Reviewerly/exHarmony huggingface dataset:
High-Dimensional Inference for Cluster-Based Graphical Models

Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper introduces a new class of cluster-based graphical models, in which variable clustering is applied as an initial step for reducing the dimension of the feature space. We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable. Two different cluster-based Gaussian graphical models are considered: the latent variable graph, corresponding to the graphical model associated with the unobserved latent variables, and the cluster-average graph, corresponding to the vector of features averaged over clusters. Our study reveals that likelihood based inference for the latent graph, not analyzed previously, is analytically intractable. Our main contribution is the development and analysis of alternative estimation and inference strategies, for the precision matrix of an unobservable latent vector $Z$. We replace the likelihood of the data by an appropriate class of empirical risk functions, that can be specialized to the latent graphical model and to the simpler, but under-analyzed, cluster-average graphical model. The estimators thus derived can be used for inference on the graph structure, for instance on edge strength or pattern recovery. Inference is based on the asymptotic limits of the entry-wise estimates of the precision matrices associated with the conditional independence graphs under consideration. While taking the uncertainty induced by the clustering step into account, we establish Berry-Esseen central limit theorems for the proposed estimators. It is noteworthy that, although the clusters are estimated adaptively from the data, the central limit theorems regarding the entries of the estimated graphs are proved under the same conditions one would use if the clusters were known....
THE USE OF TPR METHOD FOR DISABLE STUDENTS WITH DIFFERENT LEARNING STYLES IN ENGLISH VOCABULARY DEVELOPMENT

Teaching English as a foreign language to mentally retarded persons is much more complicated than normal EFL students, and it requires specific techniques and methods. The aims of the research are to find out the profile of disabled students’ learning style and to elaborate on how well Total Physical response meets the needs of disabled students with different learning styles. This research applied pre-experimental design which used to see the result of applying the TPR Method for disable students with different learning styles in vocabulary development and the data were obtained from the questionnaire, interview, and classroom observation. The research result shows that there are 3 types of learning styles in the classroom, namely visual learning, auditory and kinesthetic learning style. The data revealed that the average pre-test results of students before being given teaching material using the TPR method is 5% while the results obtained by students after giving teaching material using the TPR method is 77%. 100% of visual learners have improved the result of the post-test after being given the material. 100% of auditory learners also have an increase in the result of the post-test after the provision of teaching materials using the TPR method. However, only 50 percent of 100 percent kinesthetic learners experience an increase after the provision of teaching materials using the TPR method. There are many factors that can influence the result of the student’s test. It can be in the form of internal factors that can stimulate the concentration and the motivation of students in learning English
An Improved Algorithm and Accuracy Analysis Testing Cases of Latin-to-Balinese Script Transliteration Method based on Bali Simbar Dwijendra Font

As part of the Balinese culture, the Balinese script has rarely been used today. Its Latin-to-Balinese script transliteration knowledge is also affected by that condition. This study aims to preserve that knowledge through technological approaches. By collaboration between informatics and linguistics discipline, this research improves the Latin-to-Balinese script transliteration method from the previous research. This method can be used as a core for the web/mobile-based transliteration learning application. The improvements that were taken in this research are: 1) the utilization of Bali Simbar Dwijendra font rather than the previous Bali Simbar-B font; and 2) additional accuracy analysis testing cases to the previous accuracy analysis testing cases from The Balinese Alphabet writing rules and examples document. As main contribution in this research area, this method was considered as the first Latin-to-Balinese script transliteration method that utilizing Bali Simbar Dwijendra font. This font is the enhancement from the previous Bali Simbar-B font related to its size which are bigger and thicker for better visual at the same font size. With certain keyboard layout application, it also makes easier and more natural to type it in programs and across the web. Additional 47 testing cases to the previous 151 testing cases related to the sound killers (gantungan) that has no example at The Balinese Alphabet writing rules and examples document. Those examples were taken from another credible source, i.e. the Balinese script dictionary from the Bali Province government. Through the experiment, this method has accuracy up to 98% (195 of 198) testing cases that has passed through all additional 47 cases, and 148 cases from the previous 151 cases. Related to the previous 151 cases, this improved method maintained same accuracy rate to the previous method. Execution time for relatively more complex sentence case type gave relatively real time result at 10.99ms on average on certain hardware and Operating System platform. In the future work, this method accuracy could be improved significantly by: 1) enriching the repository consists of words belong to special words from the Balinese script dictionary; and 2) implementing semantic relation transliteration.
SAJA at TRAC 2020 Shared Task: Transfer Learning for Aggressive Identification with XGBoost.

we have developed a system based on transfer learning technique depending on universal sentence encoder (USE) embedding that will be trained in our developed model using xgboost classifier to identify the aggressive text data from English content. A reference dataset has been provided from TRAC 2020 to evaluate the developed approach. The developed approach achieved in sub-task EN-A 60.75% F1 (weighted) which ranked fourteenth out of sixteen teams and achieved 85.66% F1 (weighted) in sub-task EN-B which ranked six out of fifteen teams.
Multi-party Access Control - 10 Years of Successes and Lessons Learned

As end-users have been asked to take on management tasks for their content and online resources, access control mechanisms have played an increasingly important role in a broad range of applications. These include data management for personalized medicine, content sharing sites, online communities, and technologies for remote collaborative work To face the need of these emerging user-centered domains, an increasing body of work has recognized the importance of new multi-user (or more generally, stakeholder) access control mechanisms for multiple users. The emphasis on group-centered access control has led to a shift from the traditional approach taken in the access control community for two main reasons. First, the access control community had long investigated models and techniques to facilitate single subjects' access to resources according to well-defined locally-enforceable policies, with little attention given to group-driven access control decisions. Second, the underlying goal had been to maintain confidentiality rather than facilitate controlled sharing. As such, the decisions offered by these early mechanisms are single-user driven and often binary and based on inflexible policies. In the past ten years, researchers have investigated and proposed a variety of multiparty access control mechanisms, and defined rigorous models for content management among multiple users, also developing mechanisms for various applications \citesuch2016resolving,fogues2017sharing,hu2014,hu2011multi,rajtmajer2016constrained,SuchC18,kairam2012talking,patil2012. Some tools for practical applications have also been developed. However, we have also assisted to several "failures" where promising approaches have not gained traction, either among the research community or (even less) the applied world. In this talk I will first discuss unique needs and challenges with addressing access control for multi-owned content, and provide a perspective from various applications. Next, I will summarize main successes and failures of existing approaches, identify open research challenges for future research opportunities in this space.
Blockchain Secured “Smart Buildings” as Cyber Physical Systems

Blockchain, often associated with cryptocurrencies, has been lately realized to have immense potential across other domains too vide Blockchain 2.0 and futuristic Blockchain 3.0 versions. Blockchain has magnified itself beyond cryptocurrencies and has a universal appeal ranging across variety domains. Though still at inception stage, but a variegated options and choices have emerged on how blockchain can benefit and make the world better with its immutability, smart contracts and transparency features. Also we have evolving cyber physical systems (CPS), which are consolidations of new generation computing methods, physical and networking operations. In CPS, the embedded computing technologies and networks proctor and check the physical functions, with feedback loops where physical processes bear upon computing part and contrariwise. CPS incorporates the kinetics of the physical operations with software and networking, delivering abstractions and simulating, conceptions, and analysis techniques for the complete system. This paper will discuss and propose the deployment of the CPS Smart building (CPS-SB) network based on an Ethereum private blockchain system mechanized by smart contracts and also discuss inherent challenges on a IoT Simulator (Cupcarbon). The proposal allows for operation of self sustaining CPS-SB based on smart contracts to realize predictive building controls.
Naturalistic Programming: Model and Implementation

Naturalistic programming is defined as a programming technique that uses abstractions whose expressiveness is close to natural languages. The objective is preserving as much as possible the needs of the client in their language, while the text of these needs is simultaneously the requirements specification and the program source code. Consequently, the goal of the naturalistic paradigm is reducing the gap between problem domain and solution domain. In the literature, two main approaches are reported, one focuses on transforming controlled natural languages into high level code, such as Java and Python; in the other approach the requirements description is at the same time the program source code. While the translators employed in the first approach do not offer a new paradigm, the few naturalistic languages reported have utility in specific domains. In the absence of a naturalistic framework, this article presents the minimum elements for defining a naturalistic model that allows the creation of general-purpose languages and at the same time, the SN language is introduced as a proof-of-concept, which is a prototype language for naturalistic programming.
Towards supporting reproducibility of experimental studies in GUI visual design

Graphical User Interfaces are the most common way of interaction with the devices we use in our everyday life. Given the important and long-lasting impact that the visual design of GUIs has on User Experience, its experimental study is of high importance. However, this activity suffers from a lack of reproducibility of experimental results due to the significant amount of time and resources to conduct such experiments and create datasets. To address this problem, this thesis aims at developing an application which purpose is to facilitate the construction of datasets in the context of experimental studies of GUIs. The application parameterizes the design of experimental studies related to GUIs and automates various steps in order to facilitate their deployment and to foster their reproducibility. We explain the research approach, the workflows and features underlying the application. Finally, we discuss the current state of the thesis and the future work to be achieved.
Towards the Infeasibility of Membership Inference on Deep Models

Recent studies propose membership inference (MI) attacks on deep models. Despite the moderate accuracy of such MI attacks, we show that the way the attack accuracy is reported is often misleading and a simple blind attack which is highly unreliable and inefficient in reality can often represent similar accuracy. We show that the current MI attack models can only identify the membership of misclassified samples with mediocre accuracy at best, which only constitute a very small portion of training samples. We analyze several new features that have not been explored for membership inference before, including distance to the decision boundary and gradient norms, and conclude that deep models' responses are mostly indistinguishable among train and non-train samples. Moreover, in contrast with general intuition that deeper models have a capacity to memorize training samples, and, hence, they are more vulnerable to membership inference, we find no evidence to support that and in some cases deeper models are often harder to launch membership inference attack on. Furthermore, despite the common belief, we show that overfitting does not necessarily lead to higher degree of membership leakage. We conduct experiments on MNIST, CIFAR-10, CIFAR-100, and ImageNet, using various model architecture, including LeNet, ResNet, DenseNet, InceptionV3, and Xception. Source code: this https URL}{\color{blue} {this https URL}.
Fast Interactive Design of Scatterplots for Large Data Set Visualisation

Scatterplots are one of the most common data visualisation methods. Designing scatterplots for large data sets is challenging, as overlapping markers are likely to cause loss of information. Poorly chosen marker opacity, shape, or size may lead to e.g. overplotting or diminished visibility of outliers. The challenge is amplified by having to wait for rendering every time the design is changed. To reduce designer effort, optimisation-based approaches to scatterplot design have been proposed, most comprehensive being an algorithm by Micallef et al. (2017) that applies image-based perceptual quality measures to automatic evaluation of scatterplots. However, their approach suffers also from poor rendering performance, discouraging usage in interactive applications. This paper presents an algorithm applying abstract rendering for efficiently updating scatterplot markers regardless of data set size. We show how our approach enables fast, interactive design and adjustment of overlap in scatterplots, demonstrated with a proof-of-concept visualisation tool.
Información y pandemia. Apuntes marxistas para una comunicología posible y necesaria

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Realization of Quantum Oracles using Symmetries of Boolean Functions

Designing a quantum oracle is an important step in practical realization of Grover algorithm, therefore it is useful to create methodologies to design oracles. Lattice diagrams are regular two-dimensional structures that can be directly mapped onto a quantum circuit. We present a quantum oracle design methodology based on lattices. The oracles are designed with a proposed method using generalized Boolean symmetric functions realized with lattice diagrams. We also present a decomposition-based algorithm that transforms non-symmetric functions into symmetric or partially symmetric functions. Our method, which combines logic minimization, logic decomposition, and mapping, has lower quantum cost with fewer ancilla qubits. Overall, we obtain encouraging synthesis results superior to previously published data.
Analysis of Power-Efficient High Torque Solar Tracker through PID Controller

To provide a continuous supply of electricity, renewable energy resources, and smart technological innovation must be exploited, aiming to provide a long-term solution to the existing energy crises faced by today's world. The photovoltaic (PV) panels are deployed to receive solar energy from the sunlight and consequently convert it into electrical energy. The photovoltaic PV panel receives maximum solar radiation only when it is at 90° perpendicular to the sunlight, which is directly proportional efficiency of the system. If proper technique not used to collect the maximum amount of sunlight, solar PV generation can be a more expensive energy source as compared to conventional energy generation. The generated output power of PV panels depends on the quantity of solar energy they collect from sunlight, and this amount can be increased by exploiting tracking systems. The solar tracker tends to move PV panels perpendicular to sunlight radiations to collect the maximum amount of sunlight to increase the system's efficiency.
A Preliminary Study on Performance Evaluation of Multi-View Multi-Modal Gaze Estimation under Challenging Conditions

In this paper, we address gaze estimation under practical and challenging conditions. Multi-view and multi-modal learning have been considered useful for various complex tasks; however, an in-depth analysis or a large-scale dataset on multi-view, multi-modal gaze estimation under a long-distance setup with a low illumination is still very limited. To address these limitations, first, we construct a dataset of images captured under challenging conditions. And we propose a simple deep learning architecture that can handle multi-view multi-modal data for gaze estimation. Finally, we conduct a performance evaluation of the proposed network with the constructed dataset to understand the effects of multiple views of a user and multi-modality (RGB, depth, and infrared). We report various findings from our preliminary experimental results and expect this would be helpful for gaze estimation studies to deal with challenging conditions.
Strategic Alliance and Corporate Performance in the Oil Firms in Nigeria

There is the heightened challenged of scarcity of organisational resources desired to cope with the amplified level of organisational conflict. One of the likely ways of ensuring survival is the creation of strategic alliance with respect to conflict management amongst organisation. This study examines the relationship between the dimensions of strategic alliance and corporate performance in the oil firms in Nigeria. An extensive literature review was carried out. The study adopted the descriptive type of cross-sectional survey research and used the questionnaire method in primary data collection for the study, a total of 181 questionnaires were administered to the sample size of the oil firms under surveyed in Nigeria. Spearman’s Rank Correlation Coefficient with SPSS was used to analyze the stated hypotheses. The research finding revealed that; there is a significant positive relationship between strategic alliance and corporate performance. We therefore conclude that; strategic alliance can bring about improvement in organisational effectiveness of oil companies in Nigeria.Based on the findings the following recommendations were made; that Oil companies should focus on increasing the exploitation of how to manage conflict strategies of strategic alliance to increase their corporate performance. And that management should be sensitive to conflicts and set up effective communication channels to facilitate an improvement in conflict management.How to cite this article: Johnson UU, Irabor-Ighedosa OJ. Strategic Alliance and Corporate Performance in the Oil Firms in Nigeria. J Adv Res HR Organ Mgmt 2019; 6(3&4): 40-48.
Pruning algorithm of convolutional neural network based on optimal threshold

In the process of pruning, in order to automatically obtain an optimal pruning threshold that can balance the maximum sparse rate and the minimum error. This paper proposes a convolutional neural network pruning algorithm based on the optimal threshold. The algorithm uses the optimization ability of the greedy algorithm to select an optimal threshold, and uses the sensitivity and correlation of the node as factors to determine whether the node is important. Then by deleting the nodes whose importance is below the optimal threshold, the purpose of pruning the network is achieved. Experiments show that under the premise of loss accuracy within 2%, the algorithm can test the Lenet-5 network pruning on the M-NIST data set, which can accelerate 36.62%. This algorithm tests the VggNet network pruning on the CIFAR-10 dataset, which can speed up 43.86%. Experiments show that the algorithm effectively reduces network parameters and reduces running time.
Implementing INTACS Dynamics Enterprise Resources Planning System for Financial Statements

Accounting Information System is an accounting information system that can adjust to what kind of business that exists in Indonesia and is very important in providing corporate financial information quickly and accurate.The purpose of this study is to provide information to the businesses, especially manufacturing sector, that it is very important to make financial reports, and by using the Enterprise Resources Planning system INTACS Dynamic Manufacturing business actor can see each transaction, inventory, and financial statements quickly and precisely.This study uses a qualitative descriptive method approach with case study approach where the technique used in collecting data is done through observation, interviews, and library research by studying related literatures.Based on the results of the research and discussion, the author can conclude that by using the Enterprise Resources Planning system INTACS Dynamics then PT.Hydrocolloid Indonesia can apply well, through extensive training In managing resources for daily operations and facilitate the preparation of financial statements, maximizing the functionality of the Enterprise Resources Planning system of the Dynamics INTACS, automatic financial Report preparation as well as quality reports Financial increase.
Resolution-Exact Planner for Thick Non-Crossing 2-Link Robots

We consider the path planning problem for a 2-link robot amidst polygonal obstacles. Our robot is parametrizable by the lengths \(\ell _{1},\ell _{2} > 0\) of its two links, the thickness \(\tau \ge 0\) of the links, and an angle \(\kappa \) that constrains the angle between the 2 links to be strictly greater than \(\kappa \). The case \(\tau > 0\) and \(\kappa \ge 0 \) corresponds to “thick non-crossing” robots. This results in a novel 4DOF configuration space \(\mathbb {R}^{2}\times (\mathbb {T}^{2}\backslash \varDelta (\kappa ))\) where \(\mathbb {T}^{2}\) is the torus and \(\varDelta (\kappa )\) the diagonal band of width \(\kappa \).We design a resolution-exact planner for this robot using the framework of Soft Subdivision Search (SSS). First, we provide an analysis of the space of forbidden angles, leading to a s5oft predicate for classifying configuration boxes. We further exploit the T/R splitting technique which was previously introduced for self-crossing thin 2-link robots.Our open-source implementation in Core Library achieves real-time performance for a suite of combinatorially non-trivial obstacle sets. Experimentally, our algorithm is significantly better than any of the state-of-art sampling algorithms we looked at, in timing and in success rate.