Reviewerly/exHarmony huggingface dataset:
Nonlinear dynamic system identification with a cooperative population-based algorithm featuring a restart metaheuristic

Abstract Dynamic system identification is commonly reduced to an optimization problem which is complex and multimodal. To find a global optimum of this problem, evolutionary algorithms are often applied. However, as it was shown in many studies, conventional evolution-based algorithms do not demonstrate the acceptable performance for this class of problems, therefore, some effective modifications have been proposed so far. In our study, we combine two approaches which were previously used in linear dynamic system identification and allowed their accurate identification. More specifically, we present a cooperative evolutionary algorithm with a restart metaheuristic and apply it for the parameter identification of a nonlinear cascaded system. The experimental results prove the effectiveness of the proposed evolution-based identification compared to other known solutions of this problem.
Syntactico-Semantic Reasoning using PCFG, MEBN & PP Attachment Ambiguity

Probabilistic context free grammars (PCFG) have been the core of the probabilistic reasoning based parsers for several years especially in the context of the NLP. Multi entity Bayesian networks (MEBN) a First Order Logic probabilistic reasoning methodology is widely adopted and used method for uncertainty reasoning. Further upper ontology like Probabilistic Ontology Web Language (PR-OWL) built using MEBN takes care of probabilistic ontologies which model and capture the uncertainties inherent in the domain's semantic information. The paper attempts to establish a link between probabilistic reasoning in PCFG and MEBN by proposing a formal description of PCFG driven by MEBN leading to the usage of PR-OWL modeled ontologies in PCFG parsers. Furthermore, the paper outlines an approach to resolve the prepositional phrase (PP) attachment ambiguity using the proposed mapping between PCFG and MEBN.
Research on Chinese Short Text Clustering Ensemble via Convolutional Neural Networks

Different from traditional text, short texts are characterized by high dimensionality, sparseness, and large text size. At the same time, some existing clustering ensemble algorithms treat each clusters equally, which will lead to the problem that the clustering results are not good enough. To solve this problem, this paper proposed a short text clustering ensemble algorithm based on convolution neural network (CNN). Firstly, the word2vec model is used to preserve the semantic relationship between words and obtain the multi-dimensional word vector representation; secondly, the feature is extracted from the original vector combining with the CNN; thirdly, clustering methods are used to cluster vectors; and then finally, Gini coefficient is used to measure the reliability of clustering, and the final clustering ensemble is carried out.
Introduction to JSTQE Issue on Photonics for Deep Learning and Neural Computing

The papers in this special section examine neuromorphic photonics which combines optical physics and unconventional computing, resulting in a new class of ultrafast information processors for neuromorphic information and signal processing, machine learning, and high-performance computing. These processors can enable applications where low latency, high bandwidth, and low switching energies are paramount. Fundamentally, such computing concepts heavily depend on interconnects, a functionality where photonic processors can significantly outperform electronic systems. By combining the high bandwidth and efficiency of photonic devices with the adaptive, parallelism and complexity similar to the brain, photonic neural networks have the potential to be faster than conventional neural networks, while consuming less energy.
Automatic Segmentation of Grammatical Facial Expressions in Sign Language: Towards an Inclusive Communication Experience

Nowadays, natural language processing techniques enable the development of applications that promote communication between humans and between humans and machines.Although the technology related to automated oral communication is mature and affordable, there are currently no appropriate solutions for visualspatial languages.In the scarce efforts to automatically process sign languages, studies on non-manual gestures are rare, making it difficult to properly interpret the speeches uttered in those languages.In this paper, we present a solution for the automatic segmentation of grammatical facial expressions in sign language.This is a low-cost computational solution designed to integrate a sign language processing framework that supports the development of simple but high value-added applications for the context of universal communication.Moreover, we present a discussion of the difficulties faced by this solution to guide future research in this area. 1Examples: UNI (https://bit.ly/2M9Ywo6),Mimix Sign Language Translator (https://bit.ly/2JGWslq),Hand Talk Translator (https://bit.ly/2Fg4kpI),SignAloud Gloves (https://bit.ly/2QoHQrp).
Efficient Privacy-Preserving Skyline Queries over Outsourced Cloud

In the cloud computing paradigm, data owners could outsource their databases to the service provider, and thus reap huge benefits from releasing the heavy storage and management tasks to the cloud server. However, sensitive data, such as medical or financial records, should be encrypted before uploading to the cloud server. Unfortunately, this will introduce new challenges to data utilization. In this paper, we study the problem of skyline queries in a way that data privacy for both data owner and the client is preserved. We propose a hybrid protocol via additively homomorphic encryption system and Yao’s garbled circuits. By taking advantages of Yao’s protocol, we design a highly improved protocol which can be used to determine the skyline point and exclude the points dominated by others in an oblivious way. Based on this subroutine, we construct a fully secure protocol for skyline queries. We theoretically prove that the protocols are secure in the semi-honest model. Through analysis and extensive experiments, we demonstrate the efficiency and scalability of our proposed solutions.
Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels

In this paper, we propose a no-reference image quality assessment (NR-IQA) approach towards authentically distorted images, based on expanding proxy labels. In order to distinguish from the human labels, we define the quality score, which is generated by using a traditional NR-IQA algorithm, as “proxy labels”. “Proxy” means that the objective results are obtained by computer after the extraction and assessment of the image features, instead of human judging. To solve the problem of limited image quality assessment (IQA) dataset size, we adopt a cascading transfer-learning method. First, we obtain large numbers of proxy labels which denote the quality score of authentically distorted images by using a traditional no-reference IQA method. Then the deep network is trained by the proxy labels, in order to learn IQA-related knowledge from the amounts of images with their scores. Ultimately, we use fine-tuning to inherit knowledge represented in the trained network. During the procedure, the mapping relationship fits in with human visual perception closer. The experimental results demonstrate that the proposed algorithm shows an outstanding performance as compared with the existing algorithms. On the LIVE In the Wild Image Quality Challenge database and KonIQ-10k database (two standard databases for authentically distorted image quality assessment), the algorithm realized good consistency between human visual perception and the predicted quality score of authentically distorted images.
Data Center Selection Based on Reinforcement Learning

As demand for cloud services continues to increase, major cloud service providers have built many data centers around the world or in certain regions to ensure the quality of services. However, optimal data center selection from the distributed data centers of a cloud service provider or a cloud service alliance remains a problem, in part because of the cost of leasing data center resources, which includes the costs of network resources and computing resources. Whereas the cost of computing resources is easy to determine, the cost of network resources is relatively difficult to determine because it depends on communication paths. In this study, the distributed data centers were abstracted into an incomplete undirected graph. Furthermore, the cost of computing resources and network resources were considered together. The mathematical model of the user’s cost was established. To obtain the optimal solution, we proposed a data center selection algorithm based reinforcement learning. The experiment results showed that the algorithm effectively solved the problem.
Patient Controlled, Privacy Preserving IoT Healthcare Data Sharing Framework

Healthcare data personally collected by individuals with wearable devices have become important sources of information for healthcare professionals and medical research worldwide.User-Generated Data (UGD) offers unique and sometimes fine-grained insight into the lived experiences and medical conditions of patients.The sensitive subject-matter of medical data can facilitate the exploitation and/or control of victims.Data collection in medical research therefore restricts access control over participant-data to the researchers.Therefore, cultivating trust with prospective participants concerned about the security of their medical data presents formidable challenges.Anonymization can allay such concerns, but at the cost of information loss.Moreover, such techniques cannot necessarily be applied on real-time streaming health data.In this paper, we aim to analyze the technical requirements to enable individuals to share their real-time wearable healthcare data with researchers without compromising privacy.An extension for delay-free anonymization techniques for real-time streaming health data is also proposed.
UofC-Bayes: A Bayesian Approach to Visualizing Uncertainty in Likert Scales

Disasters demand a quick response based on incomplete information. For the Saint Himark dataset, part of the 2019 VAST Challenge, we focused on delivering a visualization which accurately conveyed that uncertainty. While our analysis was done offline, we chose techniques and algorithms which could easily be applied to realtime usage. Our visualization for the first mini-challenge was a one-screen dashboard that summarized citizen feedback.
Sarcasm detection in Text Data to bring out genuine sentiments for Sentimental Analysis

The growth of people using social media and E commerce in the modern world has influenced the people in the way they think, they communicate and act. Sentiments are expressed in the posts written by the users, views expressed by the customers and etc. Detection of sentiments in the posts on social media platforms and e commerce portals are helping to find new avenues for business Ventures. Most of the time users and customers write a comment or express a view which in depth will be opposite of what they mean to say by bringing in Irony or sarcasm in the statements. Detection of Sarcasm or Irony in sentences has become a challenging task. In this paper an attempt is made to bring out the negativity in positive sentences and positivity in negative sentences by calculating polarity scores using Sentiwordnet. Identification of sarcasm in sentences will help in bringing out genuine sentiments.
Virtual reality teaching material - virtual reality game with education

Abstract This study aims to explore the combination of virtual reality and school education, Recent years, Virtual Reality (VR) is a popular issue. The basic concept of virtual reality is to use an immersive device to isolate. Isolate the user from the real world. compare the situation of virtual reality education and traditional education to students’ learning, and explore whether virtual reality is suitable for use and promotion in schools. Can people generally accept digital technology to replace traditional education, use the convenience brought by virtual reality education and the growth of students.
Exponential Discretization of Weights of Neural Network Connections in Pre-Trained Neural Networks

To reduce random access memory (RAM) requirements and to increase speed of recognition algorithms we consider a weight discretization problem for trained neural networks. We show that an exponential discretization is preferable to a linear discretization since it allows one to achieve the same accuracy when the number of bits is 1 or 2 less. The quality of the neural network VGG-16 is already satisfactory (top5 accuracy 69%) in the case of 3 bit exponential discretization. The ResNet50 neural network shows top5 accuracy 84% at 4 bits. Other neural networks perform fairly well at 5 bits (top5 accuracies of Xception, Inception-v3, and MobileNet-v2 top5 were 87%, 90%, and 77%, respectively). At less number of bits, the accuracy decreases rapidly.
Improved Grey Wolf Algorithm Based on Nonlinear Control Parameter Strategy

Aimed at the problem of the grey wolf optimizer (GWO) algorithm of low convergence speed and easily falling into local optimum, this paper proposes a nonlinear control parameter strategy based on sinusoidal function (GWO-SIN) and a nonlinear control parameter combination (GWO-COM) strategy. In order to verify the validity and advantages and disadvantages of the proposed nonlinear control parameter strategies, selected the adjustment parameter values of GWO-COM from five different adjustment parameter values by test results of simulation of four benchmark functions. Then, this paper compares against the original GWO and four GWO based on different nonlinear control parameter strategies on a set of well-known benchmark functions. Experimental results show that the nonlinear control parameter combination (GWO-COM) strategy is superior to the others and the IGWO (an improved grey wolf algorithm for global optimization), and the optimization result of the GWO-SIN algorithm is similar to GWO-COM in the multi-peak test function.
Cooperative evolutionary classification algorithm for hyperspectral images

Reliable crop maps are a vital source of information for many economic sectors. Hyperspectral images and efficient supervised classification algorithms can help in obtaining these maps. One of the objectives is to use a spectroradiometer as a source of a priori information for training supervised classification algorithms. Although there are many supervised classification algorithms, few can be trained using collected spectral signatures, such as spectral angle mapper (SAM). This algorithm relies on setting the angle threshold values manually to separate different classes, and it is usually a random process. So, the second objective is to use an automated process to optimize the angle threshold values to improve the efficiency of the SAM algorithm. An innovative cooperative classification algorithm for hyperspectral images based on enhancing the supervised SAM algorithm performance using a genetic algorithm (GA) is presented. The improvement is based on selecting global optimal threshold angle values for different classes using GA. The efficiency of the developed cooperative evolutionary algorithm is proved by classifying hyperspectral images to create maps of major crops, such as wheat, potato, and alfalfa. The source of the hyperspectral images is the Compact High-Resolution Imaging Spectrometer onboard of the Proba satellite. The evaluation of the results showed that the new cooperative evolutionary algorithm classified hyperspectral images with the highest accuracy compared to well-known reliable supervised classification algorithms.
Age Estimation via Modern Convolutional Neural Networks

In this paper, we apply Xception deep convolutional neural network for age estimation on facial images. We adopt Label Distribution Learning age encoding, KLD loss function, and dropout techniques at fully connected layers. With IMDB-WIKI as pre-training dataset, APPA-REAL training set is used as finetune dataset, the final expected value on model output is the estimated age. We achieve the state-of-the-art results of MAE 3.635, 2.78, and 3.09 years on APPA-REAL, FG-NET, and MORPH-II test sets, respectively.
A Framework for Resilient Data Management for Smart Grids

Smart grids add information and communication technologies to traditional energy grids. This extension leads to an increased number of new devices in the energy system, like smart meters, sensors, and other electrical infrastructure devices. Collectively these devices produce a vast amount of heterogeneous data that needs to be captured, processed, and analyzed. Besides, the energy system transforms from a one-way into a bidirectional distribution of energy and information. System users not only consume the energy they also produce, store, and share it. These innovations result in potential more and new vulnerabilities. Therefore, a reliable, failure resistant, and secure data management for smart grids is required. This paper describes the problem definition and the research goals of a dissertation research project with a focus on resilient data management for smart grids. The dissertation aims to contribute to the design and development of resilient data management systems for smart grids through a systematic and structured framework that provides suitable technology suggestions and methods to address the described vulnerabilities.
Sequence-To-Sequence Neural Networks Inference on Embedded Processors Using Dynamic Beam Search

Sequence-to-sequence deep neural networks have become the state of the art for a variety of machine learning applications, ranging from neural machine translation (NMT) to speech recognition. Many mobile and Internet of Things (IoT) applications would benefit from the ability of performing sequence-to-sequence inference directly in embedded devices, thereby reducing the amount of raw data transmitted to the cloud, and obtaining benefits in terms of response latency, energy consumption and security. However, due to the high computational complexity of these models, specific optimization techniques are needed to achieve acceptable performance and energy consumption on single-core embedded processors. In this paper, we present a new optimization technique called dynamic beam search, in which the inference complexity is tuned to the difficulty of the processed input sequence at runtime. Results based on measurements on a real embedded device, and on three state-of-the-art deep learning models, show that our method is able to reduce the inference time and energy by up to 25% without loss of accuracy.
Linguistic errors made by Islamic university EFL students

In a writing activity students are required to transfer their knowledge to express their ideas on both what to say and how to say it. The difficulties in applying this knowledge have become obstacles for learners in constructing good writing. Consequently, these obstacles may cause the students to make errors. By adopting the notions of Error Analysis (EA) and the Surface Strategy Taxonomy as the theoretical frameworks, this study investigates the types of linguistic errors produced by the students. Along the way the sources behind the errors were also analyzed. This study employed the qualitative method design in which the case study approach was utilized. Ellis’ five-step procedure of EA was adopted to analyze the data of essays comprising of 150-300 words written by 23 EFL students of the fourth-semester at English Department at a state university. They were purposively selected as the participants of this study. It was found that omission was the most frequent errors identified in the students’ writings. Overall, 122 (63%) cases out of 195 cases were categorized under this type of errors. The number marker, verb-tense, article, preposition, subject-verb agreement, and pronoun were the categories of frequent errors made by the students, respectively. These were followed by addition (18%), misformation (15%), and misordering (5%). Significance to the source of errors, intralingual transfer turned out to be the main reason that provoked the blunder in the students’ writings. It was apparent that some of the interlinguistic contrast was the reason behind the errors. It appeared that the diverse systematical concept between the Indonesian language and English in terms of verb conjugation factor, inflectional morpheme, and auxiliary-verb abandonment were the strong contenders of the error sources. Additionally, interlingual transfer and context of learning also took part for the reason behind the errors.
A Face Recognition System for Assistive Robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.
Computer-Aided Diagnosis using Class-Weighted Deep Neural Network

Computer-aided diagnosis has become a major focal point of Artificial Intelligence. Interpreting medical images is often time-consuming and requires significant human expertise. Hence, there is an increasing demand to use machine learning techniques to correctly classify different medical images captured by mammography, CT scans, and MRI among others. This paper presents a deep learning method for computer-aided differential diagnosis of benign and malignant breast cancer tumors by avoiding potential errors caused by poor feature selection as well as class imbalances in the dataset. We design, develop and test an end-to-end convolutional neural network architecture for two different breast cancer datasets of fine needle aspiration biopsy samples, and show that our network outperforms the state of the art. Furthermore, we have introduced a loss coefficient which can be adjusted to fine-tune the performance of our network. The proposed method can be used to support oncologists in the detection of breast cancer with high confidence.