Reviewerly/exHarmony huggingface dataset:
A framework for speaker retrieval and identification through unsupervised learning

Speaker recognition is a task of remarkable relevance, with applications in diversified domains. Recently, mainly due to the facilities in audio-visual content acquisition, the capacity of analyzing growing datasets independent of labeled data has become a crucial advantage. This paper presents a speaker recognition approach based on recent unsupervised learning methods, which do not require any labeled data or user intervention. The approach is organized in terms of a framework which exploits a rank-based formulation. The similarity information defined by speaker modeling techniques is encoded in ranked lists, which are used as input by the unsupervised learning algorithms. Vector quantization, Gaussian mixture models and i-vectors are employed as modeling techniques, while the algorithms RL-Sim and ReckNN are used for unsupervised learning tasks. The framework was experimentally evaluated on query-by-example speaker retrieval and speaker identification tasks, both on clean and noisy speech recordings. An experimental evaluation was conducted on three public datasets, different languages, and recordings conditions. Effectiveness gains up to +56% on retrieval measures were obtained through the use of unsupervised learning algorithms over traditional speaker recognition techniques.
A Bayesian Approach to Inter-task Fusion for Speaker Recognition

In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent (among many others) are suppressed. Inter-task fusion, in which such metadata information obtained from automatic systems is used, has been shown to improve speaker recognition performance. In this paper, we explore a Bayesian approach towards inter-task fusion. Speaker similarity score for a test recording is obtained by marginalizing the posterior probability of a speaker. Gender and language probabilities for the test audio are combined with speaker posteriors to obtain a final speaker score. The proposed approach is demonstrated for speaker verification and speaker identification tasks on the NIST SRE 2008 dataset. Relative improvements of up to 10% and 8% are obtained when fusing gender and language information, respectively.
HOG and Gabor Filter Based Pedestrian Detection using Convolutional Neural Networks

Pedestrian detection is an essential research topic due to its major importance especially in the fields of automotive, surveillance and robotics. In spite of having tremendous improvements, pedestrian detection is still an open challenge and searching for more and more accurate algorithms. In the last few years, deep learning more specifically Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection, segmentation, etc. In this paper, we have proposed a pedestrian detection system based on the Gabor filter, Histogram of Oriented Gradient and Convolutional Neural Networks. two mostly used datasets, INRIAPerson and Daimler Mono Pedestrian dataset for pedestrian detection is used for both training and testing. The PennFidanPed dataset is used for testing only. From experimental results, it is shown that we have accomplished comparatively better accuracy close to state of the art approaches.
On the Performance of DIBR Methods When Using Depth Maps from State-of-the-art Stereo Matching Algorithms

In this paper we compare the quality of synthesized views produced by four DIBR methods when fed by depth maps estimated by five state-of-the-art stereo matching algorithms. Also, we compute the correlation between four popular metrics for ranking stereo matching algorithms and two metrics commonly used to evaluate synthesized views (PSNR and SSIM) plus one specific for DIBR. Among our findings, we highlight that (i) PSNR and SSIM have a weak correlation with common stereo matching metrics, (ii) using ground-truth depth does not lead necessarily to the best DIBR result; and (iii) estimated depth maps present artifacts that affect differently DIBR methods.
Convolutional Neural Networks based multi-object recognition from a RGB image

With the flow of time, the application of different kinds of intelligent systems in many sectors like security, medical operations, detecting critical diseases, space researches, industrial heavy works, automated vehicles, and many others are increasing all over the of the world, and an intelligent system works by the use of its ability of image recognition. Furthermore, image recognition has been a notable subject in the scope of digital systems in most of the works we do today. Moreover, high-dimensional data from the physical world is obtained in order to produce statistical or representative knowledge by image recognition. In this study, a mutated image recognition technique has been recommended. For the work, different objects from different images were recognized by using a trained convolutional neural network and also the accuracy of the convolutional neural network was measured to examine the performance of the system. The evaluation of the accuracy of image recognition is the main continuation of this work which was done by modifying the system of updating weights in back-propagation. We used small filters of convolution in our work. We tried in this work to demonstrate that by modifying the weight updating system a vital advancement can be reached for greater efficiency in image recognition and applying it the convolutional neural network. Inception-v3, which is trained for the ImageNet Large Visual Recognition Challenge was used in our work and we found our results to be better. It was also pointed that the correctness of the outcomes from our designed system is far better than the state-of-art standards.
Shot boundary detection based on block-wise principal component analysis

With the rapid development of digital video, shot boundary detection (SBD) has attracted much attention since it is the fundamental preprocessing for video indexing, annotation, retrieval, and other content-based operations. However, most state-of-the-art SBD methods are based on the spatial features of video image, and the overall characteristics of video shots are not fully considered. We propose a feature extraction method based on shot characteristics and a more robust SBD process. First, a video is divided into several segments, the segments containing consecutive video frames inside a shot are considered as training segments and others are called candidate segments. Afterward, using block-wise principal component analysis on the training segments, shot eigenspaces are established. The video frames in the candidate segment are then projected onto the corresponding shot eigenspace to extract the feature vectors. Finally, analysis and pattern matching for feature vectors are performed to extract the video shot boundary. Experiments on TRECVID test data demonstrate that the mean values of F1 in cut transition detection and gradual transition (GT) detection of our method are 0.901 and 0.866, respectively, obviously higher than the values of the compared methods, especially in GT detection, thus providing better accuracy in SBD.
Comparing the Effectiveness of Using Design and Code Measures in Software Faultiness Estimation

Background. Early identification of software modules that are likely to be faulty helps practitioners take timely actions to improve these modules' quality and reduce development costs in the remainder of the development process. To this end, module faultiness estimation models can be built at any point during development by using measures collected up to that time. Models available in later phases are expected to be more accurate than those available in earlier phases. However, waiting until late in the development process may reduce the impact of the effectiveness and efficacy of any software quality improvement actions and increase their cost.
Smart Stadia as Testbeds for Smart Cities: Enriching Fan Experiences and Improving Accessibility

Rapid urbanization is introducing a variety of challenges for municipalities. Technologically-enabled `Smart Cities' are rising to meet these needs, which include smarter sustainability, cybersecurity, transportation, waste management, energy efficiency, education, among many other areas, all toward enhancing the quality of life of citizens. These cities leverage the capabilities of Internet of Things (IoT) technology and the service economy to infuse sensing and information delivery into everyday life through the power of data analytics and artificial intelligence. However, the size of a city and its population make it difficult to evaluate smart solutions. In this work, the `Smart Stadium' is proposed as a testbed to trial Smart City technologies in an environment small enough to facilitate user trials yet large enough to produce scalable outcomes. Our previous proof-of-concepts are summarized to highlight the potential of this approach. We also present a more recent project on accessibility and inclusion using virtual reality with preliminary results.
A Deep Neural Network Based Maneuvering-target Tracking Algorithm

In the field of maneuvering-target tracking (MTT), the targets with changeable and uncertain maneuvering movements cannot be tracked precisely because there always exist time delays of maneuvering model estimation with traditional MT-T algorithms. To solve this problem, we propose a deep MTT (DeepMTT) algorithm based on a deep neural network, which can quickly track maneuvering targets once it has been well trained by abundant off-line trajectory data from existent ma-neuvering targets. To this end, we first build a Large-scale trajectory database to offer abundant off-line trajectory data for network training. Second, the DeepMTT algorithm is developed based on a deep neural network, which consists of three bidirectional long short-term memory layers, a filtering layer, a maxout layer and a linear output layer. The simulation results verify that our DeepMTT algorithm outperforms other state-of-the-art MTT algorithms.
MIFT: A Moment-Based Local Feature Extraction Algorithm

We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature transform algorithm based on the modified discrete Gaussian-Hermite moment (MDGHM). Taking advantage of MDGHM’s high performance to represent image information, MIFT uses an MDGHM-based pyramid for feature extraction, which can extract more distinctive extrema than the DoG, and MDGHM-based magnitude and orientation for feature description. We compared the proposed MIFT method performance with current best practice methods for six image deformation types, and confirmed that MIFT matching accuracy was superior of other SIFT-based methods.
Investigating the limits of rely/guarantee relations based on a concurrent garbage collector example

Abstract Decomposing the design (or documentation) of large systems is a practical necessity but finding compositional development methods for concurrent software is technically challenging. This paper includes the development of a difficult example in order to draw out lessons about such methods. The concurrent garbage collector development is interesting in several ways; in particular, the final step of its development appears to be just beyond what can be expressed by rely/guarantee relations. This prompts an exploration of the limitations of this well-known method. Although the rely/guarantee approach is used, most of the lessons are more general.
Hand Graph Representations for Unsupervised Segmentation of Complex Activities

Analysis of hand skeleton data can be used to understand patterns in manipulation and assembly tasks. This paper introduces a graph-based representation of hand skeleton data and proposes a method to perform unsupervised temporal segmentation of a sequence of sub-tasks in order to evaluate the efficiency of an assembly task. We explore the properties of different choices of hand graphs and their spectral decomposition. A comparative performance of these graphs is presented in the context of complex activity segmentation. We show that the spectral graph features extracted from 2D hand motion data outperform the direct use of motion vectors as features. We also make the collected hand position data available to the research community to facilitate further development in this direction.
Elucidation of essential maps for 4Cs and information-media literacy

Curriculum development is a dynamic process of iterating multi-literacy which inspires the creation of knowledge and enable to prepare students to meet the challenges of the modern world. Driven by societal imperative and contextual relevance, ideally, the 21st century learning skills and information-media literacy skill shall provide the backbone of Science teaching. Thus, this Action Research aims, to elucidate the integration of 4Cs (communication, collaboration, critical thinking and creativity) as well as to trace the development of information-media literacy and skills through essential mapping technique in order to support the ongoing implementation of the recently crafted Junior High School Science curriculum of the Philippines. As a result, learning activities promoting 4Cs are limited to: performing scientific investigations; role playing; chart & poster making; improvisation & demonstration activity; story making; role playing; multi-media & visual presentation; and simulation activity. The information-media literacy can be developed by the following learning strategies: multimedia presentation; group reporting; performing scientific investigations & experimentations; debating activity; modelling, role playing & simulation activity; portfolio making; improvisation & model-making activity; concept making; and diagram making & illustration activity. Problems such as the platforms, the teacher's socio-economic status, tedious teacher's tasks, and lacking support from the community & other stakeholders affect the implementation of the curriculum framework.
Cloud-assisted Two-Factor Protection Mechanism for Public Data in Smart Campus

Two-factor data security protection in cloud environments has become a research hotspot in recent years. With the rapid development of cloud computing, how to achieve the two-factor protection of public data resources in the cloud is a serious problem to be tackled. Furthermore, how to achieve both security and revocability is a challenge for two-factor too. In this paper, we propose a cloud-assisted two-factor protection mechanism with factor revocability for public data in smart campus. The proposed mechanism allows a data owner to send an encrypted message to user through a cloud. The data owner is required to formulate access policies and perform attribute-based encryption on messages. In order to decrypt, the user needs to possess two factors. The first factor is user's secret key. The second factor security equipment is a campus card in this paper. The ciphertext can be decrypted if and only if the user gathers the key and the security equipment at the same time. Analysis and experimental results demonstrate that the proposed mechanism is secure, efficient and practical.
A Privacy-preserving Diffusion Strategy over Multitask Networks

We develop a privacy-preserving distributed strategy over multitask diffusion networks, where each agent is interested in not only improving its local inference performance via in-network cooperation, but also protecting its own individual task against privacy leakage. In the proposed strategy, at each time instant, each agent sends a noisy estimate, which is its local intermediate estimate corrupted by a zero-mean additive noise, to its neighboring agents. We derive a sufficient condition to determine the amount of noise to add to each agent's intermediate estimate to achieve an optimal trade-off between the steady-state network mean-square-deviation and an inference privacy constraint. We show that the proposed noise powers are bounded and convergent, which leads to mean-square convergence of the proposed privacy-preserving multitask diffusion scheme. Simulation results demonstrate that the proposed strategy is able to balance the trade-off between estimation accuracy and privacy preservation.
Neuro-Fuzzy classifier for longitudinal behavioral intervention data

Fuzzy-logic based algorithms have been applied in learning longitudinal behavioral intervention data. This paper proposes a modified generalized network-based neuro-fuzzy (mGNNF) classifier for longitudinal randomized controlled trial (RCT) data with missing values. Specifically, using all available attributes, demographic, nicotine dependence, and intervention attributes, this proposed classifier is used to predict the prolonged smoking abstinence after a longitudinal smoking cessation RCT [1]. Our model comparison study shows that with the same longitudinal RCT data, mGNNF shows a higher accuracy compared to three similar fuzzy-logic based classifiers, although its computational time is slower than two of these comparators.
A Preliminary Comparison of Using Variability Modeling Approaches to Represent Experiment Families

Background: Replication is essential to build knowledge in empirical science. Experiment replications reported in the software engineering context present variabilities on their design elements, e.g., variables, materials. The understanding of these variabilities is required to plan experimental replications within a research program. However, the lack of an explicit representation of experiments' variabilities and commonalities is likely to hamper their understanding and replication planning. Aims: The goal of this paper is to explore the use of Variability Modeling Approaches (VMAs) to represent experiment families (i.e., an original study and its replications) and to investigate the feasibility of using VMAs to support experiment replication planning. Method: We selected two experiment families, analyzed their commonalities and variabilities, and represented them using a set of well-known VMAs: Feature Model, Decision Model, and Orthogonal Variability Model. Based on the resulting models, we conducted a preliminary comparison of using such alternative VMAs to support replication planning. Results: Subjects were able to plan consistent experiment replications with the VMAs as support. Additionally, through a qualitative analysis, we identified and discuss advantages and limitations of using the VMAs. Conclusions: It is feasible to represent experiment families and to plan replications using VMAs. Based on our emerging results, we conclude that the Feature Model VMA provides the most suitable representation. Furthermore, we identified benefits in a potential merge between the Feature Model and Decision Model VMAs to provide more details to support replication planning.
ADMM-based Bipartite Graph Approximation

Because the spectrum folding phenomenon affects the down-sampling of graph signals, both critically sampled and oversampled graph filter banks with down-sampling operations can only be applied to bipartite graphs. However, general graph signals may not reside on bipartite graph structures. In this paper, we present a novel bipartite graph approximation algorithm, which aims to find a bipartite graph sufficiently close to the original graph. To tackle this problem, we first show that, if the non-negativity constraint of adjacency matrices is removed, closed-form solutions can be readily obtained by the eigenvalue decomposition. Based on this fact, an alternating direction method of multipliers (ADMM) is further developed to achieve a real adjacency matrix. Experimental results show that the proposed algorithm outperforms the other proposals in terms of approximation accuracy.
A Phrase-Based Machine Translation from English to Bangla Using Rule-Based Approach

In this paper, a model of transfer architecture has been proposed which represents a Rule-Based Approach. This approach relies on the fuzzy rules. It is a tense and phrase based English to Bangla transfer system. This article represents a knowledge-based technique with a set of data. A rough set technique is used in knowledge representation system for language translation. This technique is used to categorize each English sentence to a particular group using attributes and organized in a pattern. The pattern arranges according to the rules then the system will produce the target sentence Bangla. The whole procedure completes with 6 steps: 1) Collect data 2) Tokenized by word 3) Arrange according to rules 4) Morphological Analyze 5) Reconstruct Bangla sentence using appropriate rule 6) Target sentence. Comparing the experimental result with Google translator, it has been found that the model translation system provides higher accuracy then comparing translator.
Deep learning algorithm for data classification with hyperparameter optimization method

The availability of data is an important thing in the decision-making process. With the trend of using Machine Learning and Data Mining, humans are facilitated in learning and finding hidden knowledge in the data. One technique used in the classification of data is the Deep Learning method. Deep Learning is widely used because of its ability to solve problems with satisfying results. In other words, Deep Learning has shown to be the one of the most powerful and widely used methods. However, the key of success depends on the setting the right parameters. With this reason, we need a way to optimize the use of hyperparameter in Deep Learning. The method commonly used in this case is Grid Search. However, this method requires a long time and high computation. While other methods such as Random Search and Bayesian Optimization, both are very reliable and efficient compared to Grid Search. The results of the experiments conducted in this study indicate that Bayesian Optimization is better than Random Search in performing hyperparameter optimization processes in Deep Learning.
Implementation of Security Enhanced Image Steganography with the Incorporation of Modified RSA Algorithm

In this paper, security enhanced image steganography is described. To enhance the security, cryptographic algorithm, Modified RSA in its symmetric form, is used to encrypt the message into cipher text and vice versa. The sender will encrypt the message with a key and then embed the cipher text into an image and send the image object via any transmission medium like internet along with the key to the receiver, so that the receiver can extract the cipher text from the image and then decrypt by the key that the sender have sent. The result was satisfactory and fulfilled the purpose but there arose many major issues like, visibility of the cipher pixel in case for lower resolution image; data corruption of the cipher text due to noise; limitation of grayscale image only; sender have to send a couple of image for comparison of the pixel data etc. However, noise problem have been challenged with the help of median filter but still there remains many scope to establish this algorithm into an efficient and optimized one.