Reviewerly/exHarmony huggingface dataset:
Structural decomposition for quantum two-level systems

An input–output model of a two-level quantum system in the Heisenberg picture is of bilinear form with constant system matrices, which allows the introduction of the concepts of controllability and observability in analogy with those of quantum linear systems. By means of the notions of controllability and observability, coordinate transformations, which are rotation matrices, can be constructed explicitly that transform an input–output model to a new one. The new input–output model enables us to investigate many interesting properties of the two-level quantum system, such as steady-state solutions to the Lindblad master equation, quantum decoherence-free (DF) subspaces, quantum non-demolition (QND) variables, and the realization of quantum back-action evading (BAE) measurements. The physical system in Wang and Wiseman (2001) is re-studied to illustrate the results presented in this paper.
HEVC intra frame based compressed domain video summarization

In order to overcome the shortcomings of the poor quality of the final generated video summary by reason of the incomplete selection of video feature points, a compressed domain video summarization generation algorithm is proposed based on HEVC intra-frame coding. Firstly, the weighted luminance and chrominance mode numbers are counted at the decoding end, respectively. Then, the feature vectors composed of the above two mode numbers are fused and normalized to obtain a mode feature histogram. Secondly, the normalized mode feature histograms are assigned different weight factors, and the two are merged into a new mode feature histogram model to reflect the texture features of the image. Finally, the histogram difference method is used to determine the similarity between two-frame mode feature histograms, and ultimately the histogram classification is used to generate the video summaries. The experimental results show that the accuracy of keyframe extraction on the Open Video Project dataset is 0.82, the error rate is 0.64. And that, the average recall rate achieves 82.0%, and the average F-score is 73.3%. The key frames extracted by the algorithm better reflect the image texture, and the video summary quality is further improved.
Some matroids related to sum-rank metric codes

We introduce the notion of sum-matroids and show its association with sum-rank metric codes. As a consequence, some results for sum-rank metric codes by Martínez-Peñas are generalized for sum-matroids. The sum-matroids generalize the notions of matroids and q-matroids. We define the generalized weights for sum-matroids and prove a Wei-type duality theorem which generalizes the analogous cases for matroids and q-matroids.
When autonomous agents model other agents: An appeal for altered judgment coupled with mouths, ears, and a little more tape

Agent modeling has rightfully garnered much attention in the design and study of autonomous agents that interact with other agents. However, despite substantial progress to date, existing agent-modeling methods too often (a) have unrealistic computational requirements and data needs; (b) fail to properly generalize across environments, tasks, and associates; and (c) guide behavior toward inefficient (myopic) solutions. Can these challenges be overcome? Or are they just inherent to a very complex problem? In this reflection, I argue that some of these challenges may be reduced by, first, modeling alternative processes than what is often modeled by existing algorithms and, second, considering more deeply the role of non-binding communication signals. Additionally, I believe that progress in developing autonomous agents that effectively interact with other agents will be enhanced as we develop and utilize a more comprehensive set of measurement tools and benchmarks. I believe that further development of these areas is critical to creating autonomous agents that effectively model and interact with other agents.
Nonparametric truncated spline regression on modelling mean years schooling of regencies in Java

Education is widely accepted to be a fundamental resource, both for individuals and societies. Mean Years of Schooling (MYS) is one of the two education indicators in UNDP’s Human Development Index (HDI) and can reflect important aspects related to the degree of education in some countries or regencies. The results of the preliminary identification show that the relationship patterned between MYS as response and all predictor variables does not follow particular pattern (linear or a certain degree polynomial). Therefore the analysis of nonparametric regression should be performed. The scatter plot between response and predictors variables shows patterns that change at certain sub-intervals so the suitable method used is the nonparametric regression with truncated spline estimation. Truncated polynomial spline regression is used because it has an advantage which the model tends to find its own estimate of the data pattern wherever it moves. In this regard, the objective of this research is to get estimator of nonparametric truncated spline regression and investigate the significant factors affecting mean years of schooling of 119 regencies across Java in 2017. The numbers of knot points used are one, two, three, and combination of knot points. The method of selecting optimum knots uses minimum Generalized Cross Validation (GCV) and the best model is obtained with combination knots with a coefficient of determination 87.99 percent. Based on the result of a statistical significance test, the factors that significantly affect MYS are population density, student-teacher ratio, per capita gross regional domestic products, and student-school ratio. The results of this study are expected to be the input for the government to regulate policy in order to improve the overall national mean years of schooling.
Solar Irradiation Forecasting Uses Time Series Analysis

In recent years, the ever-increasing demand on electricity cannot be followed by the amount of energy supply mainly produced from fossil. In order to mitigate this issue, many researches have been conducted in the field of renewable energy (RE), which aims at reducing the dependence on fossil based energy. In Indonesia, one form of RE that has a promising potential is solar power. Unfortunately, solar power has intermittent nature and thus, the output of RE plants (solar power plants) tends to be unpredictable. It has been found that there is a linear relationship between the amount of solar irradiation and the power produced by the solar power plants. This linear relationship can be exploited to predict the power produced by the solar power plant by performing forecasting on the amount of solar irradiation. This paper discusses three methods for forecasting solar irradiation. The first forecasting method discussed in this paper is multivariate linear regression. In this method, the forecasting is performed by using data on temperature, air pressure, humidity, and wind speed. The other two methods are support vector regression (SVR) and hybrid fast Fourier transform-autoregressive (FFT-AR). In these two methods, the forecasting process is performed based on the historical solar irradiation data sets. All the aforementioned three methods are applied to the solar irradiation data obtained from Cirata solar plant and the automatic weather station, the meteorology, climatology, and geophysics agency. Based on the study discussed in this paper, it can be concluded that the hybrid FFT-AR method offers a better performance than the other two methods. The hybrid FFT-AR method produces a normalized root mean square error (NRMSE) value of 7.18% for all scenarios. The smallest NRMSE is 4.5%, which is produced in FFT (99) - AR (48) hybrid model and which is used for short-term forecasting (7-days forecast period).
A top-down, parser-friendly approach to pied-piping and operator movement

This article explores the phenomenon of pied-piping within the context of two assumptions. The first assumption is that a realistic natural language parser must assign incoming words incrementally into the syntactic representation as it is built during the parsing process. The second assumption is that this process makes use of Merge, the core recursive operation in syntax. These assumptions lead us to consider an extension of the standard theory of Merge, in which phrase structure is derived from a linear string of words in a left-to-right/top-down fashion (e.g. Phillips, 1996, Phillips, 2003). We propose a generalization of the Phillips architecture that describes and correctly parses simple and complex pied-piping constructions in three languages: English, Italian and Finnish. We then show that the proposed hypothesis derives (without any additional assumptions) several well-known constraints to pied-piping and operator movement, such as Condition on Extraction Domains (CED), the Left Branch Condition, the extended projection principle (EPP), the complex properties of picture nouns and certain differences between relative and interrogative clauses. A computational implementation and full formalization of the hypothesis together with a test corpus is used to demonstrate the viability of the approach. We conclude that the top-down approach provides a useful tool for grammatical analysis.
The Unpopularity of the Software Tester Role Among Software Practitioners: A Case Study

As software systems are becoming more pervasive, they are also becoming more susceptible to failures, resulting in potentially lethal combinations. Software testing is critical to preventing software failures but is, arguably, the least understood part of the software life cycle and the toughest to perform correctly. Adequate research has been carried out in both the process and technology dimensions of testing, but not in the human dimensions. This work attempts to fill in the gap by exploring the human dimension, i.e., trying to understand the motivation/de-motivation of software practitioners to take up and sustain testing careers. One hundred and forty four software practitioners from several Cuban software institutes were surveyed. Individuals were asked the PROs (advantages or motivators) and CONs (disadvantages or de-motivators) of taking up a career in software testing and their chances of doing so. The results of this investigation identified 9 main PROs and 8 main CONs for taking up a testing career showing that the role of tester is perceived as a social role.
Students Evaluation in AGILE after Completing Software Engineering Course

In the arena of Software Development, the software engineering philosophy Agile is becoming more popular juxtaposed to the long-established methods. Software organizations have a prerequisite from graduates with cutting-edge knowledge of software engineering methodologies. Due to Agile's popularity universities and institutions have already added agile in the curriculum of software engineering. They have started to teach the students Agile theoretically, practically in labs and also have some seminars where empirical knowledge is shared with students. This paper aims to acquit the determination of Agile to the perceptual experience of Software Engineering undergraduate students, after completing their projects in the lab by following Agile principles, where they were trying to adopt Agile.
Experimental comparison of trajectory control and planning algorithms for autonomous vehicles

Real-time planning of feasible motion, and the accurate tracking of that motion are key aspects of autonomous vehicles. Various planning and lateral control algorithms have been proposed in the literature. However, no experimental comparison has been carried out to confront the performance of different algorithms. In this work some of the most popular algorithms have been experimentally tested to drive a scaled vehicle model along a track. A new algorithm for path tracking based on clothoid curves is here proposed and experimentally validated. The same curves are also used in a proposed new path planning algorithm. This planning is compared to the off-line solution of an optimal control problem. The results of the comparison and the validations are then reported and discussed.
UC Priced Oblivious Transfer with Purchase Statistics and Dynamic Pricing

Priced oblivious transfer (POT) is a cryptographic protocol that can be used to protect customer privacy in e-commerce applications. Namely, it allows a buyer to purchase an item from a seller without disclosing to the latter which item was purchased and at which price. Unfortunately, existing POT schemes have some drawbacks in terms of design and functionality. First, the design of existing POT schemes is not modular. Typically, a POT scheme extends a k-out-of-N oblivious transfer (OT) scheme by adding prices to the items. However, all POT schemes do not use OT as a black-box building block with certain security guarantees. Consequently, security of the OT scheme needs to be reanalyzed while proving security of the POT scheme, and it is not possible to swap the underlying OT scheme with any other OT scheme. Second, existing POT schemes do not allow the seller to obtain any kind of statistics about the buyer’s purchases, which hinders customer and sales management. Moreover, the seller is not able to change the prices of items without restarting the protocol from scratch. We propose a POT scheme that addresses the aforementioned drawbacks. We prove the security of our POT in the UC framework. We modify a standard POT functionality to allow the seller to receive aggregate statistics about the buyer’s purchases and to change prices dynamically. We present a modular construction for POT that realizes our functionality in the hybrid model. One of the building blocks is an ideal functionality for OT. Therefore, our protocol separates the tasks carried out by the underlying OT scheme from the additional tasks needed by a POT scheme. Thanks to that, our protocol is a good example of modular design and can be instantiated with any secure OT scheme as well as other building blocks without reanalyzing security from scratch.
Designing an Adaptive Software and Hardware Complex for Converting Solar Energy

The concept of a software and hardware system of converting solar electromagnetic radiation in areas of low temperatures and medium-high insolation levels into thermal and electrical energy is presented in the paper.The design of the system's project is based on the principle of modularity, which allows for the tasks of the consumer, and in the conditions of environmental constraints, to form an optimal subsystem configuration in terms of efficiency and expenses. The optimization criteria are based on the results of the climatic and geographical features analysis of the territories of potential consumers, as well as the experience and practices of introducing analogues of the proposed systems.The multi-criteria assessment performing to substantiate the functionality rationale and composition of the developed software and hardware system is implemented through an intelligent decision support system. Operational management of a configurable software and hardware system is proposed to be implemented through neuro-fuzzy control.
Robust Multi-View Clustering With a Unified Weight Learning Paradigm

Multi-view clustering, which exploits multi-view information to improve the clustering performance has attracted much attention in recent years. However, existing methods seldom consider the diverse quality of data points in different views, and assign each data point with the same importance for clustering. This way degrades the clustering performance due to the interference of low quality data points on the learned clustering indicators. In this paper, a novel robust multi-view clustering method with a unified weight learning paradigm is proposed to address this issue. The unified weight learning paradigm adaptively learns the quality of data points and the clustering capability of each view. Specifically, the reconstruction error of each data point in each view is treated as a factor to depict the quality of data point in this view. Afterwards, the clustering capability of each view is captured from the diverse quality of data points in each view. The clustering capability of each view in turn improves the learning process of data quality. An alternating iterative optimization algorithm with theoretical convergence guarantee and complexity analysis is designed to optimize the objective function. Experimental results on real-world benchmark datasets demonstrate the superiority of the proposed method.
Development of The Android-Based Interactive Physics Mobile Learning Media (IPMLM) to Improve Higher Order Thinking Skills (HOTS) of Senior High School Students

Abstract This study aims to find out: 1) generating useful android aplication IPMLM in termodynamic material on physics, 2) examining the impact of IPMLM toward students’ higher order thinking skill (HOTS). In order to find out the answer, the researcher used research and development method in 4-D model (defining, designing, developing and disseminating). The IPMLM application was assessed by media experts and physics expert. The limited test stage was conducted in SMAN 1 Kota Kupang with 32 students of 11th grade science program. Subjects were chosen by purposive sampling technique. The extensive test stage was conducted in SMAN 2 Kota Kupang, SMAN 3 Kota Kupang and SMAN 5 Kota Kupang with 210 students of 11th grade science program. Subjects were chosen by purposive sampling technique. The research design of extensive test was pretest-posttest control group design. Data collection was done by questionnaire and test. Instrument test to measure students’ HOTS instrument was reasoning multiple choice questions. The result shows that 1) the IPMLM application was eligible to be used in physics learning about thermodynamics, 2) using IPMLM in physics learning increased effectively the capability on HOTS aspect.
Towards intrinsic autonomy through evolutionary computation

This paper presents an embodied open-ended environment driven evolutionary algorithm capable of evolving behaviors of autonomous agents without any explicit description of objectives, evaluation metrics or cooperative dynamics. The main novelty of our technique is obtaining intrinsically motivated autonomy of a virtual robot in continuous activity, by internalizing evolutionary dynamics in order to achieve adaptation of a neural controller, and with no need to frequently restart the agent’s initial conditions as in traditional training methods. Our work is grounded on ideas from the enactive artificial intelligence field and the biological concept of enaction, from which it is argued that what makes a living being “intentional” is the ability to autonomously, adaptively rearrange their microstructure to suit some function in order to maintain its own constitution. We bring an alternative understanding of intrinsic motivation than that traditionally approached by intrinsic motivated reinforcement learning, and so we also make a brief discussion of the relationship between both paradigms and the autonomy of an agent. We show the autonomous development of foraging and navigation behaviors of a virtual robot.
A Multi-level Feature Fusion Network for Real-time Semantic Segmentation

Recently, convolutional neural networks (CNNs) have made a big splash in the field of semantic segmentation, achieving very high segmentation accuracy. In order to meet the requirement of real-time inference, existing methods increase inference speed by reducing the image resolution, leading to lower segmentation performance. We propose in this work a multi-level feature fusion network referred to as MLFFNet that utilizes a novel deep neural network architecture for efficient and real-time semantic segmentation. To strike a balance between speed and performance, MLFFNet substantially reduces the computational complexity by using a lightweight feature extraction network to implement feature reuse through multi-level feature fusion. In addition, MLFFNet targets at excellent segmentation performance through a channel attention mechanism and dilated convolutions with different rates. Specifically, MLFFNet achieves 72.6% mIoU on Cityscapes with the speed of 68.3 FPS on one NVIDIA Titan X card, which is significantly faster than the existing methods with comparable performance.
Pre-adjustment Based Anti-collusion Mechanism for Audio Signals

Collusion attack is considered as one of the most popular and challenging attacks for audio signals, which violates the copyright seriously. Conventional methods cannot cope with the hybrid attack which consists of collusion attack and other attacks. In this paper, we propose a pre-adjustment process (PAP) based mechanism to tackle it by destroying the perceptual quality of the colluded signal, which removes the motivation of traitors to implement collusion attack. We transform the host audio signal into DCT domain and segment the DCT coefficients into blocks. Then the DCT coefficients are modified according to a pre-designed adjustment matrix (AM) to generate the PAP signal. When multiple PAP signals are averaged to generate the colluded signal, the energy of certain frequency bands in the colluded signal will be eliminated or reduced, which degrades the perceptual quality of the colluded signal greatly. The proposed method can withstand not only collusion attack but also hybrid attacks. It is also secure, as the secret keys used in PAP will not be passed to the receiver. By combining the proposed method with other leading-edge watermarking algorithms, its performance on copyright protection can be further improved. Theoretical analysis and experimental results show the superiority of the proposed mechanism.
A High-Performance Hybrid Blockchain System for Traceable IoT Applications

Blockchain, as an immutable distributed ledger, can be the key to realize secure and trustworthy IoT applications. However, existing blockchains can hardly achieve high-performance and high-security for large-scale IoT applications simultaneously. In this paper, we propose a hyper blockchain architecture combining the security of public blockchains with the efficiency of private blockchains. An IoT anchoring smart contract is proposed to anchor private IoT blockchains into a public blockchain. An IoT device management smart contract is also designed to trace sensory data. A comprehensive analysis reveals that the proposed hybrid blockchain system can achieve the performance of private blockchains and resist tampering.
Digital Forensics Capability Analyzer: A tool to check forensic capability

Digital forensics is process of identifying, preserving, analyzing and preserving digital evidence. Due to increasing cybercrimes now a days, it is important for all organizations to have a well-managed digital forensics cell. So to overcome this, we propose a framework called digital forensics capability analyser. [1]The main advantage of developing digital analyzer is cost minimization. This tool will provide fundamental information for setting up a digital forensic cell and will also offer services like online sessions.[2] [3]It will help organizations by providing them with a perfect solution according to their requirements to start a digital forensic cell in their respective lnstitution.[4] [5]