Reviewerly/exHarmony huggingface dataset:
Depth Estimation from Single Hazy Images with 2-Phase Training

Depth estimation is essential for 3D scene understanding of images. Although this task comes naturally for human observers, it is still a challenge for computer vision. In the past, the stereo approach to depth estimation has been well-studied. Nevertheless, the same cannot be said for the monocular approach. Depth estimation from a single image alone is very ambiguous, making it a very ill-posed problem. In recent years, the usage of deep learning approaches has been explored to model the relation between single images and their 3D distance. Due to this problem's complexity, most of these approaches require multiple networks and additional computations to obtain a depth estimate. Furthermore, most depth estimation approaches utilize geometric features. However, in certain conditions the captured images will contain scattering effects. This occurs in images captured in bad weather, fog, or underwater, among others. These images will exhibit low contrast, loss of detail, occlusion issues as well as additive noise. Other works commonly attempt to remove these effects prior to further processing, while we intend to exploit them instead for additional 3D information. This research attempts to learn the relationship between a single hazy image and its depth map using deep networks. We propose a 2-phase training approach for depth estimation from single hazy images, taking advantage of two well-known deep learning architectures, e.g., UNet and Generative Adversarial Network (GAN).
Boosting Fashion Image Attributes Classification Performance with MT-GAN Training Technique

Automatic understanding of the product images, particularly the image attributes such as pattern, color, category, material, department, etc., has become a prerequisite for many downstream applications in the e-commerce ecosystem such as visually similar product retrieval and recommendation, publisher website image content monetization. With the recent advancement of deep learning techniques, multi-task deep neural networks (MTDNN) have been widely adopted for multi-class multi-label fashion image classification tasks. In this work, we propose a training technique Multi-Task Generative Adversarial Network (MT-GAN) to improve fashion image classification performance with an additional image translation task. We evaluated the proposed technique on two real-world fashion image datasets and experimental results show that both image classification and image translation benefit from this joint training setting. Specifically the image classifier, even though trained with a smaller amount of data and fewer types of labeled information, manages to outperform existing image classification models such as WTBI and DARN by 54% and 20% respectively for category classification on DeepFashion benchmark. Two main contributors to the superior performance of the image classifier trained as the discriminator in the proposed MT-GAN framework are: 1) the classifier leverages additional amount of data and the regularization effect by taking on the task of a traditional discriminator. 2) The classifier takes not only the RGB image but also the conditional input image shared between the generator and itself as its input, boosting the classification accuracy and recall by 18%. The proposed MT-GAN training technique is GAN-agnostic and can be applied to various state-of-the-art designs of generator and discriminator as well.
Action Planning for Robots using the First Order Logic Calculus of Positively Constructed Formulas

The paper presents an approach to constructing plans for a group of Lego MindStorm robots, moving small blocks to the target area. The approach is based on non-Horn logical programming in the original first-order language of positively constructed formulas (PCF). A description of the PCF formalism is given, an example of solving a problem of constructing action plans for a group of three robots is presented. Unlike other modern approaches to planning action in robotics, our approach is distinguished by a great expressiveness of means for representing knowledge in the system and about the environment.
Graph Wasserstein Correlation Analysis for Movie Retrieval

Movie graphs play an important role to bridge heterogenous modalities of videos and texts in human-centric retrieval. In this work, we propose Graph Wasserstein Correlation Analysis (GWCA) to deal with the core issue therein, i.e, cross heterogeneous graph comparison. Spectral graph filtering is introduced to encode graph signals, which are then embedded as probability distributions in a Wasserstein space, called graph Wasserstein metric learning. Such a seamless integration of graph signal filtering together with metric learning results in a surprise consistency on both learning processes, in which the goal of metric learning is just to optimize signal filters or vice versa. Further, we derive the solution of the graph comparison model as a classic generalized eigenvalue decomposition problem, which has an exactly closed-form solution. Finally, GWCA together with movie/text graphs generation are unified into the framework of movie retrieval to evaluate our proposed method. Extensive experiments on MovieGrpahs dataset demonstrate the effectiveness of our GWCA as well as the entire framework.
XplainableClusterExplorer

Human-centered machine learning is becoming an emerging field aiming to enable domain experts that do not necessarily have a data science background to make use of machine learning applications. Especially in unsupervised machine learning, e.g. cluster analysis, models cannot be autonomously tuned towards an optimal solution for a given application due to the absence of ground truth like class labels. In cluster analysis, different feature subsets may lead to different clusterings. The identification of the best subset of given features is therefore essential in order to improve the overall clustering performance and to obtain a clustering that is suitable for a given application. To support users in finding an optimal clustering solution, we propose XplainableClusterExplorer, an interactive and explorative approach suitable for feature selection for clustering. In an interactive combination of user and machine learning models, the user is supported by evaluation criteria and visualizations in determining feature subsets and adjusting hyperparameters. For feature subset selection we propose a combination with feature importances from random forests and LIME. Since this requires a supervised setting, the cluster assignments are used as tentative class labels in subsequent step. Our experimental results have shown that this subsequent classification step leveraging calculated feature importances can facilitate feature subset selection and therefore enhance overall clustering performance.
Using Prosodic and Acoustic Features for Chinese Dialects Identification

The task of spoken language identification is quite challenging when it comes to discriminating between closely related dialects of the same language. In this paper, a novel approach is proposed to learn and identify the Chinese dialects. We distinguish the difference between Chinese dialects by using the distribution of acoustic and prosodic features, and design to organize these discriminative features into a new topic model. An iterative process is further proposed to implement the model learning. Compared with other state-of-the-art methods, the experimental results show that the proposed model provides competitive performance in the task of Chinese dialects identification.
Multi-Aspect Attention Model for Aspect-based Sentiment Classification Using Deep Learning

Aspect-based sentiment classification (ABSC) (also called fine-grained sentiment analysis) is an essential and challenging task among sentiment analysis tasks. Aspect level sentiment analysis overcomes the limitation of the document and sentence level when multiple aspects appear in a review. Recently, neural network approaches like LSTM has achieved better results in ABSC than traditional machine learning algorithms. Aspect extraction and polarity detection for specific aspects in the review becoming an important task in aspect level sentiment analysis. Therefore, the paper aims to predict the sentiment polarity of each aspect in the review into 3 classes by developing the multi-aspect attention (MAA) model and combine it with the BiLSTM neural network, called MAA-BLSTM. Unlike unidirectional LSTM, the BiLSTM network runs the input in two directions: forward and backward, therefore, it can understand the context better than LSTM. In this paper, the hyperparameter tuning approach is also considered to be the high-performing model. Experiments are tested on restaurant and laptop data from SemEval (2014, 2015, and 2016) datasets and compare the result with other LSTM-based methods. Finally, the result proves that the accuracy of the proposed sentiment model reaches 89.9 on the restaurant dataset and 82.1 on the laptop dataset which are higher than other LSTM approaches.
Decomposition of Intersections with Fuzzy Sets of Operands

We investigate the operation of intersection of fuzzy sets with a fuzzy set of operands. This is a natural generalization of the corresponding operation which involves a crisp set of operands. The decomposition approach was used to study the intersections of fuzzy sets with a fuzzy set of operands. The result of this operation is a type-2 fuzzy set (T2FS). We prove several results which enable us to simplify constructing the type-2 membership function. It is shown that the resulting T2FS can be decomposed according to secondary membership grades into a finite collection of type-1 fuzzy sets. Each of these sets is the intersection of the original sets with a crisp set of operands. This crisp set is the corresponding \(\alpha \)-cut of the fuzzy set of operands. Illustrative examples are given.
Automobile Sales Forecast Based on Web Search and Social Network Data

The information on the Internet will affect consumers’ purchase decisions, especially for high-value products such as automobiles, so some data from search engine and We Media platform to a certain extent will reflect the car sales trend. This paper applies the monthly sales data of Audi from 2011 to 2019 to explore the impact of Baidu and Weibo data on automobile sales forecast. By adding car sales data, Baidu search indexes of selected keywords, Weibo text amount indexes and Weibo sentiment indexes, three machine learning models are established to compare with traditional time series model, thus key indexes helping improve the prediction accuracy are analyzed and marketing advice will be given to car companies.
Study of scale-free structures in feed-forward neural networks against backdoor attacks

Due to the computational complexities of artificial neural networks, MLaaS (machine learning as a service), which is one of the main cloud computing services, is taking the responsibility of the neural network training. With the increase in demand for third-party neural network training, there is a high possibility of adversarial attacks through malicious training. Backdoor attacks are among the most efficient attacks which cause targeted misclassification while the accuracy on clean data is not affected. In this paper, we provide the first investigation about the influence of applying scale-free networks to feed-forward neural networks (FFNNs) against backdoor attacks inserted via the MNIST dataset. It is the first time that the feed-forward neural network structure is changed to improve the network robustness against backdoor attacks using scale-free structure before the network is getting attacked. It has been achieved that scale-free neural networks with long range connections not only keep the accuracy high with strong stability but also make it independent of the number of hidden layers and prevent overfitting.
A Multiple Algorithm Approach to Textural Features Extraction in Offline Signature Recognition

Signature is a biometric trait that has piqued the interest of researchers. This is due to its high rate of acceptability. Offline signature in particular, has been around for a while and hence its suitability as a biometric trait. This paper proposes an offline signature recognition system using a multiple algorithm approach. The system accepts handwritten signature, filters the signature and crops the signature region. The Local Binary Pattern (LBP) of the signature image is then obtained. After this, Grey Level Co-occurrence Matrix (GLCM) is applied. Statistical features are then extracted. The difference in the stored features and the extracted features was obtained. The output is compared with a threshold for discrimination. This research aims at improving the performance of offline signature recognition using its textural features. The designed system gave an FRR and FAR of 8.6%, 4.6% respectively for MYCT signature database and 8.8%, 5.2% for GPDS signature database.
MMoOn Core – the Multilingual Morpheme Ontology

In the last years a rapid emergence of lexical resources has evolved in the Semantic Web. Whereas most of the linguistic information is already machine-readable, we found that morphological information is mostly absent or only contained in semi-structured strings. An integration of morphemic data has not yet been undertaken due to the lack of existing domain-specific ontologies and explicit morphemic data. In this paper, we present the Multilingual Morpheme Ontology called MMoOn Core which can be regarded as the first comprehensive ontology for the linguistic domain of morphological language data. It will be described how crucial concepts like morphs, morphemes, word forms and meanings are represented and interrelated and how language-specific morpheme inventories can be created as a new possibility of morphological datasets. The aim of the MMoOn Core ontology is to serve as a shared semantic model for linguists and NLP researchers alike to enable the creation, conversion, exchange, reuse and enrichment of morphological language data across different data-dependent language sciences. Therefore, various use cases are illustrated to draw attention to the cross-disciplinary potential which can be realized with the MMoOn Core ontology in the context of the existing Linguistic Linked Data research landscape.
Evaluating Early Fusion Operators at Mid-Level Feature Space

Early fusion techniques have been proposed in video analysis tasks as a way to improve efficacy by generating compact data models capable of keeping semantic clues present on multimodal data. First attempts to fuse multimodal data employed fusion operators at low-level feature space, losing data representativeness. This drove later research efforts to evolve simple operators to complex operations, which became, in general, inseparable of the multimodal semantic clues processing. In this paper, we investigate the application of early multimodal fusion operators at the mid-level feature space. Five different operators (Concatenation, Sum, Gram, Average and Maximum) were employed to fuse mid-level multimodal video features. Fused data derived from each operator were then used as input for two different video analysis tasks: Temporal Video Scene Segmentation and Video Classification. For each task, we performed a comparative analysis among the operators and related work techniques designed for these tasks using complex fusion operations. The efficacy results reached by the operators were very close to those reached by the techniques, pointing out strong evidence that working on a more homogeneous feature space can reduce known low-level fusion drawbacks. In addition, operators make data fusion separable, allowing researchers to keep the focus on developing semantic clues representations.
Model-Oriented Methodology for Developing a Social Based Healthcare System

Globally, the number of dependent elderly people is expected to increase significantly in the coming years. This increase requires the need to develop remote health care monitoring systems to meet the major challenges associated with aging. These issues are at the same time social, economic and above all ethical for our society. The elderly are more susceptible to multiple morbidity, loss of autonomy and also vulnerability. In this context, we present our solution, the Family Heroes System, related to the field of remote healthcare. Due to its critical and complex nature, developing such system requires to adopt a model oriented approach. This paper presents a methodology based on CATWOE and UML to analyze and model Family Heroes System.
Analysis on The Demand Evolution of Cloud Accounting Information System in The Era of Big Data

Big data can provide effective support for enterprise's financial decision-making, but how to build accounting information system(AIS) under big data environment is the primary issue that cloud accounting suppliers must consider with. Based on the analysis of the impact of cloud AIS, the implementation framework of AIS with cloud accounting is established, and the role of each module in the framework is described. Then, the complex network theory is introduced to model the AIS products of cloud accounting and calculate the structural eigenvalues. The relationship between the structural eigenvalues and the credibility attributes of the AIS products of cloud accounting is also established by the structural eigenvalues. Finally, through the analysis of cloud AIS products provided by cloud accounting suppliers, according to the needs of enterprise users as a specific sample software, it verifies the structure and credibility of the cloud accounting AIS products proposed in this paper, as well as the effectiveness of management methods.
Efficient Child Saving Robot from Open Bore Well using Hand Gesture based Robotic Arm and Gripping Belt Mechanism

In India, there have been a few revealed bore wells which are by all accounts death pits for children. The average of wells and bore wells in India is currently around twenty 7,000,000, with bore wells representing in excess of 50 percent. Bore wells are built to eradicate the requirement of water. Numerous children passing are accounted for in the past because of bore well mishaps. Nowadays, bore well mishaps are turning out to be most common concern. Children's are losing their lives in light of the carelessness of our general people. Despite the fact that safeguard is the most ideal approach to keep away from such mishaps. So we need an efficient system to pull the children from bore well. To conquer this issue, we proposed a system, in which the robot can move inside the bore well and recover the child securely. The robot consists of two broad hardware unit which is of Hand gestured robotic arm and Belt Mechanism Hardware unit. The hand gesture mechanism is controlled by using accelerometer ADXL 345 for fixing the belt to the children's hand very tightly. Then the belt mechanism is embedded with DC motor to tighten the children hand which is controlled by DPDT switch. To monitor the children inside the bore well, video surveillance is available with the use of web cam. The entire System is snared with pulley rope movement. This sort of robot can discharge trapped child from the bore well safely with in a lesser time.
Zone-based Layer Activation

Scoping behavior adaptations using dynamic extent is a crucial part of Context-oriented Programming (COP). In a synchronous execution model, dynamic extent ensures the activation of a layer for the entire duration of a block. An asynchronous execution model, however, breaks the intended semantics of dynamic extent. For example, using the await keyword postpones the execution of the block and returns to its caller. Thus, dynamic extent deactivates the behavior adaptation. Consequently, when resuming the postponed execution the layer is no longer active.
Domain models for data sources integration in HAR

The ever-increasing quantities of data generated by internet of things applications bring diverse and rich perspectives about monitored phenomena. This is the case, for example, of applications monitoring continuously human activities from wearable sensor deployments for diverse purposes like eHealth. While the profusion of modalities in these deployments could offer many benefits, current learning processes need to be further guided to relate these multisensory information more effectively. Additional knowledge about, e.g. the structure of the sensors deployments, the dynamics of human activities, physical models of the body movements, etc., in short, domain models, have the potential to bring efficiency and robustness to the process of data sources integration. In this contribution, we propose to leverage domain models in order to integrate information sources efficiently. Precisely, (1) a model of the influence of data sources w.r.t. particular human activities, (2) a model of the interactions between data sources, and (3) a model of the transitions between human activities are composed in order to sample, from the data sources, information to fuse. The proposed approach is able to perform intelligent data sampling from information sources by taking into consideration the infrastructure of the sensor deployments and their evolution. Empirical evaluations show that the proposed approach ensures an efficient trade-off between the quantities of sampled data and recognition performances. Noticeably, we get an improvement of 17.84% over the baseline setting, which exploits all available data, while reducing the number of required data sources by one-half.
Simple Yet Effective Way for Improving the Performance of Depth Map Super-Resolution

In depth map super-resolution (SR), a high-resolution color image plays an important role as guidance for preventing blurry depth boundaries. However, excessive/deficient use of the color image features often causes performance degradation such as texture-copying/edge-smoothing in flat/boundary areas. To alleviate these problems, this letter presents a simple yet effective method for enhancing the performance of the SR without requiring significant modifications to the original SR network. To this end, we present a self-selective concatenation (SSC), which is a substitute for the conventional feature concatenation. In the upsampling layers of the SR network, the SSC extracts spatial and channel attention from both color and depth features such that color features can be selectively used for depth SR. Specifically, the SSC learns to use sufficient color features for rendering sharp depth boundaries, whereas their effects are reduced in smooth regions to prevent texture-copying. The proposed SSC can be included in any existing SR networks that have the encoder-decoder structure. The experimental results show that the proposed method can further improve the performances of existing SR networks in terms of the root mean squared error and peak signal-to-noise ratio.