Reviewerly/exHarmony huggingface dataset:
Pengaruh Metode Struktural Analitik Sintetik (SAS) Berbantuan Media Audio Visual Terhadap Keterampilan Membaca dan Menulis Siswa Sekolah Dasar

This study discusses the effect of the synthetic structural analytic method (SAS) assisted by audio-visual media on students' reading and writing skills. This study aims to determine whether there is an effect of the synthetic Structural Analytical method assisted by audio-visual media on students' reading and writing skills. The type of research used is Quasi Experimental Design with the Nonequivalent Control Group Design type. The population in this study were all class I Cluster III Galesong District consisting of six schools with a total of 332 students. The sample in this study was class I students at SDN No. 190 Inpres Bura'ne, totaling 53 people using cluster random sampling technique. Data collection techniques in this study were carried out through technical tests to determine students' initial reading and writing skills in the form of questions and reading texts. The results of the study are based on the Independent Sample T Test Hypothesis 1, decision-making framework, if the significance value is less than 0.05, it can be said that the synthetic structural analytic method (SAS) assisted by audio-visual media on beginning reading skills. The significance value obtained from the independent sample t test is 0.00, which is smaller than 0.05. Therefore, it can be stated that there is an influence of the synthetic structural analytic method (SAS) assisted by audio-visual media on beginning reading skills. At the same time, the Independent Sample T Test Hypothesis 2 findings show that the significance value for the test is 0.00, which is less than 0.05. Consequently, it is possible to say that there is an influence of the synthetic structural analytic method (SAS) assisted by audio-visual media on initial writing skills. And the results of the Manova test, the significance value obtained from the Manova test is 0.00, which is smaller than 0.05. Therefore, it can be stated that there is an influence of the synthetic structural analytic method (SAS) assisted by audio-visual media on initial reading and writing skills
How is The Grammatical Relation of Batak Toba Wedding Ceremony?

This article discusses grammatical relations in Toba Batak language. Toba Batak language has formal markings, both predicates and arguments that show the relationship between clause elements, and the coding of words in the language. arguments are integrated syntactically. This research uses descriptive qualitative research. Qualitative is used to investigate an issue in order to gain a clear understanding of a particular phenomenon. The main data of this research are sentences. Sentence data in this study were obtained through traditional Toba Batak documents, namely 'marhata sinamot' which is commonly used Grammatical relations have an important role and function to identify the correctness of the syntactic process because they are considered to connect arguments with clauses. The grammatical relationship and the semantic role of the Toba Batak language are S = A/P and S = P/A. That is, the Toba Batak language treats S as A and S as P. A language is said to be of ergative type if the patient (P) of a transitive verb is treated with or coreferential with the subject (S) in an intransitive clause and different from the agent (A) of the verb transitive.
Dual‐route synthetic‐to‐real adaption for single image dehazing

Abstract Single image dehazing in real scenarios is still a particularly challenging task due to the large domain discrepancy between synthetic and real hazy images. To address this problem, the dual‐route domain‐aware adaptation framework with the bridging domain is proposed. First, inspired by the effective multistage strategy in low‐level tasks, the bridging domain is constructed with the color‐preserved adaptive histogram equalization to facilitate the adaptation and improve the performance of real hazy images. Second, the totally shared structure is relaxed and the residual dual‐path domain‐aware modules (RDDM) for synthetic and the bridging domains are proposed, which facilitates extracting the domain‐specific haze information with the different parameters. Third, a half‐cyclic constraint is proposed for the unsupervised hazy images to avoid structure distortion during the unsupervised adversarial training process. Finally, for convenience in the inference stage, feature enhancement modules (FEM) are proposed for the original real hazy images to learn the pre‐process operation in the training stage. Extensive qualitative and quantitative experiments demonstrate that the proposed method significantly improves the dehazing performance on synthetic and real hazy images.
Context‐based transfer learning for low resource code summarization

Abstract Source code summaries improve the readability and intelligibility of code, help developers understand programs, and improve the efficiency of software maintenance and upgrade processes. Unfortunately, these code comments are often mismatched, missing, or outdated in software projects, resulting in developers needing to infer functionality from source code, affecting the efficiency of software maintenance and evolution. Various methods based on neuronal networks are proposed to solve the problem of synthesis of source code. However, the current work is being carried out on resource‐rich programming languages such as Java and Python, and some low‐resource languages may not perform well. In order to solve the above challenges, we propose a context‐based transfer learning model for low resource code summarization (LRCS), which learns the common information from the language with rich resources, and then transfers it to the target language model for further learning. It consists of two components: the summary generation component is used to learn the syntactic and semantic information of the code, and the learning transfer component is used to improve the generalization ability of the model in the learning process of cross‐language code summarization. Experimental results show that LRCS outperforms baseline methods in code summarization in terms of sentence‐level BLEU, corpus‐level BLEU and METEOR. For example, LRCS improves corpus‐level BLEU scores by 52.90%, 41.10%, and 14.97%, respectively, compared to baseline methods.
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Α tool for requirements engineering using ontologies and boilerplates

Abstract The most popular technique for specification requirements is natural language. The disadvantage of natural language is ambiguity. Boilerplates are syntactic patterns which limit the ambiguity problem associated with using natural language to specify system/software requirements. Also, using boilerplates is considered a useful tool for inexperienced engineers to define requirements. Using linguistic boilerplates, constrains the natural language syntactically. Furthermore, a domain-specific ontology is used to constrain requirements semantically, as well. In requirements specification, using ontologies helps to restrict the vocabulary to entities, properties, and property relationships which are semantically related. The above results in avoiding or making fewer mistakes. This work makes use of the combination of boilerplate and ontology. Usually, the attributes of boilerplates are completed with the help of the ontology. The contribution of this paper is that the whole boilerplates is stored in the ontology and attributes and fixed elements are part of the ontology. This combination helps to correct semantically and syntactically requirement construction. This paper proposes a tool based on a domain-specific ontology and a set of predefined generic linguistic boilerplates for requirements engineering. We create a domain-specific ontology and a minimal set of boilerplates for an ATM (Automated Teller Machine). We carried out an experiment in order to obtain evidence for the effectiveness and efficiency of our method. The experiment took the form of a case study for the ATM domain and our proposed method was evaluated by users. The contribution and novelty of our methodology is that we created a tool for defining requirements that integrates boilerplate templates and an ontology. We exploit the boilerplate language syntax, mapping them to Resource Description Framework triples which have also a linguistic nature.
Multi-Prior Mixture Distribution and Arithmetic Average Fusion-Based Student’s t Filter

In this paper, a multi-prior mixture (MPM) distribution and arithmetic average (AA) fusion-based Student’s t filter (MPMAASTF) is proposed for the problem of non-stationary heavy-tailed noises filtering with only rough prior information available. Firstly, a MPM distribution is proposed to model the joint probability density function (PDF) for one-step prediction and measure likelihood, then the Kullback-Leibler Divergence (KLD) minimization technique and Jensen’s inequality are used to derive approximate posterior PDFs for states and parameters. A finite number of Student’s t PDF fusion algorithm based on AA fusion is developed to guarantee the closure of the filtering posterior PDF, and finally existing high precision moment matching technique is embedded. The proposed filter improves the filtering accuracy of non-stationary heavy-tailed noises by adaptive learning of the degrees of freedom (dof), the one-step prediction error scale matrix and the measurement noise scale matrix. Simulations validate the effectiveness and superiority of the proposed method.
Observation Is Reality? A Graph Diffusion-Based Approach for Service Tags Recommendation

Accurate service tags recommendation plays a crucial role in classifying, searching, managing, composing, and expanding services. However, many service tags recommendation studies fail to consider real-world scenarios, greatly limiting their performance and capability of handling complex situations. First, the simplification of service tags recommendation to single-tag classification or clustering overlooks the complexity and diversity of crossover services, as well as the intricate interactions between services or their tags. Second, inadequate or ambiguous descriptions of many services result in insufficient information for accurate recommendations. Third, the observation is not always reality due to the presence of unseen data or noise. To address these issues, a new graph diffusion-based graph neural network framework is proposed for multi-tags recommendation, named SpiderTags. It considers both the textual description of services and explicit relationships between services or their tags to enhance performance. Moreover, considering that the observed explicit graph may not be reality and not optimal for downstream tasks, SpiderTags introduces a graph diffusion mechanism to search for a more optimal graph for downstream tasks. A series of experiments conducted on the real-world ProgrammableWeb dataset demonstrate the effectiveness of SpiderTags in service tags recommendation task. Our code is available on https://github.com/gplinked/SpiderTags .
Anonymization-as-a-Service: The Service Center Transcripts Industrial Case

Modern Big Data Analytics services require compliance with non-functional requirements such as privacy, in order to align with the introduced legislation such as the General Data Protection Regulation (GDPR). Specifically, the Telco industry has been using Big Data Analytics solutions for service continuity, whose basic steps revolve around automatically transcribing call center text data to extract valuable insights and enhance customer service. Such data obviously contains Personal Identifiable Information (PII) which hampers privacy-sensitive service operations if not handled properly. To meet these requirements we created Deperson—an efficient rule-based data anonymization service—which enables companies to anonymize customer data effectively while preserving its utility for further analysis. As a proof-of-concept, Deperson has been integrated into an existing Big Data Analytics solution in the Customer Contact Analytics department of a major Dutch Telco provider to ensure compliance with GDPR regulations. Based on dictionary look-ups and pattern-matching rules Deperson effectively removes PII achieving an accuracy of 0.82 while maintaining the essential information necessary for analysis. Our concept shows that Deperson plays a significant role in enabling the extraction and further processing of valuable insights from customer data without risking non-compliance with GDPR.
Uncovering Implicit Bundling Constraints: Empowering Cloud Network Service Discovery

Cloud service providers (CSPs) offer their networking services (NSs) in the form of service bundles containing underlying services, not necessarily requested by the users. While service bundling is a common practice in the cloud providing multiple components as a single service, unawareness of this hidden structure of services at design time may limit their portability, compatibility, and interoperability across multiple providers. This calls for service discovery solutions that can identify and reveal such hidden bundling to cloud users so they become aware of the consequences of existing bundling before any deployment stage. This paper presents a new NSs discovery approach that takes into account and makes transparent network services bundling for cloud users.
Influence-Guided Data Augmentation in Graph Contrastive Learning for Recommendation

The graph contrastive learning approach, which combines graph convolutional networks (GCNs) with contrastive learning, has been widely applied in recommender systems and achieved tremendous success. Most graph contrastive learning (GCL) methods for recommendation perform random data augmentation operations on the user-item interaction graph to generate subgraphs, learn node embeddings through graph convolutional networks, and finally maximize the consistency of node embeddings in different subgraphs using contrastive loss. GCL improves the recommendation performance while slowing down the rate at which node embeddings tend to be similar, alleviating the over-smoothing problem to some extent. However, random data augmentation (e.g., random node dropout or edge dropout) will destroy the structure of the original input graph and change the original semantic information, leading to performance degradation. In this paper, we propose a novel graph contrastive learning model, IG-GCL, which uses the influence of elements in a graph to achieve guided data augmentation. Specifically, the model uses the mutual reinforcement network and node degree to calculate the importance scores of nodes and edges in the graph, respectively, thereby creating a more powerful data augmentation method to improve the performance of contrastive learning. We conduct extensive experiments on three real-world benchmark datasets. Experimental results demonstrate that IG-GCL can obtain performance improvements by stacking multi-layer neural networks, has the ability to mitigate the over-smoothing problem, and consistently outperforms the baseline, validating the effectiveness of the proposed influence-guided data augmentation method.
Study of Indian English Pronunciation Variabilities Relative to Received Pronunciation

Analysis of Indian English (IE) pronunciation variabilities is useful in ASR and TTS modelling for the Indian context. Prior works characterised IE variabilities by reporting qualitative phonetic rules relative to Received Pronunciation (RP). However, such characterisations lack quantitative descriptors and data-driven analysis of diverse IE pronunciations, which could be due to the scarcity of phonetically labelled data. Furthermore, the versatility of IE stems from the influence of a large diversity of the speakers’ mother tongues and demographic region differences. To address these issues, we consider the corpus Indic TIMIT and manually obtain 13, 632 phonetic transcriptions in addition to those parts of the corpus. By performing a data-driven analysis on 15, 974 phonetic transcriptions of 80 speakers from diverse regions of India, we present a new set of phonetic rules and validate them against the existing phonetic rules to identify their relevance. Finally, we test the efficacy of Grapheme-to-Phoneme (G2P) conversion developed based on the obtained rules considering Phoneme Error Rate (PER) as the metric for performance.
Investigation of Different Calibration Methods for Deep Speaker Embedding Based Verification Systems

Deep speaker embedding extractors have already become new state-of-the-art systems in the speaker verification field. However, the problem of verification score calibration for such systems often remains out of focus. An irrelevant score calibration leads to serious issues, especially in the case of unknown acoustic conditions, even if we use a strong speaker verification system in terms of threshold-free metrics. This paper presents an investigation over several methods of score calibration: a classical approach based on the logistic regression model; the recently presented magnitude estimation network MagNetO that uses activations from the pooling layer of the trained deep speaker extractor and generalization of such approach based on separate scale and offset prediction neural networks. An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system. The obtained results demonstrate that there are no serious problems if in-domain development data are used for calibration tuning. Otherwise, a trade-off between good calibration performance and threshold-free system quality arises. In most cases using adaptive s-norm helps to stabilize score distributions and to improve system performance.
Development and Research of Dialogue Agents with Long-Term Memory and Web Search

This paper describes the training process of neural network that can extract additional knowledge from the Internet and long-term memory with the goal to improve the quality of generated dialogue responses. Modern language models, due to their large size and high-quality data, can generate meaningful texts, including dialogues with other speakers. Meanwhile, their knowledge is frozen in time by the data they were trained on. Without re-training, such models are not able to acquire new relevant knowledge. We propose one of the possible solutions to this problem, in which the neural network model will be able to use the knowledge received from the Internet and long-term memory to generate dialogue responses. Using these methods we improved BLEU-1 metric by 43% and BLEU-2 metric by 45% on Toloka Persona Chat Rus dataset.
Improving Automatic Speech Recognition with Dialect-Specific Language Models

We present an end-to-end Automatic Speech Recognition (ASR) system in the context of the recent challenge tasks for Bhojpuri and Bengali. Our implementation follows the currently popular wav2vec models while we investigate ways to leverage the dialect-categorised data in order to improve ASR performance. We report overall improvements in word error rate with dialect-specific language models for each of the languages. We present an analysis that provides insights into some of the factors underlying the success of dialect-specific language models.
Addressing Effects of Formant Dispersion and Pitch Sensitivity for the Development of Children’s KWS System

The accuracy of an automatic keyword spotting (KWS) system is observed to reduce in presence of mismatches such as pitch, speaking rate, formant dispersion, and background noise. To address these mismatches to some extent, this paper proposes a simple and efficient technique through front-end speech parameterization. In the proposed approach, firstly, the formant dispersion is suppressed by temporal averaging of the short-term magnitude spectra (ST-MS) over adjacent frames. Next, the high-frequency oscillations due to pitch harmonics are smoothed out by processing through a low-pass data adaptive single pole filter (DA-SPF), whose pole value changes adaptively for each analysis frame. It provides a non-uniform spectral smoothing for voiced and non-voiced speech frames. The Mel frequency cepstral coefficient (MFCC) extracted from the smoothed spectra is appended with five logarithmically compressed resonant peaks to construct the acoustic feature termed as temporal averaged smoothed spectra (TASS)-MFCC-ARP. The TASS-MFCC-ARP results in a relative improvement of $$104.07\%$$ compared to baseline MFCC for pitch mismatched test conditions on a deep neural network - hidden Markov model (DNN-HMM) based KWS system. As the bandwidth of filters used for computation of MFCC has a direct impact on pitch harmonics of ST-MS, we have next studied the performance of the proposed feature for varying sizes of Mel-filterbank. A notable performance gain for the KWS system is shown by decreasing the Mel-filterbank size. A further improvement in pitch and speaking rate variations is also achieved by data-augmented training through prosody modification.
Gammatone-Filterbank Based Pitch-Normalized Cepstral Coefficients for Zero-Resource Children’s ASR

The work presented in this paper focuses on zero-resource children’s speech recognition task. In such tasks, adults’ speech data is used for learning the acoustic models. However, this leads to severe acoustic mismatch and hence poor recognition rates. One of the main mismatch factor is that the pitch values are higher in the case of children’s speech. In order to mitigate the ill-effects of pitch-induced acoustic mismatch, two front-end speech parameterization techniques are proposed in this study. The proposed approaches employ spectral smoothing based on either pitch-adaptive cepstral truncation or variational mode decomposition. Furthermore, we have used Gamma-tone-filterbank for warping the spectra to the ERB scale. Consequently, the cepstral coefficients exhibit lower variance than those obtained using Mel-filterbank. Therefore, the proposed features are observed to be very effective resulting in a relative reduction in word error rate by nearly $$17\%$$ over the baseline.
Topological magnetic structure generation using VAE-GAN hybrid model and discriminator-driven latent sampling

Abstract Recently, deep generative models using machine intelligence are widely utilized to investigate scientific systems by generating scientific data. In this study, we experiment with a hybrid model of a variational autoencoder (VAE) and a generative adversarial network (GAN) to generate a variety of plausible two-dimensional magnetic topological structure data. Due to the topological properties in the system, numerous and diverse metastable magnetic structures exist, and energy and topological barriers separate them. Thus, generating a variety of plausible spin structures avoiding those barrier states is a challenging problem. The VAE-GAN hybrid model can present an effective approach to this problem because it brings the advantages of both VAE’s diversity and GAN’s fidelity. It allows one to perform various applications including searching a desired sample from a variety of valid samples. Additionally, we perform a discriminator-driven latent sampling (DDLS) using our hybrid model to improve the quality of generated samples. We confirm that DDLS generates various plausible data with large coverage, following the topological rules of the target system.