Reviewerly/exHarmony huggingface dataset:
Model to Evaluate Users’ Engagement to Augmented Reality Using xAPI

This paper proposes a way of using Augmented Reality to help students accomplish remote laboratory experiments and check their engagement while their activities are carried on. For this end, we used Experience API (xAPI) to track student’s performances. First, students should access a remote laboratory. In this case, the Remote Microscope Experiment was accessed through a computer on RExLab’s website and a smartphone was used to interact with augmented elements inserted in a specific context. The users’ interactions with the elements inserted through the Augmania™ platform are withheld through xAPI and shown in the Learning Record Store of this same application. This made possible to acquire and analyze the students’ performance and engagement through data visualization dashboards and charts. RExLab (Remote Experimentation Laboratory) is physically located at Araranguá, Brasil.
An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms

This paper presents an experimental comparison among four automated machine learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on evolutionary algorithms (EAs), and the other is Auto-WEKA, a well-known AutoML method based on the combined algorithm selection and hyper-parameter optimisation (CASH) approach. The EA-based methods build classification algorithms from a single machine learning paradigm: either decision-tree induction, rule induction, or Bayesian network classification. Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms. We performed controlled experiments where these four AutoML methods were given the same runtime limit for different values of this limit. In general, the difference in predictive accuracy of the three best AutoML methods was not statistically significant. However, the EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA. We also observed that Auto-WEKA has shown meta-overfitting, a form of overfitting at the meta-learning level, rather than at the base-learning level.
Social modulation on daily variability in electric behavior

LASC 2019 was a big success that brought together 198 participants from five Latin American countries, as well as from USA, Germany, Italy, Netherlands, and Israel, with an excellent relationship between senior researchers and students (1-1.4).
A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications

In optical communication systems, frame synchronization is essential for subsequent operations, such as error correction and payload extraction. Various methods, so far, have been proposed in the published literature, but the performance is unsatisfactory under high bit error rate (BER) conditions. We present, in this work, a novel data-aided frame synchronization technique for optical packet transmission systems, in which the transmitter sends a sequence of packets with a specific synchronization word periodically inserted, and the receiver blindly recognizes the synchronization word to attain frame synchronization. The proposed algorithm detects the synchronization word based on Hough transform (HT), a classic method for line detection in digital image processing. The core principle of the algorithm is to exploit the periodicity of the frame synchronization word, which appears as black-and-white spaced stripes on a binary image when the frames are all aligned. Simulations are conducted over a 56Gbps optical QPSK transmission system, and the results show that our algorithm is still effective in attaining frame synchronization at a BER of 0.3. A comparison is also made between our algorithm and existed autocorrelation (AC)-based algorithm. The results demonstrate that our algorithm has a better error resilience performance. When the BER is higher than 0.03, our algorithm outperforms an AC-based algorithm significantly.
Pembangkitan Pola Batik dengan Menggunakan Neural Transfer Style dengan Penggunaan Cost Warna

In this research, the neural transfer styles technique was applied to transfer styles of pattern of Batik, a traditional Indonesian cloth painted using the wax-resist dyeing technique, to some certain images. The transfer was performed using a well-known convolutional neural network (CNN) architecture. Some neural transfer tests were done to produce solid color which originally came from color clustered images. The color cost function of the CNN was computed at every epoch of the iterative neural computation. The result of the transfer are images with clustered colors and a slightly apparent color gradient. The produced images can be classified as "Creative Batik".
RETRACTION: Cryptanalysis and enhancement of multi factor remote user authentication scheme based on signcryption

&lt;p style='text-indent:20px;'&gt;Advances in Mathematics of Communications, (2020)&lt;/p&gt;&lt;p style='text-indent:20px;'&gt;This paper is retracted by decision of one of the Editors in Chief of the journal Advances in Mathematics of Communications.&lt;/p&gt;
Prioritizing Software Components Risk

Technical Debt (TD) can be detected using different methods. TD is a metaphor that refers to short-term solutions in software development, which may affect the cost of the software development life-cycle. Several tools have been developed to detect, estimate, or manage TD. TD can be indicated through smells, code comments, and software metrics. Machine learning Techniques (MLTs) are used in many software engineering topics such as fault-proneness, bug severity, and code smell. In this paper we use four internal structure metrics to identify and classify Architecture Technical Debt (ATD) risk by using MLTs. We show that MLTs can identify and classify the risk of ATD on software components to help the decision-makers to prioritizing the refactoring decisions based on the level of the risk.
Preliminary Results on Different Text Processing Tasks Using Encoder-Decoder Networks and the Causal Feature Extractor

Deep learning methods are gaining popularity in different application domains, and especially in natural language processing. It is commonly believed that using a large enough dataset and an adequate network architecture, almost any processing problem can be solved. A frequent and widely used typology is the encoder-decoder architecture, where the input data is transformed into an intermediate code by means of an encoder, and then a decoder takes this code to produce its output. Different types of networks can be used in the encoder and the decoder, depending on the problem of interest, such as convolutional neural networks (CNN) or long-short term memories (LSTM). This paper uses for the encoder a method recently proposed, called Causal Feature Extractor (CFE). It is based on causal convolutions (i.e., convolutions that depend only on one direction of the input), dilatation (i.e., increasing the aperture size of the convolutions) and bidirectionality (i.e., independent networks in both directions). Some preliminary results are presented on three different tasks and compared with state-of-the-art methods: bilingual translation, LaTeX decompilation and audio transcription. The proposed method achieves promising results, showing its ubiquity to work with text, audio and images. Moreover, it has a shorter training time, requiring less time per iteration, and a good use of the attention mechanisms based on attention matrices.
A Bright Entanglement and Squeezing Generated by an External Pumping Radiation in a Correlated Emission Laser

The quantum and statistical properties of light generated by an external classical field in a correlated emission laser with a parametric amplifier and coupled to a squeezed vacuum reservoir are investigated using the combination of the master and stochastic differential equations. First, the solutions of the cavity-mode variables and correlation properties of noise forces associated to the normal ordering are obtained. Next, applying the resulting solutions, the mean photon number of the separate cavity modes and their crosscorrelation, smallest eigenvalue of the symplectic matrix, mean photon number, intensity difference fluctuation, photon number variance, and intensity correlation are derived for the cavity-mode radiation. The entanglement produced is studied employing the logarithmic negativity criterion. It is found that pumping atoms from the lower energy state to excited state, introducing the nonlinear crystal into the cavity and coupling the system to a biased noise fluctuation, generate a bright and strong squeezing and entanglement with enhanced statistical properties although the atoms are initially in the ground state.
Scaling Up Anonymous Communication with Efficient Nanopayment Channels

Abstract Tor, the most widely used and well-studied traffic anonymization network in the world, suffers from limitations in its network diversity and performance. We propose to mitigate both problems simultaneously through the introduction of a premium bandwidth market between clients and relays. To this end, we present moneTor: incentivizing nodes to join and support Tor by giving them anonymous payments from Tor users. Our approach uses efficient cryptographic nanopayments delivered alongside regular Tor traffic. Our approach also gives a degree of centralized control, allowing Tor’s managers to shape the economy created by these payments. In this paper, we present a novel payment algorithm as well as a data-driven simulation and evaluation of its costs and benefits. The results show that moneTor is both feasible and flexible, offering upwards of 100% improvements in differentiated bandwidth for paying users with near-optimal throughput and latency overheads.
IoT ink pen for ecological monitoring of daily life handwriting

The analysis of the writing gesture has been successfully investigated in the diagnosis of age-related diseases, but the current technologies and methods still do not allow the ecological daily monitoring of handwriting, mostly because they rely on standardized writing protocols. In this study, we first designed and validated a novel electronic ink pen, equipped with motion and writing force sensing, for the ecological daily-life monitoring of handwriting in uncontrolled environments. We used the pen to acquire writing activities from healthy adults, from which we computed useful handwriting and tremor indicators. We evaluated the reliability of our measurements by computing the intraclass correlation coefficients (ICC) and the minimal detectable changes (MDC). Moderate to excellent reliability were obtained for all the handwriting indicators computed in two different writing tasks. MDC values can be used as reference to discriminate a real change in the handwriting parameters from a measurement error in longitudinal studies. These results pave the way towards the use of the pen for daily life handwriting monitoring.
Post-Editing Human Translations and Revising Machine Translations

Post-editing of machine-translation output is generally considered to be a distinct process from the revision of a human-translated text. The main reasons for this assumption are the quality of machine-translation output and the fact that it might be easier to criticize the work of a machine than the work of a fellow human translator. With the global shift of statistical machine-translation systems to neural machine-translation systems, however, the quality of machine-translation output has improved. What was true about differences between revision and post-editing in 2010 might therefore no longer be true today. In addition, translators hired to revise a text are not always aware of the origin of the text. This chapter compares revision and post-editing products made by professional translation agencies when the actual origin of a text corresponded to the instructions they were given (revision of a human translation, post-editing of machine translation) and when the origin did not match the instructions (post-editing of a human translation and revision of machine translation). We look at the number of edits made, the quality of the revision and the optimality of the intervention.
Most General Algebraic Specifications for an Abstract Datatype of Rational Numbers

The notion of a most general algebraic specification of an arithmetical datatype of characteristic zero is introduced.Three examples of such specifications are given.A preference is formulated for a specification by means of infinitely many equations which can be presented via a finite number of so-called schematic equations phrased in terms of an infinite signature.On the basis of the latter specification three topics are discussed: (i) fracterm decomposition operators and the numerator paradox, (ii) foundational specifications of arithmetical datatypes, and (iii) poly-infix operations.
Self-Channel Attention Weighted Part for Person Re-Identification

Part based models have been proved to be beneficial for person re-identification (Re-ID) in recent years. Existing models usually use fixed horizontal stripes or rely on human keypoints to get each part, which is not consistent with the human visual mechanism. In this paper, we propose a Self-Channel Attention Weighted Part model (SCAWP) for Re-ID. In SCAWP, we first learn a feature map from ResNet50 and use 1x1 convolution to reduce the dimension of this feature map, which could aggregate the channel information. Then, we learn the weight map of attention within each channel and multiply it with the feature map to get each part. Finally, each part is used for a special identification task to build the whole model. To verify the performance of SCAWP, we conduct experiment on three benchmark datasets, including CUHK03-NP, Market-1501 and DukeMTMC-ReID. SCAWP achieves rank-1/mAP accuracy of 70.4%/68.3%, 94.6%/86.4% and 87.6%/76.8% on three datasets respectively.
Machine Translation for EFL

This paper reports on an ongoing study into the affordances of free online machine translation for students learning English as a foreign language (EFL) at the tertiary level in Japan. The researchers are currently collecting data from a questionnaire, task performance, and interviews with 10-15 EFL learners in an English Language Institute in a university in Japan. The paper provides some background on the changing role of translation in language learning theory and pedagogy, before focusing literature related to technical developments in machine translation technology, and its application to foreign language learning. An overview of the research methodology is provided, along with some insights into potential findings. Findings will be presented in subsequent publications.
Emotional Variability Analysis Based I-Vector for Speaker Verification in Under-Stress Conditions

Emotional conditions cause changes in the speech production system. It produces the differences in the acoustical characteristics compared to neutral conditions. The presence of emotion makes the performance of a speaker verification system degrade. In this paper, we propose a speaker modeling that accommodates the presence of emotions on the speech segments by extracting a speaker representation compactly. The speaker model is estimated by following a similar procedure to the i-vector technique, but it considerate the emotional effect as the channel variability component. We named this method as the emotional variability analysis (EVA). EVA represents the emotion subspace separately to the speaker subspace, like the joint factor analysis (JFA) model. The effectiveness of the proposed system is evaluated by comparing it with the standard i-vector system in the speaker verification task of the Speech Under Simulated and Actual Stress (SUSAS) dataset with three different scoring methods. The evaluation focus in terms of the equal error rate (EER). In addition, we also conducted an ablation study for a more comprehensive analysis of the EVA-based i-vector. Based on experiment results, the proposed system outperformed the standard i-vector system and achieved state-of-the-art results in the verification task for the under-stressed speakers.
Entanglement created in three-quantum dot system coupled to plasmonic waveguide

We investigate the entanglement properties of three-quantum dot (QD) system coupled to surface plasmonic waveguide by means of the concurrence with fully quantum-mechanical approach. High bipartite entanglement of three QDs could be achieved by adjusting the interparticle distance of the QDs, the detuning and the QD-waveguide coupling strength. We show that the bipartite entanglement of three-QD system is higher and its switching effect is better than the case of two-QD system. The discussed system with three QDs provides us rich way to realize the quantum entanglement used in the quantum information processing such as quantum computation and quantum communication.
Convolutional Neural Network Combined with Half-Quadratic Splitting Method for Image Restoration

Generally, there are mainly two methods to solve the image restoration task in low-level computer vision, i.e., the model-based optimization method and the discriminative learning method. However, these two methods have clear advantages and disadvantages. For example, it is flexible for the model-based optimization method to handle different problems, but large quantity of computing time is required for better performance. The discriminative learning approach has high computing efficiency, but the application scope is seriously limited by the fixed training model. It would be better to combine the advantages of these two methods. Luckily, with the variable splitting techniques, we insert the trained convolutional neural network (CNN) for denoising as one model to the model-based optimization method to solve other image restoration problems (e.g., deblurring and super-resolution). Final experimental results show that our denoising network is able to provide strong prior information for image restoration tasks. The image restoration effects can reach or approximate the most advanced algorithm in such three tasks as denoising, deblurring, and super-resolution. Moreover, the algorithm proposed in this paper is also the most competitive in terms of computational efficiency.
Instance Selection for Geometric Semantic Genetic Programming

Geometric Semantic Genetic Programming (GSGP) is a method that exploits the geometric properties describing the spatial relationship between possible solutions to a problem in an n-dimensional semantic space. In symbolic regression problems, n is equal to the number of training instances. Although very effective, the GSGP semantic space can become excessively big in most real applications, where the value of n is high, having a negative impact on the effectiveness of the GSGP search process. This paper tackles this problem by reducing the dimensionality of GSGP semantic space in symbolic regression problems using instance selection methods. Our approach relies on weighting functions-to estimate the relative importance of each instance based on its position with respect to its nearest neighbours-and on dimensionality reduction techniques-to improve the notion of closeness between instances, generating datasets with simplified input spaces. Experiments were performed on a set of 15 datasets and our experimental analysis shows that using instance selection by instance weighting and dimensionality reduction does improve the effectiveness of the search with almost no impact on root mean square error results.
Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing

Pose estimation is a typical problem in the field of image processing, the purpose of which is to compare or fuse images acquired under different conditions. In recent years, many studies have focused on pose estimation algorithms, but so far there are still many challenges, such as efficiency, complexity and accuracy for various targets and conditions, in the field of algorithm research and practical applications. In this paper, a multi-view-based pose estimation method is proposed. This method can solve the pose estimation problem effectively for large-scale targets and achieve good performance accuracy and stability. Compared with existing methods, this method uses different views (positions and angles), each of which only observes some features of large-size parts, to estimate the six-degree-of-freedom pose of the entire large-size parts. Experimental results demonstrate that the accurate six-degree-of-freedom pose for different targets can be obtained by the proposed method which plays an important role in many actual production lines. What is more, a new visual guidance system, applied into intelligent manufacturing, is presented based on this method. The new visual guidance system has been widely used in automobile manufacturing with high accuracy and efficiency but low cost.
Tamil News Clustering Using Word Embeddings

News aggregators support the readers to view news from multiple news providers via a single point. At the moment, the only news aggregator that supports Tamil news is Google news, which has some noticeable shortages. In this study, Term Frequency-Inverse Document Frequency and word embedding (fastText) document representation techniques were experimented with one pass and affinity propagation clustering algorithms to news title, as well as title and body in order to implement a news aggregator for the Tamil language. For this study we collected data from nine different news providers. When fastText was applied with one pass algorithm to news title and body, it managed to beat other approaches to achieve an average pairwise F-score of 81% with respect to manual clustering. Also, we were able to create a Tamil fastText word embedding model using more than 21 million words.
Participatory Design in the Development of Animated Comic on Website

Participatory design is a method that involves stakeholders in each design process, from the beginning to the end of the process. This method focuses on the opinions expressed by the participants. This method refers to Scandinavian method of participatory design. This paper presents how to build an animated comic website that fits the needs of end user. The main participants are the 11th grade students of SMAN 3 Lamongan in East Java, Indonesia, and supported by the history teacher and an IT expert.