Reviewerly/exHarmony huggingface dataset:
Leveraging CNN and Transfer Learning for Vision-based Human Activity Recognition

With the advent of the Internet of Things (IoT), there have been significant advancements in the area of human activity recognition (HAR) in recent years. HAR is applicable to wider application such as elderly care, anomalous behaviour detection and surveillance system. Several machine learning algorithms have been employed to predict the activities performed by the human in an environment. However, traditional machine learning approaches have been outperformed by feature engineering methods which can select an optimal set of features. On the contrary, it is known that deep learning models such as Convolutional Neural Networks (CNN) can extract features and reduce the computational cost automatically. In this paper, we use CNN model to predict human activities from Wiezmann Dataset. Specifically, we employ transfer learning to get deep image features and trained machine learning classifiers. Our experimental results showed the accuracy of 96.95% using VGG-16. Our experimental results also confirmed the high performance of VGG-16 as compared to rest of the applied CNN models.
Learning transferable features in meta-learning for few-shot text classification

One of the main issues accompanying the current deep learning models for classification problems is that massive data has to be fed into the training process, while obtaining sufficient annotated samples is usually time-consuming and labor-intensive. To address the problem of few-shot learning, meta-learning has made significant progress recently, which encourages fast adaptation to solve new learning tasks with only a limited number of training examples. Although it has gained increasing attention and impact in the realm of computer vision, its practical use in natural language processing has been rarely exploited. To learn transferable features effectively for few-shot text classification in a meta-learning framework, we propose a novel method which leverages the maximum mean discrepancy metric of the adaptation layer to minimize the distance between the support and query distributions within each learning task and regularize the meta-gradient update. We perform extensive empirical experiments to suggest best practices, as well as evaluate the effectiveness of our proposed method. In comparison with the well-established Model-Agnostic Meta-Learning (MAML) framework, our method achieves better generalization performance and produces remarkable accuracy gains on various text classification datasets.
An Effective Yield Estimation System Based on Blockchain Technology

Price fluctuation in agricultural products that adversely affects the actors of the market is a serious issue influenced by many factors. Uncertainty in yield estimation of the products can be counted as one of the major factors. It is a fact that an effective yield estimation method may help decision-making actors to develop effective and stable production and price policies. There are traditional and remote sensing methods aimed at calculating the correct yield estimate. However, these methods produce outputs only after the sowing season of the products. In this case, decision makers do not have enough instruments in hand to ensure price stability. Generally, the existing yield estimation systems are designed in a centralized setting. However, effective tracking may not be possible due to problems in the data flow. In this article, we will propose a blockchain-based solution that carries out yield estimation of agricultural products. Our solution brings all participants interested in agriculture together and produces an early yield estimation. Thus, the necessary precautions for the excessive imbalances that may arise in agricultural products will be planned in advance.
SIMPLE: A Remote Attestation Approach for Resource-constrained IoT devices

Remote Attestation (RA) is a security service that detects malware presence on remote IoT devices by verifying their software integrity by a trusted party (verifier). There are three main types of RA: software (SW)-, hardware (HW)-, and hybrid (SW/HW)-based. Hybrid techniques obtain secure RA with minimal hardware requirements imposed on the architectures of existing microcontrollers units (MCUs). In recent years, considerable attention has been devoted to hybrid techniques since prior software-based ones lack concrete security guarantees in a remote setting, while hardware-based approaches are too costly for low-end MCUs. However, one key problem is that many already deployed IoT devices neither satisfy minimal hardware requirements nor support hardware modifications, needed for hybrid RA. This paper bridges the gap between software-based and hybrid RA by proposing a novel RA scheme based on software virtualization. In particular, it proposes a new scheme, called SIMPLE, which meets the minimal hardware requirements needed for secure RA via reliable software. SIMPLE depends on a formally-verified software-based memory isolation technique, called Security MicroVisor (Sμ V). Its reliability is achieved by extending the formally-verified safety and correctness properties to cover the entire software architecture of SIMPLE. Furthermore, SIMPLE is used to construct SIMPLE+, an efficient swarm attestation scheme for static and dynamic heterogeneous IoT networks. We implement and evaluate SIMPLE and SIMPLE+ on Atmel AVR architecture, a common MCU platform.
Structure alignment of attributes and visual features for cross-dataset person re-identification

In cross-dataset person re-identification, it is challenging to address the problem of domain shift between training and test data. Although unsupervised domain adaptation methods have been developed, the performance is still much weaker compared with that of supervised methods because these models cannot follow a supervised optimization in unlabeled target domains. To address this problem, a transductive structure alignment-based self-reconstruction dictionary learning approach is proposed in this paper for cross-dataset person re-identification (PRID). Specifically, visual-attribute embedding is first learned to achieve knowledge transfer from the source domain to the target domain. In this process, visual-attribute structures are aligned via class prototype dictionaries to promote the discrimination of predicted semantic attributes by exploiting structure information between the visual feature and class prototype. Moreover, to mitigate domain shift, domain-invariant visual-attribute self-reconstruction is integrated into our dictionary learning framework. An identifier is then constructed by integrating the discriminativeness of attribute and compatibility matrix shared both source domain and target domain. Finally, the pre-learned model is tuned by selecting samples from the target domain which are not labeled but assigned pseudo-labels. Extensive experimental results on benchmark datasets show that our approach outperforms several state-of-the-art approaches.
A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment

Facial expression recognition, face synthesis, and face alignment are three coherently related tasks and can be solved in a joint framework. To achieve this goal, in this paper, we propose a novel end-to-end deep learning model by exploiting the expression code, geometry code and generated data jointly for simultaneous pose-invariant facial expression recognition, face image synthesis, and face alignment. The proposed deep model enjoys several merits. First, to the best of our knowledge, this is the first work to address these three tasks jointly in a unified deep model to complement and enhance each other. Second, the proposed model can effectively disentangle the global and local identity representation from different expression and geometry codes. As a result, it can automatically generate facial images with different expressions under arbitrary geometry codes. Third, these three tasks can further boost their performance for each other via our model. Extensive experimental results on three standard benchmarks demonstrate that the proposed deep model performs favorably against state-of-the-art methods on the three tasks.
Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning

The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that of three pathologists with different levels of experience to demonstrate its clinical value. In this retrospective study, a total of 1970 whole slide images of 731 cases were collected and divided into training, validation, and testing sets. Inception-v3, which is a state-of-the-art convolutional neural network, was trained to classify images into three categories: chronic nasopharyngeal inflammation, lymphoid hyperplasia, and NPC. The mean area under the curve (AUC) of the deep learning model is 0.936 based on the testing set, and its AUCs for the three image categories are 0.905, 0.972, and 0.930, respectively. In the comparison with the three pathologists, the model outperforms the junior and intermediate pathologists, and has only a slightly lower performance than the senior pathologist when considered in terms of accuracy, specificity, sensitivity, AUC, and consistency. To our knowledge, this is the first study about the application of deep learning to NPC pathologic diagnosis. In clinical practice, the deep learning model can potentially assist pathologists by providing a second opinion on their NPC diagnoses.
To chain or not to chain: A reinforcement learning approach for blockchain-enabled IoT monitoring applications

Traceability and autonomous business logic execution are highly desirable features in IoT monitoring applications. Traceability enables verifying signals' history for security or analytical purposes. On the other hand, the autonomous execution of pre-defined rules establishes trust between parties involved in such applications and improves the efficiency of their workflow. Smart Contracts (SCs) firmly guarantee these two requirements due to the blockchain's immutable distributed ledger and secure cryptographic consensus rules. Thus, SCs emerged as an appealing technology for monitoring applications. However, the cost of using public blockchains to harvest these guarantees can be prohibitive, especially with the considerable fluctuation of coin prices and different use case requirements. In this paper, we introduce a general SC-based IoT monitoring framework that can leverage the security features of public blockchains while minimizing the corresponding monetary cost. The framework contains a reinforcement learning agent that adapts to users' needs and acts in real-time to smartly set the data submission rate of IoT sensors. Results based on the Ethereum protocol show significant potential cost saving depending on users' preferences.
HieNN-DWE: A hierarchical neural network with dynamic word embeddings for document level sentiment classification

The recent rapid developments of neural networks have stimulated significant performance progress on document level sentiment classification. The existing researches focus on extracting long-distance features from documents, which fail to exploit the deep semantic information of documents. Besides, few of them have taken the representations of polysemous words into consideration, such that the semantic information of documents are not precisely exploited. To tackle these issues, we propose a novel hierarchical neural network model based on dynamic word embeddings (HieNN-DWE) for document level sentiment classification. Specifically, the dynamic word embeddings are produced by ELMo (Embedding from Language Models), which can deeply exploit the semantic information for polysemous words. The embeddings are then fed into HieNN-DWE which is a two-layer hierarchical neural network model. Note that the first layer utilizes bidirectional gated recurrent unit (BiGRU) as well as attention mechanism to encode sentences. The second layer uses both BiGRU and convolutional neural network (CNN) to capture global and local features from sentence representations. Thus, the final document representations are constructed by concatenating the outputs from BiGRU and CNN. Moreover, experimental results have shown that HieNN-DWE outperforms existing competitive methods on four public datasets for document level sentiment classification.
Cricket Sentiment Analysis from Bangla Text Using Recurrent Neural Network with Long Short Term Memory Model

Nowadays, people used to express their feelings, thoughts, suggestions and opinions on different social platform and video sharing media. Many discussions are made on Twitter, Facebook and many respective forums on sports especially cricket and football. The opinion may express criticism in different manner, notation that may comprise different polarity like positive, negative or neutral and it is a challenging task even for human to understand the sentiment of each opinion as well as time consuming. This problem can be solved by analyzing sentiment in respective comments through natural language processing (NLP). Along with the success of many deep learning domains, Recurrent Neural Network (RNN) with Long-Short-Term-Memory (LSTM) is popularly used in NLP task like sentiment analysis. We have prepared a dataset about cricket comment in Bangla text of real people sentiments in three categories i.e. positive, negative and neutral and processed it by removing unnecessary words from the dataset. Then we have used word embedding method for vectorization of each word and for long term dependencies we used LSTM. The accuracy of this approach has given 95% that beyond the accuracy of previous all method.
Mining Questions Asked about Continuous Software Engineering

Context: With the growing popularity of rapid software delivery and deployment, the methods, practices and technologies of Continuous Software Engineering (CSE) are evolving steadily. This creates the need for understanding the recent trends of the technologies, practitioners' challenges and views in this domain. Objective: In this paper, we present an empirical study aimed at exploring CSE from the practitioners' perspective by mining discussions from Q&A websites. Method: We have analyzed 12,989 questions and answers posted on Stack Overflow. Topic modelling is conducted to derive the dominant topics in this domain. Further, a qualitative analysis was conducted to identify the key challenges discussed. Findings: Whilst the trend of posted questions is sharply increasing, the questions are becoming more specific to technologies and more difficult to attract answers. We identified 32 topics of discussions, among which "Error messages in Continuous Integration/Deployment" and "Continuous Integration concepts" are the most dominant. We also present the most challenging areas in this domain from the practitioners' perspectives.
Speaker Identification Using a Hybrid CNN-MFCC Approach

In this paper, a novel architecture is proposed using a convolutional neural network (CNN) and mel frequency cepstral coefficient (MFCC) to identify the speaker in a noisy environment. This architecture is used in a text-independent setting. The most important task in any text-independent speaker identification is the capability of the system to learn features that are useful for classification. We are using a hybrid feature extraction technique using CNN as a feature extractor combines with MFCC as a single set. For classification, we used a deep neural network which shows very promising results in classifying speakers. We made our dataset containing 60 speakers, each speaker has 4 voice samples. Our best hybrid model achieved an accuracy of 87.5%. To verify the effectiveness of this hybrid architecture, we use parameters such as accuracy and precision.
A discrete wavelet transform and singular value decomposition-based digital video watermark method

Digital video watermark is widely used to protect copyright and content authentication. DWT-SVD based method, i.e., discrete wavelet transform (DWT) and singular value decomposition (SVD) based method, is one of the most popular state-of-the-art methods. There are two main criteria to evaluate it, i.e., imperceptibility and robustness. The former is measured via the peak signal to noise ratio (PSNR) and structural similarity (SSIM). They should be as high as possible after the watermark is embedded. Robustness measures how easy to restore the embedded watermark by the owner even if the watermarked video is damaged by an outside attacker. Current studies randomly choose embedded positions for watermark, which hardly achieves its highest imperceptibility and robustness. In this paper, we propose a more imperceptible and robust digital video watermarking method than the existing one. First, we introduce a method to measure video frame distortion in a wavelet transform domain. Second, we model the problem to achieve the minimum video frame distortion as an optimization problem, i.e., choose embedded positions that can maximize peak signal to noise ratio. Finally, we evaluate and compare our method with the state-of-the-art approaches by using real experiments and show its advantages in both imperceptibility and robustness.
LSTSVM classifier with enhanced features from pre-trained functional link network

In this paper, we propose an improved model for the classification problems. We use least squares twin support vector machines (LSTSVM) and pre-trained functional link to enhance the feature space. LSTSVM algorithm is used in many real world classification problems as it has lower computational complexity and solves system of linear equations instead of solving quadratic programming problems (QPPs). Since neural network models provide implicit feature representation and is one of the reasons for the success of neural networks. Here, we propose a model wherein the input feature space is enhanced by the pre-trained functional link network. Weights are generated by LSTSVM, and a non-linear function is applied on the product between input features and the weights to get the enhanced features. These features are concatenated with the input features to get the extended feature space. Final classification is done by LSTSVM based on these extended features. Numerical experiments and statistical tests conducted show that the proposed model outperforms the baseline methods.
Dual Alignment for Partial Domain Adaptation

Partial domain adaptation (PDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain based on an assumption that the source label space subsumes the target label space. The major challenge is to promote positive transfer in the shared label space and circumvent negative transfer caused by the large mismatch across different label spaces. In this article, we propose a dual alignment approach for PDA (DAPDA), including three components: 1) a feature extractor extracts source and target features by the Siamese network; 2) a reweighting network produces “hard” labels, class-level weights for source features and “soft” labels, instance-level weights for target features; 3) a dual alignment network aligns intra domain and interdomain distributions. Specifically, the intra domain alignment aims to minimize the intraclass variances to enhance the intraclass compactness in both domains, and interdomain alignment attempts to reduce the discrepancies across domains by domain-wise and class-wise adaptations. The negative transfer can be alleviated by down-weighting source features with nonshared labels. The positive transfer can be enhanced by upweighting source features with shared labels. The adaptation can be achieved by minimizing the discrepancies based on class-weighted source data with hard labels and instance-weighed target data with soft labels. The effectiveness of our method has been demonstrated by outperforming state-of-the-art PDA methods on several benchmark datasets.
Face re-identification challenge: Are face recognition models good enough?

Face re-identification (Re-ID) aims to track the same individuals over space and time with subtle identity class information in automatically detected face images captured by unconstrained surveillance camera views. Despite significant advances of face recognition systems for constrained social media facial images, face Re-ID is more challenging due to poor-quality surveillance face imagery data and remains under-studied. However, solving this problem enables a wide range of practical applications, ranging from law enforcement and information security to business, entertainment and e-commerce. To facilitate more studies on face Re-ID towards practical and robust solutions, a true large scale Surveillance Face Re-ID benchmark (SurvFace) is introduced, characterised by natively low-resolution, motion blur, uncontrolled poses, varying occlusion, poor illumination, and background clutters. This new benchmark is the largest and more importantly the only true surveillance face Re-ID dataset to our best knowledge, where facial images are captured and detected under realistic surveillance scenarios. We show that the current state-of-the-art FR methods are surprisingly poorfor face Re-ID. Besides, face Re-ID is generally more difficult in an open-set setting as naturally required in surveillance scenarios, owing to a large number of non-target people (distractors) appearing in open ended scenes. Moreover, the low-resolution problem inherent to surveillance facial imagery is investigated. Finally, we discuss open research problems that need to be solved in order to overcome the under-studied face Re-ID problem.
Robust Pose-Based Human Fall Detection Using Recurrent Neural Network

Detecting falling event from the video for providing timely assistance to the fallen person is a challenging problem in computer vision due to the absence of large-scale fall dataset and the presence of many covariate factors like varying view angle, illumination, and clothing. In this paper, to address this problem, an effective approach for fall detection has been proposed. We have developed a recurrent neural network (RNN) with LSTM architecture that models the temporal dynamics of the 2D pose information of a fallen person. Human 2D pose information, which has proven effective in analyzing fall pattern as it ignores people's body appearance and environmental information while capturing the true motion information makes the proposed model simpler and faster. Experimental results have verified that our proposed method has achieved 99.0% sensitivity on both of the benchmark datasets of fall detection FDD and URFD.
Closed-loop augmented reality towards accurate human-robot collaboration

Augmented Reality provides an interactive approach to combining the physical manufacturing environment with the computer-generated virtuality. However, it is still challenging to establish an efficient AR system due to the complexity of the actual manufacturing environment, and difficulties in human-robot collaboration. In this research, a feasible AR system is developed with a novel closed-loop structure. A feasible AR-based human-robot interaction method is also developed based on the HoloLens device, together with an advanced compensation mechanism towards accurate robot control. The proposed work is implemented and validated through case studies, and quantifiable performance assessment on system response time, compensation accuracy, etc.