Reviewerly/exHarmony huggingface dataset:
Multitask Learning with Capsule Networks for Speech-to-Intent Applications

Voice controlled applications can be a great aid to society, especially for physically challenged people. However this requires robustness to all kinds of variations in speech. A spoken language understanding system that learns from interaction with and demonstrations from the user, allows the use of such a system in different settings and for different types of speech, even for deviant or impaired speech, while also allowing the user to choose a phrasing. The user gives a command and enters its intent through an interface, after which the model learns to map the speech directly to the right action. Since the effort of the user should be as low as possible, capsule networks have drawn interest due to potentially needing little training data compared to deeper neural networks. In this paper, we show how capsules can incorporate multitask learning, which often can improve the performance of a model when the task is difficult. The basic capsule network will be expanded with a regularisation to create more structure in its output: it learns to identify the speaker of the utterance by forcing the required information into the capsule vectors. To this end we move from a speaker dependent to a speaker independent setting.
Large-Context Pointer-Generator Networks for Spoken-to-Written Style Conversion

This paper introduces a spoken-to-written style conversion method that is suitable for handling a series of text such as discourses and conversations. Spoken-to-written style conversion can increase the readability of automatic speech recognition (ASR) outputs because ASR systems transcribe input speech into text in a literal manner; however, it generates several disfluencies and redundant expressions. The most successful method of text style conversion is sequence-to-sequence mapping using pointer-generator networks that possess a copy mechanism from an input sequence. However, pointer-generator networks cannot process a series of text serially because they are developed to handle isolated text. In fact, pointer-generator networks cannot consider relationships between current processing text and all preceding text. Therefore, this paper proposes large-context pointer-generator networks that combine pointer-generator networks with large-context encoder-decoder networks. In the proposed networks, all preceding written-style text can be considered to convert current spoken-style text into written-style text. In addition, the proposed networks introduce a large-context copy mechanism that can copy tokens from both current spoken-style text and preceding written-style text. Our experiments demonstrate the proposed networks yield better performance than conventional pointer-generator networks and large-context encoder-decoder networks.
CNN Classification of the Cultural Heritage Images

The cultural heritage image classification represents one of the most important tasks in the process of digitalization. In this paper, a deep learning neural network was applied in order to classify images of architectural heritage belonging to ten categories, in particular: (i) bell tower, (ii) stained glass, (iii) vault, (iv) column, (v) outer dome, (vi) altar, (vii) apse, (viii) inner dome, (ix) flying buttress, and (x) gargoyle. The Convolutional neural network was used for image classification, with the same architecture applied on two sets of the data: the full dataset consisting of 10 categories as well as dataset with 5 different image categories. The results show that both architectures performed well and obtained accuracy of up to 90%.
Recommending Moodle Resources Using Chatbots

Recommending the most suitable educational resources has always been a challenge in the field of E-Learning. This challenge has pushed educators and researchers to implement new ideas to help learners improve their learning and their knowledge. New solutions are using Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Natural Language Processing (NLP). In this paper, we propose and describe a new recommendation approach, centered primarily on the use of a custom chatbot which can be linked to Moodle's platform using a web configuration. A chatbot is an automated communication tool, based on intents and designed to emulate communication capabilities and conduct a conversation with individuals. The proposed system should be able to answer learner's queries in real-time and able to provide a relevant set of suggestions according to their needs.
A Multiple-Attribute Decision-Making Approach to Reinforcement Learning

In the reinforcement learning (RL) system, one important issue is the tradeoff problem between exploration and exploitation. In this paper, we studied this dilemma and proposed a new approach to solving this problem by multiple-attribute decision making (MADM). The applicability of the proposed method is extended by transfer learning. The method decomposes a task into several subtasks and uses the policies of subtasks trained by RL. The proposed visual MADM method (V-MADM) is based on the state-action values of each subtask to select the action with maximal one. Meanwhile, this paper proposes a transfer learning method using a decay function with decreasing probability such that the prior experiences of the subtasks can be utilized to accelerate the learning rate. Finally, the experiment of robot confrontation and Maze walker is performed to evaluate the learning performance of the proposed method. The experimental results show that fewer training cost is needed to obtain a more effective learning performance.
Aspect Term Extraction using Graph-based Semi-Supervised Learning

Aspect based Sentiment Analysis is a major subarea of sentiment analysis. Many supervised and unsupervised approaches have been proposed in the past for detecting and analyzing the sentiment of aspect terms. In this paper, a graph-based semi-supervised learning approach for aspect term extraction is proposed. In this approach, every identified token in the review document is classified as aspect or non-aspect term from a small set of labeled tokens using label spreading algorithm. The k-Nearest Neighbor (kNN) for graph sparsification is employed in the proposed approach to make it more time and memory efficient. The proposed work is further extended to determine the polarity of the opinion words associated with the identified aspect terms in review sentence to generate visual aspect-based summary of review documents. The experimental study is conducted on benchmark and crawled datasets of restaurant and laptop domains with varying value of labeled instances. The results depict that the proposed approach could achieve good result in terms of Precision, Recall and Accuracy with limited availability of labeled data.
A method of automatically generating initial parameters for large-scale belief rule base

The traditional rule-base inference methodology using evidential reasoning approach (RIMER) needs to traverse the reference values of all antecedent attributes when constructing belief rule base (BRB). Therefore, when many attributes have multiple reference levels, the scale of BRB will face the problem of combination explosion. Thus, it is inoperable to require experts to give all the parameters of each rule of BRB model, because of the limitation of expert knowledge in complex problems. If the initial BRB parameters are set unreasonably, the optimization speed and accuracy of the model will be affected. To solve this problem, a method of automatically generating initial parameters for large-scale BRB by using part of the standard rules and cloud model is proposed in this paper. Experts determine the number and parameters of standard rules through prior knowledge based on specific practical problems, and use cloud models to convert qualitative knowledge and quantitative information to automatically generate the remaining rules. A case study is established with Intel Berkeley Research lab data set to verify the effectiveness of the proposed method.
CIMA: Compiler-Enforced Resilience Against Memory Safety Attacks in Cyber-Physical Systems

Memory-safety attacks have been one of the most critical threats against computing systems. Although a wide-range of defense techniques have been developed against these attacks, the existing mitigation strategies have several limitations. In particular, most of the existing mitigation approaches are based on aborting or restarting the victim program when a memory-safety attack is detected, thus making the system unavailable. This might not be acceptable in systems with stringent timing constraints, such as cyber-physical systems (CPS), since the system unavailability leaves the control system in an unsafe state. To address this problem, we propose CIMA – a resilient mitigation technique that prevents invalid memory accesses at runtime. CIMA manipulates the compiler-generated control-flow graph to automatically detect and bypass unsafe memory accesses at runtime, thereby mitigating memory-safety attacks along the process. An appealing feature of CIMA is that it significantly improves system availability and resilience of the CPS even under the presence of memory-safety attacks. To this end, we design our experimental setup based on a realistic Secure Water Treatment (SWaT) and Secure Urban Transportation System (SecUTS) testbeds and evaluate the effectiveness and the efficiency of our approach. The experimental results reveal that CIMA handles memory-safety attacks effectively while meeting the real-time constraints and physical-state resiliency of the CPS under test. Using CIMA, we have also discovered a memory-safety vulnerability in the firmware of programmable logic controllers and a CVE ID has already been assigned for it.
Re-projected SURF Features Based Mean-Shift Algorithm For Visual Tracking

Visual tracking is an art of tracking a moving object over video frames using non-stationary cameras, for which feature descriptors of the target are computed and applied to a motion model. In this paper, SURF is used to compute the feature descriptors and Mean-Shift algorithm as a motion model. Mean-shift algorithm is fundamentally a logical approach to track the object on an image frame where the appearance is described by histograms. Mean-Shift tracking can directly be applied to SURF features but there is a big constraint of availability of an adequate number of feature keypoints for a given object. To get over this limitation, the re-projection technique is implemented in this paper for tracking an object in any video recorded from a mobile or stationary camera. Re-projection, online updates the histogram of object template for every frame by superimposing the feature keypoints from upcoming frames to the first frame. The object can be tracked using SURF descriptors without using any secondary information (color, texture, optical flow, gradient, etc.) regarding the object, which makes it computationally inexpensive to be used in real-time systems. Also, the SURF feature is calculated only for the target object that is to be tracked, this further reduces computational complexity. The simulation results prove the effectiveness of the work.
iKarate: Improving Karate Kata

Karate is a martial art that can be practiced using hands and feet to deliver and block strikes. Karate moves must be done in a certain way, many moves are practiced incorrectly during training. In this paper, we present a fully functional system which Karate players, coaches, judges and clubs could use. The system helps in capturing Karate moves using Kinect v2 sensor and analyzing these moves using F-DTW. Real-time feedback and a report are displayed to the users using a simple GUI (Graphical User Interface) that the users can easily use, to learn how a mistake was made, and how to fix it/learn from it. F-DTW was used for proving the concept, and an average accuracy of 90% was achieved. This Paper is mainly concerned about the moves named: Age-Uke, Mae-Geri, Gedan-Barai and Soto-Uke.
Generative Adversarial Networks Based on Edge Computing With Blockchain Architecture for Security System

This paper proposes a face recognition for security system based on artificial intelligence and edge computing with a limited dataset. Generative adversarial networks (GANs) are used to manipulate the dataset in order to overcome the accuracy issue of the limited dataset. The average accuracy of GANs outperforms the limited dataset up to 92.79%. Edge computing is used to overcome a high latency of cloud computing with Jetson Nano board, which produces an average of 8.8 frame-per-second. Furthermore, the detected face will make a payment using a smart contract to the blockchain network with low static difficulty to open a gate or door's lock. In comparison, a low static difficulty outperforms the Proof-of- Work consensus algorithm in terms of transaction time around 33-39 milliseconds.
Optimization of speckle pattern based on integer programming method

The performance of digital image correlation (DIC) method heavily depends on the quality of the speckle pattern. Extensive studies have been conducted to evaluate the speckle pattern, in order to reduce the errors of DIC. It is generally understood that the sum of square of subset intensity gradients (SSSIG) is the most relevant metric to the accuracy of the DIC; and the higher SSSIG indicates the better quality of the speckle pattern. However, there are few works on the optimization of the speckle pattern. In this work, we propose an integer programming method (IPM) to obtain the subset having the highest SSSIG. The speckle pattern derived from the proposed IPM has a relatively high SSSIG and has a strong anti-noise capability. The regular patterns (e.g. checkerboard-type) can also be avoided by the proposed method. Both the simulation and experiment have been conducted to validate the effectiveness of the proposed method. The simulation results show that the standard deviation (STD) of the displacements obtained by using the proposed speckle pattern in this study is reduced by 25.2% and 10.7%, respectively, comparing with the results obtained by the conventional random speckle pattern and the optimized speckle pattern in our earlier work. In addition, the experimental results from a tensile testing also agreed to the results from the simulation.
GRAM: An efficient (k, l) graph anonymization method

There are plenty of applications that use graphs for representing the association between different entities. Many research communities are interested in publishing such graphs to study the knowledge contained in them while the privacy of involved individuals is a challenging issue. A successful computational privacy model to address the problem of data privacy in microdata publishing is k-anonymity. A relaxed version of it for graphs is called (k, l)-anonymity: identifying at most l neighbors of a vertex v in the graph, an attacker cannot limit v in a group of less than k vertices. This paper introduces the GRAM: an efficient (k, l) GRaph Anonymization Method based on edge addition. At first, the GRAM adds enough edges to the graph to meet the privacy requirement. Then, the algorithm removes some redundant added edges to minimize changes in the original graph. The necessary and sufficient conditions for removing an added edge while maintaining the privacy requirement are proposed in the paper. This makes it possible to realize the model efficiently. An extensive set of experiments shows that the GRAM usually achieves a good trade-off between data utility and privacy, while it is more efficient than similar techniques.
A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.
A Dynamic Parameter Enhanced Network for distant supervised relation extraction

Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem about classifying a bag of sentences that contains two query entities into the predefined relation classes. Most existing methods consider those relation classes as distinct semantic categories while ignoring their potential connections to query entities. In this paper, we propose to leverage this connection to improve the relation extraction accuracy. Our key ideas are twofold: (1) For sentences belonging to the same relation class, the keywords to express the relation can vary according to the input query entities, i.e., style shift. To account for this style shift, the model can adjust its parameters in accordance with entity types. (2) Some relation classes are semantically similar, and the entity types appear in one relation may also appear in others. Therefore, it can be trained across different relation classes and further enhance those classes with few samples, i.e., long-tail relations. To unify these two arguments, we developed a novel Dynamic Parameter Enhanced Network (DPEN) for Relation Extraction, which introduces a parameter generator that can dynamically generates the network parameters according to the input query entity types and relation classes. By using this mechanism, the network can simultaneously handle the style shift problem and enhance the prediction accuracy for long-tail relations. Through extensive experiments, our method which is built on the top of the non-BERT-based or BERT-based models, can achieve superior performance over the state-of-the-art methods.
Wavelength attack recognition based on machine learning optical spectrum analysis for the practical continuous-variable quantum key distribution system

The imperfect devices in the practical continuous-variable quantum key distribution (CVQKD) system leave security loopholes that an eavesdropper may exploit. For example, the wavelength attacks can be implemented successfully by utilizing the wavelength-dependent properties of beam splitters (BSs). Eliminating the potential and existing security loopholes is essential for the practical CVQKD system. In this paper, an intelligent monitoring technology based on optical spectrum analysis is proposed for the practical CVQKD system. Through the machine learning-based optical spectrum analysis technique, an abnormal optical spectrum signal can be detected automatically by using the linear discriminant analysis support vector machine (LDA-SVM) algorithm, so as to realize attack detection and intelligent monitoring of the system. Simulation and experimental results show that the original spectral data and the abnormal spectral data after the attack can be accurately identified by the LDA-SVM algorithm, and the trained model can well resist the wavelength attacks.
Implicit Preferences Discovery for Biography Recommender System Using Twitter

With the dawn of the information age, the number of choices that each person has to make each day has seen an explosive growth. Every minute, users are bombarded with so many different options for everything user do, from buying clothes to watching TV, that one live in a state of constant information overload, feeling stressed out by this imposed free will then be liberated by it. One ways in which user have now become used to mitigating this constant barrage of incessant information is by the usage of Recommender Systems. These systems are software components that use some form of rating provided by a user to generate recommendations for items that they may be interested in. Unfortunately, recommender systems face a major problem called the "Cold – Start problem", which essentially means that a recommender system in an online setting has no information about the user when the user first signs up. Many approaches have been proposed to solve this, but the one this paper shall be taking is to use information from social networks, specifically Twitter, to get this initial information. Implicitly infer user interest with good accuracy is proposed here and this understanding of interests can later be updated by observing user actions as they interact with the system. We leverage the power of the categorical information stored in the Wikipedia database to allow us to assign relative weights to entities that a user follows on Twitter. This proposed approach for creating the rating vector for each user by mining the Twitter account of the users' is evaluated using the RMSE and MAE metrics.
Online detection and classification of in-corrected played strokes in table tennis using IR depth camera

Table tennis is a complex sport with a distinctive style of play. Due to the rising interest in this sport the past years, attempts have been targeted towards enhancing the training experience and quality through various techniques. Technology has been used to support training sessions for table tennis players before, with a focus on players’ performance measures rather than technique. In this paper, we propose a methodology based on IR depth camera for detecting and classifying the efficiency of strokes performed by players in order to enhance the training experience. Our system is to based on analyzing depth data collected from IR depth camera and recognized using fastDTW algorithm. The results show an average accuracy of 88% - 100%. This is the first paper to address the usage of IR depth camera on the table tennis player to detect and classify the strokes played.
Deciding the Bisimilarity of Context-Free Session Types

Abstract We present an algorithm to decide the equivalence of context-free session types, practical to the point of being incorporated in a compiler. We prove its soundness and completeness. We further evaluate its behaviour in practice. In the process, we introduce an algorithm to decide the bisimilarity of simple grammars.