Reviewerly/exHarmony huggingface dataset:
Systematic Handling of Heterogeneous Geometric Primitives in Graph-SLAM Optimization

In this paper 1 , we propose a pose-landmark graph optimization back-end that supports maps consisting of points, lines or planes.Our back-end allows representing both homogeneous (point-point, line-line, plane-plane) and heterogeneous measurements (point-on-line, point-on-plane, line-on-plane).Rather than treating all cases independently, we use a unified formulation that leads to both a compact derivation and a concise implementation.The additional geometric information, deriving from the use of higher-dimension primitives and constraints, yields to increased robustness and widens the convergence basin of our method.We evaluate the proposed formulation both on synthetic and raw data.Finally, we made available an opensource implementation to replicate the experiments.
Crank up the Volume: Preference Bias Amplification in Collaborative Recommendation.

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine bias disparity over a range of different algorithms and for different item categories and demonstrate significant differences between model-based and memory-based algorithms.
SCOD: A Novel Semi-supervised Outlier Detection Framework

Nowadays, outlier detection has been widely used in various areas of data mining. For example, it could help to detect whether a credit card is fraudulent used. Due to its less time consuming and higher effectiveness, existing works usually focus on semi-supervised anomaly detection methods, which mainly utilize the distance between examples to detect outliers. However, such methods suffer low detection accuracy facing massive data with high density. To approach this problem, we propose a novel semi-supervised cluster-based outlier detection method (SCOD) which combines density-based method and clustering. By doing so, we could not only distinguish various behaviors in huge size of data, but also can detect anomalies with dense distance-based neighbors. In this way, the detection accuracy can be significantly improved. In addition, we use real-world datasets to evaluate our approach and the evaluation confirms the precision of SCOD against other state-of-art approaches.
Analysis of rater effect in the evaluation of second language grammatical knowledge in the context of writing: Application of a generalized linear model

Despite ample testing research involving the impact of rater background characteristics (e.g. raters’ native language) on assessment, relatively little has been known about how raters’ linguistic knowledge influences their scoring decision-making process. By associating second language acquisition research with regard to two types of linguistic knowledge (i.e. implicit and explicit knowledge), this study aims to explore the influence of raters’ linguistic knowledge and other rater factors (including teaching experience) on second language writing assessment, particularly in relation to the grammatical aspect of performance. Forty-two raters’ grammatical knowledge was measured with an untimed paper-and-pencil grammar test (explicit knowledge) and a timed computer-based experiment (implicit knowledge). The raters also responded to a background questionnaire. For scoring, the raters evaluated five examinees’ responses on two writing tasks (a personal essay and an argumentative essay), based on a six-point analytic scoring rubric. An analysis of a cumulative link mixed model revealed that the raters’ scores could be significantly related to their grammatical knowledge and teaching experience. In addition, different effects emerged across the examinees’ ability levels. These findings contribute to a better understanding of the predictors with respect to rating performance and provide practical implications for rater selection and training in second language performance assessment. (Hankuk University of Foreign Studies · Seoul National University)
Halt command in word retrieval

In this study, occurrences and temporal patterns of five types of disfluencies were analyzed that show a common feature on the surface.All of them have some kind of interruption of content words followed by some continuation.The purpose was to show whether the place of interruption of the word articulation and the durational patterns of the editing phases are characteristic of re-starts, false starts, slips of the tongue, pauses within words, and prolongations.More than 1,400 instances were processed.Both (i) the number of pronounced segments of abandoned words and the duration of the corresponding editing phases are characteristic of a specific disfluency type, and (ii) speakers select a strategy to overcome their speech planning difficulties most economically.
Rare Association Rules Mining of Diabetic Complications Based on Improved Rarity Algorithm

Although the frequent pattern mining has attracted widespread attention of scholars, it is undeniable that the rare pattern mining plays a significant role in many fields, such as medical, financial, and scientific fields. And it is more valuable to study the rare pattern mining, because it tends to find some unknown and unexpected associations. There are some previous algorithms of rare itemsets mining, however, Arima spends much time and Rarity wastes much space. So based on the Rarity algorithm, this paper presents an improved top-down approach to efficiently mine all rare itemsets and their association rules, which uses the graph structure to indicate all combinations of existing items in the database, defines a pattern matrix to record all itemsets and the support_count, and combines the hash table to accelerate support calculation to quickly find all rare itemsets, and then generate all patterns to choose useful rules according to their interesting rate. In the experiment, this paper uses the real diabetic clinical data to verify this improved approach and mines some useful rules among the diabetic complications. Moreover, compared with the two methods mentioned above, this method decreases much time and space complexity in the association rules mining.
Using convolutional neural networks for fingerspelling sign recognition in brazilian sign language

Deaf people communicate naturally using sign languages, and because of this, they have difficulty in communicating using oral or written languages. To minimize this problem, one alternative is to use machine translation systems from sign languages into oral languages, especially for scenarios where human interpreters are not viable or unavailable. In this work, we address this problem by proposing a solution for fingerspelling recognition in Brazilian Sign Language (Libras) using Convolutional Neural Networks. The system uses a 224000 images dataset created by our team, which represents the letters of the Libras alphabet signed by 12 people in different backgrounds, body arm, hand positions, and lighting patterns. The results show that the solution had an average accuracy of approximately 99% in a dependent person scenario, and had an average accuracy of 71% in an independent person scenario. This type of solution can be used together with machine translators from Brazilian Portuguese to Libras, to assist in the communication of Brazilian deaf people.
Differential Watermarking of Multilead ECG Baseline

Digital watermarking has been widely recognized as an effective tool for embedment of auxiliary data in the host record. This paper presents a new method of watermarking using lead-to-lead difference of values in the baseline of the host electrocardiogram. The method starts with delineation of the baseline and uses Kirchoff voltage law or interpolation to predict any selected lead from the remaining ones. Next, the difference between the predicted and actual value is considered as noise and subjects to measurement of level and distribution in the time frame of baseline. The watermark with patient data or results of accompanying measurements is coded accordingly to mimic the noise. Replacement of the baseline noise with the watermark data ends the process. With 12-lead CSE files and respective reference borders of PQ and TP segments, the capacity of watermark achieved 3875 bits per second, while the diagnostic value of the ECG remains untouched.
Pastel color imaging and its evaluation using video coding

In the color imaging, single sensor system with color filter array (CFA) is popular, especially with Bayer CFA. Performances of de-mosaicking for Bayer CFA are improved by using correlation of color planes, and, evaluated by reconstruction of color plane on still images. However, captured images is used for video coding recently. Therefore, it should be tested including video coding. We have proposed an imaging suitable for video coding. In this method, colors of CFA are changed to pastel color, also, the color plane reconstruction method that abstract the average component of RAW image are proposed. In this, the color of CFA is simulated by computer calculation. In this paper, we propose color plane reconstruction method using the average component and the color difference. In the experiments, we evaluate the reconstruction performance of RGB images and the overall performance include video coding. To evaluate coding performance, Y-PSNRs and coding bitrate between the original images and the decoded images are used. From the experiment, Y-PSNRs of proposal have the advantage. With the coding, the bitrate reduction is up to 30% at the same PSNR, because of less alias component.
PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) DI DAERAH TERPENCIL

Remote Area Information and Communication Technology (ICT) Learning, Post Graduate Student Education Management Study Program, University of PGRI Palembang. This paper aims to describe the benefits of Information and Communication Technology-Based Learning in explaining the concept of learning, so that the delivery of information, in the teaching and learning process is more detailed, so that it is expected to help the process of understanding the material delivered more maximally and pleasantly. The purpose of Information and Communication Technology, as a solution to, develops the creativity of both educators and students. Information and communication technology-based learning is an effort to utilize the progress of Information and Communication Technology for the learning process, and functions as a tool not as the main subject, so that technology can help humans, in solving various problems in the real world, which is currently the foundation of the workforce, so that students have basic knowledge as the main capital to enter the workforce.
CS–MMPF‐based weak target detection and tracking with range ambiguity

To address the problem of target detection and tracking with the range ambiguous radar in low signal-to-noise ratio environment, a novel weak target detection and tracking algorithm based on the compressed sensing (CS) and multiple model particle filter (MMPF) is proposed. The general solution is that constructing an over-complete atom dictionary according to the echo model of target, obtaining a sparse representation of the target echo signal with the CS method, and conducting a threshold detection with the obtained atom energy distribution and judge whether a target is present or not firstly; roughly extracting the ambiguous measurement of weak target by resolving the least square problem constrained by one-norm minimisation if a target is present then proposing an ambiguous measurement-based MMPF manoeuvring target tracking method, and realising an effective detection and tracking of manoeuvring weak target in the presence of range measurement ambiguity lastly. Simulation results demonstrate that the proposed method can improve the radar performance of target detection and tracking effectively, and succeeds in range ambiguity resolving.
Search Over Encrypted Cloud Data With Secure Updates

Searchable symmetric encryption (SSE) is a key technique which enables searching over outsourced encrypted data. Hence, SSE must provide for the functions of effective, secure privacy and usability when searching over outsourced data on the cloud. In this regard, various research into the secure search on cloud data has been done using SSE and searchable public key encryption. In this framework, we leverage a practicable searchable encryption (SE) scheme which supports secure and updatable operations on the cloud. The framework will take into consideration the advantages of both SSE and searchable public key encryption in the construction. We also construct an efficient multi-keyword search scheme using k-nearest neighbor (kNN) and Bloom Filter (BF) to achieve ranked search scheme as well as multi-keyword searching.
Individual-Topological Features of Development of Musical, Artistic and Intellectual Abilities among Pre-School Age Children

The high effectiveness of present-day educational programs within the sphere of supplementary artistic-aesthetical upbringing and continuous education of preschoolers may be provided in the light of scientifically based typologies of special abilities at the core of which lie both generic psychological and psychophysical (natural) components. In the presented exploratoryapplicative research there is an outline of a solution of several goals related to scholarly research and scholarly practice. On a diagnostic stage, in correspondence with the aim of the research a selection of diagnostic methodologies has been made and a complex diagnosis has been carried out (surveys among parents, consultation of pedagogues, testing of children) with the use of the method of contrasting groups and engaging of respondents from various preschool institutions for development of separate types of abilities. On an encompassing stage the results of expositive statistics are analyzed and a qualitative interpretation of them is carried out. As a result of comparative analysis in the groups of musical, artistic and intellectual development veracious differences are demonstrated: in the manifestation in children of separate types of sensorial sensitivity, which influences their cognitive development; in the distinct manifestation of indications of creative abilities and parameters of creative thinking in correlation with a concrete type of preschool institution and specificity of realization in it of educational programs. On the prognostic-analytical stage with the aid of correlational analysis the preschoolers show the defined specificity (character and closeness) of interconnections of audial, visual and kinesthetic types of perception and processing of information with separate indications of creative abilities and thinking. The conclusions are arrived at about the prognosticate value of examination of the indicated types of as individual-personal predictors of harmonic development of separate (general or specific) capabilities, and adequate recommendations are presented to parents and pedagogues. Keywords: creative abilities, children’s giftedness, individual-personal predictors, typological approach.
Estimating Secret Parameter and Vulnerability Analysis of a Chaos-Based RNG

A novel estimate system is proposed to discover the security vulnerabilities of a chaos based RSA algorithm and a chaos based random number generator (RNG) it contains. Convergence of the estimate system is proved using auto-synchronization. Secret parameter of the target chaos based RNG are recovered where the available information are the structure of the chaos based RSA algorithm and a scalar time series observed from the target chaotic system. Simulation and numerical results verifying the feasibility of the estimate system are given such that, next bit can be predicted while the same output bit sequence of the chaos based RNG, used together with RSA algorithm, can be regenerated.
Lattice-Based Encrypted Verifiably Encryption Signature Scheme for the Fair and Private Electronic Commence

To achieve the fairness and the privacy of the e-commence, and also to consider the quantum security of the e-commence, a lattice-based encrypted verifiably encryption signature scheme (EVES) is proposed in this paper. We achieve the strong unforgeability and the opacity properties in the standard model over lattice without the help of the lattice-based delegation technologies which is one of main tools to achieve the standard model security for the lattice-based cryptography. It is shown that the proposed scheme can be used to design a fair and privacy-preserving e-commence system in cloud, not only for business-to-customer business(B2C) but also for customer-to-customer business(C2C). The main reason is that the verifier can read the details of the EVES by easily computation operations before the EVES is verified. Then the seller who acts as a verifier in EVES scheme can ask a proxy sever to deal the business for him. Then the seller does not need to keep online for most of time. Moreover, we compare the proposed EVES with some known lattice-based VES schemes. Besides the proposed scheme achieves the more cryptographic functions, such as EVES vs VES and standard model vs random oracle model etc., the proposed scheme even has some advantage about the computation cost compared with known lattice-based VES schemes.
Wheeled robot navigation based on a unimodal potential function

This article proposes wheeled mobile robot navigation based on a model predictive control over unimodal potential function. The navigation potential function has a single minimum and no local minima. It is obtained from a discrete graph-based search algorithm that can update the environment changes. Obtained navigation function enables continuous convergent navigation where gradient direction is computed to steer the robot towards the goal. Model predictive control using particle swarm optimization is applied to find the path within areas of the lowest potential and considering the control effort. The proposed solutions feature simple implementation, at moderate computational effort and with good navigation results.
A MOVING OBSTACLES PLANNER ALGORITHM FOR A SIX-WHEELED SURVEYING ROBOT

Abstract. Nowadays, robotic systems such as ground vehicle robots are mostly used in many industrial and military applications. Therefore, the path planning problem in the robotics domain is very important. Moving Obstacles Planner (MOP) algorithms have got the researchers interests in recent years and some of the most recent ones have been implemented in Robot Operating System (ROS) which is an open source middle wear to work with robots. This paper aims to compare the state-of-the-art MOP algorithms including Rapidly exploring Random Tree (RRT) and those implemented in the ROS navigation stack such as Dynamic Window Approach (DWA) local planner coupled with Dijkstra and A* as global planners on a six-wheeled robot known as MOOR in simulation environment. The results reveal that all of these algorithms have been designed for a square shape footprint robot and thus have limitations for MOOR with a rectangular footprint shape.
Accelerating the Optimization of a Segmentation Ensemble using Image Pyramids

In this paper, we propose an image pyramid-based noisy energy function evaluation method for the local search technique simulated annealing. The method is primarily designed for the optimization of image segmentation algorithms, and it maintains solution quality with significantly reduced time requirement. The strategy to select the proper image pyramid levels during the search is theoretically determined via adapting results regarding evaluation in simulated annealing based on imprecise measurements. As a demonstrative application, we perform parameter-optimization of a segmentation ensemble dedicated to the extraction of bone structures from CT images.
Type II Excitability with Quantum Dot Lasers: Canards, Bistabilities and More

Excitable pulses are one of the necessary building blocks in achieving a functional neuromorphic photonic system. Many different semiconductor devices have been arranged in various configurations to produce excitable pulses. Of these, Quantum Dot (QD) lasers in a unidirectional optical injection set-up are great candidates for future applications. The optically injected QD laser is shown to be a truly unique set up producing a plethora of different non linear dynamics, many of which are potentially useful in neuromorphic applications. In particular, there are several different excitable regimes: Both Type I and Type II excitability can be produced. In fact, both types are produced with exactly the same master-slave configuration. By simply adjusting the control parameters only - injection strength and detuning - it is easy to navigate between these two types of excitability.
TEACHING PROGRAMMING WITH PYTHON FOR LINGUISTS: WHYS AND HOW-TOS

The article shows why it is worth introducing the basics of computational linguistics in general and programming as a method of the natural language processing in particular to the linguistics students. Computational linguistics has become the basis for solving many practical tasks in the language industry. Providing the linguistics students with the basics and the methods of the computational linguistics we widen their views on linguistics and show a perspective field of their possible future engagement to them. To get acquainted with computational linguistics, students have to learn how to work with corpora and acquire the basics of programming. This article demonstrates why Python is a good choice for linguists to start learning to programme. It also suggests an approach to teaching the fundamentals of the programming in Python for such students and gives step by step the main structures which can be used for processing texts or corpora. To such structures belong strings, variables, lists, loops, print-function, split-method, incrementation, and control structure. Combining these elements one can, for instance, split text into sentences or words, count words or sentences in text or count only some concrete elements in the text which satisfy a special condition. The article also outlines how to start working with input files. The further structures of Python are named, which can be introduced to the students next so that they become able to do more operations with texts. It is stressed that teaching programming is impossible without trying out every structure, so it is important to encourage the students to write their own code experimenting with each new element of Python and offer them enough practical tasks. Some examples of such tasks are illustrated in the article.
AN INVESTIGATIVE PARAMETER ANALYSIS OF PASTORALIST OPTIMIZATION ALGORITHM (POA): A NOVEL METAHEURISTIC OPTIMIZATION ALGORITHM

In this paper, one of the most important parameters that affects the performance of a novel population based nature-inspired metaheuristic optimization algorithm called the Pastoralist Optimization Algorithm (POA) inspired by the pastoralists herding strategies were investigated. This is to determine the suitable range of values that should be used when applying the algorithm to solve optimization problems. The parameter that was investigated is the number of pastoralist (nP), that is the number of search agents. Eight different pastoralist size (10, 20, 30, 40, 50, 60, 70 and 80) were investigated by testing the parameter on three standard test functions; unimodal Sphere function, multimodal Dejong and Shubert functions. Each test is simulated ten times, and the average optimal value and the average convergence time(s) obtained for each function and each parameter value was recorded. The experimental results obtained show that a pastoralist population size of 20 is desirable for convergence accuracy and speed.