Reviewerly/exHarmony huggingface dataset:
Hierarchical Lifelong Machine Learning With “Watchdog”

Most existing lifelong machine learning works focus on how to exploit previously accumulated experiences (e.g., knowledge library) from earlier tasks, and transfer it to learn a new task. However, when a lifelong learning system encounters a large pool of candidate tasks, the knowledge among various coming tasks are imbalance, and the system should intelligently choose the next one to learn. In this paper, an effective “human cognition” strategy is taken into consideration via actively sorting the importance of new tasks in the process of unknown-to-known, and preferentially selecting the most valuable task with more information to learn. To be specific, we assess the importance of each new coming task (e.g., unknown or not) as an outlier detection issue, and propose to employ a “watchdog” knowledge library to reconstruct each task under <inline-formula><tex-math notation="LaTeX">$\ell _0$</tex-math></inline-formula> -norm constraint. The coming candidate tasks are then sorted depending on the sparse reconstruction scores in a descending order, which is referred to as a “watchdog” mechanism. Following this, we design a hierarchical knowledge library for the lifelong learning framework to encode new task with higher reconstruction score, where the library consists of two-level task descriptors, i.e., a high-dimensional one with low-rank constraint and a low-dimensional one. Both “watchdog” knowledge library and hierarchy knowledge library can be optimized with knowledge from both previously learned tasks and current task automatically. For model optimization, we explore an alternating method to iteratively update our proposed framework with a guaranteed convergence. Experimental results on several existing benchmarks demonstrate that our proposed model outperforms various state-of-the-art task selection methods.
An Improved Reversible Data Hiding Algorithm for Image Contrast Enhancement

Recently, reversible data hiding (RDH) has been employed to perform automatic image enhancement in a reversible manner. This is useful for cloud service providers who offer the users image storage services. In this paper, we propose an efficient extension to a recent RDH that employed the popular unsharp-masking for image contrast enhancement. The extension is centered around adjusting the Laplacian values that are used for contrast enhancement in a way that addresses the problems in the original algorithm in terms of noise amplification, over enhancement artifacts and broken edges. Experimental results proved the ability of proposed algorithm in maintaining high embedding capacity, improving the image contrast, and alleviating the problems encountered in the original algorithm.
Histogram-based Fuzzy C-Means Clustering for Image Binarization

The goal of image binarization is to classify the pixels into black and white correctly. Finding a threshold to binarize the image effectively is the essence in this study. This paper introduces a new algorithm for image binarization based on clustering. The algorithm computes on the histogram and uses the membership partition based on the distance between pixels within local spatial neighbors and clustering centers to accelerate the binarization procedure. Then the weighted factor is introduced to balance the noise-immunity and details. The proposed method combines the global and local ideas in the conventional algorithms. Compared with state-of-the-art algorithms, the proposed algorithm can universally obtain a robust effect for the images within distinct features, especially for the precision images.
Weak Commutativity of Quantum Automata

Weak commutativity of quantum automata and weak commutativity of generalized quantum automata are defined. Weak commutativity of (generalized) quantum automata is discussed, and weak commutativity of general direct product, full direct product, restricted direct product, cascade product and wreath product of (generalized) quantum automata are studied, and several necessary and sufficient conditions of weak commutativity of some products of (generalized) quantum automata are obtained.
Default Negation as Explicit Negation plus Update

We argue that under the stable model semantics default negation can be read as explicit negation with update. We show that dynamic logic programming which is based on default negation, even in the heads, can be interpreted in a variant of updates with explicit negation only. As corollaries, we get an easy description of default negation in generalized and normal logic programming where initially negated literals are updated. These results are discussed with respect to the understanding of negation in logic.
Anti Camcorder Piracy Display System

Camcorder piracy has a great impact on the movie industry. According to Motion Picture Associations of America, movie piracy incurs over $6 billion annual losses to industry ( Gonzalez RC, Woods RE, Eddins SL (2018) Digital image processing using Matlab 2E, MC Graw Hill Education). Pirating movies is considered a crime in almost all countries. The situation became even worse and emerged as a crucial issue to be taken care of. This is due to the vivid availability of websites that supplies pirated videos illegally through the internet. Temporal Psycho Visual Modulation (TPVM) is a good method to solve this problem which takes the advantage that the photo reception mechanism of human eyes and camera lenses is different (Wu and Zhai in Signal Process Mag IEEE 30:136–141, 2013). Thus, recorded video quality is degraded whereas audience visual quality is not disturbed. This work aims at demonstrating the said method through a simulation model using the Simulink Video processing toolbox.
Screenshots from Screen Photography

Screenshot is a frequently used tool in our daily life, while the screenshot capturing techniques are not much discussed in computer graphics and image processing researches. Capturing a screenshot is not always as easy as it seems. Firstly, the target devices for screenshot capturing must have screenshot software installed or featured in their operating systems. Secondly, the users must have input access to control the screenshot software within the target devices. Thirdly, the target devices must have Internet access or other hardware interfaces (such as USB ports) so that the users can take their screenshots out. When these requirements are not met, people often need to use their smartphones to take photographs in front of the screens as a substitute of screenshots. This allows direct sharing of the screen content, but the fidelity of the obtained content is apparently not as good as software screenshots. Might we be able to achieve a computer graphic solution to directly convert a screen photography to a screenshot, which looks like as if it was taken using software?
Report of the ALA Core Catalog Management Interest Group Virtual Session, Core Virtual Interest Group Week, February 3, 2021

The ALA Core Catalog Management Interest Group (Core CMIG) established in fall 2020 is the successor of Catalog Management Interest Group, administered by ALA Association for Library Collections an...
An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

Convolutional Neural Networks (CNNs) models succeed in vast domains. CNNs are available in a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in ord... | Find, read and cite all the research you need on Tech Science Press
Analyzing Technical Debt of a CRM Application by Categorizing Ambiguous Issue Statements

Poor decisions and suboptimal actions taken in software development result in technical debt. In service business, technical debt may become more evident and destructive. Customer relationship management (CRM) platform is one example of such businesses where several customizations are performed to adapt the software to customers’ processes and needs. The purpose of this study is to investigate technical debt in customizations made in different Salesforce CRM organizations based on ambiguous issue statements. We categorized 300 anonymous confessions of Salesforce consultants, administrators, and developers by using three different technical debt categorization approaches. This study would improve awareness among CRM teams for potential technical debts and may serve as a starting point to determine appropriate strategies to deal with technical debt.
An AR System to Practice Drums

An augmented reality (AR) system is developed that helps beginners practice the electronic drums. The player sitting on the drums is captured by a USB camera mounted on top of a large display device placed in front of the drums and the player. Their mirror image is shown on the display device in real time with superimposed graphical instructions that indicate when and which drum to bang along a musical note. Each bang on the drum set is sent from the drum controller as a MIDI signal to the PC where it is compared with the musical note. Throughout the practice, the player only has to look straight at the display screen because everything including the drum sticks, arms, legs of his/her own, and the instructions is integrated on the screen.
A Preliminary Review on Complementary Applications of Databases and Blockchain Technology

Although blockchain was originally designed to support cryptocurrency transactions, its use has greatly expanded. Due to its security mechanisms and immutability feature, blockchain is now also used for a variety of applications. Nevertheless, blockchain is facing several challenges. On the other hand, databases have been on the market for decades, offering a wide range of affordable solutions. In this paper, we explore the possibility of combining blockchain and database technologies, taking into account the specifics of their corresponding characteristics and issues. The case study was conducted by analyzing and classifying papers into three categories: blockchain as support for databases, databases as support for blockchain, and hybrid systems. Our research focuses on the improvement of technologies features and not on the performance improvement. Obtained results show the potential in developing combined solutions in all three categories taking into account the main advantages and disadvantages of both technologies.
Advanced Memory Efficient Outlier Detection Approach for Streaming Data using Swarm Optimization

Outlier detection techniques detect abnormal behavior in data and are useful in a variety of applications. In a real-life scenario, various applications generate large-scale data every day. Outlier detection over such continuous/streaming data is a challenging task due to its volume and limitations in processing memory. This paper presents an outlier detection approach called Advanced Memory Efficient Outlier Detection (A-MEOD) that is able to find outliers in streaming data in a memory-efficient manner. The outlier detection is based on the MEOD technique and Local Correlation Integral (LOCI) algorithm. Further the A-MEOD technique reduces the LOCI calculations and finds the top M outliers using Knorr’s definition. The results of utilization of A-MEOD are compared with MiLOF and MEOD in terms of accuracy, time, and memory requirements.
An Algorithm for Determining if a BST Node’s Value Can Be Changed in Place

A binary search tree (BST) is a strictly ordered data structure that permits quick storage and access of its data. Changing a value in a node of a BST requires ascertaining that such an update will not violate the tree’s order; otherwise the update requires replacing the source node with a new node (in a different location). In this work, an algorithm for determining whether a value in a BST can be updated within an existing node will be developed. It provides experience in algorithmic development for students and may lead to improvements in more complex tree structures.
i-DetectBC: Intelligence detection of breast cancer

Abstract Detecting breast cancer lesions at an early stage can help to improve the patients' survival rates. Digital mammograms can be used to detect breast cancer lesions. However, mammographic images suffer from low image quality due to the low exposure factors used. This paper proposes an interactive way of enhancing mammographic images while improving the detection of breast cancer lesions. The Intelligence Detection of Breast Cancer (i-DetectBC) allows the radiologist or clinicians to enhance the original mammographic images by using appropriate algorithms automatically. The i-DetectBC consists of two digital image processing techniques: Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) enhancement and Multilevel Otsu Thresholding segmentation technique. The interface of i-DetectBC can be considered user-friendly with low computational methods to provide fast results, especially when identifying breast cancer types such as benign or malignant. The i-DetectBC has been performed on 322 mammographic images, which were retrieved from the MIAS database. The efficiency of the i-DetectBC is 95.7%, and the error rate is 4.3%. In summary, this i-DetectBC can be helpful in the detection and categorization of breast cancer lesions.
Using Cross Lingual Learning for Detecting Hate Speech in Portuguese

Social media growth all around the world brought benefits and challenges to society. One problem that must be highlighted is hate speech proliferation on the Internet. This article proposes a technique to hate speech detection in texts, which uses a Cross-Lingual Learning classifier. In our experiments, we used a public dataset in Portuguese and achieved one of the greatest F1 Scores within our state-of-the-art. Besides, this work is the first one to perform a cross-lingual learning task for hate speech detection using a corpus in Portuguese.
Automated Breast Cancer Diagnosis Based on Machine Learning Algorithm

Abstract – Breast Cancer classification is becoming more important with the increasing demand of automated applications especially interactive applications. It can be used to improve the performance of classifiers like Logistic Regression, Decision Tree, Random Forest, SVC etc. This study is based on learning genetic patterns of patients with breast tumors and machine learning algorithms that aim to demonstrate a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize different algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on accuracy, precision and the roc curves. The present report prepared by us proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms by reducing False Positive rate. In this paper, there were two challenges to automate the breast cancer diagnosis: (i) determining which model best classifies the data and (ii) how to automatically design and adjust the parameters of the machine learning model. We have summarized the experimental studies and the obtained results, and lastly presented the main conclusion.
Requirements in PLS, SSML and SRGS Standard—Hindi as a Case Study

The Speech Synthesis Markup Language (SSML) specification is one of the speech standard of W3C (World-Wide Web Consortium) and is aimed to provide a rich, XML (Extensible Markup Language) for assisting the generation of synthetic speech on Web and other applications. It enables authors a standard way to control various speech parameters in order to improve the quality and give natural voice to the speech synthesis applications such as TTS. Speech Recognition Grammar Specification (SRGS) is a W3C standard that enables developers to define the words and patterns of words that recognized by speech recognizer. W3C PLS (Pronunciation Lexicon Specification) represents the pronunciation information of the words for use in speech recognition and synthesis systems. Currently, these standards enable the output in a large numbers of Latin languages only. Therefore, it is essential to study the existing standards for Indian languages as Indian languages are morphologically rich languages. This paper presents the various aspects of these technologies that need to be identified for Indian languages. The papers proposed the some of the identified requirements/gaps in these speech standards.
Jointly Learning Spectral Sensitivity Functions and Demosaicking via Deep Networks

Demosaicking is reconstructing a RGB image from the mosaicked image recorded by a single sensor with a color filter array (CFA), which has achieved great progress via deep learning. Some works on joint CFA design and demosaicking via deep learning have been presented. However, almost all existing approaches focus only on optimizing filter arrangements without considering the used spectral sensitivity functions (SSFs). The commonly used SSFs are to mimic human trichromatic perception, which are not optimal for deep-learning-based de-mosaicking. In this paper, we simultaneously learn the CFA and demosaicking using a deep convolutional neural network, where both the filter arrangement and used SSFs are optimized. By modeling the applying of SSFs as a convolutional layer with physical constraints, we formulate our joint learning approach as an end-to-end autoencoder, which can be trained as the standard convolutional neural networks. Our approach allows the designing of CFA patterns with arbitrary sizes, where the filter arrangements can be predefined or not. We demonstrate the effectiveness and advantages of our approach by comparing with fixed Gaussian functions and various predefined filter arrangements.
ME-Net: Multi-Step Enhancement Network for Semantic Segmentation

research-article ME-Net: Multi-Step Enhancement Network for Semantic Segmentation Share on Authors: Xingming Zhang ZheJiang Dahua Technology CO.,LTD, China ZheJiang Dahua Technology CO.,LTD, ChinaView Profile , Bingyan Liao ZheJiang Dahua Technology CO.,LTD, China ZheJiang Dahua Technology CO.,LTD, ChinaView Profile , Shiliang Huang ZheJiang Dahua Technology CO.,LTD, China ZheJiang Dahua Technology CO.,LTD, ChinaView Profile , Yayun Wang ZheJiang Dahua Technology CO.,LTD, China ZheJiang Dahua Technology CO.,LTD, ChinaView Profile , Jun Yin ZheJiang Dahua Technology CO.,LTD, China ZheJiang Dahua Technology CO.,LTD, ChinaView Profile Authors Info & Claims ICMSSP 2021: 2021 6th International Conference on Multimedia Systems and Signal ProcessingMay 2021 Pages 53–59https://doi.org/10.1145/3471261.3471270Online:06 September 2021Publication History 0citation15DownloadsMetricsTotal Citations0Total Downloads15Last 12 Months15Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
RGB-D Scene Recognition based on Object-Scene Relation and Semantics-Preserving Attention

Scene recognition is challenging due to intra-class diversity and inter-class similarity. Previous works recognize scenes either with global representations or with intermediate representations of objects. By contrast, we investigate more discriminative sequential representation of object-to-scene relations (SOSRs) for scene recognition. Particularly, we develop an Attention-Preserving Memory-Learning (APML) model, which enforces the Memory Network of the semantic domain to guide the Learning Network of the appearance domain in the learning procedure. Accordingly, we allocate semantics-preserving attention to different objects, which is more effective to seek the key encoded SOSR and discard the misleading encoded SOSR between objects and scene without requiring extra labeled data. Based on the proposed APML networks, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.
Distributed swarm collision avoidance based on angular calculations

Collision avoidance is one of the most important topics in the robotics field. In this problem, the goal is to move the robots from initial locations to target locations such that they follow the shortest non-colliding paths in the shortest time and with the least amount of energy. Robot navigation among pedestrians is an example application of this problem which is the focus of this paper. This paper presents a distributed and real-time algorithm for solving collision avoidance problems in dense and complex 2D and 3D environments. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with ORCA one of the most important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA while is also more reliable. The proposed method is shown to enable fully autonomous navigation of a swarm of Crazyflies.
Quantifying the Qualitative Features

Machine learning and deep learning archive significant advances in the past few decades [1].
Node-Image CAE: A Novel Embedding Method via Convolutional Auto-encoder and High-Order Proximities

The network embedding method, which mainly aims to generate low-dimensional embedding vectors for nodes in a network, has received great passion from researchers and provides excellent tools for application and research. Various models have been applied to this field to gain high-quality embedding vectors and got prodigious success, such as Matrix Factorization, Multilayer Perceptron, Recurrent Neural Network, Generative Adversarial Networks, Graph Convolutional Network, and so on. However, a very powerful model—Convolutional Neural Network, which plays a significant role in Digital Image Processing, Natural Language Processing, etc.—has not been widely used (except whole-graph embedding). CNN is thought inappropriate to embed nodes into vectors because traditional CNN can only work on grid-like data such as images while a graph generally has a irregular structure. And with the development of the embedding method, high-order proximities are gradually found playing a crucial role in exploring the network’s underlying structure. However, many existing embedding methods can only preserve fixed-order proximities, limiting the scope of their application. In this paper, we try to explore a viable way to apply CNN on node-embedding task. We first build an image for each node that can flexibly preserve different order proximities and feed them into a variation of the Convolutional Neural Network, the Convolutional Auto-Encoder (CAE), to generate embedding vectors. Experiments show that our method outperforms state-of-the-art embedding methods.