Reviewerly/exHarmony huggingface dataset:
Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding

Medical ultrasound images are inherently noised with speckle noise, which may interfere with Computer Aided Diagnostics (CAD) tasks, such as automatic segmentation. A compression and speckle de-noising method is proposed and tested on real clinical breast and fetal ultrasound images. The proposed algorithm is based on the optimization of quantization coefficients when applying Wavelet representation on the image, where the optimization is held such that a pre-defined mathematical fidelity criterion with respect to a desired de-speckled image is obtained. The proposed algorithm yields effective speckle reduction whilst preserving the edges in the images, with a reduced computational burden compared to other existing state-of-the-art methods, such as Optimal Bayesian Non-Local Means (OBNLM). In addition, the images are simultaneously compressed to a target bit-rate. The proposed algorithm is evaluated using both objective mathematical fidelity criteria (such as Structural Similarity and Edge Preserve) as well as subjective radiologists tests. The experimental results demonstrate the ability of the proposed method to achieve de-speckled images with compression ratios of approximately 30:1, whilst obtaining competitive subjective as well as objective fidelity measures with respect to the desired de-speckled images.
Think Pair Share Technique to Promote Students' Reading Comprehension

The objective of the research is to promote students’ reading comprehension through Think Pair Share technique. The present research employed qualitative approach with Classroom Action Research (CAR) method. The participants involved were one class of seventh graders consisting of 22 students. The research was conducted in three cycles. Observation, test and interview were used to collect the data. The result revealed that 1) students’ score of reading comprehension increases after the implementation of Think Pair Share technique (in cycle I, only 36% students passed the passing grade, in cycle II 73% of them passed the passing grade, and in cycle III, 100% students passed the passing grade; and 2) students’ motivation and learning excitement improve. The findings lead to a conclusion that this technique can promote students’ reading comprehension. It is then recommended for English teachers to keep using this technique as a solution to assist them in teaching reading comprehension and to motivate their students to be more active in learning.
Analysis of trends in scientific publications by an NLP toolkit: A case study in Software Development Methods for Enhanced Living Environment

As the number of published scientific articles increases, the analysis of trends and state-of-the-art in software engineering is becoming very time-consuming and laborious task. To address the ever-growing demands for systematic literatures review techniques, rapid review and scoping reviews techniques have emerged. We used an NLP powered tool, which employs the PRISMA surveying methodology, to automate most of the review processes. We used it to automatically review relevant articles indexed in IEEE Xplore, PubMed and Springer digital libraries on the topic “Software Development for Enhanced Living Environments and Ambient Assisted Living”. The relevant articles identified by the NLP toolkit contained up to 21 properties clustered into 3 logical groups. We discovered that Software Development for Enhanced and Assisted living environments attracted an increased attention from the scientific communities over the last 10 years and showed several trends in the specific research topics that fall into this scope. The research uncovered that iterative software methodology had been the most attractive research topic in the field. Despite the enormous empirical evidence on application and success stories of agile development methodologies in many software development engineering, it received a little attention from the scientific community in the software development for Enhanced and Assisted Living Environments. The NLP toolkit identified the most relevant articles that contained the defined properties in the search. Hence, it significantly reduced the manual work, while also generating informative tables, charts and graphs.
W4RM: A prescriptive framework based on a wiki to support collaborative risk management in information technology projects

Despite the positive influence of risk management in Information Technology (IT) project results, many project managers are not managing risks or are managing them partially. To enhance risk management, collaborative project management has gained attention in recent years with the introduction of Web 2.0 tools. Project managers have used such tools to facilitate open communication and distribution of activities. This research introduces a prescriptive framework (W4RM – Wiki for Risk Management) based on a wiki to support collaborative risk management in IT projects. An exploratory focus group was set up and a series of interviews with practitioners was conducted to explore how a wiki can support risk management in IT projects. Findings show that project managers are facing difficulties managing risks and are the only ones responsible for identifying, registering and monitoring risks. By implementing a collaborative tool, managers can disseminate a collaboration culture and participate in risk management processes. This sense of collaboration may be used to keep the community identifying new risks, relating these risks to one or more projects, and facilitating continuous risk management. Practitioners can also adopt W4RM as a tool to support communication regarding risks status to be established for internal team stakeholders.
Incorporating User Feedback in Conversational Question Answering over Heterogeneous Web Sources

This PhD thesis will explore conversational question answering with a special emphasis on incorporating user feedback. As preliminary work, we developed a conversational passage retrieval system in the scope of the TREC Conversational Assistance Track 2019. Our current focus is to develop methods based on reinforcement learning to incorporate implicit user feedback in form of question reformulations for conversational QA over knowledge graphs. Finally, we plan to design a conversational QA system operating on heterogeneous sources.
User Guidance and Automatic Completion for Generating Planar B-Rep Models

A representation of the world as a 3D model is a common necessity in robotics and automation. In previous work, we developed a concept to generate boundary representation (B-Rep) models from multiple point clouds using a hand-held depth-camera and to register them without a prior known pose. During the online reconstruction, properties of the sensor and the system (like noise) lead to small holes in the B-Rep. To prevent tedious post-processing, holes should be closed during the reconstruction. Our goal is to automatically close identified holes. However, not every hole can be closed automatically, as it may be unreasonable. For this case we develop a visual indication for the user, so he can close the hole by recording another depth image. In an experimental validation, we conclude the usefulness of the addition to the system.
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization

The Restricted Boltzmann Machine (RBM) is a stochastic neural network capable of solving a variety of difficult tasks such as NP-Hard combinatorial optimization problems and integer factorization. The RBM architecture is also very compact; requiring very few weights and biases. This, along with its simple, parallelizable sampling algorithm for finding the ground state of such problems, makes the RBM amenable to hardware acceleration. However, training of the RBM on these problems can pose a significant challenge, as the training algorithm tends to fail for large problem sizes and efficient mappings can be hard to find. Here, we propose a method of combining RBMs together that avoids the need to train large problems in their full form. We also propose methods for making the RBM more hardware amenable, allowing the algorithm to be efficiently mapped to an FPGA-based accelerator. Using this accelerator, we are able to show hardware accelerated factorization of 16 bit numbers with high accuracy with a speed improvement of 10000x and a power improvement of 32x.
KESALAHAN PENGGUNAAN EJAAN DALAM KARANGAN EKSPOSISI SISWA KELAS X MIPA 1 SMA NEGERI 3 KOTA JAMBI

This purpose of this research is to describe the errors of spelling in expository composition of class X MIPA 1 at SMA negeri 3 Kota Jambi academic year 2018/2019. This research used qualitative descriptive method. The data of this research was sequence of sentences of students’ writing with spelling errors. The primary data was students’ expository composition of class X MIPA 1 at SMA Negeri 3 Kota Jambi Tahun academic year 2018/2019. Based on the result of the analysis, it can be found that the errors of spelling in expository composition of students’ class X MIPA 1 semeter I SMA negeri 3 kota Jambi tahun ajaran 2018/2019 were 713 errors. The errors of spelling consists of 588 errors of using capital letter in the beginning of the sentence; 4 errors of using capital letter in the beginning of the sentence with direct quote; 10 errors of using capital letter as the first letter of nation name; 2 errors of using capital letter as the first letter of days’ name; 3 errors of capital letter as the first letter of geography;14 errors of capital letter of every word in the title of their composition. Moreover, there following errors of the use of comma is; 60 errors of comma in between of elements of explanation; 4 errors of comma before conjunction; 11 errors of comma behind the conjunction of sentences; 7 errors of comma in separating direct quote from the other parts of the sentence; 2 errors of comma in between of district or country’s name which is written in sequence; 9 errors of comma on the back of the information of the beginning of the sentence which is used to avoid error of reading.
Speculative Interference Attacks: Breaking Invisible Speculation Schemes.

Recent security vulnerabilities that target speculative execution (e.g., Spectre) present a significant challenge for processor design. The highly publicized vulnerability uses speculative execution to learn victim secrets by changing cache state. As a result, recent computer architecture research has focused on invisible speculation mechanisms that attempt to block changes in cache state due to speculative execution. Prior work has shown significant success in preventing Spectre and other vulnerabilities at modest performance costs. In this paper, we introduce speculative interference attacks, which show that prior invisible speculation mechanisms do not fully block these speculation-based attacks. We make two key observations. First, misspeculated younger instructions can change the timing of older, bound-to-retire instructions, including memory operations. Second, changing the timing of a memory operation can change the order of that memory operation relative to other memory operations, resulting in persistent changes to the cache state. Using these observations, we demonstrate (among other attack variants) that secret information accessed by mis-speculated instructions can change the order of bound-to-retire loads. Load timing changes can therefore leave secret-dependent changes in the cache, even in the presence of invisible speculation mechanisms. We show that this problem is not easy to fix: Speculative interference converts timing changes to persistent cache-state changes, and timing is typically ignored by many cache-based defenses. We develop a framework to understand the attack and demonstrate concrete proof-of-concept attacks against invisible speculation mechanisms. We provide security definitions sufficient to block speculative interference attacks; describe a simple defense mechanism with a high performance cost; and discuss how future research can improve its performance.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs

Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. We find that real dynamic attributed graphs exhibit complex co-evolution of node attributes and graph structure. Learning node embeddings for forecasting change of node attributes and birth and death of links over time remains an open problem. In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence. It preserves the impact of earlier graphs on the current graph by embedding generation through the sequence. It has a temporal self-attention mechanism to model long-range dependencies in the evolution. Moreover, CoEvoGNN optimizes model parameters jointly on two dynamic tasks, attribute inference and link prediction over time. So the model can capture the co-evolutionary patterns of attribute change and link formation. This framework can adapt to any graph neural algorithms so we implemented and investigated three methods based on it: CoEvoGCN, CoEvoGAT, and CoEvoSAGE. Experiments demonstrate the framework (and its methods) outperform strong baselines on predicting an entire unseen graph snapshot of personal attributes and interpersonal links in dynamic social graphs and financial graphs.
Investigation of the effect of the measurement period on the error of manoeuvring target tracking

In this work, we aimed to develop and investigate algorithms for filtering radar information, which could provide the required accuracy of multiple-object tracking procedures. During the research, modifications to algorithms based on Kalman filtering were developed and analysed. A comparative assessment of the accuracy of the developed and existing algorithms was carried out.
A sound interpretation of Leśniewski's epsilon in modal logic KTB

In this paper, we shall show that the following translation $I^M$ from the propositional fragment $\bf L_1$ of Le\'{s}niewski's ontology to modal logic $\bf KTB$ is sound: for any formula $\phi$ and $\psi$ of $\bf L_1$, it is defined as \smallskip (M1) $I^M(\phi \vee \psi)$ = $I^M(\phi) \vee I^M(\psi),$ (M2) $I^M(\neg \phi)$ = $\neg I^M(\phi),$ (M3) $I^M(\epsilon ab)$ = $\Diamond p_a \supset p_a . \wedge . \Box p_a \supset \Box p_b . \wedge . \Diamond p_b \supset p_a,$ \smallskip \noindent where $p_a$ and $p_b$ are propositional variables corresponding to the name variables $a$ and $b$, respectively. In the last section, we shall give some open problems and my conjectures.
Integrable active atom interferometry

Active interferometers are designed to enhance phase sensitivity beyond the standard quantum limit by generating entanglement inside the interferometer. An atomic version of such a device can be constructed by means of a spinor Bose-Einstein condensate with an $F=1$ groundstate manifold in which spin-changing collisions create entangled pairs of $m=\pm1$ atoms. We use Bethe Ansatz techniques to find exact eigenstates and eigenvalues of the Hamiltonian that models such spin-changing collisions. Using these results, we express the interferometer's phase sensitivity, Fisher information, and Hellinger distance in terms of the Bethe rapidities. By evaluating these expressions we study scaling properties and the interferometer's performance under the full Hamiltonian that models the spin-changing collisions, i.e., without the idealising approximations of earlier works that force the model into the framework of SU(1,1) interferometry.
MQT-TZ: Hardening IoT Brokers Using ARM TrustZone

The publish-subscribe paradigm is an efficient communication scheme with strong decoupling between the nodes, that is especially fit for large-scale deployments. It adapts natively to very dynamic settings and it is used in a diversity of real-world scenarios, including finance, smart cities, medical environments, or IoT sensors. Several of the mentioned application scenarios require increasingly stringent security guarantees due to the sensitive nature of the exchanged messages as well as the privacy demands of the clients/stakeholders/receivers. MQTT is a lightweight topic-based publish-subscribe protocol popular in edge and IoT settings, a de-facto standard widely adopted nowadays by the industry and researchers. However, MQTT brokers must process data in clear, hence exposing a large attack surface. This paper presents MQT-TZ, a secure MQTT broker leveraging Arm TrustZone, a trusted execution environment (TEE) commonly found even on inexpensive devices largely available on the market (such as Raspberry Pi units). We define a mutual TLS-based handshake and a two-layer encryption for end-to-end security using the TEE as a trusted proxy. The experimental evaluation of our fully implemented prototype with micro-, macro-benchmarks, as well as with real-world industrial workloads from a MedTech use-case, highlights several trade-offs using TrustZone TEE. We report several lessons learned while building and evaluating our system. We release MQT-TZ as open-source.
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks

In this thesis, we disentangle the generalized Gauss-Newton and approximate inference for Bayesian deep learning. The generalized Gauss-Newton method is an optimization method that is used in several popular Bayesian deep learning algorithms. Algorithms that combine the Gauss-Newton method with the Laplace and Gaussian variational approximation have recently led to state-of-the-art results in Bayesian deep learning. While the Laplace and Gaussian variational approximation have been studied extensively, their interplay with the Gauss-Newton method remains unclear. Recent criticism of priors and posterior approximations in Bayesian deep learning further urges the need for a deeper understanding of practical algorithms. The individual analysis of the Gauss-Newton method and Laplace and Gaussian variational approximations for neural networks provides both theoretical insight and new practical algorithms. We find that the Gauss-Newton method simplifies the underlying probabilistic model significantly. In particular, the combination of the Gauss-Newton method with approximate inference can be cast as inference in a linear or Gaussian process model. The Laplace and Gaussian variational approximation can subsequently provide a posterior approximation to these simplified models. This new disentangled understanding of recent Bayesian deep learning algorithms also leads to new methods: first, the connection to Gaussian processes enables new function-space inference algorithms. Second, we present a marginal likelihood approximation of the underlying probabilistic model to tune neural network hyperparameters. Finally, the identified underlying models lead to different methods to compute predictive distributions. In fact, we find that these prediction methods for Bayesian neural networks often work better than the default choice and solve a common issue with the Laplace approximation.
Financial Management Teaching Mode Based on MOOC Under the Background of Informatization

With the improvement of China’s informatization infrastructure and the widespread popularity of mobile Internet, various new teaching modes such as video public courses and online courses have brought tremendous changes and far-reaching effects to China’s higher education. MOOC is a high-quality online course learning platform in China. It is one of the products of the effectiveness of teaching reform in China. As an emerging teaching method, MOOC not only improves the limitations of traditional teaching models, but also breaks through the constraints of time and space, and achieves the widespread dissemination of high-quality teaching resources to a certain extent. This paper takes the auditing specialty of Fuzhou University of International Studies and Trade as an example, it analyzes the problems related to the teaching reform of financial management courses and proposes corresponding reform ideas from the perspective of MOOC curriculum construction.
Construction of a Mathematical Model of Multiobjective Optimization on Permutations

ConstruCtIon oF A mAtHemAtICAl moDel oF multIoBJeCtIVe optImIZAtIon on permutAtIonsThe article is devoted to the problem of constructing and solving mathematical models of applied problems as multiobjective problems on combinatorial configurations.This question is actual branch because any task of optimal design of complex economic and technical systems, technological devices, planning and management etc. requires that the desired solution be found consider many criteria.It is used transfer to Euclidian combinatorial configurations and using of discrete optimizations methods.Method for solving such problems is considered and it includes the analyzing of structural graph of Euclidean combinatorial configurations sets.These methods can be modified by combining with other multiobjective optimization approaches depending on the initial conditions of the problem.Models for defining real estate contribution plans and production planning as multiobjective discrete problems are proposed.These models can be supplemented as needed by the required functions and, depending on the initial conditions, are presented as tasks on different sets of combinatorial configurations.
Soft Expert Reward Learning for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) requires an agent to find a specified spot in an unseen environment by following natural language instructions. Dominant methods based on supervised learning clone expert's behaviours and thus perform better on seen environments, while showing restricted performance on unseen ones. Reinforcement Learning (RL) based models show better generalisation ability but have issues as well, requiring large amount of manual reward engineering is one of which. In this paper, we introduce a Soft Expert Reward Learning (SERL) model to overcome the reward engineering designing and generalisation problems of the VLN task. Our proposed method consists of two complementary components: Soft Expert Distillation (SED) module encourages agents to behave like an expert as much as possible, but in a soft fashion; Self Perceiving (SP) module targets at pushing the agent towards the final destination as fast as possible. Empirically, we evaluate our model on the VLN seen, unseen and test splits and the model outperforms the state-of-the-art methods on most of the evaluation metrics.
Mining Design Patterns using String Encoding Format

In object oriented software, design pattern gives the particular solution for common design problems.In software engineering it's very difficult task to find out design information due to improper documentation of software systems.It is very much necessary to recover pattern instances so that system would be understandable and can do modifications in them.Actually recovery of design patterns play a significant role in object oriented programming for software developers and researchers during development of system software and their maintenance.Hence mining of design patterns are very important.The paper describes detection of design patterns from software or system design by using the String Encoding Format in which pattern and system graphs are transformed in string after that process of matching is performed to extract instances from software systems.Here we match string of system design graph and design pattern graph using structural analysis.