Reviewerly/exHarmony huggingface dataset:
A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets

We present a data set of 1460 Hindi-English code-mixed tweets consisting of 20,949 tokens labelled with Proposition Bank labels marking their semantic roles.We created verb frames for complex predicates present in the corpus and formulated mappings from Paninian dependency labels to Proposition Bank labels.With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data.We obtain an accuracy of 96.74% for Argument Identification and are able to further classify 73.93% of the labels correctly.While there is relevant ongoing research on Semantic Role Labelling (SRL) and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in Hindi-English codemixed data, to the best of our knowledge.
Multimodal Semantic Segmentation: Fusion of RGB and Depth Data in Convolutional Neural Networks

Semantic segmentation has been an active field in computer vision and photogrammetry communities for over a decade. Pixel-level semantic labeling of images is generally achieved by assigning labels to pixels using machine learning techniques. Among others, the encoder–decoder convolutional neural networks have become the baseline approach for this problem recently. The majority of papers on this topic use only RGB images as input, despite the availability of other data sources, such as depth, which can improve segmentation and labeling. In this chapter, we investigate a number of encoder–decoder CNN architectures for semantic labeling, where the depth data is fused with the RGB data using three different approaches: (1) fusion with RGB image through color space transformation, (2) stacking depth images and RGB images, and (3) using Siamese network structures, such as FuseNet or VNet. The chapter also presents our approach to using surface normals in place of depth data. The advantage of using surface normal representation is to introduce viewpoint independency, meaning that the direction of a surface normal vector remains the same when the camera pose changes. This is a clear advantage over raw depth data where the depth value for a single scene point changes when the camera moves. The chapter provides a comprehensive analysis of three different fusion approaches using SegNet, FuseNet and VNet deep learning architectures. The analysis is conducted on both the Stanford 2D-3D-Semantics indoor dataset and aerial images from the ISPRS's Vaihingen dataset. Depth images of the Stanford dataset are acquired directly by flash LiDAR, and the ISPRS dataset depth images are generated by dense 3D reconstruction. We show that the surface normal representation better generalizes to different scenes. In our experiments, using surface normals with FuseNet achieved 5% improvement compared to using depth, and this resulted in 81.5% global accuracy on the Stanford dataset.
An ambient intelligence approach for learning in smart robotic environments

Abstract Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.
Instance Segmentation Based Semantic Matting for Compositing Applications

Image compositing is a key step in film making and image editing that aims to segment a foreground object and combine it with a new background. Automatic image compositing can be done easily in a studio using chroma-keying when the background is pure blue or green. However, image compositing in natural scenes with complex backgrounds remains a tedious task, requiring experienced artists to hand-segment. In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task. Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method. It extends automatic image compositing techniques such as chroma-keying to scenes with complex natural backgrounds without the need for any kind of user interaction. The output of our approach can be considered as both refined instance segmentations and alpha mattes with semantic meanings. We provide experimental results which show improved performance results as compared to existing approaches.
A Comparison of 2D and 3D Convolutional Neural Networks for Hand Gesture Recognition from RGB-D Data

Hand gesture recognition from videos has more challenges compared with still images due to the difficulty of representing temporal features and longer training times especially in real-time applications. In this paper, we investigated the potential of 2D over 3D CNN's for representing temporal features and classification of hand gestures in videos. We mapped the frame sequence of hand gestures to a chronological tiled pattern in order to capture the dynamics of the hand movement in a single frame. Then, using 2D CNN's, we generated feature vectors containing both special and temporal features. Additionally, we proposed a new approach for fusing data and predictions through a two-stream architecture to exploit depth information. The effects of different types of augmentation techniques is also investigated. Our results confirm that appropriate usage of 2D CNNs outperforms a 3D CNN implementation in terms of recall, accuracy and time in this task.
Application of Blockchain Technology in Data Management: Advantages and Solutions

Blockchain is considered to be the key technology to lead the transformation of Information Internet to Value Internet. More and more organizations are exploring the industry applications of blockchain, and how to apply blockchain in data management has become one of the focuses of discussion. This article focuses on blockchain and provides a detailed analysis on its core components, technologies and applications. Then, combs the problems faced by data management in quality, security, sharing and so on, and analyzes the application advantages of blockchain technology in data management, and put forwards a data collaborative management model based on blockchain, which has the characteristics of decentralization, collective maintenance, automatic execution, and non-tamperable. The model covers user authentication, data verification, data logging, data sharing and other processes, and is equipped with a data management incentive system, which can achieve convenient, secure and fast data management. Applying blockchain technology to data management can further improve the effectiveness of data management and improve the quality of data, and create a positive data sharing environment.
How Deep is the Mud: Fathoming Architecture Technical Debt Using Designite

The quality of software architecture is an important concern for any software development team. Architecture smells represent quality issues at architecture granularity. Identifying and refactoring them periodically is a necessity to keep architecture quality high. We present Designite, a software design quality assessment tool, that identifies seven well-known architecture smells. Along with the identification, the tool provides supplementary information such as cause and responsible classes for each identified smell instance to help developers understand and refactor the smell. The tool is relevant and useful in both research and practice context. Software developers may use it to identify technical debt instances and to refactor them. On the other hand, software engineering researchers may use the tool to carry out large-scale empirical studies concerning code smells.
Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet

Breast cancer has always been the main killer of women. The constantly development of Convolutional Neural Network (CNN) greatly improved the possibility of early diagnostics of breast cancer owing to its high efficiency and accuracy. In this paper, we apply the architecture of Densely Connected Convolutional Network (DenseNet), and then assimilate into Squeeze-and-Excitation Network (SeNet) to perform multitask classification on Camelyon16 which is a set of images of hematoxylin and eosin (H&E) stained breast histology microscopy. Whole-slide images (WSIs) are generally stored in a multi-resolution pyramid, our dataset contains patches of Camelyon16 under ×5, ×20, ×40 three magnifications. Our multitask is to identify the magnification of the patch and distinguish whether the extracted patch belongs to metastatic tumor area of WSIs at the same time by link two classifiers at the end of the same network. Whether on multitask or a single subtask, our network has showed excellent performance, SE-DenseNet-40 has even achieved an accuracy of 92.92% on CIFAR-10.
Adaptive Neural Network PID Controller

Complex and coupled nonlinear systems cannot be efficiently controlled using conventional Proportional Integral Derivative (PID) controller. To obtain good control performance, the parameters of the PID controller should be suitably tuned and adapted to the system parameters variation. In this paper, the PID controller parameters are obtained from a Multi-Layer Perceptron (MLP) neural network and their values are online tuned using the back propagation method. The Adaptive Neural Network PID (ANNPID) controller is analyzed and compared with the conventional PID controller, the type 1 Fuzzy Logic Controller (type 1 FLC) and the type 2 Fuzzy Logic Controller (type 2 FLC) through computer simulation and experimental study. The obtained results show that the neural network PID has better performance than the other controllers and its parameters can be easily tuned to compensate the system parameters variation and the disturbances effects.
Challenges in Storing and Processing Big Data Using Hadoop and Spark

In recent years, data generated from E-commerce websites are exponentially increasing. As traditional tools and technologies are not suitable for handling such huge amounts of data, there is a need for a good framework that can store these huge amounts of data and allow the end users to perform analytics on it. This analytics, in turn, helps the industry minimize various resources like manpower and time, as well as allows taking dynamic effective decisions towards profitability. In this chapter, we discuss the need of new analytical platforms, in particular different big data frameworks like Hadoop and Spark, along with MapReduce programming concepts. In addition to that, we describe steps to develop programs in Spark Streaming, Spark SQL and GRAPHX. The objective of this chapter is to make the readers understand the challenges in storing and processing big data, as well as how to use different big data frameworks effectively to store and process big data.
Non-Random Weight Initialisation in Deep Learning Networks for Repeatable Determinism

This research is examining the change in weight values of deep learning networks after learning. These research experiments require to make measurements and comparisons from a stable set of known weights and biases before and after learning is conducted, such that comparisons after learning are repeatable and the experiment is controlled. As such the current accepted schemes of random number initialisations of the weight values may need to be deterministic rather than stochastic to have little run to run varying effects, so that the weight value initialisations are not a varying contributor. This paper looks at the viability of non-random weight initialisation schemes, to be used in place of the random number weight initialisations of an established well understood test case. The viability of non-random weight initialisation schemes in neural networks may make a network more deterministic in learning sessions which is a desirable property in mission and safety critical systems. The paper will use a variety of schemes over number ranges and gradients and will achieve a 97.97% accuracy figure just 0.18% less than the original random number scheme at 98.05%. The paper may highlight that in this case it may be the number range and not the gradient that is effecting the achieved accuracy most dominantly, although there may be a coupling of number range with activation functions used. Unexpectedly in this paper, an effect of numerical instability will be discovered from run to run when run on a multi-core CPU. The paper will also show the enforcement of consistent deterministic results on an multi-core CPU by defining atomic critical code regions aiding repeatable Information Assurance (IA) in model fitting (or learning sessions).
A Comparison of Correlation Filter-Based Trackers and Struck Trackers

In recent years, two types of trackers, namely correlation filter-based tracker (CF tracker) and structured output tracker (struck), have exhibited the state-of-the-art performance. However, there seems to be a lack of analytic work on their relations in the computer vision community. In this paper, we investigate two state-of-the-art CF trackers, i.e., spatial regularization discriminative correlation filter (SRDCF) and correlation filter with limited boundaries (CFLB), and struck, and reveal their relations. Specifically, after extending the CFLB to its multiple channel versions, we prove the relation between SRDCF and CFLB on the condition that the spatial regularization factor of SRDCF is replaced by the masking matrix of CFLB. We also prove the asymptotical approximate relation between SRDCF and struck on the conditions that the spatial regularization factor of SRDCF is replaced by an indicator function of object bounding box, the weights of SRDCF in its loss item are replaced by those of struck, the linear kernel is employed by struck, and the search region tends to infinity. The extensive experiments on public benchmarks OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we explain how detailed differences among SRDCF, CFLB, and Struck would give rise to slightly different performances on visual sequences.
Detecting Emergent Behaviors and Implied Scenarios in Scenario-Based Specifications: A Machine Learning Approach

Scenarios are commonly used for software requirements modeling. Scenarios describe how system components, users and the environment interact to complete the system functionality. However, several scenarios are needed to represent a complete system behavior and combining the scenarios may generate implied scenarios (IS) that are associated with some unexpected behavior. The unexpected behavior is commonly known as Emergent Behavior (EB), which is not evident in the requirements and design phase but may degrade the quality of service and/or cause irreparable damage during execution. Detecting and fixing EB/IS in the early phases can save on deployment cost while minimizing the run-time hazards. In this paper, we present a machine learning approach to model and identify the interactions between system components and verify which interactions are safe and which may lead to EB/IS. The experimental result shows that our approach can efficiently detect different types of EB/IS and applicable to large scale systems.
Quantum Cryptography on IBM QX

Due to the importance of securing electronic transactions, many cryptographic protocols have been employed, that mainly depend on distributed keys between the intended parties. In classical computers, the security of these protocols depends on the mathematical complexity of the encoding functions and on the length of the key. However, the existing classical algorithms 100% breakable with enough computational power, which can be provided by quantum machines. Moving to quantum computation, the field of security shifts into a new area of cryptographic solutions which is now the field of quantum cryptography. The era of quantum computers is at its beginning. There are few practical implementations and evaluations of quantum protocols. Therefore, the paper defines a well-known quantum key distribution protocol which is BB84 then provides a practical implementation of it on IBM QX software. The practical implementations showed that there were differences between BB84 theoretical expected results and the practical implementation results. Due to this, the paper provides a statistical analysis of the experiments by comparing the standard deviation of the results. Using the BB84 protocol the existence of a third-party eavesdropper can be detected. Thus, calculations of the probability of detecting/not detecting a third-party eavesdropping have been provided. These values are again compared to the theoretical expectation. The calculations showed that with the greater number of qubits, the percentage of detecting eavesdropper will be higher.
SwitchGAN for Multi-domain Facial Image Translation

Recent studies for multi-domain facial image translation have achieved an impressive performance. However, the existing methods still have limitations for some tasks, such as translating a facial image into different age groups, or translating a facial expression into other expressions. To address this problem, we propose a Switch Generative Adversarial Network (SwitchGAN) to perform delicate image translation among multiple domains. A feature switching operation is proposed to achieve features selection and fusion in our conditional modules. Experiments on Morph, RaFD and CelebA databases show that our SwitchGAN can achieve visually better translation effects than StarGAN. The attribute classification results using the trained ResNet-18 classifier also quantitatively suggest that the face images generated by SwitchGAN achieved much higher accuracy than that generated by StarGAN.
Trust Requirements in Blockchain Systems: A Preliminary Study

The focus of this position paper is to study and identify trust requirements in blockchain systems. So far, we have found that the overall trust-related requirements engineering knowledge is far from what is needed for successful engineering of various trust-related challenges in blockchain systems. We have identified an urgent need and challenge to revisit requirements engineering models to effectively include trust requirements; and to produce a trust engineering taxonomy, models, and techniques for achieving and fulfilling blockchain system trust requirements and goals.
Study on Medical Image Report Generation Based on Improved Encoding-Decoding Method

The automatic description of images has made good advances, but limited by the little-sample dataset, that the automatic generation of medical imaging reports is still a challenging problem. Aiming at the problem of training the joint model (CNN-RNN) on little-sample datasets, this paper proposes an improved encoding-decoding mode, in which the encoder uses less parameter in FCN (Fully Convolutional Network) for identifying lesions in mammography, and encoding it into a semantic vector. The decoder uses a LSTM (Long Short-Term Memory network) for solving, thereby reducing sample requirements. In addition, this paper combines multi-label classification (MLC) to assist the semantic coding process and uses post-processing such as the beam search to make the output fit in the natural language description better. Compared to existing models, our improved model on public mammography dataset (INbreast) with real-world data supplement achieved the BLEU score improvements by two points.
Experimental test of maximal tripartite nonlocality using an entangled state and local measurements that are maximally incompatible

The roles of entanglement, nonlocality, and measurement incompatibility are explored. An experimental method is introduced to prepare a special class of state, akin to the famous GHZ and Bell states, opening the door for creating multipartite states that were previously inaccessible.
Detecting System Failures with GPUs and LLVM

Since system failures cause a huge financial loss, they should be detected as early and accurately as possible and then be recovered rapidly. To detect system failures, there are mainly two methods: black-box and white-box monitoring. However, external black-box monitoring cannot obtain detailed information on system failures, while internal white-box one is largely affected by system failures. This paper proposes GPUSentinel for more reliable white-box monitoring using general-purpose GPUs. In GPUSentinel, system monitors running in a GPU analyze main memory and indirectly obtain the state of the target system. Since GPUs are isolated from the target system, system monitors are not easily affected by system failures. For easy development of system monitors, GPUSentinel provides a development environment including program transformation with LLVM. In addition, it also provides reliable notification mechanisms to remote hosts. We have implemented GPUSentinel using CUDA and the Linux kernel and confirmed that GPUSentinel could detect three types of system failures.
Detecting Proxy User Based on Communication Behavior Portrait

Abstract Proxies can help users to bypass the network filtering system, leaving the network open to banned content, and can also enable users to anonymize themselves for terminal security protection. Proxies are widely used in the current network environment. However, certain spy proxies record user information for privacy theft. In addition, attackers can use such technologies to anonymize malicious behaviors and hide identities. Such behaviors have posed serious challenges to the internal defense and security threat assessment of an organization; however, the anonymity of the proxy makes it consistent with normal network communication, and general network traffic identification methods are not able to detect it. To accurately and effectively discover proxy users in the organization based on s, a proxy user detection method based on communication behavior portrait offers the following: (1) analysis of the communication behavior from the perspective of the portrait. Based on not abandoning the effective information of the traffic itself, the label system is established by introducing exogenous data to identify the difference between proxy communication and normal communication. (2) Construction of the portrait feature set of proxy user detection based on the traffic file and external data by studying the differences between the attribute sets of communication behavior labels for proxy users and non-proxy users. (3) Design and implementation a data-driven machine learning method to supply guidance for automatic recognition of such behavior. The experimental results show that, compared with state-of-the-art methods, the detection accuracy for the proxy user exceeds 95%, and that of real network traffic environment exceeds 85%. These results indicate that the detection method proposed in this paper can accurately distinguish proxy communication and normal communication and thus achieves precise proxy user detection.