Reviewerly/exHarmony huggingface dataset:
Lifelong Learning Augmented Short Text Stream Clustering Method

Depending on the scanning mode, existing short text stream clustering methods can be divided into the following two kinds of methods: one-pass-based and batch-based. The one-pass-based method handles each text only one time, but cannot deal with the sparseness problem very well. The batch-based method obtains better results by allowing multiple iterations of each batch, but the efficiency is relatively low. To overcome these problems, this paper presents Lifelong learning Augmented Short Text stream clustering method (LAST), which incorporates the episodic memory module and sparse experience replay module of lifelong learning into the clustering process. Specifically, LAST processes each text one time, but at a certain interval it randomly samples some previously seen texts of the episodic memory to update cluster features by performing sparse experience replay. Empirical studies on two public datasets demonstrate that the performance of the LAST-based method is on a par with the batch-based method, and runs close to the speed of the one-pass-based method.
Combined Reinforcement Learning via Artificial Potential Field: A Case Study in Pommerman

Pommerman is a recently-proposed multi-agent benchmark, which is very challenging for Reinforcement Learning (RL). The main obstacles to adopt RL in Pommerman are the delayed action effects and sparse rewards. This paper presents novel approaches to mitigate these problems by introducing Artificial Potential Field (APF) in the two-dimensional Pommerman world. We propose a new framework to generate hybrid features from both APF computation and raw environment data. Meanwhile, a new reward shaping method through APF is developed to give the learning agent faster and more efficient policy iteration. The training results show that the learning speed and convergence reward are both improved on a 1v1 mode of the Pommerman game, compared to the conventional learning algorithms, A2C and ACKTR.
Triaging Autonomous Drone Faults By Simultaneously Assuring Autonomy and Security

Our premise is that assuring autonomy has operational and security components and that artificial intelligence (AI) based methods are appropriate for this task; however, making these AI techniques “Explainable” can be non-trivial. Further, our position is that a reasonable solution to this problem would require a monitor (preferably at the ground station) to access the drone's operational autonomy and individual components. The monitor could be developed to review every decision made by the native operational autonomy. When a significant number of “bad decisions” (i.e., different from the monitor) are made, the monitor can “lock out” the native autonomy and take over (establishing an explainable “safe state”). Similarly, the monitor can use data from mini-anomaly detectors to observe all of the drone's major components and aggregate the feedback to determine if the drone's security has been compromised. Essentially, we propose an “Explainable AI Security” Monitor that not only simultaneously assures the operational autonomy and security of autonomous drone fleets but also can likely output the logic that sourced its decisions. This paper will survey the current autonomy, assurance, and security literature and point out this gap (i.e., Explainable AI Security Monitor) and the critical need.
Heuristic Approach to Detect Pectoral Muscle and Nipple in Mammogram for Computer-Aided Diagnosis

In computer-aided diagnosis algorithms for mammograms, many preprocessing steps like pectoral muscle detection and the coordinates of the nipple are done automatically before the actual detection and diagnosis process begin. Normally, in medio-lateral oblique (MLO) views, the pectoral muscle should be seen to the tip of the nipple or below. Consequently, straight line estimate of pectoral muscle is parallel to the tangent drawn at nipple. Considering this heuristic anatomical relation, in the proposed work, hybrid combination of textured Hough transform with distant surround suppression was used to detect straight or curved pectoral muscle on MLO view followed by orthogonal Radon transform to detect the nipple position. The proposed algorithm was tested on 266 MLO views from miniMIAS and 74CC views from local hospital. The pectoral muscle was detected with accuracy of 97%, and detected nipple position was acceptable in 93.67% of mammograms with average error of 5.755 mm with location annotated by a radiologist.
Design of English Autonomous Learning System Based on Digital Signal Processing

Abstract The network is beginning to change people’s production and life style, which has produced earth-shaking changes in many industries. The learning method based on the network also has a great impact on the traditional education. The emergence of individualized autonomous learning mode of college English with teachers as the leading factor and students as the main body makes students change from passive learning to active learning. Many college students take acquiring knowledge and information as their important purpose in the use of the Internet, which shows that online autonomous learning has become an important means of learning in universities. On this platform, students can flexibly grasp their own learning time and progress, so as to achieve targeted learning and further improve their interest in English learning. This paper expounds the construction of college English autonomous learning environment based on digital signal processing, and puts forward corresponding management measures in view of the existing problems in autonomous learning management, in order to give full play to the effectiveness of digital signal processing technology in assisting college English autonomous learning.
Integrity guarantees over highly dynamic outsourced map data

Highly dynamic map data play nowadays a crucial role in different application contexts. Their huge volume often enforces the data owner to outsource them to the cloud, becoming this way the client of analytical queries computed by the cloud. Therefore, the classical problem of query integrity, usually considered in the scenario of location-based services (LBS) over outsourced databases, arises also in this different scenario. This means that the data owner should have guarantees that the cloud response to a submitted query is correct, complete, and fresh. On the one hand, the considered setting simplifies the problem, because public verifiability is no longer required, but poses new challenges, such as supporting very efficient insertions and deletions and, consequently, effective guarantees for freshness. This paper studies this problem by proposing a lightweight message-authentication-code-based approach fulfilling the above requirements better than the approaches available in the literature, according to an analytical cost evaluation.
Signature features with the visibility transformation

In this paper we put the visibility transformation on a clear theoretical footing and show that this transform is able to embed the effect of the absolute position of the data stream into signature features in a unified and efficient way. The generated feature set is particularly useful in pattern recognition tasks, for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values.
Comparing optimization methods for deep learning in image processing applications

Abstract Training a deep learning network requires choosing its weights such that the output minimizes a given loss function. In practice, stochastic gradient descent is frequently used for solving the optimization problem. Several variants of this approach have been suggested in the literature. We study the impact of the choice of the optimization method on the outcome of the learning process at the example of two image processing applications from quite different fields. The first one is artistic style transfer, where the content of one image is combined with the style of another one. The second application is a real world classification task from industry, namely detecting defects in images of air filters. In both cases, clear differences between the results of the individual optimization methods are observed.
DTRNG: Low Cost and Robust True Random Number Generator Using DRAM Weak Write Scheme

True Random Number Generators (TRNGs) are used in a variety of applications including cryptography, computer simulation, gambling games, machine learning and hyperdimensional computing. Dynamic Random-Access Memory (DRAM)-based TRNGs have been widely used as they are low cost and available in most modern electronic devices. However, most existing DRAM-based TRNGs produce random numbers with either hardware overhead, or low throughput. In this work, we report a novel high dense, high throughput and high entropy DRAM-based TRNG, named DTRNG, that exploits the process variations inherited by the DRAM cell's access transistor and sense amplifiers. DTRNG can be employed in commodity DRAM chips with no hardware overhead and using the standard memory controller commands. The proposed method is based on controlling the word line voltage supply part of the DRAM chip during random number mode. The novel approach provided in this work has been validated using cadence circuit tools on a constructed 8x8 DRAM chip in 65nm technology. In addition, Monte Carlo simulations are performed to verify the entropy of the generated numbers. The random sequence generated by DTRNG has passed all the NIST test without any post-processing demonstrating randomness and suitability for security scheme. DTRNG is considered a milestone towards low cost and high efficient secure hardware for AI and IoT applications.
A Generic Approach to Build Revocable Hierarchical Identity-Based Encryption

Revocable hierarchical identity-based encryption (RHIBE) is an extension of HIBE that provides the efficient key revocation function by broadcasting an update key per each time period. Many RHIBE schemes have been proposed by combining an HIBE scheme and a tree-based revocation method, but a generic method for constructing an RHIBE scheme has not been proposed. In this paper, we show for the first time that it is possible to construct RHIBE schemes by generically combining underlying cryptographic primitives and tree-based revocation methods. We first generically construct an RHIBE-CS scheme by combining HIBE schemes and the complete subtree (CS) method, and prove the adaptive security by using the adaptive security of the HIBE schemes. Thus, we obtain RHIBE schemes under the quadratic residuosity assumption, CDH assumption, and factoring assumption. Next, we generically construct an RHIBE-SD scheme with shorter update keys by combining HIBE and hierarchical single revocation encryption (HSRE) schemes, and the subset difference (SD) method to reduce the size of update keys. Finally, we generically construct an RHIBE-CS scheme with shorter ciphertexts by combining HIBE schemes with constant-size ciphertext and the CS method. Through different kind of generic combinations, we obtain various RHIBE schemes that provide a trade-off between shorter ciphertexts and shorter update keys.
Learning the Minimal Representation of a Dynamic System from Transition Data

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Attention Guided YOLOv3 for Wearing Safety Helmet Detection

Wearing safety helmet is of great importance to ensure the personal safety in power substations. The task of safety helmet detection still requires manual effort which is time-consuming and laborious. To address this problem, this paper introduces a computer vision method for automatic safety helmet detection based on YOLOv3 algorithm. We propose an attention mechanism which contains spatial-wise and channel-wise attention modules, to separately enhance the low-level and high-level features of deep convolutional encoder. Equipping YOLOv3 with our attention mechanism, the detector can improve its ability of detecting small objects, which is suitable for real-world safety helmet detection. Experiments on a publicly available helmet wearing detection dataset show that the proposed method is able to achieve good performance while running at a high speed.
Word Level LSTM and Recurrent Neural Network for Automatic Text Generation

Sequence prediction problems have been a major problem for a long time. Recurrent Neural Network (RNN) has been a good solution for sequential prediction problems. This work aims to create a generative model for text. Even though, RNN has its own limitations such as vanishing and exploding gradient descent problems, and inefficiency to keep track of long-term dependencies. To overcome these drawbacks, Long Short Term Memory (LSTM) has been a path-breaking solution to deal with sequential data and text data in particular. This paper delineates the design and working of text generation using word-level LSTM-RNN.
End-to-End Event Factuality Identification via Hybrid Neural Networks

Event factuality identification (EFI) is a task to judge the factuality of events in texts, and is also the basic task of many related applications in the field of Natural Language Processing (NLP), such as information extraction and rumor detection. Previous research on EFI relied on annotated information, which cannot be applied to real world applications directly, and some studies only considered the default source AUTHOR. To address the above issues, this paper launches an end-to-end EFI model considering different event-related sources, which constructs the candidate event sets from raw texts to capture various kinds of event-related information, and then proposes a hybrid neural network model on GCN and BiLSTM to learn semantic and syntactic features, respectively. The experimental results on FactBank show that our proposed approach outperforms the baselines.
A Multi-task Multi-view based Multi-objective Clustering Algorithm

In recent years, multi-view multi-task clustering has received much attention. There are several real-life problems that involve both multi-view as well as multi-task clustering, i.e., the tasks are closely related, and each task can be analyzed using multiple views. Traditional multi-task multi-view clustering algorithms utilize single-objective optimization based approaches and cannot apply too-many regularization terms. However, these problems are inherently some multi-objective optimization problems because conflict may be between different views within a given task and also between different tasks, necessitating a trade-off. Based on these observations, in this paper, we have proposed a novel multi-task multi-view multiobjective optimization (MTMV-MO) based clustering algorithm which simultaneously optimizes three objectives, i.e., within-view task relation, within-task view relation and the quality of the clusters obtained. The proposed methodology (MTMV-MO) is evaluated on four different datasets and the results are compared with five state-of-the-art algorithms in terms of Adjusted Rand Index (ARI) and Classification Accuracy (%CoA). MTMV-MO illustrates an improvement of 1.5-2% in terms of ARI and 4-5% in terms of %CoA compared to the state-of-the-art algorithms.
Automatic Design for Matheuristics

Matheuristics have become widespread and effective methods for tackling the generalized assignment problem (GAP) and many other NP-hard problems. In fact, in this book we have many such methods, ranging from metaheuristics and mathematical programming techniques but mainly to real matheuristics. In these methods we will see no parameter settings, but it is true: all these methods in the end rely on a good parameter setting. Here, in this chapter, we will learn of what can be seen as a good parameter setting. After having seen the parameters and the parameter configuration problem, we turn to the automatic algorithm configuration and then learn about three methods: ParamILS, SMAC, and irace. Then we learn about what these methods are good for and go into some details of automated configuration of existing algorithms, the integration of an algorithm engineering process up to the design of, in part, completely new algorithms. The chapter ends with a little example of how irace can be used to automatically configure a simple iterated local search method for the GAP.
Research on Face Detection Method Based on Deep Learning

Faster R-CNN is a popular method in object detection applications. Based on the similar characteristics of the category in face detection and the category label of target background in target detection, this paper improves the region suggestion network in Faster R-CNN method, and realizes multiscale face detection by adding multiple detectors. We train and test the multi-scale face detection network model on the Wider Face dataset. Through the experimental results, it can be obtained that the accuracy of the multi-scale face detection method is 89.6%, which has practical application value.
Bangla Handwritten Math Recognition and Simplification Using Convolutional Neural Network

Mathematical simplification—it is an interior capability in human, but machine does not have cognitional skills that can understand the problem by visual context. In this present work, we represent a new system which takes an input image of Bangla handwritten mathematical expression and automatically simplifies the problem and generates the answer as an output. Proposed pursuit can be workable in an embedded system as well as mobile application. In this scope for recognition purpose, we use a CNN model called MatheNET for segmented Bangla digits and mathematical symbols. This model dataset contains 54 classes, 10 numerals and 44 mathematical operators and symbols. The algorithm has followed for rising a system model for automatically Bangla handwritten math simplification; it has been done really good job. In the fields of the state of the art, contributions in Bangla languages are still very low. Developing an automatic math equation solver has been a desire of the researchers who worked in the field of NLP for many years.
Specifying and Executing User Agents in an Environment of Reasoning and RESTful Systems Using the Guard-Stage-Milestone Approach

Abstract For Read-Write Linked Data, an environment of reasoning and RESTful interaction, we investigate the use of the Guard-Stage-Milestone approach for specifying and executing user agents. We present an ontology to specify user agents. Moreover, we give operational semantics to the ontology in a rule language that allows for executing user agents on Read-Write Linked Data. We evaluate our approach formally and regarding performance. Our work shows that despite different assumptions of this environment in contrast to the traditional environment of workflow management systems, the Guard-Stage-Milestone approach can be transferred and successfully applied on the web of Read-Write Linked Data.
Personalized Recommendation Based on Scholars’ Similarity and Trust Degree

With the rapid development of online social networks, academic social networks (ASNs) platforms are increasingly favored by researchers. For the scholar services built on the ASNs, recommending personalized researchers have become more important, as it could promote academic communication and scientific research for scholars. We propose a personalized recommendation method combining similarity and trust degree in an academic social network. First, the text-similarity hybrid model of LDA and TF-IDF is used to calculate the similarity of scholars’ interests, moreover, the social similarity between scholars is combined as the final similarity. Second, the trust degree is calculated according to the multi-dimensional interactive behavior among scholars. Finally, the combined similarity and trust degree between scholars are used as a ranking metric. We demonstrate and evaluate our approach with a real dataset from an academic social site SCHOLAT. The experiment results show that our method is valid in recommending personalized researches.
Deep Sentiment Learning Network for Temporal-aware Recommendation Based on User Reviews

Recently, many websites encourage users to fill in reviews on items and services to improve the quality of personalized recommendation, because of the rich sentiment information hidden in user reviews. However, most existing recommendation methods based on reviews heavily emphasize extracting the rich sentiment information from reviews by using deep learning technologies as far as possible and always ignore the effects of temporal dynamical of user preferences. To address the above issues, we propose DSL- TR, a deep sentiment learning framework for the temporal-aware recommendation, using an intricate combination of bidirectional long short-term memory (BLSTM) and Convolution Neural Network (CNN). It consists of four layers: embedding layer, BLSTM layer, CNN layer and feature fusion layer. In the embedding layer, we explicitly combine the time information in the review embedding layer by regarding the review time as an independent factor. Then, we utilize the BLSTM and CNN networks to extract the long and short-terms of latent features of user and item in parallel. Finally, the factorization machine technique, as a rating predictor, is introduced on the last layer to estimate the rating based on the learned user and item latent features. We conduct extensive experiments on Amazon's four data sets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.
Review on Smile Detection

Facial expressions are a result of specific movement of face muscles, and these face expressions are considered as a visible sign of a person’s internal thought process, intensions, and internal emotional states. Smile is such a face expression which often indicates, satisfaction, agreement, happiness, etc. Though, a lot of studies have been done over detection of Facial Expression in last decade, smile detection had attracted researcher for more deeper studies. In this review paper, different type of available smile detection so far has been discussed such as Deep Convolutional Neural Network (CNN), Hidden Marcov Model(HMM), K-Nearest Neighbours(KNN), Self Similarity of Gradient(GSS), Histogram of Oriented Gradients (HOG), Gabor-Energy Filters and Local Binary Pattern(LBP) etc and classifier like HAAR Classifier, Hidden Markov Model(HMM), Adaboost Support Vector Machine (SVM),Softmax Classifier and Extreme Learning Machine(ELM).This review paper will prove beneficial for learning about smile detection and its application.
Automatic Video Editing

Automatic video editing is an artistic process involving at least the steps of selecting the most valuable footage from the points of view of visual quality and the importance of the action filmed; and cutting the footage into a brief and coherent visual story that would be interesting to watch is implemented in a purely data-driven manner. We describe a system that is capable of learning the editing style from samples extracted from the content created by professional editors, including motion picture masterpieces, and of applying this data-driven style to cut non-professional videos with the ability to mimic the individual style of selected reference samples. Visual semantic and aesthetic features are extracted by an ImageNet-trained convolutional neural network, and the editing controller can be trained by an imitation learning algorithm or reinforcement learning algorithm. As a result, during the test the controller shows signs of observing basic cinematography editing rules learned from the corpus of motion pictures masterpieces. The loss function developed for learning approaches can be efficiently applied in a global optimisation setting of the automatic video editing problem using dynamic programming.