Reviewerly/exHarmony huggingface dataset:
Improving Audiovisual Content Annotation Through a Semi-automated Process Based on Deep Learning

Over the last years, Deep Learning has become one of the most popular research fields of Artificial Intelligence. Several approaches have been developed to address conventional challenges of AI. In computer vision, these methods provide the means to solve tasks like image classification, object identification and extraction of features. In this paper, some approaches to face detection and recognition are presented and analyzed, in order to identify the one with the best performance. The main objective is to automate the annotation of a large dataset and to avoid the costy and time-consuming process of content annotation. The approach follows the concept of incremental learning and a R-CNN model was implemented. Tests were conducted with the objective of detecting and recognizing one personality within image and video content. Results coming from this initial automatic process are then made available to an auxiliary tool that enables further validation of the annotations prior to uploading them to the archive. Tests show that, even with a small size dataset, the results obtained are satisfactory.
On evaluating the resource usage effectiveness of multi-tenant cloud storage

As multi-tenant cloud storage services are becoming increasingly popular in commercial clouds, there is a growing need for evaluating their resource usage effectiveness. Although tail latency and resource utilization are the most important and popular performance metrics, it is biased to use only one of them on evaluating the resource usage effectiveness. In this paper, we present a unified framework, Stochastic Model-based Effectiveness Analyzer (SMEA), to evaluate the resource usage effectiveness of multi-tenant cloud storage systems. Instead of evaluating tail latency and resource utilization separately, we propose a new metric called resource-productivity to combine both performance. Since evaluating tail latency or resource utilization solely is challenging due to contention, queueing and burstiness of workloads, a Markov-modulated Poisson process (MMPP) is adopted in SMEA to properly characterize the behavior of workloads. Moreover, the Stochastic Network Calculus (SNC)-based network latency analysis model is extended to evaluate the end-to-end tail latency over all system components, with the principle “treating the computer as a network”. After obtaining the analyzed tail latency and resource utilization, we derive the resource-productivity mathematically. We have implemented SMEA on a small-scale testbed. Extensive trace-driven experiments demonstrate that SMEA is efficient in evaluating the resource usage effectiveness of the multi-tenant cloud storage system with relative error less than 13%.
Effects of Input Addition in Learning for Adaptive Games: Towards Learning with Structural Changes

Adaptive Games (AG) involve a controller agent that continuously feeds from player actions and game state to tweak a set of game parameters in order to maintain or achieve an objective function such as the flow measure defined by Csíkszentmihályi. This can be considered a Reinforcement Learning (RL) situation, so that classical Machine Learning (ML) approaches can be used. On the other hand, many games naturally exhibit an incremental gameplay where new actions and elements are introduced or removed progressively to enhance player’s learning curve or to introduce variety within the game. This makes the RL situation unusual because the controller agent input/output signature can change over the course of learning. In this paper, we get interested in this unusual “protean” learning situation (PL). In particular, we assess how the learner can rely on its past shapes and experience to keep improving among signature changes without needing to restart the learning from scratch on each change. We first develop a rigorous formalization of the PL problem. Then, we address the first elementary signature change: “input addition”, with Recurrent Neural Networks (RNNs) in an idealized PL situation. As a first result, we find that it is possible to benefit from prior learning in RNNs even if the past controller agent signature has less inputs. The use of PL in AG thus remains encouraged. Investigating output addition, input/output removal and translating these results to generic PL will be part of future works.
Digital Watermarking Using Discrete Wavelet Transform and Eigen Decomposition

In this paper, discrete wavelet transform (DWT) is applied on an RGB image and then eigen decomposition is done on original L-L (low-low) components. Similar operation is done on a watermark image. Then two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (IDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered image and got perfect cross-correlation. We also compare the model of the paper with three previous works in context of processing time and cross-correlation under noisy environment and found better results.
Bayesian Classification of Personal Histories - An application to the Obesity Epidemic

Bayesian classifiers are an important tool in machine learning. The Naive Bayes classifier in particular has shown itself to be a robust, computationally efficient and transparent approximation. However, due to its strong assumption of feature independence it is often too easily discarded. Understanding under what circumstances feature correlations are likely to occur and how to diagnose them is an important first step in improving the Naive Bayes approximation. Furthermore, combining features can lead to enhanced approximations. In this paper we show the benefits of a Generalised Bayes Approximation that accounts for feature correlations while, at the same time, showing that an important area where feature correlations are ubiquitous is in time series that represent aspects of human behaviour. Using data that represent historical patterns of exercise we show how more predictive and more insightful models for obesity can be constructed using a Generalised Bayes approximation that combines historical features, thereby capturing the idea of human habits. In particular, by analysing feature combinations we show that abandoning good past exercise habits is more correlated with obesity than never having had them in the first place.
A History-based Stopping Criterion in Recursive Bayesian State Estimation

In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost. In this paper, we (a) propose a criterion based on a linear combination of state posterior and its changes, (b) show that for discrete-valued state estimation scenarios the proposed objective is more likely to sort correct and incorrect estimates appropriately compared to just looking at the posterior, and finally (c) demonstrate that the method can lead to significant human intent estimation speed increase without significant loss of accuracy in a brain-computer interface application.
Multi-Resource Continuous Allocation Model for Cloud Services

Cloud service providers deliver a discrete combination of computer resources on demand with pay-as-you-go pricing. However, the discrete combination of computer resources complicates the pricing model, and users often face difficulties in comparing, identifying, and finally selecting a cloud service provider. In an effort to simplify the ever-increasing level of pricing complexity, we propose the continuous allocation model. In this model, a cloud service provider advertises continuous resource sizes while preserving discrete resource allocation. Quantization is used to map between the infinite advertised request sizes and the small number of physical quantized sizes. We use simulation to validate the multi-resource continuous allocation model for cloud services.
FRvarPSO: A Method for Obtaining Fuzzy Classification Rules Using Optimization Techniques

FRvarPSO is a new method for obtaining classification rules, which operates on nominal and/or fuzzy attributes. It combines LVQ, which is a supervised learning neural network. The search is performed through an optimization technique such as varPSO, considered a metaheuristic based on particles clusters of variable population. Each individual represents a possible solution to the problem. The proposed method uses a voting criterion, which affects the particle’s speed. This method is benchmarked against PART and C4.5, on 12 databases of the UCI repository and three databases of financial institutions from Ecuador. The results obtained were satisfactory.
Domain-invariant Adaptation Exploiting Statistical and Geometrical Properties

Domain adaptation aims to learn robust classifiers from a source data that performs well on the target data. In this paper, we propose a novel unsupervised domain adaptation method for object recognition and handwritten digit recognition applications. Our method learns two projections matrices that project the source and target domains into different low dimensional subspaces exploiting statistical and geometrical properties of data and learns a domain-invariant classifier by structural risk minimization. Extensive experiments demonstrate that our proposed method shows significant improvements in classification accuracy compared to state-of-the-art methods.
Classifying Recurrent Dynamics on Emotional Speech Signals

The current study was an attempt to understand the dynamics of emotional speech signals. The nonlinear dynamics of two speech signals having two different emotions were investigated using recurrence analysis. First, two types of speech signals were collected when the subject spoke eight sentences in the Bengali language in angry and normal emotion. The recurrence plot and the phase space plot were constructed for each signal and recurrence-based features, namely %isometry and %consecutive isometry were extracted. A marked distinction was visually observed between the normal and the angry emotion-based signals from the recurrence and the phase space plots. The variation was further confirmed from the values of the recurrence parameters. The normal emotion exhibited higher %isometry and %consecutive isometry as compared to the angry emotion. The robustness of the recurrence-based method for classifying the emotional states was established from the consistency of the results provided by the recurrence parameters in both noise-free and noisy environments.
Direct Estimation of Weights and Efficient Training of Deep Neural Networks without SGD

We argue that learning a hierarchy of features in a hierarchical dataset requires lower layers to approach convergence faster than layers above them. We show that, if this assumption holds, we can analytically approximate the outcome of stochastic gradient descent (SGD) for each layer. We find that the weights should converge to a class-based PCA, with some weights in every layer dedicated to principal components of each label class. The class-based PCA allows us to train layers directly, without SGD, often leading to a dramatic decrease in training complexity. We demonstrate the effectiveness of this by using our results to replace one and two convolutional layers in networks trained on MNIST, CIFAR10 and CIFAR100 datasets, showing that our method achieves performance superior or comparable to similar architectures trained using SGD.
An unsupervised method for automatic validation of verbal phraseological units

In this paper we present an unsupervised technique for validating the existence of verbal phraseological units in raw text. This technique employs the concept of internal and contextual attraction which basically considers a mathematical formula based on co-occurrence of terms inside and outside of the terms considered to be part of a verbal phraseological unit. The experiments carried out using a corpus of news stories report a 60% of accuracy, which highlights the challenging task of automatic validation of verbal phraseological units in raw texts.
Token-wise Training for Attention Based End-to-end Speech Recognition

In attention based end-to-end (A-E2E) speech recognition systems, the dependency between output tokens is typically formulated as an input-output mapping in decoder. Due to such dependency, decoding errors can easily propagate along output sequence. In this paper, we propose a token-wise training (TWT) method for A-E2E models. The new method is flexible and can be combined with a variety of loss functions. Applying TWT to multiple hypotheses, we propose a novel TWT in beam (TWTiB) training scheme. Trained on the benchmark Switchboard (SWBD) 300h corpus, TWTiB outperforms the previous best training scheme on the SWBD evaluation subset.
Optimization of Multimodal Benchmark Functions Using Fish Cooperative Hunting Behaviors

The rising complexity of real-world problems has inspired researchers to search for effective problem-solving techniques. Swarm intelligence and evolutionary computation techniques are outstanding examples that nature has been an unending source of motivation. Bio-inspired optimization algorithms stem from the major field of computational Intelligence (CI) domains that adopt their working principle from numerous natural phenomena particularly aimed at optimization. Recently introduced Whale Optimization Algorithm (WOA) is motivated by the special hunting mechanism of humpback type whales. The performance of WOA is very promising but the robustness and convergence need further enhancement. In this study, authors modified the original Whale Optimization Algorithm (WOA) by incorporating the `step equation' of Artificial Fish Swarm Algorithm (AFSA) to improve the convergence and robustness of the standard whale algorithm and tested on three popular multimodal benchmark functions (Schwefel, Rastrigin and Griewank). Finally, authors provide few open proposals with a certain concern of being answered in future.
Enhancing Facial Component Analysis

Capturing non-verbal information from a face is the most important phase in emotion recognition through facial expression. In order to do that, it needs to find suitable features extraction method that could help to figure out the meaning from the given face. Inspired by Liliana Dewi[1] that used a geometric feature for analyzing facial component with a good result accuracy around 98.59%. So this paper tries to improve that method in order to enhance the result of facial components analysis. This study use Hellen dataset to retrain the Active Appearance Model (AAM), add image pre-processing like image enhancement, use other geometric features and Fuzzy Rule-Based System for extract the feature on extended Cohn Kanade (CK+) dataset. The result of this research gives better in recognizing facial components than previous work with accuracy around 99.36%.
The Reflection and Outlook on the Study of Teachers’ IT-Based Teaching Ability

As a hot topic in the field of education, the study of teachers' IT-based teaching ability concerns various perspectives such as theoretical paradigm, practical approach, scholarship of teaching and so on.This paper analyzes the problems existing in the development of teachers' IT-based teaching ability.It is suggested that the improvement of IT-based teaching ability should be promoted from three aspects of school, teachers themselves and classroom teaching.
Non-local quantum functions and the distributed Deutsch-Jozsa algorithm

Non-local implementations of quantum gates are a vital part of quantum networks. We find an optimal non-local implementation of quantum functions, the quantum gate equivalent of a switch statement. Then, we apply this result to the Deutsch-Jozsa problem, obtaining a distributed Deutsch-Jozsa algorithm and we show the relative efficiency improvement. As an application, we find a non-cooperative game based upon the original Deutsch-Jozsa problem where a classical agent has at most a 50% probability of winning, while a quantum agent can win every time.
What To Do First: Ranking The Mission Impact Graph for Effective Mission Assurance

Network attacks continue to pose threats to missions in cyber space. To prevent critical missions from getting impacted or minimize the possibility of mission impact, active cyber defense is very important. Mission impact graph is a graphical model that enables mission impact assessment and shows how missions can be possibly impacted by cyber attacks. Although the mission impact graph provides valuable information, it is still very difficult for human analysts to comprehend due to its size and complexity. Especially when given limited resources, human analysts cannot easily decide which security measures to take first with respect to mission assurance. Therefore, this paper proposes to apply a ranking algorithm towards the mission impact graph so that the huge amount of information can be prioritized. The actionable conditions that can be managed by security admins are ranked with numeric values. The rank enables efficient utilization of limited resources and provides guidance for taking security countermeasures.
Stochastic Synthesizer Patch Exploration in Edisyn

Edisyn is a music synthesizer program (or "patch") editor library which enables musicians to easily edit and manipulate a variety of difficult-to-program synthesizers. Edisyn sports a first-in-class set of tools designed to help explore the parameterized space of synthesizer patches without needing to directly edit the parameters. This paper discusses the most sophisticated of these tools, Edisyn's Hill-Climber and Constrictor methods, which are based on interactive evolutionary computation techniques. The paper discusses the special difficulties encountered in programming synthesizers, the motivation behind these techniques, and their design. It then evaluates them in an experiment with novice synthesizer users, and concludes with additional observations regarding utility and efficacy.
Modernization and Development of Arts Education: Spiritual and Worldview Alternative

In the field of artistic education, the issue of modernization is considered in different contexts. In this chapter, the modernization of artistic education is considered as improvement, advancing, updating, and changes in accordance with the requirements of time and the macro-process of transition from the traditional view of education to innovation and its relationship with spiritual-ideological traditions. The main purpose of the modernization of artistic education is to improve the quality of the system through transformations and enhance its ability to respond adequately to the challenges of the time. The main challenge is globalization. Two philosophical concepts are considered—the concept of anthropological pedagogy and the concept of living knowledge. The basis for the modernization of the content of artistic education is a synergistic approach, which involves the integration of scientific knowledge, in particular, natural sciences and humanities. The task of artistic pedagogy is to establish a holistic view of a person, and it cannot be compensated at the objective level by traditional technologies. Integrity is a measure of spirituality, humanity, and the creative fiction of education. In the content of modern artistic education, special values are acquired by integrated special courses designed to guide future specialists in the specificity of the development of the individual’s spiritual potential, on the value basis to formulate conceptual positions aimed at preserving and enhancing spiritual and ideological values.
Group Support Systems: Experiments with an Online System and Implications for Same-Time/Different-Places Working

We present an analysis of the Group Explorer Group Support System (GSS) from the perspective of its implementation as technology that can support same-time/different-places group workshops. The purpose of the chapter is to report on our experiences with using a same-time/different-places GSS, introduce issues that arise from these experiences, and discuss future prospects. The chapter commences by reviewing our current understanding of GSS and how they support the use of distributed Problem Structuring Methods (PSMs) in both single-organization and multi-organization settings. The configuration and use of a cloud-based online version of the GSS is presented that highlights some of the key technological, organizational, and facilitation issues involved in supporting distributed PSM workshops. The future development of such online GSS is discussed with a particular focus on two emerging research questions: the future role of the facilitator in online GSS and the commonalities between online GSS and social media platforms as different-times/different-places group working, such as crowdsourcing, becomes prevalent in the context of increasing globalization and the ongoing decentralization of work environments.
Prototype of NFC Reader as a Attendance Sign at The Presence System

Presence processes to mark the attendance of an individual in an activity can be manipulated in various ways. Many of researches have been done to overcome the problem including the use of Student Identity Card (KTM), barcode, fingerprint, Near Field Communication (NFC) and biometric. Implementation of those technologies requires customization of a running system or creating a new system. This research will make NFC Reader connected to a computer via USB without having to change the running system. The research method uses prototyping to produce a system that suits the needs and desires of the user. This research produced some prototype results from previous repair process. The microcontroller needed is one that supports USB-Hosts such as Arduino Duemilanove, Leonardo or Micro. Tools can work as expected after using an alternative library. The test results show the NFC Reader prototype can read NFC tag in less than 1 second with various types of laptops. The hardware specification and type of laptop operating system has no significant effect on the performance of NFC Reader with an average start up time of about 17 seconds before it is ready to use.
A text sentiment analysis model based on self-attention mechanism

This paper focuses on the problem of text sentiment analysis. The task of sentiment analysis is to extract structured and valuable information from the text data that people express on various platforms. The field of sentiment analysis is attracting more and more attention from researchers. In recent years, due to the continuous development of deep learning theory, there have been many researches on the application of neural network model in sentiment analysis task. Sentiment analysis can be understood as the process of dividing text into different types according to the sentiment information expressed in the text. In this paper, we introduce the self-attention mechanism into sentiment analysis and propose a neural network model based on multi-head self-attention mechanism. In order to better capture the effective information in the text, we combine bidirectional GRU in our model. We evaluate our method on the dataset IMDB (Internet Movie Database). Experimental results show that the accuracy of the proposed model achieves 90.0 on the test dataset. It illuminates that our model outperforms other models. Our model can extract information more effectively in the task of sentiment analysis.