Reviewerly/exHarmony huggingface dataset:
Achievement of Dynamic Equivalence in the Translation of Enola Holmes Movie’s Subtitle

In translation, dynamic equivalence aims to deliver a text’s message in terms that a new recipient audience can understand. This research centered on analyzing the Indonesian translation of the movie subtitles for "Enola Holmes" from English to achieve dynamic equivalence. The main objective was to assess the level of dynamic equivalence and translation quality. The study utilized the content analysis method and employed three instruments: document analysis, a questionnaire, and interviews. The theoretical framework adopted for dynamic equivalence was Nida and Taber's (1964) theory, while the concept of translation quality was based on Nababan et al (2012) work. The findings of the study revealed the following; the accuracy was highly precise, with 78 instances out of 208 data samples. This indicates that the translation effectively conveyed the original meaning without any spelling or grammatical deviations in the use of terms. The acceptability of the translation product was 86.3 instances out of the data analyzed. This suggests that the translation felt natural, employing commonly used and familiar terms that were in line with the rules of Bahasa. Based on readability formula checkers, the translation achieved a very easy readability level suitable for readers aged 8-9 years old, equivalent to 3rd and 4th graders. The study demonstrated a close relationship between accuracy, acceptability, and readability in achieving dynamic equivalence. Furthermore, it is recommended that future researchers focus on specific objects and issues in translation studies.
Analysis of Student Satisfaction Level on its Influence in Courses Using Fuzzy Logic

One of the soft computing technologies that has been widely developed is fuzzy logic. One of the research themes that uses fuzzy logic is the assessment system in research. To do this, an application that can be used to calculate and record the results of lecturers in learning. The purpose of this study is to apply fuzzy logic with the Mamdani method for lecturer research activities at the University of Graha Nusantara Padangsidimpuan. The steps of the research using the Mamdani method: generating input variables taken from Sinta accredited articles, Simlitabmas grant articles, and articles in international and national journals. generate input variables taken from Sinta accredited articles, Simlitamas grant articles, and articles in international and national journals. In higher education, teachers play an important role in producing skilled and qualified graduates. Therefore, it is necessary to measure student satisfaction with the performance of lecturers at the UGN Padangsidimpuan campus. To determine student satisfaction, observations and questionnaires were distributed to respondents. The questionnaire as a tool and data collection method to measure student satisfaction with lecturer performance is the Mamdani fuzzy logic method.
Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm

Restocking goods is essential for bottled drinking water to ensure smooth production and maintain a stable product supply. This research aims to compare the K-Nearest Neighbor, Support Vector Machine, and the Naïve Bayes algorithm to predict the need to restock bottled water. The data set for training and training data is taken from Adimaru's Agent. The comparative analysis with three algorithms gives the results of the prediction analysis for the accuracy value of K-NN is 88.20%, SVM is 84.51%, and Naïve Bayes is 66.20%. The AUC values of the three result algorithms include Good Classification. The comparison of the K-NN and SVM with T-Test algorithms get obtained the best performance with an alpha value is 0.102. From this accuracy value, the classification method of the K-Nearest Neighbor algorithm has the best predictive model results for restocking needs of bottled water goods.
SMS Gateway Based Academic Information System as a Communication Media to support School Performance

The development of the world accompanied by rapid technological developments requires every individual or institution to continue to follow the development of information technology so that they can provide satisfactory service which is the main goal that must be achieved by every government and private institution, which includes the world of education, namely schools, especially the delivery of information. One of the delivery of information is by SMS. The old system of delivering information, among others, is by attaching grades to each class or for job vacancy information through job vacancies information boards so that the delivery of information is felt to be less fast and also if the school area is very large and the means of communication are inadequate. With this SMS-based information system, it can be a solution to solve existing problems in addition to cheap and fast SMS technology, as well as with a website system display that can be accessed by all parties who use it, an effective and efficient job will be achieved in supporting activities on the internet. schools to become educational and training institutions for producing professional and independent workers can be achieved.
Self-Efficacy and Its Correlation With Reading Comprehension Of Senior High School Students

Self-efficacy becomes one of psychological factor affecting successfully in doing some activities. By having self-efficacy, people can measure themselves in doing some tasks briefly. Self-efficacy is necessary because it influences people’s performance. Self-efficacy in reading comprehension is also called reading self-efficacy. The objective of this study is the researcher wants to know the correlation between self-efficacy and reading comprehension achievement of the eleventh grade Senior high School student. This study uses correlation quantitative research as a research design. The participant of the research was 179 students of eleventh grade of senior high school students in Indonesia. The instruments used were the reading test and self-efficacy questionnaire. For the reading test, the researcher adopted from TOEFL for Junior. Then, for self-efficacy questionnaire, the researcher adapted from MSLQ. The result showed that there was a very low correlation between self-efficacy and reading comprehension with a significance value was .020 and coefficient correlation of .174.
Patients with different cancer types are reliably stratified by CBC data

Searching for informative indices indicating cancer type and cause of the disease is of great importance. Here we tried to identify those indices from the data of complete blood analysis
LATE CARBONIFEROUS RHYOLITES OF THE AMUR FRAGMENT OF THE NORA-SUKHOTINO TERRANE: GEOCHEMISTRY AND GEOCHRONOLOGY

The publication presents the first results of geochemical, isotope-geochemical (Sm-Nd) and geochronological (U-Pb, LA-ICP-MS) studies on acid volcanic rocks collected in the Bogdanikha River basin of the Amur fragment of the Nora-Sukhotino terrane in the northeast of the South Mongoli–Khingan orogenic belt. In terms of the content of rock-forming components, the studied volcanic rocks correspond to high-silica and high-alumina rhyolites. Elevated contents of alkalis, Ga, Zr, Nb and Y, reduced concentrations of Ba, Sr, Ti, Eu, and mantle values of εNd(t) = +3.0…+3.6 enable the rhyolites from the Bogdanikha river basin to be classified as A2-type rhyolites. The concordant age of the youngest zircon population from the rhyolite, according to geochronological (U-Pb, LA-ICP-MS) studies, is 301  4 Ma which corresponds to the Late Carboniferous. Taking into account the geochemical features of the studied rhyolites and the existing models for the formation of the South Mongolian–Khingan orogenic belt, it is most likely that they formed in a collisional setting as a result of slab detachment.
Packet Sniffing in the Cyber Threat Landscape: Examining Wireshark Capabilities, Misuse, and Policy Options in the Philippines

Wireshark is an invaluable network analysis tool, yet its traffic sniffing abilities also introduce privacy and security risks. This study analyzed Wireshark’s capabilities, cyber threats enabled through its misuse, and challenges in regulating open-source software. A literature review examined technical details, risks, and legal violations including illegal access, data theft, and denial-of-service attacks. Interviews with law enforcement uncovered constraints in prohibiting publicly accessible tools like Wireshark directly. Results highlight nuanced challenges balancing innovation and cybersecurity. Recommended strategies within these limitations include access controls, professional standards, strong legal deterrents, public awareness campaigns, and robust network defenses. Ultimately, although directly policing open-source software is problematic, thoughtful multidimensional policies can still curb misuse while preserving liberties. This synthesis of technical, legal, and policy insights provides promising paths for wisely safeguarding society amidst rapid technological change.
Legal assistance as one of the priority areas of student volunteer activity

he article is devoted to the organization of volunteer activities among university students, namely legal assistance to students in the educational programs "Jurisprudence" and "Law Enforcement". Earlier, within the framework of the research project "Scientific and methodological support of students' volunteer activities in the framework of inclusive education", other issues of volunteering were considered, both in organizational and substantive aspects. The provision of gratuitous legal assistance to needy categories of persons as a volunteer can realize two goals at once: professionally and educationally, firstly, it is an opportunity for the formation of practical skills among future specialists, and secondly, the manifestation of the civic position of each cadet or undergraduate, humanity and humanity. The authors, as suggestions, put forward the issue of activization of volunteering in the field of vocational education; organization of conferences, seminars on the topics of volunteering; development of collective and author's projects for the implementation of legal volunteer assistance to persons with special (educational) needs and to consider the possibility for the teaching staff to develop scientific and methodological foundations of volunteering in professional education.
Study on networking attacks and security algorithms

The dependence of humans on computers is increasing in today's era of technology. All transactions, as well as digital data transformations, are done with the help of the network, so third parties or intruders can attack the network to access data and use it in the wrong way. Organizations and individuals use the network for their work. Due to the easy availability of the internet, there is always a risk of information falling into the wrong hands. This paper presents a detailed study of networking attacks and security algorithms, as well as an analysis of various viable network attack observation tools to detect attacks. The algorithms discussed include Symmetric Key Algorithm, Asymmetric Key Algorithm, and their respective types.
C-CLIP: Contrastive Image-Text Encoders to Close the Descriptive-Commentative Gap

The interplay between the image and comment on a social media post is one of high importance for understanding its overall message. Recent strides in multimodal embedding models, namely CLIP, have provided an avenue forward in relating image and text. However the current training regime for CLIP models is insufficient for matching content found on social media, regardless of site or language. Current CLIP training data is based on what we call ``descriptive'' text: text in which an image is merely described. This is something rarely seen on social media, where the vast majority of text content is ``commentative'' in nature. The captions provide commentary and broader context related to the image, rather than describing what is in it. Current CLIP models perform poorly on retrieval tasks where image-caption pairs display a commentative relationship. Closing this gap would be beneficial for several important application areas related to social media. For instance, it would allow groups focused on Open-Source Intelligence Operations (OSINT) to further aid efforts during disaster events, such as the ongoing Russian invasion of Ukraine, by easily exposing data to non-technical users for discovery and analysis. In order to close this gap we demonstrate that training contrastive image-text encoders on explicitly commentative pairs results in large improvements in retrieval results, with the results extending across a variety of non-English languages.
REALM: Robust Entropy Adaptive Loss Minimization for Improved Single-Sample Test-Time Adaptation

Fully-test-time adaptation (F-TTA) can mitigate performance loss due to distribution shifts between train and test data (1) without access to the training data, and (2) without knowledge of the model training procedure. In online F-TTA, a pre-trained model is adapted using a stream of test samples by minimizing a self-supervised objective, such as entropy minimization. However, models adapted with online using entropy minimization, are unstable especially in single sample settings, leading to degenerate solutions, and limiting the adoption of TTA inference strategies. Prior works identify noisy, or unreliable, samples as a cause of failure in online F-TTA. One solution is to ignore these samples, which can lead to bias in the update procedure, slow adaptation, and poor generalization. In this work, we present a general framework for improving robustness of F-TTA to these noisy samples, inspired by self-paced learning and robust loss functions. Our proposed approach, Robust Entropy Adaptive Loss Minimization (REALM), achieves better adaptation accuracy than previous approaches throughout the adaptation process on corruptions of CIFAR-10 and ImageNet-1K, demonstrating its effectiveness.
A method for Sampling Bernoulli Variables

We introduce new method for generating correlated or uncorrelated Bernoulli random variables by using the binary expansion of a continuous random variable with support on the unit interval. We show that when this variable has a symmetric probability density function around 12 , its binary expansion provides equiprobable bits over {0, 1}. In addition we prove that when the random variable is uniformly distributed over [0, 1], its binary expansion generates independent Bernoulli random variables. Moreover, we give examples where, by choosing some parameterized nonuniform probability density functions over [0, 1], samples of Bernoulli variables with specific correlation values are generated.
Automatic Concept Embedding Model (ACEM): No train-time concepts, No issue!

Interpretability and explainability of neural networks is continuously increasing in importance, especially within safety-critical domains and to provide the social right to explanation. Concept based explanations align well with how humans reason, proving to be a good way to explain models. Concept Embedding Models (CEMs) are one such concept based explanation architectures. These have shown to overcome the trade-off between explainability and performance. However, they have a key limitation -- they require concept annotations for all their training data. For large datasets, this can be expensive and infeasible. Motivated by this, we propose Automatic Concept Embedding Models (ACEMs), which learn the concept annotations automatically.
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection

Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles. Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text. Given the surge in development of new LLMs, acquiring labeled training data for supervised detectors is a bottleneck. However, there might be plenty of unlabeled text data available, without information on which generator it came from. In this work we tackle this data problem, in detecting AI-generated news text, and frame the problem as an unsupervised domain adaptation task. Here the domains are the different text generators, i.e. LLMs, and we assume we have access to only the labeled source data and unlabeled target data. We develop a Contrastive Domain Adaptation framework, called ConDA, that blends standard domain adaptation techniques with the representation power of contrastive learning to learn domain invariant representations that are effective for the final unsupervised detection task. Our experiments demonstrate the effectiveness of our framework, resulting in average performance gains of 31.7% from the best performing baselines, and within 0.8% margin of a fully supervised detector. All our code and data is available at https://github.com/AmritaBh/ConDA-gen-text-detection.
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models

This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative models, specifically Gaussian mixture models (GMMs), mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These models effectively learn the unknown channel distribution inherent in radio propagation scenarios, providing valuable prior information. Conditioning on the latent variable of these generative models yields a locally Gaussian channel distribution, thus enabling the application of the well-known Bussgang decomposition. By exploiting the resulting conditional Bussgang decomposition, we derive parameterized linear minimum mean square error (MMSE) estimators for the considered generative latent variable models. In this context, we explore leveraging model-based structural features to reduce memory and complexity overhead associated with the proposed estimators. Furthermore, we devise necessary training adaptations, enabling direct learning of the generative models from quantized pilot observations without requiring ground-truth channel samples during the training phase. Through extensive simulations, we demonstrate the superiority of our introduced estimators over existing state-of-the-art methods for coarsely quantized systems, as evidenced by significant improvements in mean square error (MSE) and achievable rate metrics.
Brief technical note on linearizing recurrent neural networks (RNNs) before vs after the pointwise nonlinearity

Linearization of the dynamics of recurrent neural networks (RNNs) is often used to study their properties. The same RNN dynamics can be written in terms of the ``activations" (the net inputs to each unit, before its pointwise nonlinearity) or in terms of the ``activities" (the output of each unit, after its pointwise nonlinearity); the two corresponding linearizations are different from each other. This brief and informal technical note describes the relationship between the two linearizations, between the left and right eigenvectors of their dynamics matrices, and shows that some context-dependent effects are readily apparent under linearization of activity dynamics but not linearization of activation dynamics.
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models

Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks. However, a challenge hindering their application in real-world scenarios, particularly regarding safety, robustness, and reliability, is their constrained semantic grounding ability, which pertains to connecting language to the physical-world entities or concepts referenced in images. Therefore, a crucial need arises for a comprehensive study to assess the semantic grounding ability of widely used LVLMs. Despite the significance, sufficient investigation in this direction is currently lacking. Our work bridges this gap by designing a pipeline for generating large-scale evaluation datasets covering fine-grained semantic information, such as color, number, material, etc., along with a thorough assessment of seven popular LVLMs' semantic grounding ability. Results highlight prevalent misgrounding across various aspects and degrees. To address this issue, we propose a data-centric enhancement method that aims to improve LVLMs' semantic grounding ability through multimodal instruction tuning on fine-grained conversations. Experiments on enhanced LVLMs demonstrate notable improvements in addressing misgrounding issues.
Two classes of reducible cyclic codes with large minimum symbol-pair distances

The high-density data storage technology aims to design high-capacity storage at a relatively low cost. In order to achieve this goal, symbol-pair codes were proposed by Cassuto and Blaum \cite{CB10,CB11} to handle channels that output pairs of overlapping symbols. Such a channel is called symbol-pair read channel, which introduce new concept called symbol-pair weight and minimum symbol-pair distance. In this paper, we consider the parameters of two classes of reducible cyclic codes under the symbol-pair metric. Based on the theory of cyclotomic numbers and Gaussian period over finite fields, we show the possible symbol-pair weights of these codes. Their minimum symbol-pair distances are twice the minimum Hamming distances under some conditions. Moreover, we obtain some three symbol-pair weight codes and determine their symbol-pair weight distribution. A class of MDS symbol-pair codes is also established. Among other results, we determine the values of some generalized cyclotomic numbers.
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation

Modern displays are capable of rendering video content with high dynamic range (HDR) and wide color gamut (WCG). However, the majority of available resources are still in standard dynamic range (SDR). As a result, there is significant value in transforming existing SDR content into the HDRTV standard. In this paper, we define and analyze the SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Our analysis and observations indicate that a naive end-to-end supervised training pipeline suffers from severe gamut transition errors. To address this issue, we propose a novel three-step solution pipeline called HDRTVNet++, which includes adaptive global color mapping, local enhancement, and highlight refinement. The adaptive global color mapping step uses global statistics as guidance to perform image-adaptive color mapping. A local enhancement network is then deployed to enhance local details. Finally, we combine the two sub-networks above as a generator and achieve highlight consistency through GAN-based joint training. Our method is primarily designed for ultra-high-definition TV content and is therefore effective and lightweight for processing 4K resolution images. We also construct a dataset using HDR videos in the HDR10 standard, named HDRTV1K that contains 1235 and 117 training images and 117 testing images, all in 4K resolution. Besides, we select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Our final results demonstrate state-of-the-art performance both quantitatively and visually. The code, model and dataset are available at https://github.com/xiaom233/HDRTVNet-plus.
Permutation Rational Functions over Quadratic Extensions of Finite Fields

Permutation rational functions over finite fields have attracted much attention in recent years. In this paper, we introduce a class of permutation rational functions over $\mathbb F_{q^2}$, whose numerators are so-called $q$-quadratic polynomials. To this end, we will first determine the exact number of zeros of a special $q$-quadratic polynomial in $\mathbb F_{q^2}$, by calculating character sums related to quadratic forms of $\mathbb F_{q^2}/\mathbb F_q$. Then given some rational function, we can demonstrate whether it induces a permutation of $\mathbb F_{q^2}$