Reviewerly/exHarmony huggingface dataset:
On the Construction of Balanced Boolean Functions with Strict Avalanche Criterion and Optimal Algebraic Immunity

Boolean functions used in the filter model of stream ciphers should have balancedness, large nonlinearity, optimal algebraic immunity and high algebraic degree. Besides, one more criterion called strict avalanche criterion (SAC) can be also considered. During the last fifteen years, much work has been done to construct balanced Boolean functions with optimal algebraic immunity. However, none of them has the SAC property. In this paper, we first present a construction of balanced Boolean functions with SAC property by a slight modification of a known method for constructing Boolean functions with SAC property and consider the cryptographic properties of the constructed functions. Then we propose an infinite class of balanced functions with optimal algebraic immunity and SAC property in odd number of variables. This is the first time that such kind of functions have been constructed. The algebraic degree and nonlinearity of the functions in this class are also determined.
ADOOPLA - Combining Product-Line- and Product-Level Criteria in Multi-objective Optimization of Product Line Architectures

Product lines of software-intensive systems have a great diversity of features and products, which leads to vast design spaces that are difficult to explore. In addition, finding optimal product line system architectures usually requires a consideration of several quality trade-offs at once, involving both product-level as well as product-line-wide criteria. This challenge cannot be solved manually for all but the smallest problems, and can therefore benefit from automated support. In this paper we propose ADOOPLA, a tool-supported approach for the optimization of product line system architectures. In contrast to existing approaches where product-level approaches only support product-level criteria and product-line oriented approaches only support product-line-wide criteria, our approach integrates criteria from both levels in the optimization of product line architectures. Further, the approach can handle multiple objectives at once, supporting the architect in exploring the multi-dimensional Pareto-front of a given problem. We describe the theoretical principles of the ADOOPLA approach and demonstrate its application to a simplified case study from the automotive domain.
Clustering Images by Unmasking – A New Baseline

We propose a novel agglomerative clustering method based on unmasking, a technique that was previously used for authorship verification of text documents and for abnormal event detection in videos. In order to join two clusters, we alternate between (i) training a binary classifier to distinguish between the samples from one cluster and the samples from the other cluster, and (ii) removing at each step the most discriminant features. The faster-decreasing accuracy rates of the intermediately-obtained classifiers indicate that the two clusters should be joined. To the best of our knowledge, this is the first work to apply unmasking in order to cluster images. We compare our method with k-means as well as a recent state-of-the-art clustering method. The empirical results indicate that our approach is able to improve performance for various (deep and shallow) feature representations and different tasks, such as handwritten digit recognition, texture classification and fine-grained object recognition.
Design and Simulation of Plant Sampling System in Complex Geographical Environment

Based on small rotor drone, the author proposal a sampling system scheme in complex geographical environment, which is composed of sampling manipulator, connection separator, image acquisition, remote controller and GPS positioning system, and realizes the function integration of plant sample, image information and geographic coordinate collection. This paper focuses on the sampling manipulator structure in detailed analysis and design, and the manipulator which is composed of ball screw and slider rocker mechanism is proposed. In order to finish further optimization, the author build dynamics model of manipulator to study the dynamics of grasping and cutting. Simulation results show that the proposed sampling system can solve the problem of plant sampling effectively in complex geographical environment, and provide technical support for the prototype development and practical application in the next stage.
Using a Fuzzy Regulator to Reduce Noise

The analysis and processing of information using the theory of fuzzy sets has been made. An analytical comparison of Kalman filtering and filtering ability using a fuzzy controller has been obtained. Comparison of filtering methods was carried out in the Matlab Simulink graphical modeling environment. Based on the results obtained, a filtering method was chosen for the roll stabilization channel of the automatic control system of the unmanned aerial vehicle when flying along a route. This article proposes the use of the theory of fuzzy sets to reduce the influence of external disturbances on the automatic control system of an unmanned aerial vehicle to stabilize the bank when flying along a route. The results were obtained on a commercial imitation modeling stand.
Implementation of Vocabulary Based Summarization of Graph (VoG) on the Web Graph

Increasing data of the web graph make it difficult to identify the data structure and get the information from that large graph. To identify the structure and get the information, some method needed to summarize the large graph into smaller subgraphs, so data structure identification can be conducted on the subgraph. This will make structure identification easier. Vocabulary based Summarization of Graph (VoG) is a method that can be used to summarize the large graph and identify the subgraph structure. In the process of structure identification, a maximum number of node in GCC (k) affects the generated subgraph. More and more k generates less of subgraph with clique and star structures but generates more subgraphs with chain structures. The k also affects the execution time of VoG. More and more k make the execution time of VoG faster. From generated and identified subgraph structure, information that can be obtained is clique subgraph represent there is a user network that polls on a question link and star subgraph represent there is one user contribute an answer on some question link on quora.com.
Fault Recognition Technology for Pipeline Systems Based on Multi-feature Fusion of Monitoring Data

Pipeline systems are important parts of modern infrastructure and complex industrial systems. Effective fault identification for pipeline systems is of great significance in ensuring safety and reliability. In this work, a method for pipeline fault identification based on data fusion analysis is proposed, which utilizes pipeline system condition monitoring data. First, monitoring data are analyzed to extract features that represent the operating state of the pipeline system, and a feature evaluation index based on sensitivity and volatility is proposed to optimize the extracted features. Second, multi-feature fusion analysis based on the Dempster-Shafer (DS) theory is performed to identify faults within the pipeline system. Meanwhile, to solve the problem of obtaining the basic probability assignment function (BPA) in DS theory, a BPA acquisition method is proposed, which considers both distance and correlation. Finally, this work is validated using case data of a residential heating test platform. The results show that the proposed method can directly use the monitoring data from the pipeline system for fault analysis and can effectively identify pipeline leakages, blockages, and other faults. The proposed method overcomes the shortcomings of traditional methods, which require a detailed mechanism analysis. It also conforms to emerging technology development trends, which utilize and apply Big Data analysis.
Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity Recognition

We propose a class-incremental learning framework for human activity recognition based on the Bag-of-Sequencelets model (BoS). The framework updates learned models efficiently without having to relearn them when training data of new classes are added. In this framework, all types of features including hand-crafted features and Convolutional Neural Networks (CNNs) based features and combinations of those features can be used as features for videos. Compared with the original BoS, the new framework can reduce the learning time greatly with little loss of classification accuracy.
Image Lightness Conversion and Sharpening Taking Account of Visual Features of Elderly Person

The performance of human visual functions degrades with age. For example, the visual field becomes dark owing to a decrease of the amount of light received. Moreover, objects appear to be hazy by a decrease in the contrast sensitivity. In particular, the contrast sensitivity decreases remarkably as the spatial frequency increases. In this paper, we propose an image enhancement method which consists of a lightness conversion and an amplification of high spatial frequency components to take account of visual characteristics of elderly persons. Concretely, the lightness conversion is conducted within the color gamut by using a tone mapping. In addition, a sharpening based on the inverse characteristic function of the sensitivity decay curve is applied. The effectiveness of the proposed method is verified by experiments using several digital images.
Design and Implementation of Human-computer interface based Cognitive Model for Examine the Skill Factor of Students

In this paper, a software is designed and developed to organize an ability test which has the ability to present the content on the screen. The software can be developed based on the cognitive model using Keystroke-Level Model (KLM) and cognitive complexity theory whcih helps in illustarting the usability metrics. The proposed software emerges as a real-time application, to conduct test in order to evaluate the skill level of the students by administering a questionnaire that contains three different types of abilities such as numerical ability, logical reasoning and perceptual speed. Basically for higher education, the students will come from different environments such as rural area, city area and metro politician city after completing their school education. So, the system needs to analyze the skill level of the students and identify the particular skill where the individual needs particular attention. The task performance of students can be retrieved from their laboratory test with end user on the basis of user interface design. The software is developed particularly to examine the task performance and to determine the exact plan using usability testing to collect the overall quantitative data. The results obtained can be analysed with a Correlation analysis model.
A Distant Optical-Center Binocular Servo System Based on TDNN with Online Fine-tuning

To solve the high-dimensional control problem in multi-view visual servo system, a distant optical-center binocular servo system is established in this paper based on time-delay neural network (TDNN) with online fine-tuning. The multi-cameras track the single objects in different part of the assembly in pixel coordinates simultaneously. The mapping relationship between pixel coordinates and robot coordinates is expressed by image Jacobian matrix, which is predicted by TDNN. The whole neural network model is put into the framework of a Kalman filter (KF). And an online fine-tuning model is applied in optimizing TDNN through KF to accelerate the convergence speed of the servo system. The system was practically tested for installing the automobile front windshield, the result demonstrated that the frame rate of image mapping processing is around 40 FPS, and the controlling can be completed within around 10 cycles. The position and angle errors are controlled within 0.5mm and 0.2°. These results indicate that the proposed system can be widely utilized for the rapid assembly of large-sized parts with industrial manipulators which has the advantages of small calculation, low cost, and close-range perception.
A New Anomaly Detection Method Based on Multi-dimensional Condition Monitoring Data for Aircraft Engine

Identifying the degradation state of aircraft engine accurately and carrying out suitable maintenance is significant for the safe and reliable operation of the aircraft system. In this paper, an anomaly detection system is proposed for multi-dimensional time series for aircraft engine. To represent the degradation degree of the device, the degradation index is proposed utilizing the Dynamic Time Warping algorithm (DTW) which possesses good similarity measure quality and can capture the changing characteristics of the indicator well. On the basis of the degradation index, three decision-making strategies including the Cumulative Sum algorithm (CUSUM), the Youden Index, and the Impact Factor are applied to determine the threshold values. For the threshold, two detection time points are obtained to discriminate different degradation stages in the device, in which the former refers to the initiation of degradation and the latter time point indicates that the device suffers from the anomaly. The experimental results demonstrate that all three decision strategies are able to detect system performance effectively before the device fails, which facilitates the incipient fault warning of aircraft engine in order to keep the equipment safe and reliable.
Clustering-Based Aggregation of High-Utility Patterns from Unknown Multi-database

High-utility patterns generated from mining the unknown and different databases can be clustered to identify the most valid patterns. Sources include the internet, journals, and enterprise data. Here, a grid-based clustering method (CLIQUE) is used to aggregate patterns mined from multiple databases. The proposed model forms the clusters based on all the utilities of patterns to determine the interestingness and the correct interval of its utility measure. The set of all patterns is collected by first mining the databases individually, at the local level. The problem arises when the same pattern is identified by all of the databases but with different utility factors. In this case, it becomes difficult to decide whether the pattern should be considered as a valid or not, due to the presence of multiple utility values. Hence, an aggregation model is applied to test whether a pattern satisfies the utility threshold set by a domain expert. We found that the proposed aggregation model effectively clusters all of the interesting patterns by discarding those patterns that do not satisfy the threshold condition. The proposed model accurately optimizes the utility interval of the valid patterns.
Automatic Extraction Tracking and Control of Robotic Based on Mean-shift

With the development of the robot field, mobile robot tracking has been applied to various fields, and it has become a hot topic of many scholars. In recent years, a large number of mobile robot tracking methods have been proposed. In this paper, for the traditional Mean-shift tracking method, the problem of the target object cannot be automatically obtained. A tracking method that automatically extracts targets and applies them to mobile robot tracking and controlling is proposed. In this work, The SAD method is first introduced and fused based on the traditional Mean-shift tracking method. Then, the motor that needs to complete the tracking and conducted experiments is modeled and closed-loop controlled in this paper. The results show that the mobile robot tracking control system that is proposed in this paper can complete the automatic recognition tracking and control of the target object.
Imbalanced Data Gravitation Classification Model Using For Internet Video Traffic Identification

In the last decade, the increasing video traffic brought big challenges for Internet management. For example, illegal videos did serious harms to Internet users. Therefore, identifying different video traffic has become an urgent issue. Unfortunately, there are few researchers concern this problem. In this paper, we try to build an accurate traffic identification model using imbalanced data gravitation classification model (IDGC). We first collected a set of video traffic data from a real network. Then a special type of feature, byte code distribution feature is extracted. Finally, IDGC is used for identification. We conduct comparing experiments between IDGC and six imbalanced learning algorithms on our data. The experimental results show the competitive performances of IDGC for video traffic identification.
Social AI for Engaging UbiComp

This short paper introduces the concept of Social AI as pivot technology between the two visions of Weiser's “calm” and Rogers's “engaging” UbiComp technologies. We discuss three types of AI solutions, Personal AI, System AI and Social AI, in relation to their interaction with ubiquitous technologies and humans. We highlight as Personal and System AI are the perfect users of calm technologies, while Social AI instantiates the vision of engaging Ubicomp. We elaborate the concept of Social AI as a technology for navigating the dual complexity of social systems and operating in synergy with humans without eroding their agency and by seeking their support through communication. We conclude by envisioning an entangled future between the research agenda on Social AI and engaging Ubicomp.
Performance Analysis of the Modified Generated S-Box for Advanced Encryption Standards

On today's world where data and information is available on almost everywhere, the security of these data and information are being attacked every day. Security enhancements are being done in order to make it more secure. One of the cryptographic techniques that is being used to secure data is the Advanced Encryption Standards (AES) and one of its primary components is the Substitution box (S-box). It is the only nonlinear part of AES thus it is prone to attacks. The paper produced a modified S-box with the combination of the original S-box and the S-box produced using the Logistic Map. Adjustments were made to meet the bijective property of an S-box. It was then evaluated using balance, bijective property, nonlinearity, bit independence criterion and strict avalanche criterion. The results have met the bijective and balance property of AES with the adjustment discussed. Its nonlinearity was tested using S-box Evaluation Tool (SET) and achieved a result of 94. Its Strict Avalanche Criterion (SAC) scored 0.5234375, and Bit Independence Criterion (BIC) has -0.0275 result. As a recommendation, this S-box can undergo cryptanalysis attacks to find its weakness further.
Applying ML Algorithms to Video Game AI

In this paper a comparison of selected algorithms used to learn intelligent behavior of characters in video games was presented. The experiment environment was created using Unity3D and TensorFlow. After brief description of the algorithms, the comparison of results is presented for three algorithms: Genetic Algorithm, Deep Q-Learning and Actor-Critic Algorithm.
Speech Speed Awareness System Slows Down Native Speaker’s Talk

Conversation between native speakers and non-native speakers is not always easy. Non-native speakers sometimes feel hard to understand what native speakers talk because of their speaking speed. Even kind native speakers who speak slower in the beginning often get back to their “natural” speed unconsciously after a while. Although non-native speakers may ask a native speaker to repeat what they cannot catch, participants of the conversation would become uneasy for too often request of repeating, which may also reduce productivity of the conversation or the meeting. In this paper, we study how conversation between native and non-native speakers is conducted regarding speech rate, how the introduction of a speech speed awareness system could alleviate this communication problem. The system recognizes speech rate in real-time and makes participants aware when their speech rate is too fast for non-native speakers.
The Stable Model Semantics of Normal Fuzzy Linguistic Logic Programs

Fuzzy linguistic logic programming is a framework for representing and reasoning with linguistically-expressed human knowledge. It is well known that allowing the representation and the manipulation of negation is an important feature for many real-world applications. In this work, we extend the framework by allowing negation connectives to occur in rule bodies, resulting in normal fuzzy linguistic logic programs, and study the stable model semantics of such logic programs.
Two improved schemes in the bitprobe model

In this paper, we describe two new explicit schemes in the bitprobe model that, to the best of our knowledge, improves upon the existing schemes in the literature. One such scheme is to store three elements using O(m2/3) amount of space and answer membership queries using two adaptive bitprobes. Previously, the maximum number of elements that could be handled using two queries and O(m2/3) amount of space is two, which is due to Radhakrishnan et al. [1]. A corollary of this result is an explicit scheme for storing three elements using three queries in the non-adaptive model, a first such scheme in the model. The next scheme uses four bitprobes in the non-adaptive bitprobe model and uses O(m2/3) amount of space to accommodate five elements using the block-superblock idea of Radhakrishnan et al. [1]. The previous scheme to store such a configuration was a non-explicit scheme due to Alon and Fiege [2], and we provide an explicit scheme for the same.
Recent Research Trends and Methods Used in Moving Object Detection

Object detection is one of the main challenges in the field of image processing from the beginning. Many methods had been established and proposed through the years, but none of them could give a high accuracy rate along with low computation time. Background subtraction is a key technique in object detection in security and surveillance purpose with a high accuracy rate, but it comes in a cost of high computational time. Frame differencing came with low computational time, but it cannot give us the accuracy that we want. Optical flow and temporal differencing are independent of irregularities of light features, but accuracy is not up to the mark. The main obstacles of detecting an object in a video are still present in proposed techniques till date. In this paper, we mainly describe the recent trends and works done in the field of object detection. We have discussed various important methods of object detection and pointed out their positive aspects and limitations.
Reinforcing Soft Part-Aligned Features for Person Re-identification

Aiming to enhance features discrimination for pose variation in person re-identification, reinforcing soft part-aligned features is proposed in this paper. By adding a channel-wise module in two-stream re-identification network, the network can be more robust to person's pose changes. Firstly, the pre-trained GoogLeNet and OpenPose is loaded to extract pedestrian appearance features and pose feature of pre-processed images. Secondly, pose features are fed into the designed channel-wise module. Finally, the two branches are trained jointly by applying bilinear pooling. From the experimental results on the Market-1501 dataset and the DukeMTMC-reID dataset, it shows that reinforcing soft part-aligned features are more discriminative and achieves higher identification accuracy over the state-of-the-arts.