Reviewerly/exHarmony huggingface dataset:
What’s in a Word? Detecting Partisan Affiliation from Word Use in Congressional Speeches

Politics is an area of broad interest to policy-makers, researchers, and the general public. The recent explosion in the availability of electronic data and advances in data analysis methods - including techniques from machine learning - have led to many studies attempting to extract political insight from this data. Speeches in the U.S. Congress represent an exceptionally rich dataset for this purpose, and these have been analyzed by many researchers using statistical and machine learning methods. In this paper, we analyze House of Representatives floor speeches from the 1981 - 2016 period, with the goal of inferring the partisan affiliation of the speakers from their use of words. Previous studies with sophisticated machine learning models has suggested that this task can be accomplished with an accuracy in the 55 to 80% range, depending on the year. In this paper, we show that, in fact, very comparable results can be obtained using a much simpler linear classifier in word space, indicating that the use of words in partisan ways is not particularly complicated. Our results also confirm that, over the period of study, it has become steadily easier to infer partisan affiliation from political speeches in the United States. Finally, we make some observations about specific terms that Republicans and Democrats have favored over the years in service of partisan expression.
Ranking Semantic Web Ontologies with ELECTRE

Ontology reuse is an important task in Semantic Web development today. To reuse ontologies, users must choose amongst the available ontologies those that meet their requirements. This is a decision-making problem. However, there has been a limited use of decision-making methods on ontologies to date. This study applies a popular multi-criteria decision making (MCDM) method, namely, elimination and choice translating reality (ELECTRE) in ontologies ranking. The decision scheme is formulated with a set of ontologies or alternatives and a number of complexity metrics or attributes of the ontologies. Seventy ontologies of the biomedical domain are used to conduct the experiments. The results display the effectiveness of ELECTRE and provide more insights on the use of decision-making methods in ontology ranking.
Learning of indiscriminate distributions of document embeddings for domain adaptation

Natural language processing (NLP) is an important application area in domain adaptation because properties of texts depend on their corpus. However, a textual input is not fundamentally represented as the numerical vector. Many domain adaptation methods for NLP have been developed on the basis of n umerical representations of texts instead of textual inputs. Thus, we develop a distributed representation learning method of words and documents for domain adaptation. The developed method addresses the domain separation problem of document embeddings from different domains, that is, the supports of the embeddings are separable across domains and the distributions of the embeddings are discriminated. We propose a new method based on negative sampling. The proposed method learns document embeddings by assuming that a noise distribution is dependent on a domain. The proposed method moves a document embedding close to the embeddings of the important words in the document and keeps the embedding away from the word embeddings that occur frequently in both domains. For Amazon reviews, we verified that the proposed method outperformed other representation methods in terms of indiscriminability of the distributions of the document embeddings through experiments such as visualizing them and calculating a proxy A-distance measure. We also performed sentiment classification tasks to validate the effectiveness of document embeddings. The proposed method achieved consistently better results than other methods. In addition, we applied the learned document embeddings to the domain adversarial neural network method, which is a popular deep learning-based domain adaptation model. The proposed method obtained not only better performance on most datasets but also more stable convergences for all datasets than the other methods. Therefore, the proposed method are applicable to other domain adaptation methods for NLP using numerical representations of documents or words.
Industrial statistics and manifold data

Complex and not only big data exist everywhere in industry and how to control and optimize systems based on these data types is an important aspect of modern Quality Engineering. One fundamental type of complexity occurs when data lies on a lower dimensional, curved subspace or manifold. We review a new approach for statistical process monitoring of point cloud, mesh and voxel data based on intrinsic geometrical features of the 2-D manifold (surfaces) of scanned manufactured parts. Monitoring intrinsic properties avoids computationally expensive registration pre-processing of the data sets. We also present a review of recent approaches for analyzing and designing experiments where either the response or the covariates lie on manifolds.
NeuralAS: Deep Word-Based Spoofed URLs Detection Against Strong Similar Samples

Spoofed URLs are associated with various cyber crimes such as phishing and ransomware etc. Most existing detection approaches design a set of hand-crafted features and feed them to machine learning classifiers. However, designing such features is a time consuming and labor intensive process. This paper proposes an approach named NeuralAS (Neural Anti-Spoofing) by segmenting URLs into word sequences and detecting spoofed URLs with recurrent neural networks. As a result, NeuralAS can perform detection with high-abstract and poor-interpretable features learned automatically, and achieve accurate detection with contextual information in sequences. We also propose a novel method to construct indistinguishable data sets of strong similar samples, which can be used to evaluate the robustness of different approaches. Extensive experimental results show that NeuralAS works well on spoofed URLs detection, and has a significant effectiveness and robustness even on strong similar data sets.
A High Performance Implementation of Quasi Delay Insensitive Booleans Functions

Digital circuit design demands critical requirements such as power consumption, robustness, performance, etc. when it is implemented in VLSI (Very Large Scale Integration). The asynchronous paradigm presents interesting features aiming to solve these requirements. An important class of asynchronous paradigm is the so-called QDI (Quasi Delay Insensitive) circuits that can also be used for critical requirements design. QDI circuits are interesting for those kinds of applications because they are robust to noise, to temperature variations, to wire and gate delays and to supply voltage variation, having also low electromagnetic emissions. QDI combinational circuits are designed as function blocks whose indicatability shows the robustness of the circuit communication with the environment. This paper presents two architectures based on basic gates and C elements as also an approach to the synthesis of the QDI function blocks. The two new architectures were prototyped in FPGAs (Field Programmable Gated Array) devices, applied in a set of benchmarks and compared with other architectures of the literature. Our proposal shows respectively a gain of up to 18.6%, 53.1%, 40.7% and 51.8% in latency, number of LUTs and power, dynamic and static, respectively.
Name Indexing in Indonesian Translation of Hadith using Named Entity Recognition with Naïve Bayes Classifier

Hadith is believed to be the main source of Islam after Qur’an. The simplicity of obtaining hadith information is currently supported by global access using the internet. The abundance of hadith literature sometimes finds difficulties to obtain the information that needed. Therefore, information extraction is required to facilitate the searching of information in hadith. In this study, the name indexing in Indonesian translation of hadith from nine narrators was built. The model was built using Named Entity Recognition with Naïve Bayes classifier. The features used in this study are title case, POS tag and unigram. This study experimented with individual features and features that were combined. Precision, recall, and F1-Score are employed as evaluation metrics. F1-Score is used in this study to measure the performance of named entity and features. The results of experiments extracted 258 people’s names from 13870 token data from 100 Indonesian hadith texts and show that implementing the combination of all features can achieve 82.63% of F1-Score.
Aspect-level Sentiment Classification with Reinforcement Learning

Aspect-level sentiment classification aims to predict the sentiment polarity of a given aspect in a sentence. However, most of the existing methods focus on the information of the entire sentence rather than a segment that describes the aspect, making it difficult to identify the mapping between an aspect and a segment. Moreover, these methods are prone to the noise in the sentence. To alleviate this problem, we propose a novel approach that models the specific segments for aspect-level sentiment classification in a reinforcement learning framework. Our approach consists of two parts: an aspect segment extraction (ASE) model and an aspect sentiment classification (ASC) model. Specifically, the ASE model extracts the corresponding segment with reinforcement learning and feeds the extracted segment into the ASC model. Then, the ASC model makes the segment-level prediction and provides rewards to the ASE model. The experimental results indicate that our proposed approach can extract the segment towards the aspect effectively, and thus obtains competitive performance. Furthermore, we provide an intuitive understanding of why our ASE model is more effective for aspect-level sentiment classification via case studies.
A Mathematical Model Based on Bayesian Theory and Gaussian Copula for the Discrimination of Gabbroic Rocks from Three Tectonic Settings

Discriminating among tectonic settings by the chemical composition of igneous rocks is a feasible method in geochemistry. In this study, the feasibility of using gabbroic rocks to discriminate among tectonic settings is analyzed, and a mathematical model based on Gaussian copula and Bayesian theory is set up to discriminate among three tectonic settings: island arc, ocean island, and mid-oceanic ridge. The derivation of the model includes three steps: (1) determine the probability density functions (PDFs) of the elements in different tectonic settings, (2) determine the joint PDFs of the geochemical components of the rocks from different tectonic settings using copula functions, and (3) determine the tectonic settings of rocks using Bayesian theory. The optimal parameters of the mathematical model are calculated using a genetic algorithm, and finally the definitive form of the model is determined with nine basic elements: TiO2, Al2O3, FeOT, CaO, MnO, K2O, Na2O, Ni, and Sr. An experiment shows that the success rates of the mathematical model on the three tectonic settings are 84.03%, 95.48%, and 91.84%, respectively. The average percent success rate is 92.13%, which is significantly higher than using discrimination diagrams and the naive Bayes algorithm. Such an ideal result indicates that using gabbroic rocks to determine the types of tectonic settings is feasible. Moreover, this study can provide support for the application of machine learning and mathematical methods in the field of geochemistry.
A New Permutation-Substitution Scheme Based on Henon Chaotic Map for Image Encryption

In this paper, a new permutation-substitution scheme based on Henon chaotic map is introduced to generate a set of secret keys equivalent to query image size (m x n). The random floating values produced by Henon map have been invested to achieve the permutation and substitution processes of the query image. The performance evaluation of the running time for encryption processes significantly guarantees that the proposed cryptographic system reduces the running time compared to the state of art chaotic based image encryption schemes. Based on the experiments, the robustness of the proposed substitution and permutation approaches based encryption scheme has been evidenced against brute force and statistical attacks. The proposed encryption scheme exhibits efficient results which have a promotion in statistical analysis parameters such as; histogram distribution, key space analysis, initial key sensitivity, entropy benchmark, correlation coefficients and peak signal to noise ratio.
Personalized Point-of-Interest Recommendation on Ranking with Poisson Factorization

The increasing prevalence of location-based social networks (LBSNs) poses a wonderful opportunity to build per-sonalized point-of-interest (POI) recommendations, which aim at recommending a top-N ranked list of POIs to users according to their preferences. Although previous studies on collaborative filtering are widely applied for POI recommendation, there are two significant challenges have not been solved perfectly. (1) These approaches cannot effectively and efficiently exploit unobserved feedback and are also unable to learn useful information from it. (2) How to seamlessly integrate multiple types of context information into these models is still under exploration. To cope with the aforementioned challenges, we develop a new Personalized pairwise Ranking Framework based on Poisson Factor factorization (PRFPF) that follows the assumption that users’ preferences for visited POIs are preferred over potential POIs, unvisited POIs are less preferred than potential POIs. The framework PRFPF is composed of two modules: candidate module and ranking module. Specifically, the candidate module is used to generate a series of potential POIs from unvisited POIs by incorporating multiple types of context information (e.g., social and geographical information). The ranking module learns the ultimate order of users’ preference by leveraging the potential POIs. Experimental results evaluated on two large-scale real-world datasets show that our framework outperforms other state-of-the-art approaches in terms of various metrics.
Gender Demography Classification on Instagram based on User’s Comments Section

Social media has become an integral part of the society. This created a rising demand on social media marketing. New breakthroughs in the field of artificial intelligence have enabled brand owners to boost social media marketing by knowing their target’s demography. This study presents a model to predict gender of social media users based on comments section on Instagram profile by using AdaBoost, XGBoost, Support Vector Machine, and Naive Bayes Classifier combined with a grid search and K- Fold validation. The model was trained with 40,000 comments and managed to get 78.64% on Naive Bayes, 73.41% on XGBoost, 74.56% on AdaBoost and 76.07% on SVM which are generally higher than related studies on this subject. This result shows that Naive Bayes produces higher accuracy on short text classification and has possibilities to help social media marketing.
Constructing a classifier with patterns

The goal of pattern extraction is to find some regularities among the objects included in a data base. When these objects are labeled the patterns can be used for the classification of unseen objects. Most of the algorithms for pattern extraction are based on finding the most frequent structures (or patterns). However, classifiers formed by the set of the most frequent patterns are not necessarily good classifiers since frequent patterns are, most of times, presents in objects of all the classes. In the current paper we propose a method to find patterns that, with a simple post-process, will form an appropriate model for classification. This method resembles to the procedure to grow decision trees, but relevant feature extraction is performed using JADE, a method based on the minimization of the distance between two indistinguishability relations.
Inference Attacks and Controls on Genotypes and Phenotypes for Individual Genomic Data

The rapid growth of DNA-sequencing technologies motivates more personalized and predictive genetic-oriented services, which further attract individuals to increasingly release their genome information to learn about personalized medicines, disease predispositions, genetic compatibilities, etc. Individual genome information is notoriously privacy-sensitive and highly associated with relatives. In this paper, we present an inference attack algorithm to predict target genotypes and phenotypes based on belief propagation in factor graphs. With this algorithm, an attacker can effectively predict the target genotypes and phenotypes of target individuals based on genome information shared by individuals or their relatives, and genotype and phenotype association from genome-wide association study (GWAS). To address the privacy threats resulted from such inference attacks, we elaborate the metrics to evaluate data utility and privacy and then present a data sanitization method. We evaluate our inference attack algorithm and data sanitization method on real GWAS dataset: Age-related macular degeneration (AMD) case/control dataset. The evaluation results show that our work can effectively defense against genome threats while guaranteeing data utility.
Person Entity Recognition for the Indonesian Qur’an Translation with the Approach Hidden Markov Model-Viterbi

Abstract Qur’an contains teachings about life given by Allah to the Prophet Muhammad. In the Qur’an, there are a lot of verses. With a large number of verses, it will be very difficult and take a long time for us to find a name. Manually searching for entities will be very difficult and take a long time to be searched. With NER, which is one of the techniques of information extraction that aims to detect entity names, such as people’s names, locations, events, and time expected search for entity names in the Qur’an will significantly simplify and shorten the time. Indonesian Qur’an translations will later be used as Inputs, and their names are entity names. The solution to the problem above is to use NER. The Named Entity (NE) Recognition (NER) system will look for name entities people from the corpus that have been created. In applying NER requires a model to detect name entities in a text. Hidden Markov Model-Viterbi is a machine learning algorithm type Supervised Learning which will be applied. In the development of a system for searching names entities for the Indonesian translation of the Qur’an dataset have best F1 results is 76%.
Coercion-Resistant E-Voting Scheme with Blind Signatures

An ideal e-voting system would allow users to go online, using web-browser or a phone application, enter their credentials and vote; it would also allow voters to verify their votes after election. The properties that make e-voting such a promising technology also raise potential privacy and efficiency problems. In literature, researchers have listed the requirements which an e-voting scheme must have. Many e-voting schemes have been proposed which are based on combination of cryptographic tools, however some schemes have efficiency problems, voter or election authority does a lot of processing at their end, and some do not fulfill all security requirements e.g. most lack coercion-resistance and receipt free-ness. In this paper, we propose an e-voting scheme based on blind signatures which fulfils important security requirements and is efficient too. In our scheme, voter does not generate any keys or sign anything, yet still is universally verifiable and coercion-resistant.
Automatic Image Annotation based on Co-Training

To learn a well-performed image annotation model, a large number of labeled samples are usually required. Although the unlabeled samples are readily available and abundant, it's a difficult task for humans to annotate large amounts of images manually. In this paper, we propose a novel semi-supervised approach based on co-training algorithm for automatic image annotation, which can utilize the labeled data and unlabeled data for the system simultaneously. Firstly, two different classifiers, namely the CNN (convolutional neural network) and the LDA-SVM, are constructed by all the labeled data. These two classifiers are independently represented as different feature views. Then, the most confident data with relevant pseudo-labels are chosen and amalgamated with the whole labeled dataset. After that, the two classifiers are retrained with the new labeled dataset until a stop condition is reached. In each iteration process, the unlabeled samples are labeled by high confidence pseudo-labels that are estimated by an adaptive weighted fusion method. Finally, we conduct experiments on two datasets, namely, IAPR TC-2 and NUS-WIDE, and measure the performance of the model with standard criteria, including precision, recall, F-measure, N+ and mAP. The experimental results show that our approach has superior annotation performance and outperforms many state-of-the-art automatic image annotation approaches.
An Enhanced Feature Selection for Text Documents

In the current digital world, a vast amount of data is recorded in a variety of forms like pictures, data, video, and audio. Generally, such type of information which is available in large voluminous form is actually not available in an organized manner which is appropriate for text processing. Text mining is a subfield of data mining which aims at exploring the useful information from the recorded resources. Document clustering helps the users to effectively navigate, review, and classify text documents into significant clusters, the knowledge that helps to handle the enormous amount of text mining. Preprocessing and feature selection are of tremendous importance in document clustering. In document clustering, preprocessing techniques applied to the documents are Bag of Words (BOW), Stop word removal, and Porter stemming. In this paper, we proposed an easy to use framework for preprocessing and Enhanced Term Frequency—Inverse Document Frequency (Enhanced TF–IDF) method for feature selection.
DeepSqueezeNet-CRF: A Lightweight Deep Model for Semantic Image Segmentation

Deep convolutional neural networks (DCNNs) has shown the powerful capability in image semantic segmentation, along with the huge model sizes and massive parameters. Our key insight is to build a new neural network for image semantic segmentation with fewer parameters and smaller model size under the premise of maintaining state-of-the-art performance. In this paper, we propose a lightweight deep model named DeepSqueezeNet, which introduces the SqueezeNet to the fullyconvolutional networks, replacing parts of convolutional layers by Fire Module layers. Our DeepSqueezeNet contains fewer parameters, smaller model size and higher accuracy than the original fully-convolutional networks. Further more, we propose DeepSqueezeNet-CRF by combining the responses at the final DCNN layer with a fully-connected Conditional Random Field (CRF) to improve localization accuracy. Both quantitative and qualitative results on PASAL VOC 2012 demonstrate the priority and feasibility of our methods, comparing with several other state-of-the-art methods. Especially, our methods can reduce the number of network parameters, model size and training time of the original fully-connected network, with even better performance.
Direct Linear Time Construction of Parameterized Suffix and LCP Arrays for Constant Alphabets

We present the first worst-case linear time algorithm that directly computes the parameterized suffix and LCP arrays for constant sized alphabets. Previous algorithms either required quadratic time or the parameterized suffix tree to be built first. More formally, for a string over static alphabet $$\varSigma $$ and parameterized alphabet $$\varPi $$ , our algorithm runs in $$O(n\pi )$$ time and O(n) words of space, where $$\pi $$ is the number of distinct symbols of $$\varPi $$ in the string.