Reviewerly/exHarmony huggingface dataset:
Agile Software Methodology with Scrum for Developing Quality Assurance System

Quality Assurance System Development is needed by ISO-Certified Higher Education to improve accreditation achievement very quickly and precisely for the higher education institutions. This study aims to develop a quality assurance system using the agile development methodology with Scrum. This research is the final stage of the previous research related to the model development of a quality assurance system based on the integration of accreditation standards from BAN-PT and ISO 9001:2008 standards. Problems that arise when developing this system application agile, and the development team are few with the products are quality and ready for release. The selection of the Agile Development Method with Scrum is considered very appropriate for the solution to this system development problem. Research with this applied research scheme used an action research approach; furthermore, the system development method used Agile Software Development with Scrum. The results of this study describe an analyze the quality assurance system development by selecting the Agile Development Methodology with Scrum. This quality assurance system is expected to contribute significantly to ISO certified higher education in achieving better Accreditation standards for their study programs.
What Is It You Really Want of Me? Generalized Reward Learning with Biased Beliefs about Domain Dynamics

Reward learning as a method for inferring human intent and preferences has been studied extensively. Prior approaches make an implicit assumption that the human maintains a correct belief about the robot's domain dynamics. However, this may not always hold since the human's belief may be biased, which can ultimately lead to a misguided estimation of the human's intent and preferences, which is often derived from human feedback on the robot's behaviors. In this paper, we remove this restrictive assumption by considering that the human may have an inaccurate understanding of the robot. We propose a method called Generalized Reward Learning with biased beliefs about domain dynamics (GeReL) to infer both the reward function and human's belief about the robot in a Bayesian setting based on human ratings. Due to the complex forms of the posteriors, we formulate it as a variational inference problem to infer the posteriors of the parameters that govern the reward function and human's belief about the robot simultaneously. We evaluate our method in a simulated domain and with a user study where the user has a bias based on the robot's appearances. The results show that our method can recover the true human preferences while subject to such biased beliefs, in contrast to prior approaches that could have misinterpreted them completely.
Searching Parallel Separating Hyperplanes for Effective Compression of Threshold Logic Networks

The threshold logic (TL) function, parameterized by a vector of weights and a threshold value, is an important class of Boolean functions that imitate neural information processing. When multiple TL functions are to be implemented in circuits or to be valuated through hardware acceleration, weight sharing among them may provide an effective way for circuit minimization or data compression. We study the condition for a set of TL functions to be implementable with a common weight vector, i.e., representable with parallel separating hyperplanes, and devise a new parameter compression technique. Experimental results demonstrate a 7-fold compression ratio for libraries of TL functions with up to 6 inputs and a data storage reduction to about 45% of the original parameter size for the depthwise convolution layers of an activation-binarized neural network aiming at CIFAR10 dataset classification.
Fast and Accurate Content Based Image Classification and Retrieval using Gaussian Hermite Moments applied to COIL 20 and COIL 100

Image retrieval based on content is gaining popularity due to the rapid progress in the usage of image a data over mobile and internet. Fast and accurate retrieval of the images is always essential in particular when the bandwidths are scarce. Many CBIR algorithms are available in the literature for various databases. This paper proposes a CBIR algorithm for improving the retrieval accuracy from COIL 20 and COIL 100. Both the databases are basically shape based ones. Even though many shape based descriptors are available image moments play an important role in the description of the shape. Some moments which are extensively used are Hu's moments, Fourier Mellin Moments, Zernike Moments, Legendre Moments, Gaussian Hermite moments, Chebycheff moments. In this work, an algorithm using Gaussian Hermite moments and SVM is proposed. Medium Gaussian kernel is used in the SVM. A cross validation of 10 fold is used for the simulations for COIL 20 database. 25% hold out validation is used for COIL 100 database due to its large size. Performance metrics used are precision and recall in addition to the average retrieval efficiency. An average retrieval accuracy of 100% is obtained for COIL 20 database. For COIL 100, the maximum average retrieval accuracy is 99.5%. 100% precision and recall are obtained for both the databases. Results are obtained with few features, the proposed algorithm is fast compared to many algorithms. Proposed algorithm is compared with the state of the art algorithms available in the literature.
The Effect of Humidity and Temperature on the Efficiency of Solar Power Panel Output in Dutsin-Ma Local Government Area (L.G.A), Nigeria

Solar energy is a large inexhaustible source of energy were the power from the sun intercepted by the Earth is over a trillion watt, which is many thousand times larger than the present consumption rate on the earth of all commercial energy sources. The sun could supply all the present and future energy need, presently the major way of extracting this energy is through solar panel and the efficiency of the energy conversion is in high consideration. Research has revealed that different factors can affect the efficiency of the panel, using a DT830B Digital Multi-meter and HTC-1 Clock/Temperature/Hygrometer on a Dp-Li21 polycrystalline solar panel. This work took the liberty of analyzing the effect of humidity and temperature on the efficiency of the solar panel. The result obtained shows that current production is highly aided by temperature within 42-45ºC and humidity below 35% and voltage production remains fairly stable with temperature below 42ºC and humidity below 60% while the power production is fairly stable with humidity below 40% and maximum with temperature at 45ºC, for power production on a panel mounted and to be mounted to be within maximum range, this analysis is to be carried out and put into consideration.
Attribute-Based Signatures for Inner-Product Predicate from Lattices

Attribute-based signature (ABS) is a versatile cryptographic primitive that allows a user possessing a set of attributes from the trusted authority to sign a message with fine-grained control over the identifying information, and the signature will reveal nothing no more than the fact that the attributes of the signer satisfy the predicate with respect to the message. In this paper, we introduce a fully secure and efficient attribute-based signature for inner-product (IP) predicate from lattice assumptions in the random oracle model, in which the admissible IP predicate is more general in contrast to those of the existing lattice-based constructions. More precisely, the proposed scheme is the first attribute-based signature from lattices to support conjunctions, disjunctions, threshold predicates, polynomial evaluations, and CNF/DNF formulas.
Short-Term Hydrothermal Scheduling Using Gray Wolf Optimization

In this paper, a new nature-inspired meta-heuristic called gray wolf optimization (GWO), is implemented for solution of short-term hydrothermal scheduling (STHTS) problems. The analytical model of GWO is based on the rigors social hierarchy and hunting procedure of gray wolves by their unique three steps: searching for prey, encircling of prey, and attacking of prey. GWO has been implemented and tested on two different types of complex constrained STHTS problems. To confirm the superiority and efficacy of GWO, the comparison of simulation results are also made with other recent reported methods available in literature.
Parallel Real-Time Tracking and 3D Reconstruction with TBB for Intelligent Control and Smart Sensing Framework

Recent advancements in aircraft controllers and increasingly flexible aircraft designs create the need for adaptive and intelligent control systems. To correctly capture the motion of a flexible aircraft wing and provide feedback to the controller large number of states (nodes along the span) must be monitored. Visual sensing method can provide the flexibility that is needed for this type of smart sensing and control. Visual sensing, however, requires to capture and track keypoint features (marker tracking), and detecting these keypoints from a feature-rich image can be a computationally intensive task. The computational effort greatly increases with larger images and or when an image stereo pair is used to find matching keypoints. In this study, a parallel approach is presented with Threading Building Blocks (TBB) to extract corresponding keypoints from an image-stereo pair and apply triangulation with the DLT (Direct Linear Transform) method to reconstruct the 3D position of the object in space. Furthermore, a flexible simulation framework is set-up using for smart sensing with a coupled unsteady aeroservoelastic model of a 3D wing and a visual model to test the method for intelligent control feedback in a simulation environment. The methodology is tested in a laboratory environment with a stereo camera setup and virtual environment, where the virtual camera parameters are reconstructed to meet a stereo setup. The approach used in this study aims to advance the are of smart sensing, particularly in the context of real-time state estimation of aeroelastic structures and control feedback. The parallel approach shows a great improvement of speed in the order of 3-6 depending on the image size, allowing real- time computation from live image stream at 50 fps. A comparison is made of parallel versus non-parallel approach for various image sizes.
Sentence-Level Readability Assessment for L2 Chinese Learning

Automatic assessment of sentence readability level can support educators in selecting sentence examples suitable for different learning levels to complement teaching materials. Although there exists extensive research on document-level and passage-level Chinese readability assessment, the sentence-level evaluation remains little explored. We bridge the gap by providing a research framework and a large corpus of nearly 40,000 sentences with ten-level readability annotation. We design experiments to analyze the influence of 88 linguistic features on sentence complexity and results suggest that the linguistic features can significantly improve the predictive performance with the highest of 70.78% distance-1 adjacent accuracy. Model comparison also confirms that our proposed set of features can reduce the bias in prediction without adding variances. We hope that our corpus, feature sets, and experimental validation can provide educators and linguists with more language resources, enlightenment, and automatic tools for future related research.
Structured Sparsification of Gated Recurrent Neural Networks

One of the most popular approaches for neural network compression is sparsification — learning sparse weight matrices. In structured sparsification, weights are set to zero by groups corresponding to structure units, e. g. neurons. We further develop the structured sparsification approach for the gated recurrent neural networks, e. g. Long Short-Term Memory (LSTM). Specifically, in addition to the sparsification of individual weights and neurons, we propose sparsifying the preactivations of gates. This makes some gates constant and simplifies an LSTM structure. We test our approach on the text classification and language modeling tasks. Our method improves the neuron-wise compression of the model in most of the tasks. We also observe that the resulting structure of gate sparsity depends on the task and connect the learned structures to the specifics of the particular tasks.
Two-photon interference between continuous-wave coherent photons temporally separated by a day

An understanding of the phenomenon of light interference forms the kernel underlying the discovery of the nature of light from the viewpoints of both classical physics and quantum physics. Here we report on two-photon interference with temporally separated continuous-wave coherent photons by using a temporal post-selection method with an arbitrary time delay. Although the temporal separation of a day between the photons is considerably longer than the coherence time of the light source, we observe the Hong–Ou–Mandel (HOM) interference of the pairwise two-photon state. Furthermore, we experimentally demonstrate the HOM interference observed in one of the interferometer-output modes by using only one single-photon detector for a large temporal separation.
A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question"

This report shares the results of a Spring 2018 survey of 35 academic libraries in the United States in regard to the research data services (RDS) they offer. An executive summary presents key findings while the results section provides detailed information on the answers to specific survey questions related to data repositories, metadata, workshops, and polices.
On Expert-Defined Versus Learned Hierarchies for Image Classification

For classification task involving a large number of classes, a decrease in recognition accuracy is observed for visually similar classes. We believe that forcing the model to learn appropriate features separately for each set of similar classes could improve classification performance. To justify our idea, we tried to improve classification performance by employing class hierarchy, which reflects visual similarities in data. More specifically, we used and compared two kinds of hierarchies to enhance classification performance of the model: (i) a hierarchy defined by experts (H-E), and (ii) a hierarchy created from performance results of a flat classifier and using DBScan clustering method (H-C). Moreover, we created a classification model that efficiently utilizes these hierarchies to learn appropriate features at different levels of the hierarchy. We evaluated the performance of the model on CIFAR-100 benchmark. Our results demonstrate that the hierarchical classification under H-C outperforms both H-E and the flat classifier.
Offline Handwritten Signature Recognition Based on SIFT and SURF Features Using SVMs

Biometric recognition for human identification plays a key role in the rapid development of computer vision and pattern recognition research areas. The biometrics, refers to the automatic identification of a person based behavioral characteristics, physiological properties or traits. Signature recognition is one such human identification method and can be performed either in offline or online mode. This paper proposed an offline handwritten signature recognition which is based on image processing technique, scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) features and support vector machines (SVMs). The handwritten signature images were then recognized through the proposed method that involves identification of regions of interest and representation of those regions as SIFT or SURF, construction of codebooks and the multi-class classification of the feature histograms using support vector machines (SVMs). Experiments have been carried out with our dataset of 1600 samples and a recognition rate in excess of 95% was obtained over the ten-fold cross validations.
Security of B92 protocol with uninformative states in asymptotic limit with composable security

The security of the Quantum Key Distribution (QKD) is coming from the fundamental principle of quantum physic where any action on the quantum system that tries to extract the information will be considered generally as a measurement which basically will modify the system. The protocol of B92 has been proven unconditionally secure by various of study based on the entanglement distillation protocol (EDP) technique. However, based on this technique it does not prove composable security which is actually important in privacy amplification in order to guarantee the partial key shared cannot revoke the security of the system. We review the security analysis of this system and its implementation in this work.
A Local-to-Global Metric Learning Framework From the Geometric Insight

Metric plays a key role in the description of similarity between samples. An appropriate metric for data can well represent their distribution and further promote the performance of learning tasks. In this paper, to better describe the heterogeneous distributions of data, we propose a semi-supervised local-to-global metric learning framework from the geometric insight. Our contributions can be summarized as: Firstly, to enlarge the application scope of local metric learning, we introduce the unsupervised information as the regularization term into our smoothly glued nonlinear metric model. Secondly, we propose two different nonlinear semi-supervised metric learning models with two different loss terms, and find that the smooth loss performs better than the hinge loss by comparison results. Thirdly, we have established not only two metric learning models, but also a nonlinear metric learning framework based on local metrics, which includes supervised and semi-supervised as well as linear and nonlinear metric learning. Moreover, we present an intrinsic steepest descent algorithm on the positive definite manifold for implementation of our semi-supervised nonlinear metric learning models with smooth triplet constrain loss. Finally, we compare our approaches with several state-of-the-art methods on a variety of datasets. The results validate that the robustness and accuracy of classification are both improved under our metrics.
Monotonic Gaussian Process Flow

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a nonparametric model of monotonic functions that allows for interpretable priors and principled quantification of hierarchical uncertainty. We demonstrate the efficacy of the proposed model by providing competitive results to other probabilistic monotonic models on a number of benchmark functions. In addition, we consider the utility of a monotonic random process as a part of a hierarchical probabilistic model; we examine the task of temporal alignment of time-series data where it is beneficial to use a monotonic random process in order to preserve the uncertainty in the temporal warpings.
Usability Evaluation and Design of Student Information System Prototype to Increase Student’s Satisfaction (Case Study: X University)

A preliminary survey that was conducted to students of the Student Information System (SIS) at X University showed that the satisfaction level SIS was low. The main purpose of this study is to increase the student’s satisfaction level by evaluating the usability of SIS. An experiment was conducted to thirty students of X University, categorized as an expert user (age 17 to 23), according to the usability testing method. The effectiveness, efficiency, and satisfaction of SIS are then measured by the Task Successful Rate, Mouse-Clicking, and System Usability Scale (SUS) scores. During the experiment, the participants were given several scenarios. The result proved that the SIS did not work well enough to satisfy the students/participants and improvements needed to be tested on the same participants. The testing process conducted using a prototype that was designed with HHS Usability Guideline. Through the usability testing, it was obtained that the effectiveness managed to increase from 58% to 85%, efficiency increased from 66% to 92%, and satisfaction scores increased from 53,83 to 70,67. Specific matters that influence the subjectivity of usability testing are noted and analyzed so that this study can be used as a practice for further study of usability evaluation in a similar student information system.
Foundations of Cryptoeconomic Systems

Blockchain networks and similar cryptoeconomic networks aresystems, specifically complex systems. They are adaptive networkswith multi-scale spatiotemporal dynamics. Individual actions towards a collective goal are incentivized with purpose-driven tokens. These tokens are equipped with cryptoeconomic mechanisms allowing a decentralized network to simultaneously maintain a universal state layer, support peer-to-peer settlement, andincentivize collective action. These networks therefore provide a mission-critical and safety-critical regulatory infrastructure for autonomous agents in untrusted networks. They also provide a rich, real-time data set reflecting all activities in their systems. Advances in data science and network sciencecan thus be leveraged to design and analyze these systems in a manner consistent with the best practices of modern systems engineering. Research that reflects all aspects of these socioeconomic networks needs (i) a complex systems approach, (ii) interdisciplinary research, and (iii) a combination of and engineering methods, here referred to as economic systems engineering, for the regulation and control of these socio-economicsystems. This manuscript provides foundations for further research activities that build on these assumptions, including specific research questions and methodologies for future research in this field.
Evidence Based Decision Making in Blockchain Economic Systems: From Theory to Practice

We present a methodology for evidence based design of cryptoeconomic systems, and elucidate a real-world example of how this methodology was used in the design of a blockchain network. This work provides a rare insight into the application of Data Science and Stochastic Simulation and Modelling to Token Engineering. We demonstrate how the described process has the ability to uncover previously unexpected system level behaviors. Furthermore, it is observed that the process itself creates opportunities for the discovery of new knowledge and business understanding while developing the system from a high level specification to one precise enough to be executed as a computational model. Discovery of performance issues during design time can spare costly emergency interventions that would be necessary if issues instead became apparent in a production network. For this reason, network designers are increasingly adopting evidence-based design practices, such as the one described herein.
Multi-label symbolic value partitioning through random walks

Feature selection and symbolic value partitioning are effective knowledge reduction techniques in the field of data mining. A large body of feature selection methods has been proposed for multi-label data. By contrast, symbolic value partitioning for such data has not been studied. In this paper, we propose the multi-label symbolic value partitioning through random walks algorithm with two stages. In the first stage, an undirected weighted graph is constructed for each attribute. Each node corresponds to an attribute value and the weight of each edge corresponds to the similarity between two nodes. Similarity is defined based on the attribute value distribution for each label. In the second stage, a random walk algorithm is used to cluster attribute values. The average weight serves as the separation operator to sharpen the inter-cluster edges. We tested the new algorithm and seven popular feature selection algorithms on 13 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm in reducing the data size and improving classification accuracy.