Reviewerly/exHarmony huggingface dataset:
Joint Optimization of Classification and Clustering for Deep Speaker Embedding

This paper proposes a method to train deep speaker embed-dings end-to-end that jointly optimizes classification and clustering. A large margin softmax loss is used to reduce classification errors. A novel large margin Gaussian mixture loss is proposed to improve clustering. With the joint optimization, the learned embeddings capture segment-level acoustic representation from variable-length speech segments to discriminate between speakers and to replicate densities of speaker clusters. We compare performance with alternative methods on large-scale text-independent speaker recognition dataset VoxCeleb1 [1] and observe that it outperforms those methods significantly, achieving new state-of-the-art results on the dataset. Moreover, because of the joint optimization, this method exhibits faster and better convergence than using classification loss alone. Our results suggest great potential of joint optimization of classification and clustering for speaker verification and identification.
Remembranza Pills: Using Alexa to Remind the Daily Medicine Doses to Elderly

European countries are facing a population widespread ageing. The percentage of third age inhabitants is becoming higher, specially in rural areas, where older adults demand special services in order to improve their life, notably when they live alone. This way, some tasks like medicine management can be a very hard day-to-day challenge. In this paper, Remembranza Pills is introduced. This is a platform that supports elderly at daily medicine takes using Alexa. The system reminds users the medicines and pills that have to be taken in each moment of the day using a web platform to manage the prescriptions. In this manner, medical staff and relatives can keep control over consumed doses.
A portable projection mapping device for medical augmented reality in single-stage cranioplasty

We introduce a portable projection mapping device (PPMD) for extracting and projecting implant geometric data in single-stage cranioplasty surgery. Our system consists of a real-time tracking device, which tracks the reference markers fixed on the skull, and a digitizing probe for localizing the contour of the cranial defect for modifying the oversized customized cranial implant (CCI). The feature points on the surface of the skull are registered to the reconstructed surface model of the skull obtained from CT image. The CT model is then projected on the surface of the skull. The digitizer is used to outline the geometry of the skull resection with sub-millimeter tracking accuracy. The proposed PPMD and its associated pipeline enable intraoperative cranial implant modification with image feedback to surgeons for the very first time. Our proposed PPMD system, an inexpensive device can also be used for other medical augmented reality applications, especially when direct projection mapping is preferred over the use of wearable head-mounted displays.
Efficient Recoverable Cryptographic Mosaic Technique by Permutations

Mosaic is a popular approach to provide privacy of data and image. However, the existing demosaicing techniques cannot accomplish efficient perfect-reconstruction. If the receiver wants to recover the original image, the extra transmission of the original subimage to be mosaicked is necessary, which consumes much channel resource and is therefore inefficient. In this paper, we propose a novel efficient recoverable cryptographic mosaic technique by permutations. A mosaic, or a privacy-protected subimage, can be constructed through either of the three permutations (Busch's, Wu's, and Sun's/Minmax). These three permutations are designed to maximize the objective function as the sum of the absolute row/column index-differences. This objective is related to the sum of the pixel-to-pixel cross-correlation by our pertinent theoretical study. To measure the effectiveness of the image-mosaicing methods, we propose two image-discrepancy measures, namely summed cross-correlation (SCC) and Kullback-Leibler divergence of discrete cosine transform (DCT-KLD). Compared to the big majority of random permutations for image-mosaicing, our proposed three permutation methods can achieve much better performances in terms of SCC. Nevertheless, the advantage of the three proposed permutation methods over random permutations is not obvious according to DCT-KLD.
Remote Sensing Image Classification Based on AlexNet Network Model

With the continuous migration and development of remote sensing data application scenarios, the problem of low automation of data and the poor timeliness of information extraction makes it difficult to meet the real-time detection, such as fire monitoring, flying object reconnaissance and so on. With great research, the author select a new method of remote sensing image classification based on AlexNet network model which can shorten training time and improved classification accuracy. The paper used UCM spaceflight remote sensing object detection dataset. Experiments are carried out on the improved network model using the training method of weight transfer. The experimental results show that the improved network model and the training method of weight transfer can improve the classification accuracy by 27.9% without increasing the training time.
Enhanced clustering models with wiki-based k-nearest neighbors-based representation for web search result clustering

Information retrieval is a difficult process due to the overabundance of information on the web. Nowadays, search result responds to user queries with too many results although only a few are relevant. Therefore, the existing clustering methods that fail in clustering snippets (short texts) of web documents due to the low frequencies of document terms should be deeply investigated. One of the approaches that can be used to solve this problem is the expansion of document terms with semantically similar terms. Hence, a list of terms with their closest and accurate semantically similar words (word representation) must be built. This study aims to design and develop a new framework to enhance the performance of web search result clustering (WSRC). The research also presents a new unsupervised distributed word representation scheme where each word is represented by a vector of its semantically related words; such as scheme expands snippets and user queries. The proposed framework consists of several activities, such as (1) various standard datasets (Open Directory Project [ODP]-239 and MORESQUE) that are used for evaluating search result clustering algorithms for most cited dataset works, (2) text pre-processing, (3) document representation based on a new wiki-based k-nearest neighbors (KNN) representation method, (4) effect of the proposed model on the performance of traditional clustering methods (k-means, k-medoids, single-linkage, and complete-linkage) for WSRC, and (5) evaluation stage of the proposed method. Results indicate that enhanced clustering methods, according to the new wiki-KNN based representation method in comparison with the baseline methods, show a significant improvement in WSRC. Furthermore, the new data representation scheme has enhanced the overall performance of clustering methods.
Online Batch Normalization Adaptation for Automatic Speech Recognition

Deep Neural Network (DNN) acoustic models are sensitive to the mismatch between training and testing environments. When a trained model is tested on unseen speakers, domain, or environment, recognition accuracy can degrade substantially. In such a case, offline adaptation with a fair amount of field data can improve recognition accuracy significantly, and is commonly applied to ASR systems in practice. Ideally, such kind of adaptation should be done online as well in order to catch any unexpected dynamic changes in the environments during the inference process. However, online adaptation is subject to strict constraints on computational cost. On the other hand, the small amount of available data and the nature of unsupervised adaptation make online adaptation a very challenging task, especially for DNN acoustic models which normally contain millions of parameters. In this paper, we introduce a simple and effective online adaptation technique to compensate training and testing mismatch for DNN acoustic models. It is done via online adaptation of the parameters associated with the batch normalization applied to the model training process. Our results show that this technique can improve accuracy significantly in a domain mismatched scenario for different DNN architectures.
Secure Naïve Bayes Classification Protocol over Encrypted Data Using Fully Homomorphic Encryption

Machine learning classification has a wide range of applications. In the big data era, a client may want to outsource classification tasks to reduce the computational burden at the client. Meanwhile, an entity may want to provide a classification model and classification services to such clients. However, applications such as medical diagnosis require sensitive data that both parties may not want to reveal. Fully homomorphic encryption (FHE) enables secure computation over encrypted data without decryption. By applying FHE, classification can be outsourced to a cloud without revealing any data. However, existing studies on classification over FHE do not achieve the scenario of outsourcing classification to a cloud while preserving the privacy of the classification model, client's data and result. In this work, we apply FHE to a naïve Bayes classifier and, to the best of our knowledge, propose the first concrete secure classification protocol that satisfies the above scenario.
A Cost-Sensitive Centroid-based Differential Evolution Classification Algorithm applied to Cancer Data Sets

Nowadays, the collected or generated data for some real-life applications such as in the Medical domain and Intrusion Detection, are typically imbalanced. Imbalanced data sets consist of data where one class-label (minority) includes significantly fewer instances compared to other class labels. The misclassification of the minority class-label could be costly in some circumstances. Therefore, the extraction of valuable information from this kind of data poses a challenge to the scientific community. During the last decades, the researchers proposed a centroid-based classification algorithm using differential evolution (CDE) to solve data classification. However, CDE shows an inefficient performance especially when applied to imbalanced binary data sets. In this paper, we propose a cost-sensitive version of CDE based on a new objective function in order to overcome this drawback. We are using four cancer data sets that are imbalanced namely Breast, Lung, Uterus, and Stomach. Furthermore, we analyzed and investigated the performance of our proposed version of CDE for predicting the survivability of cancer patients compared to the performance of the current variants of CDE. Moreover, we compared the performance of our proposed version of CDE with the performance of five cost-sensitive machine learning algorithms. The experimental results demonstrate that our proposed version of CDE improves the performance of CDE when applied to imbalanced binary data sets. Furthermore, the performance of our proposed CDE algorithm outperformed the performance of the current variants of CDE on all data sets in terms of Area Under Curve and G-mean.
Improving the Efficiency of Ensemble Classifier Adaptive Random Forest with Meta Level Learning for Real-Time Data Streams

New challenges have emerged in data mining as the traditional techniques have floundered with real-time data streams. The traditional technique needs refurbishing so as to acclimatize with concept drifting data streams. Thus dealing with the concept changes is the most imperative task of stream data mining. Ensemble classifiers have the ability to automatically adapt with the incoming drifts and, therefore, it is the most interesting research area in data stream mining. Bagging, Boosting and Random forest generation are the common ensemble techniques and are the most popular machine learning approaches in the current scenario for static data (Gomes HM, Bifet A, Read J, Barddal JP, Enembreck F, Pfharinger B, Abdessalem T (2017) Adaptive random forests for evolving data stream classification. Mach Learn 106(9–10):469–1495, [1]). A large number of base classifiers in an ensemble can cause computational overhead. Data mining classifiers for real-time data streams, therefore, need to be updated constantly and retrained with the labeled instances of the newly arrived novel classes in data streams and to cope with concept drift; otherwise, the mining models will become less and less accurate as time passes by. However, for data streams, adaptive random forest algorithms have been widely used for ensemble generation due to its competence to handle different types of drifts. This paper proposes a modified adaptive random forest with meta level learner algorithm and concept adaptive very fast decision tree to overcome the concept drift problem in real-time data streams. The proposed algorithm is experimentally compared with state-of-the-art adaptive random forest algorithm on several real synthetic datasets. Results indicate its efficiency in terms of accuracy and processing time.
Dual Imaging–Can Virtual Be Better Than Real?

In this paper, a dual imaging technique is used for high-precision reconstruction of an observed 3D scene. In contrast to stereo vision, dual imaging systems use a camera and a projector instead of a camera pair. We propose a multiresolution approach based on the sum-to-one transform, coupled with compressive sensing principles, for efficient estimation of the light transport matrix (LTM). The LTM contains information on both optical systems and the 3D scene. In our setup, the camera sensor is intentionally chosen to be low resolution to prove the future use of inexpensive sensors in nonvisible regions of the light spectrum, as well as the potential design of simplified multiview and light field acquisition systems. We show that a high-precision estimation of the LTM from a reduced set of measurements is possible. Virtual measurements, instead of physical, are conducted to obtain the 3D reconstruction. We show that 3D scene reconstruction from the proposed virtual measurements corresponds with the actual physical acquisition. Moreover, this approach provides much more detail in the reconstruction. The computational complexity of the proposed methods is reduced to such a level that practical implementations are feasible.
Evolving General Regression Neural Networks for Learning from Noisy Datasets

In this work, a novel algorithm for evolving general regression neural networks is suggested for regression tasks with noisy data. The proposed approach divides the training dataset into slices and then applies the evolving algorithm to create and delete networks to improve the training accuracy. The suggested algorithm performance is verified using a variety of benchmarking datasets where random noise is added to every dataset. Also, different portions of the datasets are utilized for the training, namely, 70% and 50% were used to measure the consistency of the algorithm and its generalization abilities when the training data shrinks. The algorithm results demonstrate its ability to perform well especially when the dataset size is large. The algorithm not only results in reasonable performance in noisy regression tasks but also helps to reduce the network size significantly for many datasets. It is also noted that the training and testing errors are close to each other for many of the tested datasets which ensure the ability of the algorithm not only in the learning stage but also in the generalization stage.
FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels

Automated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc., which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. In our work, we propose a semi-supervised learning detection model, FraudJudger, to analyze user behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from raw data with the help of Adversarial Autoencoder (AAE). Compared with other state-of-the-art fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. Besides, we deploy FraudJudger on a real-world financial platform, and the experiment results show that our model can well generalize to other fraud detection contexts.
Robust Hybrid Image and Audio Watermarking Using Cyclic Codes and Arnold Transform

Hybrid watermarking is a prevailing technique in the latest research and has a significant amount of real-time applications. Digital watermarking is utilized to protect copyright and security to multimedia content. In this paper, an algorithm is an amalgamation of image watermarking and audio watermarking algorithms. In case of image watermarking algorithm discrete cosine transform (DCT) and in case of audio, discrete cosine transforms (DCT) and discrete wavelet transform (DWT) are used to propose this Hybrid watermarking algorithm. Moreover, to refine the performance of the proposed scheme, error correction technique and Arnold transforms are employed. Peak sound to noise ratio and Bit Error Rate (BER) is applied to estimate the performance of this scheme to counter the Time-Scale Modification and numerous attacks possible on images and audio as well.
End-to-End Reinforcement Learning for Multi-agent Continuous Control

In end-to-end reinforcement learning, an agent captures the entire mapping from its raw sensor data to actuation commands using a single neural network. End-to-end reinforcement learning is mostly studied in single-agent domains, and its scalability to multi-agent setting is under-explored. Without effective techniques, learning effective policies based on the joint observation of agents can be intractable, particularly when sensor data perceived by each agent is high-dimensional. Extending the multi-agent actor-critic method MADDPG, this paper presents Rec-MADDPG, an end-to-end reinforcement learning method for multi-agent continuous control in a cooperative environment. To ease end-to-end learning in a multi-agent setting, we proposed two embedding mechanisms, joint and independent embedding, to project agents' joint sensor observation to low-dimensional features. For training efficiency, we applied parameter sharing and the A3C-based asynchronous framework to Rec-MADDPG. Considering the challenges that can arise in real-world multi-agent control, we evaluated Rec-MADDPG in robotic navigation tasks based on realistic simulated robots and physics enable environments. Through extensive evaluation, we demonstrated that Rec-MADDPG can significantly outperform MADDPG and was able to learn individual end-to-end policies for continuous control based on raw sensor data. In addition, compared to joint embedding, independent embedding enabled Rec-MADDPG to learn even better optimal policies.
BigOptiBase: Big Data Analytics for Base Station Energy Consumption Optimization

Mobile Network Operators develop new technologies, as the 5G network, to handle the constantly increasing network traffic, while they put less effort on optimizing their operations. However, more energy efficient approaches are essential for cost reduction and compliance with energy footprint principles. Network usage and IoT data produced in the base stations can be used to develop such approaches. Considering the above, the BigOptiBase platform has been designed. Through this platform we will offer a big data analytics subsystem developed to provide elastic energy efficient solutions for the base stations using data analytics and machine learning technologies.
Inattentional Blindness for Redirected Walking Using Dynamic Foveated Rendering

Redirected walking is a Virtual Reality(VR) locomotion technique which enables users to navigate virtual environments (VEs) that are spatially larger than the available physical tracked space. In this work we present a novel technique for redirected walking in VR based on the psychological phenomenon of inattentional blindness. Based on the user's visual fixation points we divide the user's view into zones. Spatially-varying rotations are applied according to the zone's importance and are rendered using foveated rendering. Our technique is real-time and applicable to small and large physical spaces. Furthermore, the proposed technique does not require the use of stimulated saccades but rather takes advantage of naturally occurring saccades and blinks for a complete refresh of the framebuffer. We performed extensive testing and present the analysis of the results of three user studies conducted for the evaluation.
Novel Enhanced Teager Energy Based Cepstral Coefficients for Replay Spoof Detection

Replay attack on voice biometric, refers to the fraudulent attempt made by an imposter to spoof another person's identity by replaying the pre-recorded voice samples in front of an Automatic Speaker Verification (ASV) system. In an attempt to develop countermeasures against replay attack, this paper proposes to use a new feature set, namely, Enhanced Teager Energy Cepstral Coefficients (ETECC) using the recently introduced concept of signal mass. Results obtained on ASVspoof 2017 version 2.0 dataset suggest that the proposed feature set performs better than the original Teager Energy Cepstral Coefficients (TECC) feature set because the Enhanced Teager Energy Operator (ETEO) gives a better estimate of signal's energy as compared to the Teager Energy Operator (TEO). We obtained 53.3% and 51.35% reduction in EER on development and evaluation dataset, respectively, with respect to the baseline system.
Ant Colony Optimization in Data Mining: Critical Perspective from 2015 to 2020

Ant colony optimization (ACO) is a modern metaheuristic advance for resolving firm optimization problems. ACO is based on perception of swarm intelligence (SI). Many algorithms that are motivated by scavenging activity of ant colonies were utilized to result of complex distinct optimization problems. This paper intends to provide a detailed idea about the importance of ACO for data mining problems with a broad analysis on the structure of ACO as well as its applicability from 2015 to 2020. Many variations of ACO have also been utilized for solving diverse complex problems of data mining area. The main intent of this study is to inspire the researchers of optimization board to make use of ACO for explaining to those kinds of problems that were not used yet.