Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review on Recognition Techniques for Bangla Handwritten Characters

Handwritten character recognition is a challenging task in OCR and for a cursive and complex character set like Bangla, it is even harder to implement. Many researchers have proposed different methods for recognizing Bangla Handwritten character set. It is done through analyzing the structure of the characters or through some machine learning process. This paper represents an analysis and overview of the existing methods for recognizing handwritten basic and compound characters. Methods, success rate, limitations and future scope has been mentioned in this paper. The purpose of this paper is to find out the fields in which the systems necessitate improvement and contribute to establish an ideal Bangla Handwritten Character Recognition System.
Learning to Select Important Context Words for Event Detection

It is important to locate important context words in the sentences and model them appropriately to perform event detection (ED) effectively. This has been mainly achieved by some fixed word selection strategy in the previous studies for ED. In this work, we propose a novel method that learns to select relevant context words for ED based on the Gumbel-Softmax trick. The extensive experiments demonstrate the effectiveness of the proposed method, leading to the state-of-the-art performance for ED over different benchmark datasets and settings.
Finding a Secure Place: A Map-Based Crowdsourcing System for People With Autism

People with autism have idiosyncratic sensory experiences, which may impact on how they live the “spaces” of their everyday life. Starting from an investigation of their conception and experience of “secure places,” we defined a series of user requirements for designing technology that supports their everyday movements in the urban environment. On the basis of such requirements, we developed an interactive system that leverages crowdsourcing mechanisms to map places that are perceived as secure by the population with autism.
Using Utterance Recall to Assess Second Language Proficiency

Abstract Obtaining quick and reliable evidence regarding the proficiency of learners is a perennial issue in second language (L2) learning research. In this study, we examined naturalistic utterance recall as a measure of L2 learning proficiency that can be easily extracted from videos and automatically scored using the video's captions. In our recall task, learners listen to audio clips and write down as much of the utterance as they can remember. We evaluated this naturalistic recall task with a sample of English native speakers who are learning Spanish at beginner to advanced levels, as well as Spanish native speakers. The results suggest that our recall measure is a better predictor of a learner's ability to translate heard sentences than a shortened version of a standardized listening multiple‐choice comprehension test. Our findings suggest naturalistic utterance recall can offer an accurate and efficient method for predicting foreign language proficiency.
Visual Search Target Inference in Natural Interaction Settings with Machine Learning

Visual search is a perceptual task in which humans aim at identifying a search target object such as a traffic sign among other objects. Search target inference subsumes computational methods for predicting this target by tracking and analyzing overt behavioral cues of that person, e.g., the human gaze and fixated visual stimuli. We present a generic approach to inferring search targets in natural scenes by predicting the class of the surrounding image segment. Our method encodes visual search sequences as histograms of fixated segment classes determined by SegNet, a deep learning image segmentation model for natural scenes. We compare our sequence encoding and model training (SVM) to a recent baseline from the literature for predicting the target segment. Also, we use a new search target inference dataset. The results show that, first, our new segmentation-based sequence encoding outperforms the method from the literature, and second, that it enables target inference in natural settings.
Delving Deeper in Drone-Based Person Re-Id by Employing Deep Decision Forest and Attributes Fusion

Deep learning has revolutionized the field of computer vision and image processing. Its ability to extract the compact image representation has taken the person re-identification (re-id) problem to a new level. However, in most cases, researchers are focused on developing new approaches to extract more fruitful image representation and use it in the re-id task. The extra information about images is rarely taken into account because the traditional person re-id datasets usually do not have it. Nevertheless, the research in multimodal machine learning has demonstrated that the utilization of the information from different sources leads to better performance. In this work, we demonstrate how a person re-id problem can benefit from the utilization of multimodal data. We have used the UAV drone to collect and label the new person re-id dataset, which is composed of pedestrian images and its attributes. We have manually annotated this dataset with attributes, and in contrast to the recent research, we do not use the deep network to classify them. Instead, we employ the continuous bag-of-words model to extract the word embeddings from text descriptions and fuse it with features extracted from images. Then the deep neural decision forest is used for pedestrians classification. The extensive experiments on the collected dataset demonstrate the effectiveness of the proposed model.
Segmentation-validation based handwritten Arabic CAPTCHA generation

A Completely Automated Public Turing test to tell Computers and Human Apart (CAPTCHA) is an effective way to identify human users and prevent cyber-attacks. In this research, we present a novel approach for generating and validating handwritten Arabic CAPTCHAs. With high Internet penetration among Arabic speakers, Arabic CAPTCHAs have recently gained interests among the researchers. Handwritten text based CAPTCHAs have been shown as more secured compared to printed text based CAPTCHAs. Therefore, the proposed method uses synthesized handwritten Arabic words to generate CAPTCHAs. In the user-validation phase of our method, users solve the CAPTCHAs by finding the segmentation points in the cursive Arabic words. To the best of our knowledge, such a segmentation-validation based approach for CAPTCHAs is reported for the first time in this paper. In addition, this work introduces the use of synthesized words for handwritten Arabic CAPTCHAs. The proposed method is sufficiently flexible to generate CAPTCHAs with various degrees of difficulty. The usability and security aspects of the current method have been evaluated through experimental studies with very promising results.
Visual BMI estimation from face images using a label distribution based method

Body mass index (BMI) analysis from face images is an interesting and challenging topic in machine learning and computer vision. Recent research shows that facial adiposity is associated with BMI prediction. In this work, we investigate the problem of visual BMI estimation from face images by a two-stage learning framework. BMI-related facial features are learned from the first stage. Then a label distribution based BMI estimator is learned by an optimization procedure that is implemented by projecting the features and assigned labels to a new domain which maximizing the correlation between them. Two label assignment strategies are analyzed for modeling the single BMI value as a discrete probability distribution over a range of BMIs. Extensive experiments are conducted on FIW-BMI, Morph II and VIP_attribute datasets. The experimental results show that the two-stage learning framework improves the performance step by step. More importantly, the proposed BMI estimator efficiently reduces the error. It outperforms regression based methods, two label distribution methods and two deep learning methods in most cases.
Benchmarking Microsoft Azure Virtual Machines for the use of HPC applications

In this paper, we present the results of performance analysis for the Microsoft Azure virtual machines. The tests used NASA’s well known NAS parallel benchmarks for High Performance Computing (HPC) applications. Several tests were executed using different HPC cluster configuration. Both computation and communication times were measured to study the effect of Virtual Machines (VMs) computing and networking features on the execution and communication time of the benchmarks. We concluded that the communication behavior of any benchmark or application has the most influence on the HPC cluster configuration selection.
Necessary and sufficient conditions for the existence of solution of generalized fuzzy relation equations A ⇔X = B

In 2013 Li and Jin studied a particular type of fuzzy relational equations on finite sets, where the introduced min-bi-implication composition is based on Łukasiewicz equivalence. In this paper such fuzzy relation equations are studied on a more general level, namely complete residuated lattice valued fuzzy relation equations of type ⋀y∈Y(A(x,y)↔X(y)=B(x) are analyzed, and the existence of solutions S is studied. First a necessary condition for the existence of solution is established, then conditions for lower and upper limits of solutions are given, and finally sufficient conditions for the existence of the smallest and largest solutions, respectively, are characterized. If such general or global solutions do not exist, there might still be partial or point wise solutions; this is a novel way to study fuzzy relation equations. Such point wise solutions are studied on Łukasiewicz, Product and Gödel t-norm based residuated lattices on the real unit interval.
New approach to evaluating predictive models of photovoltaic systems

Performance models are used for forecasting the energy output of photovoltaic systems. The ability of a model to accurately predict the output must be carefully assessed because the capital cost for photovoltaic system implementation is tied to the reliability of the forecast. The customary approach to model evaluation is based on the root-mean-square-error, mean-absolute-error and mean-bias-error. The values of these statistics strongly depend on the scale of the quantities they compare and, therefore, can be misleading. This paper presents a multifaceted approach to model evaluation, comprising qualitative and quantitative assessment, thus increasing the reliability of the evaluation outcome. The qualitative assessment is based on graphical residual analysis. The quantitative assessment is based on two new, scale independent, metrics. The results show good agreement between the outcomes of the qualitative and the quantitative assessment, when it is done with the new metrics, but a discrepancy between the two, when the traditional metrics are used. The new metrics can be used to assess the forecasting accuracy of different performance models of the same photovoltaic system as well as to compare the accuracy of models of different systems. The new approach is applied in the evaluation of models of two photovoltaic systems and the results are compared with the results that would have been obtained, had the customary metrics been used.
Firefly algorithm and flower pollination algorithm

The firefly algorithm is a swarm intelligence-based algorithm, and its nonlinearity in search mechanisms can usually lead to subdivision and multiswarms, which means that it can be potentially more effective than single-swarm algorithms. This chapter introduces the main ideas of the firefly algorithm, followed by the introduction of the flower pollination algorithm. Both implementation details and examples will be presented to show how these algorithms work. Suggestions on modifications and multiobjective optimization will also be discussed.
Human-machine collaboration in online customer service – a long-term feedback-based approach

Abstract The rising expectations of customers have considerably contributed to the need for automated approaches supporting employees in online customer service. Since automated approaches still struggle to meet the challenge to fully grasp the semantics of texts, hybrid approaches combining the complementary strengths of human and artificial intelligence show great potential for assisting employees. While research in Case-Based Reasoning (CBR) already provides well-established approaches, they do not fully exploit the potential of CBR as hybrid intelligence. Against this background, we follow a design-oriented approach and develop an adapted textual CBR cycle that integrates employees’ feedback on semantic similarity, which is collected during the Reuse phase, into the Retrieve phase by means of long-term feedback methods from information retrieval. Using a real-world data set, we demonstrate the practical applicability and evaluate our approach regarding performance in online customer service. Our novel approach surpasses human-based, machine-based, and hybrid approaches in terms of effectiveness due to a refined retrieval of semantically similar customer problems. It is further favorable regarding efficiency, reducing the average time required to solve a customer problem.
An efficient self-certified multi-proxy signature scheme based on elliptic curve discrete logarithm problem

Multi-proxy signature schemes (MPSS) allow n proxy signers to generate a valid signature on behalf of the original signer. A new efficient self-certified MPSS with message recovery based on the Elliptic Curve Discrete Logarithm Problem (i.e., ECDLP) is proposed in this paper. A rationale for proposing a new scheme is to improve the verification process performance by combining the verification process with message recovery in one single stage (i.e., simultaneously). The security and performance analysis shows, the new scheme has many advantages over the traditional MPS schemes. These benefits include decreasing costs (i.e., certificate sizes, consumption time, and bandwidth requirements) and increasing speed.
Chinese clinical named entity recognition based on stacked neural network

Summary The precise named entity recognition (NER) is a key component in Chinese clinical natural language processing. Although clinical NER systems have attracted widespread attention and been studied for decades, the latest NER research usually relies on a shallow text representation with one‐layer neural encoding, which fails to capture deep features and limits its performance improvement. To capture more features and encode the clinical text efficiently, we propose a deep stacked neural network for Chinese clinical NER. The neural network stacks two bidirectional long‐short term memory and gated recurrent unit layers to encode the text twice, followed by a conditional random fields (CRF) layer to recognize named entities in Chinese clinical text. Extensive empirical results on three real‐world datasets demonstrate that the proposed method significantly outperforms six state‐of‐the‐art NER methods. Especially compared with the conventional CRF model, our method has at least 3.75% F 1 ‐score improvement on these public datasets.
P-SFA: Probability based Sigmoid Function Approximation for Low-complexity Hardware Implementation

A probability-based sigmoid function approximation (P-SFA), which is based on piecewise linear function and neuron's values statistical probability distribution in each layer, is proposed to lower the complexity of neural network hardware implementation with only addition circuit. The sigmoid function is divided into three fixed regions, and the number of sub-regions with different sizes in each fixed region is adapted to neuron's values distribution in each layer to reduce the error between the sigmoid function and P-SFA function. The experimental results on FPGA show that the P-SFA function is efficient in terms of power and speed, and the recognition accuracies in DNN and CNN for MNIST with P-SFA are the highest among the state-of-the-art methods, up to 97.46% and 99.02% respectively.
Deep Collaborative Filtering Based on Outer Product

Recently, deep neural networks are widely used in recommendation systems, but most of them are used to process auxiliary information of recommendation systems such as items' descriptions and images.When it comes to how to learn a better interaction function to model the relation between user latent features and item latent features, which is the most critical step in a recommendation task, most works employ matrix factorization together with the inner product.However, it is sub-optimal because of ignoring many correlations between latent factors.As deep neural networks perform well in building more complex non-linear models, we employ deep neural networks to improve the collaborative filtering algorithm, solving the problem of implicit feedback which is the most common scene in real applications.Some recent work has contributed to finding better interaction function, but these functions are not exact enough to model comprehensive correlations among latent features.In this work, we propose the Convolutional Neural Networks based Deep Collaborative Filtering model (CNN-DCF) to solve the key problem in the recommendation system.Based on the outer product and deep neural networks, we develop a correlation extraction module that can learn high-order correlations between item latent features and user latent features.Extensive experiments on the public implicit feedback dataset Yelp show that the proposed CNN-DCF model brings significant improvements over the state-of-the-art methods.
Design and Virtex-7-Based Implementation of Video Chaotic Secure Communications

In this paper, a Virtex-7-based video chaotic secure communication scheme is investigated. First, the network sending and receiving controller Intellectual Property (IP) cores are designed. Next, the chaotic encryption and decryption IP cores are implemented using fixed-point algorithm, pipeline operation, and state machine control. Thus, video capturing, video displaying, network sending, network receiving, chaotic encrypting, and chaotic decrypting can be achieved via IP core integration design. An improved 7D chaotic stream cipher algorithm for resisting divide-and-conquer attack is then designed and realized on a Virtex-7 high-end FPGA platform. Hardware experimental results are also given to verify the feasibility of the scheme.
A high speed and memory efficient algorithm for perceptually-lossless volumetric medical image compression

With the advancements in modern medical imaging systems, the diagnostic image data has increased exponentially. The future medical applications seek medical imaging devices to be portable. Image quality and real-time processing are of prime importance in medical image compression. Therefore the volumetric medical images need to be compressed in perceptually lossless manner. Also the time taken to compress the images before transmitting (or storing), should be small. In this paper, an algorithm for lossless and perceptually-lossless medical image compression is proposed. The proposed algorithm uses two small lists and two small state-tables to encode an image. The compression efficiency is comparable to the state-of-the-art lossless compression techniques. Also the computational complexity and memory requirement are realistic for portable medical imaging devices. Combining all the three features, it is obvious that the proposed algorithm is a better candidate for image compression in comparison to all the state-of-art compression algorithms that we know of, for volumetric-medical imaging systems.
Mobile Information Security Awareness Among Students in Higher Education : An Exploratory Study

Higher education students live their lives through their mobile technology devices. The availability of broadband networks and the associated connectivity capabilities of modern mobile devices as well as the numerous mobile apps on offer enable students to be constantly connected. Consequently, large amounts of sensitive information is being stored and processed through a variety of mobile devices, making the individual students and their higher education institutions easy targets for cyberattacks. This study assesses factors that impact mobile security awareness of students (N=397) at a higher education institution in South Africa. It proposes a conceptual model of mobile information security awareness, which draws upon a combination of constructs from the Knowledge-Attitude-Behavior (KAB) model and the Theory of Planned Behavior (TPB) to measure mobile information security awareness. The results show a significant relationship between students' knowledge and behavioral intentions about information security threats and their levels of security awareness. The study also shows the existence of the gap between knowing about security and the lack of concomitant safety behaviors. The study concludes with a discussion of the practical implications of the research outcomes.
Generation of quantum coherence in two-qubit cavity system: qubit-dipole coupling and decoherence effects

Abstract The intrinsic decoherence effect for two qubits interacting with a coherent field, under the dipole-dipole interaction and two-photon resonance, is analytically described. We investigate numerically the population inversion and the quantum coherence. The results show that the generated mixture entropy and the entanglement negativity, can be enhanced and protected by the dipole-dipole interaction and by reducing the initial coherent field intensity. In particular, we find that, the collapses and revivals of the population inversion present high sensitivity to these physical parameters. The nonlinearity of the two-photon processes leads to a generation of a strong two-qubit entanglement. This generated entanglement depends on the initial coherent field intensity, the dipole-dipole interaction and the intrinsic decoherence.
Fast and improved backpropagation learning of multi‐layer artificial neural network using adaptive activation function

Abstract In the conventional backpropagation (BP) learning algorithm used for the training of the connecting weights of the artificial neural network (ANN), a fixed slope−based sigmoidal activation function is used. This limitation leads to slower training of the network because only the weights of different layers are adjusted using the conventional BP algorithm. To accelerate the rate of convergence during the training phase of the ANN, in addition to updates of weights, the slope of the sigmoid function associated with artificial neuron can also be adjusted by using a newly developed learning rule. To achieve this objective, in this paper, new BP learning rules for slope adjustment of the activation function associated with the neurons have been derived. The combined rules both for connecting weights and slopes of sigmoid functions are then applied to the ANN structure to achieve faster training. In addition, two benchmark problems: classification and nonlinear system identification are solved using the trained ANN. The results of simulation‐based experiments demonstrate that, in general, the proposed new BP learning rules for slope and weight adjustments of ANN provide superior convergence performance during the training phase as well as improved performance in terms of root mean square error and mean absolute deviation for classification and nonlinear system identification problems.