Reviewerly/exHarmony huggingface dataset:
Multi-Modal Language Analysis with Hierarchical Interaction-Level and Selection-Level Attentions

As an emerging research area in natural language processing, multi-modal human language analysis spans language, vision and audio modalities. Understanding multi-modal language requires not only the modeling of independent dynamics within each modality (intra-modal dynamics), but also more importantly interactive dynamics among different modalities (inter-modal dynamics). In this paper, we propose a hierarchical approach to multi-modal language analysis with two levels of attention mechanism, namely interaction-level, which captures the intra-modal and inter-modal dynamics across different modalities with multiple types of attention, and selection-level attention, which selects the effective representations for final prediction by calculating the importance of each vector obtained from interaction-level. Empirical evaluation demonstrates the effectiveness of our proposed approach to multi-modal sentiment classification, sentiment regression and emotion recognition.
Model of Recognition Algorithms for Objects Specified as Images

The paper deals with issues related to the construction of a model of recognition algorithms focused on the classification of objects specified in the form of images. As an initial model, a model of recognition algorithms based on radial functions was considered. A distinctive feature of the approach under consideration is the formation of subsets of interrelated features and the construction of a distance function based on an interconnectivity model. The main advantage of the proposed model of recognition algorithms is the allocation of the preferred function of the distance with the subsequent calculation of the assessment of the belonging of objects and ensuring a significant reduction in the number of computational operations when recognizing unknown objects. This feature is very important for real-time recognition systems. To test the performance of the proposed model, experimental studies were carried out in solving the model problem and the problem of identifying the person from the images of auricles. This model can be used in the preparation of various programs aimed at solving problems of classification of objects specified in the form of images.
Image analysis by two types of Franklin-Fourier moments

In this paper, we first derive two types of transformed Franklin polynomial: substituted and weighted radial Franklin polynomials. Two radial orthogonal moments are proposed based on these two types of polynomials, namely substituted Franklin-Fourier moments and weighted Franklin-Fourier moments &#x0028 SFFMs and WFFMs &#x0029, which are orthogonal in polar coordinates. The radial kernel functions of SFFMs and WFFMs are transformed Franklin functions and Franklin functions are composed of a class of complete orthogonal splines function system of degree one. Therefore, it provides the possibility of avoiding calculating high order polynomials, and thus the accurate values of SFFMs and WFFMs can be obtained directly with little computational cost. Theoretical and experimental results show that Franklin functions are not well suited for constructing higher-order moments of SFFMs and WFFMs, but compared with traditional orthogonal moments &#x0028 e.g., BFMs, OFMs and ZMs &#x0029 in polar coordinates, the proposed two types of Franklin-Fourier Moments have better performance respectively in lower-order moments.
A Sensor Glove for the Interaction with a Nursing-Care Assistive Robot

According to World Health Organization, the number of elderly people will rise rapidly between 2015 and 2050. Rates of physical disability or mobility problems are increasing due to population aging. With the continuous evolution of robotic technology, assistive robot shows great prospect to expand the existing healthcare services to address the above problems. As a result, nursing-care robotic systems are playing more and more important role to the elderly and disabled. In this paper, a sensor glove which is employed for human-robot interaction in an assistive robotic system is designed, produced and evaluated. The sensor glove is based on flex sensors which is utilized to detect user's finger gestures and inertial measurement unit (IMU) which is used to detect arm movements of the user and other off-the-shelf electronic components. The main goal of this research is to make the interaction between human and assistive robots more intuitive and reliable. This paper mainly deals with three issues: 1) inventing a design pattern of this kind of sensor gloves, 2) performance evaluation of sensor gloves based on flex sensors, and 3) method of analyzing the human-in-the-loop system with the presence of the sensor glove. Our experimental results suggested that by using this design pattern, the sensor glove can be a reliable interface in controlling the assistive robotic system. Besides, our estimation model can be used in analyzing this human-in-the-loop assistive robotic system and able to find suitable parameters of this system theoretically.
An modeling processing method for video games based on deep reinforcement learning

In the reinforcement learning field, the traditional Q-Learning method can make the agent get a good grades on some simple games whose states are limited. Meanwhile, if the model of game is relatively simple, the continuous states can also be transformed into finite states through discretization so that the agent can achieve good results. However, traditional Q-Learning will face many difficulties for video games with image output, such as too complicated image states to store. In this paper, an algorithm of deep reinforcement learning(DRL), called Deep Q-Network(DQN), which is originated from Q-Learning and combining with artificial neural network, is used to model the video games with its image output. Then in the model training process, some image processing optimization methods and neural network structure are put forward for the preparation of test. Finally, from the experimental results, it is concluded that DQN can make the agent get high scores from video game, and the human game data can make the model training faster and better.
HoloHome: An Augmented Reality Framework to Manage the Smart Home

This paper introduces the HoloHome, an Augmented Reality framework which aims to provide new means of interaction with the Smart Home and its components. HoloHome integrates multiple technological paradigms encompassing Internet of Things, Ambient Assisted Living, and Augmented Reality to offer the ultimate tailored comfort experience for the Smart Home's inhabitants including frail elderlies and people with mild cognitive disabilities. The main purpose of the HoloHome is to provide a Mixed Reality environment implemented on the Microsoft HoloLens, to allow the user interaction with the Smart Home devices and appliances through the Augmented objects. HoloHome tackles the issue of real world locations of the objects and alignment of the virtual objects on the real objects within the spatial environment, using Vuforia image processing engine. It also defines the modality of the interconnection between the Mixed Reality application and the distributed network of smart devices through the communication channel of WiFi-enabled microcontrollers. Two use cases depict the HoloHome interaction methods and functionalities in two typical scenarios: regulating the domestic devices – such as turning on/off light – through the Augmented Reality framework, and providing visual hints and clues to help the users with mild cognitive impairments to find the objects they need easily.
Graph Embedding Using Frequency Filtering

The target of graph embedding is to embed graphs in vector space such that the embedded feature vectors follow the differences and similarities of the source graphs. In this paper, a novel method named Frequency Filtering Embedding (FFE) is proposed which uses graph Fourier transform and Frequency filtering as a graph Fourier domain operator for graph feature extraction. Frequency filtering amplifies or attenuates selected frequencies using appropriate filter functions. Here, heat, anti-heat, part-sine and identity filter sets are proposed as the filter functions. A generalized version of FFE named GeFFE is also proposed by defining pseudo-Fourier operators. This method can be considered as a general framework for formulating some previously defined invariants in other works by choosing a suitable filter bank and defining suitable pseudo-Fourier operators. This flexibility empowers GeFFE to adapt itself to the properties of each graph dataset unlike the previous spectral embedding methods and leads to superior classification accuracy relative to the others. Utilizing the proposed part-sine filter set, which its members filter different parts of the spectrum in turn, improves the classification accuracy of GeFFE method. Additionally, GeFFE resolves the cospectrality problem entirely in tested datasets.
Jointly Solving Deblurring and Super-Resolution Problems with Dual Supervised Network

Thanks to the vigorous development of deep learning techniques, the solutions of super-resolution (SR) and deblurring have made great progress in recent years. However, in real-world scenarios, the low-quality images not only suffer from the loss of spatial information (resolution), they are often degradated by complicated non-uniform motion blur as well. As a result, traditional SR and deblurring methods are not robust enough to restore those blurred and low-resolution (LR) images to sharp and high-resolution (HR) images. Therefore, we propose a novel dual supervised network (DSN) to jointly solve the SR and deblurring problems. In the beginning we use a deblurring module to make blurry input much sharper and feed the deblurred feature to a reconstruction module, which aims to produce the final HR result from the deblurred feature. Moreover, we employ dual supervised structure to fully exploit the mutual dependencies between LR and HR patches. Experiments show that our network outperforms the state-of-the-art methods.
An Innovative Neural Network Framework for Glomerulus Classification Based on Morphological and Texture Features Evaluated in Histological Images of Kidney Biopsy

Medical Imaging Computer Aided Diagnosis (CAD) systems could support physicians in several fields and recently are also applied in histopathology. The goal of this work is to design and test a novel CAD system module for the discrimination between glomeruli with a sclerotic and non-sclerotic condition, through the elaboration of histological images. The dataset was constituted by 26 kidney biopsies coming from 19 donors with Periodic Acid Schiff (PAS) staining. Preparation, digital acquisition and glomeruli annotations have been conducted by experts from the Department of Emergency and Organ Transplantation (DETO) of the University of Bari Aldo Moro (Italy). Starting from the annotated Regions Of Interest (ROIs), several feature extraction techniques were evaluated. Feature reduction and shallow artificial neural network were used for discriminating between the glomeruli classes. The mean and the best performances of the best ANN architecture were evaluated on an independent dataset. Metric comparison and analysis were performed to face the unbalanced dataset problem. Results on the test set asses that the proposed workflow, from the feature extraction to the supervised ANN approach, is consistent and reveals good performance in discriminating sclerotic and non-sclerotic glomeruli.
A (2,2) XOR-based visual cryptography scheme without pixel expansion

Visual cryptography (VC) is a technique that encodes the content of a secret image into two or more images, which are called shares. These shares are printed on transparencies and superimposed to reveal the original secret image. Frequently, VC techniques require a pixel expansion and a good alignment, which reduces the final spatial resolution. In this paper, we propose a physical VC scheme that only requires two shares and do not demand a pixel expansion. The first share is a colored transparency printed on a Polyvinyl Chloride (PVC) surface of 3 mm, while the second share is a colored image displayed on a smartphone screen. The decoded pixels of the proposed scheme have defined colors and a good resolution. We perform a physical evaluation of the color interference properties of the two shares, to find the most adequate color space, and test the proposed method with practical examples.
Automated Genre Classification of Books Using Machine Learning and Natural Language Processing

In today's world due to ever increasing demand to make computers perform tasks of humans, machine learning is used. It is a tedious task to manually read the entire book and classify it based on its genre. Novice writers find it troublesome to figure out the genre of their book, which can affect its reach to the right audience. The proposed method gains knowledge from a large number of words from the books and transforms them into a feature matrix. During transformation, the size of the initial matrix is reduced using Wordnet and Principle Component Analysis. Then, AdaBoost classifier is applied to predict the genres of the books.
Face Friend-Safe Adversarial Example on Face Recognition System

Deep neural networks (DNNs) provide the excellent service on deep learning tasks such as image recognition, speech recognition, and pattern recognition. In the field of face recognition, researches using DNN have been carried out. However, face adversarial example is a serious threat in face recognition system. Face adversarial example adding a little of noise to the original face image can cause the misrecognition in the face recognition system. For example, an attacker intentionally modifies a face image with small distortion, which could cause the face recognition system to misidentify another person. It also shows the possibility of wrong recognition by another person when it is modulated by several points on the face. However, the concept of face friend-safe adversarial example can be useful in a military situation where friend and enemy forces are mixed. In the face recognition field, face friend-safe adversarial example may be needed that is correctly recognized by a friend face recognition system and misidentified by an enemy face recognition system. In this paper, we propose a face friend-safe adversarial example targeting the FaceNet face recognition system. The proposed scheme generates a face friend-safe adversarial example that is misrecognized by a enemy face recognition system but is correctly recognized by friend face recognition system with minimum distortion. For experiment, we used VGGFace2 and Labeled Faces in the Wild (LFW) as a dataset and Tensorflow as a machine learning library. Experimental results show that the proposed method has a 92.2% attack succes rate and 91.4 % friend accuracy with only 64.22 distortion.
A Single-Shot Oriented Scene Text Detector with Learnable Anchors

Current regression based text detectors mainly use fixed anchors, where scales and positions can not be changed during network training. As scene texts tend to have large variation in orientations, aspect ratios and sizes, fixed anchors are insufficient to cover all varieties. This paper proposes a novel text detector with learnable anchors, named LATD. LATD contains two prediction branches. One aims to refine scales and locations of anchors according to the characteristics of scene texts. The other one receives refined anchors as defaults and regresses their offsets to text regions. These two branches are optimized jointly without sacrifices much speed. Meanwhile, we explore the class-imbalance issue between texts and backgrounds, and replace softmax loss with focal loss. Extensive experiments on both oriented and horizontal benchmarks demonstrate the effectiveness of LATD with new state-of-the-art performance. By visualizing qualitative results, as expected, LATD provides more accurate locations and lower rate of missed detections.
An Integrative Framework for Artificial Intelligence Education

Modern introductory courses on AI do not train students to create intelligent systems or provide broad coverage of this complex field. In this paper, we identify problems with common approaches to teaching artificial intelligence and suggest alternative principles that courses should adopt instead. We illustrate these principles in a proposed course that teaches students not only about component methods, such as pattern matching and decision making, but also about their combination into higher-level abilities for reasoning, sequential control, plan generation, and integrated intelligent agents. We also present a curriculum that instantiates this organization, including sample programming exercises and a project that requires system integration. Participants also gain experience building knowledge-based agents that use their software to produce intelligent behavior.
Optimum Linear Codes With Support-Constrained Generator Matrices Over Small Fields

We consider the problem of designing optimal linear codes (in terms of having the largest minimum distance) subject to a support constraint on the generator matrix. We show that the largest minimum distance can be achieved by a subcode of a Reed–Solomon code of small field size and with the same minimum distance. In particular, if the code has length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , and maximum minimum distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> (over all generator matrices with the given support), then an optimal code exists for any field size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q\geq 2n-d$ </tex-math></inline-formula> . As a by-product of this result, we settle the GM–MDS conjecture in the affirmative.
A Novel Cybersecurity Framework for Countermeasure of SME's in Saudi Arabia

Cyber security is a core issue of the world. Every country and each organization is trying their best to defend their assets by spending a huge amount of their budget on Cybersecurity issues. It has been noticed that cyber security funds have not be utilized in a proper way to defend the assets of an organization. Each organization has their special needs and sensitivity to their assets. Some assets are critical to be protected in an organization while a few assets are trivial to be protected as organizational backup plan works. In this connection, we have proposed three basic models for Small and Medium Enterprise (SMEs) and one holistic model to cover the needs in the life cycle of diverse SMEs. These models are based on the sensitivity of assets and organizational requirements. These model can be further assessed when extensive survey/interviews based on the target organization taken placed.
Evolution of Educational Robotics in Supplementary Education of Children

The paper presents a retrospection of educational robotics and supporting technologies via the analysis of the evolution of a robotic competition, its rules and designs of participating robots. Corresponding evolution of tools for technical education is discussed in a context of digital fabrication and educational robotics focused on schoolchildren of 6 to 18 years old. Blended learning development is suggested to increase technical literacy among larger groups of children.
Improved ROEWA SAR Image Edge Detector Based on Curvilinear Structures Extraction

By introducing curvilinear structures extraction (CSE) instead of watershed algorithm (WA) or nonmaximum suppression (NMS) to edge strength map (ESM), an improved ratio of exponentially weighted averages (ROEWA) edge detector with better capacity for weak edges detection is proposed to extract smooth edges and edge direction of synthetic aperture radar (SAR) images. Using the ROEWA, the ESM is calculated. Then the CSE algorithm is employed to extracted edges, by acquiring eigenvectors and eigenvalues of the Hessian matrix, the improved ESM (IESM) is obtained, which ensures the good weak edge detection capacity and smoothness of edges. Experimental results on simulated and real SAR images show that the improved ROEWA based on CSE attains better performance than the one using WA or NMS.
Goal-Setting of Modern Students: Inverse Analysis

The article presents the results of a study of the goal-setting of undergraduate students enrolled in the areas of training “Personnel management”, “State and municipal management”, “Psychology”, and “Construction”. The study jointly analyzed the goals stated by students for 1 year, 5 years and more than 5 years (that is, for the rest of their lives). The theoretical basis of the study was the analysis of inverse relations in hierarchical systems. It is assumed that the value preferences, goals and motives form hierarchical systems in which the lower, subordinate elements under certain circumstances are able to claim the dominant value (this is the phenomenon of inversion). The development of inversions in these hierarchies can lead to the escalation of social contradictions. The study identified gender-specific goal-setting; Fisher transformation was used to identify the significance of gender differences. In particular, it was found that female respondents are significantly more likely to point to goals related to building a family, giving birth to children, purchasing real estate, getting favorite work; male respondents pay more attention to high earnings. Particular importance was attached to the identification of inversions in the motivational hierarchy, the relationship between narrow and broad social motives. In particular, it was found that narrow social motives related to personal and / or family well-being strongly prevail in the goal-setting of students over broad social motives, as well as over cognitive and research motives. In this case, there is an inversion, as a result of which prosocial motives are inferior to selfish motives, and this inspires some anxiety. At the same time, we observe an evident desire of the vast majority of students to arrange their own family life, despite the crisis of family values identified by many researchers. The motivation to maintain family ties with the older generation is also important. All this can be viewed as a manifestation of prosocial tendencies .
Adapting Semantic Segmentation of Urban Scenes via Mask-Aware Gated Discriminator

Training a deep neural network for semantic segmentation relies on pixel-level ground truth labels for supervision. However, collecting large datasets with pixel-level annotations is very expensive and time consuming. One workaround is to utilize synthetic data where we can generate potentially unlimited data with their corresponding ground truth labels. Unfortunately, networks trained on synthetic data perform poorly on real images due to the domain shift problem. Domain adaptation techniques have shown potential in transferring the knowledge learned from synthetic data to real world data. Prior works have mostly leveraged on adversarial training to perform a global aligning of features. However, we observed that background objects have lesser variations across different domains as opposed to foreground objects. Using this insight, we propose a method for domain adaptation that models and adapts foreground objects and background objects separately. Our approach starts with a fast style transfer to match the appearance of the inputs. This is followed by a foreground adaptation module that learns a foreground mask that is used by our gated discriminator in order to adapt the foreground and background objects separately. We demonstrate in our experiments that our model outperforms several state-of-the-art baselines in terms of mean intersection over union (mIoU).