Reviewerly/exHarmony huggingface dataset:
Towards Empirical Evidence on the Comprehensibility of Natural Language Versus Programming Language

In software design teams, communication between programmers and non-programming domain experts is an ongoing challenge. In this communication, source code documents could be a valuable artifact as they describe domain logic in an unambiguous way. Some programming languages, such as the Smalltalk programming language, try to make source code accessible. Its concise syntax and message-passing semantics are so close to basic English, that it is likely to appeal to even non-programming domain experts. However, the inherent obscurity of technical programming details still poses a significant burden for text comprehension. We conducted a code-reading study in form of a questionnaire through Amazon Mechanical Turk and SurveyMonkey. The results indicate that even in simple problem domains, a simple English text is more comprehensive than a simple Smalltalk program. Consequently, source code in its current text form should not be used as a reliable communication medium between programmers and (non-programming) domain experts.
Nonclassical properties of photon-added q-deformed cat states

The nonclassical properties of photon-added q-deformed cat states with q>1 is studied. The excitation number m has significant influence on their nonclassical properties. The squeezing effect of the photon-added q-deformed even cat state deepens with the increase of the excitation number m. The anti-bunching effect disappears faster with the increase of the excitation number m. The anti-bunching effect exists in the q-deformed photon-added odd and even cat states in a limited range, and disappears faster with the increase of the excitation number m.
An A3AOs Method of Software Tools Integration in the Complex System Development

Abstract Companies commonly face cost pressures due to the increasing industry rivalry and competition. Software tools in this regard has become more and more popular in assisting the complex systems development. However, the lack of a proper system development process that integrates the usage of software tools will hinder the company efficiency. This is especially visible as each engineer may use software tools differently. In the context of complex system developments, this research investigates the optimization of the system development process by integrating software tools. We propose an architecture of a new system development process using A3 Architectural Overviews. Based on a real‐life case study and quick‐prototyping techniques, the architecture evolves into an optimized system development process that can increase efficiency, quality and consistency for the case company.
From Parameter Estimation to Dispersion of Nonstationary Gauss-Markov Processes

This paper provides a precise error analysis for the maximum likelihood estimate â(u) of the parameter a given samples u = (u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ... , u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> )T drawn from a nonstationary Gauss-Markov process U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = αU <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i-1</sub> + Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , i ≥ 1, where α > 1, U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> = 0, and Zz's are independent Gaussian random variables with zero mean and variance σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We show a tight nonasymptotic exponentially decaying bound on the tail probability of the estimation error. Unlike previous works, our bound is tight already for a sample size of the order of hundreds. We apply the new estimation bound to find the dispersion for lossy compression of nonstationary Gauss-Markov sources. We show that the dispersion is given by the same integral formula derived in our previous work [1] for the (asymptotically) stationary GaussMarkov sources, i.e., |α|<; 1. New ideas in the nonstationary case include a deeper understanding of the scaling of the maximum eigenvalue of the covariance matrix of the source sequence, and new techniques in the derivation of our estimation error bound.
FittingGAN: Fitting image Generation Based on Conditional Generative Adversarial Networks

Recent studies have shown remarkable success in image generations using generative adversarial networks (GANs). However, how to deal with the fitting image generation, which is a task that generates a reasonable dressing image containing the input clothes is still an open problem. In this paper, we propose a condition generation model named FittingGAN which can achieve the generation of fitting scenes. The results show that It can generate fitting images with high resolution and realistic details, and FittingGAN have achieved good results in both qualitative and quantitative evaluations.
The Potential of Text-to-Speech Synthesis in Computer-Assisted Language Learning

This chapter describes the potential of text-to-speech synthesis (TTS) as a tool that can transform CALL platforms. Illustrating this point, a specific platform, An Scéalaí, is presented. By incorporating TTS, this platform facilitates the training of literacy skills, writing, and reading, with an emphasis at all times on the spoken language. The platform is described, as is the way in which it functions as a personalised tutor, prompting the learner towards self-correction. The prompts are delivered in both spoken/auditory form (using TTS voices) and in written form. The auditory feedback enables prooflistening, as well as spoken instructions pertaining to specific errors not picked up in the prooflistening process. The learner's progress is monitored throughout and records of the process are harvested for future research. The language in focus is Irish, and the linguistic complexities being targeted in the present implementation are explained, along with the relevant sociolinguistic context.
An Image Retrieval System for Video

Since the 1970's the Content-Based Image Indexing and Retrieval (CBIR) has been an active area. Nowadays, the rapid increase of video data has paved the way to the advancement of the technologies in many different communities for the creation of Content-Based Video Indexing and Retrieval (CBVIR). However, greater attention needs to be devoted to the development of effective tools for video search and browse. In this paper, we present Visione, a system for large-scale video retrieval. The system integrates several content-based analysis and retrieval modules, including a keywords search, a spatial object-based search, and a visual similarity search. From the tests carried out by users when they needed to find as many correct examples as possible, the similarity search proved to be the most promising option. Our implementation is based on state-of-the-art deep learning approaches for content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine using similarity functions.
Piece-Wise Approximation of Distributed Cumulative Histogram Features for Face Classification

The statistical features of distributed cumulative image histograms for their use in the processes of classification and search are researched. To check their similarity on the proposed features are used faces of different intensity. Segmented 2D image histograms give the slightest difference for faces made for experiments.
Rounding based continuous data discretization for statistical disclosure control

Abstract “Rounding” can be understood as a way to coarsen continuous data. That is, low level and infrequent values are replaced by high-level and more frequent representative values. This concept is explored as a method for data privacy with techniques like rounding, microaggregation, and generalisation. This concept is explored as a method for data privacy in statistical disclosure control literature with perturbative techniques like rounding, microaggregation and non-perturbative methods like generalisation. Even though “rounding” is well known as a numerical data protection method, it has not been studied in depth or evaluated empirically to the best of our knowledge. This work is motivated by three objectives, (1) to study the alternative methods of obtaining the rounding values to represent a given continuous variable, (2) to empirically evaluate rounding as a data protection technique based on information loss (IL) and disclosure risk (DR), and (3) to analyse the impact of data rounding on machine learning based models. Here, in order to obtain the rounding values we consider discretization methods introduced in the unsupervised machine learning literature along with microaggregation and re-sampling based approaches. The results indicate that microaggregation based techniques are preferred over unsupervised discretization methods due to their fair trade-off between IL and DR.
Characterizing quantum correlation with the correlated part of partial coherence

One of the fundamental features of the quantum world that distinguishes it from the classical counterpart is the existence of quantum correlations. In this paper, we study the quantum correlation from the viewpoint of partial coherence. Partial coherence in a bipartite system often contains local coherence of a subsystem. By removing the local coherence, we show that the remaining part of partial coherence can be regarded as quantum correlations. In this way, we introduce two quantum correlations: partial correlated coherence (PCC) and minimal partial correlated coherence (MPCC). Furthermore, we prove that PCC and MPCC induced by contractive pseudodistances are good candidates of quantum correlations. For contractive pseudodistances, we also show that the induced geometric partial correlated coherence (GPCC) and geometric minimal partial correlated coherence are actually the upper and lower bound of corresponding geometric quantum discord. Particularly, GPCCs based on a family of distances are proved to be bona fide measures of quantum correlations. By connecting to local quantum uncertainty, we provide an operational meaning for geometric quantum discord and GPCC induced by affinity of distance.
From Classical to Semi-Quantum Secure Communication

In this work we introduce a novel QKD protocol capable of smoothly transitioning, via a user-tuneable parameter, from classical to semi-quantum in order to help understand the effect of quantum communication resources on secure key distribution. We perform an information theoretic security analysis of this protocol to determine what level of "quantumness" is sufficient to achieve security, and we discover some rather interesting properties of this protocol along the way.
Effect of Weight Initialization on Training of Sigmoidal FFANN for Back Propagation Algorithms

For finding the performance of neural network choice of training algorithm is very important. Various algorithms are available and are used for testing the performance. In this paper we have used twelve variants of Back propagation algorithm for training & testing. We have used four different weight initialization methods namely random, Nguyen-Widrow and two proposed weight initialization methods. We have taken ten different data sets. We have compared the results obtained by applying all the twelve algorithms and found that our proposed methods are giving better results when compared with random and Nguyen-Widrow weight initialization methods. We have found that we obtained best results when air quality data set was taken as an input and scaled conjugate gradient back propagation algorithm as training algorithm out of all the twelve training algorithms taken during experimentation.
On Dual Codes in the Doob Schemes

The Doob scheme D(m, n' + n'') is a metric association scheme defined on E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> X F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n'</sup> X Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n''</sup> , where E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> = GR(4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) or, alternatively, on Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m</sup> X Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n'</sup> X Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n''</sup> . We prove the MacWilliams identities connecting the weight distributions of a linear or additive code and its dual. In particular, for each case, we determine the dual scheme, on the same set but with different metric, such that the weight distribution of an additive code C in the Doob scheme D(m, n' +n'') is related by the MacWilliams identities with the weight distribution of the dual code C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> in the dual scheme. We note that in the case of a linear code C in E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> X F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n'</sup> , the weight distributions of C and C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> in the same scheme are also connected.
Innovative Talent Cultivation Pattern of Software Engineering under Internet Plus

Recently, the combination of between Internet and the traditional business shows a fast development trend, and this further accelerates the fusion of IT technology and business applications. Therefore, multiple new innovative applications emerge for better serving daily life of the public. Facing this new demand and trend, our talent cultivation pattern of traditional software engineering no longer meets the current innovative needs. Therefore, it is urgent to explore the new talent cultivation pattern from the traditional software engineering to the one of internet plus pattern. This paper focuses on the new characteristics of software engineering education under Internet plus environment, and designs innovative methods for improving the talent cultivation of software engineering under the perspective of Internet plus. Furthermore, we build a new talent cultivation framework and propose an innovative talent pattern by analyzing the shortage of existing talent cultivation pattern. We cultivate more innovative software talents by this pattern, and further promote the development of innovation and entrepreneurship.
Sample Efficient Reinforcement Learning for Navigation in Complex Environments

Navigation of mobile robots in unstructured, time-varying environments is challenging. It becomes even more complicated in disaster scenarios where logistical difficulties, as well as technical issues such as reactive and time-varying obstacles, exist. These scenarios are too complex for classical obstacle avoidance methods to navigate through successfully. This paper presents a sample efficient reinforcement learning algorithm for navigation in complex environments. The approach augments training data with randomly generated target location data to accelerate learning. A Q-learning approach is implemented, which is capable of quick training with limited episodes. The procedure is tested in four scenarios in Gazebo and one scenario in a real-world experiment. In the two simulation scenarios with no obstacles, the method can learn to navigate towards the target in fewer than 200 episodes. For environments with moving obstacles, training takes slightly longer, but the process is still able to learn an effective policy quickly.
Dialog State Tracking for Unseen Values Using an Extended Attention Mechanism

Recently, discriminative models using recurrent neural networks (RNNs) have shown good performance for dialog state tracking (DST). Yoshida, Takami However, the models have difficulty in handling new dialog states unseen in model training. This paper proposes a fully data-driven approach to DST that can deal with unseen dialog states. Iwata, Kenji The approach is based on an RNN with an attention mechanism. The model integrates two variants of RNNs: a decoder that detects an unseen value from a user’s utterance using cosine similarity between word vectors of the user’s utterance and that of the unseen value; and a sentinel mixture architecture that merges estimated dialog states of the previous turn and the current turn. Fujimura, Hiroshi We evaluated the proposed method using the second and the third dialog state tracking challenge (DSTC 2 and DSTC 3) datasets. Experimental results show that the proposed method achieved DST accuracy of 80.0% for all datasets and 61.2% for only unseen dataset without hand-crafted rules and re-training. For the unseen dataset, the use of the cosine similarity-based decoder leads to a 26.0-point improvement from conventional neural network-based DST. Akamine, Masami Moreover, the integration of the cosine similarity-based decoder and the sentinel mixture architecture leads to a further 2.1-point improvement.
Project Portfolio Management (PPM) in Education Domain Using Skill Matcher Model

Project Portfolio Management has spurred significant research due to its importance in systematic management of corporate, government and academic projects. In this paper, we propose a skill matcher model for PPM which optimally matches students with various skills to a set of projects with specific skills requirements. We extract the skills required to execute a project from its description. We then generate a concept lattice of projects-skills by applying Formal Concept Analysis. The concepts so generated are assigned to students in an optimal manner by invoking the Stable Marriages Algorithm, keeping in view certain matching criteria. The experimental results show that the project teams so formed consists of all the skills required for completion of the project.
A Knowledge-Gated Mechanism for Utterance Domain Classification

Utterance domain classification (UDC) is a critical pre-processing step for many speech understanding and dialogue systems. Recently neural models have shown promising results on text classification. Meanwhile, the background information and knowledge beyond the utterance plays crucial roles in utterance comprehension. However, some improper background information and knowledge are easily introduced due to the ambiguity of entities or the noise in knowledge bases (KBs), UDC task remains a great challenge. To address this issue, this paper proposes a knowledge-gated (K-Gated) mechanism that leverages domain knowledge from external sources to control the path through which information flows in the neural network. We employ it with pre-trained token embedding from Bidirectional Encoder Representation from Transformers (BERT) into a wide spectrum of state-of-the-art neural text classification models. Experiments on the SMP-ECDT benchmark corpus show that the proposed method achieves a strong and robust performance regardless of the quality of the encoder models.
Study of contrast variations with depth in focused plenoptic cameras

A focused plenoptic camera has the ability to record and separate spatial and directional information of the incoming light. Combined with the appropriate algorithm, a 3D scene could be reconstructed from a single acquisition, over a depth range called plenoptic depth-of-field. In this Letter, we study the contrast variations with depth as a way to assess plenoptic depth-of-field. We take into account the impact of diffraction, defocus, and magnification on the resulting contrast. We measure the contrast directly on both simulated and acquired images. We demonstrate the importance of diffraction and magnification in the final contrast. Contrary to classical optics, the maximum of contrast is not centered around the main object plane, but around a shifted position, with a fast and nonsymmetric decrease of contrast.
Algorithm for k-anonymity based on ball-tree and projection area density partition

K-anonymity is a popular model used in microdata publishing to protect individual privacy. This paper introduces the idea of ball tree and projection area density partition into k-anonymity algorithm.The traditional kd-tree implements the division by forming a super-rectangular, but the super-rectangular has the area angle, so it cannot guarantee that the records on the corner are most similar to the records in this area. In this paper, the super-sphere formed by the ball-tree is used to address this problem. We adopt projection area density partition to increase the density of the resulting recorded points. We implement our algorithm with the Gotrack dataset and the Adult dataset in UCI. The experimentation shows that the k-anonymity algorithm based on ball-tree and projection area density partition, obtains more anonymous groups, and the generalization rate is lower. The smaller the K is, the more obvious the result advantage is. The result indicates that our algorithm can make data usability even higher.
Sparse Control and Data plane Telemetry features for BGP anomaly detection

Nowadays, the paradigm of detection of anomalous behavior in data networks is changing with the availability of data of high dimension and resolution, i.e. telemetry data. However, using such data requires careful analysis to decide which features are actually important for operators. In this paper we propose Sparse-RFE, a learning-based process that constructs a very small subset of telemetry features suitable for detection tasks. We apply Sparse-RFE on monitoring data of a BGP data center network described by a large amount of YANG instances. Our results show that collecting such subset of features from 1/3 of the nodes in the network results in instantiating only about 5% of the original features. Detectors based on Sparse-RFE features achieve high detection performance comparable to detectors that need all data from every node.