Reviewerly/exHarmony huggingface dataset:
Efficient TBox Reasoning with Value Restrictions—Introducing the $$\mathcal {F\!L}_{o}{} \textit{wer}$$ Reasoner

The Description Logic (DL) $${\mathcal {F\!L}_0}$$ uses universal quantification, whereas its well-known counter-part $$\mathcal {E\!L}$$ uses the existential one. While for $$\mathcal {E\!L}$$ deciding subsumption in the presence of general TBoxes is tractable, this is no the case for $${\mathcal {F\!L}_0}$$ . We present a novel algorithm for solving the ExpTime-hard subsumption problem in $${\mathcal {F\!L}_0}$$ w.r.t. general TBoxes, which is based on the computation of so-called least functional models. To build a such a model our algorithm treats TBox axioms as rules that are applied to objects of the interpretation domain. This algorithm is implemented in the $$\mathcal {F\!L}_{o}{} \textit{wer}$$ reasoner, which uses a variant of the Rete pattern matching algorithm to find applicable rules. We present an evaluation of $$\mathcal {F\!L}_{o}{} \textit{wer}$$ on a large set of TBoxes generated from real world ontologies. The experimental results indicate that our prototype implementation of the specialised technique for $${\mathcal {F\!L}_0}$$ leads in most cases to a huge performance gain in comparison to the highly-optimised tableau reasoners.
Semantic Understanding of Natural Language Stories for Near Human Question Answering

Machine understanding of natural language stories is complex, and automated question answering based on them requires careful knowledge engineering involving knowledge representation, deduction, context recognition and sentiment analysis. In this paper, we present an approach to near human question answering based on natural language stories. We show that translating stories into knowledge graphs in RDF, and then restating the natural language questions into SPARQL to answer queries can be successful if the RDF graph is augmented with an ontology and an inference engine. By leveraging existing knowledge processing engines such as FRED and NLQuery, we propose the contours of an open-ended and online flexible query answering system, called Omniscient, that is able to accept a natural language user story and respond to questions also framed in natural language. The novelty of Omniscient is in its ability to recognize context and respond deductively that most current knowledge processing systems are unable to do.
Overview of Pheromone Control Method based on Ant Colony Algorithm in Wireless Communication

Ant colony optimization has become an important tool for solving complex optimization problem, and has been widely applied in many areas. In ant colony algorithm, pheromone is the key to realize swarm intelligence, and it is an important medium for artificial ants to realize indirect communication and cooperation between themselves. In order to improve the overall performance of ant colony algorithm and improve the pheromone control method, this paper studies the pheromone control method and its diffusion model. First, this paper introduces the research significance and research status of pheromone control method. Then, the new methods of pheromone control are reviewed, such as Max-min Ant System (MMAS), Ant Colony System (ACS) and Nearest Neighbor Ants (NN-Ants), and their advantages and disadvantages are analyzed and compared. Finally, combining with some related work has been carried out in the research group, the research progress of pheromone diffusion model is given based on path and correlation distance of multidimensional knapsack problem application. The existing research shows that the control strategies on pheromone, which includes its retaining, initialization, updating and evaporation, influence the performance of ant colony algorithm significantly.
Traffic Sign Image Recognition Using Gabor Wavelet and Principle Component Analysis

The image recognition system of traffic signs is one of the important elements needed by autonomous vehicles, which also these signs are a guide for autonomous vehicles. In this paper we propose a traffic signs image recognition by implementing Gabor wavelet feature extraction. The images of traffic signs will be captured by cameras. These images will then be processed for recognition. Where in the process the features of the traffic sign images will be extracted so that the identifier for each traffic sign is obtained. We use 8 orientations and 5 scales Gabor bank and PCA in recognition process to get a good accuracy as well as efficiency. Easiness and high accuracy was the avantages from proposed Gabor method. This method as seen from the experimental results demonstrate fair reasonable in terms of preciseness, with 90.55% overall average recognition rate are obtained for the data of 100 traffic sign images.
Face Morphing Detection using Generative Adversarial Networks

Facial recognition system is used by various entities, including social media, identity verification, and security services. However, face morphing techniques present an adversarial challenge to the current facial recognition system. This is particularly the case for face morphing using Generative Adversarial Networks (GAN) method. To combat the face morphing we studied the GAN to detect morphed facial images. This has been achieved by using GAN to generate a large number of morphed images. Then, the morphed images are used to retrain the GAN for detecting the morphed images. The performance of this method is evaluated using facial images dataset and network structures.
Research on Hotel Recommendation Systems Based on AHP and Collaborative Filtering Combination Algorithm

The Internet brings a lot of information to people. While satisfying people's demand for information, it also brings the problem of information overload. Recommendation systems show great potential in solving information overload and personalized needs. This paper studies the recommendation system and hotel recommendation problem for the specific recommendation object of the hotel. By analyzing the current situation and difficult problems of hotel recommendation, this paper proposes a hotel recommendation method combining AHP analytic hierarchy process and collaborative filtering algorithm. This method can quantify the weight of the main factors affecting customer selection, and predict the degree of preference of another customer for a hotel through the rating of similar customer groups, so as to select hotels that are more in line with customer preferences for recommendation.
Unsupervised Feature Selection via Local Total-Order Preservation

Without class label, unsupervised feature selection methods choose a subset of features that faithfully maintain the intrinsic structure of original data. Conventional methods assume that the exact value of pairwise samples distance used in structure regularization is effective. However, this assumption imposes strict restrictions to feature selection, and it causes more features to be kept for data representation. Motivated by this, we propose Unsupervised Feature Selection via Local Total-order Preservation, called UFSLTP. In particular, we characterize a local structure by a novel total-order relation, which applies the comparison of pairwise samples distance. To achieve a desirable features subset, we map total-order relation into probability space and attempt to preserve the relation by minimizing the differences of the probability distributions calculated before and after feature selection. Due to the inherent nature of machine learning and total-order relation, less features are needed to represent data without adverse effecting on performance. Moreover, we propose two efficient methods, namely Adaptive Neighbors Selection(ANS) and Uniform Neighbors Serialization(UNS), to reduce the computational complexity and improve the method performance. The results of experiments on benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods. Compared to the competitors by clustering performance, it averagely achieves $$31.01\%$$ improvement in terms of NMI and $$14.44\%$$ in terms of Silhouette Coefficient.
Validating a Heuristic Evaluation Method An Application Test

The usefulness of a set of heuristics for the evaluation of sociotechnical systems will be examined. The usefulness of this evaluation is comparable to heuristics of interactive systems. The criterion of such usefulness include, whether persons who are not necessarily experts at the intersection between work design and sociotechnical design find more aspects with the help of sociotechnical heuristics which give critical or positive indications for the evaluation of a sociotechnical solution. An experiment with 60 participants shows that the intuitive assumption that more of such aspects could be found with the knowledge of heuristics cannot be confirmed for individual students. The use of heuristics supports a more even distribution of the found aspects across different categories. After evaluating the outcome of this study, it becomes clear that more detailed investigations on the effectiveness of sociotechnical heuristics are necessary and which special methodological requirements have to be observed.
Eye Tracking for Quality Control in Automotive Manufacturing

Eye tracking is the process of recording a person’s eye movement for the better understanding of his/her’s visual perception. Due to low costs and highly accurate systems the process of eye tracking is used in devices and applications to increase computer interaction and to study human behavior. Eye tracking is used in a wide range of applications but is almost absent in the engineering domain. The cause could be that engineering is an exact science and product design is based on a rigorous set of physical, functional and legal constraints. This study deals with the usage of the Eye tracking technique in the automotive environment. In particular it will be used to optimize the quality assurance of paint and the manufacturing of head-up displays in automotive manufacturing. The objective of the studies is to examine the quality assurance cycles with regard to optimized working conditions and improved production quality.
On shortened 3D local binary descriptors

The wide-spread mobile systems nowadays desire ultra lightweight local geometric features to accomplish tasks relying on correspondences. Nonetheless, most existing 3D local feature descriptors, though shown to be distinctive and robust, still are real-valued and/or high-dimensional. Accordingly, this paper conducts a comparative study on current bit-selection methods with a focus on shortening 3D local binary descriptors. By analyzing several bit-selection techniques, we develop and evaluate various approaches to obtain a shortened version of a state-of-the-art feature remaining discriminative and robust. Through extensive experiments on four standard datasets with different data modalities (e.g., LiDAR and Kinect) and application scenarios (e.g., 3D object retrieval, 3D object recognition, and point cloud registration), we show that a small subset of representative bits are sufficient to achieve promising feature matching results as the initial descriptor. Moreover, the shortened binary descriptors still hold competitive or better distinctiveness and robustness compared to several state-of-the-art real-valued descriptors, e.g., spin image, SHOT, and RoPS, albeit being dramatically more efficient to match and store. Key to the foreseen research trend of local geometric feature description is dealing with compact binary descriptors; thus, our work may pave the way for this new research direction.
Evaluation of a Dependability Mechanism for Cyber Physical Systems

Cyber-Physical-Systems (CPS) are systems of collaborating computational entities. Concepts such as autonomous cars, smart electric grid, implanted medical devices and smart manufacturing are some practical examples of CPS. However, the open and cooperative nature of CPS poses a significant new challenge in assuring dependability. The DEIS project addresses this important and unsolved challenge by developing technologies that facilitate the efficient synthesis of components and systems based on their dependability information. The key innovation that is the aim of DEIS is the corresponding concept of a Digital Dependability Identity (DDI). A DDI contains all the information that uniquely describes the dependability characteristics of a CPS or CPS component. DDIs are synthesised at development time and are the basis for the (semi-)automated integration of components into systems during development, as well as for the fully automated dynamic integration of systems into systems of systems in the field. In this paper we present an overview of the DDI. Additionally, we provide metrics for evaluating the DDI’s impact on CPS dependability, and the results of an evaluation of the DDI’s impact on dependability in four CPS industrial systems. These results demonstrate the positive impact of the DDI on the dependability of CPS.
Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Attention and Neural Collaborative Filtering

Sentiment classification which aims to predict a user’s sentiment about a product is becoming more and more useful and important. Some neural network methods achieved improvement by capturing user and product information. However, these methods fail to incorporate user preferences and product characteristics reasonably and effectively. What’s more, these methods all only use the explicit influences observed in texts and ignore the implicit interaction influences between user and product which cannot be observed in texts. In this paper, we propose a novel neural network model HUPSA-NCF (Hierarchical User Product Separated Attention and Neural Collaborative Filtering Network) to address these issues. Firstly, our model uses hierarchical user and product separated attention on BiLSTM to incorporate user preferences and product characteristics into specific text representations respectively. Secondly, our model uses neural collaborative filtering to capture the implicit interaction influences between user and product. Lastly, our model makes full use of both explicit and implicit informations for final classification. Experimental results show that our model outperforms state-of-the-art methods on IMDB and Yelp datasets.
How Many Weak-Keys Exist in T-310 ?

Abstract T-310 is an important Cold War cipher. The cipher is extremely complex and it outputs extremely few bits from the internal state. A recent paper [C ourtois , N. T.: Decryption oracle slide attacks on T-310 , Cryptologia, 42 (2018), no. 3, 191–204] shows an example of a highly anomalous key such that T-310 can be broken by a slide attack with a decryption oracle. In this paper, we show that the same attacks are ALSO possible for regular keys which satisfy all the official KT1 requirements. Two other recent papers [C ourtois , N. T.—G eorgiou , M.—S carlata , M.: Slide attacks and LC-weak keys in T-310 , Cryptologia 43 (2019), no. 3, 175–189]; [C ourtois , N. T.—O prisanu , M. B.—S chmeh , K.: Linear cryptanalysis and block cipher design in East Germany in the 1970s , Cryptologia (published online), December 5, 2018] show that some of the KT1 keys are very weak w.r.t. Linear Cryptanalysis. In this paper, we show that a vast number of such weak keys exist and study the exact pre-conditions which make them weak. In addition we introduce a new third class of weak keys for RKDC (Related-Key Differential Cryptanalysis). We show that the original designers in the 1970s have ensured that these RKDC properties cannot happen for 4 rounds. We have discovered that these properties can happen for as few as 5 rounds for some keys, and for 10 to 16 rounds they become hard to avoid. The main reason why we study weak keys is to show that none of these properties occur by accident, rather that they are governed by precise pre-conditions which guarantee their existence, and countless other keys with the same properties exist. Eventually, this is how interesting attacks can be found.
The research on intelligent RGV dynamic scheduling based on hybrid genetic algorithm

Abstract With the rapid development of automation, mechatronics, information technology and other fields, production and processing systems are constantly moving towards unmanned and intelligent development. The RGV rail-guided vehicle system integrates various high-new technologies and is widely used in modern processing systems. Compared with the circular RGV orbit system, the linear reciprocating RGV system adopts a linear orbit, which is not easy to be blocked during operation, but its scheduling response mechanism is not perfect enough, which makes the operation efficiency of the whole system low. Therefore, how to improve the response mechanism and the operational efficiency of the RGV intelligent processing system has become an important factor in prompting the rapid development of the modern intelligent processing industry. In this paper, aiming at the intelligent linear reciprocating scheduling problem, the main influencing factors are analyzed according to the shortest distance priority response and the shortest completion time. Mathematical analysis and mathematical modeling are carried out and a rule-based genetic algorithm is designed to solve the problem. The algorithm of replacing the fixed parameters in the traditional genetic algorithm with the adaptive cross mutation probability, and improves the genetic algorithm’s tendency to fall into the local optimal solution.
Investigation of Frustration

Frustration often occur in an interaction with a technical device. It has a strong impact on the quality of interaction and should be carefully considered in planning how humans and machines should interact with each other.
Smile to Me

Emotions are part of human communication shaping mimics and revealing feelings. Therefore, conveying emotions has been integrated in text-based messaging applications using emojis. While visualizing emotions in text messages has been investigated in prior work, we studied the effects of emotion sharing by augmenting the WhatsApp Web user interface. For this, we designed and developed four different visualizations to represent emotions detected through facial expression recognition of chat partners using a web cam. Investigating emotion representation and its effects, we conducted a four weeks longitudinal study with 28 participants being inquired via 48 semi-structured interviews and 64 questionnaires. Our findings revealed that users want to maintain control over their emotions, particularly regarding sharing, and their preference to view positive emotions avoiding unpleasant social situations. Based on that, we phrased four design recommendations stimulating novel approaches for augmenting chats.
Visual Surveillance Algorithm for Dense Crowd

In this paper, the traditional pedestrian detection and tracking method fails due to the pedestrian occlusion in the dense crowd background. The development of Dense Crowd Visual Tracking algorithm based on Deep Learning (DTDL) is introduced. DTDL is mainly composed of pedestrian detection algorithm and Visual tracking framework. Experimental results show that DTDL is more robust than other tracking algorithms, and its running speed is close to real-time tracking.
Usability and UX of a Gaze Interaction Tool for Front Seat Passengers

Input modalities generally as well as in cars are evolving quickly regarding their spread and reliability. One possible input technique is gaze interaction, a topic still being researched. What are the front seat passengers' feelings towards gaze interaction, are they comfortable using it and do they think it is necessary? A laboratory experiment was conducted with 13 student participants, using a driving simulator, eye tracker, lamp and a driving wheel. Qualitative data was collected during and after the experiment through observation and a semi-structured interview. Quantitative data was collected through questionnaires (ATI, CTAM, SUS). The results were that the usability of the system was high but participants did not feel well using it.
Fast Breadth-First Search in Still Less Space

It is shown that a breadth-first search in a directed or undirected graph with n vertices and m edges can be carried out in $$O(n+m)$$ time with $$n\log _2 3+O((\log n)^2)$$ bits of working memory.
Multilayer Perceptron New Method for Selecting the Architecture Based on the Choice of Different Activation Functions

Multilayer perceptron has a large amount of classifications and regression applications in many fields: pattern recognition, voice, and classification problems. But the architecture choice in particular, the activation function type used for each neuron has a great impact on the convergence and performance. In the present article, the authors introduce a new approach to optimize the selection of network architecture, weights, and activation functions. To solve the obtained model the authors use a genetic algorithm and train the network with a back-propagation method. The numerical results show the effectiveness of the approach shown in this article, and the advantages of the new model compared to the existing previous model in the literature.
Constrained Kalman Filter for Adaptive Prediction in Minidrone Flight

The minidrone Parrot Mambo® is a promising robotic platform for education control purposes. An important limitation is that its SDK provides sensor data with a maximum nominal frequency of just 2 Hz, creating objective difficulties for feedback control. This paper proposes an observer capable of generating prediction on the data, which allows feeding the controller with a much faster rate than the one allowed by the slow sensor data rate. The predictions are generated by a linear model, whose parameters are identified on-line using a Constrained Kalman Filter. The strategy is successfully validated via extensive experiments with real drones performing altitude stabilisation and trajectory tracking tasks. In particular, the constrained model identification preserves a stable prediction (which is physically meaningful), and hence safe flight, even in the presence of large disturbances.
Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface

The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app.