Reviewerly/exHarmony huggingface dataset:
Salient Latent Features For Zero-shot Learning

Zero-shot recognition (ZSR) refers to predict the categories of the unseen new images that never appear in the training set. Seen classes belong to the training set and unseen classes belong to the test set. Typically, most of the previous works focus on solving how to embed the space between visual and semantic representation. In this paper, we argue that the salience of the image can provide more discriminative information for ZSR. We propose a framework that first detects the salient objects in image, and then locates the most interested salient region and extracts the features in it. We use the convolutional neural network (CNN) to extract the visual features of the salient region and maps them to both user-defined and latent attributes. Ablation study on two challenging ZSL datasets is conducted. The experimental results demonstrate the salient latent features extracting from the salient origins can improve the recognition accuracy in a great deal.
Study on Computational Visual Attention System and Its Contribution to Robotic Cognition System

The Human visual system is being studied for more than one century. The Design of computational visual attention (CVA) system derived its roots from the theories of human attention system. These models have shown remarkable growth in computer vision and image processing application. Computational models combined with intelligent system have resulted in successful application in the dynamic real-time processing of mobile robots. The prime objective of this study is to focus on the contribution of visual attention system in the field of robotics. This study throws light on CVA models broadly summed up under Bottom-Up (BU) and Top-Down (TD) Categories. It presents a review of the different ways in which the CVA model has been used in navigation, detection and Human-Robotic Interaction. The conclusion derived from the study shows that the framework of Visual attention model has the evidence of scalability of the method to solve challenges in autonomous robots.
On the Dependence Structure Between Learners' Response-time and Knowledge Mastery: If Not Linear, Then What?

Popular approaches in learner modeling explore response-time as observational data supplemental to response correctness, to enrich the predictive models of learner knowledge. It has been argued that the relationship between response-time and knowledge mastery is non-linear. Determining the degree of association (dependence structure) between those two observations is an open question. To address this objective, we propose an approach based on copulas, i.e., a statistical tool suitable for capturing dependence structure between two variables. All of the information about the dependence structures can be estimated by copula models separately, allowing for the construction of more flexible joint distributions than existing multivariate distributions. This paper puts into practice a two-step pipeline for building the analytical models. Specifically, we propose a flexible copula-based approach that describes the dependence structure between students' response-time and mastery, in learning and testing contexts, and apply the methodology on four datasets. The two datasets are coming from Intelligent Tutoring Systems and are shared via an online repository, and the other two were collected during the validation of an (adaptive) assessment system. The results reveal five generic patterns of associations across-datasets, for various types of activities, domains and learner characteristics (i.e., not across-contexts). We elaborate on those findings and on the implications of our approach for adaptive systems.
Intelligent Identification and Classification of Product Quality Incidents Based on Rules and Statistics

Abstract Product quality and safety incidents are closely related to the lives of ordinary people, and have always been the focus of all countriesgovernments. Using related technologies and methods, how to intelligently identify and classify product quality and safety incidents, and find out the evolution and spread of events at the first time, and deal with them quickly, has always been a difficult problem for product quality and safety supervision departments to solve. Based on rules and statistics, a multi-layer text classifier is built to identify and classify product quality incidents. The first layer implements rough segmentation of the news concerning product quality incidents based on rule classifier. The second layer matches the news in a level-wise manner from the top down by calculating the text similarity until the corresponding subclass is found. The experiments show that the proposed classifier can effectively identify the product quality incidents in Web news.
An Image Authentication Method Based on Deep Convolutional Generative Adversarial Network

In the past, there has been a lot of research in the development of digital watermark technology in image authentication. In this paper, we will propose a Deep Convolutional Generative Adversarial Network (DCGAN) architecture extended by the Generative Adversarial Network (GAN) to conduct training and verification evaluation through three subnets. Try to use a deep learning-based architecture for image authentication. Through the training of the model, the features of the authentication image can be more accurately extracted and the tampered local features can be found too.
PHILOSOPHICAL PROBLEMS OF THE MENTALISTIC LOGIC

Paper deals with philosophical problems of mentalistic logic. Mentalistic logic is a formal system that concentrates on underpinning processes of mental life instead of certain elements of extrinsic rational behavior as most of existing logics (like BDI calculi) do. The project is compared to the existing logics of actions. Mentalistic logic is patually a formal system and partually phenomenological study of human mind. We presume formal signs such as propositions and modal operators refer to mental states and can describe the general structure of mental activity. That is purely the approach of classical phenomenology – the study of experience and its structures. On the other hand the usage of formal logic is a classic analytic philosophy of mind. So the things are getting more complicated when taking in consideration that the initial framework of a study is analytic philosophy and not continental phenomenology. Phenomenology is of different intellectual and methodological tradition than any type of analytic philosophy including analytic philosophy of mind. From that stanpoint it may be said that paper is also interesting as a purely methodological project – it tries to find bridges between phenomenology and philosophy of mind. As for the action logics, mentalistic logic also studies rational behavior but does it on the other lever and often with a different purpose. The main problem in this case is philosophical interpretation of modality. Minor problems include shared content, many leveled self-referential structures and vagueness. The paper also studies brain in a vatt thought experiment as a methodological concept. The research will have implications for philosophy of logic, artificial intelligence and theory of reference.
Font Recognition of English Letters Using Normalized Central Moments Features

Optical font recognition is an important process applied before or after optical character recognition. This paper presents a system for recognizing English fonts of character images. Feature selection plays a major role in recognizing the font; hence, we used normalized central moments (NCM) as the feature of choice in this study. What differentiates this study from others is the attempt to use another popular feature (distance profile features) used by other researchers and compare the results of the two. The support vector machine (SVM) method is used in training and testing. A system is developed that extracts the two features and trains two SVM models. Simulation results based on a dataset of 27,620 images belonging to three English fonts show that the proposed system can achieve an overall 94.9% correct recognition rate based on normalized central moments, while the system can achieve an overall 94.82% correct recognition rate when using distance profile features.
An Analysis and Comparison Performance of DNA and Chaotic Method Combination for Image Encryption

Abstract There is a variety of important and personal information stored in an image that is often shared via the internet. Cryptography is one way to protect these image files, so that private information cannot be read by malicious third parties. The DNA and Chaos methods are one of the cryptographic methods that are widely proposed and combined. Some previous research proposes a combination of these two methods with a sequence of different methods and different testing techniques where all the research claims the results are very good. This research aims to analyze and compare the performance of two methods namely DNA-Chaos and Chaos-DNA with more complete measurement tools such as PSNR and SSIM to measure results visually, there is also entropy to measure the probability of encryption results can be solved, NPCR and UACI to find out encryption strength from differential attacks and image histogram analysis. From the test results, it appears that both methods have their own strengths and weaknesses. Chaos-DNA is superior in entropy, and UACI, while DNA-Chaos is superior in PSNR, SSIM and NPCR values, but the difference is not significant, so it can be said that the performance of the two methods is quite identical. The results of the histogram analysis also showed that the shape of the histogram generated from the encrypted image was also quite similar.
Optimal virtual machine placement with multiple resources constraints in cloud data centers

In cloud data centers, different virtual machine placement strategies will affect the resource utilization efficiency of the whole system. How to improve the overall resource utilization of the system by adjusting the virtual machine placement strategy under limited resources is the focus of current researchers. In this paper, a new virtual machine placement strategy is proposed, which is based on the comprehensive constraints of various system resources. In the process of solving the problem, a multi-objective ant colony optimization algorithm is used. Simulation results show that this method can effectively reduce the number of physical machines activated in the data center, and the accuracy is high.
The uncertainty property of electromagnetic field in optical thin microcavity

The uncertainty property of electromagnetic field in thin microcavity is exactly derived in this paper. The intensity of the electromagnetic field at the center of thin microcavity is indeterminate while other positions intensity are determinate. The product of the deviation of momentum and the deviation of position at the center of thin microcavity is larger than a certain constant. By introducing the energy quantization condition (E=Nħω), we verify this constant has the same physical dimension as Planck’s constantħ and the value is an integral multiple of Planck’s constant, the integer is the smallest volume element at the center of thin microcavity. Furthermore, the perimeter of the smallest volume element is an integer multiple of the wavelength of the incident electromagnetic wave, which satisfies the assumption of DE Broglie matter wave. Our results are the first to find that the uncertainty property of electromagnetic field in thin microcavity and further derived the quantum uncertainty principle through the quantization of energy.
An Evaluation Model for Authors’ Academic Influence Based on Multi-source Heterogeneous Database in Bilingual Environment

Abstract As the internationalization of academic rise up, it is insufficient to evaluate a scholar’s influence only taking domestic academic contribution as reference. To have a better understanding of scholars’ academic contribution in an international scale, this paper proposes a new evaluation system of academic influence based on the optimal dataset of entity recognition in bilingual environment. The new evaluation system has been experimented on a real literature dataset of DBLP. The research is conducted by the following steps. First, handle bilingual data. Second, optimize the literature data with entity recognition technology. Third, make an academic influence evaluation. Experiment results show that the new evaluation system is feasible and reasonable.
An Acoustic QR Code that is Audible

Compared to image-based QR codes, acoustic QR codes have their advantages. However, an acoustic QR scanner cannot recognize an acoustic QR code at a distance of more than 2 meters from an acoustic QR announcer. To this end, we propose a new kind of acoustic QR codes, called Audible Acoustic QR Codes (AAQRC), which employ humanly audible sound to carry users' information directly. At first, a user's string of characters is translated into a string of pitches. And then, some related algorithms can be applied to convert a string of pitches into a playable audio file. Thus, an AAQRC is produced, consisting of the audio itself. And AAQRC recognition has the opposite process of AAQRC generation. Compared with the existing approach for acoustic QR codes, the new method can recognize acoustic QR codes at a longer distance.
Manufacturing and Production Planning via Fish Swarm Optimization Method and Its Hybridizations

This research aims to solve manufacturing problems including an aggregate production planning or APP for the herb cosmetic company in Thailand, where the demand levels of products are different. The fish swarm optimization metaheuristic or FSA including its hybridizations, are used to determine the preferable levels of manufacturing variables. Using the data obtained from an undisclosed manufacturer, computational experiments are conducted under various constraints. The results from the experiments show that the hybridization of the FSA with the evolutionary equation concerning the iteration or HFSA2 can solve various problems with better efficiency compared with other methods under reasonable time. For the APP model, the implementation of numerical results required many actions such as preparation of detailed information about production process or production capacity including changeover times of the machines.
Parsing Romanian Texts

The paper describes the experiments of automatic parsing on a Romanian corpus that contains various types of Romanian texts. The corpus has morphological and syntactic annotations using the dependency grammar model. We have experimented with various parsing models and combinations of the training parts. The results of the experiments are encouraging and we believe that by enlarging the corpus we can obtain satisfactory accuracy even with difficult n on-standard texts.
Research on Restoration Algorithm of Two-dimensional Degraded Image Based on Deep Learning

The recognition and application of two dimensional code has been seriously affected by the image degradation produced during the process of acquisition and transmission of it. In order to fix that, analysis of the traditional image processing technology has revealed that adopting the advanced image restoration algorithm which is based on deep learning, building restoration model of two dimensional code by using the multilayer neural network, adding binary loss constraint on the basis of L1 norm, constructing the non-linear image degradation and restoration process can solve the too complicated structure, easily affected by noise and poor robustness problems caused of traditional method. The experimental result shows that this method has a good restoration effect on the degraded image of two-dimensional code, and also improves the recognition efficiency while shortens the network training time, more over it meets the needs of practical application of two-dimensional code.
Time-optimal movement of an elliptical pendulum

The problem of the optimal speed of horizontal movement of the pendulum on the trolley (at the required distance from the lower state of equilibrium to a similar one) under the action of one limited control force applied to the trolley is solved. For different mass ratios, trajectories that satisfy the necessary optimality conditions, are constructed numerically.
Designing “Concentrated Listening” for Advantageous Adult Learning With Multimedia

In the online learning space, adult learners have access to a wide variety of sound-based content (podcasts, audio books, audio recordings, and others) and multimodal contents of which the audio element is a central part. This work explores research methods used to enhance “concentrated listening” to enable learners to acquire the most from the following: pre-recorded sound files, live audio, natural language discussions through speech, and other types of auditory-based learning online. This work explore some aspects of (1) learning designs to enhance both adult “concentrated listening” in an online learning context (both synchronous and asynchronous) and (2) some tactical designs of learning based around sound for general learning (vs. domain-specific case-based learning).
Optimization Scheme of Massive Meteorological Data Storage Based on OpenStack Swift

In order to solve the problem that cloud storage system will produce lots of metadata information and reduce the storage efficiency of the system when processing massive meteorological small files. In this paper, we propose an optimized scheme for storing massive meteorological data, which is implemented in OpenStack Swift. The scheme consists of load balancing module and merge storage module. On the one hand, load balancing module combines HAproxy and UCARP to alleviate the uneven load distribution when Swift stores a huge number of meteorological files. On the other hand, the scheme adopts the merge storage strategy to improve the reading and writing performance of meteorological small files in Swift. Experiments show that this optimization method can not only reduce the reading delay of massive meteorological files in Swift system, but also improve the throughput of Swift.
Towards Virtual Technology Vision in Critical Cases

Abstract Undoubtedly, Information and Communication Technology (ICT) is the most popular and widespread trend of many fields around the globe, and a smart tool for the emergence of several technological services such as web service and virtual technology. Nowadays, development and growth in computers and communications field are dynamically changing. ICT plays a key role in the digital transformation which led to the appearance of a new age called the digital age. Hence, many organizations and countries have supported modern technological trends such as the Virtual University (VU), Virtual Reality (VR) and Virtual Learning Environments (VLE), as a virtual technology. ICT can be used in positive and negative aspects, so it must be observed and considered. As a virtual technology, VU can offer extraordinary opportunities to avoid obstacles caused by critical circumstances.
On the Complexity in Task-oriented Spoken Dialogue Systems

The recent rise of personal voice-assistants shows that research in dialogue systems has gone great lengths from its beginnings many decades ago. We argue that recent research on dialogue complexity has concentrated on already known problems and has remained rather static. We present an overview of past work and argue that increasing dialogue complexity should move again to the centre of interest of new research endeavours.
Research on the Application of Computer Recording Technology in Modern Chinese Bopomofo

Abstract With the development of science and technology, the computer has gradually replaced the pen and paper, and even is more important in many occasions. Also, people’s dependence on the computer is deepening. While the importance of Bopomofo is in decline, Bopomofo teaching has been in a dangerous position. But actually, Bopomofo is the foundation of learning Chinese, the importance of language learning is self-evident. This paper mainly discusses the application of modern Chinese Bopomofo in computer recording technology.
Improving Energy-Efficiency of CNNs via Prediction of Reducible Convolutions for Energy-Constrained IoT Devices

Convolutional neural networks (CNNs) are widely used in various pattern recognition and computer vision applications due to their state-of-the-art performance. This superior performance comes with a tremendously high degree of computational complexity, leading to energy inefficiencies. Such inefficiencies hinder their applications on energy-constrained IoT devices, especially when it is required to perform the data processing near the source, on the edge device, for real-time requirements or security and privacy reasons. In this paper, we propose an approach to reduce a compute-intensive convolution operation by factorizing it into an inexpensive pooling operation followed by a 1 × 1 convolution operation. To identify the reducible convolutions, a lightweight, multi-class CNN-based predictor is proposed, which determines the type of the pooling operation to be applied to each activation predicted as reducible, as well. The empirical results demonstrate the great potential chances of convolution reductions using factorization by incurring a negligible storage overhead.
Graph-Assisted Attention for Path Finding in Question Answering Task

Attention-based memory networks, a class of deep learning algorithms in Natural Language Processing (NLP), capture long-range dependencies present in text data and is a popular recipe in currently available question answering (QA) systems. However, multi-hop QA systems pose additional challenges that these memory networks cannot comfortably handle with their attention spans. Path-finding tasks are a flavor of such multi-hop QA, and it does not have the additional complexity of implicit reasoning. A directed knowledge graph of entities, built from any text corpus, can capture both direction and implicit reasoning. This work attempts to address the bottleneck faced by sophisticated memory network implementations on a dataset that is appropriate for solving such path-finding tasks over multiple hops. It uses the knowledge graph approach for the directional information to address the search challenge in path finding. The experiments conducted using this approach have produced impressive results and can be extended in the future for higher-order challenges that consist of implicit reasoning.
Estimating Precisions for Multiple Binary Classifiers Under Limited Samples

Machine learning classifiers often require regular tracking of performance measures such as precision, recall, F1-score, etc., for model improvement and diagnostics. The population over which accuracy metrics are evaluated can be too large for a full ground-truth assessment and so only small random samples are chosen for estimation. Ground-truthing often requires human review, which is expensive. Moreover, in some business applications, it may be preferable to minimize human contact with the data in order to improve privacy safeguards. Thus, sampling methods that can provide estimates with low margin of error, high confidence, and small sample size are highly desirable. With an ensemble of multiple binary classifiers, choosing the right sampling method with these desired properties and small size for the collective sample becomes even more important. We propose a sampling method to estimate the precisions of multiple binary classifiers that exploits the overlaps between their prediction sets. We provide theoretical guarantees that our estimators are unbiased and empirically demonstrate that the precision metrics estimated from our sampling technique are as good (in terms of variance and confidence interval) as those obtained from a uniform random sample.