Reviewerly/exHarmony huggingface dataset:
Scalable Uncertainty Quantification via GenerativeBootstrap Sampler

It has been believed that the virtue of using statistical procedures is on uncertainty quantification in statistical decisions, and the bootstrap method has been commonly used for this purpose. However, nowadays as the size of data massively increases and statistical models become more complicated, the implementation of bootstrapping turns out to be practically challenging due to its repetitive nature in computation. To overcome this issue, we propose a novel computational procedure called {\it Generative Bootstrap Sampler} (GBS), which constructs a generator function of bootstrap evaluations, and this function transforms the weights on the observed data points to the bootstrap distribution. The GBS is implemented by one single optimization, without repeatedly evaluating the optimizer of bootstrapped loss function as in standard bootstrapping procedures. As a result, the GBS is capable of reducing computational time of bootstrapping by hundreds of folds when the data size is massive. We show that the bootstrapped distribution evaluated by the GBS is asymptotically equivalent to the conventional counterpart and empirically they are indistinguishable. We examine the proposed idea to bootstrap various models such as linear regression, logistic regression, Cox proportional hazard model, and Gaussian process regression model, quantile regression, etc. The results show that the GBS procedure is not only accelerating the computational speed, but it also attains a high level of accuracy to the target bootstrap distribution. Additionally, we apply this idea to accelerate the computation of other repetitive procedures such as bootstrapped cross-validation, tuning parameter selection, and permutation test.
On Learning Associative Relationship Memory among Knowledge Concepts

A B S T R A C TA high-level associative memory modelling method was developed to explore the realization of associative memory.In the proposed method, two stage procedures are progressively performed to construct a unified associative knowledge network.In the first stage, some direct weighted associative links are created according to original context relations, and in the second stage, dynamic link reduction operations are executed to optimize associative access efficiency.Moreover, two kinds of link reduction strategies are designed including a global link reduction strategy and a dynamic link reduction strategy based on Hebb learning rule.Two independent datasets are considered to examine the performance of proposed modelling method.By means of reasonable performance indices, the experimental results displayed that, about 70% original links can be reduced almost without associative access failure but better total associative access efficiency.Particularly, the dynamic reduction strategy based on Hebb learning rule may achieve better associative access performance.
Privacy-Aware Best-Balanced Multilingual Communication

In machine translation (MT) mediated human-to-human communication, it is not an easy task to select the languages and translation services to be used as the users have various language backgrounds and skills. Our previous work introduced the best-balanced machine translation mechanism (BBMT) to automatically select the languages and translation services so as to equalize the language barriers of participants and to guarantee their equal opportunities in joining conversations. To assign proper languages to be used, however, the mechanism needs information of the participants' language skills, typically participants' language test scores. Since it is important to keep test score confidential, as well as other sensitive information, this paper introduces agents, which exchange encrypted information, and secure computation to ensure that agents can select the languages and translation services without destroying privacy. Our contribution is to introduce a multi-agent system with secure computation that can protect the privacy of users in multilingual communication. To our best knowledge, it is the first attempt to introduce multi-agent systems and secure computing to this area. The key idea is to model interactions among agents who deal with user's sensitive data, and to distribute calculation tasks to three different types of agents, together with data encryption, so no agent is able to access or recover participants' score.
A Novel Approach for Remote Compilation using Docker Containers

The number of programming languages is getting more and more and developers are facing a tough time in installing all the compilers, libraries and supporting files for the development activities. Most of the time they want to experiment with new technologies, where the efforts required creating a complete environment to run the programs may not be feasible. On the other hand, several companies have started recruiting developers through their online programming platforms. In such situations, it is essential to protect the resources of the server from malicious programs written by the users by purpose or inadvertently. The client environment has to be as lighter as possible and the server environment must be as secure and efficient as possible. There are several existing solutions to meet this objective with plenty of demerits. In this paper we propose a novel method which overcomes most of the problems in the existing solutions and we have experimented the effectiveness of the proposed solution. In our proposed method we develop a docker based sandbox to run the client programs and display the output. We have developed a complete web interface to test the solution and created a backend to manage the users, sessions, tested programs and the outcomes which can be used for analytics too.
Balanced binary search tree multiclass decomposition method with possible non-outliers

Multiclass decomposition algorithms are the means by which binary classification algorithms like support vector machine are used for multiclass classification problems. The popular multiclass decomposition algorithms like one against one (OAO), one against all (OAA), etc., perform the decomposition in a naive manner. This paper presents a novel heuristic-based decomposition algorithm that takes the Hausdorff distance between two classes to decide the decomposition. During the decomposition, rules are made to ensure a balanced binary search tree structure. To model the uncertainty and class noise present in the data, an unsupervised outlier detection technique has been used so that only possible non-outliers take part in the decomposition process. The presented algorithm has been evaluated and compared against OAO and OAA methods across 6 datasets. While evaluating the decomposition algorithms, fuzzy support vector machine has been used to model the class noise during each binary classification. The comparison shows that presented method not only provides comparable performance, but also in all cases, can classify the test samples with fewer average number of support vectors, thus leading to faster test performance. The paper further observes that the proposed approach can provide statistically better performance when the decomposition structure is learned only using the possible non-outliers, as compared to the scenario where the decomposition structure is learned using all samples.
Construction of In-situ Coastal Experimental Station to Continue Assessing Shoreline Protection Performance of Geotextile Sandbags

Li Y.-Q.; You Z.-J.; Shi H.-Y., and Ren, B., 2020. Construction of in-situ coastal experimental station to continue assessing shoreline protection performance of geotextile sandbags. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 267–271. Coconut Creek (Florida), ISSN 0749-0208.Storm-induced coastal erosion is one of main natural hazards on the coast of China and appears over one-third of the Chinese coastline. How to protect the valuable coastline from extensive erosion is of enormous economic and social values. In this study, a 74 m-long erosive coastline with high dune of about 8 m on the coast of Chudao in China has been ecologically protected with durable geotextile sandbags and then served as an in-situ permanent experimental station to continuously assess the shoreline protection performance of geotextile sandbags. The in-situ station consists of three different test segments (S1, S2, S3) and each of them was designed differently to optimize the stability and construction cost of geotextile sandbags. The sand-filled geotextile bags are designed to have the same dimensions of 1.3 m long, 0.8 m wide, and 0.2 m high and last for about 20∼30 years. Before and after the in-situ station has been built, the essential field data are collected: (1) monthly surveying of 20 permanent beach profiles to evaluate the beach profile changes; (2) monthly collection of numerous sand samples to analyze sediment grain size changes; and (3) monthly monitoring of maximum wave runup levels and sandbags stability. In analyzing the collected filed data, it is found that high wave runup is main driver for the shoreline erosion at this study site and the test segment (S2) together with geogrids is most effective and economic of the three test segments. The test segment (S2) will be redesigned to further assess the effects of sandbag size, revetment height and geogrid meshing on the performance of geotextile sandbags at this in-situ station.
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search

Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands of domain-specific data samples. Inspired by how biological motifs such as cells are sometimes extracted from their natural environment and studied in an artificial Petri dish setting, this paper proposes the Synthetic Petri Dish model for evaluating architectural motifs. In the Synthetic Petri Dish, architectural motifs are instantiated in very small networks and evaluated using very few learned synthetic data samples (to effectively approximate performance in the full problem). The relative performance of motifs in the Synthetic Petri Dish can substitute for their ground-truth performance, thus accelerating the most expensive step of NAS. Unlike other neural network-based prediction models that parse the structure of the motif to estimate its performance, the Synthetic Petri Dish predicts motif performance by training the actual motif in an artificial setting, thus deriving predictions from its true intrinsic properties. Experiments in this paper demonstrate that the Synthetic Petri Dish can therefore predict the performance of new motifs with significantly higher accuracy, especially when insufficient ground truth data is available. Our hope is that this work can inspire a new research direction in studying the performance of extracted components of models in an alternative controlled setting.
A new Rule to Constrain Convolution Neural Network Architecture in Face Recognition System

Abstract Face recognition (FR) system is an essential part of a biometric security system, which runs faster than other security methods and done remotely. One of the most important techniques that used in FR is the convolutional neural network (CNN). Traditionally, the choice of the CNN architecture is achieved by experimental trails. In this paper, a new approach is proposed to build a mathematical model that helps to select a proper architecture. This model is built from the experimental results by applying different architectures on the well-known dataset (Vggface2). By changing the class number, image number and convolutional layer number, where the accuracy of each case is recorded. Finally, the proposed model is evaluated on the sets of datasets (Essex. FEI, Caltech), where the accuracies of (99.13, 98.51, 97.78) respectively, are achieved. The evaluation results proved that the proposed model is an efficient for many types of small and middle scale dataset.
Chook -- A comprehensive suite for generating binary optimization problems with planted solutions

We present Chook, an open-source Python-based tool to generate discrete optimization problems of tunable complexity with a priori known solutions. Chook provides a cross-platform unified environment for solution planting using a number of techniques, such as tile planting, Wishart planting, equation planting, and deceptive cluster loop planting. Chook also incorporates planted solutions for higher-order (beyond quadratic) binary optimization problems. The support for various planting schemes and the tunable hardness allows the user to generate problems with a wide range of complexity on different graph topologies ranging from hypercubic lattices to fully-connected graphs.
Extended Inter-Device Digital Rights Sharing and Transfer Based on Device-Owner Equality Verification Using Homomorphic Encryption

In this paper, we propose homomorphic encryption based device owner equality verification (HE-DOEV), a new method to verify whether the owners of two devices are the same. The proposed method is expected to be used for credential sharing among devices owned by the same user. Credential sharing is essential to improve the usability of devices with hardware-assisted trusted environments, such as a secure element (SE) and a trusted execution environment (TEE), for securely storing credentials such as private keys. In the HE-DOEV method, we assume that the owner of every device is associated with a public key infrastructure (PKI) certificate issued by an identity provider (IdP), where a PKI certificate is used to authenticate the owner of a device. In the HE-DOEV method, device owner equality is collaboratively verified by user devices and IdPs that issue PKI certificates to them. The HE-DOEV method verifies device owner equality under the condition where multiple IdPs can issue PKI certificates to user devices. In addition, it can verify the equality of device owners without disclosing to others any privacy-related information such as personally identifiable information and long-lived identifiers managed by an entity. The disclosure of privacy-related information is eliminated by using homomorphic encryption. We evaluated the processing performance of a server needed for an IdP in the HE-DOEV method. The evaluation showed that the HE-DOEV method can provide a DOEV service for 100 million users by using a small-scale system in terms of the number of servers.
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses

This paper presents our modeling and architecture approaches for building a highly accurate low-latency language identification system to support multilingual spoken queries for voice assistants. A common approach to solve multilingual speech recognition is to run multiple monolingual ASR systems in parallel and rely on a language identification (LID) component that detects the input language. Conventionally, LID relies on acoustic only information to detect input language. We propose an approach that learns and combines acoustic level representations with embeddings estimated on ASR hypotheses resulting in up to 50% relative reduction of identification error rate, compared to a model that uses acoustic only features. Furthermore, to reduce the processing cost and latency, we exploit a streaming architecture to identify the spoken language early when the system reaches a predetermined confidence level, alleviating the need to run multiple ASR systems until the end of input query. The combined acoustic and text LID, coupled with our proposed streaming runtime architecture, results in an average of 1500ms early identification for more than 50% of utterances, with almost no degradation in accuracy. We also show improved results by adopting a semi-supervised learning (SSL) technique using the newly proposed model architecture as a teacher model.
Educational needs of the primary education teachers in inclusive education of the pupils from socially disadvantaged backgrounds in Slovakia

The study presents the results of the research examining the educational needs of the teachers and their interest about the themes of courses and educational programmes in connection with actual needs arising from changes in the Slovak education system due to the gradual promotion of inclusion. The main research method was the questionnaire aimed at detection of primary level teachers’ needs for the professional development and trainings, the identification of primary education teachers’ needs for successful implementation of inclusion in the learning of the pupils from socially disadvantaged backgrounds. This study presents if there exist statistically significant differences in self-reflexive perception of the need to improve in topics related to the implementation of inclusive education and the characteristics of respondents (age, length of the teaching practice, willingness to grow personally and to educate).
A Study on the development of indexes for operation of process-fortified assessment in schools

2015 revised National Curriculum has emphasized the process-fortified student assessment aligned with students’ activities in the classroom. This study aimed to develop indexes, which can be used by an individual school and/or teacher, to refer the procedure and the contents of process-fortified assessment. As a result, the present study came up with the definition of the process-fortified assessment as “the assessment that collects the information about the students in the classroom learning activities from various aspects, and provides feedback to support the students’ growth, based on the assessment plan which is based on the curriculum and the alignment of learning and assessment. And, the indexes for the process-fortified assessment were developed based on the literature and delphi method. The the upper category of indexes are ‘Assessment Planning’, ‘Developing Assessment tasks’, ‘Implementation’ and ‘Applying(using) the results of the Assessment’. With regard to these categories, total of 18 implementation indexes and 60 detailed indexes were developed.
Design and Implementation of an Efficient Solar Tracker System

Abstract The world is highly interested in using solar energy as an alternative to fossil energy. Changing the shape of solar systems was adopted to improve its work efficiency. Although the solar tracker is made to be more efficient than the fixed solar system, the work to develop it is yet uncompleted. In this paper, the solar tracker system is used, which works by (LDR) sensors, microcontroller Arduino UNO for controlling the system, and DC motors. The extent of the impact of the solar tracker was studied with its work time. The worked electric circuit for the timer was run the system for one minute and shut-down the system circuit for 29 minutes, the comparison of the work of the tracker was done with a timer and without timer. Finally, according to the amount of gain and loss of energy, it was found that the energy gains about 96.25% from timer system only, and the lost energy from the solar panel because used the timer is about (0.238 to 0.475) % only. The workplace lies in (32o01’49.08” N, 44o22’21.83” E, in Iraq), in August, to real solar panel type.
Computing the Shortest String and the Edit-Distance for Parsing Expression Languages

A distance between two languages is a useful tool to measure the language similarity, and is closely related to the intersection problem as well as the shortest string problem. A parsing expression grammar (PEG) is an unambiguous grammar such that the choice operator selects the first matching in PEG while it can be ambiguous in a context-free grammar. PEGs are also closely related to top-down parsing languages. We consider two problems on parsing expression languages (PELs). One is the r-shortest string problem that decides whether or not a given PEL contains a string of length shorter than r. The other problem is the edit-distance problem of PELs with respect to other language families such as finite languages or regular languages. We show that the r-shortest string problem and the edit-distance problem with respect to finite languages are NEXPTIME-complete, and the edit-distance problem with respect to regular languages is undecidable. In addition, we prove that it is impossible to compute a length bound $$\mathcal {B}(G)$$ of a PEG G such that L(G) has a string w of length at most $$\mathcal {B}(G)$$.
I am Not an Object: Reframing 3D Body Scanning for Co-Design

3D scanning technologies provide designers with tools to generate a digital representation of the human body that can be used in the design of ultra-personalized apparel and wearables. However, prior work shows that the body scanning process can be an uncomfortable experience for users. In this work, we take a first-person perspective to identify frictions in the experience of being body scanned compared to having one's body measurements taken by a professional tailor. Based on our findings, we offer a reframing of body scanning as a collaborative process, and discuss implications for the design of tools and processes that shift agency in the generation of body data towards users. Our paper is relevant to design researchers and practitioners interested in taking a co-design approach to ultra-personalization.
A Batch Variable Learning Rate Gradient Descent Algorithm With the Smoothing L 1/2 Regularization for Takagi-Sugeno Models

A batch variable learning rate gradient descent algorithm is proposed to efficiently train a neuro-fuzzy network of zero-order Takagi-Sugeno inference systems. By using the advantages of regularization, the smoothing L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> regularization is utilized to find more appropriate sparse network. Combining the second-order information of the smoothing error function, a variable learning rate is chosen along the steep descent direction, which avoids line search procedure and may reduce the cost of computation. In order to appropriately adjust the Lipschitz constant of the smoothing error function in the learning rate, a new scheme is proposed by introducing a hyper-parameter. Also the article applies the modified secant equation for estimating the Lipschitz constant, which makes the algorithm greatly reduce the oscillating phenomenon and improve the robustness. Under appropriate assumptions, a convergent result of the proposed algorithm is also given. Simulation results for two identification and classification problems show that the proposed algorithm has better numerical performance and promotes the sparsity capability of the network, compared with the common batch gradient descent algorithm and a variable learning rate gradient-based algorithm.