Reviewerly/exHarmony huggingface dataset:
An Intelligent Framework and Process Optimization Algorithm for Foreign Propaganda Machine Translation based on Multi-Cultural Information Collection and Clustering

An intelligent framework and process optimization algorithm for foreign propaganda machine translation based on multi-cultural information collection and clustering is designed and implemented in the paper. This paper establishes a term translation model and a joint translation model. The term translation model binds the translation probability of a term to the topic distribution of the document in which it is located, so as to guide the selection of translations that match the topic during term translation. With these theoretical information, the novel machine translation model will be defined. Phrase-based statistical machine translation does not conduct the in-depth linguistic analysis of the core source language and target language, but divides the source language sentences to be translated into word strings. The proposed research article integrates the multi-cultural information collection and clustering model to design the intelligent framework and process optimization algorithm for foreign propaganda machine translation. Through the experiment, the performance is finally validated.
Determining the Number of Bit Encryption That Is Optimum for Image Steganography in 8 Bit Images

The most challenging task is to hide an image into another without the viewer getting an indication of the presence of secret data while looking at it. The message could be in any form of digital media like text, codes, or images. The paper proposes image steganography using LSB (Least Significant Bit) encryption. This technique can be applied to install secret messages in an 8-digit, 24-bit, or grayscale structure. In our research, 8-bit format has been used. The transmitter encrypts the secret image with respect to cover image to form stego image. Receiver decrypts the encrypted image by dividing 8 bit of secret image into ‘x’ bit of the cover image and ‘8-x’ bit of secret image. The paper has also evaluated the PSNR (Peak Signal to Noise Ratio) of retrieved secret and cover image with respect to its original secret and original cover image. As a result, this paper explains 4-bit encryption is best for the purpose of steganography as PSNR of retrieved images (cover and secret both) is highest here.
INCREASING STUDENTS’ READING COMPREHENSION ABILITY THROUGH MIND MAPPING TECHNIQUE OF REPORT TEXT

This research was aimed at examining: (i) whether there is significant increase on students’ reading comprehension ability after being taught through Mind Mapping Technique of report text and (ii) the aspect of reading skills that increased the most and the least. The subjects of this research were 28 students in class X. The data in form of scores were collected using multiple choices and analized using SPSS 16.0. The result of this research showed that there was significant increased of students’ reading comprehension to a report text after being taught through Mind Mapping technique since t-value &gt; t-table (7.796 &gt; 2.051) with significant value was 0.05. The average score of pre-test was 82.36 and the average score of post-test was 95.02. It gained 10.16 and the most increased aspect of reading skills is vocabulary whereas the least increased is finding detail information.
A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor.
Label-Adversarial Jointly Trained Acoustic Word Embedding

Query-by-example spoken term detection (QbE-STD) is a task of using speech queries to match utterances, and the acoustic word embedding (AWE) method of generating fixed-length representations for speech segments has shown high performance and efficiency in recent work. We propose an AWE training method using a label-adversarial network to reduce the interference information learned during AWE training. Experiments demonstrate that our method achieves significant improvements on multilingual and zero-resource test sets.
HUMAN BEHAVIOR PREDICTION FOR CITYSCAPE IMAGES USING MULTIMODAL DEEP LEARNING

This study aimed to estimate human willingness to visit cityscape images via artificial intelligence (AI) using multimodal deep learning. In this study, gaze information was acquired through subject experiments using a measurement device. We added gaze information when humans felt motivated to visit the cityscape image, and confirmed whether the estimation accuracy of AI would improve. We also created an AI model that generated gaze-view images, and used it for multimodal deep learning. We used pix2pix to generate the images. Finally, we verified the accuracy of the proposed multimodal deep learning approach, when the generated pseudo-gaze image was attached.
Health Record Maintenance Using Cloud Computing and Multi Authority Attribute-Based Encryption

The health records of various patients are monitored, stored and accessed using cloud providers. There may a chance of accessing and data exposure to third party servers which are unauthorized. In order to provide a secured access to data, encryption is considered to be a most promising method. Though many issues are concerned in encryption such as key management, efficient access and user revocation the most prominent changeling is ensuring fine grained. This proposed method states various framework and architectures which would provide a control over the data access and also helps in storing the patient’s record in semi trusted servers. In order to achieve fine grained and secured data access over health records, Multi Authority Attribute-based encryption (MA ABE) technique helps in encrypting each and every person health record. The suggested encryption standards have a centric view on multiple data that divides the users into multiple domains which in reduces the complicity of key management for both users and owners. High level of privacy is provided by exploiting multi authority encryption method. This also governs dynamic change in accessing policies and also supports efficient user revocation under emergency situation. The experimental results help in providing the scalability, efficiency of proposed method.
Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

&lt;span lang="EN-US"&gt;Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method.&lt;/span&gt;
Findings of the Third Workshop on Neural Generation and Translation

This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.
Discernment of Unsolicited Internet Spamdexing Using Graph Theory

With the advent of technology, spamming of web pages which is a methodology by which the imposter pages get a higher rank than the true or genuine pages in the search engine’s results to have become prevalent. It poses a gigantic issue for search engines, making it essential for search engines to be able to catch hold and identify spam web pages during crawling. One of the ways in which this issue can be visualized is by considering the underlying web graph structure and the directed URL links(edges) between different spam and true web hosts(nodes), as well as the content attributes of each of the web pages. This graph structure on being fed to an inductive graph neural network model for training purposes will enforce the model to efficiently categorize the new, previously unseen web hosts as web spam or genuine. A GraphSAGE model was developed for carrying out node classification that would leverage the web content attribute data to create node embeddings for the entirely new, unseen web host nodes.
Os processos e sistemas em Organização do Conhecimento análise de verbetes da Enciclopédia da ISKO

The Knowledge Organization Systems (KOS) as tools used to interpret knowledge structuresare considered, helping in the organization, representation and retrieval of information. It is discussed to what extent the entries as Knowledge Organization (KO)tools contribute to understanding and greater applicability, in the sense of contributing to the Information Science area in what concerns information retrieval. For this, a data survey was carried out considering the terms retrieved in ISKO, identified by the ISKO Encyclopedia of Knowledge Organization(IEKO). The analysis was performed using the content analysis methodology. Based on the approach proposed by this work, 32 articles were retrieved to discuss the concepts of KOSin the Brazilian Information Science literature. As a result, we identified the entries that made it possible to direct some theoretical and practical contributions in the context of KO. The entries retrieved in IEKO are classification systems and showthat this system orders and highlights the KOprocess for its particular use, butthat its practice does not contribute for the knowledge to be retrieved, being necessary theconjunction with another process, such as indexing, so that it can be retrieved by subject, which allows us to conclude that indexing assigns representative terms based on the concepts of the material, allowing us to observe how IEKOhas understood the KO processes and systems. For several authors, KOis approached from different thoughts and points of view: the KOSas a process addresses the representation, organization, description of documents, using classification, indexing and cataloging as a procedure; and KOSas a tool, uses rules and standards such as classification systems, thesauri, ontologies and so on.
Reconstruction-Based One-Class Classification Anomaly Detection for Tabular Data

Most anomaly detection based on tabular data requires training data sets containing normal and abnormal samples. In practice, it is usually the case that there are few or no abnormal samples, and it is necessary to pay a high price to obtain abnormal samples. In order to solve this problem, some scholars have proposed anomaly detection based on one-class classification, which only needs to learn the characteristics of normal samples to obtain the decision boundary that distinguishes normal samples from other samples. In this paper, it propose a reconstruction-based one-class classification anomaly detection method, which includes two stages: data preprocessing and adaptive encoder network model. This paper used the f1-score function to evaluate the accuracy of our proposed method for anomaly detection. The experimental results show that the anomaly detection for tabular data sets of network security and medical shows a significant improvement over the most advanced methods.
Few-shot News Recommendation via Cross-lingual Transfer

The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these solutions is limited by the amount of records available from warm users to use. Thus, building a recommendation system based on few interaction records from a few users still remains a challenging problem for unpopular or early-stage recommendation platforms. This paper focuses on solving the few-shot recommendation problem for news recommendation based on two observations. First, news at different platforms (even in different languages) may share similar topics. Second, the user preference over these topics is transferable across different platforms. Therefore, we propose to solve the few-shot news recommendation problem by transferring the user-news preference from a many-shot source domain to a few-shot target domain. To bridge two domains that are even in different languages and without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. A user encoder is constructed on top of the aligned news encoding and transfers the user preference from the source to target domain. Experimental results on two real-world news recommendation datasets show the superior performance of our proposed method on addressing few-shot news recommendation, comparing to the baselines.
Students’ Satisfaction in Campus Cafeterias: An Empirical Study on Public Universities at Klang Valley in Malaysia

Cafeteria facilities are a key component of a wholesome arrangement that impacts personal happiness for both native and foreign students. For university students, the first option for dining is on-campus food services which imply the importance of the quality of the cafeteria service and must be reviewed and improved regularly. Considering the importance of students’ satisfaction this study was conducted to investigate the link between variables such as pricing, food quality, customer service, and the atmos-phere of students’ satisfaction. The convenience sampling method was used to collect quantitative data from 250 students at public universities in Klang valley of Malaysia which are IIUM, UPM, UM, UiTM, UKM and UTM. The survey was conducted via google form in September 2021. For the data analysis, descriptive, reliability, exploratory factor analysis and regression analysis have been conducted by using SPSS. The study revealed that students’ satisfaction was associated with the four independent variables. All the factors had a significant and positive influence on students’ satisfaction. From the regression analysis, it was found that customer service and price fairness had the most significant influence over student satisfaction which was followed closely by the other two variables which were food quality and atmosphere. It was also concluded that all of the independent factors included in the study had a sub-stantial influence on the dependent variable, which was student satisfaction.
Java Island Health Profile Clustering using K-Means Data Mining

Health is the best gift in life, because with health humans can carry out daily activities. Administratively, Java Island consists of 85 administrative regions and 34 cities. Therefore, it is very important to understand the health level of each area. The main objective of this research is to divide each region (district and city) into several groups and use the K-means method to determine health status based on 8 data parameters into certain groups. Algorithm in groups, will place the data based on the similarity of characteristics between groups. The results showed that there were 4 clusters of health profiles in Java, with 1 high health quality cluster in Central Jakarta, 55 regencies/municipalities with low health quality, 52 regencies/cities with low health quality. and the quality of health is quite low there are 13 districts/cities, it can be concluded that the health indicators in Java
Research on automatic parking based on A* search algorithm

Automatic parking is one of the most landing scenarios in autonomous driving technology. Automatic parking refers to the automatic parking process of cars in parking lots. In this paper, we consider three parking models under vertical, horizontal and bending conditions. We first establish physical models according to different conditions, introducing A* search algorithm is d as the optimal path selection algorithm for practical cases. We also set up a constrained optimization problem for the situation of car occupancy in the garage. By solving this constraint, we can efficiently calculate the optimal parking path to avoid the situation of car occupancy.
Psychological effects associated with preventive isolation by COVID-19 in early childhood in Colombia

Background: There are enough documented records worldwide to date on the number of infections and deaths from the COVID-19 pandemic, and it is foreseeable that there will be a worsening of psychological health in the entire population, however, there are few studies that have addressed the emotional and psychological effects that preventive isolation has caused in Colombia in early childhood so the objective of this study was to know these effects through a survey carried out with caregivers of children in Colombia from different social strata.
Boosting Semantic Segmentation Via Feature Enhancement

A B S T R A C TSemantic segmentation aims to map each pixel of an image into its corresponding semantic label. Most existing methods either mainly concentrate on high-level features or simple combination of low-level and high-level features from backbone convolutional networks, which may weaken or even ignore the compensation between different levels. To effectively take advantages from both shallow (textural) and deep (semantic) features, this paper proposes a novel plug-and-play module, namely feature enhancement module (FEM). The proposed FEM first uses an information extractor to extract the desired details or semantics from different stages, and then enhances target features by taking in the extracted message. Two types of FEM, \emph{i.e.}, detail FEM and semantic FEM, can be customized. Concretely, the former type strengthens textural information to protect key but tiny/low-contrast details from suppression/removal, while the other one highlights structural information to boost segmentation performance.
On the structure of repeated-root polycyclic codes over local rings

This paper provides the Generalized Mattson Solomon polynomial for repeated-root polycyclic codes over local rings that gives an explicit decomposition of them in terms of idempotents that completes the single root study. It also states some structural properties of repeated-root polycyclic codes over finite fields in terms of matrix product codes. Both approaches provide a description of the $\perp_0$-dual code of a given polycyclic code.
Quest for Speed: The Epic Saga of Record-Breaking on OpenCV Connected Components Extraction

Connected Components Labeling (CCL) represents an essential part of many Image Processing and Computer Vision pipelines. Given its relevance on the field, it has been part of most cutting-edge Computer Vision libraries. In this paper, all the algorithms included in the OpenCV during the years are reviewed, from sequential to parallel/GPU-based implementations. Our goal is to provide a better understanding of what has changed and why one algorithm should be preferred to another both in terms of memory usage and execution speed.
SOV as the Canonical Word Order in Modern Chinese

The debate on modern Chinese being SVO or SOV is facing a dilemma: the word order is SVO in an unmarked declarative sentence in Chinese, while Chinese exhibits many features shared by SOV languages. To tackle this difficult situation, researchers should focus on language types, but not the relative orders of subject, verb, and object. Based on the usage of modern Chinese, we have checked ten universals generalized in Greenberg (1963) which are relevant to this topic. It is shown that 90% of the universals support Chinese being a SOV language, and only one universal is on the SVO side. Modern Chinese is therefore argued to be located very close to the SOV end of a continuum.
Edge detection of printed matter based on improved Canny operator

In order to solve the problem of irregular edge and more noise in printed products, this paper proposes an improved method of Canny operator to detect edges. Firstly, the filter is changed from gaussian filter of traditional Canny operator to bilateral filter, so as to improve the noise reduction of gaussian filtering, there may be a problem of unclear noise detection; secondly, the gradient amplitude and direction of the printed product image are calculated based on the 3 × 3 gradient template of sobel operator, and the final edge detection image is obtained. The final experiment also verifies that the image edge obtained by the improved algorithm is relatively complete, and the effect is slightly improved compared with the traditional Canny operator.
An improved pagination widget based on PYQT5

The Model-View framework of PyQt5 has no pagination function to display the data well, this paper separates the pagination work from the whole project, but also use the “signals and slot” to keep the parameters synchronization. So we can design forwards, backwards, records per page and other pagination functions in the widget, and then we can use “promote” function of PYQT5 to apply in our own code with simple modification only. we constructed a more powerful, easily reusable widget base on the class inheritance of Python and richly functional components of PyQt5.
Temporal Coherence of an Optical Field in the Presence of Entanglement

We demonstrate in SU(1,1) interferometers that temporal coherence of an optical field depends on not only its spectrum but also its entanglement property. This study reveals the relationship among interference visibility, photon distinguishability and entanglement.
Applications of a Quantum Phase Estimation Algorithm

Means for extracting higher-order phase information and sub-Heisenberg accuracies within an SU(2) interferometer utilizing coherent-state light are discussed.