Reviewerly/exHarmony huggingface dataset:
A Dual-Context Sequent Calculus for S4 Modal Lambda-Term Synthesis

In type-based program synthesis, the search of inhabitants in typed calculi can be seen as a process where a specification, given by a type A, is considered to be fulfilled if we can construct a λ-term M such that M : A, or more precisely if Γ ` M : A holds, that is, if under some suitable assumptions Γ the term M inhabits the type A. In this paper, we tackle this inhabitation/ synthesis problem for the case of modal types in the necessity fragment of the constructive logic S4. Our approach is human-driven in the sense of the usual reasoning procedures of modern theorem provers. To this purpose we employ a so-called dual-context sequent calculus, where the sequents have two contexts, originally proposed to capture the notions of global and local truths without resorting to any formal semantics. The use of dual-contexts allows us to define a sequent calculus which, in comparison to other related systems for the same modal logic, exhibits simpler typing inference rules for the operator. In several cases, the task of searching for a term having subterms with modal types is reduced to the quest for a term containing only subterms typed by non modal propositions.
Semi-Automatic Alignment of Multilingual Parts of Speech Tagsets

We cast the problem of mapping a pair of Parts of Speech (POS) tagsets as a labelled tree mapping problem and present a general-purpose semi automatic POS tree alignment algorithm to solve the alignment. This algorithm can be used to align two POS tagsets of different languages or the same language. We evaluate its usefulness using POS tagsets of two languages: Tamil and Sinhala. The proposed approach shows that manual effort in prior approaches is drastically reduced due to the proposed algorithm and eliminates the need to create new POS tagsets.
Toward Relevance Term Logic

Term Functor Logic is a term logic that recovers some important features of the traditional, Aristotelian logic; however, it turns out that it does not preserve all of the Aristotelian properties a valid inference should have insofar as its class of theorems includes some inferences that may be considered irrelevant. Given this situation, in this contribution we tweak a tableaux method in order to avoid said irrelevance.
Improving Question Analysis for Arabic Question Answering in the Medical Domain

Question analysis is a basic module in a question answering (QA) system, and its quality affects the performance of QA system. In this paper, we address the problem of Arabic question analysis in the medical domain where several specific challenges are met. The major challenging issue in processing Arabic medical question is the need for ambiguity resolution. Nevertheless, this issue has not been well studied in related works. Our question analysis uses dictionaries and transducers to analyze any medical question, factoid or complex. This module detects important elements of the question, including: different words in the question that identify what the user wants to ask for, and the nature of the expected answer. To identify well these elements a step of disambiguation is applied. Then, the words used in the question will be extended by adding new words that connect semantically to those in the question. Experimentation of the question analysis module of our Arabic medical question answering system show interesting results.
A Product Review Writing Recommender System based on LDA and TF-IDF

Twitter is a micro-blogging platform where people broadcast their views and opinions to fellow users in crisp messages called Tweets. However, the platform's format of restricted character limit makes it challenging for many users to express their views exhaustively. The paper proposes a recommender system to help in writing effective product review Tweets within the restricted character limit of Twitter. The approach is divided into two phases where, the first phase uses the Latent Dirichlet Allocation (LDA) algorithm to find pivotal features from the training corpus and suggests them to the users while writing new Tweets. In the second phase, the approach suggests the most appropriate opinion words to describe the respective features by using an approach based on the occurrence frequency of opinion words and TF-IDF. The evaluation results show significant improvement in the quality of product review Tweets. The percentage of good reviews corresponding to a parameter such as correct usage of feature words is found to be 17.85% higher, whereas an improvement of 23.22% is reported with regard to the correct use of opinion words using the generated recommendations.
Constructing Vietnamese WordNet: A Case Study

WordNets are commonly used in tasks such as summarizing documents, extracting information, translating and creating other lexical resources. This paper presents experiments in constructing a Vietnamese WordNet (VWN) from a variety of freely published resources in several languages. The VWN has the same structure as the Princeton WordNet. Our algorithm translates several existing WordNets to Vietnamese using a freely available machine translator, removes translation ambiguities by applying ranking methods based on occurrence counts and Google distances on translation candidates. We also establish connections between synsets and extract glosses for synsets. Finally, we carefully look at the VWN created and identify problematic issues in the VWN due to differences in culture and agglutinative morphology of Vietnamese and other languages used.
Measuring the Quality of Low-Resourced Statistical Parametric Speech Synthesis Trained with Noise-Degraded Data Supported by the University of Costa Rica

After the successful implementation of speech synthesis in several languages, the study of robustness became an important topic so as to increase the possibility of building voices from non-standard sources, e.g. historical recordings, children's speech, and data freely available on the Internet. In this work, a measure of the influence of noise in the source speech of the statistical parametric speech synthesis system based on HMM is performed, for a case of a low-resourced database. For this purpose, three types of additive noise were considered at five signal-to-noise ratio levels to affect the source speech data. Using objective measures to assess the perceptual quality of the results and the propagation of the noise through all the processes of building speech synthesis, the results show a severe drop in the quality of artificial speech, even for the cases of lower levels of noise. Such degradation seems to be independent of the noise type, and is at lower proportion to the noise level. This results are of importance for any practical implementation of speech synthesis from degraded data in similar conditions, and shows that applying denoising processes became mandatory in order to keep the possibility of building intelligible voices.
Implementation of fuzzy logic model for fish supplier selection

This study aims to select the best supplier in the Dizma Koi Blitar business by using two variables as indicators of supplier assessment, namely low prices and minimal number of defective products. The data used in this study is secondary data in the form of a history of koi fish purchases to suppliers during the period from January to December 2021.The data analysis method used in this study is fuzzy logic mamdani programming. The results of the study in the form of measuring instruments determining the quality of koi fish suppliers provide calculations of two different sets of prediction data; the first prediction uses two input factors, while the second prediction uses five input variables. Both projections are quite close to the results of the first fish purchase, and the prediction of fish purchases is targeted for each month in 2021. The trial of the program using a price input of IDR 415,000.00 with defective products in one bag of 10 heads turned out to provide the predicted result of the supplier's quality value of 2.5282. The result of the predicted value if converted, the supplier is included in the very good category because with prices that are included in the affordable class and defective products are included in the very low category intervals get an assessment of the quality of suppliers in the very good category. When viewed using MAPE and MSE, the MSA value obtained is 2.3129, this indicates that the error rate in forecasting is very small. The fact that the MAPE value for test data with 2 input factors is the minimum possible value suggests that subsequent predictions can be made more accurately using this model, specifically by using more than 2 input variables.
Narrative interest and meaning of oral folktales transmission group using text mining technique: For the digital archive of 〈Korea Oral Literature Daegye〉

This paper examines the aspect and meaning of narrative interests of the whole tradition group and the tradition group according to gender by using text mining technique targeting each 26,542 oral narratives in the digital archive of 〈Korea Oral Literature Daegye〉. For this purpose, the nouns were extracted by analyzing the morphemes from the titles of 26,542 oral narratives using the Mecab class of the KoNLPy package, and then the high frequencies were extracted using the Counter class of the collection package.&#x0D; As a result, looking at the 30 nouns occupying the highest frequency, the oral folktale tradition group consists of ‘the story of the origin of the place name’, the tale of the ‘tiger’, the story of the ‘goblin’, the narrative showing the ‘relationship of family relationships’, ‘wealth, filial piety, fortune, it is confirmed that he mainly told stories about the values pursued by humans’, such as famous places.&#x0D; The result of the most oral tales of ‘place name origin’ shows that the narrators perceive ‘story that is worth investigating’ as a place name origin story. The story of ‘Tiger’ brought laughter to the enjoyment class and revealed the value of ‘filial piety’. The story of ‘Goblin’ is a means to satisfy ‘story pleasure’ by conveying the fictional situation in the story as if it were a real event and arousing interest to the enjoyment class to feel ‘creep’ or to solve problems that are difficult to explain in reality. it became. Among the stories that show ‘family relations’, in particular, the story about ‘daughter-in-law’ was overwhelmingly told, which shows that the family relationship that causes the most conflict in reality is that surrounding the daughter-in-law. Nouns such as ‘son’, ‘myeong-dang’, ‘filial piety’, ‘rich man’, ‘grave’, ‘blessed’, ‘filial piety’, etc. It shows that he cares about ‘filial piety’ above all other ideologies. The character’s earnest wish for this is shown to be realized through ‘Myeongdang’, and it can be interpreted in this context that many stories about ‘myongdang’ and ‘grave’ are handed down.&#x0D; Meanwhile, narrative interest according to gender was examined through 15,088 stories told by 2,916 male narrators and 7,467 stories narrated by 2,004 female narrators. Male narrators recounted many stories about the origin of the place name, and female narrators enjoyed telling stories that showed problems between families. This shows that the lives of female speakers are focused on ‘in the home’ with a ‘family’, which is different from male speakers who enjoy telling stories about ‘Park Moon-soo’, ‘Middle’, and ‘Jeong-seung’ and expand their area of interest outside the house. will be.
Threat led advanced persistent threat penetration test

Cyber security attacks have been on the rise in recent years. One of the most destructive attacks are known as advanced persistent threat (APT) attacks which can inflict massive damages to a network. A common approach of testing the security of an IT infrastructure is a penetration test (PT), which identifies security flaws in a system. However, this approach covers only the present vulnerabilities in a system without minding any new potential ones that could harm such systems. The goal in this research paper is to implement an enhanced PT approach called threat led APT PT to test the security of a target network against present vulnerabilities. The enhanced PT approach presented in this research work resulted in significantly enhancing the security of a network up to 28.5%. The proposed PT approach is compared to other custom PT approaches, which failed to achieve the same results as the proposed approach.
Educational Application of an Online Context Sensitive Speech Dictionary

As part of an online freshmen courseware, a context sensitive speech dictionary is implemented to improve learning efficiency of scientific terminologies. The meaning system is based on WordNet, a context sensitive semantic database developed in Princeton University. Speech functionality is achieved by implementing a Java speech interface for the TTS (Text-To-Speech) engine. These features are targeted to help the students to understand the online reading material more accurately and more efficiently. Microsoft SAPI (Speech Application Programming Interface) and TTS (Text to Speech) engine are selected as the audio driver. A server program is implemented based on Component Object Model (COM) standard and provides a simple online speech interface. The interface can also be easily used for other software to provide fast and reliable speech service on the Internet The toolkit is prepared for the general chemistry education courseware in New Jersey Institute of Technology (NJIT). Index Terms—Dictionary, WordNet, Speech, TTS, Java, Microsoft SAPI, COM (Common Object Model)
Signal Flow Graphs

Signal flow graph theory is concerned with the development of a graph theoretic approach to solving a system of linear algebraic equations. Two closely related methods proposed by Coates and Mason have appeared in the literature and have served as elegant aids in gaining insight into the structure and nature of solutions of systems of equations. This chapter develops these two methods. Coates and Mason’s methods may be viewed as generalizations of a basic theorem in graph theory due to Harary, which provides a formula for finding the determinant of the adjacency matrix of a directed graph. The chapter determines the different collections of vertex-disjoint directed circuits of the Mason graph.
Automatic Generation of Motion Activity Descriptors in Video Sequences

Recently, motion activity which is defined as amount of motion in a video sequence has been included as a descriptor in MPEG-7 standard. The motion activity descriptors (MADs) which describe this motion activity need to enable efficient content analyzing, indexing, browsing, and querying of video data. To address this issue, first, we propose a novel technique for automatic measurement of motion activity using accumulation of quantized pixel differences among the frames of given video segment. As a result, accumulated motions of shot are represented as a two dimensional matrix. Also, we investigate an efficient and scalable technique to compare these matrices and generate MADs that are representing various motions of shots effectively. Not only the degrees (amounts) but also the locations of motions are computed and presented accurately. Our preliminary experimental studies indicate that the proposed techniques are effective in capturing and comparing motion activities.
How Human Factors Impact on Software Process Maturity

Software process is a repeatable path toward the final goals of a software project and is also one of the most important subjects on building a high quality software. Better process not only ensures the quality of the software, but also improves the productivity of the project. Among the Capability Maturity Model (CMM), the IS09000 series, and best practices, the CMM is the one specially focusing on the software development. In this paper, we demonstrate that the process maturity of software development organizations can be negatively affected by leadership and other human factors. Even for those organizations that are assessed for certain levels of process maturity, with no periodical audit from CMM assessors, their process maturity still under influence of these factors. These human factors, based on the industry experience, are identified and possible resolutions are discussed.
A New Image Based Lighting Method: Practical Shadow-Based Light Reconstruction

In this paper we present a practical image based lighting method. Our method is based on a simple and easily constructible device: a square plate with a cylindrical stick. We have developed a user-guided system to approximately recover illumination information (i.e. orientations, colors, and intensities of light sources) from a photograph of this device. Our approach also helps to recover surface colors of real objects based on reconstructed lighting information.
Continuous Planning for Inertial-Aided Systems

Inertial-aided systems require continuous motion excitation among other reasons to characterize the measurement biases that will enable accurate integration required for localization frameworks. This paper proposes the use of informative path planning to find the best trajectory for minimizing the uncertainty of IMU biases and an adaptive traces method to guide the planner towards trajectories which aid convergence. The key contribution is a novel regression method based on Gaussian Process (GP) to enforce continuity and differentiability between waypoints from a variant of the RRT* planning algorithm. We employ linear operators applied to the GP kernel function to infer not only continuous position trajectories, but also velocities and accelerations. The use of linear functionals enable velocity and acceleration constraints given by the IMU measurements to be imposed on the position GP model. The results from both simulation and real world experiments show that planning for IMU bias convergence helps minimize localization errors in state estimation frameworks.
bGSL: An imperative language for specification and refinement of backtracking programs

We present an imperative refinement language for the development of backtracking programs and discuss its semantic foundations. For expressivity, our language includes prospective values and preference — the latter being a variant of Nelson's biased choice that backtracks from infeasibility of a continuation. Our key contribution is to examine feasibility-preserving refinement as a basis for developing backtracking programs, and several key refinement laws that enable compositional refinement in the presence of non-monotonic program combinators.
KLASIFIKASI STATUS GIZI BAYI POSYANDU KECAMATAN BANGUN PURBA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

This research was conducted to apply the Support Vector Machine algorithm in the process of classifying the nutritional status of infants under five. The nutritional status of early childhood can determine what kind of human resources as successors of a nation in the future. Good nutritional status plays an important role in determining the success or failure of efforts to increase human resources, so that data on the nutritional status of toddlers such as at the Posyandu, Bangun Purba District can be classified using Data Mining techniques using the Support Vector Machine algorithm. The results of this study using 80% of the data as training data and 20% of the data as training data are f1 score 0.865, accuracy 0.876, precision score 0.871, and recall score 0.876. The results showed that from a total of 347 data on the nutritional status of infants, there were 284 infants with good nutrition, 15 infants with poor nutrition, 23 infants with less nutrition, 8 infants with excess nutrition, 6 infants with obesity, and 11 infants at risk of overnutrition. Based on these results, there were 304 baby nutrition data that were classified correctly from a total of 347 baby data that were used as testing data. From this research, it can be concluded that the Support Vector Machine algorithm can classify infant nutrition data at the Posyandu, Bangun Purba District, well.
Boundary-Enhanced Self-supervised Learning for Brain Structure Segmentation

To alleviate the demand for a large amount of annotated data by deep learning methods, this paper explores self-supervised learning (SSL) for brain structure segmentation. Most SSL methods treat all pixels equally, failing to emphasize the boundaries that are important clues for segmentation. We propose Boundary-Enhanced Self-Supervised Learning (BE-SSL), leveraging supervoxel segmentation and registration as two related proxy tasks. The former task enables capture boundary information by reconstructing distance transform map transformed from supervoxels. The latter task further enhances the boundary with semantics by aligning tissues and organs in registration. Experiments on CANDI and LPBA40 datasets have demonstrated that our method outperforms current SOTA methods by 0.89% and 0.47%, respectively. Our code is available at https://github.com/changfeng3168/BE-SSL .
Numerical Demonstration of Unsupervised-Learning-Based Noise Reduction in Two-Dimensional Rayleigh Imaging

The conventional denoising method in Rayleigh imaging in a general sense requires an additional hardware investment and the use of the underlying physics. This work demonstrates an alternative image denoising reconstruction model based on unsupervised learning that aims to remove Mie scattering and shot noise interference from two-dimensional (2D) Rayleigh images. The model has two generators and two discriminators whose parameters can be trained with either feature-paired or feature-unpaired data independently. The proposed network was extensively evaluated with a qualitative examination and quantitative metrics, such as PSNR, ER, and SSIM. The results demonstrate that the feature-paired training network exhibits a better performance compared with several other networks reported in the literature. Moreover, when the flame features are not paired, the feature-unpaired training network still yields a good agreement with ground truth data. Specific indicators of the quantitative evaluation show a promising denoising ability with a peak signal-to-noise ratio of ~37 dB, an overall reconstruction error of ~1%, and a structure similarity index of ~0.985. Additionally, the pre-trained unsupervised model based on unpaired training can be generalized to denoise Rayleigh images with extra noise or a different Reynolds number without updating the model parameters.
Adversarial example generation with AdaBelief Optimizer and Crop Invariance

Deep neural networks are vulnerable to adversarial examples, which are crafted by applying small, human-imperceptible perturbations on the original images, so as to mislead deep neural networks to output inaccurate predictions. Adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, most existing adversarial attacks often achieve relatively low success rates on adversarially trained networks and advanced defense models. In this paper, we propose AdaBelief Iterative Fast Gradient Method (ABI-FGM) and Crop-Invariant attack Method (CIM) to improves the transferability of adversarial examples. ABI-FGM and CIM can be readily integrated to build a strong gradient-based attack to further boost the success rates of adversarial examples for black-box attacks. Moreover, our method can also be naturally combined with other gradient-based attack methods to build a more robust attack to generate more transferable adversarial examples against the defense models. Extensive experiments on the ImageNet dataset demonstrate the method's effectiveness. Whether on adversarially trained networks or advanced defense models, our method has higher success rates than state-of-the-art gradient-based attack methods.