Reviewerly/exHarmony huggingface dataset:
Surveillance Video Authentication Using Universal Image Quality Index of Temporal Average

Inter-frame forgery is a common type of surveillance video forgery where a tampered process occurs in a temporal domain such as frame deletion, insertion, and shuffling. However, there are a number of methods that have been proposed for detecting this type of tampering, most of the methods have been found to be deficient in terms of either accuracy or running time. In this paper, a new approach is proposed as an efficient method for detecting frame deletion, insertion, and shuffling attacks. Firstly, the video is extracted into frames and the temporal average for each non-overlapping subsequence of frames is computed for examination instead of exhaustive checking which can be reduced the running time. Then, the universal image quality index is used for detecting the inter-frame forgery and determining its location. The experimental results show the efficiency of the proposed method for detecting inter-frame forgery with high accuracy and low running time.
Development of a Real-Time Damage Estimation System

Assessing the extent of damage quickly following a major natural disaster is crucial to ensuring that effective decisions are made to establish an appropriate first response system and implement response measures. Therefore, a real-time earthquake damage estimation system was developed. Among other things, the system estimates the distribution of seismic ground motion, structural damage, and casualties based on observation records obtained immediately after a major earthquake. In addition, the system is equipped with a function for assessing actual damage using a variety of sources and techniques. Damage estimates generated by the system were used for emergency response during actual disasters, including the 2016 Kumamoto Earthquakes, and the system’s effectiveness has been confirmed. This study evaluates the functions and performance of the system, examines its potential applications, and discusses future innovations and challenges.
A method towards Web service combination for cross-organisational business process using QoS and cluster

How to quickly combine various Web services to support cross-organisational business processes is the key technical problem in service computing. Because of the changeability of QoS of Web services, the traditional methods are unadaptable to the new environments. In this paper, a new service composition method, called Improved Self-organising neural network Method for Web Service Composition, is proposed to achieve QoS-aware Web service combination, according to using the clustering technology. First, fuzzy mathematics is used to express each QoS attribute and the improved self-organising neural network is used to cluster services to reduce the number of candidate services. Secondly, all the centre of each cluster is selected and formed a composite service by using an exhaustive algorithm. Thirdly, the service cluster that is represented by the concrete services. Finally, the optimal service combination can be selected using integer programming or genetic algorithm. The experimental result shows the efficiency of Web service composition and demonstrates the applicability.
Registration Error and Intensity Similarity Based Label Fusion for Segmentation

Label fusion is a core step of Multi-Atlas Segmentation (MAS), which has a decisive effect on segmentation results. Although existed strategies using image intensity or image shape to fuse labels have got acceptable results, there is still necessity for further performance improvement. Here, we propose a new label fusion strategy, which considers the joint information of intensity and registration quality. The correlation between any two atlases is taken into account and the probability that two atlases both give wrong label is used to compute the fusion weights. The probability is jointly determined by the registration error and intensity similarity of the two corresponding atlas-target image pairs. The proposed label fusion algorithm is named Registration Error and Intensity Similarity based Label Fusion (REIS-LF). Using 3D Magnetic Resonance (MR) images, the proposed REIS-LF algorithm is validated in brain structure segmentation including the hippocampus, the thalamus and the nuclei of the basal ganglia. The REIS-LF algorithm has higher segmentation accuracy and robustness than the baseline AQUIRC-W algorithm. Taking the registration quality, the inter-atlas correlations and intensity differences into account in label fusion benefits to improve the object segmentation accuracy and robustness.
Competitive Bridge Bidding with Deep Neural Networks

The game of bridge consists of two stages: bidding and playing. While playing is proved to be relatively easy for computer programs, bidding is very challenging. During the bidding stage, each player knowing only his/her own cards needs to exchange information with his/her partner and interfere with opponents at the same time. Existing methods for solving perfect-information games cannot be directly applied to bidding. Most bridge programs are based on human-designed rules, which, however, cannot cover all situations and are usually ambiguous and even conflicting with each other. In this paper, we, for the first time, propose a competitive bidding system based on deep learning techniques, which exhibits two novelties. First, we design a compact representation to encode the private and public information available to a player for bidding. Second, based on the analysis of the impact of other players' unknown cards on one's final rewards, we design two neural networks to deal with imperfect information, the first one inferring the cards of the partner and the second one taking the outputs of the first one as part of its input to select a bid. Experimental results show that our bidding system outperforms the top rule-based program.
Instance Segmentation of Auroral Images for Automatic Computation of Arc Width

The width of auroral arc is one of the most important factors in understanding and examining its physical mechanisms. In this letter, we propose a fully automatic method for computing the width of auroral arcs based on the instance segmentation of auroral images. To accurately detect and segment auroral arcs with oriented bounding boxes, we adapt a state-of-the-art instance segmentation model, Mask region-based convolutional neural network, by designing a two-stage inference process combined with an indispensable random rotation training strategy and designing an effective feature extraction architecture. Given the segmented masks of individual auroral arcs, we present a method for computing the arc width automatically. In our experiments, the instance segmentation model achieves 86.8% of mean average precision on the human-labeled data set. By automatically evaluating the width of 29 938 detected auroral arcs in 18 417 auroral arc images, we obtain a similar arc width distribution to that evaluated by the semiautomatic approach, which demonstrates the effectiveness of our proposed method.
Variation and security enhancement of block ciphers by embedding

This paper proposes a method of embedding block cipher algorithms in a new algorithm in order to create variations and improving security of the original algorithm. The method is concretely explained for embedding AES128 block cipher. In practice there is often a need to develop a bank of secure cipher algorithms in short time. Designing such algorithms from first principles is a very long drawn process. The embedding method allows creating a family of ciphers which have an upgraded level of security over a chosen original algorithm and have the same key length with an option to extend the key length. The embedding method is chosen such that the efficiency of operation of the new algorithm is kept close to that of the original algorithm.
Secondary professional education for an innovative economy

Presented in this article is a review, which evaluates the current state of Russia’s secondary professional education system (SPE), and the degree to which it is prepared to train specialists who would be in demand by an innovative economy. Unlike the higher professional education system, during the last decade SPE has not only ceased to be a government priority, but has also “suffered” to a certain degree due to increased attention on behalf of the federal government towards universities, which have attained special statuses – such as “federal”, “research”, “supporting”. Limited financial resources have been redistributed for the benefit of higher educational facilities, this leading to a decrease in funding for training personnel within SPE, while also harming the resource base. This took a serious toll on those technical colleges which trained specialists in new, promising professions. Given such a situation, communicating with employers becomes one of the key factors. However, the cooperation between SPE facilities and enterprises has been experiencing crisis due to the general decline of a number of manufacturing plants during the post-Soviet period. The study is based on the analysis of official statistical data, materials from sociological surveys, as well as expert opinions. This article consistently analyzes the current state of the SPE system, evaluates the training level of teachers, students’ motivation for receiving secondary education, as well as the most popular professions. Special attention is devoted to corporate training acting as the equivalent of SPE, in the form of additional professional training. The article is concluded by examining possible directions for the development of the SPE system, in order to facilitate its adaptation to the demands of an innovative economy. Three possible directions are offered, which might help increase the quality of education within the SPE system, given the current conditions of financial limitations and issues associated with the quality of human resources. This includes cooperation with higher educational facilities, transferring a certain part of educational programs to the premises of employers, and conducting personnel training and advanced training by means of participation in independent programs such as WorldSkills and other such initiatives.
New SIEM System for the Internet of Things

Based on the available standards, the generalized architecture and the reference model of the IoT as a security object to be protected are presented. On the IoT layers, different security controls collecting data for further detection of security-related events are located. The security incident management process for the IoT needs automation, for which Security Information and Event Management (SIEM) systems are the best applicable solutions. But modern challenges dictate the need to modify these systems for the IoT. A new blockchain-based SIEM system for the IoT is proposed.
Generic Approach to Outsource the Decryption of Attribute-Based Encryption in Cloud Computing

The notion of attribute-based encryption with outsourced decryption (OD-ABE) was
Vocal Tract Length Normalization using a Gaussian mixture model framework for query-by-example spoken term detection

A speech spectrum is known to be changed by the variations in the length of the vocal tract of a speaker. This is because of the fact that speech formants are inversely related to the vocal tract length (VTL). The process of compensating spectral variation due to the length of the vocal tract is known as Vocal Tract Length Normalization (VTLN). VTLN is a very important speaker normalization technique for speech recognition and related tasks. In this paper, we used Gaussian Posteriorgram (GP) of VTL-warped spectral features for a Query-by-Example Spoken Term Detection (QbE-STD) task. This paper presents the use of a Gaussian Mixture Model (GMM) framework for VTLN warping factor estimation. In particular, the presented GMM framework does not require phoneme-level transcription. We observed the correlation between the VTLN warping factor estimates obtained via a supervised HMM-based approach and an unsupervised GMM-based approach. In addition, a phoneme recognition and speaker de-identification tasks were conducted using GMM-based VTLN warping factor estimates. For QbE-STD, we considered three spectral features, namely, Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), and MFCC-TMP (which uses Teager Energy Operator (TEO) to exploit implicitly magnitude and phase information in the MFCC framework). Linear frequency scaling variations for VTLN warping factor are incorporated into these three cepstral representations for the QbE-STD task. Similarly, VTL-warped Gaussian posteriorgram improved the Maximum Term Weighted Value by 0.021 (i.e., 2.1%), and 0.015 (i.e., 1.5%), for MFCC and PLP feature sets, respectively, on the evaluation set of the MediaEval SWS 2013 corpus. The better performance is primarily due to VTLN warping factor estimation using unsupervised GMM framework. Finally, the effectiveness of the proposed VTL-warped GP is presented to rescore using various detection sources, such as depth of detection valley, Self-Similarity Matrix, Pseudo Relevance Feedback and weighted mean features.
Experiments in online expectation-based novelty-detection using 3D shape and colour perceptions for mobile robot inspection

Abstract Novelty detection is a very useful function for detecting abnormal data in any application. An expectation-based novelty-detection approach has been introduced that learns the dynamic relationship model among normal data in order to predict the next expected data. Most novelty-detection systems use an offline approach with a fixed structure, a system type that has limitations when the data count in the environment is unknown. A new expectation-based novelty-detection system features an online recurrent neural network approach that learns the data by inserting new nodes or deleting unused nodes from its structure. Generally, to detect novelties, a global novelty threshold is defined to filter out all input data as novel whenever the prediction error of the network exceeds the threshold. However, because a neural network cannot learn to predict all classes of input data perfectly, using a global novelty threshold leads to the misclassification of the insufficiently learned normal data as novel. To overcome this problem, the novelty-detection system has been improved to learn local novelty thresholds alongside its learning to predict expectations. The proposed algorithm is applied to an online novelty detection using colour and depth data obtained from a Kinect sensor on a mobile robot. The performance of the expected novelty detector and its limitations during experiments are analysed and shown. Furthermore, colour and depth data as inputs into the novelty filter are separately analysed and their contributions on the overall novelty detection highlighted. In conclusion, the performance of the novelty filter could further be improved by applying a better feature-selection technique to extract more interesting features from high-dimensional input data.
Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data

For many applications of hand gesture recognition, a delay-free, affordable, and mobile system relying on body signals is mandatory. Therefore, we propose an approach for hand gestures classification given signals of inertial measurement units (IMUs) that works with extremely short windows to avoid delays. With a simple recurrent neural network the suitability of the sensor modalities of an IMU (accelerometer, gyroscope, magnetometer) are evaluated by only providing data of one modality. For the multi-modal data a second network with mid-level fusion is proposed. Its forked architecture allows us to process data of each modality individually before carrying out a joint analysis for classification. Experiments on three databases reveal that even when relying on a single modality our proposed system outperforms state-of-the-art systems significantly. With the forked network classification accuracy can be further improved by over 10 % absolute compared to the best reported system while causing a fraction of the delay.
Modeling the Influence of Oil Film, Position and Orientation Parameters on the Accuracy of a Laser Triangulation Probe

The laser triangulation probe conveniently obtains surface topography data of a measured target. However, compared to the touch probe, its reliability and accuracy can be negatively affected by various factors associated with the object being measured and the probe itself. In this paper, to identify potential compensation strategies to improve the accuracy of depth measurement for laser triangulation probe, the measuring errors caused by an oil film on the measured surface, and the probe's position and orientation parameters with respect to the measuring object (including scan depth, incident angle, and azimuth angle), were studied. A theoretical model based on the geometrical optics, and an empirical model from the error evaluations, were established to quantitatively characterize the error influence of oil film and probe's parameters, respectively. We also investigated the influence pattern of different filtering methods with several comparison experiments. The verification procedures, measuring both a free-form surface (chevron-corrugated plate) and a gauge block covered with an oil film, demonstrate that these models and measurement suggestions are viable methods for predicting theoretical error and can be used as compensation references to improve the accuracy of depth measurement to the laser triangulation probe.
Model-driven photometric stereo for in-process inspection of non-diffuse curved surfaces

This paper presents an in-process inspection approach for quality control of non-diffuse curved surfaces based upon an evolution of conventional photometric stereo. An inverse reflectance model is proposed to reveal the non-linear reflectance behaviour of general non-diffuse surfaces from images based on a neural network. The model can directly be used to drive a high accurate photometric stereo which only requires a single RGB image with a pre-captured collocated image. This allows the technique to realize in-process inspection of moving surfaces with micro-second level capturing rate. Experimental study confirms the excellent texture recovery and defect detection capabilities in mass production.
An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors

With the continuous improvement of automation in industrial production, industrial process data tends to arrive continuously in many cases. The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms. According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data. The ILES aggregates multiple sublearning machines by different weights for better accuracy. When new data set arrives, a new submachine will be trained and aggregated into ensemble soft sensor model according to its weight. The other submachines' weights will be updated at the same time. Then a new updated soft sensor ensemble model can be obtained. The weight updating rules are designed by considering the prediction accuracy of submachines with new arrived data. So the update can fit the data change and obtain new information efficiently. The sizing percentage soft sensor model is established to learn the information from the production data in the sizing of industrial processes and to test the performance of ILES, where the ELM (Extreme Learning Machine) is selected as the sublearning machine. The comparison is done among new method, single ELM, AdaBoost.R ELM, and OS-ELM, and the test of the extensions is done with three test functions. The results of the experiments demonstrate that the soft sensor model based on the ILES has the best accuracy and ability of online updating.
Provable Order Amplification for Code-Based Masking: How to Avoid Non-Linear Leakages Due to Masked Operations

Code-based masking schemes have been shown to provide higher theoretical security guarantees than Boolean masking. In particular, one interesting feature put forward at CARDIS 2016 and then analyzed at CARDIS 2017 was the socalled security order amplification: under the assumption that the leakage function is linear, it guarantees that an implementation performing only linear operations will have a security order in the bounded moment leakage model larger than d - 1, where d is the number of shares. The main question regarding this feature is its practical relevance. First of all, concrete block ciphers do not only perform linear operations. Second, it may be that actual leakage functions are not perfectly linear (raising questions regarding what happens when one deviates from such assumptions). In this paper, we show that the issue of only linear operations can be provably avoided and that it is possible to obtain security order amplification for any functionality to implement. We then show that (not so) slightly non-linear leakage functions do not annihilate the nice properties (i.e., that the code-based schemes we consider remain interesting compared to the Boolean masking). We conclude with a performance evaluation of the proposals, showing that the performance overheads are moderate for a reasonable number of shares (we studied when the number of the shares d = 2,3,4). In additional, our results could be specified to the case of provable security for low entropy masking, which can be considered as a side bonus of our contributions. We give some preliminary results on how to construct the low entropy masking schemes with provable high security order against linear leakage.
Unleashing the Full Potential of Blockchain Technology for Security-Sensitive Business Applications

Companies are eager to invest in the emerging blockchain technology, but often fail to integrate this technology into real-world business applications that go beyond cryptocurrencies or pure demonstrators. The reason often lies in specific requirements related to data security and privacy. Current blockchain solutions are not able to meet these requirements, preventing a broad adoption of blockchain technology in business applications. To tackle this problem, we introduce UBI: a Universal Blockchain Integrator. UBI acts as a middleware between a company's legacy IT infrastructure and off-the-shelf blockchain solutions. UBI enriches the functionality of these solutions so that they meet specific security and privacy requirements. Its successful implementation shows the feasibility of the proposed solution. First evaluation results demonstrate its potential for real-world business applications.