Reviewerly/exHarmony huggingface dataset:
A fuzzy reasoning method based on compensating operation and its application to fuzzy systems

In this paper, we present a new fuzzy reasoning method based on the compensating fuzzy reasoning (CFR). Its basicidea is to obtain a new fuzzy reasoning result by moving and deforming the consequent fuzzy set on the basis of themoving, deformation, and moving-deformation operations between the antecedent fuzzy set and observation information.Experimental results on real-world data sets show that proposed method significantly improve the accuracy and timeperformance of fuzzy neural network learning.
On the $p$-divisibility of class numbers of cyclotomic function fields

Let $\mathbb{F}_q$ be the finite field with $q=p^r$ elements, where $p$ is a prime. For a monic $m \in \mathbb{F}_q[T]$, let $h_m$ be the class number of the $m$th cyclotomic function field. The goal of this paper is to determine the $p$-divisibility of $h_m$ when $q = p$ $(r \ge 2)$ and $\deg m=2$.
Group ORAM for privacy and access control in outsourced personal records

Cloud storage has rapidly become a cornerstone of many IT infrastructures, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. Putting user data in the direct control of cloud service providers, however, raises security and privacy concerns relate d to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. Furthermore, even if the cloud provider is trusted, users having access to outsourced files might be malicious and misbehave. These concerns are particularly serious in sensitive applications like personal health records and credit score systems. To tackle this problem, we present ΠGORAM, a definitional framework for Group Oblivious RAM, in which we formalize several security and privacy properties such as secrecy, integrity, anonymity, and obliviousness. ΠGORAM allows per entry access control, as selected by the data owner. ΠGORAM is the first framework to define such a wide range of security and privacy properties for outsourced storage. Regarding obliviousness, we tackle two different attacker models: our first definition protects against an honest-but-curious server while our second definition protects against such a server colluding with malicious clients. In the latter model, we prove a server-side computational lower bound of Ω(n) where n is the number of entries in the database, i.e., every operations requires to process a constant fraction of the database. Furthermore, we present two constructions: a pure cryptographic instantiation, which achieves an O(n) amortized communication and computation complexity and a construction based on a trusted proxy with logarithmic communication and server-side computational complexity. The second construction bypasses the previously established lower bound leveraging a trusted party. Both schemes achieve secrecy, integrity, and obliviousness with respect to a server colluding with malicious clients, but not anonymity due to the deployed access control mechanism. In the former model, we present a cryptographic system that achieves secrecy, integrity, obliviousness, and anonymity. In the process of designing an efficient construction, we developed three new, generally applicable cryptographic schemes, namely, batched zero-knowledge proof of shuffle correctness, the hash-and-proof paradigm, which even improves upon the former, and an accountability technique based on chameleon signatures, which we consider of independent interest. We implemented our constructions in Amazon Elastic Compute Cloud (EC2) and ran a performance evaluation demonstrating the scalability and efficiency of our construction.
Graph and Neural Network-Based Intelligent Conversation System

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.
How Can a Cybersecurity Student Become a Cybersecurity Professional and Succeed in a Cybersecurity Career?

The need for cybersecurity professionals extends across government and private industries. Estimates place the shortage of cybersecurity professionals at 1.8 million by 2022. This chapter provides aspiring cybersecurity students a clear understanding of the various educational pathways they can choose to achieve their goals. The authors describe educational categories and include an assessment of each that students will want to consider based on their own situation. The authors discuss how the study of cybersecurity can be accomplished from a computer science, engineering, and business perspective. Students with STEM skills can accomplish their goals in numerous cybersecurity roles including cyber engineer, architect, and other technical roles. Finally, students with cyber business interest can accomplish their goals with a focus on strategy, compliance, awareness, and others. Organizations need employees with all these skills. This chapter concludes with the recommendation for continual learning, the value of networking, and the encouragement for students to start creating a cyber career.
ToothPic: Camera-Based Image Retrieval on Large Scales

Being able to reliably link a picture to the device that shot it is of paramount importance to give credit or assign responsibility to the author of the picture itself. However, this task needs to be performed at large scales due to the recent explosion in the number of photos taken and shared. Existing methods cannot satisfy those requirements. Methods based on the photo response nonuniformity (PRNU) of digital sensors are able to link a photo to the device that shot it and have already been used as proof in the Court of Law. Such methods are reliable but so far, they can be only used for small-scale forensic tasks involving few cameras and pictures. ToothPic, an acronym for “Who Took This Picture?,” is a novel image retrieval engine that allows to find all the pictures in a large-scale database shot by a given query camera.
Aesthetic Composition Indicator Based on Image Complexity

Several systems and indicators for multimedia devices have appeared in recent years, with the goal of helping the final user to achieve better results. Said indicators aim at facilitating beginner and intermediate photographers in the creation of images or videos with more professional aesthetics. The chapter describes a series of metrics related to complexity which seem to be useful for the purpose of assessing the aesthetic composition of an image. All the presented metrics are fundamental parts of the prototype “ACIC” introduced here, which allows an assessment of the aesthetics in the composition of the various frames integrating a video.
Generating research of image caption based on improved NIC algorithm

The image caption generation algorithm allows computer to understand the picture and generate sentences that comply with grammar rules and picture features. Under the Encoder-Decoder framework, the CNN (Convolutional Neural Networks) model is widely used as an encoder to extract image features and the RNN (Recurrent Neural Networks) model as a decoder to generate the description sentence to solve the problem of image caption generation. The most famous algorithm is the NIC, which used Inception-v3 as the encoder, and the LSTM (Long Short-term Memory) as the decoder. However, there are too many parameters in LSTM, and the quality of generated sentences is not high. In the field of visual features, deepening the network structure can improve the feature extraction ability, but the network will degenerate. Therefore, the NIC algorithm is improved. The Inception-ResNet-v2 network is used as the encoder, and the LSTMP network is introduced as the decoder. Taking BLUE-4, ROUGE, METEOR, and CIDEr as evaluation indicators, MSCOCO and Flickr30k are used as datasets to make comparative test between the NIC and the improved NIC. Experimental results show that the improved NIC algorithm outperforms the NIC algorithm in all four evaluation indicators.
Application of Multi-Column Heterogeneous Convolutional Neural Networks in image classification

Image classification is an important research direction of computer vision. Convolutional neural network is a deep feedforward neural network model. It uses the deep learning idea and shows good performance in multiple image classification fields such as speech recognition, face recognition, motion analysis, and medical diagnosis. However, a single-structure convolutional neural network is prone to overfitting problems. The main reason for the overfitting problem is that the learning model overfits the training set and results in the lack of generalization performance, which affects the feature extraction and judgment of the test set. This paper presents a structure model for Multi-Column Heterogeneous Convolutional Neural Networks. Multi-Column Heterogeneous Convolutional Neural Networks are used in image classification. We construct several convolutional neural networks with different structures by setting different size of convolution kernels and different number of feature maps. Image features are learned from multiple perspectives. Each convolutional neural network model is trained on the training set, and the different network models are fitted to the training set. Finally, through the sliding window, the output of each network is fused to obtain a relatively better prediction result. Experiments show that Multi-Column Heterogeneous Convolutional Neural Networks reduce the overfitting problem to a certain extent, and the accuracy of object recognition is improved compared to the single structure convolutional neural network.
Real-time Context-aware learning System for IoT Applications

This paper introduces a real-time context-aware learning system that runs on mobile devices, collects data from the sensors, learns about the user-defined context, makes predictions in real-time, and manage IoT devices accordingly. However, the computational power of the mobile devices makes it challenging to run machine-learning algorithms with acceptable accuracy. Hence, existing works implement machine-learning algorithms on the server and transmit the results to the mobile devices. Although the context-aware predictions made by the server are more accurate than their mobile counterpart is it heavily depends on the network connection for the delivery of the results to the devices, which negatively affects real-time context learning. Therefore, in this paper, we propose a context-learning algorithm for mobile devices, which is less demanding on the computational resources and maintains the accuracy of the prediction by updating itself from the learning parameters obtained from the server periodically. Experimental results show that the proposed lightweight context-learning algorithm can achieve mean accuracy up to 97.51% while mean execution time requires only 11ms.
Australian Undergraduate Information Systems Curricula: A Comparative Study

The paper describes the first comprehensive comparative study of Undergraduate (UG) Information Systems (IS) degree programs in Australia using the model curricula outlined in ACM/AIS IS2010 as a reference point. The study had three broad aims: (1) to compare the Australian IS curriculum with that of other major IS education systems internationally, (2) to identify what subject areas are considered mandatory and what are considered optional in Australian IS programs, and (3) to understand if the host academic division within different disciplines (e.g. Business or Science/Engineering/Information Technology) have an influence on the variations in the degrees. In a first phase, 2017 IS program data was obtained from university websites. In a second phase, this data was validated in consultation with academic staff from those universities offering the programs. The conclusion is that a high level of adherence to the IS2010 curricula was evident in core courses; considerable diversity was found in a long tail of non-core offerings; and the location of the host academic unit within Business or Science/Engineering/Technology influenced the subject areas offered.
Construction of CCZ transform for quadratic APN functions

Almost perfect nonlinear (APN) function is an important type of function in cryptography, especially quadratic APN function. Since the notion of CCZ-equivalence developed, the construction of CCZ transform for APN functions to obtain new APN functions became a critical issue in cryptography. Inspired by the result of Budaghyan who used Gold functions, this article gives the construction of CCZ transform for all quadratic vectorial Boolean functions and proves that for quadratic APN functions, the functions transformed have algebraic degree 3, thus EA-inequivalent to all quadratic functions, and have minimum algebraic degree 2, thus EA-inequivalent to all power functions.
Public Key Encryption Resilient to Post-challenge Leakage and Tampering Attacks

In this paper, we introduce a new framework for constructing public-key encryption (PKE) schemes resilient to joint post-challenge/after-the-fact leakage and tampering attacks in the bounded leakage and tampering (BLT) model, introduced by Damgård et al. (Asiacrypt 2013). All the prior formulations of PKE schemes considered leakage and tampering attacks only before the challenge ciphertext is made available to the adversary. However, this restriction seems necessary, since achieving security against post-challenge leakage and tampering attacks in its full generality is impossible, as shown in previous works. In this paper, we study the post-challenge/after-the-fact security for PKE schemes against bounded leakage and tampering under a restricted yet meaningful and reasonable notion of security, namely, the split-state leakage and tampering model. We show that it is possible to construct secure PKE schemes in this model, tolerating arbitrary (but bounded) leakage and tampering queries; thus overcoming the previous impossibility results. To this end, we formulate a new notion of security, which we call entropic post-challenge IND-CCA-BLT secure PKE. We first define a weaker notion called entropic restricted post-challenge IND-CCA-BLT secure PKE, which can be instantiated using the (standard) DDH assumption. We then show a generic compiler from our entropic restricted notion to the entropic notion of security using a simulation-extractable non-interactive zero-knowledge argument system. This requires an untamperable common reference string, as in previous works. Finally, we demonstrate the usefulness of our entropic notion of security by giving a simple and generic construction of post-challenge IND-CCA-BLT secure PKE scheme in the split-state leakage and tampering model. This also settles the open problem posed by Faonio and Venturi (Asiacrypt 2016).
Strong Leakage Resilient Encryption by Hiding Partial Ciphertext

Leakage-resilient encryption is a powerful tool to protect data confidentiality against side channel attacks. In this work, we introduce a new and strong leakage setting to counter backdoor (or Trojan horse) plus covert channel attack, by relaxing the restrictions on leakage. We allow bounded leakage at anytime and anywhere and over anything. Our leakage threshold (e.g. 10000 bits) could be much larger than typical secret key (e.g. AES key or RSA private key) size. Under such a strong leakage setting, we propose an efficient encryption scheme which is semantic secure in standard setting (i.e. without leakage) and can tolerate strong continuous leakage. We manage to construct such a secure scheme under strong leakage setting, by hiding partial (e.g. 1%) ciphertext as secure as we hide the secret key using a small amount of more secure hardware resource, so that it is almost equally difficult for any adversary to steal information regarding this well-protected partial ciphertext or the secret key. We remark that, the size of such well-protected small portion of ciphertext is chosen to be much larger than the leakage threshold. We provide concrete and practical examples of such more secure hardware resource for data communication and data storage. Furthermore, we also introduce a new notion of computational entropy, as a sort of computational version of Kolmogorov complexity. Our quantitative analysis shows that, hiding partial ciphertext is a powerful countermeasure, which enables us to achieve higher security level than existing approaches in case of backdoor plus covert channel attacks. We also show the relationship between our new notion of computational entropy and existing relevant concepts, including All-or-Nothing Transform and Exposure Resilient Function. This new computation entropy formulation may have independent interests.
Detailed analysis and improvement of an efficient and secure identity-based public auditing for dynamic outsourced data with proxy

For data owners of restricted cloud access with a delegated proxy, public auditing technology for cloud data integrity, plays critical roles in ensuring powerful productivity that flexible cloud services provide for their business. In order to address the scalability of data owners and storage clouds for secure public auditing, Yu et al. (2017) proposed an Identity-Based Public Auditing for Dynamic Outsourced Data with Proxy Processing (https://doi.org/10.3837/tiis.2017.10.019), which also overcomes complicated public key certificates management issue. In this article, we figure out that this scheme is vulnerable to data loss attack where clouds could pass integrity auditing without original data. Meanwhile, a threat of system security is demonstrated, i.e., any entities are able to recover proxy private keys and impersonate proxy to forge proxy tag, with two arbitrary data tag pairs of same data owner. To enable secure identity-based batch public auditing with proxy processing, we propose an improved scheme without these security flaws and prove its security under CDH hard problem in the random oracle model. With complexity analysis, our scheme shows better efficiency over identity-based proxy-oriented data uploading and remote data integrity checking in public cloud (ID-PUIC) in a single owner effort on a single cloud. Especially, we give the detailed analysis for how efficiently the attacks on Yu et al.’s scheme could be launched with an experiment, and demonstrate complete reduction on probability and time for proving security of our improved scheme. For potential application in big data storage, we first evaluate the error detection probability varying on number of auditing blocks, and then conduct detailed performance analysis by simulating our scheme and ID-PUIC scheme on the different number of data owners and storage clouds, with up to 106 data blocks.
Third Gen AI as Human Experience Based Expert Systems

We are reviewing various notions of Artificial Intelligence (AI) based on its unsuccessful history (as the success of AI has too many reports). Then we suggest changes based on notions of possibility from fuzzy math, ergodic process for examining possibility space rather than a stochastic time space, speed and memory in neural networks, which follow the supervised least mean square (LMS) deep learning or unsupervised gradient descent learning based on minimizing cost function of Helmholtz free energy, etc. The first Gen AI is Marvin Minsky's rule-based AI which has taken into account implicitly the first attribute Poincare-Neumann Ergodicity that has foregone the small Rosenblatt neural nets. The second Gen AI is Google learnable rule-based system added either to LMS Deep Learning or Maxwell–Boltzmann irreversible thermodynamics Unsupervised Deep Learning (unlabeled inputs) that has beaten Go champion Lee Se-Dol. The third Gen AI shall take into account open set possibilities used in Fuzzy Logic (not yet in digital automation computer scientists [ACS]). In order to compute “where we are,” in turns of 5W (when, where, what, why, and how) needed for route planning, geolocation, and situation awareness, we generated the data from the dynamic equations of motion of Newton, Kubo-Langevin's Einstein fluctuation-dissipation, multiple satellite triangulation GPS, advanced EO, IR, RF sensors suite, as well as Lyaponov Control theory. We wish to tabulate the peak occurrence frequency of those dynamic data near the peak of a triangle shape function in rounded brackets into Zadeh-Freeman fuzzy membership functions (FMF).
Simple method for generating evaluation data for scene flow algorithms

A method which simply generates evaluation data for scene flow testing is presented, in which most algorithms for scene flow estimation require a pair of depth and colour images. We scanned the 3D geometry of a real object and rendered it in graphical space to acquire its depth and colour information. Next, surface deformation to generate natural motion for the scene flow is employed. The surface deformation enabled the generation of non-rigid as well as rigid motion. Next, barycentric coefficients were exploited to compute the ground truth scene flow. The coefficients enabled the acquired dense scene to flow accurately without dense point tracking and colour information. On the other hand, each flow vector was computed per pixel and a pixel could come from more than one point due to occlusion. We compute the flow maps dividing the geometry into the front area and the occluded area by modifying a z-buffer algorithm. Experimental results showed that the proposed method reliably provided evaluation data (a pair of depth and colour images and a scene flow map) based on a scanned model with occlusion.
Dependency Graph Based Detection of Semantically Equivalent Questions in Online Forums

In the present, people increasingly rely on online user forums for clearing their doubts and seeking answers to varied questions. On many online forums, users encounter a similar question asked in different formats. This results in confusion for the user and he may not be able to find appropriate answer to his question even though the appropriate answer exists on some other page, i.e., on the page resultant of a differently formed question. Currently, online forums like Quora only give suggestions to the user about the questions he could ask but do not show all the semantically equivalent questions. This article eases the work of the users searching for answers on online user forums. The proposed technique will allow the users to look at all the existing semantically equivalent questions. A dependency graph-based matching algorithm is applied to accomplish this objective.
Entanglement and Measurement-induced quantum correlation in Heisenberg spin models

Correlation beyond entanglement is a subject of interest in quantum information. Here we have shown the existence of quantum correlation without entanglement in Heisenberg $XYZ$ spin model with external magnetic field, using different versions of measurement-induced nonlocality. However maximally entangled states are shown to possess maximum correlation.