Reviewerly/exHarmony huggingface dataset:
VQ-CL: Learning Disentangled Speech Representations with Contrastive Learning and Vector Quantization

Voice Conversion(VC) refers to converting the voice characteristics of audio to another one as it is said by other people. Recently, more and more studies have focused on disentangle-based VC, which separates the timbre and linguistic content information from an audio signal to effectively achieve VC tasks. However, It’s still challenging to extract phoneme-level features from frame-level hidden representations. This paper proposed a novel zero-shot voice conversion framework that utilizes contrastive learning and vector quantization to encourage the frame-level hidden features closer to the phoneme-level linguistic information, called VQ-CL. All objective and subjective experiment results show that VQ-CL has better performance than previous studies in separating content and voice characteristics to improve the sound quality of generated speech.
In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models

The phenomena of in-context learning has typically been thought of as "learning from examples". In this work which focuses on Machine Translation, we present a perspective of in-context learning as the desired generation task maintaining coherency with its context, i.e., the prompt examples. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. Next, we investigate coherency for the in-domain setting, which uses prompt examples from a moving window. We study this with respect to other factors that have previously been identified in the literature such as length, surface similarity and sentence embedding similarity. Our results across 3 models (GPTNeo2.7B, Bloom3B, XGLM2.9B), and three translation directions (\texttt{en}$\rightarrow$\{\texttt{pt, de, fr}\}) suggest that the long-term coherency of the prompts and the test sentence is a good indicator of downstream translation performance. In doing so, we demonstrate the efficacy of In-context Machine Translation for on-the-fly adaptation.
Cervical Cancer Classification using Machine Learning with Feature Importance and Model Explainability

Cervical cancer is still one of the most common gynecological cancers in the world. The rate at which cervical cancer develops is the fourth highest among common female disorders. It's one of the diseases that threaten women's health worldwide, and early symptoms are notoriously hard to see. In the fields of gynecology and computer science, there has been a dearth of studies focusing on the diagnosis of cervical cancer based on machine learning. Early-stage prediction of cervical cancer can be a great solution as it aware women to control their lifestyles also. This study classifies cervical cancer from secondary data using machine learning algorithms. Also, the top features responsible for cervical cancer are found out in this study. Support Vector Machine, Random Forest, and Logistic Regression are used as classifiers, and Boruta feature selection technique is used to find the best features to train the model. Two model explainable tools Explain like I'm 5 (eli5) and SHapley Additive exPlanations (SHAP) are used to rank the top feature, and their effect on the model are also analyzed. RF performs better than other classifiers using the Synthetic Minority Oversampling Technique with Tomek links (SMOTETomek) data balancing technique that greatly impacts model accuracy. It shows 99.85% accuracy with 100% precision, recall and f1 score. This proposed paradigm will help the medical domain people to predict the early stage of cervical cancer more accurately and can explain the behind information.
Hybrid plaintext attack for a cryptosystem based on interference and the phase-retrieval technique

In this paper, the security strength of an enhanced cryptosystem based on interference and the phase-retrieval technique is evaluated. The security strength of the optical cryptosystem was improved through the phase-retrieval technique used to generate a phase-only mask (POM) as the ciphertext. Due to the complex mathematical model of the phase-retrieval technique, it seems that a silhouette problem existing in the conventional interference-based scheme was removed. However, we noted that the random phase mask (RPM) regarded as the only private key was fixed in the encryption path, which is not related to the plaintext and makes it possible to be recovered using a known-plaintext attack (KPA). Moreover, we also found that the RPM has high key sensitivity, and it should be recovered precisely to retrieve information of plaintexts during the attack. Thus, a hybrid KPA where three pairs of known plaintexts and their ciphertexts are regarded as the amplitude and phase constraints to obtain the precise estimation of the RPM is proposed. Then, with the help of the estimated private key, information of the original plaintexts encoded using the cryptosystem under study could be retrieved from an arbitrarily given ciphertext without any knowledge of the private key. Our cryptoanalysis shows that the cryptosystem based on interference and the phase-retrieval technique is vulnerable to the proposed attack, and there is a security leak in it. Numerical simulations have been carried out to demonstrate the performance of our proposed attack.
FashionTex: Controllable Virtual Try-on with Text and Texture

Virtual try-on attracts increasing research attention as a promising way for enhancing the user experience for online cloth shopping. Though existing methods can generate impressive results, users need to provide a well-designed reference image containing the target fashion clothes that often do not exist. To support user-friendly fashion customization in full-body portraits, we propose a multi-modal interactive setting by combining the advantages of both text and texture for multi-level fashion manipulation. With the carefully designed fashion editing module and loss functions, FashionTex framework can semantically control cloth types and local texture patterns without annotated pairwise training data. We further introduce an ID recovery module to maintain the identity of input portrait. Extensive experiments have demonstrated the effectiveness of our proposed pipeline. Code for this paper are at https://github.com/picksh/FashionTex.
A systemic mapping study of Mobile Assisted Language Learning methods and practices: a qualitative literature review

Pedagogical use of mobile applications has become a novel approach for language teaching, resulting in higher student engagement and motivation. There are a number of methods and practices that have been created so far to incorporate into language classroom at the disposal of language educators. However, extracting relevant and suitable information from growing bodies of research and teaching practices to apply in a specific teaching context is tedious and time-consuming requirement for those who want to use the state-of-the-art methods of mobile assisted language teaching in their classroom. Thus, the aim of this systematic mapping study is to structure the knowledge of using Mobile Assisted Language Learning methods and practices and provide a comprehensive overview of the current state of the art in this domain of language teaching. Besides giving the structure of relevant research areas and the portfolio of key publications, the paper also outlines classification of mobile assisted language learning solution proposals and the visual status of the target research areas. Finally, it represents future research agenda and draw implications.
Het interesseert me geen ene je-weet-wel – Minimaliserende strategieën in het hedendaagse Nederlandse Nederlands

This paper focuses on minimizing strategies in present-day Netherlandic Dutch. The minimizing construction under investigation consists of (1) an element of negation (geen); (2) a predicate and (3) a minimizer, a noun that denotes a small quantity and that is used to reinforce sentential negation (e.g. geen woord begrijpen ‘to understand not a word’). First, we situate the minimizing construction in relation to other, pragmatic minimizing strategies. Then, we provide a semantic typology of the minimizers in our dataset. Moreover, we give an overview of the different strategies that are available to extend the slot of the minimizer, such as analogical and context-based extensions. This shows the productivity (in terms of lexical scope) of the minimizing construction.
Correlations in Compositional Data without Log-Transformations

The article proposes a method for determining the p-value of correlations in compositional data, i.e., those data that arise as a result of dividing the original values by their sum. Data organized in this way are typical for many fields of knowledge, but there is still no consensus on methods for interpreting correlations in such data. In a space closed by normalizing quantity, correlation coefficients behave differently than under normal conditions: their probabilities of occurrence do not coincide with those inherent in the standard scale of estimates. In the tens of the new millennium, almost all newly emerging methods for estimating correlation in compositional data began to require mandatory log-transformation of the variable values. In the method proposed here there are no log-transformations. We return to the early stages of attempting to solve the problem and rely on negative shifts in correlations in the multinomial distribution. In modeling the data, we use a hybrid method that combines the hypergeometric distribution with the distribution of any other law. During our work on the calculation method, we found that the number of degrees of freedom in compositional data measures discretely only when all the normalizing sums are equal and that it decreases when the sums are not equal, becoming a continuously varying quantity. Estimation of the number of degrees of freedom and the strength of its influence on the magnitude of the shift in the distribution of correlation coefficients is the basis of the proposed method.
Analytical Bounds for Nonasymptotic Asymmetric State Discrimination

Two types of errors can occur when discriminating pairs of quantum states. Asymmetric state discrimination involves minimizing the probability of one type of error, subject to a constraint on the other. We give explicit expressions bounding the set of achievable errors, using the trace norm, the fidelity, and the quantum Chernoff bound. The upper bound is asymptotically tight and the lower bound is exact for pure states. Unlike asymptotic bounds, our bounds give error values instead of exponents, so can give more precise results when applied to finite-copy state discrimination problems.
Achieving a Secure and Traceable High-Definition Multimedia Data Trading Scheme Based on Blockchain

The Internet has penetrated into every aspect of life. Large amounts of data are generated by multimedia collection equipment every day. As an asset, data can achieve value circulation through transactions. However, the existing centralized transaction model is not secure enough, has the risk of user privacy leakage, and the protection of data copyright is insufficient. In this paper, in order to solve the transaction security and traceability problems of multimedia data, especially high-definition data such as vector graphics, we implement a transaction scheme STTS without third-party based on blockchain. For high-definition multimedia data, we use zero watermarking combined with oblivious transfer to embed copyright information. A two-stage verification process is then implemented by using group signature and a secret sharing scheme to complete data distribution. Finally, the smart contract is used to complete copyright tracking. We test the performance of our scheme by simulating the real transaction environment in the Internet of Things (IoT) and demonstrate the feasibility of our scheme, which can be applied to large-scale multimedia data trading schemes in the IoT.
Retrieving SSH Journals Citation Information from three datasets (COCI, META and ERIH-PLUS) - Workflow v2

Purpose The main purpose of our research is to answer to three different question and find out: by looking at citations data contained in COCI, the number of citations included in Meta which refer to publication in SSH (Social Sciences and Humanities) journals indicated in ERIH-PLUS the disciplines citing the most VS the disciplines cited the most the citations from/to publication contained in Meta which are not included in SSH journals We want to create a connection between these three different datasets for having an overall view of the citations present in each of them. Methodology For this reason, we approach the problem from a computational point of view. We started by extracting only the relevant data by operating a first processing of COCI, ERIH-PLUS and META's datasets. Then we built a python software able to analyze the CSV format data, querying them in order to retrieve the info needed and to present the results in a clear and understandable way, which will be available in CSV format. Findings The findings show that the majority of citations come from and go to Psychology publications, and a good amount of citations according to the three datasets is involved in SSH publications. Originality/Value The research conducted by us has the purpose to add information to existing resources with the aim of facilitating their use and allowing the researchers to have a clearer view of the data contained in each dataset. Further development can be made, for example analyzing other disciplines, to have the same overview as the one created by us but related to other fields.
Revisiting Relation Extraction in the era of Large Language Models

Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a \emph{sequence-to-sequence} task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer

Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and the necessary computational resources for training, directly applying these models for video stylization remains difficult. Also, given that the noise addition process on the input content is random and destructive, fulfilling the style transfer task's content preservation criteria is challenging. This paper proposes a zero-shot video stylization method named Style-A-Video, which utilizes a generative pre-trained transformer with an image latent diffusion model to achieve a concise text-controlled video stylization. We improve the guidance condition in the denoising process, establishing a balance between artistic expression and structure preservation. Furthermore, to decrease inter-frame flicker and avoid the formation of additional artifacts, we employ a sampling optimization and a temporal consistency module. Extensive experiments show that we can attain superior content preservation and stylistic performance while incurring less consumption than previous solutions. Code will be available at https://github.com/haha-lisa/Style-A-Video.
Key Posture Extraction Method using a Small Number of Motion Datasets

Although human movement can be inferred from various datasets, a method is required to recognize the change in movement from a small amount of new data. A deep learning-based method cannot be used to learn motion from limited data. To use existing feature extraction methods, a process is required to extract key postures from the entire dataset. The proposed method extracts key postures from an entire frame, whereby the differences in distance from the starting posture are added and displayed as a graph. The key task is to determine an inflection point in the graph, divide the data, and designate the frame corresponding to the inflection as an intermediate frame. Kinect and inertial sensor data are used to demonstrate the results of the proposed method. The proposed method can be used to determine similar motions on the basis of key postures, using existing datasets.
Spin, Magnetic Moment, Structure, Shape and Sizes of an Electron, Muon, and Proton

In particle physics, spin is an intrinsic form of angular momentum carried by the elementary particles. It is believed that spin is a solely quantum-mechanical phenomenon, which does not have a counterpart in classical mechanics. We have derived the simplest equations of spin and magnetic moment of a charged elementary particle. These formulas enable us to understand the physical nature of the spin and the structure of elementary particles. It is shown that the electron and muon are in the shape of a thin ring with a circular cross section (torus), and the proton is in the form of a ring with a non-circular cross section (toroid). The connection between the photon&amp;rsquo;s spin of and the propagation of electromagnetic waves is considered.
Research on the Evaluation Model of School Management Quality in the Compulsory Education Stage Based on Big Data Technology

With the spread of compulsory education emerged school management problems continued, and the quality of school management in compulsory education has attracted a great deal of attention in China. However, the application of information technology in the field is not yet detailed and wide, resulting in problems of heavy workload and high difficulty in the whole evaluation process. Accordingly, we use big data technologies such as Apache Spark, Apache Hive, and SPSS to carry out data cleaning, correlation analysis, dynamic factor analysis, principal component analysis, and visual display on 1760 sample data from 40 primary and secondary schools in Q Province in China, and constructs a model school management of quality evaluation in the compulsory education stage, which reduces the 22 management tasks required for previous evaluation to 5, greatly reducing the workload and difficulty of evaluation. It has improved the efficiency and accuracy of evaluation, and further promoted the simultaneous development of education of five domains and education equity in the compulsory education stage.
Locally private estimation of conditional probability distribution for random forest in multimedia applications

The application of artificial intelligence models to raw multimedia data is susceptible to various data inference attacks, posing a significant risk in terms of sensitive input information leakage. Most of the existing studies on privacy-preserving multimedia applications based on artificial intelligence, focus on a single intelligent model and thus have various limitations. In this paper, we attempt to directly perturb the core component of many multimedia intelligent models in Bayesian networks and deep learning. That is, we apply conditional probability distribution estimation to guarantee the privacy of the models. At first, we present the formal problem formulation of private conditional probability distribution estimation and apply it to random forest for task classification in multimedia applications. Then, we design a simple perturbation approach called NaivePrivDistEst, to add noise to all the elements of probability estimation in random forest. Next, we present an improved approach called FastLRG, that utilizes the taxonomy tree to discretize the continuous attributes, thereby combining the attribute features to improve the prediction accuracy of random forest. Finally, we perform extensive experiments to evaluate the performance of random forest based on the proposed estimation algorithms. The experimental results indicate that the proposed models have better performance compared with existing private decision trees.
Development of Integrated Thematic Electronic Worksheets With RADEC Learning Model Using Nearpod

This research was motivated by that LKPD in schools is still in the form of handouts that are printed by teachers and distributed to students. The purpose of this research is to produce E-LKPD products using the RADEC-based Nearpod application. This type of research is development research with the ADDIE development model. Research instruments in the form of interviews, questionnaires, tests, and documentation. The research results obtained were Integrated thematic E-LKPD assisted by the RADEC-based Nearpod application in the elementary school stated in the very valid category. In the practicality questionnaire for the teacher's response score and the score for the student's practicality questionnaire in the very practical category. On the effectiveness used the N-Gain score percent test with the effective category. So, it can be concluded that the developed ELKPD has fulfilled the categories of valid, practical, and effective to improve student learning outcomes.
Cypriot Arabic: Language Contact and Linguistic Deviations from Mainstream Arabic Norms

I was fascinated when I came to know that there is “Arabic” spoken in Cyprus, which aroused several questions in me as to how this language came into existence, how it is spoken now, what changes to its linguistic modules take place due to language contact with other languages, what its status now is, etc. This article, thus, aims to investigate these questions, focusing mainly on how Cypriot Arabic (CyA) deviates from Mainstream Arabic (MA) varieties in all linguistic modules as a result of the language contact with the dominant language, i.e. Cypriot Greek. Specifically, I investigate CyA linguistic modules: phonology, morphology, syntax and lexicon and how language contact leads to deviations from Mainstream Arabic norms. Language decay and death are also investigated. Cypriot Arabic is a dialect of Arabic spoken in Cyprus, having evolved as a purely oral (unwritten) variety in isolation from any written or spoken variety of Arabic after the twelfth century. It is spoken by the Maronite Cypriots who have immigrated mostly from Syria and Lebanon to Cyprus and have inhabited Kormakiti(s) village until the Turkish invasion of the northern part of the Island in 1974 (Borg, 1985; inter alia). The main contact CyA has is with (Cypriot) Greek. In this paper, I employ the comparative and analytic approaches to linguistic phenomena under study. Findings indicate that CyA is severely endangered and hence should urgently be documented and revitalized. Thus, this research, to the best of my knowledge, is the first to be conducted by a linguist who is a native speaker of Arabic (with a little knowledge of Greek), and here lies its significance. It provides a reliable investigation and contributes to the existing body of literature on this language variety.