Reviewerly/exHarmony huggingface dataset:
Scale, translation and rotation invariant Wavelet Local Feature Descriptor

In this paper is presented a novel scale, translation and rotation (STR)-invariant 1D-descriptor, named Wavelet Local Feature Descriptor (WLFD). The methodology to construct the WLFD locates in three different scale pyramids keypoints to extract representative features of the image. These scale pyramids are built using the 2D multi-resolution representation by Haar wavelet. Hence, the translation invariance was achieved using keypoints, and the scale invariance was obtained via the scale pyramids. The rotation invariance is worked out by adding the intensity values of the given image filtered by a binary mask with a circumference of radius r centered on the keypoint; the result was stored in the descriptor. For each keypoint, forty binary masks were used (r=1,2,…,40 pixels); thus the STR-invariant 1D-WLFD is a vector of forty entries. The robustness of the STR-invariant 1D-WLFD was tested using images with different scales and rotations and comparing the results with the Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) descriptors. The WLFD shows results similar to SIFT and much higher than SURF. Another advantage that WLFD shows is being faster than SIFT, building the descriptors up to four times faster. Therefore, WLFD is a fast, efficient and easy-to-implement methodology.
Materializing interactions with paper prototyping: A case study of designing social, collaborative systems with older adults

Paper prototypes have been successful for gathering concrete, grounded user feedback. Most of these prototypes are used to elicit feedback during bounded design sessions. However, for systems that support new social practices these bounded sessions do not fully materialize the interactions the prototype supports. This paper presents a case study of a six-month paper prototype deployment that materialized collaborative interactions in care retirement community. Throughout this deployment the prototype was situated within the lives of participants, where they shaped the interactions and usage of the prototype over time. As these interactions materialized, the evoked community practice was unlike what participants envisioned during our first, more traditional paper prototyping and design sessions. This article shows how our interactive, paper prototype provided critical guidance in how realistic our imagined practice would be.
Improving Deep Binary Embedding Networks by Order-Aware Reweighting of Triplets

In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be effective for hashing retrieval. However, most of the triplet-based deep networks treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations of the binary codes and ignore the hash encoding when learning the feature representations. To this end, we propose an order-aware reweighting method to effectively train the triplet-based deep networks, which up-weights the important triplets and down-weights the uninformative triplets via the rank lists of the binary codes. First, we present the order-aware weighting factors to indicate the importance of the triplets, which depend on the rank order of binary codes. Then, we reshape the triplet loss to the squared triplet loss such that the loss function will put more weights on the important triplets. The extensive evaluations on several benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines.
SMT-Based Refutation of Spurious Bug Reports in the Clang Static Analyzer

We describe and evaluate a bug refutation extension for the Clang Static Analyzer (CSA) that addresses the limitations of the existing built-in constraint solver. In particular, we complement CSA's current heuristics for removing spurious bug reports. We encode the path constraints produced by CSA as Satisfiability Modulo Theories (SMT) problems, use SMT solvers to precisely check them for satisfiability, and remove bug reports whose associated path constraints are unsatisfiable. Our refutation extension refutes spurious bug reports in 8 out of 12 widely used open-source applications; on average, it refutes ca. 7% of all bug reports, and never refutes any true bug report. It incurs only negligible performance overheads, and on average adds 1.2% to the runtime of the full Clang/LLVM toolchain. A demonstration is available at https://www.youtube.com/watch?v=ylW5iRYNsGA.
Moon image segmentation with a new mixture histogram model

This paper proposes an algorithm named Histogram Mixture Model Genetic algorithm combining histograms and mixture models to segment images. The histogram of an image is approximated by several Gaussian functions with parameters. The parameters are obtained by the genetic algorithm with a fitness function deﬁned by an error function. The algorithm improves the drawbacks of Otsu and non-parametric methods. In the experiments, the algorithm has better results than Matlab function graythresh for Otsu method as compared to the theoretical optimal value. The experimental results of moon images show that the hole in Marius Hills is segmented.
Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces

Word embedding techniques heavily rely on the abundance of training data for individual words. Given the Zipfian distribution of words in natural language texts, a large number of words do not usually appear frequently or at all in the training data. In this paper we put forward a technique that exploits the knowledge encoded in lexical resources, such as WordNet, to induce embeddings for unseen words. Our approach adapts graph embedding and cross-lingual vector space transformation techniques in order to merge lexical knowledge encoded in ontologies with that derived from corpus statistics. We show that the approach can provide consistent performance improvements across multiple evaluation benchmarks: in-vitro, on multiple rare word similarity datasets, and invivo, in two downstream text classification tasks.
Computationally and Statistically Efficient Truncated Regression

We provide a computationally and statistically efficient estimator for the classical problem of truncated linear regression, where the dependent variable $y = w^T x + \epsilon$ and its corresponding vector of covariates $x \in R^k$ are only revealed if the dependent variable falls in some subset $S \subseteq R$; otherwise the existence of the pair $(x, y)$ is hidden. This problem has remained a challenge since the early works of [Tobin 1958, Amemiya 1973, Hausman and Wise 1977], its applications are abundant, and its history dates back even further to the work of Galton, Pearson, Lee, and Fisher. While consistent estimators of the regression coefficients have been identified, the error rates are not well-understood, especially in high dimensions. Under a thickness assumption about the covariance matrix of the covariates in the revealed sample, we provide a computationally efficient estimator for the coefficient vector $w$ from $n$ revealed samples that attains $l_2$ error $\tilde{O}(\sqrt{k/n})$. Our estimator uses Projected Stochastic Gradient Descent (PSGD) without replacement on the negative log-likelihood of the truncated sample. For the statistically efficient estimation we only need oracle access to the set $S$.In order to achieve computational efficiency we need to assume that $S$ is a union of a finite number of intervals but still can be complicated. PSGD without replacement must be restricted to an appropriately defined convex cone to guarantee that the negative log-likelihood is strongly convex, which in turn is established using concentration of matrices on variables with sub-exponential tails. We perform experiments on simulated data to illustrate the accuracy of our estimator. As a corollary, we show that SGD learns the parameters of single-layer neural networks with noisy activation functions.
Agriculture as a Stimulant for Sustainable Development in ECOWAS

This study utilized secondary data sourced from the World Development Indicators (WDI), International Labour Organisation (ILO), United Nations Educational, Scientific and Cultural Organization (UNESCO), and the System Generalized Method of Moments (SGMM) econometric technique was used to analyze the data. Sustainable Development Goal 1, a proxy for poverty, was used as the dependent variable, while agriculture value added, employment in the agricultural sector, inequality, literacy rate, population growth rate, and gross domestic savings were the explanatory variables. The study found that both agriculture value added and employment in the agricultural sector were statistically significant in explaining poverty and negatively related to poverty in the Economic Community of West African States (ECOWAS) subregion. Therefore, based on the findings, the study recommends that the governments of ECOWAS countries should focus more on agriculture in order to become exporters of cash crops to boost their economies and increase savings that can be used to alleviate and eliminate poverty among the people.
Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models

Component insertion is a common task in robotic assembly, and is widely used for manufacturing a variety of electronic devices. This task is generally characterized by low tolerances, thus requiring high precision during assembly. An early detection of a fault in the mating during the insertion process enables quality control of the end products, as well as safeguards the robotic equipment. We propose to use Gaussian Process Regression-based methods to learn the force profile during successful insertions, as well as quantify permissible deviations from this profile. The GPR model is then used to detect anomalies in case the observed force profile deviates significantly from the expected range. Apart from the standard GPR formulation, we consider two other variants - the Heteroscedastic GPR and the local GPR for better modeling accuracy and computational time efficiency, respectively. We report an accuracy of 100% in differentiating between normal and faulty insertions. The modeling and detection results indicate that our approach is accurate and robust to severe uncertainties due to process (e.g., force drift) and measurement noise.
Data-Driven Lexical Normalization for Medical Social&#x0D; Media

In the medical domain, user-generated social media text is increasingly used as a valuablecomplementary knowledge source to scientific medical literature. The extraction of this knowledge iscomplicated by colloquial language use and misspellings. However, lexical normalization of suchdata has not been addressed effectively. This paper presents a data-driven lexical normalizationpipeline with a novel spelling correction module for medical social media. Our method significantlyoutperforms state-of-the-art spelling correction methods and can detect mistakes with an F1 of 0.63despite extreme imbalance in the data. We also present the first corpus for spelling mistake detectionand correction in a medical patient forum.
Unveiling Trends and Predictions in Digital Factories

The emergence of the Industrial Internet of Things paves the way for enhancing the real-time monitoring capabilities of contemporary manufacturing enterprises through the extensive utilization of physical and virtual sensors. This paradigm enables the detection of early warning signals concerning systems' degradation and facilitates the prompt decision making and actions performed ahead of time. Currently, even large manufacturing companies have not yet developed a complete Predictive Maintenance strategy and appropriate sensor-driven, real-time systems in order to utilize these benefits. In this paper, we propose a failure prediction system for complex IT systems in the steel industry. The novelty of our work lies in the exploitation of Deep Learning techniques from streaming operational sensor data, enabling earlier failure predictions through a Neural Networks approach. To evaluate the proposed framework, real-life data are collected and analyzed based on daily operational and maintenance activities within the production line. We further demonstrate the framework's potential by presenting some early results in modeling and predicting the complex and dynamic behavior in the manufacturing settings.
Image-Image Translation to Enhance Near Infrared Face Recognition

With the rapid development of facial recognition, the research field of near infrared (NIR) face recognition, which is less sensitive to illumination levels, has attracted increased attention. Unfortunately, directly applying the face recognition model trained using visible light (VIS) data to NIR face data does not produce a satisfactory performance. This is due to the domain bias between the NIR images and the VIS images. To this end, we created the Outdoor NIR-VIS Face (ONVF) database and Indoor NIR Face (INF) database to increase the number of near infrared facial images for system training and evaluation. In this paper, we propose an efficient NIR face recognition method, which consists of face detection and alignment, NIR-VIS image translation and face embedding. The NIR-VIS image conversion model is capable of transforming near-infrared facial images into their corresponding VIS images whilst maintaining sufficient identity information to enable existing VIS facial recognition models to perform recognition. Extensive experiments using the INF dataset and the CSIST database have demonstrated that the proposed method yields a consistent and competitive performance for near infrared face recognition.
On Fenchel Mini-Max Learning

Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling. Practical considerations often force modeling approaches to make compromises between these objectives. We present a novel probabilistic learning framework, called Fenchel Mini-Max Learning (FML), that accommodates all four desiderata in a flexible and scalable manner. Our derivation is rooted in classical maximum likelihood estimation, and it overcomes a longstanding challenge that prevents unbiased estimation of unnormalized statistical models. By reformulating MLE as a mini-max game, FML enjoys an unbiased training objective that (i) does not explicitly involve the intractable normalizing constant and (ii) is directly amendable to stochastic gradient descent optimization. To demonstrate the utility of the proposed approach, we consider learning unnormalized statistical models, nonparametric density estimation and training generative models, with encouraging empirical results presented.
Talk2Learn: A Framework for Chatbot Learning

The rapid expansion of technologies in the education sector has led to the development of innovative pedagogical approaches being integrated with new technologies for enhancing the learning experience. Virtual assistants or chatbot technologies have been one of the primary focus in streamlining and enhancing learning processes by integrating pedagogic approaches with innovative technologies. This paper focuses on analyzing the recent developments in educational chatbots, as well as the identified issues in the design, development, and application of chatbots in e-Learning. Accordingly, a framework that reflects the various factors that need to be considered in chatbot design and developments in e-Learning is proposed and discussed in this paper.
Echo State vs. LSTM Networks for Word Sense Disambiguation

Inspired by bidirectional long short-term memory (LSTM) recurrent neural network (RNN) architectures, commonly applied in natural language processing (NLP) tasks, we have investigated an alternative bidirectional RNN structure consisting of two Echo state networks (ESN). Like the widely applied BiLSTM architectures, the BiESN structure accumulates information from both the left and right contexts of target word, thus accounting for all available information within the text. The main advantages of BiESN over BiLSTM are the smaller number of trainable parameters and a simpler training algorithm. The two modelling approaches have been compared on the word sense disambiguation task (WSD) in NLP. The accuracy of several BiESN architectures is compared with that of similar BiLSTM models trained and evaluated on the same data sets.
DCAR: Deep Collaborative Autoencoder for Recommendation with Implicit Feedback

In recent years, deep neural networks have been widely applied to recommender systems. Although there are extensive explorations of deep neural networks on the collaborative filtering problem in item recommendation, most of the existing methods employ a similar loss function, i.e., the prediction loss of user-item interactions, and only change the form of the input, which may limit the model’s performance. To address this problem, we present a novel framework, named DCAR, short for Deep Collaborative Autoencoder for Recommendation. Specifically, with the implicit feedback matrix as the input, we employ the autoencoder module to obtain the latent representations of users and items respectively. Then, to predict the matching score of corresponding user-item pairs, an interaction prediction module is designed based on the neural network architecture. The two parts are coupled together and employ alternating training to learn. We conduct extensive experiments on several real-world datasets and the results empirically verify the superior performance of DCAR on item recommendation. The code related to this paper is available at: https://github.com/strange-jiong/DCAR .
Real-time subtraction-based calibration methods for deformation measurement using structured light techniques

Calibration of the optical trigonometric relationships in structured light is essential for the accuracy of out-of-plane displacement measurement. A simple calibration mechanism based on real-time image subtraction is proposed. Details of calibrating the height-to-phase ratio of the digital fringe projection method, the relationship between out-of-plane and in-plane displacements of the digital projection-speckle correlation method, and the scale factor of the optical system are described. The calibration methods are applied to the deformation measurement of a clamped Plexiglas plate, which demonstrates the effectiveness of the calibration methods. The real-time subtraction-based calibration methods provide alternatives for calibrating structured light techniques used for out-of-plane displacement measurement.
A workflow-based solution to support the assessment of collaborative activities in e-learning

Purpose The purpose of this paper is to focus on assessing individuals’ problems in learning groups/teams and should lead to the assessment of the group/team itself as a learning entity. Design/methodology/approach In this paper, an extension of the IMS-Learning Design (IMS-LD) meta-model is proposed in order to support the assessment of collaborative activities in e-learning. Besides, the software architecture which consists of a set of components forming a web wizard to create, track and assess the collaborative assessment processes is described as to support that extension of the IMS-LD meta-model. Findings With the proposed solution we can: make assessment fairer using individual and collective assessment indicators to assign final scores to learners; make an assessment step by step for better individual and collective monitoring activities; and divide the assessment into lighter phases for the correctors. Consequently, the evaluator will have more detailed information about his/her students and the quality of judgment will be better. This could also be useful for the evaluator in order to plan further examinations. Research limitations/implications Further experimentations are necessary to test the effectiveness of the proposed system in order to analyze its performances under a massive usage. In addition, the authors plan to use a survey to collect learners’ opinions to know the effectiveness of the proposal in terms of fairness in the assessment of collaborative activities in an online community. Originality/value This paper addresses important issues in the educational area, especially assessment of collaborative activities. In fact, to reduce subjectivity and increase fairness in assessing learners in collaborative work, for example, using the peer assessment, in order to try reducing subjectivity and fairly assessing learners. However, while assessing group work, the same mark is attributed for all group members and authors have concluded that it is not the right approach to make a fair and more objective assessment.
The Multilayer Perceptron (MLP)

The multilayer perceptron (MLP) is the fundamental example of a deep neural network. The architecture of a MLP consists of multiple hidden layers to capture more complex relationships that exist in the training dataset. Another name for the MLP is the deep feedforward neural network (DFN). An illustration of an MLP is shown in Figure 31-1.
3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images

Convolutional Neural Networks (CNNs) have achieved remarkable performance in many 2D computer vision and medical image analysis tasks. In clinical practice, however, a large part of the medical imaging data available are in 3D. This has motivated the development of 3D CNNs in order to benefit from more spatial context. Although weight sharing in CNNs significantly reduces the number of parameters that have to be learned, state-of-the-art 3D methods still depend on patch processing due to GPU memory restrictions caused by moving to fully 3D. The size of the input patch is usually small if no specialized hardware with large GPU memory is used, limiting the incorporation of larger context information for a better performance. In this paper, we propose a 3D Tiled Convolution (3D-TC) which learn a number of separate kernels within the same layer. 3D-TC has the advantage of significantly reducing the required GPU memory for 3D medical image processing task but with improved performance. Results obtained from comprehensive experiments conducted on both hip T1 MR images and pancreas CT images demonstrate the efficacy of the present method. Our implementation can be found at: https://github.com/guoyanzheng/LPNet .
Lithuanian Speech Recognition Using Purely Phonetic Deep Learning

Automatic speech recognition (ASR) has been one of the biggest and hardest challenges in the field. A large majority of research in this area focuses on widely spoken languages such as English. The problems of automatic Lithuanian speech recognition have attracted little attention so far. Due to complicated language structure and scarcity of data, models proposed for other languages such as English cannot be directly adopted for Lithuanian. In this paper we propose an ASR system for the Lithuanian language, which is based on deep learning methods and can identify spoken words purely from their phoneme sequences. Two encoder-decoder models are used to solve the ASR task: a traditional encoder-decoder model and a model with attention mechanism. The performance of these models is evaluated in isolated speech recognition task (with an accuracy of 0.993) and long phrase recognition task (with an accuracy of 0.992).