Reviewerly/exHarmony huggingface dataset:
Measurement of form and mid-spatial-frequency errors of specular freeform surfaces

The manufacturing of optical components introduces varying surface errors with diverse impact to the optical performance. In this paper we propose a measurement technique to detect form and mid-spatial-frequency errors of specular freeform surfaces. Results from simulation and experimental measurements are presented. The aim of a manufacturer of optical components is to produce its products as precise as possible according to the given parameters of the designer. However, errors due to the manufacturing process are not avoidable. Regarding surface deviations, one distinguishes between form deviations, mid-spatial-frequency errors and roughness. The proposed measurement technique in this paper is able to detect form and mid-spatial-frequency errors in one measurement. Therefore, the investigated surface is scanned with a single laser beam. The direction if the reflected beam is measured using Experimental Ray Tracing. From the direction of the incident and the reflected beam, the surface gradient at the investigated position can be determined. Proper integration methods lead to the reconstruction of the surface. Knowing the model of the investigated optical components, the form deviations and the mid-spatial-frequency errors can be calculated. Considering the model as unknown, the mid-spatial frequency-errors can still be determined, by separating the mid-spatial-frequency components from the low-frequency form information of the reconstructed surface. In this paper we propose a measurement technique for the measurement of form and mid-spatial-frequency errors of specular freeform surfaces. The measurement principle as well as results from simulation and experimental measurements of freeform surfaces are shown and evaluated.
Airport Aircraft Detection Based on Local Context DPM in Remote Sensing Images

Airport aircraft detection is a research hotspot in the field of automatic target detection in optical remote sensing images. The existing detection methods generally have low efficiency and poor detection accuracy due to the uncertainty of aircraft scale and orientation and to the complexity of airport remote sensing images. To address these problems, this paper presents an effective aircraft detection framework called Local Context DPM (LC-DPM). Our method is conducted in two main stages. (1) During aircraft candidate region extraction, we propose a non-flat region extraction method and a regular region elimination method to extract aircraft candidate regions in airport. (2) We propose LC-DPM during identification of the aircraft candidate region, we achieve accurate identification by constructing the local context histogram of oriented gradients (HOG) feature pyramids, which combine the local context information and HOG features of the aircraft targets. In addition, in order to further improve the efficiency of the method, we use the circle- frequency filter to predict the orientation of the suspected aircraft target, before the identification at various orientations based on LC-DPM. We test the proposed method on complex airport area sets with varying types of aircraft. The experimental results show that the proposed method is both highly accurate and computationally efficient.
Detecting the Detection Limit

Detection limits are highly controversial in practice despite decades of developmental efforts by analytical scientists and regulators. This high resistance to obtaining effective and accepted detection limit solutions indicates our collective failure in how we traditionally approach this problem. The analytical chemist approaches it from analytical science, the statistician from statistical theory, the regulator from a desire for regulatory needs, the instrument manufacturer from the standpoint of selling instruments, the contract laboratory from the standpoint of selling services, and the regulated from the standpoint of reducing the cost of regulation. This exquisite mix of science, misapplication of science, regulatory needs, and business concerns highlights why serious trouble abounds. Figuring out how to simultaneously address these constraints is not a trivial task. Our journey to navigate the detection limit obstacle course successfully begins with a broad view of the problem focused on developing a conceptual understanding of key detection limit concepts and limitations. Throughout the presentation, key detection limit concepts, including the problems caused by our natural biases, will be explored. Issues in detection limit and rule-set application will be probed because it is at the application level that detection limit standards will succeed or fail. This presentation does not require anything other than a rudimentary knowledge of statistics. Although statistics plays a key role in any good detection limit definition, statistics needs to be the last and final piece of solving the detection limit puzzle. In fact, to succeed, statistical practice must come last. Statistics must not drive the definition process; the definition and the problem environment must drive the statistical process's development.
A Decomposition Method of Object Transfiguration

Existing deep learning-based object transfiguration methods are based on unsupervised image-to-image translation which shows reasonable performance. However, previous methods often fail in tasks where the shape of an object changes significantly. In addition, the shape and texture of an original object remain in the converted image. To address these issues, we propose a novel method that decomposes an object transfiguration task into two subtasks: object removal and object synthesis. This prevents an original object from affecting a generated object and makes the generated object better suited to the background. Then, we explicitly formulate each task distinguishing a background and an object using instance information (e.g. object segmentation masks). Our model is unconstrained by position, shape, and size of an original object compared to other methods. We show qualitative and quantitative comparisons with other methods demonstrating the effectiveness of the proposed method.
Research on Image Segmentation Method Based on Fuzzy Clustering

Abstract Image segmentation is to divide an image into several continuous non-overlapping regions with similar characteristics, such as gray level, color and texture information. Through image segmentation, we can extract specific objects from complex background. The image has great uncertainty and fuzziness. Traditional segmentation methods often fail to achieve ideal segmentation effect, which directly affects the subsequent target feature extraction and result analysis. However, the fuzzy theory can well describe the characteristics of the image. Therefore, more and more scholars have studied image segmentation and proposed various related segmentation methods. Firstly, this paper analyses the hierarchical structure of image engineering. Then, this paper analyses the basic concepts of the fuzzy clustering algorithm. Finally, this paper analyses the image segmentation effect of the fuzzy algorithm by experiments.
JavaScript Function in Creating Animations

In many cases, the focus is to create dynamically-driven animations while avoiding excessive use of keyframes. In such a situation, expressions can be utilized. Provided are a number of functions for a certain automation in 2D/3D animation for the effect of counting numerical values and movement in 2D/3D space on the surface of a sphere and ellipsoid. Various pitfalls will be explored regarding direct control of an expression’s speed or frequency and potential solutions to overcome the issues. In this paper we present specific solutions as expressions in JavaScript for the purposes of automating animation.
MLCA: A Multi-label Competency Analysis Method Based on Deep Neural Network

The goal of human resource management is to select the right people to the right positions, no matter by recruitment, assessment or promotion. To achieve this goal, competency analysis is an effective way. We can obtain the employee’s competency and the position’s requirements by the analysis. The competency analysis also provide a strong intellectual support in the downstream works, such as assessing or promoting employees, or establishing employee files. The multi-label text classification model, which is proposed in this paper based on deep neural network, can successfully complete the competency analysis, and its performance is much better than the current text multi-label classification method. We also construct a multi-label classification dataset in human resource field, which is the first one focused on competency analysis, as far as we know.
Verifying the Security of Enclaved Execution Against Interrupt-based Side-channel Attacks

Computing platforms sometimes provide hardware support for enclaves or trusted execution environments. On such platforms, security critical code can execute in an enclave or secure world, isolated from all other software on the platform.
Irreducibility of extensions of Laguerre polynomials

For integers $a_0,a_1,\ldots,a_n$ with $|a_0a_n|=1$ and either $\alpha =u$ with $1\leq u \leq 50$ or $\alpha=u+ \frac{1}{2}$ with $1 \leq u \leq 45$, we prove that $\psi_n^{(\alpha)}(x;a_0,a_1,\cdots,a_n)$ is irreducible except for an explicit finite set of pairs $(u,n)$. Furthermore, all exceptions other than $n=2^{12},\alpha=89/2$ are necessary. The above result with $0\leq\alpha \leq 10$ is due to Filaseta, Finch and Leidy and with $\alpha \in \{-1/2,1/2\}$ due to Schur.
Recommendation Algorithm for Potential Candidates in Human Resources Based on the Integration of Dynamic Decision Diagram

A recommendation algorithm is put forward for the potential candidate recommendation technology in human resources. The dynamic decision diagram is combined in this method based on collaborative filtering technology and partition clustering technology. Firstly, the computational matrix that integrates the dynamic decision diagram recommendation algorithm is established so that the algorithm can recommend information in reference to the matrix. Secondly, the assignment range of the matrix is improved, so that the recommendation algorithm can integrate the evaluations of all users. Finally, the evaluation value and the update coefficient are added, which makes the dynamic update of the algorithm possible, thus recommending the most satisfactory information to the users. On this basis, an advanced recommendation algorithm based on partition clustering is proposed, which has further improved the precision and real-time performance of the algorithm. In addition, experiments are carried out to verify that the ultimate commendation algorithm based on partition clustering is the optimal recommendation algorithm for potential candidates in human resources, which can provide the most satisfactory information to users.
Public Outcomes of Publicly Funded Socio-Technical Projects

In this short essay, we present our reflections on public outcomes of publicly funded socio-technical projects in relevance to discussions on empowerment, participation, and researcher responsibility in the field of Human Computer Interaction (HCI). Our work offers a reflective analysis and raises critical questions for further communal discussion about the life of research projects when the funding ends. When a publicly funded research project for empowering users comes to an end, how do we as HCI researchers measure the social impact of our interventions? What are the public outcomes and what are our responsibilities after we leave the field? To address these questions, first, we introduce two publicly funded socio-technical projects, situate our work with respect to current discourse on empowering users in HCI, present findings and insights based on our analysis of the projects, and conclude with a set of questions that are intended to further communal discussion.
Implementation bayes network based for classification study of completion

Abstract Many factors influence the completion of the study. However, it is likely that some variables are dependent on each other. Method Bayes Network is used because it can illustrate the cause-effect relationship between completion of study with the factors that allegedly influenced as a graphical model representation so that more easily understood. With empirical data, the presence or absence of relationships among factors is calculated using mutual information. If the structure of the network has been established then subsequently calculated the probability of relationship for each event. The probability value obtained is shown to obtain a complete causality diagram. The results of the research on the classification process using student data in 2012 with Bayes Network method resulted in an accuracy of 71.09%.
About the values of generating polynomials of cyclotomic classes

Abstract The trace representation of sequences is a powerful tool for the analysis and for the design of pseudorandom sequences. Z. Dai et al. (2011) reduce the problem of determining trace representation of series of binary e th power residue sequences to that of determining the values of generating polynomials of cyclotomic classes. We derive the values of generating polynomials of cyclotomic classes of order 4, 6, 8 and consequently solve three problems pointed by Z. Dai et al. In fact, we study the discrete Fourier transform of cyclotomic sequences of order 4, 6, 8.
Semantic Bayesian Network

In spite of the fact that BN is inherently capable of representing, learning, and reasoning with uncertain knowledge , the performance of BN highly depends on the size of available training dataset. A proper learning of the network needs large amount of observed data be available during the training procedure. Otherwise, it may result in strongly biased inference due to parameter learning uncertainty . The recent research indicates that a prior knowledge about the respective domain may help in reducing such epistemic uncertainty . This chapter discusses on semBnet, a semantically enhanced Bayesian network model, which has inherent capability of incorporating external knowledge over the associated domain. Consequently, semBnet is potential enough to model spatio-temporal relationships in an improved manner with incorporated domain knowledge . The performance of semBnet-based prediction model has been evaluated in comparison with a number of conventional statistical and state-of-the-art space-time prediction models, with respect to a case study on climatological time series data . Experimental result demonstrates the superiority of semBnet over the other models considered.
Target Assignment for Anti-Ship Missile Groups based on Fuzzy Cognitive Maps

The problem of assigning a group of anti-ship missiles to the targets in a fleet is studied in this paper. To describe this problem, mathematical models of cooperative target assignment, path planning, ship defense and missile attacking are built at first. Then, multi-targets assignment method is proposed by considering the cooperative defense of warships in formation. A two-level decision-making model based on fuzzy cognitive maps is constructed to evaluate the attack efficiency under the condition of cooperative defense. Finally, the effectiveness of the target assignment approach is verified by the simulations. The results show that the proposed method can obtain a reasonable solution for the missile target assignment problem, which can make it have a strong application prospect.
3D Model Visualization for Brain Tumour using MRI Images

Abstract The inaccurate prediction of tumour is because of the 2D images do not present the complete natural tumour representation as in 3D visualization. In order to construct the 3D model, the contour of 2D MRI images must be merged together. In this work, the edge detection of brain tumour on 2D images is done using the Geodesic Active Contour (GAC) model based on Additive Operator Splitting (AOS), then 3D model is visualized using Image Manifold (IM) and Volume Estimation (VE) methods. The comparison of performance for both methods is calculated. In conclusion, the execution time for VE method is higher than IM method since more images are used in VE method to calculate the volume of the tumour accurately. In terms of iteration number, Volume Estimation has a higher number of iterations, but for error and root mean square error, both methods have roughly the same value.
Changing ACM computer communication review (1988--1991)

A brief description of how SIGCOMM's quarterly newsletter evolved into an entry-level journal.
New Classes of Efficient MDS Transformations

MDS transformation plays an important role in resisting against differential cryptanalysis (DC) and linear cryptanalysis (LC). Recently, M. Sajadieh, et al.[15] designed an efficient recursive diffusion layer with Feistel-like structures. Moreover, they obtained an MDS transformation which is related to a linear function and the inverse is as lightweight as itself. Based on this work, we consider one specific form of linear functions to get the diffusion layer with low XOR gates for the hardware implementation by using temporary registers. We give two criteria to reduce the construction space and obtain six new classes of lightweight MDS transformations. Some of our constructions with one bundle-based LFSRs have as low XOR gates as previous best known results. We expect that these results may supply more choices for the design of MDS transformations in the (lightweight) block cipher algorithm.
Square attack on 4 round Midori64

Midori64 is a block cipher, which is resistant to integral attacks since it was obtained a 3.5 round integral distinguisher using only one active nibble. In this work, we performed the integral attack which is a square attack on Midori64 using all possible positions and amount of active nibbles, under the assumption that all active nibbles have the same permutation. The research gave 3 types of integral distinguisher, i.e., 3.5 round integral distinguisher, 2.5 round integral distinguisher, and 1.5 round integral distinguisher for certain scenario. It is proved that a 3.5 round integral distinguisher can be used to recover the subkey of the fourth-round; meanwhile a 2.5 round integral distinguisher can be used to recover the subkey of the third round key. From the two subkeys which are recovered on the fourth and third rounds, we can derive the input key (seed) using key scheduling algorithm. The data complexity to recover the seed is 3.24+4.24, for which the time complexity of 3.212+214 is needed.
An Automatic Grabbing Method of Manipulator Based on Monocular Ranging

Abstract Most existing studies on manipulator focus on the field of teaching and research. Other manipulators used for industrial production also have several disadvantages, including high cost, complicated control and low flexibility. Accordingly, in this paper, a simple automatic grabbing method of manipulator is proposed based on monocular ranging. The method is developed via the openmv vision platform. First, the target object is identified in the lens, the linear distance of the lens from the target object is calculated through monocular ranging, and then the inverse kinematics of the manipulator is exploited to calculate the rotation angle of each steering engine. Next, the contradiction between system speed and impact is balanced using the five-time curve trajectory optimization algorithm. The results of tests suggest that the method can achieve accurate capture of the target object in the grab range. The method, compared with the teaching and even the engineering manipulator system, is characterized by lower cost, simple and flexible control. Besides, it can be used to implement simple tasks in industry.
Characterizing Phenotype Abnormality by Variational Auto Encoder

Variational auto-encoder (VAE) acquires not only a low dimensional representation of the data, but also the probability distribution of the data, in its latent space. Therefore, if an input data is not from the trained category, it fails to find an appropriate point in the latent space, which leads to a poor reconstruction. Thus, it can be used for data abnormality detection. We use VAE to detect a phenotype abnormality in a biological system. When each of lethal genes in early embryogenesis is knocked down by RNAi technique, an observed abnormality can be linked to the corresponding gene function. We train VAE by the wild type (WT) data without any gene manipulation, and use to characterize a phenotype of RNAi embryo in two cell stage. Abnormality is defined by a data reconstruction error, and several genes are found whose absence causes the abnormality, including some already known genes.
Development of a Virtual Reality-Based Working at Heights Safety Awareness Framework

Working at heights leads to serious injuries and deaths globally each year in part, due to poor working conditions, improper use of protective equipment, and lack of training. Currently, safety is enforced by requiring mandatory working at heights certification whereby trainees take written and practical tests in regular classrooms or specialized facilities (if available), to become licensed/certified. However, despite training, certification, and policy efforts, a number of accidents are still occurring when working at heights. In this paper, we present a virtual reality-based simulation that safely exposes and immerses trainees to more realistic training at heights scenarios. The simulation also includes a practical test component that involves 100% tie-off travel restraint on top of a building.
Video Retrieval Using Textual Queries and Spoken Text

The expanded accessibility of broadband associations has prompted an increment in the utilization of Internet webcasting. Most webcasts are filed and got to various times reflectively. One test to skimming through such documents is the absence of text transcripts of the webcasts channel. Translation of addresses is a testing task, both in acoustic and in dialect illustrating. Recording lectures and putting them on the Web for access by under studies has turned into a general pattern at different colleges. To take full pick up of the information database that is manufactured by these records involved inquiry usefulness that must be given that goes past pursuit on meta-information level, however, performs an itemized examination of the relating multimedia reports. Videos and Texts demonstrated in lecture are nearly identified with the substance of the lecture, gives an important source for recovering lecture videos and indexing. Text substance may be separated, then analyze and deducted consequently by OCR (Optical Character Recognition) strategies. In this paper, for remedying lapses in the OCR Transcriptions, we investigated two separate systems connected just to unmatched question words. In any case, methodology delivers another arrangement of n-gram strings to coordinate the unedited OCR Transcriptions. These n-grams incorporate strings with an altered separation of 1 character and all conceivable n-gram substrings with no less than 3 characters. The second system for redressing OCR includes the word reference of spelling adjustment strategy gave in MS Words. The peculiarities of MS Word 2000, an OCR perceived string was extended through an application program interface into its corrected spellings. An exceptionally progressive style, just growing words that MS Word had flagged as erroneously spelled which we have depicted before. This significantly decreased the number of spurious word competitors and maintained a strategic distance from false matches.