Reviewerly/exHarmony huggingface dataset:
Finding the disjointness of stabilizer codes is NP-complete

The disjointness of a stabilizer code is a quantity used to constrain the level of the logical Clifford hierarchy attainable by transversal gates and constant-depth quantum circuits. We show that for any positive integer constant $c$, the problem of calculating the $c$-disjointness, or even approximating it to within a constant multiplicative factor, is NP-complete. We provide bounds on the disjointness for various code families, including the Calderbank-Shor-Steane codes, concatenated codes, and hypergraph product codes. We also describe numerical methods of finding the disjointness, which can be readily used to rule out the existence of any transversal gate implementing some non-Clifford logical operation in small stabilizer codes. Our results indicate that finding fault-tolerant logical gates for generic quantum error-correcting codes is a computationally challenging task.
Research on Parking Planning Method based on AI Planning

As a key factor affecting urban traffic efficiency, parking can effectively improve traffic congestion and urban landscape. AI Planning can provide economical and efficient planning resources for planning tasks. This paper introduces intelligent planning technology into the parking lot navigation system, and proposes a parking guidance method based on intelligent planning. Although there are currently a variety of methods that can be used in parking guidance, there is no research on using automatic planning methods to guide parking lot navigation. This paper provides a set of modeling methods for intelligent planning applications by formulating artificial intelligence planning problems, and provides a reference for intelligent planning in other fields. Experiments show that this planning method can quickly obtain planning actions, and guide the driver to park the vehicle to the designated parking space according to the plan.
Bridging the Gap between Atomic and Complex Activities in First Person Video

In this work, we describe a system for classifying activities in first-person video using a fuzzy inference system. Our fuzzy inference system is built on top of traditional object-and motion-based video features and provides a description of activities in terms of multiple fuzzy output variables. We demonstrate the application of the fuzzy system on a well known dataset of unscripted first person videos to classify actions into four categories. Comparing the results to other supervised learning techniques and the state-of-the-art, we find that our fuzzy system outperforms alternatives. Further, the fuzzy outputs have the potential to be much more descriptive than conventional classifiers due to their ability to handle uncertainty and produce explainable results.
The Effect of Financial Development on Economic Growth in Developing Countries

The financial system provides `real` services to the economy that are crucial for economic activity and long-run growth. Specifically, the financial system facilitates transactions, eases risk management, mobilizes savings, allocates savings, and monitors the behavior of managers after funding projects. These five financial services provide a rough definition of financial development. Financial systems that are better at providing these services are better developed financially. This study investigates the influence of financial development on economic growth in developing countries. It selects four indicators to measure the level of financial development, two indicators of the size of the financial sector (Liquid liabilities and Central bank assets) and two indicators of the activity of the financial sector (Bank credit and Deposit money-Central bank). The study applies an Ordinary Least Squares (OLS) regression to examine the effect of these indicators on the level of economic growth for a set of 42 developing countries in the period between 1970 and 1995. According to the analysis conducted, the results show a high and significant effect of the level of financial development on economic growth for these developing countries.
Identifying Handwork with Machine Learning Data Sets from Sensors Built into Gloves

To digitalize handwork that remains at manufacturing sites, we developed a glove with built-in sensors that capture fingertip pressure and work sounds. To create an identification model with small amounts of training data, we developed a handwork identification model based on machine learning, in which a hidden Malkov model (HMM) extracts features from time-series information of sensor data and a support vector machine (SVM) identifies the type of handwork. The developed gloves and model identify specific handwork every 0.01 seconds. We experimentally confirmed that our proposed model can distinguish two typical types of handwork—connector insertion and screw tightening with a pistol grip electric screwdriver—with 82% accuracy from only 20 seconds of training data for each. This technology can be applied to recording handwork evidence and automation of incorrect work detection.
Qualitative assessment of deterioration embankment dam using index condition and annual probability of failure (APF) using event tree method

Abstract The construction of dams can offer many advantages, but their major deterioration could result in failure, including loss of life and property destruction. The purpose of this study is development of the model to predict deterioration using condition index and determine the risk of the failure using Event Tree Analysis. This method is combined between ICOLD and Public Work and Public Housing (PWPH) Guidelines. The results of condition index in Sagung is good condition. The results of annual probability of failure (APF) using Event Tree method is 1.1111 × 10 −5 .
Dynamic self-dual DeepBKZ lattice reduction with free dimensions and its implementation

Lattice problems are algorithmic problems for lattices, whose hardness assures the security of lattice-based cryptography. Given a basis of a lattice, the goal of lattice basis reduction is to seek a new basis of the same lattice whose basis vectors are relatively short and nearly orthogonal to each other. Such reduction algorithms give a fundamental tool to solve lattice problems such as the shortest and the closest vector problems. In this paper, we provide a new self-dual variant of the DeepBKZ reduction algorithm. Our variant dynamically switches performing primal and dual reductions for local blocks of an input basis to effectively reduce the whole basis. In particular, we introduce a parameter of free dimensions to determine the sizes of local blocks to be reduced. We also implement our self-dual DeepBKZ to report its experimental results for random lattice bases.
A Robust Bangla Spell Checker for Search Engine

The spell-checking system assists to detect spelling-errors and advise the appropriate corrections in the word documents automatically. This paper is concerned with the implementation of an interactive Bangla spell checking tool, and demonstrating how to use it in a general-purpose search engine effectively. From the literature, it is seen that Bangla has complex grammatical and orthographical rules for spelling, and the study addressed different challenges in generating suggestions for Bangla phonetic-errors. The study also presented a combined technique of the string-matching algorithm and a reverse dictionary lookup method for detecting the typographical-errors as well as cognitive phonetic-errors in Bangla language. Both algorithms have been implemented into a search engine app for locating a misspelled word, and listing the suggested candidate-words. Finally, the intended word is chosen from the candidate words that were generated by the spell checker, and used it for searching in the search engine platform. Two-level of databases (the lexicon and secondary database) were used to design the proposed system. The lexicon database contains about 58,319 correctly spelled Bangla words, and a secondary database contains about 46,881 stemmed-key data.
Changing Input Shape Dimension Using VGG16 Network Model

In computer vision, transfer learning is a common method because it helps us to quickly create accurate models. In this work, consider the outcome of the convolutional network depth with VGG16 on its accuracy in the large-scale image recognition setting. Rather than using a Convolutional Neural Network, Transfer Learning can be used on images with different image dimension inputs (CNN) and was originally trained on by using Keras to fine-tune the input from tensor dimensions. In this paper, we demonstrate how the VGG16 network handles new image input dimensions of 128x128x3 pixels from eligible VGG16 224x224x3 pixels images that are cut before the recognition is implemented. Our results show that Convolutional Neural Network can manage small datasets and can produce ideal validation accuracy of 93% from small images and better results from higher resolution images.
Multicriteria group decision making based on neutrosophic analytic hierarchy process: Suggested modifications

To avoid any conflict toward a previous work[1],we clarify certain parts thatneed explanationandsuggest some modifications that will enhance the performance of the suggested algorithm.We enhance the algorithm by making it work in any environment and under various conditions without the occurrence of any warnings.Moreover, wesuggest various future directions that will help researchers in the application of neutrosophic analytic hierarchy process. Given that scientific research is always renewed and developed, we always strive to reach the best methods and solutions. Thus,we present this study as a good guide forresearchersin their future works.
Inverse Reinforcement Learning for Strategy Identification

In adversarial environments, one side could gain an advantage by identifying the opponent’s strategy. For example, in combat games, if an opponent’s strategy is identified as overly aggressive, one could lay a trap that exploits the opponent’s aggressive nature. However, an opponent’s strategy is not always apparent and may need to be estimated from observations of their actions. This paper proposes to use inverse reinforcement learning (IRL) to identify strategies in adversarial environments. Specifically, the contributions of this work are 1) the demonstration of this concept on gaming combat data generated from three pre-defined strategies and 2) the framework for using IRL to achieve strategy identification. The numerical experiments demonstrate that the recovered rewards can be identified using a variety of techniques including visual analysis, cluster analysis, and supervised classification.
The Comparison of Native and Hybrid Mobile Applications for Android System

In the age of smartphones and tablets popularity of mobile applications is rising extremely quickly. There are countless purposes of using them - communication, social media, news, sending emails, shopping, paying, watching videos and streams, playing games. Google's Android is currently the most used mobile system in the world. The main aim of this article is to compare native and hybrid mobile applications created for this system. The comparison was based on mobile applications implemented in Java 8 language and Ionic framework.
A Scalable Blockchain Consensus Model

Blockchain is a distributed immutable ledger supporting strong secure transactions across various nodes in a Blockchain network after the mutual consensus of all the nodes in the associated network. Blockchain technology is the foundation technology of various crypto currencies. However, it has the potential to contribute to several mission-critical applications (MCA) or real-time applications such as Healthcare, e-Voting, Government Regulatory to securely regulate business processes or to identify any fictitious business transaction. Unfortunately, Blockchain implementations in MCAs are still being challenged by rigorous requirements. The most pressing among these challenges is scalability. The performance time in the current consensus model (related to scalability) is still questionable, especially in MCAs. That is, the model is lacking in high efficiency in many aspects such as transaction throughput, transaction latency, network bandwidth, and storage, despite strong reliability (in terms of advanced security and privacy) through an immutable shared distributed ledger. Hence, there is an exigent need for a new consensus model which could mitigate the scalability issues in MCAs. This paper is therefore aimed to propose a scalable consensus model to improve the scalability issue in MCA by improving the node syndicating time. This contribution is to elevate MCAs (other than Bitcoin)to positively impacting on social/business environment.
Design Patterns Discovery in Source Code: Novel Technique Using Substring Match

The role of design pattern mining is a very significant strategy of re-engineering as with the help of detection one could easily understand complex systems. Of course, identifying a design pattern is not always a simple task. Additionally, pattern recovering methods often encounter problems dealing with space outburst for extensive systems. This paper introduces a new way to discover a design pattern based on an Impact Analysis matrix followed by substring match. UML diagrams corresponding to codes are created using Visual Paradigm Enterprise. Impact Analysis matrices of these UML diagrams are converted to string format. Considering system code string as main string and design pattern string as a substring, the main string is further decomposed. A substring match technique is developed here to discover design patterns in the source code. Overall, this procedure has the potential to convert the representation of system design and design pattern in ingenious shapes. In addition, this method has the advantage of moderation in the size. Therefore, this approach is beneficial for Software professionals and researchers due to its simplicity.
ThiNet Based Pruning Method for GAN Based Steganography Framework UT-GAN

With the application of deep-learning framework, both the steganography and steganalysis gain superior performance than before, which has been testified by the previous researchers through experiments. Though the deep-learning models bring the excellent performance, they also result in the over-parameterized models and redundancy in computation and memory storage which make it hard for them to deploy. In this paper, we propose a ThiNet based scheme to prune the state-of the-art steganographic model based on GAN--UTGAN. We evaluate and sort the significance of the channels of a layer according to the output of the next layer by using the greedy algorithm and obtain the channels which have the similar impact on the next layer compared with all the channels before pruning. The experimental results conducted on SZUBase, BOSSBase, and BOWS2 dataset demonstrate that our proposed ThiNet based method could compact the original UT-GAN model effectively with about only two percent of parameters of the original model and have about only a little decline in detection error rate when analyzed by SRM with ensemble classifier, which has outperformed the ASDL-GAN method.
Blockchain Module for Securing Data Traffic of Industrial Production Machinery on Industrial Platforms 4.0

Industry 4.0 (the Fourth Industrial Revolution) is a concept devised for improving the operation of modern factories through the use of the latest technologies, under paradigms such as the Industrial Internet of Things (IIoT) or Big Data. One of such technologies is Blockchain, which is able to provide industrial processes with security, trust, traceability, reliability and automation. This paper proposes a technological framework that combines an information sharing platform and a Blockchain platform. One of the main features of the this framework is the use of smart contracts for validating and auditing the content received throughout the production process to ensure the correct traceability of the data. The conclusion drawn from this study is that this technology is under-researched and has significant potential to support and enhance the industrial revolution. Moreover, this study identifies areas for future research.
HTNN: Deep Learning in Heterogeneous Transform Domains with Sparse-Orthogonal Weights

Convolutional neural networks (CNNs) achieved great success on various tasks in recent years. Their applications to low power and low cost hardware platforms, however, have been often limited due to extensive complexity of convolution layers. We present a new class of transform domain deep neural networks (DNNs), where convolution operations are replaced by element-wise multiplications in heterogeneous transform domains. To further reduce the network complexity, we propose a framework to learn sparse-orthogonal weights in heterogeneous transform domains co-optimized with a hardware-efficient accelerator architecture to minimize the overhead of handling sparse weights. Furthermore, sparse-orthogonal weights are non-uniformly quantized with canonical-signed-digit (CSD) representations to substitute multiplications with simpler additions. The proposed approach reduces the complexity by a factor of 4.9– 6.8 $\times$ without compromising the DNN accuracy compared to equivalent CNNs that employ sparse (pruned) weights. The code is available at https://github.com/unchenyu/HTNN.
Extension of Interaction Flow Modeling Language for Geographical Information Systems

The Web-based Geographical Information Systems (WebGIS) are used to store, analyze and spread the geospatial information, facilitating an efficient decision-making. However, the issues of complexness and changing requirements in WebGIS severely delay the overall development process. Therefore, a Model Driven Architecture paradigm, along with the use of Interaction Flow Modeling Language (IFML), can be a great way to reduce the development complexities. While the IFML has several concepts pertaining to the modeling of user interface components in web applications, the concepts that are specifically required to model WebGIS user interactions are found to be missing in its standard specifications. Consequently, this article has extended the standard IFML meta-model with a UML profile, which allows to model user interactions for WebGIS. The validity of the proposed profile has been demonstrated via a real-world case study. The results prove that the proposed profile may be used for the modeling of simple and complex scenarios of WebGIS development. Based on the proposed UML profile, the IFML Editor Palette can be updated with the proposed stereotypes. Writing a Model to Text (M2T) transformation for transforming the system models into an executable java code is a milestone to be achieved in future.
An Hour-Ahead Photovoltaic Power Forecasting Based on LSTM Model

The extensive integration of the large-scale Photovoltaic (PV) plants into the power grid requires the development of new forecasting methods, for the prediction of the PV output with high accuracy. Despite the statistical and the Machine Learning (ML) approaches which have been extensively studied in the literature, the Deep Learning (DL) methods are not yet fully examined. Considering this, the present paper proposes a forecasting model based on the Long-Short Term Memory (LSTM) algorithm. Except of the solar irradiance, the module’ temperature and the historical PV data, the influence of the clearness index to forecasting process has been also examined. The results indicate that the employment of the clearness index can improve the performance of the forecaster.
A Fuzzy Aggregation based Ensemble Framework for Accurate and Stable Feature Selection

A novel ensemble feature selection (FS) framework using fuzzy aggregation is proposed in this paper. It consists of three main steps: distribution generation of feature importance, distribution ensemble using fuzzy aggregation, and defuzzification for feature ranking. Based on four state-of-the-art FS methods (named as base selectors in our algorithm) selected from different method categories, different fuzzy aggregation operators were implemented to achieve ensemble learning for decision making. A training data repository that consists of eight datasets was used for parameter tuning of the proposed framework. The proposed framework using drastic sum aggregation achieved the best performance and was subsequently evaluated on eight independent testing datasets. Remarkably, the proposed method achieved the best classification accuracy and the highest stability compared with the four base FS methods. It also outperformed our previously proposed score based ensemble method [1].
Study of phase congruency quantization function properties for image edge detection

Phase congruency is a recently developed, but still rather unexplored and unknown technique for edge detection, allowing to determine the location of edges, ridges and valleys in images by analyzing the phase of the signal's frequency components. Phase congruency identifies edges based on the phase of the signal's frequency components. One of its main uses is image segmentation where the region of interest is separated from the background. The segmentation result varies according to a mathematical function, used to quantify the phase congruency, whose main properties are that it is centered at the origin, of even symmetry and whose global maximum is one. In addition, according to its form, a function allows better edge detection. Thus, several mathematical functions fulfill the necessary conditions for measuring phase congruency. However, these conditions have not yet been studied and, therefore, the type of changes they produce in phase congruency when varying this function is unknown. Therefore, in this work, an evaluation of the characteristics of the functions used for the quantification of phase congruency is presented, observing their properties and the behavior of phase congruency, allowing to find the most appropriate functions depending on the type of edges to be detected.
Investigation of the Relationship between Subjective and Objective Video Quality Metrics for Multiparty Video Calls on Mobile Devices

Today, various solutions and configurations exist for enabling audiovisual communications. One of the main driving forces has been the evolution of the technologies and APIs related to the open source WebRTC standards, enabling browser-to-browser real-time communication with built-in real-time audio and video functions without requiring any plugins. However, given the strict delay and high bandwidth requirements associated with video conferencing services, along with variable mobile network resource availability and limited mobile end user device capabilities, challenges arise in terms of setting and adapting video encoding parameters so as to achieve acceptable end-user perceived quality. In this paper, we extend our previous studies with further exploration of the impact of different video encoding parameters on perceived video quality. Furthermore, we focus on investigating the relationship between subjective video quality ratings and selected objective video quality metrics (namely blurriness and blockiness) to determine to what extent objective metrics correlate with subjective scores in the specific context of multiparty video calls on mobile devices. Our findings, reported based on the results of a user study (n=27), indicate that perceived video quality has very strong and strong correlation with objective blockiness and blurriness values, respectively.