Reviewerly/exHarmony huggingface dataset:
Semantic Learning and Emulation Based Cross-Platform Binary Vulnerability Seeker

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries. In this paper, we present BinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph, BinSeeker can quickly identify M candidate functions that are most similar to the vulnerability from the target binary. The value of M is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these M candidates to obtain their dynamic signature sequences. By comparing signature sequences, BinSeeker produces top-N functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation, BinSeeker seeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate that BinSeeker outperforms three state-of-the-art tools Genius, Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate, BinSeeker achieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively.
Generalized Pareto Model Based on Particle Swarm Optimization for Anomaly Detection

Anomaly detection of time series has been widely used in various fields.Most detection methods depend either on assumptions about data distribution or manual threshold setting.If the assumption is incorrect, the effectiveness of detection technology will be greatly reduced.To deal with this problem, we propose a maximum likelihood estimation method based on particle swarm optimization for generalized Pareto model to detect outliers of time series, which can be called Generalized Pareto Model Based on Particle Swarm Optimization (GPMPSO).Because the generalized Pareto model is multidimensional, we introduce a comprehensive learning strategy to improve search ability of particle swarm algorithm.Due to the multiple peaks of the log-likelihood function of generalized Pareto model, we apply dynamic neighbors to reduce the possibility of particle swarm optimization falling into local optimum.Moreover, we propose a new processing model Big Drift Streaming Peak Over Threshold (BDSPOT) to enhance the capability of the data stream processor.Our algorithm is tested on various real-world datasets which demonstrate its very competitive performance.
A geometric measure of non-classicality

This paper aims to stress the role of the Cahill-Glauber quasi-probability densities in defining, detecting, and quantifying the non-classicality of field states in quantum optics. The distance between a given pure state and the set of all pure classical states is called here a geometric degree of non-classicality. As such, we investigate non-classicality of a pure single-mode state of the radiation field by using the coherent states as a reference set of pure classical states. It turns out that any such distance is expressed in terms of the maximal value of the Husimi $Q$ function. As an insightful application we consider the de-Gaussification process produced when preparing a quantum state by adding $p$ photons to a pure Gaussian one. For a coherent-state input, we get an analytic degree of non-classicality which compares interestingly with the previously evaluated entanglement potential. Then we show that addition of a single photon to a squeezed vacuum state causes a considerable enhancement of non-classicality, especially at weak and moderate squeezing of the original state. By contrast, addition of further photons is less effective.
Transmission Control of MPTCP Incast Based on Buffer Balance Factor Allocation in Data Center Networks

The multi-home host will become increasingly popular within IPv6, especially in data center Fat-tree networks. MPTCP protocol is designed to transmit over multipath load, providing users with transparent multi-path utilization and high data transmission rate. As the characteristics of Many-to-one pattern and a large number of short bursts, all of flows collide on the shared link and same output port, creating an inevitable bottleneck collapse during transmission process. In order to avoid the collapse of throughput and improve the throughput of the receiving end, the MPTCP protocol in the data center network should have the characteristics of minimal packet loss and rapid convergence. In this paper, we focus on throughput collapse avoidance and design a MPTCP Transmission Control method based Queuing Cache Balance Factor called QCBF in Multi-homed FatTree topology. This method can realize the sub-flows are allocated by the size of ToRs cluster cache. We first propose a buffer pool balance factor and build a ToRs cluster cache balance queue. Then according to ToRs cluster cache allocation to calculate a sub-flow congestion window value. QCBF not only avoids the MPTCP Incast problem effectively, but also makes full use of the aggregate bandwidth. Based the software NS3, the experiment results of experiment show the QCBF transmission control method could avoid loss of packet, and achieve the load balance of MPTCP data transmission.
Primary School Teacher’s Social Competence in the Postgraduate Education

The main point of the article is that among the factors that contribute to the social success of students, the developed social competence (SC) and ability of primary school teachers to reflect are extremely important.The process of development of the reflexive component of social competence of elementary school teacher in the postgraduate process is defined as the object.According to the hypothesis of the work, its development can become a multifaceted process, an integral part of effective professional training, with appropriate willingness of teachers, their involvement in practice-oriented activities to solve vital social problems and the implementation of socially significant projects; pedagogical support of teachers on diagnostic basis.Theoretical studies of definitions of social competence have made it possible to distinguish among its various approaches its reflective component.In view of the lack of a single universally recognized toolkit, the content, criteria, indicators and levels of the phenomenon under consideration are determined on the basis of the theory and methodology of different fields of general pedagogy, vocational education.To analyze and evaluate the reflexive component, the methods of introspection and self-assessment were selected and the author's method "Self-assessment of teachers' readiness to increase their social competence" was created.In accordance to the hypothesis and developed concepts of the model of activity, a system of integrated sustainable development of social competence for primary school teachers was created.Comparisons of initial and final results showed their greatest positive dynamics in the experimental group, whereas in the control group there was only a tendency to improve it (the results were tested for reliability by means of mean, paired comparisons, correlation analysis (student t-test)).The developed system has provided continuous, detailed, multi-level methodological and social support for teachers during the course and intercourse periods for five years.It helped them to carry out a socially oriented professional activity on the students' social competence formation, their values, motivation and behavior.
Agoraphilic Navigation Algorithm in Dynamic Environment with and without Prediction of Moving Objects Location

This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched- marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations.
Fusing Two Directions in Cross-Domain Adaption for Real Life Person Search by Language

Person search by language is an important application in video surveillance. The existing huge visual-semantic discrepancy and the cross-domain difficulty of emerging pedestrian images with new identities while no language description for training in real life application make this problem non-trivial to be addressed. In this paper, we first propose a concise and effective framework for image-sentence alignment to deal with the visual-semantic discrepancy. Second, we innovatively fuse the two opposite directions, i.e., source to target and target to source, for cross-domain adaption. Extensive experiments have validated the significant superiority of the proposed method on both source domain and target domain, and we have obtained the state-of-the-art performance and won the 1st place in competition.
How Do Secondary Minerals in Granite Help Distinguish Paleo- from Present-Day Permeable Fracture Zones? Joint Interpretation of SWIR Spectroscopy and Geophysical Logs in the Geothermal Wells of Northern Alsace

The investigation of permeable hydrothermally altered and fractured zones and their distribution is a key issue for the understanding of fluid circulation in granitic rocks, on which the success of geothermal projects relies. Based on the use of short-wave infrared (SWIR) spectroscopy applied to rock cuttings coupled with interpretation of geophysical logs, we propose an investigation of the clay signature of fault and fracture zones (FZ) inside the granitic basement. This methodology was applied to two geothermal wells: GRT-2 from the Rittershoffen and GPK-1 from the Soultz-sous-Forêts (Soultz) geothermal sites, both located in the Upper Rhine Graben (URG). A total of 1430 SWIR spectra were acquired and analysed. Variations in the 2200 nm absorption band area are correlated with hydrothermal alteration grades. The 2200 nm absorption band area is found to reflect the illite quantity and its variations in the granitic basement. Low, stable values are observed in the unaltered granite facies, showing good reproducibility of the method, whereas scattered high values are associated with high hydrothermal alteration and FZs. Variations in the 2200 nm absorption band area were correlated with the gamma ray and electrical resistivity logs. This procedure allowed us to confirm that illite mainly controls the resistivity response except inside the permeable FZs, where the resistivity response is controlled by the geothermal brine. Thus, the architecture of these permeable FZs was described precisely by using a combination of the 2200 nm absorption band area data and the electrical resistivity log. Moreover, by correlation with other geophysical logs (temperature (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>T</mml:mi></mml:math>), porosity, and density), paleo-permeable and currently permeable FZs inside the reservoir were distinguished. The correlation of SWIR spectroscopy with electrical resistivity logs appears to be a robust tool for geothermal exploration in granitic reservoirs in the URG.
PENGEMBANGAN MEDIA PEMBELAJARAN IPA BERBASIS POWERPOINT INTERAKTIF UNTUK SISWA KELAS V SEKOLAH DASAR NEGERI GONDOLAYU

Based on the necessary with the teacher, it is concluded that teachers need information technology (IT) based on learning media in the teaching and learning process. The main purpose of this study is to know about the research procedure, to know about the quality of the media that developed, and the expert's respond. This study is a Research and Development (R&amp;D). The on procedure of the procedure uses 9 steps, including 1)Potentials and problems, 2)Data collection, 3)Product design, 4)Design validation, 5)Design revision, 6)Product try-out, 7)Product revision, 8)Application try-out, 9)Product revision until the desired products interactive powerpoint learning media are complete. The result of the study showed that the media were appropriate to be implemented. This showed from media experts of the validation result that average score 3,34 with a very good category. Material expert student's that average score 3,40 with very good category and from linguists expert result that average score 3,22 with the good category. Next, the average validation score of the teacher is 3,62 with a very good category and peers of the validation result that average score 3,44 with a very good category. The last response of the students in applications try-out responses that average score 92,75 with a very good category.
Robust path planning for avoiding obstacles using time-environment dynamic map

Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algorithm and local trace-back model is designed in order to discover sub-optimal feasible path rapidly in unmanned aerial vehicles’ field of view. Finally, the simulation results have illustrated that the proposed method can ensure unmanned aerial vehicles’ autonomous path planning safely and effectively in urban space crowded with obstacles.
Leadership and Functional Competence Development in Teachers: World Experience

Teacher leadership is closely related to the level of professional competence.However, at the present development stage of the education system, this concept is not always fully understood and taken into account.The purpose of our research is to study the level of leadership of Russian school and university teachers, as well as to identify problems and suggest possible solutions.The survey involved 111 school teachers and university lecturers.The selection criterion is explained by the fact that leadership may vary depending on the level of education.This sample will allow us to consider the issue more widely.In the course of our research, the authors identified the following problems: the concept and importance of teacher leadership are not fully understood; consequently, teacher leadership is not fully implemented.This fact negatively affects professional competence development.These problems can be solved by the creation of special courses, the exchange of experience, the encouragement of teacher leaders and the introduction of the idea of teacher leadership in pedagogical universities.
On Leveraging the Visual Modality for Neural Machine Translation

Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics.Recently, Caglayan et al. posit that the observed gains are limited mainly due to the very simple, short, repetitive sentences of the Multi30k dataset (the only multimodal MT dataset available at the time), which renders the source text sufficient for context.In this work, we further investigate this hypothesis on a new large scale multimodal Machine Translation (MMT) dataset, How2, which has 1.57 times longer mean sentence length than Multi30k and no repetition.We propose and evaluate three novel fusion techniques, each of which is designed to ensure the utilization of visual context at different stages of the Sequence-to-Sequence transduction pipeline, even under full linguistic context.However, we still obtain only marginal gains under full linguistic context and posit that visual embeddings extracted from deep vision models (ResNet for Multi30k, ResNext for How2) do not lend themselves to increasing the discriminativeness between the vocabulary elements at token level prediction in NMT.We demonstrate this qualitatively by analyzing attention distribution and quantitatively through Principal Component Analysis, arriving at the conclusion that it is the quality of the visual embeddings rather than the length of sentences, which need to be improved in existing MMT datasets.
Challenges of IoT Payments in Smart Services

Many applications including Industry 4.0, Smart City, Building Management, Car of the Future, Smart Home and Connected Health use IoT (Internet of Things).IoT is changing the way businesses and consumers, even whole cities interact through connected devices.In the future, with Internet of Everything (IoE), every entity (people, machines, animals, cars, buildings, etc.) will have possibilities for direct interconnections.Forecasts show that over 20 billion connected devices will reach the market by 2020.Ninety percent of cars are expected to be connected, too.These wide range of industry and consumer applications have growing needs for IoT and many common elements such as decentralization, real-time requirements, sensory or measurement capabilities, a communications layer and Big Dataanalytics/intelligence.Besides these well know common elements, also in many applications exit the need for payment.This additional requirement is known as the idea of IoT payments.In this paper is present some of challenges in implementation of IoT payments.
Continuous-time Quantum Error Correction with Noise-assisted Quantum Feedback

We address the standard quantum error correction using the three-qubit bit-flip code, yet in continuous-time. This entails rendering a target manifold of quantum states globally attractive. Previous feedback designs could feature spurious equilibria, or resort to discrete kicks pushing the system away from these equilibria to ensure global asymptotic stability. We present a new approach that consists of introducing controls driven by Brownian motions. Unlike the previous methods, the resulting closed-loop dynamics can be shown to stabilize the target manifold exponentially. We further present a reduced-order filter formulation with classical probabilities. The exponential property is important to quantify the protection induced by the closed-loop error-correction dynamics against disturbances. We study numerically the performance of this control law and of the reduced filter.
A New Localization Objective for Accurate Fine-Grained Affordance Segmentation Under High-Scale Variations

Fine-grained affordance segmentation for object parts can greatly benefit robotics and scene understanding applications. In this work, we propose an instance-segmentation framework that can accurately localize functionality and affordance of individual object parts. We build on the standard Mask-RCNN framework and propose two novelties to the localization objective that can lead to improved part detection and affordance segmentation results. Specifically, we notice two problems with the conventional IOU based regression loss, (a) the small boxes, that are specially relevant for fine-grained detection, have a higher risk of being ignored during the optimization process and (b) a constant value of IOU for non-overlapping candidates means no supervision is available to encourage the reduction in loss function. To address these limitations, we propose a novel Angular Intersection Over Larger (AIOL) measure. Our experiments show consistent improvement over other baselines and state of the art localization loss functions for the fine-grained affordance segmentation task.
An Approach to Segment and Track-Based Pedestrian Detection from Four-Layer Laser Scanner Data

Pedestrian detection is a critical perception task for autonomous driving and intelligent vehicle, and it is challenging due to the potential variation of appearance and pose of human beings as well as the partial occlusion. In this paper, we present a novel pedestrian detection method via four-layer laser scanner. The proposed approach deals with the occlusion problem by fusing the segment classification results with past knowledge integration from tracking process. First, raw point cloud is segmented into the clusters of independent objects. Then, three types of features are proposed to capture the comprehensive cues, and 18 effective features are extracted with the combination of the univariate feature selection algorithm and feature correlation analysis process. Next, based on the segment classification at individual frame, the track classification is conducted further for consecutive frames using particle filter and probability data association filter. Experimental results demonstrate that both back-propagation neural network and Adaboost classifiers based on 18 selected features have their own advantages at the segment classification stage in terms of pedestrian detection performance and computation time, and the track classification procedure can improve the detection performance particularly for partially occluded pedestrians in comparison with the single segment classification procedure.
Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Identifikasi Jenis Anggrek (Orchidaceae)

Orchidaceae is the Latin name of an orchid that has various shapes, colors and sizes of flowers with unique features. The shape and color of the lips or labellum is a unique orchid that is a differentiator from other plants. In general, the types of orchids have similar colors, textures and flower petals, this causes a person to have difficulty identifying orchid species, especially people who do not know the characteristics of some types of orchids. Therefore the process of identifying orchids needs to be done automatically with a computer system so that it is expected to make it easier to identify the types of orchids. In this study using the GLCM method for feature extraction and KNN method for the process of identifying orchids or orchidaceae. Stages of identification of orchid images are changing the initial size of the image, conversion into gray degrees, median filters, feature extraction of the GLCM method and identification with the KNN method. The success rate of identifying Orchidaceae or orchids reaches 80% with an average of 77%. K value influences the success rate of identification, the greater the K value the smaller the accuracy.