Reviewerly/exHarmony huggingface dataset:
Role of anti-squeezing noise in continuous-variable quantum cryptography

Quadrature squeezing is known to enforce continuous-variable quantum cryptography. However, in practice, squeezed states are never generated perfectly and always contain anti-squeezing quadrature noise concerned with losses in the optical parametric oscillator sources. We therefore address the role of anti-squeezing noise in continuous-variable quantum cryptography over attenuating and noisy quantum channels. We first assume that anti-squeezing noise is trusted, hence, out of control of a potential eavesdropper, capable of collective attacks. In this regime, anti-squeezing noise can be even helpful by tightening the bound on the information leakage in the quantum channel. Moreover, in the asymptotic limit of infinitely strong trusted anti-squeezing noise, the bound can be expressed analytically. We then assume that anti-squeezing noise is controlled by an eavesdropper, thus being a side-channel in the source implementation. In this regime, the anti-squeezing noise leads to the security break both in direct and reverse reconciliation scenarios. We compare the bounds on the untrusted phase-sensitive anti-squeezing noise to the typical phase-insensitive channel excess noise and show that protocols are more robust to the anti-squeezing noise, while the presence of such noise makes the protocol more sensitive to the noise in the main quantum channel. The obtained results reveal the important role of anti-squeezing noise in continuous-variable quantum cryptography and suggest the need of pure squeezed state preparation in the case of untrusted sources.
The Acquisition of Khmer-Chinese Parallel Sentence Pairs From Comparable Corpus Based on Manhattan-BiGRU Model

Bilingual parallel sentence pairs are an important resource for cross-lingual processing. Aiming at the shortage of Khmer-Chinese parallel texts and the complex structure of research methods, a method of obtaining Khmer-Chinese parallel sentence pairs from comparable corpus based on Manhattan-BiGRU model is proposed. In this method, feature information and bilingual word embedding are concatenated together and then jointly used as input. Then the word embedding is coded into sentence embedding through the BiGRU network. Finally, the similarity of sentence embedding is calculated according to the Manhattan distance algorithm, it achieves the acquisition of parallel sentence pairs. Compared with other methods of obtaining parallel sentence pairs based on neural network model, the experimental results show that this method achieves good results, in which the accuracy of obtaining parallel sentence pairs reaches 82.2% and the recall rate reaches 70.7%.
Adaptive quarkonial domain decomposition methods for elliptic partial differential equations

Abstract This paper is concerned with new discretization methods for the numerical treatment of elliptic partial differential equations. We derive an adaptive approximation scheme that is based on frames of quarkonial type, which can be interpreted as a wavelet version of $hp$ finite element dictionaries. These new frames are constructed from a finite set of functions via translation, dilation and multiplication by monomials. By using nonoverlapping domain decomposition ideas, we establish quarkonial frames on domains that can be decomposed into the union of parametric images of unit cubes. We also show that these new representation systems are stable in a certain range of Sobolev spaces. The construction is performed in such a way that, similar to the wavelet setting, the frame elements, the so-called quarklets, possess a certain number of vanishing moments. This enables us to generalize the basic building blocks of adaptive wavelet algorithms to the quarklet case. The applicability of the new approach is demonstrated by numerical experiments for the Poisson equation on $L$-shaped domains.
Persistent Connected Power Constrained Surveillance with Unmanned Aerial Vehicles

Persistent surveillance with aerial vehicles (drones) subject to connectivity and power constraints is a relatively uncharted domain of research. To reduce the complexity of multi-drone motion planning, most state-of-the-art solutions ignore network connectivity and assume unlimited battery power. Motivated by this and advances in optimization and constraint satisfaction techniques, we introduce a new persistent surveillance motion planning problem for multiple drones that incorporates connectivity and power consumption constraints. We use a recently developed constrained optimization tool (Satisfiability Modulo Convex Optimization (SMC)) that has the expressivity needed for this problem. We show how to express the new persistent surveillance problem in the SMC framework. Our analysis of the formulation based on a set of simulation experiments illustrates that we can generate the desired motion planning solution within a couple of minutes for small teams of drones (up to 5) confined to a 7 × 7 × 1 grid-space.
LSB and RLE Based Approach for Increasing Payload and Security of Stego Images

Data security is a major issue for Internet communication. This problem is solved by using various cryptography and steganography techniques. The challenges for different steganography methods are security, payload (embedding capacity) and robustness. This paper suggests spatial domain technique where the conventional least significant bit (LSB) substitution method is used with run-length encoding (RLE) for improving security and payload. The methods mentioned in literature give embedding capacity 20–50%, [1] whereas the work presented here shows embedding capacity up to 88% for three-bit evaluation (Table 4). At the same time, it maintains low distortion of the vessel image which is shown by calculating MSE and PSNR at each stage (Table 1). Also, there is an insignificant effect on various moments before and after embedding (Table 2).
Visual Password Scheme Using Bag Context Shape Grammars

In this paper, we implemented the similar images generated by bag context shape grammars as distractors in a prototype visual password scheme. A bag context shape grammar is a shape grammar that uses spatial rules to generate images in a regulated manner. That is, during the generative process, bag context is used as a technique to control when a shape grammar rule should be applied. The prototype visual password scheme is used to measure user experience as to whether users can remember their passwords immediately after enrolment and one week after enrolment. This is to ascertain whether the similar images generated using bag context shape grammars are good as a distractor for visual password scheme. The prototype visual password scheme is also built for shoulder surfing and guessing attacks resistance. The outcome of this study shows that bag context shape grammars are good for the generation of similar images as distractors for visual password schemes.
Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o
Effectiveness of Using Advertisement in Improving Writing Skills among the Islamic Senior Higher School’s Students

This study analyzes the effectiveness of using advertisement in improving the writing skills of students at the Islamic State Senior High School, Madrasah Aliyah Negeri (MAN) 1 Langsa, Aceh Province, Indonesia.To gather the data, the interviews are conducted with selected lecturers and students and then qualitatively analyzed.The analysis was conducted through three steps; data reduction, data display, and conclusion.The study found that the students were glad, enthusiastic, and motivated in writing lessons by using advertisement.The use of advertisement in teaching has improved students' ability in writing skills.However, some students faced difficulties in expressing their ideas in writing lessons due to the lack of vocabulary.Based on these findings, it is suggested that all teachers should use advertisement intensively in their teachings to further enhance the writing skills of their students.
Opposition Relations Between Prophecies

This paper presents two versions of opposition relations for prophetical statements, the first one is an application of “Ockham’s thesis” in Classical propositional Logic. The second one is a reinterpretation of that thesis in the logic $$MRS^{P}$$ .
Algorithm of acid temperature setup model based on the CBR

In the continuous pickling line, the acid temperature setup accuracy was low, which not only caused excessive pickling defects, but also caused excessive consumption of hydrochloric acid and a decrease in pickling efficiency. In order to solve this problem, the acid temperature setup model based on case-based reasoning (CBR) technology was established by using a large amount of production data with good temperature setting precision. After the model was put into operation on a 1450mm pickling and rolling line, the average pickling defect ratio decreased from 14.99904% to 11.7332% and the fluctuation range of the pickling defect ratio was effectively controlled. Practical application showed that the setup accuracy of the acid temperature model was effectively improved. As the number of strip steel production increases, the model has the adaptive ability to change with the working conditions, which is suitable for industrial promotion.
A novel depth estimation approach based on bidirectional matching for stereo vision systems

Recently, the extraction efficiency and quality of depth data have become the major concern in stereo vision. This work presents stereo depth estimation method based on Bidirectional Matching Method (BM2) aiming at achieving a good speed-accuracy trade-off. In this study, a novel bidirectional matching process is proposed for matching cost computation. Additionally, a robust cost function is designed for computing color, gradient and local binary pattern similarity between potential matching points. Next, an efficient adaptive spatially smooth edge-preserving filter is applied to minimize matching ambiguities. Following this, a Knock-Out Disparity Selection Technique (KO-DST) that iteratively solves a sequence of discrete binary optimization problem is used to generate disparity map. Finally, a discontinuity preserving disparity refinement is applied to correct disparity errors and refine the resulting disparity map. Rigorous experiments and analysis on Middlebury datasets validated the effectiveness of the presented idea and its superiority over some state-of-the-art algorithms. The proposed method runs approximately two times faster than the standard bidirectional matching.
Efficient DDPG via the Self-Supervised Method

In this paper, embedded with self-supervised learning network, an efficient DDPG(Deep Deterministic Policy Gradient) RL algorithm is investigated. With more essential characteristics of observing data included, the inputs of actor network and critic network of DDPG are replaced by the high-dimensional outputs from feature extracting layers and forward network respectively. Additionally, the parameters of these auxiliary layers are optimized with a self-supervised method by minimizing predicting errors, and thus both optimizing progresses can run parallelly and simultaneously. Lastly, an antagonistic air-fight simulation with a novel customized training index is introduced to perform the effectiveness and rising efficiency of our self-supervised DDPG RL algorithm.
Using The SVO Grid Table as a Beginner’s Guide to Write English Sentences and Improve Writing Skills among Low Proficiency Pupils in Primary School

The pupils in primary school are taught to learn English through skills such as listening, speaking, writing and reading.In a suburban school in West Malaysia, a group of low proficiency pupils were introduced to the SVO Grid Table to see the impact in learning one of the skills, the writing skill.It was aimed to help the pupils to write simple present tense sentences with subject-verb agreement.The focus of the SVO Grid Table is to guide low proficient pupils to write English sentences with subject, verb and object.There were two experimental and control group in the study and the instrument used was assessments.The findings indicated the experimental group showed a difference in their writing assessment marks.Overall, the SVO Grid Table shows a positive effect on the pupils' performance in writing simple sentences with the present tense.Lastly, the SVO Grid Table is hoped to give better impact on writing especially among low proficiency pupils in primary school and be usedby teachers in the next future.
Challenges in Building the Administrative Capacity of Career Centers in Higher Education Institutions

Purpose. An important priority for higher education institutions is the career development and realization of the students. In the dynamic labor market in Bulgaria this is becoming a serious challenge. It is important that these processes provide prerequisites for career centers to participate at all stages of the career path of students. The main objective of the present study is to build on the existing good practices and work of career centers to highlight the main factors for increasing their administrative capacity. Methods. The applied methods of study allow the basic objective to be achieved. These are: empirical analysis and synthesis, as well as an analysis of the existing regulatory framework. Results. The results obtained are in the direction of building and developing the administrative capacity of the higher education career centers. Conclusions. The conclusions outline the main challenges facing the higher education career centers and the opportunities for increasing their administrative capacity.
Educational Inclusion: Teachers’ Technical Needs and Supports

The Erasmus + Project “European diversity design for inclusive education” (EURODDIP-e), co-financed by the European Union, aims to provide education professionals with the knowledge, tools and devices necessary to assess the individual needs of the Students with difficulties in the upper limbs and personalize the Information and communication technologies (ICT) resources (interface, software, buttons, etc.). A Questionnaire Teacher (QT) with α = 0.75, has been prepared to find out the needs of professionals in the teaching-learning process, taking into account the characteristics of the students and the existing technological possibilities, both in paper format and by using Google Forms is anonymous, and collects sociodemographic data, as well as 27 other items (16 yes/no answers, 10 Likert-type answers and the last open-ended answer. 1237 teachers from public and private schools in Belgium, Italy, Portugal and Spain, have responded. Items related to the training and performance of teachers in their teaching with inclusion students and use of ICT are analyzed. The need for training and greater resources is unanimous. English, French and Spanish versions in https://riubu.ubu.es/handle/10259/5262. It is the beginning for the elaboration of a methodology for the detection of capacities for all partner countries.
On Motion Analysis of Multiple Time-Variant Objects in Video Sequences

Motion analysis and speed estimation from video signals is a topic of increasing interest, e.g., in the field of traffic monitoring and road surveillance. Moving objects in a considered video sequence may undergo several modifications either due to perspectival issues, that depend on the camera placement, or periodic behaviours. Hence, geometrical transformations, such as scaling and rotations, and periodic features are often superimposed to the motion of an object of interest. In this paper we present a novel method to speed estimation that applies to video streams framing multiple objects which experience a dynamic change throughout the video duration. Applications and results are presented to assess the robustness of the proposed algorithm.
Optimizing Data Transmission from IoT Devices Through Weighted Online Data Changing Detectors

The real-time data analysis requires an integrated approach to know the last known state of variables of a concept under monitoring. Thereby, the Internet-of-Thing (IoT) devices have provided alternatives to address distributed data collection strategies. However, the autonomy of IoT devices represents one of the main challenges to implement the collecting strategy. Battery autonomy is affected directly by the energy consumption derived from data transmissions. The Data Stream Processing Strategy (DSPS) is an architecture oriented to the implementation of measurement projects based on a measurement and evaluation framework. Its online processing is guided by the measurement metadata informed from IoT devices associated with a component named Measurement Adapter (MA). This paper presents a new data buffer organization based on measurement metadata articulated with online data filtering to optimize the data transmissions from MA. As contributions, a weighted data change detection approach is incorporated, while a new local buffer based on logical windows is proposed for MA. Also, an articulation among the data buffer, a temporal barrier, and data change detectors is introduced. The proposal was implemented and released on the pabmmCommons library. A discrete simulation on the library is here described to provide initial applicability patterns. The data buffer consumed 568 Kb for monitoring 100 simultaneous metrics. The online estimation of the mean and variance based on the Statistical Process Control consumed 238 ns. However, as a limitation, other scenarios need to be addressed before generalizing results. As future work, new alternatives to filter noise online will be addressed.
Speaker Recognition Using Composite Vector Stochastic Processes Model representation

The authors presenting a study of the automatic speaker verification for short utterances. The verification method of the speaker using word's sound parametric spectrum factorization in composite vector stochastic process representation on the base of multiplicative autoregressive model is presented in the paper. The developed method enables to receive the words features with stable characteristics for the same speaker and differ for the different speakers. The results presented in the paper showed the high correct identification probability. The features analysis for different words showed that these features also can be used in the task of connected speech recognition.
Multiscale Anisotropic Morlet Wavelet for Texture Classification of Interstitial Lung Diseases

Interpretation of various lung textures found in the interstitial lung disease (ILD) is essential for the assessment of the disease, and it constitutes a vital step in diagnosis. In the present study, high-resolution computed tomography images are used in classifying healthy lung texture and five other texture patterns that mostly occur in ILDs. A texture classifier based on anisotropic Morlet wavelet has been developed, and images are analysed with the mathematical model of the Morlet wavelet in various scales, orientations and anisotropic ratios. Support vector machine (SVM) as classifier is used, and the input feature vector for the SVM is created from the quantised values of wavelet coefficients. Quantisation of the wavelet coefficients is done by calculating Rényi’s entropy and estimating the parameters of generalised Gaussian distribution from the coefficient distribution. The results highlight that considering anisotropic property in the wavelet results in precision increase for a few texture patterns. The influence of scale on classification is also studied.
Decision Support System with the HFLTS Method in Student Achievement Selection

The determination of high achieving students must be done as objectively as possible with a minimum error rate, where several criteria need to be considered, namely: Cumulative Achievement Index (GPA), Outstanding Achievement / Ability, and English Language Ability. For the GPA criteria, it will not be difficult because it is based on the track record of achieving student achievement index while studying. The Assessors determine objectively based on the existing assessment standards. Even though they have clear assessment guidelines, in some cases for students who have similar achievements, this problem will be difficult. This difficulty occurs because each assessor has difficulty in describing the results of their assessment due to criteria other than GPA. After all, the existing assessment is qualitative. The qualitative assessment has its difficulties, and sometimes other aspects need to be considered such as how confident the team members are with their assessment and whether there is an element of doubt in making an assessment. Based on this, we need a Decision Support System (DSS) for Student Achievement Determination using Hesitant Fuzzy Linguistic Term Sets (HFLTS). The results showed that the DSS with HFLTS Method was successful in determining outstanding students.
Figurative Languages Employed by EFL Lecturers in Teaching

The objectives of this study are to find out (1) kinds of figurative languages used by EFL lecturers (2) reasons for using figurative languages in teaching, and (3) students' responses on the use of figurative languages by EFL lecturers in teaching. The current research employed the descriptive qualitative method. The subjects of this research are two EFL lecturers and students at English Education Study Program of STKIP Dampal Selatan. The results of the research indicated that (1) kinds of figurative languages employed by EFL lecturers in teaching are: rhetoric, simile, metonymy, personification, repetition, paradox, metaphor, and litotes, (2) the reasons for using figurative languages are: to help students' understanding the lessons, in motivating students in learning process, for introducing language and culture, as a feedback to increase students' understanding on the lessons, and to train students' critical thinking. In addition, (3) the students' responses on the use of figurative language is the students easily in association the material with the real world absorbed because those make closer with their environment; those also could enrich their knowledge on languages; and figurative languages used by lecturers help them to understand the lesson.
The Lorentz Groups $${\circledast }$$

In this chapter we study a class of linear Lie groups known as the Lorentz groups. As we will see, the Lorentz groups provide interesting examples of homogeneous spaces. Moreover, the Lorentz group SO(3, 1) shows up in an interesting way in computer vision.