Reviewerly/exHarmony huggingface dataset:
IMM Box Particle Filtering for Bearings-Only Maneuvering Target Tracking

Aiming at bearings-only maneuvering target tracking, a new multisensor interacting multiple model box particle filtering algorithm is proposed, which uses the interactive multiple model algorithm to estimate the state of each box particle. In the update stage of the proposed filter, the quadratic contraction algorithm is put forward to increase the interval of innovation and the survival probability of the box particle, which achieves the purpose to improve the diversity of the box particle and the performance of target state estimation. Simulation results are presented to demonstrate the improved performance of the proposed filtering algorithm.
Partial Orders, Residuation, and First-Order Linear Logic

We will investigate proof-theoretic and linguistic aspects of first-order linear logic. We will show that adding partial order constraints in such a way that each sequent defines a unique linear order on the antecedent formulas of a sequent allows us to define many useful logical operators. In addition, the partial order constraints improve the efficiency of proof search.
Computerized Counting of Individuals in Ottoman Population Registers with Deep Learning

The digitalization of historical documents continues to gain pace for further processing and extract meanings from these documents. Page segmentation and layout analysis are crucial for historical document analysis systems. Errors in these steps will create difficulties in the information retrieval processes. Degradation of documents, digitization errors and varying layout styles complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics and different writing styles make it even more challenging to process Arabic historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s–1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.
Maintaining control over sensitive data in the Physical Internet: Towards an open, service oriented, network-model for infrastructural data sovereignty

Changing market dynamics force organizations evermore to share data in supply chains. Misuse of the data shared can cause major damage to the business and reputation of organizations. Hence, being in control over the terms-of-use for sharing data (i.e., data sovereignty) is a key prerequisite for sharing potentially sensitive data. This, however, provides a major challenge as data sovereignty concepts are currently mainly provided in communities with their own specific data sovereignty solutions. This faces data providers with a threat of lock-in and major integration efforts in case of data sharing with a multitude of data consumers. As alternative, a network-model approach for providing generic infrastructural data sovereignty can overcome these challenges. Its technical concepts are currently maturing. Its business and service concepts however are still under development. This paper proposes an open, service-oriented, network-model approach for infrastructural data sovereignty. The goal is to support a broad variety of end-user and service provider options for maintaining sovereignty in the data sharing processes. It uses an illustrative and representative logistics scenario and describes how infrastructural data sovereignty may stimulate adoption of sharing of (potentially sensitive) operational data as required for realizing the physical Internet.
Facial Image Indexing Using Locally Extracted Sparse Vectors

Amidst all biological characteristics in a human, face emerged to be the most common biometric identifier used by humans due to its distinctive and prominent features. Moreover, the collection of facial images is a friendly, direct and a non-intruding activity, which makes it easily acceptable in nature by the users because of its non-infringement identification technology. We intend to take complete advantage of these developments and propose a novel methodology to achieve identification of the subjects by examining the facial images of the prospects. In the proposed methodology, preprocessed images are passed onto logically adaptive regression kernel for coherent facial feature extraction. The patterns recorded from these feature vectors are condensed using LDA to reduce the computational load. These sparse vectors are quantized and aggregated using VLAD with an intention to classify the descriptors in the later stages of the pipeline. Classification is achieved using CAT Boost and multi-layered perceptron to demonstrate the results using a comparative paradigm. The proposed system has been tested on benchmark datasets such as Grimace, Faces95 and Faces96. Evaluation of these datasets has been done considering the precision, recall and F1-score with an intention to perceive the best one among the proposed alternatives.
Road vehicles Cybersecurity system evaluation method

Abstract With the continuous improvement of automobile intelligence and networking, automobile cybersecurity has gradually become the focus of attention from enterprises and research institutions. Whether it is domestic or international, there are many organizations and institutions on the Internet of vehicles cybersecurity is widely concerned, ISO 21434 road vehicle network security project, is an international automotive network security standard, which covers the entire life cycle of automotive products (from design to retirement), is currently in the development. From the perspective of ISO 21434 cybersecurity system requirements, this paper proposes an cybersecurity system evaluation method for automobile companies, in order to evaluate whether or to what extent each automobile company meets the cybersecurity status of ISO 21434 standard.
Identification of Shoplifting Theft Activity Through Contour Displacement Using OpenCV

Ambiguous surveillance and analysis of the theft activity are one of the main reasons for inadequate security management of a place. The place can be our home, office, shops, etc. In this research work, an automated system has been proposed, which uses contour displacement points resulting in eternal surveillance for detection of the theft activity through CCTV camera. The algorithm developed in this work, utilises object detection technique in presenting real-time detection characteristics of the stolen object and identity of the theft in the form of camera frames. Through massive literature work, it is found that ideas adopted in developing various intelligent surveillance systems are majorly theoretically driven and lacks with required strong output modules. This research work is intended to demonstrate the implications of a convolutional neural network towards the prediction of visual objects from a previously trained data set and then form a suitable model for identifying any theft activity. For the experimental purpose, authors have used OpenCV library [], which tends to be a very effective component to accomplish the entire work objectively. At the last, results have been analysed and all results and developed conditional algorithm.
Voluminis: An Augmented Reality Mobile System in Geometry Affording Competence to Evaluating Math Comprehension

The spatial competence for learning three-dimensional objects in the geometry course makes Augmented Reality a perfect ally, due to its popularity among children allowing them to experience more realistic learning. Also, the use of augmented reality allows to improve the interactive and spatial skills with three-dimensional objects. A mobile interactive augmented reality system with 3D models was developed for the teaching of geometry, allowing the teacher to obtain real-time results of the student’s spatial and mathematical competence in a real-world environment. In the mobile system, a playful environment capable of visualizing geometric figures (Cube, Rectangular Prism, Triangular Prism, Pyramid, Cone, Cylinder and Sphere), and formulas to calculate its volume are proposed. After learning the formulas, the student must continue with the game and obtain the highest score without visualizing the formulas in such a way that in a competitive environment they can experience more realistic and beneficial learning to improve their spatial competence. The students evaluated are in the primary level (sixth grade) of two classrooms in the city of Arequipa, Peru, enrolled in a one-week course (4 h a week) entitled “Mathematical Logic”, and divided into a experimental group (they used Voluminis) and a control group (traditional methodology). A performance test (post-test) and a satisfaction questionnaire were used. In addition, a pre-test to both groups to determine the same level of knowledge about spatial geometry. The results revealed a positive impact on performance, greater academic motivation of students and above all increased competition among students in the Voluminis game. Thanks to the results in real time the professor observed the learning difficulties of the students and obtained the immediate qualifications of the space competition. Voluminis can be part of the qualified evaluation of the course, because it provides the evolution of each student in the area of geometry. It also helps to strengthen space skills thanks to the competition activities that occur in the game.
Development of Electronic Module Hikayat Text Based on Project Based Learning (PJBL) Class X Students of High Schools

This study aims to describe the development of a valid electronic learning module based on saga text based onProject Based Learning (in terms of content, presentation, linguistic, and graphic), practical (in terms of easeof use and suitability with time), and effective (in terms of activities students, learning outcomes, andaffective students) used by high school students in class X. This type of research is research and development.This study uses a 4-D model (defining, designing, developing, and distributing). The subjects of this studywere class X students of SMA Negeri 3 Padang, totaling 36 students. This research data consists of qualitativedata and quantitative data. DaQualitative data were obtained based on filling out electronic module validationquestionnaires, electronic module practicality questionnaires, and student activity observation sheets.Meanwhile, quantitative data were obtained based on student learning outcomes in learning to develop sagatext. The results of this study indicate that the Project Based Learning -based saga text learning moduleelectronics are valid, practical, and effective. This is evidenced from the results of the validity, practicality,and effectiveness of electronic modules. The validity of the electronic modules from the experts was 88,70 %so the category was very valid. Electronic module pre- activity was obtained from the practicality value of theteacher at 98.33 % with a very practical category and student practicality at 90.26 % with a very practicalcategory. Student learning activities obtained by 93.61 % with a very active category. The effectiveness ofelectronic modules based on student performance test results on average the value obtained by students is88.44 with the title of A. The effectiveness of the electronic module based on student knowledge the averagevalue obtained by students is 91.53 with the title of A. The effectiveness of the electronic module based onthe assessment of aspects the hood obtained a value of 88.89 with the title A categorized as very effective.
A Contextualized Entity Representation for Knowledge Graph Completion

Knowledge graphs (KGs) have achieved great success in many AI-related applications in the past decade. Although KGs contain billions of real facts, they are usually not complete. This problem arises to the task of missing link prediction whose purpose is to perform link prediction between entities. Knowledge graph embedding has proved to be a highly effective technology in many tasks such as knowledge reasoning, filling in the missing links, and semantic search. However, many existing embedding models focus on learning static embeddings of entities which pose several problems, most notably that all senses of a polysemous entity have to share the same representation. We, in this paper, propose a novel embedding method, which is named KG embedding with a contextualized entity representation (KGCR for short), to learn the contextual representations of entities for link prediction. KGCR encodes the contextual representations of an entity by considering the forward and backward contexts of relations which helps to capture the different senses of an entity when appearing at different positions of a relation or in different relations. Our approach is capable to model three major relational patterns, i.e., symmetry, antisymmetry, and inversion. Experimental results demonstrate that KGCR can capture the contextual semantics of entities in knowledge graphs and outperforms existing state-of-the-art (SOTA) baselines on benchmark datasets for filling in the missing link task.
A Dual Fusion Model for Attributed Network Embedding

Attributed network embedding (ANE) maps nodes in network into the low-dimensional space while preserving proximities of both node attributes and network topology. Existing methods for ANE integrated node attributes and network topology by three fusion strategies: the early fusion (EF), the synchronous fusion (SF) and the late fusion (LF). In fact, different fusion strategies have their own advantages and disadvantages. In this paper, we develop a dual fusion model named as DFANE. DFANE integrated the EF and the LF into a united framework, where the EF captures the latent complementarity and the LF extracts the distinctive information from node attributes and network topology. Extensive experiments on eight real-world networks have demonstrated the effectiveness and rationality of the DFANE.
IC Microscopic Image Stitching Based on Improved Line Matching

Abstract Integrated Circuit(IC) microscopic image stitching is an important process in IC reverse engineering. However, IC is highly integrated and miniaturized, with many similar structures and dense distribution, and the overlapping region of some IC microimages may be the low-texture region. As a result, the traditional image stitching method is prone to mismatch, and the stitching effect is poor. This paper proposes an IC microscopic image stitching method based on improved line matching. The method first extract line segments from two IC microscopic images to be stitched by using Edline algorithm. Secondly, the intersection of line segments is generated by using the position relation of line segments in a certain strategy. Then, combined with the 2D capture method of the IC microscopic image, we limit the matching region. Finally, image registration is completed by combining points and lines, and a transformation matrix is obtained to stitch IC microimages. Experimental results demonstrate that the proposed method reduces the possibility of mismatching in the stitching process while using the stable line features and the position constraints between line segments in IC microscopic images. Moreover, the image stitching guided by line features can also achieve good stitching results in low-texture IC micrograph.
Deterrence Games and the Disruption of Information

Deterrence is a generic situation, where player 2 threatens to bash player 1, should he harm her. Typical player 1’s are bomb builders, market invaders, or computer hackers. 2’s decision whether to bash will depend on a noisy signal. 1 may have the ability to disrupt the signal. The game has a unique equilibrium. If it involves mixed strategies, signal reliability benefits both players. Signal disruption hurts 2 for sure and only benefits 1 if 2 believes he is unlikely to possess it.
Analysis of Skype Digital Evidence Recovery based on Android Smartphones Using the NIST Framework

Cybercrime is an activity utilizing electronic devices and network technology as tools or media to commit crimes. One of them uses the Skype application that is installed on the smartphone. In finding evidence from a cybercrime case, a forensic activity known as digital forensic must be carried out. This study aims to recover digital evidence that has been erased using the NIST framework and forensic tools such as Oxygen and Belkasoft. The results of digital evidence recovery from smartphone Samsung J2 in the removal scenario via the application manager, the Oxygen tool cannot recover deleted data and the percentage of success using Belkasoft is 26%. While the results of data recovery with the manual removal method the percentage of success using Oxygen was 63% and Belkasoft was 44%. Digital evidence recovery results from smartphones Andromax A on the erase scenario through the application manager, Oxygen and Belkasoft tools cannot recover deleted data. While manual removal of Oxygen by 61% and Belkasoft cannot restore data. It can be concluded the results of data recovery from both smartphones that are used according to the erasure method through the application manager, Belkasoft has better performance than Oxygen, and data recovery according to the method of erasing manually, Oxygen has better performance than Belkasoft.
What does ‘secure by design’ actually mean?

‘Secure by design’ is not a new concept. Much discussed as part of General Data Protection Regulation (GDPR) compliance, it has recently re-emerged as a discipline that's integral to strategic security planning. But what does secure by design really mean? A quick search will bring up plenty of options to choose from, covering everything from IoT security to software development. It's so much more than a tagline, however.
Feature-Based Sentiment Analysis and Classification Using Bagging Technique

With the ingress of exponential advancement of Internet technologies & social media platforms, there is a potential increase that can be seen in the development of online commercial websites. With time, people started to buy goods from these websites. So, there is also a great increase in selling goods on the Internet. These sites also facilitate their customers to leave their reviews and share their experiences with other users also. These customer reviews help others to make their decision before buying that product. In other words, these reviews help to show the quality of the product. Hence for this process, mining and understanding of the reviews are very important. In this research work, authors aimed to tackle one of the natural language processing (NLP) problems, i.e., sentiment polarity classification. The authors have performed a study to compare the baseline and statistical method (machine learning) for polarity classification. This work is also intended to compare the baseline method and machine learning method, to understand which method is better and more appropriate toward sentiment classification problems with the help of Python programming. The experimental results found to be satisfactory and compared with the existing literature.
A Text Summarization Method Based on Semantic Similarity Among Sentences

In recent years, more and more attention has been paid to the graph-based text summarization. In terms of this method, the document is transformed into a text graph and then sentences are ranked by taking into account the global information of the document. However, the similarity measure be-tween sentences is mostly limited to word layer in previous studies, which makes it difficult to measure the semantic similarity accurately; besides, the features of sentence has also been disregarded. In view of the defects mentioned above, this paper proposes a semantic-word two layer similarly measure model and defines topic relativity as well as position sensitivity, so as to optimize the sentence ranking results. Through the evaluation on DUC datasets, performance of this approach shows greater improvement compared with a number of baseline systems.
Specifics of The Educational Process Organization at Universities When Training Students for Midterm Assessment in The Worldskills Format

The relevance of the problem is determined by the need to identify, select, and provide pedagogical and psychological support for talented youth, its successful career guidance, and efficient self-realization. The educational process organization in training for midterm assessment in the WorldSkills format helps students achieve a new quality of education associated not only with educational results, but also with a value attitude to themselves and others. The goal of the study is to identify the specifics of the educational process organization at universities when training students for midterm assessment in the WorldSkills format. The research methods include theoretical analysis of pedagogical theory, practice, and experience in vocational training; modeling, comparison, referencing, and generalization; expert assessment method; and methods of qualitative and quantitative processing of research results. The main results of the study are the following: the content of the process of organizing practical classes and independent work on the discipline “Literary education of preschool children” has been determined; the activity forms and methods for their implementation have been developed, the objective criteria for their assessment have been determined; the functional responsibilities have been distributed between students and the teacher in the educational process; and the point rating system for assessing students’ activities while mastering the discipline in the WorldSkills format has been created.
Monotonic Opaqueness in Deontic Contexts

The paper begins with a quick review of monotonic inferences in natural language. We illustrate that the inferences sometimes fail with sentences that contain both quantifiers and deontic modalities. A distinction between a narrow and wide scope of readings is made. A first-order deontic event model is proposed to study those sentences. Inspired by the philosophy of Allan Gibbard, we set a normative system as a component of our model to interpret the modalities. This leads to a general result that enables us to explain the failed monotonic inferences.
Increasing the sensitivity of laser triangulation systems using structured optical surfaces

Laser triangulation is an optical method for measuring distances to an object. A laser beam is directed towards a measurement surface, and the diffusely reflected light is collected by an imaging system onto a detector. The absolute distance can be obtained by using known geometric relations of the system and the position of the laser spot on the detector. Therefore, a change in the measurement distance results in a corresponding movement of the imaged laser spot, defining the sensitivity of the system in pixels per millimeter. This value depends on the geometrical and optical design of the laser triangulation setup, especially the base distance between the laser and the imaging lens, as well as its focal length. As those parameters also influence the geometric dimensions and the possible measurement range of the device, the sensitivity cannot be increased arbitrarily. Thus, the sensitivity of a standard laser triangulation system is limited to a certain value. In this contribution, structured optical surfaces are applied onto the measurement surface to further increase the sensitivity. Through the spatial modulation of the imaged laser spot intensity distribution, the calculated laser spot displacement is larger than its actual geometrical displacement. This effect is examined through simulations with a bar structure, which leads to an improvement of the sensitivity by a factor of up to 5.7 at a distance of 1 m and a measurement range of 2 mm. Eventually, the concept is proven in measurements and feasible implementations of such a structure are considered.