Reviewerly/exHarmony huggingface dataset:
Anticanonical codes from del Pezzo surfaces with Picard rank one

We construct algebraic geometric codes from del Pezzo surfaces and focus on the ones having Picard rank <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="1"> <mml:semantics> <mml:mn>1</mml:mn> <mml:annotation encoding="application/x-tex">1</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and the codes associated to the anticanonical class. We give explicit constructions of del Pezzo surfaces of degree <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="4"> <mml:semantics> <mml:mn>4</mml:mn> <mml:annotation encoding="application/x-tex">4</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="5"> <mml:semantics> <mml:mn>5</mml:mn> <mml:annotation encoding="application/x-tex">5</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, and <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="6"> <mml:semantics> <mml:mn>6</mml:mn> <mml:annotation encoding="application/x-tex">6</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, compute the parameters of the associated anticanonical codes, and study their isomorphisms arising from the automorphisms of the surface. We obtain codes with excellent parameters, and some of them turn out to beat the best known codes listed on the database <italic>codetable</italic>.
STEAM EDUCATION AS A BENCHMARK FOR INNOVATIVE TRAINING OF FUTURE TEACHERS OF LABOUR TRAINING AND TECHNOLOGY

The purpose of the article is to substantiate an innovative methodology for implementing the content of STEAM (science, technology, engineering, arts and math) education through the organization of creative projects of future teachers of labour training and technology.The future teacher should be able to fulfil the tasks of integration of different subject areas, to use knowledge from different disciplines to describe the system of transformative activity; to make well-founded structural and technological decisions and put them into practice; to choose rational ways, to plan, predict and evaluate efficiency of transformative activity; to create spatial models of products or processes.Investigating this problem, we used the methods of research: analysis and synthesis to clarify the basic concepts and categories of STEAM education; conceptual analysis for comparing traditional and STEAM-oriented approaches, curricula and programs, psychological-pedagogical and scientific-methodical literature, materials of scientific-practical conferences on the problem of research, advanced innovative pedagogical experience.The organization of project activities contributes to the formation of sustainable motivation in teaching the disciplines on which STEAM education is based. Creating a product from conception to implementation, students are aware of the integral theoretical and practical importance of knowledge in the natural-mathematical and technical disciplines of cycles of general and vocational training.
Exploring software reusability metrics with Q&amp;A forum data

Question and answer (Q&A) forums contain valuable information regarding software reuse, but they can be challenging to analyze due to their unstructured free text. Here we introduce a new approach (LANLAN), using word embeddings and machine learning, to harness information available in StackOverflow. Specifically, we consider two different kinds of user communication describing difficulties encountered in software reuse: 'problem reports' point to potential defects, while 'support requests' ask for clarification on software usage. Word embeddings were trained on 1.6 billion tokens from StackOverflow and applied to identify which Q&A forum messages (from two large open source projects: Eclipse and Bioconductor) correspond to problem reports or support requests. LANLAN achieved an area under the receiver operator curve (AUROC) of over 0.9; it can be used to explore the relationship between software reusability metrics and difficulties encountered by users, as well as predict the number of difficulties users will face in the future. Q&A forum data can help improve understanding of software reuse, and may be harnessed as an additional resource to evaluate software reusability metrics.
Wrist in Motion: A Seamless Context-Aware Continuous Authentication Framework Using Your Clickings and Typings

In this paper, we propose enhanced continuous authentication by supplementing keystroke and mouse dynamics with wrist motion behaviors. Our method bridges the security gap when neither the mouse nor the keyboard is being used, such as during transitions from mouse to keyboard and vice versa, or during intermittent pauses when wrist movement is captured. Context-aware keystroke latency feature cell generation improves performance and solves latency fluctuation - different diagraphs have different latencies in different words. Based on two Random Forest Ensemble Classifiers (RFECs) recognizing the mouse and keystroke actions with corresponding wrist motions and one Sequential Sampling Analysis (SSA) or SSA Dynamic Trust Model (SSA-DTM), the identity of the user can be continuously verified no matter the operation mode - mouse clicking or keyboard typing. Experimental results, based on 44 subjects, show that the proposed approach can reach an FRR of 0.92% for genuine users and an FAR of 0 for attackers. The approach is shown to be more superior in efficient and timely authentications by making an authentication decision within only 35 mixed actions - mouse clicks or keystrokes, compared with conventional methods solely based on the mouse geometry and locomotion features or keystroke latency features.
Adaptive Secure Nearest Neighbor Query Processing Over Encrypted Data

Nearest neighbor query processing is a fundamental problem that arises in many fields such as spatial databases and machine learning. This article aims to address the Secure Nearest Neighbor (SNN) problem in cloud computing. Prior SNN schemes are both insecure and inefficient. In this article, we formally prove and experimentally demonstrate that the SNN scheme ASPE is actually insecure against even ciphertext only attacks. Although prior work proved that it is impossible to construct an SNN scheme even in much relaxed standard security models, we point out the flaws of the hardness proof. We propose an SNN scheme and prove that it is secure against adaptive chosen keyword attacks. Our scheme is efficient as its query processing complexity is logarithmic. To evaluate the efficiency of our SNN scheme, we implemented our scheme in C++ and compared its performance with a plain text scheme, binary scheme, and a PIR scheme on a large set of over 10 million real-world data points. Experimental results show that our scheme is fast (0.124 millisecond per query when data set size is 10 million) and scalable in terms of the number of data points.
A Framework for In-Vivo Testing of Mobile Applications

The ecosystem in which mobile applications run is highly heterogeneous and configurable. All layers upon which mobile apps are built offer wide possibilities of variations, from the device and the hardware, to the operating system and middleware, up to the user preferences and settings. Testing all possible configurations exhaustively, before releasing the app, is unaffordable. As a consequence, the app may exhibit different, including faulty, behaviours when executed in the field, under specific configurations.In this paper, we describe a framework that can be instantiated to support in-vivo testing of a mobile app. The framework monitors the configuration in the field and triggers in-vivo testing when an untested configuration is recognized. Experimental results show that the overhead introduced by monitoring is unnoticeable to negligible (i.e., 0-6%) depending on the device being used (high- vs. low-end). In-vivo test execution required on average 3s: if performed upon screen lock activation, it introduces just a slight delay before locking the device.
Construction of knowledge graph of maritime dangerous goods based on IMDG code

The International Maritime Dangerous Goods Code (IMDG Code) is the most important regulation in the international maritime transport chain of dangerous goods. Any international ship carrying dangerous goods must be strictly observed. Integrating and correlating cumbersome knowledge of IMDG Code and simplifying the query process are of great significance to the safe transportation and storage of dangerous goods. As a new method of knowledge representation and management, knowledge graph has been successfully applied in many industries. It can present the complex relationship between domain knowledge and correlate trivial and scattered knowledge, which provides a new way to solve this problem. This article starts with the knowledge system, structure, and classification of IMDG Code, and then analyses the related concepts of knowledge graph of maritime dangerous goods. Based on the above analysis, the authors construct the knowledge graph of maritime dangerous goods. It is helpful to simplify the retrieval process of dangerous goods professional knowledge, realise the automatic judgment of cargo stowage and segregation, and promote the intelligent transportation of dangerous goods.
Classification of handwritten digits using the Hopfield network

Abstract The paper presents the results of the classification of handwritten digits from the MNIST database using the Hopfield network. A strong correlation of training binary patterns does not allow the use of the standard Hebbian learning method. The application of the Storkey learning method increases the capacity of associative memory, and the optimized pattern binarization threshold and pattern size reduce the correlation of patterns. By optimizing these parameters, a network achieved a classification accuracy of 56.2% on a set of validation data used for network training. The selection of the optimal binarization threshold for a separate set of test images increased the classification accuracy to 61.5%.
Relation Extraction with Explanation

Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences.Efforts thus far have focused on improving extraction accuracy but little is known about their explainability.In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models.We demonstrate that replacing the entity mentions in the sentences with their fine-grained entity types not only enhances extraction accuracy but also improves explanation.We also propose to automatically generate "distractor" sentences to augment the bags and train the model to ignore the distractors.Evaluations on the widely used FB-NYT dataset show that our methods achieve new state-of-the-art accuracy while improving model explainability.
Attitude control of an inverted-pendulum-type robotic wheelchair to climb stairs considering dynamic equilibrium

Abstract The wheelchair is the major means of transport for elderly and physically disabled people in their daily lives. However it cannot overcome architectural barriers such as curbs and stairs. In this study, we developed an inverted-pendulum-type robotic wheelchair for climbing stairs. This wheelchair has a seat slider and two rotary links between the front and rear wheels on each side. When climbing stairs, the wheelchair rotates the rotary links while maintaining an inverted state of a mobile body by controlling the position of the center of gravity using a seat slider. In previous research, we confirmed that the wheelchair can climb by applying the control method consisting of a center-of-gravity control phase and rotary link control phase. However, it took approximately 15 s to rotate the rotary links during climbing because faster climbing causes the movement of wheels and the wheelchair to fall. This paper focuses on a control method to restrain the movement of the wheels when the stair climbing speed is increased. We realized that the movement was caused by forces acting on the pitch angle, such as the inertial force and the reaction of the driving force. We proposed the method considering the dynamic equilibrium of the pitch angle and confirmed the effect of the restraining wheels’ movement when the proposed method was applied.
Moving Object Detection under a Moving Camera via Background Orientation Reconstruction

Moving object detection under a moving camera is a challenging question, especially in a complex background. This paper proposes a background orientation field reconstruction method based on Poisson fusion for detecting moving objects under a moving camera. As enlightening by the optical flow orientation of a background is not dependent on the scene depth, this paper reconstructs the background orientation through Poisson fusion based on the modified gradient. Then, the motion saliency map is calculated by the difference between the original and the reconstructed orientation field. Based on the similarity in appearance and motion, the paper also proposes a weighted accumulation enhancement method. It can highlight the motion saliency of the moving objects and improve the consistency within the object and background region simultaneously. Furthermore, the proposed method incorporates the motion continuity to reject the false positives. The experimental results obtained by employing publicly available datasets indicate that the proposed method can achieve excellent performance compared with current state-of-the-art methods.
FingerTouch: Touch Interaction Using a Fingernail-Mounted Sensor on a Head-Mounted Display for Augmented Reality

This study proposes FingerTouch, a method of touch interaction using a head-mounted display for mobile augmented reality. FingerTouch allows users to manipulate virtual content with one-finger touch interaction regardless of the material or tilt of the plane the finger is touching. In addition, users can interact freely with virtual contents using FingerTouch. As the prototype developed in this study uses only one inertial measurement unit sensor attached to a fingernail, it features high mobility and allows users to feel natural tactile feedback, which is important for performing everyday tasks. The user evaluation of FingerTouch indicated that it provides high accuracy, with an average cursor navigation error of 2.15 mm and an average finger gesture recognition accuracy of 95% across 22 participants, two surface orientations, and three surface materials.
Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework

Nowadays, bot detection from Twitter attracts the attention of several researchers around the world. Different bot detection approaches have been proposed as a result of these research efforts. Four of the main challenges faced in this context are the diversity of types of content propagated throughout Twitter, the problem inherent to the text, the lack of sufficient labeled datasets and the fact that the current bot detection approaches are not sufficient to detect bot activities accurately. We propose, Twitterbot+, a bot detection system that leveraged a minimal number of language-independent features extracted from one single tweet with temporal enrichment of a previously labeled datasets. We conducted experiments on three benchmark datasets with standard evaluation scenarios, and the achieved results demonstrate the efficiency of Twitterbot+ against the state-of-the-art. This yielded a promising accuracy results (&gt;95%). Our proposition is suitable for accurate and real-time use in a Twitter data collection step as an initial filtering technique to improve the quality of research data.
Calibration routine for a telecentric stereo vision system considering affine mirror ambiguity

A robust calibration approach for a telecentric stereo camera system for three-dimensional (3-D) surface measurements is presented, considering the effect of affine mirror ambiguity. By optimizing the parameters of a rigid body transformation between two marker planes and transforming the two-dimensional (2-D) data into one coordinate frame, a 3-D calibration object is obtained, avoiding high manufacturing costs. Based on the recent contributions in the literature, the calibration routine consists of an initial parameter estimation by affine reconstruction to provide good start values for a subsequent nonlinear stereo refinement based on a Levenberg–Marquardt optimization. To this end, the coordinates of the calibration target are reconstructed in 3-D using the Tomasi–Kanade factorization algorithm for affine cameras with Euclidean upgrade. The reconstructed result is not properly scaled and not unique due to affine ambiguity. In order to correct the erroneous scaling, the similarity transformation between one of the 2-D calibration plane points and the corresponding 3-D points is estimated. The resulting scaling factor is used to rescale the 3-D point data, which then allows in combination with the 2-D calibration plane data for a determination of the start values for the subsequent nonlinear stereo refinement. As the rigid body transformation between the 2-D calibration planes is also obtained, a possible affine mirror ambiguity in the affine reconstruction result can be robustly corrected. The calibration routine is validated by an experimental calibration and various plausibility tests. Due to the usage of a calibration object with metric information, the determined camera projection matrices allow for a triangulation of correctly scaled metric 3-D points without the need for an individual camera magnification determination.
Norm-based Access Control

Collaborative systems, such as online social networks or Internet of Things, host vast amounts of content that is created and manipulated by multiple users. Co-edited documents or group pictures are prime examples of such co-owned content. Respecting privacy of users in collaborative systems is difficult because the co-owners of the shared content can have conflicting access policies about the content. To address this problem, recent approaches employ group decision making techniques, such as auctions. With these approaches, when a content is to be shared, all co-owners express their privacy preferences through the mechanism (e.g., by bidding) and the group decision mechanism reaches a decision to enable or deny access to the content. However, such mechanisms have to be carried out per content, making them impractical for most realistic settings. We argue that rather than employing a group decision mechanism on each content separately, it is more practical to watch for privacy norms that emerge in systems and make decisions using these norms, when possible. This paper borrows ideas from philosophy to represent privacy norms and develops algorithms to compute them in collaborative systems. We show that when privacy norms are identified correctly, they can enable collaborative systems respect users' privacy as well as decrease the need to engage in a group decision mechanism considerably.
Designing Games for Healthy Sleep

A sleep deficit has far-reaching consequences, but for many people, healthy sleep is not a priority or a possibility. We explore the potential for "sleepy games" as a genre of transformational games. To explore this design space, we prototyped nine games through an iterative design process. Based on analysis of design decisions and the games as artifacts, we identify seven design challenges for sleepy games: agency and control; physiological and mental arousal; intervention timing; social embeddedness; multisensory experience; vulnerability; and identity and values. We expand on three games with playtesting to show how these design challenges unfold for players in practice, show the impact on players' lives, and discuss sleepy games as creative, social, and situated practices.
Multimodal Brain MRI Translation Focused on Lesions

Registered multimodal images are lacking in many medical image processing tasks. To obtain sufficient registered multimodal data, in this paper, we propose a new unsupervised scheme for medical image translation based on cycle generative adversarial networks (CycleGAN), which can generate registered multimodal from single modality and retain the lesion information. We improve parameter initialization method, upsampling method and loss items to speed up model training and improve translation quality. Compared with previous studies that focus only on the overall quality of translation, we attach more importance to the lesions information in medical images, so we propose a method for the preservation of lesions information in the translation process. We perform a series of multimodal translation experiments on the BRATS2015 dataset, verify the effect of each of our improvements as well as the consistency of the lesions information between translation images and original images. And we also verify the effectiveness and availability of the lesions information in translation images.
Integrating expert’s knowledge constraint of time dependent exposures in structure learning for Bayesian networks

Learning a Bayesian network is a difficult and well known task that has been largely investigated. To reduce the number of candidate graphs to test, some authors proposed to incorporate a priori expert knowledge. Most of the time, this a priori information between variables influences the learning but never contradicts the data. In addition, the development of Bayesian networks integrating time such as dynamic Bayesian networks allows identifying causal graphs in the context of longitudinal data. Moreover, in the context where the number of strongly correlated variables is large (i.e. oncology) and the number of patients low; if a biomarker has a mediated effect on another, the learning algorithm would associate them wrongly and vice versa. In this article we propose a method to use the a priori expert knowledge as hard constraints in a structure learning method for Bayesian networks with a time dependant exposure. Based on a simulation study and an application, where we compared our method to the state of the art PC-algorithm, the results showed a better recovery of the true graphs when integrating hard constraints a priori expert knowledge even for small level of information.
Clustering-Based Trajectory Outlier Detection

The improvement in mobile computing techniques has generated massive trajectory data, which represent the mobility of moving objects like vehicles, animals, and people.Mining trajectory data and especially outlier detection in trajectory data is an attractive and challenging topic that fascinated many researchers.In this paper, we propose a Clustering-Based Trajectory Outlier Detection algorithm (CB-TOD).The proposed algorithm partitions a trajectory into line segments and decreases those line segments to a smaller set (Summary-trajectory SS(t)) without affecting the spatial properties of the original trajectory.After that the CB-TOD algorithm using a clustering method to detect the cluster with the smallest number of segments for a trajectory and a small number of neighbors to be sub-trajectory outliers for this trajectory.Also, our proposed algorithm can detect outlier trajectories in the dataset.The main advantage of CB-TOD algorithm is reducing the computational time for outlier detection especially for big trajectory data without affecting the efficiency of the outlier detection results.Experimental results demonstrate that CB-TOD outperforms the state of art existing algorithms in identifying outlier sub-trajectories and also outlier trajectories in real trajectory dataset.