Reviewerly/exHarmony huggingface dataset:
AttGAN based Simultaneous Modification of the given Multiple Face Attributes

Facial attributes are interesting because they show the identity of a person, and manipulating these facial attributes is challenging as it requires the preservation of the identity while making the required modifications to the appearance. Many deep-neural-network-based approaches have been proposed for attribute manipulation. However, these existing approaches are incapable of effectively modifying the attributes simultaneously. In this paper, we address the problem of simultaneous modification of face attributes by manipulating the class information of the attributes using an attribute generative adversarial network. Based on the proposed approach, the latent representation of the given image and its corresponding attribute information are used to modify the appearance by negating the given attribute information. Experiments on the CelebA dataset show that our method effectively performs simultaneous attribute editing with the preservation of other intact facial details.
Optimization of K-medoids Algorithm for Initial Clustering Center

Abstract This paper studies the k-medoids of the partitioning clustering algorithm. A variance-based density optimization algorithm is proposed to solve the problem of random selection of initial clustering centers, slow convergence speed and unstable clustering results in K-medoids algorithm. Based on the mean square deviation and distance mean of the sample set, the density radius of the sample set is calculated according to the size of the sample set. Under the same density radius, the samples in dense regions have high density. By dynamically selecting the samples are selected as initial clustering centers from different dense regions, in the clustering process local optimization is used to accelerate the convergence speed. These operations solve the shortcomings of K-medoids algorithm. In order to test the clustering effect, this algorithm is applied to data set of UCI machine learning. The experimental results show that the initial clustering centers selected by the algorithm are located in the dense area of the sample set, which is more in line with the original distribution of the data set. The algorithm has higher clustering accuracy, more stable clustering results and faster convergence speed on data sets.
An Action Recognition Method Based on Deformable Convolution Network

Abstract In order to solve the problem of behavior recognition in short video, this paper first proposes a key frame extraction algorithm based on mutual information entropy, which uses sliding window to preserve the timing information between frames. Based on the key frame extraction, a based on Deform-GoogLeNet, a two-stream CNN method for deformable convolutional networks, uses the two-stream network to extract the RGB features and optical Flow characteristics of the image separately, and uses the weighted average method to obtain the results of behavior recognition. On the public dataset Charades dataset, mAP is 22.9, which is higher than the similar fusion algorithm, which proves that the proposed algorithm has a good effect in short video behavior recognition.
Precision variable anonymization method supporting transprecision computing

Recently, the number of Internet of Things (IoT) sensors has been increasing rapidly; hence, various data are gathered. As a secondary use of the data, they are useful in providing new services, such as the demand response service in the Smart Grid. However, data services cause several problems in preserving privacy and during computation. This study focuses on these two significant problems. First, the invasion of privacy while using the data to provide such new services is problematic. A lot of private information is available in the data. For example, power consumption data may reveal the lifestyle of the residents, and the technique of obtaining information is known as nonintrusive load monitoring. Second, the penetration of IoT devices and sensors increases the computational and communicating energy consumption for processing the data and for providing various services using the data. In this paper, a new method is proposed to solve these two problems. This method is based on the fact that the anonymization process reduces the amount of information itself, as well as the quantity of computational resources required. This leads to a trade-off between anonymization level and computational cost. For example, raw data have a maximum amount of information and maximum computational cost. In contrast, fully generalized data (all zero data) have minimum amount of information and minimum computational cost. Compared to the conventional method, the proposed method demonstrated lower precision and a higher error rate. Therefore, the proposed method aims to control the trade-off and enables the provision of anonymized data with less information, the required anonymity level, and low computational cost compared to the conventional method. The proposed method is practiced using power consumption data gathered from the Urban Design Center Misiono (UDCMi) and the demand response service is evaluated as an experiment using the data. In this evaluation, a simple model of energy consumption was used in the calculation, which uses the required bit width of the arithmetic logic unit (ALU) for providing the service. The computational efficiency of the proposed method was increased by 60% when k = 2 and by 65% when k = 3, 4, 5, 6 compared to the conventional method. The method can also maintain an acceptable range of service error. The transprecision platform can restrict energy consumption by reducing the bit width of the data. Therefore, the proposed anonymization method can also reduce energy consumption by achieving lower usage of the ALU based on the transprecision architecture.
Low-power activity recognition from triaxial accelerometer data

In this work, a low power triaxial accelerometer data acquisition prototype is presented, which is used to derive physical activity recognition algorithms to be used for implantable or wearable applications. There different characterization methods were implemented and can predict the different activities with good accuracy (380 cases out of 386).
Social Data Assisted Multi-Modal Video Analysis For Saliency Detection

Video saliency should be taken into consideration to facilitate optimization of the end-to-end video production, delivery and consumption ecosystem to improve user experience at lowered cost. Although recent studies have significantly increased the accuracy of saliency prediction, the approaches are mostly video-centric, without considering any prior "bias" that viewers may have with regard to the video contents. In this paper, we propose a novel learning-based multi-modal method for optimizing user-oriented video analysis. In particular, we generate a face-popularity mask using face recognition results and popularity information obtained from social media, and combine it with conventional content-only saliency analysis to produce multi-modal popularity-motion features. A convolutional long short-term memory (ConvL- STM) network discovers temporal correlation of human attention across frames. Experiments show that our method outperforms the state-of-the-art video saliency prediction approaches in representing human viewing preferences in real world applications, and demonstrate the necessity as well as the potential for integrating user bias information into attention detection.
Blind Digital Watermarking Algorithm in Wavelet Domain Using Artificial Immune Optimization

With the development of Internet technology, copyright of digital image has become more and more important. In order to enhance digital watermark image and further balance between robustness and invisibility, a blind digital watermarking algorithm in wavelet domain using artificial immune optimization is proposed. Firstly, the algorithm divides low frequency coefficient matrix into several blocks with length denoted as n and width denoted as m. Secondly, the watermark embedding position is found according to the JND mean of sub-blocks based on the JND model, and the watermark is embedded by ks grouping scheme. The best value of ks is obtained by artificial immune optimization algorithm. Results of experiment show that the proposed algorithm has good invisibility and better robustness.
Application of Hybrid Intelligent Algorithms in Path Planning

Abstract Combining with the classical combinatorial optimization problem in the field of mathematics, this paper decides to take the optimal path planning problem as the research object. A new neighborhood search operator is introduced into the basic worker bee colony algorithm, and the optimization strategy of the algorithm is updated by combining the simulated annealing algorithm. Finally, the improved algorithm is applied to the optimal layout of the path planning, and the optimization results are compared with those of other algorithms. The simulation test shows that the intelligent optimization scheme proposed in this paper can get better optimization results, and has certain feasibility in the path planning. It provides valuable reference for solving the classical problems of the same type.
A Low-Dimensionality Method for Data-Driven Graph Learning

In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian sparsity, with various additional constraints, followed by variations of the edge weights in the graph domain or the eigenvalues in the graph spectral domain. These domains are high-dimensional, since their dimension is at least equal to the order of the number of vertices. In this paper, we propose a numerically efficient method for estimating of the normalized Laplacian through its eigenvalues estimation and by promoting its sparsity. The minimization problem is solved in quite a low-dimensional space, related to the polynomial order of the underlying system on a graph corresponding to the the observed data. The accuracy of the results is tested on numerical example.
Entropy-Based Exposure Time Setting for an Adaptive Multi-Exposure Image Fusion

We investigated the setting method of exposure times for an adaptive multi-exposure image fusion. Because the range of luminance in the natural environment is wide and objects in captured image have inherent optical properties, the vision system need to adapt to the input light. The proposed system comprises an image sensor module and a field-programmable gate array (FPGA) which is composed of control signal generator for an image sensor, HDR (High-dynamic-range) image generator/evaluator. In this study, we employed and evaluated maximum entropy of HDR image as the exposure time setting method.
A Label Distribution Topic Model for Multi-label Classification

At present, multi-label supervised topic model is a kind of effective multi-label classification model applied to various domain. However, due to the limitation of traditional label-topic correspondence in existing multi-label supervised topic model, there are still some aspects that need to be improved. This paper proposed a label distributed LDA model(LD-LDA) for providing more complete label description, which overcomes the disadvantage that labels can only be associated with a fixed hidden topic set or a set of non-overlapping hidden topics, so as to describe labels by the form of probability distribution of all hidden topics. The experimental results show that LD-LDA model has better prediction effect than comparative models in protein function prediction. Although the description of observable variables, parameters and hidden variables in LD-LDA model is based on the problem of protein function prediction, LD-LDA model is essentially a multi-label topic model, which is also applicable to various multi-label application scenarios.
Organization of Educational Process in Conditions of Modern Polytechnic Education Development

The need for reforms in the field of polytechnic training of the younger generation requires scientific understanding of this problem in order to further optimize and align with rapidly changing production and economic realities. The authors analyze various styles and criteria for managing the educational process, which allow us to efficiently organize and motivate educational activities in a polytechnic approach to education, with an emphasis on the practical application of the knowledge gained. It is well known that effective motivation leads to an improvement in the quality of students’ learning, increases their activity and efficiency, and develops skills for successful teamwork. Therefore, the authors believe that the transition from an authoritarian to a participatory style of management of the educational process, improvement of methods and forms of educational work, and also the use of an interdisciplinary approach form the skills of productive professional and scientific activities, creative thinking, and an active life position among the future university graduates. This contributes not only to successful professional implementation, but also to continuous self-education and self-development throughout the rest of the student’s life. This study also raises the problem of organizing an effective industrial practice in the process of studying at the university, which is an urgent part of modern polytechnic education.
MSAHTA: Mixed Spatial Attention and Hierarchical Temporal Aggregation for Action Recognition

Spatial-temporal feature modeling in videos is a fundamental yet knotty problem in computer vision. In this paper, we present a novel model, termed as Mixed Spatial Attention and Hierarchical Temporal Aggregation (MSAHTA), to highlight key spatial feature and aggregate temporal information in the multi-scale and hierarchical manner. MSAHTA is constructed by Spatial Attention module (SA) and Hierarchical Temporal Aggregation module (HTA). Specifically, the SA module aims to enhance the discrimination of spatial-feature by soft attention, while the HTA module aggregates the enhanced spatial-feature of each frame over time by means of a set of hierarchical 3D convolution operation. Furthermore, MSAHTA is an extensible module that can be used as a plugin for the existing 2D CNN. Our method obtains state-of-the-art results on two widely applicable datasets: UCF101 and HMDB51.
DGAN: Disentangled Representation Learning for Anisotropic BRDF Reconstruction

Accurate reconstruction of real-world materials' appearance from a very limited number of samples is still a huge challenge in computer vision and graphics. In this paper, we present a novel deep architecture, Disentangled Generative Adversarial Network (DGAN), which performs anisotropic Bidirectional Reflectance Distribution Function (BRDF) reconstruction from single BRDF subspace with the maximum entropy. In contrast to previous approaches that directly map known samples to a full BRDF using a CNN, a disentangled representation learning is applied to guide the reconstruction process. In order to learn different physical factors of the BRDF, the generator of the DGAN mainly consists of a fresnel estimator module (FEM) and a directional module (DM). Considering the fact that the entropy of different BRDF subspace varies, we further divide the BRDF into He-BRDF and Le-BRDF to reconstruct the interior part and the exterior part of the directional factor. Experimental results show that our approach outperforms state-of-the-art methods.
Long Short-Term Memory with Smooth Adaptation

Long Short-Term Memory (LSTM) is a type of recurrent neural network that has become important in machine learning research thanks to its high precision to solve problems such as speech recognition, handwriting recognition, natural text compression, sequential data processing among others. Although classic LSTM are powerful tools to solve such problems, their adaptation is far from showing a smooth behavior which represents a drawback to LSTM be used in applications such as real-time control of physical systems in which to fulfill restrictions of ranges of values of the control variables is important in order to preserve the physical integrity of the systems. In this paper we present a design of architecture of LSTM that overcomes the non-smooth adaptation problem by using a single forget gate for all the LSTM units and furthermore improves the accuracy of classic LSTMs in problems such as rebber grammar learning, time series forecasting and control of physical systems as it is shown in the experimental and comparison results.
Enterprise Information retrieval using Collaborative Fusion and Active Feedback

Enterprise information retrieval is quite challenging than web based information retrieval due to retrieval from heterogeneous distributed data sources and need for higher accuracy in retrieval [17]. Large corpus of data from heterogeneous information sources available as distributed systems make querying for most relevant and updated information in less number of trials. Similar users tend to prefer similar search results. Applying three dimensions of user similarity, user-document search history and document similarity, collaborative information fusion based retrieval is proposed in this work. The Fuzzy method for mapping document and search queries, to concept is proposed and using it the similarity between documents is modeled. Active feedback based search result revision is proposed to get highly relevant results in less number of trials.
Two-Layer 3DoF+ System Based on Ultra-High Resolution Panoramic Video

3DoF+ supports limited movements of the user to better interact with the video contents. It provides user with more detailed information by utilizing the ultra-high resolution panoramic video. However, such high-resolution video poses a great challenge to network bandwidth and client performance. In this paper, a two-layer based 3DoF+ panoramic video system is proposed utilizing the ultra-high resolution panoramic video. According to the user's viewpoint, this system will select different layer videos to display to provide users with a real overall zooming viewing experience, while effectively saving bandwidth. Experimental results show that the proposed system saves 85.95% transmission bandwidth than the traditional system in average.
A Multi-target Tracking Algorithm Based on AGMM-PHD

Aiming at the problem that traditional tracking algorithms can not effectively track maneuvering multi-target in clutter environment, a new maneuvering multi-target tracking algorithm is proposed in this paper. By combining the Adaptive Grid method with the PHD filtering algorithm, the adaptive adjustment of the model set is realized, so that the tracking algorithm can adapt to the state change of the maneuvering targets. The simulation results show that, compared with the traditional fixed structure multi-model tracking algorithm, the algorithm proposed in this paper has better tracking performance and higher cost-effectiveness ratio. It has broad application prospects in multi-target tracking.
The Orthogonal Defect Classification-based Software Error Pattern Ontology Construction Researches

Orthogonal defect classification is a multi-dimensional measurement system with both qualitative and quantitative features, which is widely used in the software industry. However, the level of abstraction is high, which leads to limited semantic information. Therefore, it has a limited effect in the process of software engineering of complex software systems. To solve this problem, this paper first analyses the software error lifetime from the perspective of knowledge-based software engineering. Furthermore, based on the orthogonal defect classification, the concept of software error pattern is proposed, and its constituent elements and value sets are given. Moreover construct the software error pattern ontology in the requirements analysis phase, focusing on the elements such as “scenario”, “error-manifestation” and “solution”. The example verification part takes unmanned aerial vehicle flight control and management system software as an example to carry out the software requirements error pattern ontology representation and conducts software verification activities based on this and measures the development quality. The results show that the software error pattern can effectively guide the verification of complex software systems and measure the development quality. Therefore, the proposal of software error pattern is of great significance to improve the quality of software development and verification.
Semantic Augmentation Hashing for Zero-Shot Image Retrieval

Hashing technique has been widely applied to large-scale image retrieval due to its efficacy in storage and retrieval. However, due to the explosive growth of multimedia data on the web, existing hashing approaches can hardly achieve satisfactory performance on the newly-emerging images of new classes. In this paper, we propose a novel Semantic Augmentation Hashing (SAH) for zero-shot image retrieval. The class semantic embeddings are used as an intermediate space between visual features and binary codes to align visual features to corresponding class semantics and to transfer knowledge from seen classes to unseen classes simultaneously. Extensive experiments conducted on two datasets with different scales demonstrate the superiority of our method as compared against the state-of-the-arts.
Comparative Study on Punjabi Document Clustering

The objective of clustering, a class of techniques that fall under the category of machine learning is to consequently isolate information into groups called clusters. Clustering of Punjabi documents finds numerous applications in the domain of natural language processing. Currently, not much work has been done for native languages such as Punjabi. This study presents the results of certain common document clustering techniques such as agglomerative and K-means experimented with different feature extraction methods to compare its performance using intrinsic and extrinsic measures. The recently released pre-trained Punjabi word vector model by Facebook has also been experimented as one of the feature extraction methods. This study is conducted to know which combination of clustering algorithm and feature extraction technique gives the most optimum results. This study also uses a supervised approach to evaluate the results of an unsupervised learning algorithm such as clustering.
The Relevance of Using Message Brokers in Robust Enterprise Applications

The paper covers main message broker protocols and the patterns which are used to manage technical problems. The article provides the reader with typical software engineering design issues which might lead to conceptual violations, software non-functional requirements nonconformity, which is high coupling problem, synchronous operation performance loss, and system constituent integration problem. The solutions which come as a result of applying messaging are brought covering each problem above, in particular establishing the communication between loosely coupled applications, researching the application performance speedup with the Amdahl's law and applying Read Model architectural pattern leveraging message brokers capabilities.