Reviewerly/exHarmony huggingface dataset:
Silhouettes and Quasi Residual Plots for Neural Nets and Tree-based Classifiers

Classification by neural nets and by tree-based methods are powerful tools of machine learning. There exist interesting visualizations of the inner workings of these and other classifiers. Here we pursue a different goal, which is to visualize the cases being classified, either in training data or in test data. An important aspect is whether a case has been classified to its given class (label) or whether the classifier wants to assign it to a different class. This is reflected in the (conditional and posterior) probability of the alternative class (PAC). A high PAC indicates label bias, that is, the possibility that the case was mislabeled. The PAC is used to construct a silhouette plot which is similar in spirit to the silhouette plot for cluster analysis. The average silhouette width can be used to compare different classifications of the same dataset. We will also draw quasi residual plots of the PAC versus a data feature, which may lead to more insight in the data. One of these data features is how far each case lies from its given class. The graphical displays are illustrated and interpreted on datasets containing images, mixed features, and tweets. Supplementary materials for this article are available online.
“Spanish Políglota”: an automatic Speech Recognition system based on HMM

The goal of this ASR system is to be able to recognize audio queries that request static translation of a given Spanish word into a specified language. We call this ASR system as the Spanish Políglota. The pronunciation dictionary for the language model is obtained by applying grapheme to phoneme conversion. It was developed via Festival Speech Synthesis Scheme scripts and the SPPAS Spanish lexicon. The possible audio queries are restricted by a BNF grammar we designed for this project. A triphone acoustic model was generated from a set of 1621 words audio recordings. This acoustic model is based on a N-gram model that estimates its probabilities based on the maximum likelihood estimation MLE. We evaluated the prediction of individual words, as well as of synthetic phrases. We generated 1577 synthetic phrases concatenating the words of our audio set. The performance was also measured over a new set of audio recordings from a different speaker. Evaluation of isolated word recognition achieved 77.91% of correct predictions. Nevertheless, the performance dropped when evaluating the synthetic phrases as well as the second speaker's speech. We consider it is an initial step towards the development of a fully functional automatic speech recognition system.
Hybrid Recommender System Using A-priori Algorithm

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Inventory control and distribution of medicine stocks by using a just in time method based on interactive web applications

Abstract Management of an inventory data within an agency requires an interactive management system to provide ease and informative data presentation. Data processing using an interactive system makes it easy for users to understand and interact with data, especially if the agency concerned is an agency that is in charge of long-term data storage, complex data processing, data collection and data distribution processes, controlling inventory availability, and data collection conducted every day. Data storage and conventional data processing has several weaknesses such as high error rate in data processing, the system is not interactive so that it troubles and impedes the inventory data collection process by users, and can produce data ambiguity that can cause misinformation. By using the inventory control model Just-In-Time (JIT) Inventory method that allows the compilation of data collection and inventory stock requests as needed so that it can easily distribute and minimize wasted stock, the processing, storage, and data collection of a particular data inventory can be controlled well and can provide clearer information. As a result, implementing inventory control methods into a web application can provide web applications that have better functionality in processing, storing, and collecting data.
Rock beats Scissor: SVM based gesture recognition with data gloves

Hand gestures play an important role in human communication, particularly when auditory communication is limited. Akin to speech recognition, hand gesture recognition can therefore be a useful tool to facilitate communication and for more immersive computer interaction. In this paper, we examine the mobile recognition of hand gestures using data recorded with sensor gloves. We design a system based on Support Vector Machines (SVM), capable of recognizing 5 different hand gestures. In an experiment with 11 participants, we determine applicable hyperparameters based on performance on the training set which translates into 100% classification accuracy on the test set. In an additional practical experiment with 9 participants, our system achieves up to 98% in a personalized and up to 87.5% in a generalized model setting.
Joint Graph Embedding and Alignment with Spectral Pivot

Graphs are powerful abstractions that naturally capture the wealth of relationships in our interconnected world. This paper proposes a new approach for graph alignment, a core problem in graph mining. Classical (e.g., spectral) methods use fixed embeddings for both graphs to perform the alignment. In contrast, the proposed approach fixes the embedding of the 'target' graph and jointly optimizes the embedding transformation and the alignment of the 'query' graph. An alternating optimization algorithm is proposed for computing high-quality approximate solutions and compared against the prevailing state-of-the-art graph aligning frameworks using benchmark real-world graphs. The results indicate that the proposed formulation can offer significant gains in terms of matching accuracy and robustness to noise relative to existing solutions for this hard but important problem.
Problem of integration of digital data analytics services: teacher's competence in work with educational data

In the context of the development of data-based management, the transition to decision-making models based on Big Data technologies, in the context of the development of the national data management system, the issues of educational data analytics, the problems of the fragmentation of digital data analytics services, as well as the need to develop the teacher’s competencies in working with educational data. The structural and functional analysis of digital resources of educational data with which a modern teacher should work is presented. The competencies of the teacher in the field of analysis of educational data are characterized. The concept of “Pedagogy based on data” is considered as a new area of pedagogical knowledge and a methodological basis for the development of teachers' competencies in the field of data analysis. The author analyzes the possibilities of modernization and integration of digital data analytics services in the context of the implementation of the National Data Management System in Russia.
Discontinuous neural networks and discontinuity learning

In the framework of discontinuous function approximation and discontinuity interface detection, we consider an approach involving Neural Networks. In particular, we define a novel typology of Neural Network layers endowed with new learnable parameters and discontinuities in the space of the activations. These layers allow to create a new kind of Neural Networks, whose main property is to be discontinuous, able not only to approximate discontinuous functions but also to learn and detect the discontinuity interfaces. A sound theoretical analysis concerning the properties of the new discontinuous layers is performed, and some tests on discontinuous functions are proposed, in order to assess the potential of such instruments.
A Scale and Rotational Invariant Key-point Detector based on Sparse Coding

Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this article, we propose a solution to this challenging problem by incorporating Scale and Rotation Invariant design (named SRI-SCK) into a recently developed Sparse Coding based Key-point detector (SCK). The SCK detector is flexible in different scenarios and fully invariant to affine intensity change, yet it is not designed to handle images with drastic scale and rotation changes. In SRI-SCK, the scale invariance is implemented with an image pyramid technique, while the rotation invariance is realized by combining multiple rotated versions of the dictionary used in the sparse coding step of SCK. Techniques for calculation of key-points’ characteristic scales and their sub-pixel accuracy positions are also proposed. Experimental results on three public datasets demonstrate that significantly high repeatability and matching score are achieved.
Analytical potential of 3D data in the ballistic analyses of lithic projectiles

The total entry area of a projectile to the impacted object is a key ballistic parameter of lithic projectiles. Together with the assumed or experimentally testified penetration depth, it forms the first reception of the wounding capability of an individual projectile point, i.e., of the permanent wounding cavity volume. Mathematical operations are used to calculate the entry area, for which the manually measured dimensions of the projectile form input data. Nevertheless, these methods reconstruct only the cross-sectional area at only one point of the longitudinal axis of the object. Here, we propose a method of using high-resolution 3D data for precise measurements of the real entry area of a projectile, even in the case of barbed projectiles whose barbs exceed the width of the projectile body, as well as in cases of projectiles with an asymmetric cross-section. The proposed method of using 3D models to read the numerically expressed values of ballistic parameters was used to analyse almost 400 lithic archery projectiles found in the eastern part of Central Europe, dating from the Late Eneolithic and Early Bronze Age, a culminating period for lithic archery projectiles in the archaeological record.
aiai at the FinSim-2 task: Finance Domain Terms Automatic Classification Via Word Ontology and Embedding

This paper describes the method that we submitted to the FinSim-2 task on learning similarities for the financial domain. This task aims to automatically classify the Financial domain terms into the most relevant hypernym (or top-level) concept in an external ontology. This paper shows the result of experiments using the Catboost, Attention-LSTM, BERT, RoBERTa to develop an automatic finance domain classifier via word ontology and embedding. The experiment result demonstrates that each model could be an effective method to tackle the FinSim-2 task, respectively.
A Classification Model for Software Bug Prediction Based on Ensemble Deep Learning Approach Boosted with SMOTE Technique

In the software development process, the testing phase plays a vital role in assessing software quality. Limited resources pose a challenge in achieving this purpose efficiently. Therefore, early stage procedures such as software fault prediction (SFP) are utilized to facilitate the testing process in an optimal way. SFP aims to predict fault-prone components early based on some software metrics (features). Machine learning (ML) techniques have proven superior performance in tackling this problem. However, there is no best classifier to handle all possible classification problems. Thus, building a reliable SFP model is still a research challenge. The purpose of this paper is to introduce an efficient classification framework to improve the performance of the SFP. For this purpose, an ensemble of multi-layer perceptron (MLP) deep learning algorithm boosted with synthetic minority oversampling technique (SMOTE) is proposed. The proposed model is benchmarked and assessed using sixteen real-world software projects selected from the PROMISE software engineering repository. The comparative study revealed that ensemble MLP achieved promising prediction quality on the majority of datasets compared to other traditional classifiers as well as those in preceding works.
PAKT: A Position-Aware Self-attentive Approach for Knowledge Tracing

Knowledge Tracing aims to model a student’s knowledge state from her past learning interactions and predict her performance in future. Although structures such as positional encoding or forgetting gate have already been used in Knowledge Tracing models, positional information with great potential is not fully utilized. In this paper, we propose a Position-aware Self-Attentive Knowledge Tracing (PAKT) model with a position supervision mechanism. Massive experimental results show that PAKT outperforms other benchmarks on several popular datasets.
Analysis &amp;amp; Improvement of Wireless Network Security Based on Biometrics

The acceptance of mobile products and cloud computing amenities are increasing every day. Symmetric safety expertise addresses the problems of verified candid user which is a basic defect in traditional decryption. As far as, the authentication and recognition is concerned, the Traditional symmetric applications have been examined thoroughly since ten years and it is moving towards an important improvement. However, many problems remain unsolved and the related research continues. This research focuses on four research issues problems received during the application of biometric safety expertise in mobile and cloud computing expertise. This research consist a unique Bio-Pattern Recognition method for efficient acknowledgement of Bio-cryptic designs. This method was equated with the prevailing method. Where accuracy, acknowledgment duration, True Positive Identification Rate and False Negative Identification Rate were measured by prime guidelines. Lastly, empirical outcomes express the comprehensive work is enhanced in design identification in relation to duration taken.
Generative Flows with Invertible Attentions

Flow-based generative models have shown an excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations. Yet, learning attentions in generative flows remains understudied, while it has made breakthroughs in other domains. To fill the gap, this paper introduces two types of invertible attention mechanisms, i.e., map-based and transformer-based attentions, for both unconditional and conditional generative flows. The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows. The masked scheme allows for invertible attention modules with tractable Jacobian determinants, enabling its seamless integration at any positions of the flow-based models. The proposed attention mechanisms lead to more efficient generative flows, due to their capability of modeling the long-term data dependencies. Evaluation on multiple image synthesis tasks shows that the proposed attention flows result in efficient models and compare favorably against the state-of-the-art unconditional and conditional generative flows.
Human Action Recognition Based on Motion Feature and Manifold Learning

Human action recognition is an important task in the fields of video content analysis and computer vision. Since the performance of most existing action recognition frameworks depends on the representation of features, many researches aim to construct more discriminative features. In this paper, we propose a manifold learning framework based on optical flow for action recognition. First, we calculate the dense optical flow field of the original video sequence, and the attention pooling layer (AP) is adopted to separate target area and background area to eliminate background interference. On this basis, motion features (MF) based on the physical characteristics of dense optical flow are developed to characterize human motion information. After that, manifold learning is introduced to calculate the motion variance features (MVF), which reflect the change rate of motion features and measure the spatial correlation between features in non-Euclidean space. Finally, fusing the MVF obtained by manifold learning and MF, feeding fusion features into two fully connected layers (FC) in series for action classification and recognition. Experiments on several classic datasets show that the proposed method achieves 0.98%, 1.86% and 0.99% performance improvement on UCF 101, HMDB51 and JHMDB.
Learning hierarchical probabilistic logic programs

Abstract Probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning and reasoning in relational domains characterized by uncertainty. Still, learning the parameter and the structure of general PLP is computationally expensive due to the inference cost. We have recently proposed a restriction of the general PLP language called hierarchical PLP (HPLP) in which clauses and predicates are hierarchically organized. HPLPs can be converted into arithmetic circuits or deep neural networks and inference is much cheaper than for general PLP. In this paper we present algorithms for learning both the parameters and the structure of HPLPs from data. We first present an algorithm, called parameter learning for hierarchical probabilistic logic programs (PHIL) which performs parameter estimation of HPLPs using gradient descent and expectation maximization. We also propose structure learning of hierarchical probabilistic logic programming (SLEAHP), that learns both the structure and the parameters of HPLPs from data. Experiments were performed comparing PHIL and SLEAHP with PLP and Markov Logic Networks state-of-the art systems for parameter and structure learning respectively. PHIL was compared with EMBLEM, ProbLog2 and Tuffy and SLEAHP with SLIPCOVER, PROBFOIL+, MLB-BC, MLN-BT and RDN-B. The experiments on five well known datasets show that our algorithms achieve similar and often better accuracies but in a shorter time.
Effort prediction for the software project construction phase

Abstract The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR‐SMO‐PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR‐SMO‐PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε‐ SVR and ʋ‐ SVR in the seven data sets. Thus, the SVR‐SMO‐PUK is useful to software managers to predict CE.
Addressing Assumptions About Expert Information Leveraging the Transferable Belief Model

Quite often the state of information provided by disparate sources, or experts, is not conducive to fusion without additional processing. The focus of this paper involves processing information to help improve fusion results in classification problems. Specifically, this paper presents and integrates three necessary techniques to address assumptions about expert information, which are misaligned with reality. These three alignment techniques address the following three causes of misalignment: (1) over confident predictions made by experts, (2) different (but relatable) sets of classifications used by experts to make predictions, and (3) ambiguity in object-to-object association between experts. Addressing these three causes of misalignment avoids the inappropriate weighting, the improper interpretation, and/or the misapplication of expert information, any of which could lead to poor fusion results. Additionally, an expert's use of poorly informed priors (in the Bayesian sense) can prevent the alignment techniques from being most effective, which makes information representation at the credal level (in the transferable belief model sense) an ideal domain to execute these alignment techniques. Zadeh's and another similar example are used to demonstrate the necessity of addressing assumptions misaligned with reality to improve fusion results.
FPGA Implementation of Parallel Adder Using Reversible Logic Gates

Reversible digital technology can now start taking a more desirable direction for low dissipation of power, higher processing speeds. Here, we suggested the construction of an 8-, 16-, 32-, 64-bit multiplier using the carry-save adder, the Kogge stone adder, and the HNFG adder with the high operating speed of the proposed HNFG gate adder. The architecture of the device and the logic gates which are reversible can be implemented using the Vedic multiplier. The output of the accumulator operation is dependent on the multiplier unit and the adder units. Here, the development of a multiplier and an adder can be built using reversible gates to achieve high operating speeds, and the use of a Vedic multiplier for greater efficiency. Also fewer area and partial elements are used. A comparative study is being conducted between the conventional [] and the Kogge stone adder based on HNFG. Finally, it has been shown that the proposed HNFG gate with multiplier adder has a relatively high-speed over the conventional method. The whole process of simulation and synthesis is done using Xilinx 14.7 tool.
Spry Farm: A Portal for Connecting Farmers and End Users

Agriculture is considered to be the backbone of India. There are many people who are involved in farming mostly belong to the lower class and are stuck in poverty. The emergence of middlemen in the Indian agricultural marketing sector can be traced back to as early as bartender system times. The interference of middlemen has led to poor lifestyle for the farmers since time immemorial. The development of a portal will serve as a way for the farmers to sell their products across the country. The portal helps the farmers in registering themselves easily and selling their produce. The farmers can gain more profit than usual by using the web portal since it forms a direct connect to the customers. Removing the intervention of the middlemen. The total sale and the earned profit for the sold products, and customer needs are better known by the farmers through the portal. This would overall give a clear idea to the farmer as to what the customer requirements are and to how to grow only the required crops and needed investments for the same as alike. The max-prior algorithm used helps in allocating the highest requirement customer to the farmers to gain better profit. It also helps the farmers in selling their produce quicker. Thus, by this portal the farmers gain more profit hence increasing the country's economy.