Reviewerly/exHarmony huggingface dataset:
Random locations of periodic stationary processes

We consider a family of random locations, called intrinsic location functionals, of periodic stationary processes.This family includes but is not limited to the location of the path supremum and first/last hitting times.We first show that the set of all possible distributions of intrinsic location functionals for periodic stationary processes is the convex hull generated by a specific group of distributions.We then focus on two special subclasses of these random locations.For the first subclass, the density has a uniform lower bound; for the second subclass, the possible distributions are closely related to the concept of joint mixability.
Identification of Cervical Cancer Cells using Backpropagation

Abstract Cervical cancer is the growth of abnormal cells in the cervix. In general, cervical cancer can be early detected through Pap smear. This examination aims to see the existence of changes in cell shape from digital microscopic images, but it is still manually performed. Therefore, a method is required to identify the cell of cervical cancer automatically. Under this research, the proposed method consists of three primary processes. The initial stage is image input. The process continues to the pre-processing phase which involves scaling, grey-scaling, median filter and thresholding. The final step is identification process using Backpropagation to determine the normal and abnormal cells of cervical cancer. In this research, the training and testing processes used 40 data and 20 data respectively. According to the conducted test, 18 data were correctly identified as the expected output. The study concluded the proposed method performs well in identifying the cervical cancer cells with an accuracy rate of 90%.
FPGA vs. SIMD: Comparison for Main Memory-Based Fast Column Scan

The ever-increasing growth of data demands reliable data-base system with high-throughput and low-latency. Main memory-based column store database systems are state-of-the-art on this perspective, whereby data (values) in relational tables are organized by columns rather than by rows. In such systems, a full column scan is a fundamental key operation and thus, the optimization of the key operation is very crucial. This leads to have compact storage layout based fast column scan techniques through intra-value parallelism. For this reason, we investigated on different well-known fast column scan techniques using SIMD (Single Instruction Multiple Data) vectorization as well as using Field Programmable Gate Arrays (FPGA). Moreover, we present selective results of our exhaustive evaluation. Based on this evaluation, we find out the best column scan technique as per implementation mechanism–FPGA and SIMD. Finally, we conclude this paper via mentioning some lessons learned for our ongoing research activities.
Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs

Multi Word Expressions (MWEs) are one of the most widely used term in linguistics which mainly deals with combination of words rather than single word. In Hindi language, MWEs have become significant and popular for text processing and research related activities. The nicety of any term in linguistics is justified by using statistical measures. Many of these statistical measures are based on frequency of occurrence of a particular word pattern in a corpus. Syntactic fixedness is one of the important statistical measure, which can be used for measuring the degree of lexical and syntactic restrictiveness in the MWEs extraction and analysis process. This paper mainly focuses on evaluating the degree of lexical and syntactic fixedness and justifying their role for Hindi MWEs. The corpus used for experimental purpose is collected from the famous Hindi novel “Godaan”. Total 36 text files of the novel are used for the evaluation purpose. The degree of lexical and syntactic fixedness are measured for many classes of 2-grams Hindi MWEs and results are analyzed for accuracy.
Efficient learning of neighbor representations for boundary trees and forests

We introduce a semiparametric approach to neighbor-based classification. We build off the recently proposed Boundary Trees algorithm by Mathy et al. (2015) which enables fast neighbor-based classification, regression and retrieval in large datasets. While boundary trees use an Euclidean measure of similarity, the Differentiable Boundary Tree algorithm by Zoran et al. (2017) was introduced to learn low-dimensional representations of complex input data, on which semantic similarity can be calculated to train boundary trees. As is pointed out by its authors, the differentiable boundary tree approach contains a few limitations that prevents it from scaling to large datasets. In this paper, we introduce Differentiable Boundary Sets, an algorithm that overcomes the computational issues of the differentiable boundary tree scheme and also improves its classification accuracy and data representability. Our algorithm is efficiently implementable with existing tools and offers a significant reduction in training time. We test and compare the algorithms on the well known MNIST handwritten digits dataset and the newer Fashion-MNIST dataset by Xiao et al. (2017).
Hybrid Algorithm for Small Doppler Shifts Evaluation

Improving performance of Doppler shift evaluation could be generally attempted by enhancement of hardware or software. The costly and inflexible nature of the former motivates us to gather as much as possible from the signal processing. The aim of our research has been to develop a real-time algorithm improving precision of Doppler shift evaluation for a standard, low-cost CW (continuous wave) Doppler radar. Emphasis has been put on close-to-zero Doppler shifts (i.e. low velocities), where standard algorithms usually do not provide accurate and reliable results. The proposed algorithm uses simple neural networks in order to provide the approximate estimate of the Doppler shift. According to their results, a proper processing method is chosen for the evaluation of the accurate Doppler shift value.
Hierarchical Competition Framework for Particle Swarm Optimization

Particles in PSO algorithms evolve only in one group, or in many groups but without interaction between different groups. Inspired by the concept of social class evolution process, a hierarchical competition framework is proposed in this paper. Through the competition mechanism, particles can flow dynamically between different levels, and this can reduce the probability of top-level particles leading to a wrong direction and in this way enhance the global search ability. In this paper, the proposed framework is tested in combination with the canonical PSO and one of the most famous variant particle warm optimizers, named quantum-behaved particle swarm optimizer. All the experiments are run on the CEC’2013 benchmark function database, and the results show that the global search ability and the convergence speed are both improved compared to the basic optimizers.
An Axiomatization for Quantum Processes to Unifying Quantum and Classical Computing

We establish an axiomatization for quantum processes, which is a quantum generalization of process algebra ACP (Algebra of Communicating Processes). We use the framework of a quantum process configuration 〈p, ϱ〉, but we treat it as two relative independent part: the structural part p and the quantum part ϱ, because the establishment of a sound and complete theory is dependent on the structural properties of the structural part p. We let the quantum part ϱ be the outcomes of execution of p to examine and observe the function of the basic theory of quantum mechanics. We establish not only a strong bisimilarity for quantum processes, but also a weak bisimilarity to model the silent step and abstract internal computations in quantum processes. The relationship between quantum bisimilarity and classical bisimilarity is established, which makes an axiomatization of quantum processes possible. An axiomatization for quantum processes called qACP is designed, which involves not only quantum information, but also classical information and unifies quantum computing and classical computing. qACP can be used easily and widely for verification of most quantum communication protocols.
Are there needles in a moving haystack? Adaptive sensing for detection of dynamically evolving signals

In this paper, we investigate the problem of detecting dynamically evolving signals.We model the signal as an n dimensional vector that is either zero or has s non-zero components.At each time step t ∈ N the nonzero components change their location independently with probability p.The statistical problem is to decide whether the signal is a zero vector or in fact it has non-zero components.This decision is based on m noisy observations of individual signal components collected at times t = 1, . . ., m.We consider two different sensing paradigms, namely adaptive and non-adaptive sensing.For non-adaptive sensing, the choice of components to measure has to be decided before the data collection process started, while for adaptive sensing one can adjust the sensing process based on observations collected earlier.We characterize the difficulty of this detection problem in both sensing paradigms in terms of the aforementioned parameters, with special interest to the speed of change of the active components.In addition, we provide an adaptive sensing algorithm for this problem and contrast its performance to that of non-adaptive detection algorithms.
Semantic matchmaking as a way for attitude discovery

Powerful data analysis techniques are currently applied to 3D motion sensing devices like Microsft Kinect for posture and gesture recognition. Though effective, they are computationally intensive and require complex training. This paper proposes an approach for on-the-fly automated posture and gesture recognition, exploiting Kinect and treating the detection as a semantic-based resource discovery problem. A proper data model and an ontology support the annotation of body postures and gestures. The proposed system automatically annotates Kinect data with a Semantic Web standard logic formalism and then attempts to recognize postures by applying a semantic-based matchmaking between descriptions and reference body poses stored in a Knowledge Base. In addition, sequences of postures are compared in order to recognize gestures. The paper presents details about the prototype implementing the framework as well as an early experimental evaluation on a public dataset, in order to assess the feasibility of both ideas and algorithms.
Paper-based vs. AI computer-based test results

The concept of this paper focuses on the area of testing university students while using tools of artificial intelligence. The sample set of students were students of the third year of bachelor studies of Applied Informatics and the test was focused on testing English: grammar, vocabulary, IT English. The introductory part describes current possible ways of testing students, mainly in paper- or computer-based tests. The paper-based method is the traditional one while the computer-based one can take advantage of modern tools of artificial intelligence, such as expert systems and neural networks. The core of this paper lies in the comparison of the results that are acquired from paper-based and computer-based testing which uses artificial intelligence tools. The final part envisages the most optimal ways of testing university students.
A Study of an Instructional Model (ASKS Model) in Physical Education to Improve Social Skills: Focusing on Team Organization*

In school education, it is important to encourage students to improve their social skills. Umegaki et al. (2016b) have developed an instructional model known as the Acquisition of Social Knowledge in Sport (ASKS) Model for facilitating improvement in social skills in the context of physical education. They consider that the ASKS Model with heterogeneous team organization would improve social skills that would be applicable to daily life outside of physical education classes and help students to maintain these social skills. However, no previous study has examined whether the ASKS Model would be effective for homogeneously organized teams. Therefore, the present study was designed to examine the type of team organization that would be most effective for the ASKS Model by comparing physical education classes with the ASKS Model based on homogeneous teams and heterogeneous teams. The study focused on physical education classes for male students in the second year of junior high school. The classes included those without the ASKS Model, those with the ASKS Model based on homogeneous teams, and those with the ASKS Model based on heterogeneous teams. A formative evaluation of friendship-building and the KiSS-18 questionnaire on paper were administered before and after each class. The study confirmed two points: First, the ASKS Model appeared to be effective when heterogeneous teams were organized. Second, the effectiveness was suggested to be improved when heterogeneity of motor skill was maintained rather than heterogeneity of social skills.
Pupil Segmentation Method Based on Treasure Theory and Region Growing

Aiming at the problems of blurred pupil edge and occlusion of upper and lower eyelids in low resolution images, a heuristic pupil region segmentation method is proposed, which mainly uses the gray level features of pupil region to locate. Firstly, N points are randomly initialized on the whole graph. Each point is iterated in a random step and direction to find the location of the pixel whose gray value is less than the global minimum gray value. When all points in the graph are not moving, all points are selected as seed stores to grow the region. The region after growing is the pupil region. Finally, isolated points are removed by morphological changes. And fill the hole. The results show that the algorithm can overcome the pupil pose change, eyelid, eyelash occlusion and other problems, and show good robustness and accuracy compared with other algorithms.
The Improvement of Technology Commercialization Performance of Public Research Institutes in Information and Technology : Focused on Equity based Technology Transfer

본 연구에서는 지분형 기술이전이 미진한 원인을 파악하고 지분형 기술이전을 활성화시키기 위한 정부정책/기관 측면의 방안을 제시한다. 이를 위해 전문가 FGI를 활용하여 연구를 수행하였다. 연구결과, 주요 문제점으로는 법·제도의 미흡, 기업가정신 부족, 사업화 역량 부족, 사후관리 부족 등이 지적되었다. 따라서 지분형 기술이전을 활성화하기 위해 정책적 측면으로는 기술료 관련 법령의 근거규정 명확화, 연구자 및 기술이전 참여자 인센티브 강화 등이 필요하다. 기관운영 측면에서는 기술사업화 사후지원 강화, 단위조직들의 명확한 전략과 목표 수립, 민간 전문기관(기술거래기관, 중개기관, 기술평가기관)과의 협업 활성화가 필요하다. 본 연구는 초기 연구단계인 지분형 기술이전에 대한 학문적 토대를 제공하는 것을 주목적으로 하지만 유관 정부부처의 정책수립과 공공연구기관의 기술사업화 전략수립에도 기여할 것으로 예상된다.This study conducted a research using the expert FGI to identify the cause of equity transfer, and to suggest policy and institutional aspects to promote equity-based technology transfer. As a result of the research, the main problems were insufficient laws and institutions, lack of entrepreneurship, insufficient commercialization capacity, and lack of follow-up management. In order to promote equity-based technology transfer, in terms of policy, it is necessary to clarify basis rules, reinforce incentives for participants, establish standards for institutional evaluation, reflect performance, and establish support projects. In terms of institutional operation, it was necessary to support the life-cycle of technology commercialization, to establish institutional level strategy, to strengthen cooperation with private professional organizations. This study is expected to contribute to the establishment of the policy of the related ministries and the establishment of the technology commercialization strategy of the public research institute..
Offline fitting Markov switching model

Markov regime switching models remain enormously popular in speech recognition, economics, finance, etc. Nonparametric segmentation in switching models without probability assignment of jump moments is in many papers by Brodsky and Darkhovsky. We model all regimes as long SCOT strings. Stochastic C Ontext Tree (abbreviated as SCOT) is m-Markov Chain (m-MC) with every state of a string independent of the symbols in its more remote past than the context of length determined by the preceding symbols of this state. A parallel super-fast fitting and asymptotically optimal inference in a sparse SCOT model including the nonparametric homogeneity test are described in our previous papers. Our segmentation method is a combination of preliminary online change point detection with its subsequent offline Maximal Likelihood update.
Biodigital

"Biodigital" is a Sci-Fi interactive story set in the year 2117 that combines VR film, immersive 3D environments, and VR data visualization. It turns data into a cinematic experience where a user is enmeshed as a character in the story. This VR storytelling tells the tale of humanity a hundred years from now. It also encourages us to think "How should we live in the future?"
Line Codes Generated by Finite Coxeter Groups

Using an algebraic approach based on the theory of Coxeter groups, we design, and describe the performance of, a class of line codes derived from permutation modulation, useful for parallel transmission of b bits over b + 1 wires, and admitting especially simple encoding and decoding algorithms. With these codes, resistance to common-mode noise is obtained by using codewords whose components sum to zero, simultaneous switching output noise is reduced by using constant-energy signals, and the effects of intersymbol interference are reduced by having decisions based on only two values at the input of the final slicers. Codebook design is based on the theory of Group Codes for the Gaussian Channel, as specialized to Coxeter matrix groups generated by reflections in orthogonal hyperplanes. A number of designs are exhibited, some of them being novel or improving on previously obtained codes.
Xploro

Xploro is an educational game for iOS which combines augmented-reality (AR) technology and spatial computing multi-user game-play mechanics. It was created by the UTS Animal Logic Academy (UT-SALA) 2018 cohort to educate children aged 8--12 in a fun and social way. Xploro uses emerging augmented reality technology to create the hide and seek fun of "Wheres Wally" alongside educational aspects similar to "Carmen Sandiego".
Inter-frame Relationship Graph Based Near-Duplicate Video Clip Detection Method

The detection of Near-Duplicate Video has been studied by many scholars in recent years. This paper proposes a method for classifying and identifying infringing video by extracting video visual features, obtaining inter-frame relationship graphs, and using CNN classification network. We apply the improved weighting technique of text mining to video solution and still achieve better results. This method is robust in experiments and does not require retraining of a model in practical applications. At the same time, this method can also be applied to large-scale video retrieval. Our method has an F1 score of 95.74%.
A Frequent Itemset Hiding Toolbox

Advances in data collection and storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous amounts of data to be stored efficiently. Useful knowledge can be mined from these data, which can be used in several ways depending on the nature of the data. Quite often companies and organizations are willing to share data for the sake of mutual benefit. However, the sharing of such data comes with risks, as problems with privacy may arise. Sensitive data, along with sensitive knowledge inferred from this data, must be protected from unintentional exposure to unauthorized parties. One form of the inferred knowledge is frequent patterns mined in the form of frequent itemsets from transactional databases. The problem of protecting such patterns from being discovered, is known as the frequent itemset hiding problem. In this paper we present a toolbox, which provides several implementations of frequent itemset hiding algorithms. Firstly, we summarize the most important aspects of each algorithm. We then introduce the architecture of the toolbox and its novel features. Finally, we provide experimental results on real world datasets, demonstrating the capabilities of the toolbox and the convenience it offers in comparing different algorithms.
Unsupervised Dimension Selection Using a Blue Noise Graph Spectrum

Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed spaces. Consequently, dimension selection (DS) aims to solve the combinatorial problem of identifying the top-k dimensions, which is required for effective experiment design, reducing data while keeping it interpretable, and designing better sensing mechanisms. In this paper, we develop a novel approach for DS based on graph signal analysis to measure feature influence. By analyzing synthetic graph signals with a blue noise spectrum, we show that we can measure the importance of each dimension. Using experiments in supervised learning and image masking, we demonstrate the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high dimensional spaces, using only a small subset of the original features.
Efficient Ballot Casting in Ranked Based Voting System Using Homomorphic Encryption

Elections conducted on paper consumes many resources. Online voting system is very faster, cheaper and more suitable. Recent in online voting system improve the security guarantees for elections, because of confidentiality of voters and their integrity and validity. For security purpose, three election models are used for online voting: the mix-net model, the blind signature model, and the homomorphic encryption model. However only homomorphic encryption gives direct tallying without decrypting every votes. In this paper, we are focusing on ballot casting and tallying for ranked based voting system using Paillier homomorphic and Elgamal homomorphic encryption schemes and at the end we will compare results of both encryption schemes.
Asynchronous COMID: The Theoretic Basis for Transmitted Data Sparsification Tricks on Parameter Server

Asynchronous FTRL-proximal and L2 norm done at server are two widely used tricks in Parameters Server which is an implement of delayed SGD. Their commonness is leaving parts of updating computation on server which reduces the burden of network via making transmitted data sparse. But above tricks’ convergences are not well-proved. In this paper, based on above commonness, we propose a more general algorithm named as asynchronous COMID and prove its regret bound. We prove that asynchronous FTRL-proximal and L2 norm done at server are applications of asynchronous COMID, which demonstrates the convergences of above two tricks. Then, we conduct experiments to verify theoretical results. Experimental results show that compared with delayed SGD on Parameters Server, asynchronous COMID reduces the burden of the network without any harm on the mathematical convergence speed and final output.