Reviewerly/exHarmony huggingface dataset:
Exploring the usability of the text-based CAPTCHA on tablet computers

This paper analyses and discusses the usability aspect of the text-based CAPTCHA in terms of response time and success in solving the CAPTCHA on tablet computers. The response time is the time spent by the user to find a solution to the CAPTCHA. The analysis is separately conducted on text-based CAPTCHA with only text and numbers. Then, the results are compared and the differences in response time and success in solving the two types of CAPTCHA are underlined. This is accomplished by asking 125 Internet users to solve the text-based CAPTCHA on the tablet computer. Their gender, age, education level, Internet experience, response time and success in solving two types of text-based CAPTCHA are collected in a dataset. Then, advanced statistical analysis by association rule mining is performed. It shows the dependence of the response time and success in solving the CAPTCHA on co-occurrence of gender, age, education level and Internet experience and the strength of this dependence by support, confidence and lift measures. This study provides relevant information for designing new CAPTCHAs which may be more accustomed to specific types of Internet users.
Recursive Maximum Margin Active Learning

Active learning is an important technique to alleviate the problem when there is abundant unlabeled data but scarce labeled data. It aims to choose the most valuable samples to label in order to build powerful predictive models with minimal supervision. However, under the setup of active learning, when the data are characterized by high-dimensional features, it will be difficult to get a reliable estimate on the model parameters, as the labeled data are limited. Most existing works tackle this problem by learning a low-dimensional representation of data before active learning, but it cannot guarantee to obtain promising results, as traditional feature extraction techniques and active learning algorithms are designed independently. In this paper, we propose an efficient hybrid active learning algorithm, called Recursive Maximum Margin Active Learning. We optimize the active learning and semi-supervised feature extraction under a unified framework to tackle the high-dimensional features' problems and select the most representative samples in the low-dimensional space. By introducing the semi-supervised maximum margin criterion into active learning, we can conduct sample selection and feature extraction recursively at each iteration of active learning to learn more accurate models. The extensive experimental results show that the proposed method outperforms several state-of-the-art active learning methods on publicly available datasets.
Assessing transferability of adversarial examples against malware detection classifiers

Machine learning (ML) algorithms provide better performance than traditional algorithms in various applications. However, some unknown flaws in ML classifiers make them sensitive to adversarial examples generated by adding small but fooled purposeful distortions to natural examples. This paper aims to investigate the transferability of adversarial examples generated on a sparse and structured dataset and the ability of adversarial training in resisting adversarial examples. The results demonstrate that adversarial examples generated by DNN can fool a set of ML classifiers such as decision tree, random forest, SVM, CNN and RNN. Also, adversarial training can improve the robustness of DNN in terms of resisting attacks.
Null Objects in Korean: Experimental Evidence for the Argument Ellipsis Analysis

Null object (NO) constructions in Korean and Japanese have received different accounts: as (a) argument ellipsis ( Oku 1998 , S. Kim 1999 , Saito 2007 , Sakamoto 2015 ), (b) VP-ellipsis after verb raising ( Otani and Whitman 1991 , Funakoshi 2016 ), or (c) instances of base-generated pro ( Park 1997 , Hoji 1998 , 2003 ). We report results from two experiments supporting the argument ellipsis analysis for Korean. Experiment 1 builds on K.-M. Kim and Han’s (2016) finding of interspeaker variation in whether the pronoun ku can be bound by a quantifier. Results showed that a speaker’s acceptance of quantifier-bound ku positively correlates with acceptance of sloppy readings in NO sentences. We argue that an ellipsis account, in which the NO site contains internal structure hosting the pronoun, accounts for this correlation. Experiment 2, testing the recovery of adverbials in NO sentences, showed that only the object (not the adverb) can be recovered in the NO site, excluding the possibility of VP-ellipsis. Taken together, our findings suggest that NOs result from argument ellipsis in Korean.
Punctured Bent Function Sequences for Watermarked DS-CDMA

In this letter, we investigate the effect of inserting some randomly generated watermarking chips into known spreading sequences in terms of periodic correlations; moreover, we give two design criteria for good watermarked sequences in the sense of: 1) reducing the average correlation value and 2) minimizing the variance of correlations. For n = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> with even m, we propose a set of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-1</sup> punctured bent function sequences of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> punctured by the Singer difference set. The maximum non-trivial correlation magnitude of the proposed set turns out to be 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + 1, which is asymptotically two times the Welch bound.
Research on Hybrid Recommendation Model Based on PersonRank Algorithm and TensorFlow Platform

At present, the borrowing data of university libraries in western China generally has the problem of large sparseness and inaccurate recommendation results. However, the traditional recommendation algorithm does not solve the problem. Therefore, solving the data sparsity problem and improving the recommendation accuracy has become a very important issue. Through research, the author proposes a hybrid recommendation model that combines the PersonRank algorithm with the neural network trained by Google's artificial intelligence framework TensorFlow in the context of excessive data sparseness in western college libraries. First, using the existing borrowing records to model through the bipartite graph probability random walk method, the author obtains a list of books that the reader may be interested in and calculates the probability value of interest. After that, the author converts the probability value into a pseudo-scoring table, and uses TensorFlow to calculate by pseudo-scoring, and supplements the data training set and book classification information to obtain the recommendation result. The model effectively solves the problem of data sparsity in the recommendation system. Finally, it compares with the traditional recommendation algorithm and an improved recommendation algorithm. The accuracy of the model is evaluated by AUC index, and the accuracy of the proposed model is higher.
Synthesizing Cluster Management Code for Distributed Systems

Management planes for data-center systems are complicated to develop, test, maintain, and evolve. They routinely grapple with hard combinatorial optimization problems like load balancing, placement, scheduling, rolling upgrades and configuration management. To tackle these problems, developers are left with two bad choices: (i) develop ad-hoc mechanisms for systems to solve these optimization problems, or (ii) use specialized solvers that require steep engineering effort.
A Multi-population QUasi-Affine TRansformation Evolution Algorithm for Global Optimization

In this paper, we propose a new Multi-Population QUasi-Affine TRansformation Evolution (MP-QUATRE) algorithm for global optimization. The proposed MP-QUATRE algorithm divides the population into three sub-populations with a sort strategy to maintain population diversities, and each sub-population adopts a different mutation scheme to make a good balance between exploration and exploitation capability. In the experiments, we compare the proposed algorithm with DE algorithm and QUATRE algorithm on CEC2013 test suite for real-parameter optimization. The experimental results indicate that the proposed MP-QUATRE algorithm has a better performance than the competing algorithms.
Intelligent ERP Based Multi Agent Systems and Cloud Computing

The use of agents makes ERP systems more intelligent. They provide capabilities to independently interact with their surrounding environment and performing autonomous actions while cooperating with other systems Regarding the cloud computing, its use is almost obvious. It provides access to full-function applications at a rational cost without a substantial upfront for hardware and software spending. In this paper, we propose an Intelligent ERP system based on new technologies: agents and cloud computing. This combination makes ERP users (managers, customers, employees, etc.) expect data to be made available to them via the most widely used communication mobile device.
KALD: Detecting Direct Pointer Disclosure Vulnerabilities

Modern operating system kernels deploy Kernel Address Space Layout Randomization (KASLR) to mitigate control-flow hijacking attacks. KASLR randomizes the base addresses of the kernel's code and data segments. However, it randomizes both with a single offset and does not randomize the internal layout of either of these segments, so relative addresses remain known to adversaries. If the kernel discloses a single code or global data pointer, an adversary can therefore infer the entire layout of the kernel's code segment and bypass KASLR. In this paper, we present Kernel Address Leak Detector (KALD), a tool that finds direct disclosure vulnerabilities by statically analyzing the kernel source code. KALD can analyze the source code of modern operating system kernels and find previously unreported leaks. KALD compiles a list of functions that can leak information to user-space accessible locations, and it uses the results of a points-to analysis to determine whether individual invocations of such functions can disclose kernel pointers. We show that KALD successfully detects several direct disclosure vulnerabilities in the Linux kernel and that it is flexible enough to be useful in practice.
Rethinking General-Purpose Decentralized Computing

While showing great promise, smart contracts are difficult to program correctly, as they need a deep understanding of cryptography and distributed algorithms, and offer limited functionality, as they have to be deterministic and cannot operate on secret data. In this paper we present Protean, a general-purpose decentralized computing platform that addresses these limitations by moving from a monolithic execution model, where all participating nodes store all the state and execute every computation, to a modular execution-model. Protean employs secure specialized modules, called functional units, for building decentralized applications that are currently insecure or impossible to implement with smart contracts. Each functional unit is a distributed system that provides a special-purpose functionality by exposing atomic transactions to the smart-contract developer. Combining these transactions into arbitrarily-defined workflows, developers can build a larger class of decentralized applications, such as provably-secure and fair lotteries or e-voting.
Scaling Agile Beyond Organizational Boundaries: Coordination Challenges in Software Ecosystems

The shift from sequential to agile software development originates from relatively small and co-located teams but soon gained prominence in larger organizations. How to apply and scale agile practices to fit the needs of larger projects has been studied to quite an extent in previous research. However, scaling agile beyond organizational boundaries, for instance in a software ecosystem context, raises additional challenges that existing studies and approaches do not yet investigate or address in great detail. For that reason, we conducted a case study in two software ecosystems that comprise several agile actors from different organizations and, thereby, scale development across organizational boundaries, in order to elaborate and understand their coordination challenges. Our results indicate that most of the identified challenges are caused by long communication paths and a lack of established processes to facilitate these paths. As a result, the participants in our study, among others, experience insufficient responsivity, insufficient communication of prioritizations and deliverables, and alterations or loss of information. As a consequence, agile practices need to be extended to fit the identified needs.
Walking Assist Method for VR Zombie

Our goal of the research is to propose and develop walking assist methods in real space while playing immersive VR without reducing the user's immersive feeling as much as possible. In this paper, we proposed a navigation system which automatically calculates the route the user should walk so that the user avoid to collide with surrounding obstacles when using VR. Also, we simulated the real environment and conducted the experiment. As a result, in a situation assumed that the system can calculate a safe route, by displaying only the walking route to the destination, the user can safely reach the destination without reducing the immersive feeling in the VR content. In addition, we found that users' preferences are greatly divided between 6-DoF and 3-DoF for DoF conditions when using VR while walking in real space.
Scheme and research of realizing regional substation by using private cloud technology

In recent years, the technology of Internet of things and information communication technology have developed rapidly. With the construction of intelligent substation, the intelligent level of main equipment is constantly improving, and the power data network is constantly improving, which provides the basic conditions for the realization of regional private cloud. In this paper, the concept, advantages, implementation scheme, construction process and advanced application of regional substation are proposed. And the application mode and scenario are discussed.
A Smart System for Text-Lifelog Generation from Wearable Cameras in Smart Environment Using Concept-Augmented Image Captioning with Modified Beam Search Strategy

During a lifetime, a person can have many wonderful and memorable moments that he/she wants to keep. With the development of technology, people now can store a massive amount of lifelog information via images, videos or texts. Inspired by this, we develop a system to automatically generate caption from lifelog pictures taken from wearable cameras. Following up on our previous method introduced at the SoICT 2018 conference, we propose two improvements in our captioning method. We trained and tested the model on the baseline MSCOCO datasets and evaluated on different metrics. The results show better performance compared to our previous model and to some other image captioning methods. Our system also shows effectiveness in retrieving relevant data from captions and achieve high rank in ImageCLEF 2018 retrieval challenge.
A Normalized Generalized Curvature Scale Space for 2D Contour Representation

Here, we intend to propose a discrete normalization of the Generalized Curvature Scale Space (GCSS). The GCSS is an Euclidean invariant planar contour descriptor. It consists on the convolution of the contour by Gaussian functions with different scales. The points having the same curvature values as the selected extremums are the considered key points. This representation implies different number of descriptors from a shape to another. Thus, a step of redistribution of the key points is requested. Therefore, a discrete normalization approach is proceeded. The descriptor is composed by the curvature variation of the key points at the smoothed curve. Several datasets were used to carry on the experiments and to verify the accuracy, the stability and the robustness of the novel description. The Dynamic Time Warping distance is the similarity metric used. Experimental results show that considerable rates of image retrieval are reached comparing to the state of the art.
Agile Methods Knowledge Representation for Systematic Practices Adoption

Abstract The popularity of agile methods is constantly increasing. Information and feedback on how these frameworks were adopted can easily be found in academia and industrial knowledge bases. Such a collective experience allowed the development of many approaches in the aim of simplifying the adoption process and maximizing the chances of success. These approaches provide practitioners with guidelines to help them find the practice that suits their team best. Nonetheless, these approaches are not systematic and practitioners need to go through a long process. For instance, they need to identify the important situational factors that can have a positive/negative effect on the agile practice adoption. Available experiences thus require lots of effort to be discovered. This research proposes an agile methods knowledge representation using an ontology so that the knowledge and experience on agile adoption reported in literature may be reusable and systematic. Based on this model, added knowledge and inference rules, practitioners will systematically be able to decide which practice to select and adopt, i.e, for a given goal, practitioners can retrieve which practices to achieve; from a situation, teams can tell what can be harmful and what can be useful for adopting a practice or what problems they may encounter; etc.
Hierarchical Trust Chain Framework for IoT Services

To support blockchain enabled Internet of Things (IoT) services, a lightweight approach is necessary in order to reduce the processing overhead and memory redundancy. We propose the hierarchical trust chain framework for secured and reliable IoT service provisioning, which consists of local trust chains and global distributed trust chain for scalable and trusted IoT services. The proposed framework provides the time slot based delayed verification algorithm and the blockchain configuration method based on the trust analysis mechanism.
A multiple model adaptive SVSF-KF estimation strategy

State estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The Smooth Variable Structure Filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF – KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.
Non-Markovianity in the presence of multiple thermal environments via collision model

By means of the collision model, we study the non-Markovianity of an open quantum system S being coupled to M thermal reservoirs. In our model, each reservoir is modeled as a chain of ancillas whose intracollisions account for the occurrence of non-Markovian dynamics. We show that by incorporating M reservoir ancillas into the system, the non-Markovian dynamics of S can be embedded in the extended system that experiences a completely Markovian dynamics. The number M of involved reservoirs can thus be identified as the memory depth and determines the degree of the non-Markovianity. In the equilibrium case with identical temperatures for all the reservoirs, we show that though the non-Markovianity is proportional to M in the zero and relatively low temperature regimes, in the relatively high temperature regime such proportional relation holds only for the weak intracollisions of the reservoir ancillas. In the nonequilibrium situation, we examine the effect of temperature difference of reservoirs on the non-Markovianity. Focusing on a simple situation with two reservoirs, we observe that the nonzero temperature difference has a significant impact on the non-Markovianity.
Augmented Multi-Task Learning by Optimal Transport

Multi-task learning (MTL) provides an effective approach to improve generalization error for multiple related prediction tasks by learning the tasks jointly, assuming there is a common structure shared by their model parameters. Despite its successes, the shared parameter assumption is ineffective when the sample sizes for some tasks are too small to infer the task relationships correctly from data. To overcome this limitation, we propose a novel framework for increasing the effective sample size of each task by augmenting it with pseudo-labeled instances generated from the training data of other related tasks. Incorporating training data from other tasks is a challenge for regression problems as their data distributions may not be consistent due to the co-variate shift and response drift problems. Our proposed framework addresses this challenge by coupling multitask regression with a series of optimal transport steps to iteratively learn the pseudo-labeled instances by identifying relevant training instances from other source domains and refining the pseudo-labels until they are consistent with the training instances of the target domain. Experimental results on both synthetic and real-world data showed that our framework consistently outperformed other state-of-the-art MTL methods.
Adversarial Style Transfer for Long Sentences

Language style transfer is the task of generating a new sentence with the same content but of a different style. This task is a part of natural language generation and an attractive research topic. There is currently a spike in research on neural networks, and many studies are focused on language style transfer. One approach is a sequence-to-sequence model with multiple decoders. However, this approach cannot avoid the conventional problem of recurrent neural networks: it is difficult to handle long sentences. In this paper, we propose an adversarial style transfer model that generates a style-transferred sentence from a given sentence. To tackle the problem, we adopted a transformer structure to our model because this structure has proven effective for long sequences and requires lower computation costs. For evaluation, the model transferred the sentiments of sentences from a Yelp review dataset. As an experimental result, our proposed model outperforms the baseline and shows robustness for long sentences.