Reviewerly/exHarmony huggingface dataset:
Types of CAPTCHA

This chapter introduces a categorization of the main types of CAPTCHA. Then, each category of CAPTCHA is described and some examples are shown. The main advantages and problems related to the different categories of CAPTCHA are shortly discussed. This represents an introductive overview preceding the subsequent chapters where some types of CAPTCHA belonging to this categorization are described in more detail.
Design of Quantum Circuits for Cryptanalysis and Image Processing Applications

Quantum circuits for arithmetic functions over Galois fields such as squaring are required to implement quantum cryptanalysis algorithms. Quantum circuits for integer arithmetic such as multiplication are required to implement scientific computing algorithms and quantum image processing algorithms on quantum computers. Reliable quantum circuits require error correcting codes and gates that are fault tolerant in nature. Quantum circuits of many qubits are challenging to implement making designs with low qubit cost desirable. In this work, we present quantum arithmetic circuits for applications in quantum cryptanalysis and quantum image processing. We present a proposed algorithm for synthesizing gate cost, qubit cost and depth optimized Galois field (GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> )) squaring circuits for quantum cryptanalysis applications. In addition, these squaring circuits are incorporated into a proposed quantum circuit for inversion in GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). This work also presents a proposed quantum integer conditional addition circuit and a quantum integer multiplication circuit optimized for T-count and qubit cost. The quantum conditional addition circuit and quantum multiplier are incorporated into proposed quantum circuits for bilinear interpolation optimized for T-count cost that can be used in quantum image processing applications.
GPA-ES Algorithm Modification for Large Data

This paper discusses improvement of Genetic Programming Algorithm to large data sets with respect to future extension to big data applications. On the beginning it summarizes requirements on evolutionary system to be applicable in the area of big data and ways of their satisfaction. Then GPAs and especially their improvements by solution constant optimization (so called hierarchical and hybrid genetic programming algorithms) are discussed in this paper. After a discussion of few experiment results of introduced novel evaluation scheme approach with floating data window is presented. Novel evaluation scheme applies floating data window to fitness function evaluation. After one evaluation step of GPA including tuning of parameters (solution constants) by embedded Evolutionary Strategy algorithm data window moves to new position. Presented results demonstrate that this strategy can be faster and more efficient than evolution of whole training data set in each evolutionary step of GPA algorithm. This modification can be starting point of future applications of GPA in the field of large and big data analytic.
An Efficient Architectural Framework for Non-obtrusive and Instantaneous Real-Time Identification of Clones During the Software Development Process in IDE

Code Clones are well-known Source Code Smells that impacts the Software maintenance thus research community proposed various real-time clone detection approaches to proactively manage them during the software development process. The present-day real-time Code Clone identifiers have at least one of the five inadequacies: (a) entails Developer involvement to start the Clone Detection process, (b) despite of having focused search capability from few tools, Clone Detection necessitates to be triggered by the Developer, (c) in spite of few plug-in tools instigating concentrated search a large portion of available plug-ins procedure Clones in bunch mode and in this way expends much time to find clones, (d) despite being plugins to the IDEs, current tools require Software Programmer to trigger the visualization of Clone Detection results thus deficits instantaneous real-time Clone recognition functionality, (e) uses indexing techniques that can further be replaced by other available more efficient techniques to reduce the response time. This paper presents the Architectural Framework of underdevelopment real-time Code Clone Detection plug-in tool, which is proficiently adequate as a resolution to all the above-unveiled issues. The tool architecture description clearly indicates the proficiency of our approach in the application of automatic triggering of Clone Detection process as well as focused block level search on interception of block end leading to instantaneous real-time identification of clones and immediate recommendation mechanism.
Multi-keyword Search Based on Attribute Encryption

To address the problems of key leakage and large computation amount in current attribute-based searchable encryption (SE) schemes, this study proposes a multi-keywords ranked search scheme based on attribute encryption in cloud environment. Firstly, the scheme not only supports fine-grained search authorisation, but also has constant ciphertext length. Secondly, users' private keys are blinded before being submitted to the cloud server, This process ensures the security and confidentiality of users' keys. Thirdly, to reduce search complexity, this study uses $$ TF \times IDF $$ rules to calculate the relevance scores of documents and given keywords, and then sorts the documents. Lastly, formal evidence shows that our scheme exhibits selective plaintext security and is indistinguishably secure against chosen keyword attacks. Experimental results demonstrate that our scheme is efficient.
Representing search tasks in an information use environment: a case of English primary schools

Purpose To design effective task-responsive search systems, sufficient understanding of users’ tasks must be gained and their characteristics described. Although existing multi-dimensional task schemes can be used to describe users’ search and work tasks, they do not take into account the information use environment (IUE) that contextualises the task. The paper aims to discuss these issues. Design/methodology/approach With a focus on English primary schools, in four stages a multi-dimensional task scheme was developed that distinguishes between task characteristics generic to all environments, and those that are specific to schools. In Stage 1, a provisional scheme was developed based upon the existing literature. In the next two stages, through interviews with teachers and observations of school children, the provisional scheme was populated and revised. In Stage 4, whether search tasks with the same information use can be distinguished by their characteristics was examined. Findings Ten generic characteristics were identified (nature of work task, search task originator, search task flexibility, search task doer, search task necessity, task output, search goal, stage in work task, resources and information use) and four characteristics specific to primary schools (curricular area, use in curricular area, planning and location). For the different information uses, some characteristics are more typical than others. Practical implications The resulting scheme, based on children’s real-life information seeking, should be used in the design and evaluation of search systems and digital libraries that support school children. More generally, the scheme can also be used in other environments. Originality/value This is the first study to develop a multi-dimensional task scheme that considers encompasses the IUE.
A Formal Model for Semantic Computing Based on Generalized Probabilistic Automata

In most previous research, “semantic computing” refers to computational implementations of semantic reasoning. It lacks support from the formal theory of computation. To provide solid foundations for semantic computing, researchers propose a different understanding of semantic computing based on finite automata. This approach provides a computer theoretical approach to semantic computing. But finite automata are not capable enough to deal with imprecise knowledge. Therefore, in this paper, we provide foundations for semantic computing based on probabilistic automata. Even though traditional probabilistic automata can handle imprecise knowledge, their limitation resides in their being defined on a fixed finite input alphabet. This deeply restricts the abilities of automata. In this paper, we rebuild traditional probabilistic automata for semantic computing. Furthermore, our new probabilistic automata are robust enough to handle any alphabet as input. They have better performances in many applications. We provide an application for weather forecasting, a domain for which traditional probabilistic automata are not effective due to their finite input alphabet. Our new probabilistic automata can overcome these limitations.
Understanding Attestation: Analyzing Protocols that Use Quotes

Attestation protocols use digital signatures and other cryptographic values to convey evidence of hardware state, program code, and associated keys. They require hardware support such as Trusted Execution Environments. Conclusions about attestations thus depend jointly on protocols, hardware services, and program behavior. We present a mechanized approach to modeling these properties, combining protocol analysis with axioms, that formalize hardware and software properties. Here, we model aspects of Intel’s SGX mechanism. Above the underlying manufacturer-provided protocols, we build a modular user-level that uses its attestations to make trust decisions.
The Network Improvement and Connection Refinement for Multi-Person Pose Estimation

Human pose estimation is one of the important challenges in computer vision tasks. In the past three years, various algorithms for pose estimation have emerged one after another and have achieved good results. However, from time to time, there are some keypoints that cannot be accurately predicted due to the ambiguity in images. To address such problems, we make corresponding improvements on RMPE multi-person pose estimator. On the one hand, we introduce a channel-attention mechanism to enhance the predictive ability of heat map on keypoints. On the other hand, we design a geometry constraint strategy during the test phase to control the way of keypoint connection which can compensate insufficient network functions and training samples. We carry out experiments on a standard benchmark for multi-person pose estimation, and the results prove that the proposed method can more effectively distinguish ambiguous keypoints and estimate poses.
Transparency in the New gTLD Era: Evaluating the DNS Centralized Zone Data Service

The centralized zone data service (CZDS) was introduced by the Internet Corporation for Assigned Names and Numbers (ICANN) to facilitate sharing and access to zone data of the new generic Top-Level Domains (gTLDs). CZDS aims to improve the security and transparency of the naming system of the Internet. In this paper, we investigate CZDS's transparency by measurement and evaluation. By requesting access to zone data of all gTLDs listed in the CZDS portal, we analyze various aspects of CZDS, including access status, responsiveness and provided reasons for granting access or denial. Among other findings, we find that while a large percent of the gTLD admins respond within a reasonable time, more than 10% of them have a long request-to-decision waiting time, and sometimes requests go unanswered even after six months of a request. Furthermore, we find that denial cases were for unjustified reasons, where administrators who denied the requests have asked for information that was already provided in the request form. We discuss implications, and how to enforce better outcomes of CZDS using insight from our measurement and evaluation.
Ethics in AI: Collaborative filtering based approach to alleviate strong user biases and prejudices

Generally, recommender systems tend to satisfy users' tastes and preferences by showing them the extreme versions of the content they like. This over time results in reinforcement of user's biases, which has far reaching socioeconomic and political consequences. For example, Facebook played a major part in inciting the genocide against Rohingya minority in Myanmar in 2017 by reinforcing users preferences for content containing hate speech to maximize impressions and clicks. This paper proposes to blend user's recommendations with items which are of “high quality” but are inversely correlated to user's taste, which might help in loosening up user's biases or at least help prevent heavy reinforcement of user's biases. A collaborative filtering based algorithm is proposed to find “high quality” items which are inversely correlated to user's tastes. The proposed algorithm is implemented and tested on the MovieLens 1M dataset and the results are compared with that of a traditional collaborative filtering approach on the basis of two metrics - quality and diversity of recommendations for each user. The proposed algorithm shows better performance in both metrics.
Methods for Derivation of Density Matrix of Arbitrary Multi-Mode Gaussian States from Its Phase Space Representation*

Abstract We present a method for derivation of the density matrix of an arbitrary multi-mode continuous variable Gaussian entangled state from its phase space representation. An explicit computer algorithm is given to reconstruct the density matrix from Gaussian covariance matrix and quadrature average values. As an example, we apply our method to the derivation of three-mode symmetric continuous variable entangled state. Our method can be used to analyze the entanglement and correlation in continuous variable quantum network with multi-mode quantum entanglement states .
Evaluation of the Implementation of an Abstract Interpretation Algorithm using Tabled CLP

Abstract CiaoPP is an analyzer and optimizer for logic programs, part of the Ciao Prolog system. It includes PLAI, a fixpoint algorithm for the abstract interpretation of logic programs which we adapt to use tabled constraint logic programming . In this adaptation, the tabling engine drives the fixpoint computation, while the constraint solver handles the LUB of the abstract substitutions of different clauses. That simplifies the code and improves performance, since termination, dependencies, and some crucial operations (e.g., branch switching and resumption) are directly handled by the tabling engine. Determining whether the fixpoint has been reached uses semantic equivalence , which can decide that two syntactically different abstract substitutions represent the same element in the abstract domain. Therefore, the tabling analyzer can reuse answers in more cases than an analyzer using syntactical equality. This helps achieve better performance, even taking into account the additional cost associated to these checks. Our implementation is based on the TCLP framework available in Ciao Prolog and is one-third the size of the initial fixpoint implementation in CiaoPP. Its performance has been evaluated by analyzing several programs using different abstract domains.
Research on UPFC Fault Diagnosis Based on KFCM and Support Vector Machine

In order to make full use of limited data to protect the normal and stable operation of Unified Power Flow Controller (UPFC) prospectively, a fault diagnosis method based on KFCM and Support Vector Machine (SVM) is proposed in the study. Above all, the DC voltage signal is used as the monitoring signal to determine seven kind of characteristic quantity corresponding to the simulated fault types to obtain the initial data sources. Then, the data are sent to KFCM algorithm for clustering to verify the validity of the feature vectors and the number of classifications. Finally, the feature data are sent to SVM to further verify the classification results. In conclusion, the validity and accuracy of the method were proved by simulation experiments and shown that the UPFC fault mentioned in this paper can be recognized through the proposed method.
Study on the Performance of Hybrid Quantum-Classical Optical Networks

Aiming at the hybrid transmission of quantum signals and classical optical signals in the same networks, we analyze the noise interference of classical high-intensity optical signals on quantum signals to obtain the performance of the practical hybrid quantum-classical networks. With two common quantum key distribution protocols, BB84 and measurement-device-independent (MDI) protocol, we discuss the influence of the spontaneous Raman scattering noise from the classical optical signals and the insertion loss of the main network components on the quantum key generation rate, secure transmission distance and number of users of the networks. The numerical results show that under the same conditions, compared with the BB84 hybrid optical networks, the quantum signals in the MDI hybrid optical networks are more strongly interfered by the classical glare signals, and the performance degradation is more obvious. For the small and medium-sized access networks (the number of users does not exceed 31), the transmission distance of the MDI protocol is farther. For larger-scale networks, the key generation rate of the BB84 protocol is higher when the transmission distance does not exceed 46 km.
Random Sequence Coding Based Privacy Preserving Nearest Neighbor Query Method

Location based service has been widely used in people' life. It brings convenience to the people, in parallel with the risk of query user's location privacy disclosure. As a result, privacy preserving location based nearest neighbor queries witness its thriving in recent years. Private information retrieval (PIR) based solutions receives continuous attention for in privacy preserving for its merits in high level privacy protection strength and independence of the trusted third parties. However, existing PIR based methods fall short in high time consuming of encoding, querying efficiency and poor security to mode attacks. To address above issues, random sequence is introduced to encode POI data, which can resist mode attacks and reduce the time of data encoding. As a consequence, location privacy protection effectiveness is improved. Meanwhile, to accelerate query efficiency, a hash table structure is built at the server-side to store rules of POI distribution in the manner of space bitmap, which can position nearing POI quickly and reduce the I/O cost of database visiting efficiently. Theoretical analysis and experimental results demonstrates our solution's efficiency and effectiveness.
Are We in the Right Path in Using Early Warning Systems?

This paper focusses on the recent tsunami in Indonesia and the factors led to the mass killing. We also discussed the failure of early warning systems, steps, methods, and technologies, in general, to improve the early warning systems in the future to mitigate the loss of lives and property during these impending disasters. We believe that this paper is timely as Indonesia has seen one of the worst tsunamis in recent years and the threat is still on. Hence, we stress the importance of improving and strengthening the existing early warning systems.
Human-Computer Interaction

Human-computer interaction is a key element in the communication between a human and a computer. Hence, the most advanced computer system takes a significant attention in improving this communication. This chapter analyzes the elements of human-computer interaction paying special attention to elements that the usability consists of. A particular consideration is dedicated to visual and contact devices as well as to their comparison. Then, the factors of the usability are defined as: (i) tasks, (ii) system, and (iii) users. Each of them is carefully explained with its constituent components. At the end, their mutual interaction is discussed.
HcRPC: Highly Compact Reachability Preserving Graph Compression With Corrections

Graphs are used in numerous applications to model real-world systems and phenomena. The ever increasing size of graphs makes them difficult to query and analyze. In this paper, we propose HcRPC, a Highly compact Reachability Preserving Graph compression algorithm with Corrections, which is capable of preserving the reachability relations between the nodes in original graph. The highly compressed representation of a given graph consists of a compressed graph and a set of corrections. The original graph is compressed on the basis of equivalence class obtained via the reachability relations between nodes in the original graph. In the compressed graph, each node corresponds to a set of nodes from the original graph with similar ancestors and descendants, and each edge represents linkage between the original nodes in any two node sets. The corrections portion specifies the set of corrections, including equivalent class-node corrections and node-node corrections. MinHash technique is utilized to speed up checking whether equivalence classes are structure-similar and the pair of equivalence classes with high similarity are thus merged to acquire a highly compressed graph. Besides, we develop an algorithm for preserving compressed graph with a set of corrections in response to changes to the original graph. We evaluate our algorithms on real-life graph data sets and the results indicate that graph data sets can be highly compressed while preserving the reachability relations between nodes.
A Proposal of Neural Networks with Intermediate Outputs

The automatic data classification is an essential problem in machine learning, and it applies to different contexts such as people detection, health or astronomy. In recent years, deep neural networks have gained extensive attention due to their excellent performance on large and complex datasets. A neural network is a supervised method for classification, therefore typically requires a set of inputs and targets for the training process. However, it is possible to include auxiliary outputs that characterize aspects of the object of interest, which can accelerate the learning process. For example, in an image, a person may have extra outputs like attributes given by the presence of a hat or beard. However, the classical neural networks do not consider the presence of explicit auxiliary outputs. Furthermore, these outputs might be at a lower semantic level. We propose a framework that allows for using auxiliary outputs connected to hidden layers that complement the output connected to the output layer of the network. The key idea is to improve the training process of a neural network through a variant of the standard backpropagation algorithm that considers these auxiliary outputs. The article presents experimental evidence of the advantages of the proposed idea in various real datasets. Results also show new research venues and practical applications into image recognition considering a deep learning setting.