Reviewerly/exHarmony huggingface dataset:
Localized Online Weather Predictions with Overnight Adaption

ThisZauner, Michael paperKillian, Michaela extendsKozek, Martin the results presented in a conference paper showcasing an approach for online forecasting of ambient temperature and solar irradiation. The proposed method creates a localized prediction with an improvement over the available weather predictions ranging from 52 to 92% in ambient temperature forecast and 8–42% for solar irradiation forecast. This localized forecast can be used for improved predictions in smart homes or PV power plants for a more efficient operation. A new method for adapting the parameters of the autoregressive model with external input (ARX) for the solar irradiation over the night is proposed. This allows the model to be tuned to changing weather conditions without relying on external inputs.
Understanding Factors Affecting the Use of English Writing Software in Indonesia

Technology has affected crucial changes in language learning. Notably, in English as a Foreign Language (EFL) context, technology has been regarded as beneficial for the improvement of English writing skills. However, there is not much attention given to the EFL students' needs for writing software to improve their writing. This study investigated the needs of Indonesian EFL tertiary students for writing software. The participants of this study were EFL students in one of the state universities in Malang City, East Java, Indonesia. Data were elicited by using a questionnaire which was adopted and modified from a unified theory of acceptance and use of technology (UTAUT). The results showed that most of the participants use Microsoft Word more than other useful writing software. Mostly, they used writing software to check their spelling. There were so many benefits that students can get beside spelling checker feature. Moreover, the students believe that writing software can help them improve their performance in writing. This suggests that writing software designers should improve the software in terms of quality, feature variety, and practicality.
A GPU assisted space frequency method for dynamic defect detection in optical metrology

The study of defect dynamics is an important problem in the field of non-destructive testing, crack propagation and fracture mechanics. Optical interferometric techniques are extensively used for this purpose because of their non-invasive behaviour and full field operation. The fringe patterns obtained from these techniques serve as good indicators for finding defects. In dynamic defect analysis, a large number of fringe patterns are captured and processed. The regions where the fringe density varies significantly are classified as defects. Thus, a fast, reliable and robust algorithm for identifying the rapid variations of fringe density is required. In this paper, we propose a graphics processing unit (GPU) assisted space frequency method based on windowed Fourier spectrum analysis for processing the dynamically varying fringe patterns. The main advantage of this approach is high computational efficiency achieved using GPU computing framework. The performance of the proposed method is demonstrated using 100 simulated fringe patterns, each of size 2048 x 2048 pixels. This large data stack was efficiently processed using the proposed method within only a minute, and thus, the proposed method offers the feasibility of high speed defect analysis. The practical application of the proposed method is explored by processing the fringe patterns obtained from the propagation of micron sized defects in an experimental configuration based on common-path diffraction phase microscopy setup.
Performance Study of Some Recent Optimization Techniques for Energy Minimization in Surveillance Video Synopsis Framework

In the age of the smart city, each activity is under surveillance. The employment of plentiful surveillance video cameras produces the gigantic amount of redundant video data. For ease of investigations, video synopsis competently shrinks the length with the preservation of all activities presents in the original video. The outcome of the video synopsis technology greatly depends on the central module, the optimization framework, and its minimization. This paper evaluates the performance of various optimization techniques, namely simulated annealing (SA), NSGA II, cultural algorithm (CA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), forest optimization algorithm (FOA), JAYA algorithm, elitist-JAYA algorithm, self-adaptive multi-population-based JAYA algorithm (SAMP-JAYA), to minimize the energy in the field of object-based surveillance video synopsis. The experimental results and analysis direct the need for an optimization algorithm which can efficiently and consistently solve the minimization problem in connection to video synopsis.
Fusion Estimation for Nonlinear Systems with Heavy-tailed Noises

In some target tracking scenarios, the process noise and the measurement noise are both heavy-tailed noises. This type of noise can not be modeled as Gaussian noise, since it has quite different characteristics. Existing algorithms for fusion estimation of nonlinear systems with Gaussian noises are no longer applicable for systems with heavy-tailed noises. In this paper, estimation of multisensor data fusion for nonlinear systems with heavy-tailed process noise and measurement noise in target tracking is studied. Based on the robust Student's t based nonlinear filter (RSTNF), a filtering method using unscented transformation (UT) for state estimation of nonlinear systems with heavy-tailed noises, we present a modified nonlinear filter in case of package drop out exists. For fusion estimation of multisensor nonlinear systems, we present the centralized fusion based on the modified filter. Our results from Monte Carlo simulations on a target tracking example demonstrate the effectiveness and the robustness of the presented algorithm.
The sentiment recognition of online users based on DNNs multimodal fusion

ABSTRACT Online users are more willing to express their opinions and emotions on social platforms in online public opinion events, through combing texts and images rather than texts alone. Facing the change of emotional expressions, it is extremely vital to recognize the sentiments of online users in online public opinion events in an appropriate way. In this paper, we propose a novel Deep Neural Networks (DNNs) model to recognize online users' sentiments in online public opinion events by analyzing the sentiments of texts and attached images together. Also, we compare two fusion strategies, feature‐level fusion and decision‐level fusion, to combine the affective information from texts and images. In feature‐level fusion, fine‐tuned Convolutional Neural Networks (CNNs) and Bidirectional Long Short‐Term Memory (BiLSTMs) are employed to extract visual and textual features respectively. Then the features are concatenated and fed to a DNNs system to complete classification. In decision‐level fusion, a rule is employed to fuse the output from unimodality, generating final predicted labels. Experimental results showed that the proposed DNNs multimodal model achieved better performance than unimodal sentiment recognition model. In fusion strategy, the feature‐level fusion performed better in our experiments.
Power Consumption Attack Based on Improved Principal Component Analysis

Accompanied with the status quo and problems that the low efficiency in the traditional methods of principal component analysis (PCA) when we face the problems of correlated power attack with large amount of data, we presents an improved method to reduce the noise of power data by wavelet packet transform (WPT) and then reduce the dimension by traditional principal component analysis, based the conclusion we have arrived about the advantage of wavelet packet transform in signal processing. It is more productive than common methods in the data processing phase of the related power attack, especially on the occasion that we own high dimensional data with low signal to Noise Ratio (SNR). Just to show you where we can optimize, the middle position of SM4 encryption algorithm was selected to measure the power consumption, and compared with the results of traditional principal component analysis. The results show that not only is the number of curves has been significantly reduced, but the computational complexity has been decreased easily, by all means, the computational time is less than the original required time so that the attack efficiency is significantly improved. Aiming at the goal with a highly targeted way to reduce the amount of data which are needed to crack the key especially for course of power analysis, the proposal submitted by us have the certain advantages under this circumstance when we face the high latitude data with low SNR within the process of correlated power attack.
Using Balanced Training to Minimize Biased Classification

In this paper, we classify semantic zone in a document image and observe how a balanced training influences the classification performance. Unlike holistic document which normally distinguishes in content and structural layout, semantic zone introduces stronger inter-class ambiguity as it loses layout feature. Zone extraction from documents often results in unbalanced class distribution. Our experiment shows that training on such data leads to a biased classification. We classify semantic zone by using AlexNet which is a Convolutional Neural Network (CNN). It works on 3 corpora: University of Washington (UW) III, German historical document images (OCRD), and combination of both data sets. Because zone distribution is heavily unbalanced, we augment the data and balance the training distribution to prevent over expression by major classes. To maintain accuracy, we adopt transfer learning from larger document corpus (RVLCDIP). Besides deep learning, we also use heuristic approach to compare performance between balanced and unbalanced training. The result shows that balanced training can alleviate biased performance.
Performance of Korean spontaneous speech recognizers based on an extended phone set derived from acoustic data*

We propose a method to improve the performance of spontaneous speech recognizers by extending their phone set using speech data.In the proposed method, we first extract variable-length phoneme-level segments from broadcast speech signals, and convert them to fixed-length latent vectors using an long short-term memory (LSTM) classifier.We then cluster acoustically similar latent vectors and build a new phone set by choosing the number of clusters with the lowest Davies-Bouldin index.We also update the lexicon of the speech recognizer by choosing the pronunciation sequence of each word with the highest conditional probability.In order to analyze the acoustic characteristics of the new phone set, we visualize its spectral patterns and segment duration.Through speech recognition experiments using a larger training data set than our own previous work, we confirm that the new phone set yields better performance than the conventional phoneme-based and grapheme-based units in both spontaneous speech recognition and read speech recognition.
An Improved Location Model for Pedestrian Detection

Abstract Pedestrian detection in complex scenes has always been a research difficulty in computer vision. The performance of current methods was seriously degraded when the pedestrian is occluded or the size of pedestrians are too small, etc. In this paper, we propose a novel approach based on location model for the detection of multiple pedestrians, which aims to improve the efficiency of algorithms in complex scenarios. In the model, a fully convolutional neural networks for the classification of pedestrian and non-pedestrian are trained to learn pedestrian features first. Then trained model are used to search for pedestrian regions, and the regions where pedestrian may be present will be activated and marked with boxes. Finally, we fine tune the boxes to overlap them with the ground truth more precisely. Compared with the current methods on two pedestrian datasets, experimental results demonstrate the comparable performance of our approach in term of miss rates.
Content Adaptive Image Steganalysis in Spatial Domain Using Selected Co-occurrence Features

In this paper, a general content adaptive image steganography detector in the spatial domain is proposed. We assemble conventional Haar and LBP features to construct local co-occurrence features, then the boosted classifiers are used to assemble the features as well as the final detector, and each weak classifier of the boosted classifiers corresponds to the co-occurrence feature of a local image region. Moreover, the classification ability and the generalization power of the candidate features are both evaluated for decision in the feature selection procedure of boosting training, which makes the final detector more accuracy. The experimental results on standard dataset show that the proposed framework can detect two primary content adaptive stego algorithms in the spatial domain with higher accuracy than the state-of-the-art steganalysis method.
An Improved Adaptive Kalman Filter for Altitude Estimation of Quadrotors

This paper studies the altitude estimation problem of quadrotors in the indoor complex environment in which more than one altitude sensors are installed on the quadrotor in order to obtain precise and stable information. First of all, the measurement model is established. Then, an improved adaptive Kalman filter to deal with the problems that the measurement value of a sensor remains unchanged or the constant measurement errors occur. An altitude estimation algorithm is given based on the proposed improved adaptive Kalman filter. Simulation and experimental results show the feasibility and effectiveness of the algorithm.
Superpixel Description and Indexing for Visual Loop Closure Detection

Recognizing whether the current place has been visited before or not is an important task in robotic navigation, since it helps to reduce the inconsistencies produced by the inherent noise of navigation sensors. When a camera is used as input for navigation, this process is known as visual loop closure detection. Under this context, in this work we propose a loop closure detection method based on superpixels and a Bag of Words scheme. A novel image description method for superpixels is proposed. Our approach also makes use of the concept of dynamic islands, which allows us to group images close in time and to avoid images taken from the same place to compete among them as loop closure candidates. The proposed method is validated using several outdoor public image sequences and compared to other state-of-the-art solutions.
Capsule Networks for Chinese Opinion Questions Machine Reading Comprehension

In recent years, machine reading comprehension is becoming a more and more popular research topic. Promising results were obtained when the machine reading comprehension task had only two inputs, context and query. In this paper, we propose a capsule networks based model for Chinese opinion machine reading comprehension task which has three inputs: context, query and alternatives. First, we use a bi-directional LSTM to encode the three inputs. Second, model the complex interactions between context and query with a multiway attention layer. In addition to the attention mechanism used in BiDAF, the other two attention functions are designed to match the relationship between inputs. Finally, we present a capsule networks layer to route the right alternative. Specifically, we use two strategies to improve the dynamic routing process to filter noisy capsules, which may contain useless information such as stop words. Our single model achieves competitive results compared to the baseline methods on a Chinese dataset and obtains a significant improvement of 2.45% accuracy.
Budgeted Algorithm for Linearized Confidence-Weighted Learning

This paper presents a novel algorithm for performing linearized confidence-weighted (LCW) learning on a fixed budget. LCW learning has been applied to solve online classification problems in recent years. To make better classification performance, it is common to combine with kernel functions through the kernel trick. However, the trick makes the LCW learning vulnerable to the curse of kernelization that causes unlimited growth in memory usage and run-time. To address this issue, we first re-interpret the LCW learning by using a resource perspective deeming every instance as a potential resource to exploit. Based on the perspective, we then propose a budgeted algorithm that approximates the LCW learning under a finite constraint on the number of available resources. The proposed algorithm enjoys finite complexities of time and space and thus is able to break the curse. Experiments on several open datasets show that the proposed algorithm approximates the LCW learning well and is competitive to state-of-the-art budgeted algorithms.
Arbiter: a lightweight, secured and enhanced access control mechanism for cloud computing

The demand for Cloud computing has been increasing for the past few years due to its significance in the IT industry, cost effectiveness and on demand services. To benefit from this technology, access control mechanism need to be applied to restrict the access to legitimate users and prevent the system from unauthorized access whether coming from people or malicious programs. The increase in the number of unauthorized access recently required more work to reduce it. The main problem is that most of the cloud access control is already built based on weak access control mechanism comparing to other mechanisms. In addition to this weakness, the process of changing it to trust mechanism is complex, expensive and takes a great deal of decision-making. This paper investigates the status of existing access control mechanism in the cloud and provides lightweight, secured, and a new approach based on Mandatory Access Control (MAC) mechanism called Arbiter. The proposed architecture aims to reduce the cost and effort of change the entire operating system to meet the requirement of a trusted system. This paper uses design science methodology to design a technical tool for those who are involved in planning, designing and managing access control in the cloud.
Using Mobile Application for Word Reordering Problems to Enhance Learning Analytics

The authors have developed a mobile application for word reordering problems (WRPs) and its learning management system (LMS) based on the WRP web application in order to collect learners' study logs. Various parameters such as "total distance of movements" and "answer time" are generated by learners' trajectories to detect their hesitations in answering WRPs. The main objective of this research is to exploit the practical use of new data types provided by physical sensors embedded in mobile devices, with the belief that specific new data types possibly benefit learning analytics. The authors used acceleration data and force data to detect abnormal trajectories - a series of trajectories caused by unstable environments resulting in low accuracy of detecting hesitations. The authors conducted an experiment to determine the most suitable pair of thresholds (acceleration and force parameter values). By setting this pair, f-measure to identify abnormal trajectories has reached approximately 0.727. This means that we are able to detect abnormal trajectories at a certain rate and cut some of them off to get more accurate results to detect hesitations using mobile devices.
Video Görüntülerinde Şiddet İçeren Aktivitelerin Lstm Ağı İle Tespiti

Although action recognition is a widely studied area on both RGB videos and depth map, violent activity detection is a relatively less studied area. With developing technology and the growing internet, large-scale video data become easily accessible. This also makes videos with violent scenes accessible. Labeling the violent scenes in videos is important for the content-based multimedia retrieval systems. Standard surveillance systems are incapable of detecting violent and improper activities. It is impossible for a human operator to watch all the records for a large-scale surveillance system. On the other hand, there is an increasing demand for automatic labeling systems to check the videos uploaded into video streaming sites. For these reasons, the automatic detection of violent activities is becoming more and more important. In this study, we propose a Transfer Learning and LSTM (Long Short-Term Memory) network-based method. Deep features extracted form GoogleNet by using RGB sequences, velocity sequences and acceleration sequences computed from the first and second derivative of the pixels values are given into the LSTM network as input. The proposed method is tested with Hockey Fight and Violent Flow datasets that are commonly used in the literature. Experimental results are comparable to those in the literature.
Target tracking algorithm based on convolutional neural network and particle filtering

Aiming at the phenomenon that the target particle tracking algorithm is affected by illumination changes and occlusion in the target tracking process, the goal is lost. A target tracking algorithm based on convolutional neural network and particle filtering is proposed. The algorithm uses the convolutional neural network to automatically learn the depth features of the target, and extracts the more abstract semantic information of the target. The semantic information makes the algorithm robust to the apparent changes of the target, which can alleviate the drift problem to some extent. The algorithm can effectively combine the target apparent model based on convolutional neural network with the particle filter framework. The experimental results show that compared with the other five algorithms in the particle filter framework, the algorithm can track the moving targets with partial occlusion and morphological changes more robustly.
Opportunities, Challenges and Strategies for the Internationalization Development of Higher Vocational Education in the New Situation

The opening-up of national education has come into a new period of development, the internationalization of higher vocational and technical education should conform to the overall situation of service, for our own use, raise the level and intention of the development trend, and at the same time, should cooperate with the "One Belt One Road" construction, to help China's education quality capacity to go out.The internationalization development level of higher vocational and technical colleges in China has the characteristics of the coexistence of strategic development and imbalance, the coexistence of advantages and disadvantages, opportunities and challenges, from strategic planning and system construction, self-support and the pursuit of resources, docking strategy and characteristic development, intelligence introduction and resource output synchronization; operating schools openly and party building promote each other to discuss the effective path of developing education internationalization in higher vocational and technical colleges under the new background.
Analysis and Comparison of Monte Carlo Tree Search versus ACO Algorithms in Distribution of Resources and Environment

Swarm intelligence (CI) has been widely studied in the past decades. The most famous CI algorithm is ant colony algorithm (ACO), which is used to solve complex path search problems. AlphaZero program, through self-reinforcement learning of Monte Carlo tree algorithm from scratch, has achieved transcendental results in go, chess and general chess. By analyzing and comparing the Monte Carlo Tree Search (MCTS) and ACO algorithms on Distribution of resources and environment, the reasons for AlphaZero's success are revealed. It is not only because of deep neural networks and intensive learning, but also because the algorithm is essentially an evolutionary algorithm of CI emergence.