Moment formula to determine the direction of the correlation and the magnitude of the correlation between students' interest in speaking and student achievement. The results of this study indicate that there is a relationship between students' interest in speaking and the achievement of speaking skills in class XI MAN 2 Bukittinggi in the 2020/2021 school year. The researcher found that the correlation coefficient between students' interest in speaking and the achievement of speaking skills in class XI MAN 2 Bukittinggi is 0.508. The correlation was positive and significant. This means that Ho is rejected and Ha is accepted. Thus, there is a positive relationship between students' interest in speaking and the achievement of speaking skills in class XI MAN 2 Bukittinggi. Based on the results of the study, it can be concluded that the higher the student's interest in speaking, the better the achievement of speaking skills.
Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects’ fMRI data to explore the brain’s visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as “matching”, and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the “matching” information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.
Analysis of online user sentiment and behavior in the tourism sector in Vietnam based on reviews and comments

As an attractive destination, Vietnam’s tourism industry attracts a large number of tourists and has become a spearhead industry that plays an important role in the country’s GDP. The large number of tourists leads to the development of the hotel industry, especially online hotel business services. Today, the explosion of social platforms and e-commerce sites has helped businesses collect large amounts of data from customer feedback. Exploiting this endless resource will give businesses a competitive advantage over competitors. Within the scope of this research, the sentiment analysis based on reviews and comments is focused on considering. Sentiment analysis is very useful to help business managers understand the needs and desires of customers. From there, come up with appropriate strategies to develop and improve the quality of products and services. The data of the study was collected on the online travel platform agoda.com with 272,835 comments. Research using machine learning models and algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) to evaluate and analyze customer views in the business sector online hotel business in Vietnam. The research results have high accuracy up to 90% in both English and Vietnamese.
A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual Analytics

Guidance can support users during the exploration and analysis of complex data. Previous research focused on characterizing the theoretical aspects of guidance in visual analytics and implementing guidance in different scenarios. However, the evaluation of guidance-enhanced visual analytics solutions remains an open research question. We tackle this question by introducing and validating a practical evaluation methodology for guidance in visual analytics. We identify eight quality criteria to be fulfilled and collect expert feedback on their validity. To facilitate actual evaluation studies, we derive two sets of heuristics. The first set targets heuristic evaluations conducted by expert evaluators. The second set facilitates end-user studies where participants actually use a guidance-enhanced system. By following such a dual approach, the different quality criteria of guidance can be examined from two different perspectives, enhancing the overall value of evaluation studies. To test the practical utility of our methodology, we employ it in two studies to gain insight into the quality of two guidance-enhanced visual analytics solutions, one being a work-in-progress research prototype, and the other being a publicly available visualization recommender system. Based on these two evaluations, we derive good practices for conducting evaluations of guidance in visual analytics and identify pitfalls to be avoided during such studies.
Unsupervised Techniques for Identifying the Mode of a Multi-Functional Radar with Varying Pulse Sequences

A multifunctional radar (MFR) with varying pulse sequences can change its signal characteristics and/or pattern, based on the presence of targets and to avoid being jammed. To take a countermeasure against an MFR, it is crucial for an electronic warfare (EW) system to be able to identify and separate a MFR's modes via analyzing intercepted radar signals, without a priori knowledge. In this article, two correlation-based methods, one taking the signal's order into account and another one ignoring the signal's order, are proposed and investigated for this task. The results demonstrate their great potential.
A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification

Classification of car vehicle types has been carried out using CNN. There are weaknesses in the CNN algorithm so that it can be continued in the research we propose. This study aims to improve the previous accuracy by using the Alexnet architecture. To improve the results of the data set used we use threshold and brightness adjustment and data augmentation techniques for Reflection, Rotation, and Translation. Sample images with a resolution of 227x227x3 totaling 840 images used to represent 8 class types of cars, including Avanza, Fortuner, Freed, Inova, Pajero, Terios, Xenia, and Xpander. Alexnet with 10 epochs consisting of a total of 760 iterations, and validation is carried out every 30 iterations, the test results show that the use of the "sgdm" optimization function achieves a training accuracy of 99.74%, while the use of the "adam" optimization function produces an accuracy of 96.85%. This experiment shows the model's ability to classify the types of trainers after a success rate of 100%.
The Current Situation of Chinese Teaching in Higher Vocational Colleges

The construction mechanism of high-quality student source base in Colleges and high schools is not perfect In order to keep up with the construction requirements of relevant education departments, some colleges and universities have some coping behaviors in the construction of high-quality student source base, and there is no perfect mechanism in the construction of high- quality student source base. For example, some colleges and universities have not carried out active promotion and implementation of supporting facilities after the establishment of high-quality student source base, as well as contact and interaction with students in this field, which leads to the active state of colleges and universities, while the high school is in a passive state. Over time, the driving force of cooperation between the two gradually declines, which is also an important factor affecting the cooperation between the two One. Therefore, in view of this phenomenon, colleges and high schools need to establish a scientific and reasonable evaluation system, and regularly evaluate the cooperation content and effect.
Merit: an on-demand IoT service delivery and resource scheduling scheme for federated learning and blockchain empowered 6G edge networks with reduced time and energy cost

Federated learning (FL) can improve the privacy-preserving issue of users' IoT devices, in which users complete the local training and transfer the updated model data to the central server for a global update. Due to high latency, the central server-based FL may suffer from huge energy loss at local user devices. MEC-based FL can improve the model accuracy and energy consumption at user devices via edge server-based task execution. Along with FL, blockchain can improve data security via permission-based access. Existing works explored only single type of IoT task without any appropriate resource scheduling for multiple tasks with different preferences, FL, and blockchain operations. This paper provides a merit-based resource scheduling scheme for different tasks with preferences, blockchain, and FL operations by checking resources, deadlines, delays, and resource costs. The simulation results verify that 45% running time and 53% cost gain is achieved in proposed scheme over the baseline schemes.
A Computational Design Pipeline to Fabricate Sensing Network Physicalizations

Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.
THE APPLICATION OF SPEED READING TO INCREASE READING COMPREHENSION FOR THE FIRST GRADE STUDENTS OF MAS AL- AMIN NORTH LABUHANBATU

This research observed the application of speed reading to increase reading comprehension for the first grade students of SMA Antartika Sidoarjo. This research was undertaken in class X-1 comprising 40 students involving a writer and an observer. The research was conducted through a classroom action research (CAR). The result showed that speed reading technique could be used to increase the students reading skills. The use of speed reading technique could be effective if it was supported by the ability of the teacher on planning learning in the classroom involving activities proportionally in the instructional process. Carrying a classroom climate was conducive by adequating learning facilities and infrastructure. The successful application of speed reading technique could be seen from the presence of students’ positive response and their active participation in each learning activities.
A Combination of Vigenere Cipher and Advanced Encryption Standard for Image Security

In an era where digital information security is paramount, this research addresses the pressing need for robust encryption methods. We propose a novel approach that combines the Vigenere Cipher and the Advanced Encryption Standard (AES) for secure digital image transmission. Our study recognizes the research gap in secure image transmission methods and aims to bridge it with a powerful encryption solution. We implement this hybrid encryption approach using the Vigenere Cipher in C++ and the AES algorithm in MATLAB. Our experiments validate the effectiveness of our program in concealing and restoring digital images during transmission. This hybrid encryption technique has promising applications in healthcare, military, and confidential business operations, bolstering image security in real-life scenarios. By enhancing image security, our research can contributed to safeguarding sensitive information in the digital age