Reviewerly/exHarmony huggingface dataset:
Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks

Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in knowledge discovery. We study the problem of learning the sparse DAG structure of a BN from continuous observational data. The central problem can be modeled as a mixed-integer program with an objective function composed of a convex quadratic loss function and a regularization penalty subject to linear constraints. The optimal solution to this mathematical program is known to have desirable statistical properties under certain conditions. However, the state-of-the-art optimization solvers are not able to obtain provably optimal solutions to the existing mathematical formulations for medium-size problems within reasonable computational times. To address this difficulty, we tackle the problem from both computational and statistical perspectives. On the one hand, we propose a concrete early stopping criterion to terminate the branch-and-bound process in order to obtain a near-optimal solution to the mixed-integer program, and establish the consistency of this approximate solution. On the other hand, we improve the existing formulations by replacing the linear "big-$M$" constraints that represent the relationship between the continuous and binary indicator variables with second-order conic constraints. Our numerical results demonstrate the effectiveness of the proposed approaches.
Possibilistic Bounds for Granular Counting

Uncertain data are observations that cannot be uniquely mapped to a referent. In the case of uncertainty due to incompleteness, possibility theory can be used as an appropriate model for processing such data. In particular, granular counting is a way to count data in presence of uncertainty represented by possibility distributions. Two algorithms were proposed in literature to compute granular counting: exact granular counting, with quadratic time complexity, and approximate granular counting, with linear time complexity. This paper extends approximate granular counting by computing bounds for exact granular count. In this way, the efficiency of approximate granular count is combined with certified bounds whose width can be adjusted in accordance to user needs.
Sampling Techniques in Bayesian Target Encoding

Target encoding is an effective encoding technique of categorical variables and is often used in machine learning systems for processing tabular data sets with mixed numeric and categorical variables. Recently en enhanced version of this encoding technique was proposed by using conjugate Bayesian modeling. This paper presents a further development of Bayesian encoding method by using sampling techniques, which helps in extracting information from intra-category distribution of the target variable, improves generalization and reduces target leakage.
A Fair, Traceable, Auditable and Participatory Randomization Tool for Legal Systems

Many real-world scenarios require the random selection of one or more individuals from a pool of eligible candidates. One example of especial social relevance refers to the legal system, in which the jurors and judges are commonly picked according to some probability distribution aiming to avoid biased decisions. In this scenario, ensuring auditability of the random drawing procedure is imperative to promote confidence in its fairness. With this goal in mind, this article describes a protocol for random drawings specially designed for use in legal systems. The proposed design combines the following properties: security by design, ensuring the fairness of the random draw as long as at least one participant behaves honestly; auditability by any interested party, even those having no technical background, using only public information; and statistical robustness, supporting drawings where candidates may have distinct probability distributions. Moreover, it is capable of inviting and engaging as participating stakeholders the main interested parties of a legal process, in a way that promotes process transparency, public trust and institutional resilience. An open-source implementation is also provided as supplementary material.
MLE-guided parameter search for task loss minimization in neural sequence modeling

Neural autoregressive sequence models are used to generate sequences in a variety of natural language processing (NLP) tasks, where they are evaluated according to sequence-level task losses. These models are typically trained with maximum likelihood estimation, which ignores the task loss, yet empirically performs well as a surrogate objective. Typical approaches to directly optimizing the task loss such as policy gradient and minimum risk training are based around sampling in the sequence space to obtain candidate update directions that are scored based on the loss of a single sequence. In this paper, we develop an alternative method based on random search in the parameter space that leverages access to the maximum likelihood gradient. We propose maximum likelihood guided parameter search (MGS), which samples from a distribution over update directions that is a mixture of random search around the current parameters and around the maximum likelihood gradient, with each direction weighted by its improvement in the task loss. MGS shifts sampling to the parameter space, and scores candidates using losses that are pooled from multiple sequences. Our experiments show that MGS is capable of optimizing sequence-level losses, with substantial reductions in repetition and non-termination in sequence completion, and similar improvements to those of minimum risk training in machine translation.
Inter-Relationship between Economic Development and Human Development- Analytical Study of selected Arab Countries

espanolLa investigacion tiene como objetivo estudiar la interrelacion entre el desarrollo economico y los indicadores de desarrollo humano para paises seleccionados (Jordania, Egipto, Reino de Arabia Saudita y Bahrain) mediante el uso de la prueba de regresion de minimos cuadrados de dos pasos. Los resultados de la prueba mostraron que existe una relacion de causalidad entre el desarrollo economico y el desarrollo humano. Descubrimos que el desarrollo economico tiene un efecto claro sobre los indicadores de desarrollo humano en los paises arabes seleccionados. EnglishThe research aims to study the inter-relationship between economic development and human development indicators for a selected countries (Jordan, Egypt, Kingdom of Saudi Arabia and Bahrain) by using two stage least Square Test. The results of the test showed that there is a causality relation between the economic development and human development. We found economic development has clear effect on human development indicators in the selected Arab countries.
Theoretical Research and Practical Exploration on the Teaching Reform of Higher Mathematics

Higher mathematics is a core basic course offered by various majors in colleges and universities, which plays a very important role in improving students’ mathematical literacy and cultivating logical thinking. At present, there are many problems in the teaching of higher mathematics, especially with the development of network platform and technology, students’ learning patterns and the way of thinking have changed greatly. Therefore, it is necessary for us to reform it to meet the needs of the times. Based on the theory of modern education and teaching, combined with the current situation of research at home and abroad and the reality of teaching reform, this paper studies teaching materials, the teaching mode combining the first classroom with the second classroom, online and offline, the construction of the test bank, the teaching evaluation and soon.
Mobile Learning Features Preferred: An Examination of Students in The United Arab Emirates

The purpose of the current study was to examine the preferred mobile learning features higher education Emirati students use to make sense of mathematics and to determine if these Emirati students valued a mobile device as an essential tool to learn mathematics. Both students and instructors used the Texas Instruments Nspire: Computer Algebra System (CAS) App in a semester mathematics course. The app has such features as enter expressions in proper mathematics notation, import images, dynamically linked notes, share work across platforms (emails, iTunes, Dropbox, etc.), and save documents to mobile device. Participates included sixty-three undergraduate students enrolled in a mathematics course at Zayed University (Dubai campus, United Arab Emirates). Based on a pre- and post-survey, students’ positive perspectives of mathematics increased, negative perspectives decreased, and instructor influence on mobile learning increased. The pre- and post-questionnaire revealed an increase in the participants’ opinion that mobile learning is an essential tool to help make sense of mathematics.
Linguistic Means for Creating an Image in Online Advertising (by the Material of the Georgian Cuisine Restaurants’ Websites)

The aim of the study is to examine linguistic means used for creating a restaurant’s image in online advertising. The article analyses specifics of online advertising. Special attention is paid to a company’s website as a type of the electronic advertising text. The research is novel in that it studies the role of linguistic means employed for creating a restaurant’s advertising image on the establishment’s website. The article concludes that these linguistic means (the most productive being brand names, epithets, metaphors and phraseological units) create a full-fledged advertising image evoking appropriate marketing associations in consumers’ minds.
Unsupervised Transfer Learning with Self-Supervised Remedy

Generalising deep networks to novel domains without manual labels is challenging to deep learning. This problem is intrinsically difficult due to unpredictable changing nature of imagery data distributions in novel domains. Pre-learned knowledge does not transfer well without making strong assumptions about the learned and the novel domains. Different methods have been studied to address the underlying problem based on different assumptions, e.g. from domain adaptation to zero-shot and few-shot learning. In this work, we address this problem by transfer clustering that aims to learn a discriminative latent space of the unlabelled target data in a novel domain by knowledge transfer from labelled related domains. Specifically, we want to leverage relative (pairwise) imagery information, which is freely available and intrinsic to a target domain, to model the target domain image distribution characteristics as well as the prior-knowledge learned from related labelled domains to enable more discriminative clustering of unlabelled target data. Our method mitigates nontransferrable prior-knowledge by self-supervision, benefiting from both transfer and self-supervised learning. Extensive experiments on four datasets for image clustering tasks reveal the superiority of our model over the state-of-the-art transfer clustering techniques. We further demonstrate its competitive transferability on four zero-shot learning benchmarks.
The use of innovations during the crime scene investigation

Introduction. Criminalistics is a science that aims to satisfy the needs of the practice and not only the investigator, but also the prosecutor's, lawyer's and expert's. And this is especially true of the current stage of society, where it faces many challenges and modern technologies are rapidly evolving. This puts forensics with complex and varied tasks, both related to the provision of practice and the training of relevant professionals. The issue of introduction of innovations into the practice of investigation of criminal offenses was of great relevance. An important aspect of the introduction of the latest technologies and innovations in the process of investigation of criminal offenses is their use during investigative actions, in particular, the crime scene investigation. However, the coverage of some aspects of the use of innovation during the crime scene investigation remains relevant and needs to be addressed. The purpose of the paper is to look at specific issues related to the use of innovation during the crime scene investigation. Results. The paper deals with the use of innovation in the investigation process, in particular in such investigative action as the crime scene investigation. Some aspects, as well as the current state of the use of technical and forensic tools during this investigative action, are investigated. Ways to improve and improve the performance of the review by using innovation during its conduct are analyzed. Specific issues related to the use of innovation during the crime scene investigation are addressed. Innovation in this investigative action is an urgent need of time, especially since technologies have reached a high level and allow them to perform various tasks quickly and effectively. Conclusion. Criminalistics should evolve dynamically both as a science and as a discipline. She must respond to the practice's requests in a timely manner. And this, accordingly, requires a high level of training of both future practitioners and scientists who will later become at the forefront of forensics. The latest technologies and innovations should be actively introduced by criminalistics into the practice of criminal investigation.
UPAYA PENINGKATAN PRODUKSI KOPI DENGAN PANEN PETIK MERAH DI KABUPATEN REJANG LEBONG

This study aims to examine the best forecasting model for the export price of Indonesian coffee. The data used in this study are monthly data on coffee prices from January 2012 to September 2019. Three price forecasting models used are moving average, single exponential smoothing and trend analysis are applied to determine the best model based on the lowest MAPE, MAD, and MSE values. The results showed the best model for forecasting the export price of coffee is the moving average (MA1) model because it has the smallest MAPE, MAD and MSE values ​​compared to other models.&#x0D; Keywords: Price; Coffee; Forecasting; Export
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level

Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spatial attention networks are known to learn this relationship and focus its gaze on salient objects in the image. Thus, we propose to utilize spatial attention networks for image-level visual-textual fusion preserving local (word) and global (phrase) information to refine region proposals with an in-network Region Proposal Network (RPN) and detect single or multiple regions for a phrase query. We focus only on the phrase query - ground truth pair (referring expression) for a model independent of the constraints of the datasets i.e. additional attributes, context etc. For such referring expression dataset ReferIt game, our Multi-region Attention-assisted Grounding network (MAGNet) achieves over 12\% improvement over the state-of-the-art. Without the context from image captions and attribute information in Flickr30k Entities, we still achieve competitive results compared to the state-of-the-art.
Learning Restricted Boltzmann Machines with Sparse Latent Variables

Restricted Boltzmann Machines (RBMs) are a common family of undirected graphical models with latent variables. An RBM is described by a bipartite graph, with all observed variables in one layer and all latent variables in the other. We consider the task of learning an RBM given samples generated according to it. The best algorithms for this task currently have time complexity $\tilde{O}(n^2)$ for ferromagnetic RBMs (i.e., with attractive potentials) but $\tilde{O}(n^d)$ for general RBMs, where $n$ is the number of observed variables and $d$ is the maximum degree of a latent variable. Let the MRF neighborhood of an observed variable be its neighborhood in the Markov Random Field of the marginal distribution of the observed variables. In this paper, we give an algorithm for learning general RBMs with time complexity $\tilde{O}(n^{2^s+1})$, where $s$ is the maximum number of latent variables connected to the MRF neighborhood of an observed variable. This is an improvement when $s &lt; \log_2 (d-1)$, which corresponds to RBMs with sparse latent variables. Furthermore, we give a version of this learning algorithm that recovers a model with small prediction error and whose sample complexity is independent of the minimum potential in the Markov Random Field of the observed variables. This is of interest because the sample complexity of current algorithms scales with the inverse of the minimum potential, which cannot be controlled in terms of natural properties of the RBM.
Do RNN and LSTM have Long Memory

The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in mind, this paper raises the question - do RNN and LSTM have long memory? We answer it partially by proving that RNN and LSTM do not have long memory from a statistical perspective. A new definition for long memory networks is further introduced, and it requires the model weights to decay at a polynomial rate. To verify our theory, we convert RNN and LSTM into long memory networks by making a minimal modification, and their superiority is illustrated in modeling long-term dependence of various datasets.
English-Korean Machine Translation System with the Improved Ability to Resolve Linguistic Differences by Pre- and Post-Processing

The linguistic differences between English and Korean cause difficulties in text processing, especially in machine translation. An English-Korean machine translation system must effectively solve these linguistic differences in order to be used practically. In this paper, we propose pre-processing as a method of solving the linguistic differences. We present pre-processing tasks for the differences and some accompanying post-processing procedures. We explore the differences requiring the pre-processing, devise pre-processing procedures, and determine the proper position where the procedures can be applied during the translation process. In an experiment, we show the usefulness of the proposed pre-processing and post-processing tasks for improving translation quality.
An all-optical proteretic switch using semiconductor ring lasers

Proteresis, a complementary effect to well-known hysteresis, is an interesting phenomenon to be explored. As we know, the hysteretic device is a bistable switch whose states depend on the system’s history whereas the proteretic device’s states depend on the future. Though an all-optical implementation of the hysteretic device has been demonstrated but its complementary behavior has yet to be contrived. Therefore, this work presents the design and development of an all-optical proteretic system using semiconductor ring lasers. The design was achieved by providing a constraint feedforward path to a hysteretic feedback system. Additionally, the role of various parameters was studied in order to achieve proteresis. It is observed that the proteretic effect can be achieved only for specific conditions otherwise the system behaves as a traditional hysteretic device. The experimental results match well with the theoretical Simulink results.
FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT

Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.
Inclusive literature as an innovative marketing resource of publishing activities

Inclusive literature as a component of the process of real involvement of people with disabilities in active social life remains out of the attention of researchers. The paper aims to study the state and existing problems in publishing literature for people with disabilities and the use of inclusive topics by Ukrainian publishers. The results were obtained due to the theoretical comprehension of the empirical materials, which were based on a survey of 42 operating various domestic publishing houses. The vast majority of them expressed an understanding of the importance of publishing literature about people with disabilities and accessible editions for them. One-third of those surveyed publishers have experience in publishing inclusive literature. About 24% of respondents have books in accessible formats. They mainly used such publishing or engineering technologies as books with large print, picture-books, and books with pictograms. The study also found that among a few current insurmountable restraining factors of development of inclusive literature is the commercial inexpediency of such projects. The results of this study will contribute to the formation of collective public opinion on the importance of inclusive literature and, consequently, the attraction of the financing of inclusive publications of patrons, sponsors, crowdfunding platforms, etc.
Instance segmentation of buildings using keypoints

Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.