Reviewerly/exHarmony huggingface dataset:
Using Graphical Models as Explanations in Deep Neural Networks

Despite its remarkable success, deep learning currently typically operates as a black-box. Instead, can models produce explicit reasons to explain their decisions? To address that question, we propose to exploit probabilistic graphical models which are declarative representations of our understanding of the world (e.g., what the relevant variables are, and how they interact with each other), and are commonly used to perform causal inference. More specifically, we propose a novel architecture called Deep Explainable Bayesian Networks whose main idea consists in concatenating a deep network with a Bayesian network, and to rely on the latter one to provide the explanations. We conduct extensive experiments on classical image, and text classification tasks. First, the results show that deep explainable Bayesian networks can achieve comparable accuracy than models that are trained on the same datasets but without producing explanations. Second, the experiments show promising results: The average accuracy of the explanation ranges from 68.3% to 84.8%.
LAWS: Locality-AWare Scheme for Automatic Speech Recognition

Automatic Speech Recognition (ASR) systems are changing the way people interact with different applications on mobile devices.Fulfilling such user-interactivity requires not only a highly accurate, large-vocabulary recognition system, but also a real-time, energy-efficient solution.However, these ASR systems need high memory bandwidth and power budget, which may be impractical for most of small form-factor battery-operated devices.In this paper, we propose two combined techniques implemented on top of a state-of-the-art ASR accelerator in order to significantly reduce its energy consumption and memory requirements.First, by leveraging the locality among consecutive segments of the speech signal, we develop a Locality-AWare-Scheme (LAWS) which exploits the on-chip recently-explored data while removing most of the off-chip accesses during the ASR's decoding process.As a result, we remove up to 60% of ASR's workload.As the second step, we introduce an approach to improve LAWS's effectiveness by selectively adapting the amount of ASR's workload, based on run-time feedback.In particular, we exploit the fact that the confidence of the ASR system varies along the recognition process.When confidence is high, the ASR system can be more restrictive and reduce the amount of work.The end design including both techniques provides a saving of more than 87% in the memory requests and 2.3x reduction in energy consumption, and a speedup of 2.1x with respect to a state-ofthe-art baseline design.
From Consensus to Innovation. Evolving Towards Crowd-based User-Centered Design

The arising of new technologies ranging from smartphones to social networks is constantly increasing interactions between people. In the Information and Communications Technology (ICT) community, adapting technology to human nature is the key concern of User-Centered Design (UCD). However, UCD tends to neglect the emerging social dimension of technology: users are consulted in the design process, but they do not have any direct involvement or creative control over the developed technological solutions. On the other hand, the collaborative and social nature of the design process is getting increasingly explicit in the Product Design community, where well-established participatory approaches are applied to involve stakeholders, designers, and end-users in the creative process of new products. In this paper, we first provide a deep analysis of the state of the art of participatory approaches in the research literature. Then, we investigate how their integration with UCD leads not only to empower the role of end-users as active collaborators of designers towards a more democratic crowd-based UCD process, but also to create innovation in the design process. We advocate that such innovation can be obtained by giving the right voice not only to the users who reach consensus in the design process but also to the marginals. We provide an explorative model, some experiments, and a sketch of the user interface to support our claim.
Cache efficient Value Iteration using clustering and annealing

Value Iteration (VI) is a powerful, though time consuming, approach to solve Markov Decision Processes (MDPs). Existing algorithms for VI incur a large number of cache misses. Motivated by the observation that, on modern computers, the cost of a cache miss is two to three orders of magnitude more than that of an arithmetic operation, we explore the possibility of improving the performance of VI by reducing the number of cache misses, possibly at the expense of increasing the number of arithmetic operations. Cache efficiency is obtained by performing VI on partitions of the MDP state space that fit in the lowest level cache of the computational platform on which the code is to run. Further performance improvement, motivated by the use of MDP partitions, is obtained using a clustering scheme to construct the partitions and an annealing schedule to converge to the target accuracy. We demonstrate experimentally that incorporating partitioning, clustering, and annealing into state-of-the-art VI software result in speedups of up to a factor of 8.1 on our computational platforms.
The Performance Cost of Software-based Security Mitigations

Historically, performance has been the most important feature when optimizing computer hardware. Modern processors are so highly optimized that every cycle of computation time matters. However, this practice of optimizing for performance at all costs has been called into question by new microarchitectural attacks, e.g. Meltdown and Spectre. Microarchitectural attacks exploit the effects of microarchitectural components or optimizations in order to leak data to an attacker. These attacks have caused processor manufacturers to introduce performance impacting mitigations in both software and silicon. To investigate the performance impact of the various mitigations, a test suite of forty-seven different tests was created. This suite was run on a series of virtual machines that tested both Ubuntu 16 and Ubuntu 18. These tests investigated the performance change across version updates and the performance impact of CPU core number vs. default microarchitectural mitigations. The testing proved that the performance impact of the microarchitectural mitigations is non-trivial, as the percent difference in performance can be as high as 200%.
Automatic Counting of People in Crowded Scenes, with Drones That Were Applied in Internal Defense Operations on October 20, 2019 in Ecuador

Eleven days of public protests, unrest and chaos happened in October 2019 in Ecuador. The national security forces acted immediately implementing internal defense operations. In this changing and highly uncertain context the automatic crowd counting based on the Gaussian density estimation combined with the multi-scale convolutional neural networks is used to generate electronic intelligence. This solution is set in a real scene obtaining excellent results and reducing the training time without being the performance affected. In this way, with the information obtained in real-time about the quantities of forces and dispositive. The national security forces reaction is immediate guaranteeing the control, the resupply and reorganization of themselves.
Algorithm of Pre-whitening on the Basis of the Polynomial Canonical Expansion of Random Sequences

The algorithm of whitening which can be used during the initial stage of functioning of the cryptographic system of information security is obtained in the work. The algorithm allows to remove stochastic relations of a source message which will make it harder to crack it by intruders. Non-linear canonical expansion of random sequences forms the basis of the algorithm. The results of the experimental investigations show high efficiency of the offered whitening method.
Assistance For Visually Impaired Using Finger-Tip Text Reader Using Machine Learning

Visually impaired people report large number of difficulties in their day to day life. One of the main and most important difficulty is reading texts. With the help of the latest technologies, we tend to help such difficulties by providing them with a device which could assist them in their everyday activities and also help them in studies to improve reading and learning contents. This device is a vital part in visually impaired person's life as it assists them with almost everything they come across in their typical day. This device captures image when pointed by the user and locates the text present in the image. The text is then extracted from the image and is further converted into audio to give the user with a clarified outcome. This device can be used in any paper printed texts and also handwritten texts, thus providing users an effective and efficient in time output. This project helps us identify various difficulties in detecting and recognizing text in real time by an average visually impaired person and come up with solutions to help them. In our approach we have used Fully Convolutional neural network for text level predictions and the then we use NMS to obtain the boxed geometry output of all the texts in the images. Then for the purpose of recognition the text we pass it on to tesseract OCR to obtain the extracted text, and then we convert the text to speech for the final outcome. The main motivation behind our project is to help the visually impaired people to better recognize all the text in front of them and help them live their day to day life just like any other normal person.
Modeling as Computational Thinking Language

Modeling or building models is a widely spread concept that includes several competences relevant for every domain. Our Erasmus+ project "Modeling at school", hence, aims at integrating modeling techniques from the field of computer science such as class or activity diagrams in order to support teaching and learning in all subjects and levels of primary and secondary education. On the one hand, we introduce modeling as effective and brain-based learning strategy and, on the other hand, we use it as language that expresses, describes and trains computational thinking integrated in everyday life situations and topics. This paper describes the development of a reference framework following the Common European Framework of Reference for Languages (CEFR) that will serve as guideline and assessment tool for teachers without computer science background, who teach the Austrian curriculum "Basic Digital Education".
Shape-Based Analysis on Component-Graphs for Multivalued Image Processing

Abstract Connected operators based on hierarchical image models have been increasingly considered for the design of efficient image segmentation and filtering tools in various application fields. Among hierarchical image models, component-trees represent the structure of grey-level images by considering their nested binary level-sets obtained from successive thresholds. Recently, a new notion of component-graph was introduced to extend the component-tree to any grey-level or multivalued images. The notion of shaping was also introduced as a way to improve the anti-extensive filtering by considering a two-layer component-tree for grey-level image processing. In this article, we study how component-graphs (that extend the component-tree from a spectral point of view) and shapings (that extend the component-tree from a conceptual point of view) can be associated for the effective processing of multivalued images. We provide structural and algorithmic developments. Although the contributions of this article are theoretical and methodological, we also provide two illustration examples that qualitatively emphasize the potential use and usefulness of the proposed paradigms for image analysis purposes.
Robust Review Rating Prediction Model based on Machine and Deep Learning: Yelp Dataset

Public reviews for a business are very important and help the business to measure the quality and excellence in different directions which leads to predict the worth of a business in the market. In other words, reviews have a very high impact on business revenue. In this paper, we focus on reviews for all kinds of restaurants business and have proposed a sentiment analysis and opinion mining model to perform the classification on business reviews. In order to achieve robust results both binary and multilabel classification are used used by using a large and rich text reviews dataset provided by Yelp Dataset Challenge round -13. Extensive and series of experiments have been done and compare the results of a machine learning based algorithm “Multinomial Naive Bayes” and deep learning algorithm “convolution Long Short Term Memory'” (CLSTM) with word2vec and Global Vector (Glove). After analyzing the performance of each model with different metrics, it has been observed that the best model for classifying the review ratings is CLSTM. We have also found the role of bias in the machine and its importance in explaining the performance differences observed on specific problems.
fcvalid: Olasılıklı ve Olabilirlikli Bölümleyici Kümelemede Bulanık Geçerlilik İndeksleri için Bir R Paketi

In exploratory data analysis and machine learning, partitioning clustering is a frequently used unsupervised learning technique for finding the meaningful patterns in numeric datasets. Clustering aims to identify and classify the objects or the cases in datasets in practice. The clustering quality or the performance of a clustering algorithm is generally evaluated by using the internal validity indices. In this study, an R package named 'fcvalid' is introduced for validation of fuzzy and possibilistic clustering results. The package implements a broad collection of the internal indices which have been proposed to validate the results of fuzzy clustering algorithms. Additionally, the options to compute the generalized and extended versions of the fuzzy internal indices for validation of the possibilistic clustering are also included in the package.
Towards Rigorously Designed Preference Visualizations for Group Decision Making

Group decision making is when two or more individuals must collectively choose among a competing set of alternatives based on their individual preferences. In these situations, it can be helpful for decision makers to model and visually compare their preferences in order to better understand each others’ points of view. Although a number of tools for preference modelling and inspection exist, none are based on detailed data and task models that capture the demands of group decision making in particular. This paper is a first step in addressing this gap. By going through the four stages of the nested model of visualization design, we have developed and tested a prototype to support group decision making when decision makers express their preferences directly on the alternatives.
Long Short-term Memory for Tibetan Speech Recognition

Tibetan is the common language of Tibetan people in China. Tibetan is divided into U-Tsang, Kham and Ando three dialect areas. This paper proposes a Tibetan speech recognition method based on long short-term memory to deal with the problem of the existing methods have low recognition rate. We firstly designed a Tibetan text corpus and a Tibetan pronunciation dictionary, and we also recorded a Tibetan speech corpus, and trained an LSTM-based Tibetan acoustic model. Moreover, we compared our method with the DNN-based method and the CNN-based method under the same test set. The results show that the recognition rate of our model is increased by 8.03% and 6.95% than DNN-based method and CNN-based method.
Technology of Forming Competence of Pedagogical Design in Graduate and Postgraduate Programs

The approaches to pedagogical design as an invariant teacher’s activity are analyzed and theoretical and methodological provisions of pedagogical design are highlighted. The content and structure of pedagogical design in the development of educational materials are disclosed. The characteristic of stages of students’ activity in the development of educational materials is presented. The highlighted characteristics of modern teaching materials allow researchers to consider them as an indicator of the quality teacher’s work. The requirements for educational materials are identified and a methodology for assessing their quality is presented. The principles of pedagogical design as a component in the structure of pedagogical activity are formulated. The structure and content of the model for constructing educational materials and methodology for the examination of educational literature have been developed. Experience and implementation of technology development for the formation of competence in pedagogical design of teaching materials in the training of postgraduates and graduate students are analyzed. The technology of teaching the pedagogical design of teaching materials and assessing their quality has been developed and tested, which allows forming a design competence and the competence of an expert assessment of the quality of educational materials.
Improving Prediction Accuracy for Power Consumption in Virtual Environments

Modern processors with multi-cores and several power states make power modeling of servers challenging. This issue becomes more complex in virtualized environments, owing to the presence of hypervisors. Requests and instructions as processed at the guest (i.e Virtual Machine, VM) level cannot be straightforwardly related to instructions processed at the host, and this relation varies with changing virtual environment configuration. However, observing performance at both levels, host and guest, might be helpful in developing realistic performance models. Also, the scale of virtual systems in modern computing environments such as data centers and clouds is very large, and these systems have massive data to manage, in terms of their allocation, scheduling, migration, etc. For such heterogeneous systems and large scale data, machine learning (ML) methodologies can play a vital role. Developing power and performance models using effective performance counters at host and guest level can provide significant features for training data. In this research work correlated performance counters for the running applications are monitored and trained for different models. This novel approach of monitoring performance counters at both levels provides in-depth performance information about individual VMs and servers. Results show that estimating power using these models reduces the prediction error, and hence can help in providing more effective power-aware decisions.
Estimating Viewed Images with Natural Language Question Answering from fMRI Data

We propose a model for free-form visual question answering (VQA) from human brain activity. The task of VQA is leading to an answer given an image and a question about the image. Given brain activity data measured by functional magnetic resonance imaging (fMRI) and a natural language question in terms of the viewed image, the proposed method can provide an accurate natural language answer with the VQA algorithm. Visual questions selectively approach various areas of an image such as objects and backgrounds. As a result, a more detailed understanding of the image and complex reasoning are typically needed than general image captioning models. In this paper, we propose a method of answering a given question about a viewed image from fMRI data based on the VQA algorithm. We estimate the relation between fMRI data and visual features extracted from viewed images. Based on the relationship, we convert fMRI data into visual features. Finally, the proposed method can answer to a visual question from fMRI data measured while subjects are viewing images. Experimental results show that the proposed method enables accurate answering for questions about viewed images.
Sentimental Style Transfer in Text with Multigenerative Variational Auto-Encoder

Style transfer is an emerging trend in the fields of deep learning's applications, especially in images and audio data this is proven very useful and sometimes the results are astonishing. Gradually styles of textual data are also being changed in many novel works. This paper focuses on the transfer of the sentimental vibe of a sentence. Given a positive clause, the negative version of that clause or sentence is generated keeping the context same. The opposite is also done with negative sentences. Previously this was a very tough job because the go-to techniques for such tasks such as Recurrent Neural Networks (RNNs) [1] and Long Short-Term Memories(LSTMs) [2] can't perform well with it. But since newer technologies like Generative Adversarial Network(GAN) and Variational AutoEncoder(VAE) are emerging, this work seem to become more and more possible and effective. In this paper, Multi-Genarative Variational Auto-Encoder is employed to transfer sentiment values. Inspite of working with a small dataset, this model proves to be promising.
Question-Worthy Sentence Selection for Question Generation

The problem of automatic question generation from text is of increasing importance due to many useful applications. While deep neural networks achieved success in generating questions from text paragraphs, they mainly focused on a whole paragraph in generating questions, assuming all sentences are question-worthy sentences. However, a text paragraph often contains only a few important sentences that are worthy of asking questions. To that end, we present a feature-based sentence selection method for identifying question-worthy sentences. Such sentences are then used by a sequence-to-sequence (i.e., seq2seq) model to generate questions. Our experiments show that these features significantly improves the question generated by seq2seq models.
Selecting the Right Game Concept for Social Simulation of Real-World Systems

Game theoretical models can be used for social simulation of real-world systems. These models describe the interaction between actors who have to make a decision. Before application of those models, the game concept that describes the situation at hand needs to be selected. Selecting the right game concept is crucial when choosing a model to simulate the process, but not trivial. In this paper we present a taxonomy of game concepts to select a set of suitable models.
An Extension of Prototypical Networks

Meta-learning is playing an increasingly important role in the field of machine learning and artificial intelligence. Few-shot learning, which only exploits a few data to learn different tasks, has been evolved greatly using meta-learning approaches like metric learning, optimization learning and etc. In this paper, we develop Prototypical Networks in two aspects: first one is that we propose a novel feature extractor architecture which not only being trained to learn useful feature but also learn to reduce useless feature. Secondly, we propose a regularization method that tends to increase entropy of output, improving flexibility under the paradigm of few-shot learning. Our experiments show that our extension of prototypical networks outperforms the vanilla version.