Reviewerly/exHarmony huggingface dataset:
Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data

A typical issue in Pattern Recognition is the nonuniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions. Generally, the data is considered non-uniformly sampled when in a specific area of data space, they are not enough, leading us to misclassification problems. This issue cut down the goal of the one-class classifiers decreasing their performance. In this paper, we propose a one-class classifier based on the minimum spanning tree with a dynamic decision boundary (OCdmst) to make good prediction also in the case we have non-uniformly sampled data. To prove the effectiveness and robustness of our approach we compare with the most recent one-class classifier reaching the state-of-the-art in most of them. The code and all results are available online https://gitlab.com/artelabsuper/ocdmst.
Subjective Assessments of Legibility in Ancient Manuscript Images - The SALAMI Dataset

The research field concerned with the digital restoration of degraded written heritage lacks a quantitative metric for evaluating its results, which prevents the comparison of relevant methods on large datasets. Thus, we introduce a novel dataset of Subjective Assessments of Legibility in Ancient Manuscript Images (SALAMI) to serve as a ground truth for the development of quantitative evaluation metrics in the field of digital text restoration. This dataset consists of 250 images of 50 manuscript regions with corresponding spatial maps of mean legibility and uncertainty, which are based on a study conducted with 20 experts of philology and paleography. As this study is the first of its kind, the validity and reliability of its design and the results obtained are motivated statistically: we report a high intra- and inter-rater agreement and show that the bulk of variation in the scores is introduced by the image regions observed and not by controlled or uncontrolled properties of participants and test environments, thus concluding that the legibility scores measured are valid attributes of the underlying images.
Foraging-based optimization of menu systems

The problem of computational design for menu systems has been addressed in some specific cases such as the linear menu (list). The classical approach has been to model this problem as an assignment task, where commands are assigned to menu positions while optimizing for users’ selection performance and grouping of associated items. However, we show that this approach fails with larger, hierarchically organized menus because it does not take into account the ways in which users navigate hierarchical structures. This paper addresses the computational menu design problem by presenting a novel integer programming formulation that yields usable, well-ordered command hierarchies from a single model. First, it introduces a novel objective function based on information foraging theory, which minimizes navigation time in a hierarchical structure. Second, it models the hierarchical menu design problem as a combination of the exact set covering problem and the assignment problem, organizing commands into ordered groups of ordered groups. The approach is efficient for large, representative instances of the problem. In a controlled usability evaluation, the performance of computationally designed menus was ∼25% faster to use than existing commercial designs. We discuss applications of this approach for personalization and adaptation.
Enhanced AES algorithm based on 14 rounds in securing data and minimizing processing time

Abstract Computer, Internet technology have grown exponentially, and constant evolution until today. The usage of digital data such as text, images, audio, animation and videos are commonly used in many aspects of daily activity. The continuous increase in the use of digital data transmission over a network and it exposed to the various kinds of attacks, unauthorized access and network hacking. Thus, it is very hard to ensure that the digital data transmission are secure from any attacks and unauthorized access especially for sensitive and important digital data. This has been raised researcher’s concerns on security of the digital data. Digital data security has become one of the most important aspects in communication. Cryptography is one of the most important technology for protecting digital data. As there is need for secure communication, efficient and secure cryptographic processing is needed for desirable platform overall performance. Improvement of any communication platform with secure and complicated cryptographic algorithms incredibly relies on ideas of data safety that is essential within the current technological global. This paper propose a Secured Modified Advanced Encryption Standard Algorithm with decreasing the rounds of Advanced Encryption Standard (AES) to 14 rounds in order to minimize encryption and decryption process time and increasing digital data security as well. The results have been proved that the proposed technique provides higher efficiency in term of encryption and decryption process time compared to other researches while increase security which has been proved by using avalanche effect test.
Detection of First-Episode of Schizophrenia Brain MRI Images Using Random Forest Classifier

Schizophrenia (SCZ) is a mental disorder, that is, persons loss their normal life and they supposed to live in an imaginary world of hallucination or delusions. Segmentation of brain tissue is an important task for the accurate detection of the disease SCZ. Here, proposing a novel method to segment the brain tissue and obtained the dominated features for the detection. Medical imaging is used and is a technique that processes and creates visual representation of the interior of a body for clinical analysis and medical intervention. Here, integrating two segmentation procedures is based on probability distribution and various intensity level heuristic segmentations, which make use of swarm optimization. A better result can be made using the extracted features of gray matter. ANOVA was used to analyze the statistical performance. The result shows that it yields accuracy 99.09%, sensitivity 100% and specificity 98.14% for schizophrenic detection with the use of random forest classifier.
Microblog Text Classification System Based on TextCNN and LSA Model

With the development of the internet technology, kinds of short text information are growing explosively. Information explosion has become an urgent problem. To find the target information accurately from a large number of short texts and to find the short text with the same semantic meaning of the target information, we propose and build a microblog text classification system. Take the official microblog of Peking University as an example; the TextCNN model based on Convolutional Neural Network (CNN) is used for the classification of the text of Peking University's Weibo. The text is pre-processed accordingly and converted into word vectors through unsupervised learning. TextCNN is used for training to realize the classification of Weibo text. Finally, the paper sets up a web server to interact with the users, and uses the Latent Semantic Analysis (LSA) algorithm to recommend relevant categories of Weibo based on user search content. The experimental results show that the classification accuracy of the TextCNN model used in the article is 85.94%. This paper realizes the function of microblog content classification and accurate search. We help users quickly and accurately find the content they want, reducing the time of browsing useless information.
The role of blockchain for increase trust on software components and services

Software components and services are the main building blocks for distributed software projects, enabling them to make software engineering really global. However, the trust for binary components or service is still an open question. Because the components and services are provided to the system without the possibility of the detailed inspection of its internals, global system integrators are walking on edge to approve such building blocks inside the system. In the times when software quality now is critically important (as the financial data), we need solutions as secure as those which are used to preserve financial data. In this paper, the authors discuss the main issues of the software components and services trust and to present the blockchain-based model of software metadata registration and retrieval to increase the trust for the software composition entities.
Review of question answering technology based on Text to SQL

Text to SQL technology is to convert natural language problems into corresponding SQL statements to help users query massive data from the database. This paper introduces the construction of text to SQL data set, the analysis of various technologies of the task model and the application fields based on the technology. It is found that the current model still has a lot of room for improvement for wikisql, spider and other large-scale annotation data sets, while there is little research on Chinese text to SQL technology. Finally, it summarizes some problems that should be paid attention to in future research.
Secure Door on Cloud: A Secure Data Transmission Scheme to Protect Kafka's Data

Apache Kafka, which is a high-throughput distributed message processing system, has been leveraged by the majority of enterprise for its outstanding performance. Unlike common cloud-based access control architectures, Kafka service providers often need to build their systems on other enterprises' high-performance cloud platforms. However, since the cloud platform belongs to a third party, it is not necessarily reliable. Paradoxically, it has been demonstrated that Kafka's data is stored in the cloud in the plaintext form, and thus poses a serious risk of user privacy leakage. In this paper, we propose a secure fine-grained data transmission scheme called Secure Door on Cloud (SDoC) to protect the data from being leaked in Kafka. SDoC is not only more secure than Kafka's built-in security mechanism, but also can effectively prevent third-party cloud from stealing plaintext data. To evaluate the performance of the SDoC, we simulate normal inter-entity communication and show that Kafka with SDoC integration has a lower data transfer time overhead than that of Kafka with built-in security mechanism opened.
Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

Due to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are authentication, integrity veri cation, and tampering detection of the digital contents.In this paper, text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents.The proposed approach embeds and detects the watermark logically without altering the original English text document.Based on hidden Markov model (HMM), the fourth level order of the word mechanism is used to analyze the contents of the given English text to nd the interrelationship between the contexts.The extracted features are used as watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering, the proposed approach has been implemented and validated with attacked English text.Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion, reorder, and deletion attacks.The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks.Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.
Kernel Subspace Clustering with Block Diagonal Prior

We present in this paper a kernel subspace clustering with block diagonal prior (KSCBD) method for nonlinear data analysis. Unlike most existing block diagonal constrained subspace clustering methods can only be performed explicitly on the original data space, our KSCBD embed the block diagonal prior into the kernel Hilbert space. Specifically, our KSCBD first projects the data to a high-dimension linear space by kernel method. Then, the block diagonal prior is embedded to the kernel Hilbert space for keeping the block diagonal structure. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.
Constructing three-qubit unitary gates in terms of Schmidt rank and CNOT gates

It is known that every two-qubit unitary operation has Schmidt rank one, two or four, and the construction of three-qubit unitary gates in terms of Schmidt rank remains an open problem. We explicitly construct the gates of Schmidt rank from one to seven. It turns out that the three-qubit Toffoli and Fredkin gate, respectively, have Schmidt rank two and four. As an application, we implement the gates using quantum circuits of CNOT gates and local Hadamard and flip gates. In particular, the collective use of three CNOT gates can generate a three-qubit unitary gate of Schmidt rank seven in terms of the known Strassen tensor from multiplicative complexity.
OpenSurgery for Topological Assemblies

Surface quantum error-correcting codes are the leading proposal for fault-tolerance within quantum computers. We present OpenSurgery, a scalable tool for the preparation of circuits protected by the surface code operated through lattice surgery. Lattice surgery is considered a resource efficient method to implement surface code computations. Resource efficiency refers to the number of physical qubits and the time necessary for executing a quantum computation. OpenSurgery is a first step towards methods that aid quantum algorithm design informed by the realities of the hardware architectures. OpenSurgery can: 1) lay out arbitrary quantum circuits, 2) estimate the quantum resources used for their execution, 3) visualise the resulting 3D topological assemblies. Source code is available at http://www.github.com/alexandrupaler/opensurgery.
Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution

Single-image super-resolution, i.e., reconstructing a high-resolution image from a low-resolution image, is a critical concern in many computer vision applications. Recent deep learning-based image super-resolution methods employ massive numbers of model parameters to obtain quality gain. However, this leads to increased model size and high computational complexity. To mitigate this, some methods employ recursive parameter-sharing for better parameter efficiency. Nevertheless, their designs do not adequately exploit the potential of the recursive operation. In this paper, we propose a novel super-resolution method, called a volatile-nonvolatile memory network (VMNet), to maximize the usefulness of the recursive architecture. Specifically, we design two central components called volatile and nonvolatile memories. By means of these, the recursive feature extraction portion of our model performs effective recursive operations that gradually enhance image quality. Through extensive experiments on $\times 2$ , $\times 3$ , and $\times 4$ super-resolution tasks, we demonstrate that our method outperforms existing state-of-the-art methods in terms of image quality and complexity via stable progressive super-resolution.
An Overview of Recommender Systems in the Context of Smart Cities

The concept of smart city appeared following technological, societal and organizational changes. A smart city uses huge number of equipments connected to internet, to gather data and use it to effectively manage resources and enhance urban services quality.The use of Recommender Systems(RS) in smart cities plays take a leading part to guid citizens in the process of finding services that match with their preferences. Recommendations provided allow users to satisfy their needs in an efficient and easy way and make their daily lifes less complicated. This paper introduces an overview of Recommender Systems in Smart Cities, also presents real-world application of RS in IoT and in Smart Cities.
Modeling of Ground Handling Processes in Simio Software

More and more attention is being devoted to increasing the capacity of airspace and airports (airport capacity) due to the increase in air traffic. The effects of the increase in global air traffic are felt not only when managing the movement of aircraft in the air, but also when performing operations at airports. An increasing number of airports must solve the problem of airport capacity. This problem can be solved by developing the airport infrastructure but it is extremely important to analyze processes at the airport. Airports have the necessary infrastructure to enable safe and efficient implementation of airport traffic. Airport traffic is divided into the movement of aircraft performing take-off and landing operations, the movement of aircraft during taxiing after landing and before departure and apron operations. The aircraft handling process is combined with the handling of passengers, luggage, mail and other cargo. Particular attention should be paid to the ground handling process and coordination between relevant entities. Ground handling includes activities performed when an aircraft is parked at an aircraft stand. Among the operations at the aircraft stand, one can distinguish several categories of services performed at the airport for airlines. There is a close relationship between aircraft service at an aircraft stand, taxiway and runway operations. Operations occurring in ground aerodrome traffic can be described as a sequence of operations elementary for aircraft movement in the airport area and on aprons. The organization of ground handling operations performed at an aircraft stand affects the moment of completion of ground handling and the issue of aircraft take off authorization. The identified aerodrome operations at the airport interact with each other to form a structure consisting of a series of activities occurring in parallel or in series. The article examines the process of ground handling of aircraft and includes an analysis based on real-time measurements of steps taken during ground handling. A model was developed and a ground handling model was simulated using Simio software. The developed model was verified on real measurements taken at an airport. An analysis of the duration of performance of individual activities was carried out for different types of aircraft. On this basis, the reliability of the model was confirmed and several simulations were performed. As part of the study of the results, critical paths were analyzed. The model will be used to introduce interference and analysis processes involving a stand to estimate the impact of interference on the timely execution of elementary operations.
The Insider Threat: Reasons, Effects and Mitigation Techniques

The insider threat is increasingly becoming extremely important for companies, organizations and even governments. A malicious, or even a careless, insider can cause severe damage to the resources and the reputation of an organization. In this article, we provide an overview of the basic characteristics of insider cyber-security threats and we present current approaches and controls of mitigating such threats.
CARNAP’S PROBLEM FOR MODAL LOGIC

Abstract We take Carnap’s problem to be to what extent standard consequence relations in various formal languages fix the meaning of their logical vocabulary, alone or together with additional constraints on the form of the semantics. This paper studies Carnap’s problem for basic modal logic. Setting the stage, we show that neighborhood semantics is the most general form of compositional possible worlds semantics, and proceed to ask which standard modal logics (if any) constrain the box operator to be interpreted as in relational Kripke semantics. Except when restricted to finite domains, no modal logic characterizes exactly the Kripkean interpretations of $\Box $ . Moreover, we show that, in contrast with the case of first-order logic, the obvious requirement of permutation invariance is not adequate in the modal case. After pointing out some known facts about modal logics that nevertheless force the Kripkean interpretation, we focus on another feature often taken to embody the gist of modal logic: locality. We show that invariance under point-generated subframes (properly defined) does single out the Kripkean interpretations, but only among topological interpretations, not in general. Finally, we define a notion of bisimulation invariance—another aspect of locality—that, together with a reasonable closure condition, gives the desired general result. Along the way, we propose a new perspective on normal neighborhood frames as filter frames, consisting of a set of worlds equipped with an accessibility relation, and a free filter at every world.
On Saliency Maps and Adversarial Robustness

A very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts that focus solely on good interpretations or robustness against adversaries. Works have shown that adversarially trained models exhibit more interpretable saliency maps than their non-robust counterparts, and that this behavior can be quantified by considering the alignment between the input image and saliency map. In this work, we provide a different perspective to this coupling and provide a method, Saliency based Adversarial training (SAT), to use saliency maps to improve the adversarial robustness of a model. In particular, we show that using annotations such as bounding boxes and segmentation masks, already provided with a dataset, as weak saliency maps, suffices to improve adversarial robustness with no additional effort to generate the perturbations themselves. Our empirical results on CIFAR-10, CIFAR-100, Tiny ImageNet, and Flower-17 datasets consistently corroborate our claim, by showing improved adversarial robustness using our method. We also show how using finer and stronger saliency maps leads to more robust models, and how integrating SAT with existing adversarial training methods, further boosts the performance of these existing methods.
Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment

Automated surgical gesture recognition is of great importance in robot-assisted minimally invasive surgery. However, existing methods assume that training and testing data are from the same domain, which suffers from severe performance degradation when a domain gap exists, such as the simulator and real robot. In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Specifically, we first propose a Motion Direction Oriented Kinematics feature alignment (MDO-K) to align kinematics, which exploits temporal continuity to transfer motion directions with smaller gap rather than position values, relieving the adaptation burden. Moreover, we propose a Kinematic and Visual Relation Attention (KV-Relation-ATT) to transfer the co-occurrence signals of kinematics and vision. Such features attended by correlation similarity are more informative for enhancing domain-irreverent of the model. Two feature alignment strategies benefit the model mutually during the end-to-end learning process. We extensively evaluate our method for gesture recognition using DESK dataset with peg transfer procedure. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in Accuracy and 20.16% in F1score without using any annotations in real robot.