Reviewerly/exHarmony huggingface dataset:
An Asset-Based Assistance for Secure by Design

With the growing numbers of security attacks causing more and more serious damages in software systems, security cannot be added as an afterthought in software development. It has to be built in from the early development phases such as requirement and design. The role responsible for designing a software system is termed an “architect”, knowledgeable about the system architecture design, but not always well-trained in security. Moreover, involving other security experts into the system design is not always possible due to time-to-market and budget constraints. To address these challenges, we propose to define an asset-based security assistance in this paper, to help architects design secure systems even if these architects have limited knowledge in security. This assistance helps alert threats, and integrate the security controls over vulnerable parts of system into the architecture model. The central concept enabling this assistance is that of asset. We apply our proposal on a telemonitoring case study to show that automating such an assistance is feasible.
Experimental implementation of arbitrary entangled operations

Abstract Quantum entanglement lies at the heart of quantum mechanics in both fundamental and practical aspects. The entanglement of quantum states has been studied widely both theoretically and experimentally, however, the entanglement of operators has not been studied much experimentally in spite of its importance. Here, we propose a scheme to realize arbitrary entangled operations based on a coherent superposition of local operations with a non-zero probability of failure. Then, we experimentally implement several intriguing two-qubit entangled operations in photonic systems. We also discuss the generalization of our scheme to extend the number of superposed operations and the number of qubits. Due to the simplicity of our scheme, we believe that it can reduce the complexity or required resources of the quantum circuits and provide insights to investigate properties of entangled operations.
Generative Adversarial Network Using Multi-modal Guidance for Ultrasound Images Inpainting

Medical image inpainting not only helps computer-aided diagnosis systems to eliminate the interference of irrelevant information in medical images, but also helps doctors to prognosis and evaluate the operation by blocking and inpainting the lesion area. However, the existing diffusion-based or patch-based methods have poor performance on complex images with non-repeating structures, and the generate-based methods lack sufficient priori knowledge, which leads to the inability to generate repair content with reasonable structure and visual reality. This paper proposes a generative adversarial network via multi-modal guidance (MMG-GAN), which is composed of the multi-modal guided network and the fine inpainting network. The multi-modal guided network obtains the low-frequency structure, high-frequency texture and high-order semantic of original image through the structure reconstruction generator, texture refinement generator and semantic guidance generator. Utilizing the potential attention mechanism of convolution operation, the fine inpainting network adaptively fuses features to achieve realistic inpainting. By adding the multi-modal guided network, MMG-GAN realizes the inpainting content with reasonable structure, reliable texture and consistent semantic. Experimental results on Thyroid Ultrasound Image (TUI) dataset and TN-SCUI2020 dataset show that our method outperforms other state-of-the-art methods in terms of PSNR, SSIM, and relative l1 measures. Code and TUI dataset will be made publicly available.
Geometrical versus time-series representation of data in quantum control learning

Recently machine learning techniques have become popular for analysing physical systems and solving problems occurring in quantum computing. In this paper we focus on using such techniques for finding the sequence of physical operations implementing the given quantum logical operation. In this context we analyse the flexibility of the data representation and compare the applicability of two machine learning approaches based on different representations of data. We demonstrate that the utilization of the geometrical structure of control pulses is sufficient for achieving high-fidelity of the implemented evolution. We also demonstrate that artificial neural networks, unlike geometrical methods, posses the generalization abilities enabling them to generate control pulses for the systems with variable strength of the disturbance. The presented results suggest that in some quantum control scenarios, geometrical data representation and processing is competitive to more complex methods.
TINGKAT PARTISIPASI MAHASISWA DALAM PERKULIAHAN ELEARNING MELALUI FORUM DISKUSI DI PGSD FKIP UNSYIAH

One of the demands of universities today is to implement an online learning system. During the pandemic, all activities are carried out online, including lecture activities. Based on observations during learning during the pandemic, especially in the odd semester of 2020/2021, many lecturers implemented e-learning, from 30% to 85%, this was a very significant increase. This also had a significant effect on students, especially the level of participation in lectures increased. Student participation has an important role in learning success. The active participation of students is able to achieve learning goals so that academic achievement increases. Discussion forums are a vehicle for students to develop ideas and thoughts. From the results of the observation the discussion forum can increase their participation in the discussion, it can even trigger them to give their opinion so that students have confidence in conveying their ideas. This forum also provides space and flexibility so that all students actively participate in lectures. The student participation rate in e-learning courses reaches 90% of each topic discussed.
A Tree-Structure Convolutional Neural Network for Temporal Features Exaction on Sensor-Based Multi-resident Activity Recognition

With the propagation of sensor devices applied in smart home, activity recognition has ignited huge interest and most existing works assume that there is only one habitant. While in reality, there are generally multiple residents at home, which brings greater challenge to recognize activities. In addition, many conventional approaches rely on manual time series data segmentation ignoring the inherent characteristics of events and their heuristic hand-crafted feature generation algorithms are difficult to exploit distinctive features to accurately classify different activities. To address these issues, we propose an end-to-end Tree-Structure Convolutional neural network based framework for Multi-Resident Activity Recognition (TSC-MRAR). First, we treat each sample as an event and obtain the current event embedding through the previous sensor readings in the sliding window without splitting the time series data. Then, in order to automatically generate the temporal features, a tree-structure network is designed to derive the temporal dependence of nearby readings. The extracted features are fed into the fully connected layer, which can jointly learn the residents labels and the activity labels simultaneously. Finally, experiments on CASAS datasets demonstrate the high performance in multi-resident activity recognition of our model compared to state-of-the-art techniques.
A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns. A prominent example is hardware transient faults that are growing in frequency due to the progressive technology scaling, and can lead to failures in DNNs. This work proposes Ranger, a low-cost fault corrector, which directly rectifies the faulty output due to transient faults without re-computation. DNNs are inherently resilient to benign faults (which will not cause output corruption), but not to critical faults (which can result in erroneous output). Ranger is an automated transformation to selectively restrict the value ranges in DNNs, which reduces the large deviations caused by critical faults and transforms them to benign faults that can be tolerated by the inherent resilience of the DNNs. Our evaluation on 8 DNNs demonstrates Ranger significantly increases the error resilience of the DNNs (by 3x to 50x), with no loss in accuracy, and with negligible overheads.
Kreol Morisien to English and English to Kreol Morisien Translation System using Attention and Transformer Model

Translation from one language to another is extensively carried out by travellers, students and many other people.Unfortunately, less popular languages, such as Kreol Morisien (KM), are not catered for by popular translation systems.The objective of this work is to develop a system that translates English into Kreol Morisien and vice-versa.The size of the dataset which was used in the model consists of 19,650 pairs of English and Kreol Morisien sentences.The Kreol Morisien words are up to date since they were taken from the third edition of the Diksioner Morisien.A neural machine translation system has been used in this work.A transformer model with attention was then developed which was trained for several iterations.Evaluation of the system is done using the standard BLEU scores.The English to Kreol Morisien model achieves a BLEU score of 0.20 and the Kreol Morisien to English model achieves a BLEU score of 0.23.Both are much higher than existing systems.Furthermore, user evaluation has been carried out in the form of two surveys.Each survey consisted of 25 pairs of sentences in the source language and the target language.Responses were gathered from people with different age groups living in both rural and urban regions and including both students and professionals.We received more than 90 responses for each survey.Evaluation and testing of the translation model using the BLEU score showed that the model can produce satisfactory translations.
ConnectionChain: the Secure Interworking of Blockchains

Blockchain is a core technology to manage the value of cryptocurrencies, or to record trails of important business trades. The Smart Contract on blockchain is expected to improve security on blockchain system with automated operation, but it cannot be the solution when the application service required to operate tightly related blockchain ledgers as service business logic. This paper proposed the method to extend the functionality of traditional Smart Contract on blockchain, and introduced the prototype system, named 'ConnectionChain'.
SHIFTRY: WEB SERVICE FOR DIACHRONIC ANALYSIS OF RUSSIAN NEWS

We present the ShiftRy web service. It helps to analyze temporal changes in the usage of words in news texts from Russian mass media. For that, we employ diachronic word embedding models trained on large Russian news corpora from 2010 up to 2019. The users can explore the usage history of any given query word, or browse the lists of words ranked by the degree of their semantic drift in any couple of years. Visualizations of the words’ trajectories through time are provided. Importantly, users can obtain corpus examples with the query word before and after the semantic shift (if any). The aim of ShiftRy is to ease the task of studying word history on short-term time spans, and the influence of social and political events on word usage. The service will be updated with new data yearly.
Faster dense deformable image registration by utilizing both CPU and GPU

Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
Quantum Simulation of Non‐Perturbative Cavity QED with Trapped Ions

Abstract The simulation of non‐perturbative cavity‐QED effects is discussed using systems of trapped ions. Specifically, the implementation of extended Dicke models with both collective dipole‐field and direct dipole–dipole interactions is addressed, which represent a minimal set of models for describing light–matter interactions in the ultrastrong and deep‐strong coupling regime. It is shown that this approach can be used in state‐of‐the‐art trapped ion setups to investigate excitation spectra or the transition between sub‐ and superradiant ground states, which are currently not accessible in any other physical system. The analysis also reveals the intrinsic difficulty of accessing this non‐perturbative regime with larger numbers of dipoles, which makes the simulation of many‐dipole cavity QED a particularly challenging test case for future quantum simulation platforms.
Machine knowledge and human cognition

Intelligent machines are knowledge systems with unique knowledge structure and function.In this paper, we discuss issues including the characteristics and forms of machine knowledge, the relationship between knowledge and human cognition, and the approach to acquire machine knowledge.These issues are of great significance to the development of artificial intelligence.
Heterogeneous Data-Aware Federated Learning

Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework, we identify several problems hampering its successful deployment, such as presence of non i.i.d data, disjoint classes, signal multi-modality across datasets. In this work, we address these problems by proposing a novel method that not only (1) aggregates generic model parameters (e.g. a common set of task generic NN layers) on server (e.g. in traditional FL), but also (2) keeps a set of parameters (e.g, a set of task specific NN layer) specific to each client. We validate our method on the traditionally used public benchmarks (e.g., Femnist) as well as on our proprietary collected dataset (i.e., traffic classification). Results show the benefit of our method, with significant advantage on extreme cases.
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences

Dynamic reconstructions (3D+T) of coronary arteries could give important perfusion details to clinicians. Temporal matching of the different views, which may not be acquired simultaneously, is a prerequisite for an accurate stereo-matching of the coronary segments. In this paper, we show how a neural network can be trained from angiographic sequences to synchronize different views during the cardiac cycle using raw x-ray angiography videos exclusively. First, we train a neural network model with angiographic sequences to extract features describing the progression of the cardiac cycle. Then, we compute the distance between the feature vectors of every frame from the first view with those from the second view to generate distance maps that display stripe patterns. Using pathfinding, we extract the best temporally coherent associations between each frame of both videos. Finally, we compare the synchronized frames of an evaluation set with the ECG signals to show an alignment with 96.04% accuracy.
Process-Genre Approach for Teaching Writing of English Text

Although writing has been learned by students since they start to learn English language but it still become the most difficult skill to be acquired than speaking, reading and listening. It is possibility caused by the students have limitation of knowledge about writing roles, lack of vocabulary, lack of motivation, lack of master of language structure, and lack of ability in generating a topic. In addition, writing is also called as the most complexity skill and it can be seen from the process of writing, rules that must be followed by students, and the influence of non-linguistics factors during of writing activity. Therefore, students must be serious to acquire this skill. In this article, the writer would like to introduce a suitable approach to teaching writing of English texts through process-genre approach. After reading this article, the writer expects that this article can help English teachers to apply this approach for teaching writing of English texts at Junior and Senior High Schools in Indonesia.&#x0D;
Function-as-a-Service Benchmarking Framework

Cloud Service Providers deliver their products in form of 'as-a-Service', which are typically categorized by the level of abstraction. This approach hides the implementation details and shows only functionality to the user. However, the problem is that it is hard to measure the performance of Cloud services, because they behave like black boxes. Especially with Function-as-a-Service it is even more difficult because it completely hides server and infrastructure management from users by design. Cloud Service Prodivers usually restrict the maximum size of code, memory and runtime of Cloud Functions. Nevertheless, users need clarification if more ressources are needed to deliver services in high quality. In this regard, we present the architectural design of a new Function-as-a-Service benchmarking tool, which allows users to evaluate the performance of Cloud Functions. Furthermore, the capabilities of the framework are tested on an isolated platform with a specific workload. The results show that users are able to get insights into Function-as-a-Service environments. This, in turn, allows users to identify factors which may slow down or speed up the performance of Cloud Functions.
Meta-Neighborhoods

Making an adaptive prediction based on one's input is an important abilityfor general artificial intelligence. In this work, we step forward in thisdirection and propose a semi-parametric method, Meta-Neighborhoods, wherepredictions are made adaptively to the neighborhood of the input. We show thatMeta-Neighborhoods is a generalization of $k$-nearest-neighbors. Due to thesimpler manifold structure around a local neighborhood, Meta-Neighborhoodsrepresent the predictive distribution $p(y \mid x)$ more accurately. To reducememory and computation overhead, we propose induced neighborhoods thatsummarize the training data into a much smaller dictionary. A meta-learningbased training mechanism is then exploited to jointly learn the inducedneighborhoods and the model. Extensive studies demonstrate the superiority ofour method.
Token-Curated Registry with Citation Graph

In this study, we aim to incorporate the expertise of anonymous curators into a token-curated registry (TCR), a decentralized recommender system for collecting a list of high-quality content. This registry is important, because previous studies on TCRs have not specifically focused on technical content, such as academic papers and patents, whose effective curation requires expertise in relevant fields. To measure expertise, curation in our model focuses on both the content and its citation relationships, for which curator assignment uses the Personalized PageRank (PPR) algorithm while reward computation uses a multi-task peer-prediction mechanism. Our proposed CitedTCR bridges the literature on network-based and token-based recommender systems and contributes to the autonomous development of an evolving citation graph for high-quality content. Moreover, we experimentally confirm the incentive for registration and curation in CitedTCR using the simplification of a one-to-one correspondence between users and content (nodes).
Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.
Data Literacy for Libraries – A Local Perspective on Library Carpentry

Abstract In the past decades, libraries have developed into sophisticated information centres. This evolution requires a new set of computational expertises and competencies but also a change in the general attitude toward continuous learning. “Library Carpentry”, as a lesson programme of the “The Carpentries” community offers an elaborate and evidence-based collection of approaches for efficient teaching of the required computational skills and concepts. This article introduces Library Carpentry and presents how it is applied at ZB MED-Information Centre for Life Sciences as a foundation for teaching digital literacy.
A Three-Valued Semantics for Typed Logic Programming

Types in logic programming have focused on conservative approximations of program semantics by regular types, on one hand, and on type systems based on a prescriptive semantics defined for typed programs, on the other. In this paper, we define a new semantics for logic programming, where programs evaluate to true, false, and to a new semantic value called wrong, corresponding to a run-time type error. We then have a type language with a separated semantics of types. Finally, we define a type system for logic programming and prove that it is semantically sound with respect to a semantic relation between programs and types where, if a program has a type, then its semantics is not wrong. Our work follows Milner's approach for typed functional languages where the semantics of programs is independent from the semantic of types, and the type system is proved to be sound with respect to a relation between both semantics.