Reviewerly/exHarmony huggingface dataset:
Heuristic Pairwise Alignment in Database Environments

Biological data have gained wider recognition during the last few years, although managing and processing these data in an efficient way remains a challenge in many areas. Increasingly, more DNA sequence databases can be accessed; however, most algorithms on these sequences are performed outside of the database with different bioinformatics software. In this article, we propose a novel approach for the comparative analysis of sequences, thereby defining heuristic pairwise alignment inside the database environment. This method takes advantage of the benefits provided by the database management system and presents a way to exploit similarities in data sets to quicken the alignment algorithm. We work with the column-oriented MonetDB, and we further discuss the key benefits of this database system in relation to our proposed heuristic approach.
Quantum dense coding network using multimode squeezed states of light

We present a framework of a multimode dense coding network with multiple senders and a single receiver using continuous variable systems. The protocol is scalable to arbitrary numbers of modes with the encoding being displacements while the decoding involves homodyne measurements of the modes after they are combined in a pairwise manner by a sequence of beam splitters, thereby exhibiting its potentiality to implement in laboratories with currently available resources. We compute the closed form expression of the dense coding capacity for the cases of two and three senders that involve sharing of three- and four-mode states, respectively. The dense coding capacity is computed with the constraint of fixed average energy transmission when the modes of the sender are transferred to the receiver after the encoding operation. In both the cases, we demonstrate the quantum advantage of the protocol using paradigmatic classes of three- and four-mode states. The quantum advantage increases with the increase in the amount of energy that is allowed to be transmitted from the senders to the receiver.
Using devices for immersion in virtual reality when studying the section "Input and output devices" at the informatics lesson

The article discusses the possibilities of using devices for immersion in virtual reality when studying the section "Input and output devices" of the school informatics course. The possible stages of the lesson "Peripheral devices. Devices for immersion in virtual reality" are described in the article. At the "Organizational moment" stage, the "Unusual photo" technique is considered, at the "Summarizing the lesson" stage, the "Reﬂective target" technique is considered. Revealing the content of the stages of the lesson, the article describes the main devices used for immersion in virtual reality: base stations, virtual reality helmet, controllers. Instructions are given: how to properly put on a virtual reality helmet; how to put the controller on your hand. The safety precautions that are recommended to be observed in the process of working in virtual reality are considered. The article describes examples of tasks that can be oﬀered to students at each stage of the lesson. Most of the tasks in the lesson are performed by students in pairs.
A Neuro-Symbolic Classifier with Optimized Satisfiability for Monitoring Security Alerts in Network Traffic

We introduce in this paper a neuro-symbolic predictive model based on Logic Tensor Networks, capable of discriminating and at the same time of explaining the bad connections, called alerts or attacks, and the normal connections. The proposed classifier incorporates both the ability of deep neural networks to improve on their own through learning from experience and the interpretability of the results provided by the symbolic artificial intelligence approach. Compared to other existing solutions, we advance in the discovery of potential security breaches from a cognitive perspective. By introducing the reasoning in the model, our aim is to further reduce the human staff needed to deal with the cyber-threat hunting problem. To justify the need for shifting towards hybrid systems for this task, the design, the implementation, and the comparison of the dense neural network and the neuro-symbolic model is performed in detail. While in terms of standard accuracy, both models demonstrated similar precision, we further introduced for our model the concept of interactive accuracy as a way of querying the model results at any time coupled with deductive reasoning over data. By applying our model on the CIC-IDS2017 dataset, we reached an accuracy of 0.95, with levels of satisfiability around 0.85. Other advantages such as overfitting mitigation and scalability issues are also presented.
Unsupervised historical map registration by a deformation neural network

Image registration that aligns multi-temporal or multi-source images is vital for tasks like change detection and image fusion. Thanks to the advance and large-scale practice of modern surveying methods, multi-temporal historical maps can be unlocked and combined to trace object changes in the past, potentially supporting research in environmental science, ecology and urban planning, etc. Even when maps are geo-referenced, the contained geographical features can be misaligned due to surveying, painting, map generalization, and production bias. In our work, we adapt an end-to-end unsupervised deformation network that couples rigid and non-rigid transformations to align scanned historical map sheets at different time stamps. To the best of our knowledge, we are the first to use unsupervised deep learning to register map images. We address the sparsity of map features by introducing a loss based on distance fields. When aligning the displaced landmark locations by our proposed method, the results are promising both quantitatively and qualitatively. The generated smooth deformation grid can be applied to vector features directly to align them from the source map sheet to the target map sheet.
A method of value model convergence and profit optimization for crossover services

Value models have been employed in value-based requirements engineering (RE) to express value exchange relations between stakeholders. The value chain of crossover services spans multiple domains, so it is necessary to converge the value models of multiple domains when conducting requirements analysis. Existing value modeling methods, however, mostly focus on describing the value transmission relations in a single domain and have their limitations in coping with complex value models convergence analysis of multiple domains in crossover scenarios. After the convergence of value models, the value exchange relations between stakeholders has changed. How to make crossover services providers earn maximum profit is also omitted in these methods. In this study, we proposed a value model convergence method and a profit optimization method with consideration of meeting the value proposals of all stakeholders for crossover services value network models convergence analysis. We use an experimental application to illustrate the capabilities and efficiency of the proposed value models convergence method. Furthermore, we compare the performance of the proposed method with other methods in completing profit optimization tasks. The experimental results demonstrate that the proposed method has better performance and efficiency than alternative methods.
UncensoredLibrary Project: astrategy to circumvent censorship in countries predatory of press freedom

Introduction: The UncensoredLibrary is a virtual anti-censorship library, created in 2020 within the game Minecraft by Reporters WithoutBorders to circumvent censorship in countries where access to information is restricted.Objective: Its purposeis to discuss The Uncensored Library as a means of access to censored information in countries identified as limiting freedom of the press; examine their adequacy and the characteristics of virtual libraries and reflect on the application of content curation in their collection composition. Methodology: As a method, an exploratory study of a theoretical nature was undertaken, configured as a casestudy. Systematic observation, bibliographic and documentary research were usedfor data collection.Result:verifies it can be characterized as a virtual library with limitations. In the construction of the collection,it is noticed that a still rustic form of content curation has been applied, but that the process has the potentialto be applied in other initiativesof this kind.Conclusion: The Uncensored Library has limitations on access to hosted items, however, it achieves the purpose of sharing censored articles in countries predatory of press freedom
Features of the method of teaching the For loop in Python language in secondary school

Today, in many educational institutions, the study of the Python programming language is included in the informatics curriculum. Possessing indisputable advantages, which are noted by both research educators and practicing teachers, the Python language has a number of features that students should pay attention to in order to prevent mistakes that they may make in the future when switching to other programming languages. This also applies to the study of cyclic constructions, in particular the For loop. The implementation of the For loop in the Python language is fundamentally diﬀerent from the implementation of the loop with a parameter used in procedural programming languages that were studied earlier in the school curriculum (BASIC, Pascal, C/C++, etc.). Today, we have a situation where the method of learning a loop with a parameter cannot be applied to the For loop of the Python language. The article is devoted to the methods of studying this cyclic construction.
LetterSampo–Historical Letters on the Semantic Web: A Framework and Its Application to Publishing and Using Epistolary Data

Epistolary data about historical letters are typically distributed in different archives depending on where the letters were sent to and received, and the data are represented using local heterogeneous data models and different natural languages. To study such letter data on a global level, the heterogeneous, distributed data in local siloes need to be aggregated and harmonized into larger services where local metadata can enrich each other to complement missing information. This article presents a new framework, LetterSampo, for representing, publishing, and using epistolary data as Linked Open Data (LOD) on the Web for Digital Humanities (DH) research. The framework is used for creating LOD services and for building individual LetterSampo portals on top of them. To test and demonstrate the framework, it has been applied to the epistolary CKCC dataset of ca. 20,000 letters of the Huygens Institute, the Netherlands, to the correspSearch dataset of ca. 151,000 letters aggregated by the Berlin-Brandenburg Academy of Sciences and Humanities, and to the Early Modern Letters Online (EMLO) data of ca. 170,000 letters published by the University of Oxford. The CKCC and correspSearch datasets were published as LOD services, SPARQL endpoints, and as data dumps at Zenodo.org for re-use, and a demonstrational portal LetterSampo: Historical Letters on the Semantic Web was created based on this data. A novelty of the LetterSampo portals is to use faceted semantic search for filtering data of interest in flexible ways from multiple perspectives on two conceptual levels, and then visualize and analyze the results and data by seamlessly integrated data analytic tools—programming skills are not needed for using the portal. In addition to using the tools of the portal, the SPARQL endpoints can be used with modest knowledge about programming for DH research.
Communication-Efficient Secure Federated Statistical Tests from Multiparty Homomorphic Encryption

The power and robustness of statistical tests are strongly tied to the amount of data available for testing. However, much of the collected data today is siloed amongst various data owners due to privacy concerns, thus limiting the utility of the collected data. While frameworks for secure multiparty computation enable functions to be securely evaluated on federated datasets, they depend on protocols over secret shared data, which result in high communication costs even in the semi-honest setting.In this paper, we present methods for securely evaluating statistical tests, specifically the Welch’s t-test and the χ2-test, in the semi-honest setting using multiparty homomorphic encryption (MHE). We tested and evaluated our methods against real world datasets and found that our method for computing the Welch’s t-test and χ2-test statistics required 100× less communication than equivalent protocols implemented using secure multiparty computation (SMPC), resulting in up to 10× improvement in runtime. Lastly, we designed and implemented a novel protocol to perform a table lookup from a secret shared index and use it to build a hybrid protocol that switches between MHE and SMPC representations in order to calculate the p-value of the statistics efficiently. This hybrid protocol is 1.5× faster than equivalent protocols implemented using SMPC alone.
The Dark Side of the Entangled Informational Universe

After having explored some basic theoretical concepts about the quantum information approach, we focus on Entropic Information Theory which is an informational approach mathematically based on the mass of the bit of information; massbit = \(\frac{kTIn(2)}{c^2}\). The mass of the bit of information and the new entropy formulae associated to it,S = k2\(\frac{TIn(2)t}{h}\) , and its alternative writings lead to new formulation,SBH = K\(\frac{c^3In(2)tevap}{16\pi^2GM}\), to calculate the entropy of black holes independently of the law of area. Being able to express the fine-grained gravitational entropy of a black hole using the rules of gravity, we can, at this level, speak of quantum gravity as emerging through the fundamentality of entangled quantum information by considering that information emerges from degree of freedom; indeed, information being a quantum state change due to the modification of one degree of freedom from the considered quantum system. In addition, we calculated the informational content of the observable universe using the entropic information formula, to obtain, 1.57 1099 bits, a result remarkably close to some previous estimates to account for all the dark matter missing in the visible Universe. After that, we calculated the amount of energy associated with this informational content using Landauer's principle, to obtain, 3.50 1076 Joules, a result that we can relate to dark energy estimates. Moreover, some deep considerations based on the perspectives of Entropic Information Theory have been explored. This new complete mathematical framework of Entropic Information Theory can explain various processes being several aspects of the same, entangled information, by considering that information emerges from degree of freedom, it is the theoretical framework of the entangled informational universe.
ULAN: A Universal Local Adversarial Network for SAR Target Recognition Based on Layer-wise Relevance Propagation

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.
One-Class Convolutional Neural Networks for Water-Level Anomaly Detection

Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging from leakage to destruction of infrastructures, leading to economic and life losses. Thus, apprehending the nature of abnormalities that may interrupt or aggravate the service or cause the destruction is at the core of their business model. Normally, companies use sensor networks to monitor these systems and record operational data including any fluctuations in water levels considered abnormalities. Detecting abnormalities allows water companies to enhance the service's sustainability, quality, and affordability. This study investigates a 2D-CNN-based method for detecting water-level abnormalities as time-series anomaly pattern detection in the One-Class Classification (OCC) problem. Moreover, since abnormal data are usually scarce or unavailable, we explored a cheap method to generate synthetic temporal data and use them as a target class in addition to the normal data to train the CNN model for feature extraction and classification. These settings allow us to train a model to learn relevant pattern representations of the given classes in a binary classification fashion using cross-entropy loss. The ultimate goal of these investigations is to determine if any 2D-CNN-based model can be trained from scratch or if transfer learning of any pre-trained CNN model can be partially trained and used as the base network for one-class classification. The evaluation of the proposed One-Class CNN and previous approaches have shown that our approach has outperformed several state-of-the-art approaches by a significant margin. Additionally, in this paper, we mention two interesting findings: using synthetic data as the pseudo-class is a promising direction, and transfer learning should be dealt with considering that underfitting can happen because the transferred model is too complicated for training data.
Earthquake Event Recognition on Smartphones Based on Neural Network Models

Using sensors embedded in smartphones to study earthquake early warning (EEW) technology can effectively reduce the high construction and maintenance costs of traditional EEW systems. However, due to the impact of human activities, it is very difficult to accurately detect seismic events recorded on mobile phones. In this paper, to improve the detection accuracy of earthquakes on mobile phones, we investigated the suitability of different types of neural network models in seismic event detection. Firstly, we collected three-component acceleration records corresponding to human activities in various scenarios such as walking, running, and cycling through our self-developed mobile application. Combined with traditional strong-motion seismic event records fusing typical mobile phone accelerometer self-noise, all records were used for establishing the training and testing dataset. Finally, two types of neural network models, fully connected and convolutional neural networks, were trained, validated, and tested. The results showed that the accuracy rates of the neural network models were all over 98%, and the precision rate for seismic events and the recall rate for non-earthquake events could both reach 99%, indicating that the introduction of neural networks into the earthquake recognition on smartphones can significantly enhance the accuracy of seismic event recognition. Therefore, we can exceedingly reduce the amount of data transmitted to the processing server, further lowering the load on the server processor and effectively increasing the lead time at each target site for an EEW system.
Proposed Hybrid Technique in Cryptanalysis of Cryptosystem Based on PSO and SA

Modern ciphers are one of the more difficult to break cipher systems because these ciphers high security, high speed, non - propagation error and difficulty in breaking it. One of the most important weaknesses of stream cipher is a matching or correlation between the output key-stream and the output of shift registers. This work considers new investigation methods for cryptanalysis stream cipher using ciphertext only attack depending on Particle Swarm Optimization (PSO) for the automatic extraction for the key. It also introduces a cryptanalysis system based on PSO with suggestion for enhancement of the performance of PSO, by using Simulated Annealing (SA). Additionally, it presents a comparison for the cryptanalysis system results that were obtained by the proposed technique, which is called Modified PSO (MPSO) with classical PSO and GA. These algorithms can be used for reducing the number of attempts or trials of key space, which are needed to reach to the optimal solution (the exact initial setting of Linear Feedback Shift Register (LFSR)),and increase the speed of the search process to find the best solution. Based on the obtained results, these algorithms were shown to be effective at finding the optimal solution and the MPSO method operates better compared with PSO in the term of time and accuracy. Well known stream cipher systems were attacked by the two soft computing methods as the cases of study, which are Geffe , Brȕer and Shrinking stream cipher systems.
Mineralogy of Recent Sediments of AL-Teeb River Basin East Missan Governorate Southeastern Iraq

The main aim of this research is to define the mineralogical composition of recent sediments deposited around the Al-Teeb river basin in eastern Missan, trying to determine the provenance or the source of these sediments. The study area represents the southeastern edge of the Mesopotamian Plain and is part of it. Quaternary deposits cover most of the area. It is clayey with old sea and river deposits and part of aeolian deposits. These sediments cover 95% of the study area, while the older rocks, which date back to the Tertiary (Late Miocene – Pliocene), exposed in the area east and northeast of the Al-Teeb area, made up hills which back to the undifferentiated Pliocene Mukdadiya and Bai-Hassan formations. The light components of these sediments consist mainly of quartz, feldspars (potash and plagioclase feldspar), sedimentary rock fragments (carbonate rock fragments, chert rock fragments, evaporates fragments), igneous rock fragments, and metamorphic rock fragments The heaviest minerals are opaque, amphiboles, pyroxenes, chlorite, epidotes, biotite, garnet, muscovite, zircon, kyanite, staurolite, and rutile. These sediments are typically formed by sedimentary rocks (single or many cycles), low and high-rank metamorphic rocks, acidic and basic igneous rocks, and pegmatite rocks. The high percentage of opaque heavy minerals in clastic sediment refers to unstable clastic sediments. The stability issue to the areas during the study shows that there are significant variances over the several places, indicating dissimilar sources and types of source rocks