Reviewerly/exHarmony huggingface dataset:
Ensemble Bag-of-Audio-Words Representation Improves Paralinguistic Classification Accuracy

A recently introduced, effective feature extraction technique for computational paralinguistics is that of Bag-of-Audio-Words (BoAW), where we cluster the frame-level training vectors, and represent each speech utterance based on the cluster of its frames. Over the past few years, several improvements have been proposed for the original BoAW approach, but none of them has examined the impact of the stochastic nature of the clustering step. In this study we demonstrate experimentally that the random factor present in the BoAW clustering step is indeed propagated into the next classification step, eventually leading to suboptimal classification performance. As a solution, we propose to train an ensemble of classifiers; that is, we repeat the BoAW codebook selection step several times, train separate classifier models for these BoAW representation versions and combine their predictions. Our results, obtained for three different paralinguistic datasets, demonstrate that this ensemble technique makes the whole paralinguistic classification process more robust, and it leads to improvements in the classification performance. We tested this technique on three different paralinguistic datasets, and achieved the highest Unweighted Average Recall score reported so far on the iHEARu-EAT corpus.
A Lightweight Super-resolution for Compressed Image

In recent years, there are significant improvements in super-resolution task using deep neural networks. However, these models have a problem that artifacts become stronger when upscaling lossily compressed images such as JPEG. Conventional super-resolution algorithms are trained on uncompressed images, but the images mainly dealt with on-device such as consumer electronics are compressed ones. Therefore, to perform on-device super-resolution, a lightweight model is required, which can reduce compression artifacts. We propose a novel loss and training scheme that can produce high-resolution images while minimizing artifacts for compressed ones with a single network. Experimental results show that our method can reduce artifacts significantly with less computation than existing approaches.
A Condition Monitoring Data Cleaning Method for Power Equipment Based on Correlation Analysis and Ensemble Learning

The abnormal and missing data in the original condition monitoring dataset of power equipment have adversely affected the equipment's condition assessment and fault diagnosis. This paper proposes a data cleaning method based on grey correlation analysis and ensemble learning. The condition monitoring data that to be cleaned is collected and preprocessed to achieve synchronization and standardization. The grey correlation analysis method is applied to select the parameters with high correlation degree, and the key parameter set is established, which effectively reduces the data dimension and the complexity of the model. Then a data cleaning model based on ensemble learning method (random forest) is established. After the model trained by the data in the key parameter set, the cleaning data is predicted. A distance discriminant method is used to detect abnormal data between the prediction results and measured values, and then the missing data is filled. The example shows that the method presented in this paper can identify a large number of abnormal data correctly and fill in the missing data accurately. The quality of data after cleaning is obviously improved, which benefits for data mining, condition assessment and fault diagnosis for power equipment.
Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping

The paper introduces a radar signal processing method for goal-oriented, collision-free navigation in mobile robotic platforms. The derived algorithm creates an enhanced perception of the area in front of the sensor through accumulating a sequence of radar pulses that is constantly updated, and uses previously obtained perception to inform future robot steering actions on the fly, thus creating a form of working memory. The algorithm is analytically described, and experimentally confirmed in laboratory conditions with a ground mobile robot operating in real-time.
Exploring the Role of Input and Output Layers of a Deep Neural Network in Adversarial Defense

Deep neural networks are learning models having achieved state of the art performance in many fields like prediction, computer vision, language processing and so on. However, it has been shown that certain inputs exist which would not trick a human normally, but may mislead the model completely. These inputs are known as adversarial inputs. These inputs pose a high security threat when such models are used in real world applications. In this work, we have analyzed the resistance of three different classes of fully connected dense networks against the rarely tested non-gradient based adversarial attacks. These classes are created by manipulating the input and output layers. We have proven empirically that owing to certain characteristics of the network, they provide a high robustness against these attacks, and can be used in fine tuning other models to increase defense against adversarial attacks.
Nonlinear objects as a part of plane-and-height basis of sattelite imagery reference

The description of the method of using nonlinear objects of circular, rounded and parabolic shape (beds, road fences, markings, cylindrical oil storages, wells, etc.) as the plane-and-height basis is given. The necessity of using nonlinear objects is due to the insufficient availability of reference points on the territory covered by the image. The use of nonlinear objects will further enhance the geo-bridging capabilities of the push-broom images. An analytical solution of amendments determination (shift, affine and polynomial amendments) to the model of space push-broom images in the form of RPC polynomials using reference points, linear objects, and nonlinear objects of circular, rounded, and parabolic shape as the high-raised basis is described. The advantages of joint-use of reference points, linear and nonlinear objects for solving the task of orienting space push-broom images are shown. Practical examples are given. The proposed technique enables the joint using of reference points, linear objects and nonlinear objects of circular, rounded and parabolic shape as a plane-and-height basis.
Classifying Common Vulnerabilities and Exposures Database Using Text Mining and Graph Theoretical Analysis

Although common vulnerabilities and exposures data (CVE) is commonly known and used to keep vulnerability descriptions. It lacks enough classifiers that increase its usability. This results in focusing on some well-known vulnerabilities and leaving others during the security tests. Better classification of this dataset would result in finding solutions to a larger set of vulnerabilities/exposures. In this research, vulnerability and exposure data (CVE) is examined in detail using both manual and computerized content analysis techniques. Later, graph theoretical techniques are used to scrutinize the CVE data. The computerized content analysis made it possible to find out 94 concepts associated with the CVE records. The author was able to relate these concepts to 11 logical groups. Using the network of the relationships of these 94 concepts further in the graph theoretical analysis made it possible to discover groups of contents, thus, the CVE items which have similarities. Moreover, lacking some concepts pointed out the problems related to CVE such as delays in the review CVE process or not being preferred by some user groups.
BlockConfess: Towards an Architecture for Blockchain Constraints and Forensics

Although Blockchain is still an emerging technology it has the potential to serve as a general purpose information technology platform. Already, smart contract / chaincode platforms, such as Ethereum and Hyperledger Fabric, provide support for the execution of arbitrary computations. However, the suitability of these platforms for specifying and enforcing data and service usage constraints (e.g., usage policies, regulatory obligations, societal norms) and providing guarantees with respect to conformance has yet to be determined. In order to address this gap, in this position paper we argue that symbolic artificial intelligence techniques in the form of semantic technology based policy languages and business process conformance tools and techniques, can together be used to provide guarantees with respect to the behaviour of autonomous smart contract / chaincode applications.
Mapping the Behavioral Change Evaluation of Elderly with Mild Cognitive Impairment in a Smart Environment

Mild cognitive impairment (MCI) can occur while a person is getting old and it is associated with more serious cognitive deteriorations than the normal ageing-related ones. As it may involve a significant decrease in the quality of life, health status and independence of the elderly, the early diagnosis and a personalized assistance become crucial in this ageing worldwide context. Current and emerging technologies provide the needed support for implementing appropriate new models of healthcare delivery. This paper highlights how RO-SmartAgeing system, which aims to provide customized monitoring and assistance for an elderly in his home, is designed for addressing a particular health condition as MIC. The associated smart environment is presented, together with the data flow chart and architecture, with an emphasis on MIC approach.
Low-rank Shared Dictionary Learning with Incoherence Constraint for Endoscopic Gastrointestinal Image Classification

Endoscope has been widely used in clinical examination of gastrointestinal diseases. Many automatic endoscopic image classification algorithms based on dictionary learning are proposed to assist doctors in diagnosing diseases, where the learning method of shared dictionary and class-specific dictionaries enables training dictionary to be more discriminative. Nevertheless, in the process of dictionary learning, the appearance of common features in class-specific dictionaries may cause low classification accuracy. To remedy this deficiency, herein we introduce a coherence constraint between low-rank shared dictionary and class-specific dictionaries. The proposed dictionary learning method is applied to the classification system of endoscopic gastrointestinal images, including normal, polyp and ulcer images, whose experimental results prove that it has promising classification performance.
LABYRINTHS, LIMINALITY AND EKPHRASIS: THE GRAPHICAL IMPETUS IN THE MUSIC OF KENNETH HESKETH

Abstract This article is an introduction to the graphical impetus in the music of Kenneth Hesketh (b. 1968), from around 2012 to the present day, including his recent work, Viae (2020). Through examination of Hesketh's graphical control of a number of musical parameters, the article will demonstrate that the idea of a ‘musical ekphrasis’ can be extended through the incorporation of spatial aspects in determinate music to result in what the author deems a graphical ekphrasis. Whilst in no way exhaustive in its exploration of Hesketh's multi-parametric approach to composition, the article provides an analysis of selected compositional methodologies relating to both composition design and audience communication, situating them alongside other contemporary musical examples and trends.
Power and Performance Analysis of Deep Neural Networks for Energy-aware Heterogeneous Systems

The driver of technology innovation is shifting from raw computing performance to performance delivered per watt. Therefore, it is crucial to conduct heterogeneous (CPU-GPU) system performance analysis in terms of power utilization. The main objective of our experimental study is to provide a detailed analysis of performance and power utilization of Convolution Neural Network for image classification of CIFAR-10 tiny images. We present an approach to calculate one convolution-layer power utilization for heterogeneous CPU-GPU systems by employing CUDA and OpenCL environments. The purpose of power, performance and hardware utilization analysis is to promote green computing and to assist system designers and AI specialists in choosing a green neural network architecture for energy-aware high-performance heterogeneous systems.
A Pedestrian Re-identification Method Based on Multi-frame Fusion Part-based Convolutional Baseline Network

In recent years, with the increasingly perfect monitoring system, how to make full use of the existing monitoring system to do security work has become a concern in the security field. Face recognition can be used in the security field, but it is difficult to play a role in the surveillance field because it usually requires the cooperation of pedestrians. Therefore, the pedestrian recognition technology without the cooperation of pedestrians has been widely concerned. In this paper, in order to realize a given sequence of monitoring pedestrian images and retrieve pedestrian images across devices, we proposed a new method to realize high- precision pedestrian recognition. First, because surveillance video is a series of pedestrian sequences, we proposed a Crossover Filtering Module (CFM) to screen video sequences for key frames. Then, we propose a network named Multi-frame Fusion Part- based Convolutional Baseline (MFPCB) to extract the features of screened keyframes. Finally, we use the cosine distance to measure the features and find the pedestrian image across the device. This paper mainly studies feature comparison and extraction, which can solve the problems of pedestrian occlusion and location under different cameras. Experiment confirms that MFPCB allows pedestrian recognition to gain another round of performance boost. For instance, on the Mars dataset, we achieve 77.3% mAP and 88.6% rank-1 accuracy, surpassing the state of the art by a large margin.
A Bilateral Filtering Based Ringing Elimination Approach for Motion-blurred Restoration Image

We describe an approach that uses a bilateral filter to reduce the ringing artifact in motion-blurred restoration image. It takes into account the specific physical structure of the ringing artifact combined with the properties of the human visual system. To properly reduce the ringing artifact, each of the adjacent pixels is limited in a straight line which has a given direction. To protect the edges and the texture regions of an image, our algorithm divides the image into texture regions and flat regions, and the artifact reduction algorithm is only applied to the flat region. Finally, we use 8 typical images and 5 objective quality evaluation indices to evaluate our algorithm. Experimental results show that our algorithm can obtain better results in subjective visual effect and in objective image quality evaluation.
Cosmonaut Training Complex with Remote Access.

The paper deals with approaches to the creation of cosmonaut training complex with remote access capability based on the modern hardware and software technologies at the Gagarin Research &amp; Test Cosmonaut Training Center. An actual objective of providing remote access in the course of training session using video conferencing, electronic manuals and other assets as well as remote working with software of functional-modeling stands is discussed. The existing groundwork for work in this line, sugges-tions on the content of the training complex with the allocation of unified classrooms, virtual computer complex, and the data transfer system in overall structure are also given here.
Distilling Issue Cycles From Large Databases: A Time-Series Analysis of Terrorism and Media in Africa

Analyzing issue cycles usually begins with observing selected events and then tracking the course of media coverage. This approach collapses when the events of interest are hidden, overlain, or even distorted by extensive coverage of other events. One such complicated case is news about terrorism in Africa. While previous studies have started from single media hypes, we propose modeling the general pattern of such issue cycles with distributed lag models on a large-scale data basis. In order to assess the utility of distributed lag models, two basic principles of issue cycles are derived from theory and empirically tested. Furthermore, using the Global Database of Events, Language, and Tone, we evaluate the usefulness of automated methods for news research. Although the data are quite noisy, automated content analysis combined with distributed lag models is a promising approach for studying issue cycles. The model can be used to visualize issue cycles. In the case of news about terrorism in Africa, we found a sudden increase in coverage, followed by a second local maximum after a few weeks.
Performance of AV1 Real-Time Mode

With COVID-19, the interest for digital interactions has raised, putting in turn real-time (or low-latency) codecs into a new light. Most of the codec research has been traditionally focusing on coding efficiency, while very little literature exist on real-time codecs. It is shown how the speed at which content is made available impacts both latency and throughput. The authors introduce a new test set up, integrating a paced reader, which allows to run codec in the same condition as real-time media capture. Quality measurements using VMAF, as well as multiple speed measurements are made on encoding of HD and full HD video sequences, both at 25 fps and 50 fps to compare the respective performances of several implementations of the H.264, H.265, VP8, VP9 and AV1 codecs.
Boosting and Residual Learning Scheme with Pseudoinverse Learners

The traditional gradient descent based optimization algorithms for neural network are subjected too many vulnerabilities, such as slow convergent rate, gradient vanishing and falling into local minima. Therefore, the alternative non-gradient descent learning algorithm was proposed and prevalently applied in kinds of domains, such as pseudoinverse learning algorithm (PIL). However, when a special variant of the PIL, taking the random configuration of weight parameters, is adopted, the generalization ability needs further improvement although it has excellent training efficiency. Thus, on consideration of integrating the idea of ensemble learning, we proposes two methods to enhance basic PIL. One method is equivalent to an additive model, which can raise the network's performance by introducing boosting mechanism, and the other is to adopt a recursive way to rectify the hidden layer output of the neural network, then the relative better model is used in the subsequent prediction. Comprehensive evaluating experiments are conducted on several datasets, and the experimental results illustrate that the our proposed methods are effective on the classification accuracy.
Vehicle Seismic Signal Recognition based on Recurrent Neural Network (RNN)

Vehicle recognition is a challenging task in unattended ground sensor (UGS) system. Firstly, this paper briefly introduces the development of UGS and seismic signal recognition. Then this paper introduces the acquisition of vehicle seismic signal and the feature analysis. Next we propose a vehicle recognition method based on recurrent neural network (RNN) and compare the recognition effects of MFCC and PSD feature. The results show that the RNN model based on MFCC feature has higher accuracy rate and stronger robustness than that based on PSD feature.
Discovering Business Problems Using Problem Hypotheses: A Goal-Oriented and Machine Learning-Based Approach

Discovering business problems hindering business goals and a deep understanding of those problems are often more important than finding solutions. However, business organizations face difficulties finding business problems hidden in Big Data using machine learning. The specific difficulties might include a lack of methods in exploring potential problems, figuring out necessary data features associated with potential problems, and validating potential problems. This paper presents the Metis+ framework that supports the discovery of business problems using problem hypotheses. Metis+ consists of essential modeling concepts, semantic reasoning methods, and processes for ensuring that potential business problems are analyzed in the context of business goals, hypothesized problems are systematically and explicitly mapped to relevant data features in a data set, and supervised machine learning models are built to get insights into problem hypotheses. Semantic reasoning methods are then utilized to validate or invalidate hypothesized problems. The Metis+ framework is illustrated with a loan case study in the PKDD'99 Financial Data Set. The experiment results show that Metis+ can help explore and validate problem hypotheses, leading to identifying the most significant problem.
Multiframe Astronomical Image Registration Based on Block Homography Estimation

Due to the influence of atmospheric turbulence, a time-variate video of an observed object by using the astronomical telescope drifts randomly with the passing of time. Thereafter, a series of images is obtained snapshotting from the video. In this paper, a method is proposed to improve the quality of astronomical images only through multiframe image registration and superimposition for the first time. In order to overcome the influence of anisoplanatism, a specific image registration algorithm based on multiple local homography transformations is proposed. Superimposing registered images can achieve an image with high definition. As a result, signal-to-noise ratio, contrast-to-noise ratio, and definition are improved significantly.
SACT: A New Model of Covert Communication Based on SDN

Anonymous tracking technology of network watermarking is limited by the deployment of tracking devices in traditional network structure, resulting in poor scalability and reusability. Software Defined Network (SDN) boasts more freedom thanks to its separation of the control plane from the data plane. In this paper, a new anonymous communication tracking model SDN-based Anonymous Communication Tracking (SACT) is proposed, which introduces network watermarking into SDN and combines IP time hidden channel and symbol expansion technology. In addition, we introduce a hopping protection mechanism to improve the anti detection ability of the watermark as well. The experimental results show that in a variety of simulated network environments, SACT achieves excellent detection rate and bit error rate, thus it is sufficient to determine the communication relationship between the two parties. Meanwhile, SACT solves the deployment problem of anonymous tracking and improves the availability and scalability of covert communication.
Insights into the interrelationship between the design process and user practice: a case study on user engagement in a platform for professional learning

This article reports on a study of an online system for supporting professional learning within higher education. The system was developed through an iterative user-centred design process to ensure that it supported the needs of academics. Results indicated there were a number of practice-related factors that emerged from within the user’s environment that triggered micro challenges at the user level and macro challenges at the organisational level. The article offers two considerations for capturing practice-related barriers in complex environments. These considerations are: engaging a multidisciplinary team and designing online tools to be flexible to respond to complex environmental factors.