Reviewerly/exHarmony huggingface dataset:
Management of evaluation and assessment processes in lower secondary schools in Slovenia

In today's society, acquiring knowledge is not enough. Learners and students have to be provided with problem-solving competences and become trained for an independent, lifelong process of learning and constructive self-criticism, and accepting responsibility. This is especially important in teaching interdisciplinary and multidisciplinary fields, such as STEM (science, technology, engineering and mathematics). Evaluation and assessment is an indispensable element also of the students self-evaluation process. The two basic research questions in this study were: What are the goals of testing and assessment? Do teachers manage to regularly monitor the students progress? The presented research focuses mainly on the strategies and methods for testing, evaluation and assessment in the interdisciplinary field of Science, Technology and Engineering (STE). The main research results showed that too little time is spent on formative assessment of individual students, and that students are thus unable to regularly monitor their learning progress.
A Fuzzy Based MCDM Methodology for Risk Evaluation of Cyber Security Technologies

Cyber security that also known as information technology security is to protect computers, mobile devices, servers, electronic systems and networks from malicious digital attacks. In recent years, cyber security threats have been a growing problem for any critical digital infrastructure and various cyber-attacks created over the Internet are also becoming a big issue for the society. Therefore, the use of technologies developed to provide cyber security is very important. However, the risks of cyber security technologies should be taken into account when choosing among cyber security technologies. For this aim, we have proposed a multi-criteria decision making (MCDM) methodology based on hesitant fuzzy sets (HFSs) that gives experts extra flexibility in using linguistic terms to evaluate the criteria and alternatives to determine the best cyber security technology. For this aim, a study has also been discussed which deals with risk factors in the selection of cyber security technologies via fuzzy MCDM process.
Augmented Quaternion Extreme Learning Machine

As an efficient training strategy for single hidden layer neural networks, extreme learning machine, and its variants have been widely used due to its fast learning speed and superior generalization performance. However, when used for quaternion signal processing, extreme learning machine cannot make full use of the cross-channel correlation and quaternion statistics and thus often provides only suboptimal solutions. To this end, in this paper, we extend the extreme learning machine to quaternion domain and then propose two augmented quaternion extreme learning machine models for quaternion signal processing. These two models incorporate the involutions of the network input and hidden nodes, respectively, and can fully capture the second-order statistics of quaternion signals. In order to overcome the possible overfitting problem, two corresponding regularized augmented algorithms are also derived with the help of the generalized HR calculus. The superiority of the proposed models is verified by the simulation results.
Quantitative Interpretation of Cryptographic Algorithms

Encryption is the method of sending messages via code. It essentially jumbles up the content of a message so that it would not make sense to a third party trying to gain access to the material. Both the sender and the recipient have the right key which encrypts and decrypts the message. It is the most effective method of securing communication. Encryption algorithms are divided into two categories: symmetric and asymmetric. In this paper, some of the most widely used encryption algorithms 3DES, AES and RSA have been implemented using C# programming language and .NET framework. After the implementation of these algorithms, they have been compared on the following parameters—encryption time, decryption time and throughput.
Quantitative discrimination of biological tissues by micro-elastographic measurement using an epi-illumination Mueller matrix microscope

We propose a method for estimating the stiffness of bio-specimens by measuring their linear retardance properties under applied stress. For this purpose, we employ an epi-illumination Mueller matrix microscope and show the procedures for its calibration. We provide experimental results demonstrating how to apply Mueller matrix data to elastography, using chicken liver and chicken heart as biological samples. Finally, we show how the histograms of linear retardance images can be used to distinguish between specimens and quantify the discrimination accuracy.
Structured Traversal of Search Trees in Constraint-Logic Object-Oriented Programming

In this paper, we propose an explicit, non-strict representation of search trees in constraint-logic object-oriented programming. Our search tree representation includes both the non-deterministic and deterministic behaviour during execution of an application. Introducing such a representation facilitates the use of various search strategies. In order to demonstrate the applicability of our approach, we incorporate explicit search trees into the virtual machine of the constraint-logic object-oriented programming language Muli. We then exemplarily implement three search algorithms that traverse the search tree on-demand: depth-first search, breadth-first search, and iterative deepening depth-first search. In particular, the last two strategies allow for a complete search, which is novel in constraint-logic object-oriented programming and highlights our main contribution. Finally, we compare the implemented strategies using several benchmarks.
A Lindström Theorem for Intuitionistic Propositional Logic

It is shown that propositional intuitionistic logic is the maximal (with respect to expressive power) abstract logic satisfying a certain topological property reminiscent of compactness, the Tarski union property and preservation under asimulations.
Neural Network Prediction of Censorable Language

Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House’s annual Freedom on the Net report, more than half the world’s Internet users now live in a place where the Internet is censored or restricted. China has built the world’s most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention ‘sensitive’ topics or authored by ‘sensitive’ users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50% accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.
Quantum Privacy-Preserving Price E-Negotiation

Privacy-preserving price e-negotiation (3PEN) is an important topic of secure multi-party computation (SMC) in the electronic commerce field, and the key point of its security is to guarantee the privacy of seller's and buyer's prices. In this study, a novel and efficient quantum solution to the 3PEN problem is proposed, where the oracle operation and the qubit comparator are utilized to obtain the comparative results of buyer's and seller's prices, and then quantum counting is executed to summarize the total number of products which meets the trading conditions. Analysis shows that our solution not only guarantees the correctness and the privacy of 3PEN, but also has lower communication complexity than those classical ones.
A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters

Convolutional Neural Networks (CNNs) are one of the most extensively used tools in machine learning, but they are still not well understood and in many cases they are over-parameterized, leading to slow inference and impeding their deployment on low-power devices. In the last few years, many methods for decreasing the number of parameters in a network by pruning its output channels have been suggested, but a very recent work has argued that random pruning of channels performs on-par with state-of-the-art pruning methods. While random and other pruning methods might be effectively used for lowering the number of parameters in a CNN, none of these methods can be used to gain any further understanding of the model that the CNN has built. In this work, we propose a novel method for pruning a network, that at the same time can lead to a better understanding of what the individual filters of the network learn about the data. The method proposed aims to keep only the filters that are "important" for a class. We define a filter as important for a class if its removal has the highest negative impact on the accuracy for that class. We demonstrate that our method is better than random pruning on two networks used on the EMNIST and CIFAR10 datasets. By analyzing the important filters, we find that the important filters in the pruned networks learn features which are more general across classes. We demonstrate the importance and applicability of that observation in two transfer-learning tasks.
MsCoa: Multi-Step Co-Attention Model for Multi-Label Classification

Multi-label text classification (MLC) task, as one of the sub-tasks of natural language processing, has broad application prospects.On the basis of studying the previous research work, this research takes the relationship among text information, leading label information and predictive label information as the frame and analyzes the information loss of original text and leading label, decoding error accumulation.We propose an improved multi-step multi-classification model to mitigate the phenomenon of error prediction, label repetition and error accumulation.The model uses multi-step and multi-classification task to complete multi-label prediction.It uses the leading label and the original text as input, and the next to-be-predicted label as output.The co-attention mechanism is operated between the original text and the leading label.The attention of the original text to the leading label is helpful to filter out the error accumulation problem caused by the error prediction.The features is combined in a manner of difference and concatenation, which highlights the auxiliary effect of the model structure on feature extraction.In order to avoid the influence of the feature dimension on the performance of long short-term memory (LSTM), a multi-layer fully-connected classifier is used instead to predict the label.Through experimental validation, the performance of our model on the multi-label text classification task shows the current optimal level, which fully proves the superiority of our model.
Multimedia Data Retrieval Using Data Mining Image Pixle Comparison Techniques

Information extraction is one of the challenging factors for may researchers today. This task more complicated for multimedia data sets because of the quality of the data sets, it gives more challenging factor today. Recording the images convenient even for unprofessional user because of the growing technology allows, every day to produce a large amount of image, audio, video data sets are uploaded by different user community around the world. From this huge content extracting the needed information's are too complex activity. Image extracting done by either text based query or image attribute based retrieval. Extracting the need information from this complex data sets user need additional knowledge about the domain. This gives more attention on this filed. This research paper focuses image retrieval using hierarchical clustering technique. Process divided into two steps initially input data sets need to be trained with help of image pixel value. Second step though image query data's are retrieved from the trained data sets. The proposed technique works well, experimental results also verified this.
Verification of data integrity and co-operative loss recovery for secure data storage in cloud computing

In Cloud Computing, the data stored in the external servers may be tampered or deleted by unauthorized persons or selfish Cloud Service Providers (CSPs). Hence, the Cloud Data Owners (CDOs) have to provide assurance to the integrity and correctness of the stored data in the server. In this paper, a Verification of Data Integrity and Co-operative Loss Recovery technique for secure data storage in Cloud Computing is proposed. In this technique, a ciphertext file is split into various cipher blocks and distributed to randomly selected cloud service providers (CSPs) by the cloud data owner (CDO). If a cloud data user (CDU) wants to access any file, the corresponding ciphertext file is reconstructed from the blocks and downloaded by the user. The file can be decrypted if the attribute set of the user matches the access policy of the application. By simulation results, we show that the proposed technique enhances the data integrity and confidentiality.
Strong Tenant Separation in Cloud Computing Platforms

Cloud computing, with its large, homogeneous infrastructures, has a high sensitivity to security incidents due to the multitude of affected services and tenants. To contain a potential attacker within a compromised subsystem, a strict separation between individual resources, e.g. virtual servers and networks, must be established. However, this separation usually relies on the integrity of the entire cloud platform. Due to the considerable size and complexity of the software powering these platforms and recently found attacks on hardware components, i.e. Spectre and Meltdown, this may not always represent a reasonable assumption.
Concept Mining in Online Forums Using Self-corpus-Based Augmented Text Clustering

This paper proposes a self-corpus-based text augmentation technique with clustering for concept mining in a discussion forum. Sparseness in text data, which challenges the distance and density measures in determining the concepts in a corpus, is handled through self-corpus-based document expansion via matrix factorization. Experiments with a real-world dataset show that the proposed method is able to infer useful concepts.
Detecting Spiky Corruption in Markov Decision Processes.

Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem within the formalism of Corrupt Reward Markov Decision Processes (CRMDPs). We show that if the reward corruption in a CRMDP is sufficiently spiky, the environment is solvable. We fully characterize the regret bound of a Spiky CRMDP, and introduce an algorithm that is able to detect its corrupt states. We show that this algorithm can be used to learn the optimal policy with any common reinforcement learning algorithm. Finally, we investigate our algorithm in a pair of simple gridworld environments, finding that our algorithm can detect the corrupt states and learn the optimal policy despite the corruption.
Optimal control for multi-parameter quantum estimation with time-dependent Hamiltonians

We investigate simultaneous estimation of multi-parameter with time-dependent Hamiltonians. We analytically obtain the maximum quantum Fisher information matrix for two-parameter encoded in time-dependent three-level systems. The optimal coherent control scheme is proposed to increase the simultaneous multi-parameter estimation precisions. In an example of a spin-1 particle in a uniformly rotating magnetic field, the optimal coherent Hamiltonians for different parameters can be chosen to be completely same. However, in general, the optimal coherent Hamiltonians for different parameters are incompatible. In this situation, we suggest a variance method to obtain the optimal coherent Hamiltonian for simultaneously estimating multiple parameters, and obtain the optimal simultaneous estimation precision of two-parameter in a three-level Landau-Zener Hamiltonian.
Analyzing Coreference Tools for NLP Application

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Comprehensive Survey on Internet of Things, Architecture, Security Aspects, Applications, Related Technologies, Economic Perspective, and Future Directions

Internet of Things (IoT) is the paradigm of network of Internet-connected things as objects that constantly sense the physical world and share the data for further processing. At the core of IoT lies the early technology of radio frequency identification (RFID), which provides accurate location tracking of real-world objects. With its small size and convenience, RFID tags can be attached to everyday items such as books, clothes, furniture and the like as well as to animals, plants, and even humans. This phenomenon is the beginning of new applications and services for the industry and consumer market. IoT is regarded as a fourth industrial revolution because of its massive coverage of services around the world from smart homes to artificial intelligence-enabled smart driving cars, Internet-enabled medical equipment, etc. It is estimated that there will be several dozens of billions of IoT devices ready and operating until 2020 around the world. Despite the growing statistics, however, IoT has security vulnerabilities that must be addressed appropriately to avoid causing damage in the future. As such, we mention some fields of study as a future topic at the end of the survey. Consequently, in this comprehensive survey of IoT, we will cover the architecture of IoT with various layered models, security characteristics, potential applications, and related supporting technologies of IoT such as 5G, MEC, cloud, WSN, etc., including the economic perspective of IoT and its future directions.
Statically Detecting Vulnerabilities by Processing Programming Languages as Natural Languages

Web applications continue to be a favorite target for hackers due to a combination of wide adoption and rapid deployment cycles, which often lead to the introduction of high-impact vulnerabilities. Static analysis tools are important to search for vulnerabilities automatically in the program source code, supporting developers on their removal. However, building these tools requires programming the knowledge on how to discover the vulnerabilities. This article presents an alternative approach in which tools <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> to detect flaws automatically by resorting to artificial intelligence concepts, more concretely to natural language processing. The approach employs a sequence model to learn to characterize vulnerabilities based on an annotated corpus. Afterwards, the model is utilized to discover and identify vulnerabilities in the source code. It was implemented in the DEKANT tool and evaluated experimentally with a large set of PHP applications and WordPress plugins. Overall, we found several thousand vulnerabilities belonging to 15 classes of input validation vulnerabilities, where 4143 of them were zero-day.
Boundary-induced effect encoded in the corrections to the geometric phase acquired by a bipartite two-level system

We present a bipartite two-level system coupled to electromagnetic quantum vacuum fluctuations through a general dipolar coupling. We derive the master equation in the framework of an open quantum system, assuming an environment composed of (i) solely vacuum fluctuations and (ii) the vacuum fluctuations and a conducting plate located at a fixed distance from the bipartite system. For both cases considered, we study the dynamics of the bipartite system and the temporal evolution of the concurrence of an initial entangled bipartite state. We further analyze the generation of entanglement due to the vacuum structure. Finally, we study the different induced contributions to the correction of the unitary geometric phase of a bipartite quantum state so as to explore the possibility of future experimental setups by exploring the influence of boundaries conditions in vacuum.
Quantum circuits for calculating the squared sum of the inner product of quantum states and its application

In this paper, we propose a quantum circuit for calculating the squared sum of the inner product of quantum states. The circuit is designed by the multi-qubits controlled-swapping gates, in which the initial state of each control qubit is [Formula: see text] and they are in the equilibrium superposition state after passing through some Hadamard gates. Then, according to the control rules, each basis state in the superposition state controls the corresponding quantum states pair to swap. Finally, the Hadamard gates are applied to the control qubits again, and the squared sum of the inner product of many pairs of quantum states can be obtained simultaneously by measuring only one control qubit. We investigate the application of this method in quantum images matching on a classical computer, and the experimental results verify the correctness of the proposed method.
The value of threat intelligence

Computer Fraud & SecurityVol. 2019, No. 10 RegularsThe value of threat intelligenceLiv RowleyLiv RowleyBluelivSearch for more papers by this authorLiv RowleyPublished Online:5 Nov 2021https://doi.org/10.1016/S1361-3723(19)30109-5AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 January 2019Volume 2019Issue 10ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 5 November 2021 Published in print 1 January 2019 Information© MA Business Ltd.PDF download
One-way quantum state transfer in a lossy coupled-cavity array

Quantum state transfer plays an important role in quantum information processing, and it has been obtained many of the theoretical and experimental triumphs. But designing a dissipation-assisted scheme to transfer a quantum state is still by no means trivial. Here we put forward an easier scheme to dissipatively transfer an arbitrary quantum state from a sender to a receiver with two four-level atoms and three lasers in a lossy coupled-cavity array, and make the quantum state stable at the receiver via the photon loss of optical cavities. Owing to the assistance of the dissipation, the target state becomes the steady state of the whole process. Thus there is no requirement on external time-dependent controls. Furthermore, the atomic spontaneous emission can be significantly suppressed as the adiabatic elimination of the excited states. We also discuss the experimental feasibility of this scheme with the current experimental technologies and a high fidelity of the transferred state in the receiver can be above 98%.