Reviewerly/exHarmony huggingface dataset:
A Glimpse of the Matrix

Matrix is a new message-oriented data synchronization middleware, used as a federated platform for near real-time decentralized applications. It features a novel approach for inter-server communication based on synchronizing message history by using a replicated data structure. We measured the structure of public parts in the Matrix federation as a basis to analyze the middleware's scalability. We confirm that users are currently cumulated on a single large server, but find more small servers than expected. We then analyze network load distribution in the measured structure and identify scalability issues of Matrix' group communication mechanism in structurally diverse federations.
Study on the performances of dwell time algorithms in ion beam figuring

Dwell time calculation is one of the most important process in a highly deterministic computer-controlled Ion Beam Figuring (IBF) process. It is modeled as a deconvolution process between the desired removal map and the beam removal function, which is an ill-posed inverse problem. A good dwell time solution should fulfill four requirements: 1) it must be non-negative; 2) it should closely duplicate the shape of the desired removal map; 3) the total dwell time is expected to be as short as possible; 4) the calculation time is reasonable. Dwell time algorithms, such as Fourier transform-based algorithm, matrix-based algorithm, and Bayesian-based algorithm, have been proposed and applied to IBF. However, their performances were never clearly examined and described accordingly. In this research, we provide a quantitative study on the performances of these dwell time algorithms based on the aforementioned four requirements.
A Neuroevolution-Based Learning of Reciprocal Maneuver for Collision Avoidance in Quadcopters Under Pose Uncertainties

Abstract This paper focuses on the idea of energy efficient cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. For realism, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain — thereby presenting substantial challenges to undertaking reciprocal actions. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burden-some under these uncertainties. Instead, an (unsupervised) neuroevolution algorithm, called AGENT, is employed to learn a neural network (NN) model that takes the initial (uncertain) pose as state inputs and maps it to a robust optimal action. In neuroevolution, the NN topology and weights are simultaneously optimized using a special evolutionary process, where the fitness of candidate NNs are evaluated over a set of sample (in this case, various collision) scenarios. For further computational tractability, a surrogate model is used to estimate the energy consumption and a classifier is used to identify trajectories where the controller fails. The trained neural network shows encouraging performance for collision avoidance over a large variety of unseen scenarios.
Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers

Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this article, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. We propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers.
A Semi-Automatic Annotation Interface for Named Entity and Relation Annotation on Document Images

To be able to use supervised machine learning methods in natural language processing, there is a need of labeled data in large quantities. In some cases, especially when there are multiple tasks conducted on the same data, the annotation process may become exhausting and time consuming for both the annotatore and interpreters. Thus, an effective annotation tool becomes crucial in order to both increase the annotation quality and reduce the annotation time. In this paper, a semi-automatic annotation tool, which aims to decrease the manual work and user faults, is proposed. The interface of the tool is designed in a user-friendly manner in order to ease the process. The characteristics and input/output formats of the tool is explained in detail within the paper. The effects on the speed and accuracy of the users are analyzed as well as automatic labeling accuracy with conducted performance tests. It is noted that a deep-learning model trained with a small dataset can decrease the manual entity annotation workload up to 78,43%.
Evaluasi Desain Antarmuka Portal Akademik Menggunakan Metode Heuristic Evaluation

Evaluation of interface design is part of human and computer interaction (HCI-Human Computer Interaction). The focus of this study is to conduct heuristic evaluations on the interface design of an academic portal to determine the extent to which the academic portal meets the concept of usability. Academic portal is a website accessed by academics through a web browser. This portal can be accessed by students, lecturers, academic supervisors, Final Project supervisors, and other academics such as: Academic Administration Section (BAA), Study Programs, Libraries, Student Affairs, and Public Administration and Personnel Section (BAUK). This study uses heuristic evaluation as an approach in evaluating a human-machine system (man-machine system) in relation to academic portal ease of use (usability) and also severity ratings in addition to heuristic checklist. This study involved several evaluators who would conduct an assessment by filling in the heuristic checklist. The expected results with the evaluator's heuristic evaluation method found usability problems related to the general interface. These results are expected to be used by Mikroskil's academic portal system developers to make improvements to the academic portal currently in use
Analysis of Regression Algorithm to Predict Administration, Production, and Delivery to Accuracy of Delivery of Products in Cosmetic Industry

Abstract The purpose of this study is to apply a linear regression algorithm to predict the accuracy of product shipments using administration, production and delivery as attributes that will be compared with existing targets which are the results of agreements between companies and customers used. as the dependent variable. In this study it was found that a linear method is feasible and effective for predicting administration, production and delivery of targets for accuracy of product shipments in the company. From the results of the prediction formula obtained, the prediction formula is implemented in the July 2018 data as test data. The results obtained from the prediction formula have an accuracy of 91.67%. From previous studies, which say that linear regression is feasible and effective for data use, they must make predictions. Likewise with this research where the results obtained in testing data are feasible and effective for the company.
Sleak

We present a novel approach to automatically recover information about the address space layout of remote processes in the presence of Address Space Layout Randomization (ASLR). Our system, dubbed Sleak, performs static analysis and symbolic execution of binary executable programs, and identifies program paths and input parameters leading to partial (i.e., only a few bits) or complete (i.e., the whole address) information disclosure vulnerabilities, revealing addresses of known objects of the target service or application. Sleak takes, as input, the binary executable program, and generates a symbolic expression for each program output that leaks information about the addresses of objects, such as stack variables, heap structures, or function pointers. By comparing these expressions with the concrete output of a remote process executing the same binary program image, our system is able to recover from a few bits to whole addresses of objects of the target application or service. Discovering the address of a single object in the target application is often enough to guess the layout of entire sections of the address space, which can be leveraged by attackers to bypass ASLR.
Interactive scenario analysis of banking credit risks in intuitive fuzzy space

Purpose The purpose of the paper is to bring attention to documentary credits and the efforts to reduce debt obligations in credit history is recognized as an important source of uncommitted bank earnings. Credit risk has a significant impact on the stability of the banking system. This paper identifies the types of credit risk in the banking supply chain. Design/methodology/approach The authors model the types of credit risk using the intuitive fuzzy failure modes and effects analysis (IFMEA) and intuitive fuzzy cognitive mapping. The population of the study that is needed for the interviews and expert panels comprises senior managers and experts of a leading bank in Iran. The respondents are experienced in credit and banking risk and were selected through judgment sampling and snowballing. Findings The findings suggest that reducing the risks of the foreign letters of credit contracts can mitigate the risk in the agricultural sector, the specific risks of rent-to-own contracts, the risk of the long-term facilities and the specific risk of the domestic letter of credit contracts. Originality/value This research investigates Iran Tejart Bank’s credit risk, formulates a model of the types of credit risk present and analyzes them using the intuitive fuzzy failure modes and effects analysis and intuitive fuzzy cognitive map. Through this credit risk model, one can then facilitate risk management for better financial stability. Also, the model can be used to evaluate the risk indicators.
An Approach to Support the Selection of Relevant Studies in Systematic Review and Systematic Mappings

A Systematic Mapping or Systematic Review is a good alternative for surveying the state of the art of a specific area. One of its most important features is its ability to get an overview of a research area. However, those kind of studies are still a manual, costly and labor-intensive process. This paper aims to support the initial selection of studies, in Systematic Mapping and Systematic Review, as it is one of the stages that require more effort, and therefore requires a longer time to complete. The approach proposed in this paper uses a different combination of techniques in order to get better results of effort reduction. An experiment was carried out in which 5 systematic mappings were analyzed and our approach was able to reduce the effort in the initial selection of studies in Systematic Mapping by up to 50% with an average loss of 5% of relevant articles.
Effects of Anonymity, Ephemerality, and System Routing on Cost in Social Question Asking

Online platforms provide new channels for people in need to seek help from friends and strangers. However, individuals often encounter psychological barriers that deter them from asking for help. For example, people might have different concerns about asking for help, including acknowledging incompetence, bothering others, and accruing social debt. These perceived social costs limit the potential benefits of help solicitations. In this study, we attempt to investigate whether anonymity (posting a question anonymously), ephemerality (allowing questions to be visible for only a short period), and system routing (having the system handle the question routing) could reduce social costs in a typical online help-seeking behavior-question asking. We built a platform to support these three features and conducted a controlled within-subjects experiment to test their effects on the social costs of posting questions. Results suggest that the presence of anonymity, ephemerality, and system routing reduce social costs. Further, we find that employing anonymity and system routing features did not lower the quality and quantity of answers to the questions in our system.
Inspirations from Nature for Meta-Heuristic Algorithms: A Survey

Background: Nature-Inspired Algorithms (NIAs) are the most efficient way to solve advanced engineering and real-world optimization problems. Since the last few decades, various researchers have proposed an immense number of NIAs. These NIAs get inspiration from natural phenomenon. A young researcher attempting to undertake or solve a problem using NIAs is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some scores over others. Objective: This paper presents a comprehensive study of seven NIAs, which have new and unique inspirations. This study shall useful to easily understand the fundamentals of NIAs for any new entrant. Conclusion: Here, we classify the NIAs as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, well-establish and relatively new NIAs, namely- Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), Water Cycle Algorithm (WCA), Jaya Algorithm and Anti-Predatory NIA (APNIA), have been studied. This study presents a theoretical perspective of NIAs in a simplified form based on its source of inspiration, mathematical formulations, control parameters, features, variants and area of application, where these algorithms have been successfully applied.
Practical Thinking and Innovative Strategy of Diagnosing and Improving Teaching Work in Higher Vocational Colleges

Diagnosing and improving teaching work is an important means to enhance the responsibility of the managers in higher vocational colleges and improve the quality of running schools. However, there are difficulties in the process of practical promotion, which further affect the expected results. In this paper, based on the practice and research of teaching quality monitoring for more than ten years, the reality is analyzed from three aspects of concept, management and implementation, and the problem is solved from three aspects of changing concept, comprehensive examination and careful deployment, aiming to open up a new path and provide new thinking for the establishment of a diagnosing and improving system in teaching work.
Machine Learning Based Parametric Estimation Approach for Poll Prediction

Background: The process of election prediction started long back when common practice for election predictions were traditional methods like pundits, hereditary factor etc. However, in recent times new methods and techniques are being used for election forecasting like Data mining, Data Science, Big data, and numerous machine learning techniques. By using such computational techniques the whole process of political forecasting is changed and poll predictions are carried out through them. Objective: The main objective of this research work is to propose an election prediction model for developing areas especially for the state of Jammu and Kashmir (India). Methods: The election prediction model is developed in Jupyter notebook web application using different supervised machine learning techniques. To obtain the optimal results, we perform the hyperparameter tuning of all the proposed classifiers. For measuring the performance of poll prediction system we used confusion matrix along with AUROC curve which depicts that this methods can be well suited for political forecasting. An important contribution of this article is to design a Prediction system which can be used for making prediction in other fields like cardiovascular disease predictions, weather forecasting etc. Results: This model is tested and trained with real-time dataset of the state Jammu and Kashmir (India). We applied features selection techniques like Random Forest, Decision Tree Classifier, Gradient boosting Classifier and Extra Gradient Boosting and obtained eight most important parameters like (Central Influence, Religion Followers, Party Wave, Party Abbreviations, Sensitive Areas, Vote Bank, Incumbent Party, and Caste Factor) for poll predictions with their mean weightages. By applying different classifier to get mean weightage of different parameters for this election prediction models, it has been observed that Party wave got maximum mean weightage of 0.82% as compared to others parameters. After obtaining the vital parameters for political forecasting, we applied various machine learning algorithms like Decision tree, Random forest, K-nearest neighbor and support vector machine for the early prediction of elections. Experimental results show that Support Vector Machine outperformed with a higher accuracy of 0.84% in contrast to others classifiers. Conclusion: In this paper, a clear overview of election prediction models, their potentials, techniques, parameters as well as limitations are outlined. We conclude this work by stating that election predictions can indeed be forecasted with significant parameters however, with caution due to the limitations which were outlined in developing nations like sensitive areas, social unrest, religion etc. This research work may be considered as the first attempt to use multiple classifier for forecasting the Assembly election results of the state Jammu and Kashmir (India).
Quantum transmitter engineering based on transition metal adatoms

We suggest a model of nanoscale quantum transmitter, based on isotropic exchange interactions between transition metal atoms on a surface. Under consideration is nanosystem of antiferromagnetically coupled dimers, forming a ferromagnetic conducting line. It was shown that spatially localized external magnetic influence can serve as the binary signal to be sent through the line without significant loss, providing a technologically applicable method of short-ranged quantum communication, which is critically important in the development of novel quantum computation modules.
Automatic Detection and Pennation Angle Measurement of Muscle Fascicles in Ultrasound Images Using Belt Linear Summation Transform

Ultrasound images of muscle fascicles have been widely used to investigate muscle properties for diagnosis and rehabilitation assessment.The existing automatic fascicle detection and measure methods are based on line detection techniques which do not exactly coincide with the true state of the muscle images. This affects their detection and measure accuracy and depresses their robustness to background interference. In this work, a novel discrete transform namely Belt Linear Summation (BLS) transform is proposed. Unlike the line transform techniques which calculate the sum of pixels on a straight line in images, the BLS transform intends to determine the weighted summation of a belt of pixel values. Based on BLS transform, an automatic fascicle detection and measure method is designed. The performance of the proposed method is compared to the recent automatic fascicle detection and measure methods using both simulated images and clinical images. Experimental results show that the proposed method is robust to background and noise interference, accurate in terms of muscle pennation angle measurement, and feasible for analyzing clinical data.
What Can the Sentiment of a Software Requirements Specification Document Tell Us?

Sentiment analysis tools are becoming increasingly more prevalent in the software engineering research community. In this data showcase paper, we present a set of twenty-two software requirements specification (SRS) documents that have been preprocessed and subsequently analyzed using the Senti4SD sentiment analysis tool. As part of our preliminary research, we compared the result of the sentiment analysis of the SRS documents and other non-related documents and found that the SRS documents were 6% more neutral than other non-related documents. Finally, we also present a number of research questions that we believe the research community might be able to answer using our published data.
Meta-Learning Deep Energy-Based Memory Models

We study the problem of learning associative memory -- a system which is able to retrieve a remembered pattern based on its distorted or incomplete version. Attractor networks provide a sound model of associative memory: patterns are stored as attractors of the network dynamics and associative retrieval is performed by running the dynamics starting from a query pattern until it converges to an attractor. In such models the dynamics are often implemented as an optimization procedure that minimizes an energy function, such as in the classical Hopfield network. In general it is difficult to derive a writing rule for a given dynamics and energy that is both compressive and fast. Thus, most research in energy-based memory has been limited either to tractable energy models not expressive enough to handle complex high-dimensional objects such as natural images, or to models that do not offer fast writing. We present a novel meta-learning approach to energy-based memory models (EBMM) that allows one to use an arbitrary neural architecture as an energy model and quickly store patterns in its weights. We demonstrate experimentally that our EBMM approach can build compressed memories for synthetic and natural data, and is capable of associative retrieval that outperforms existing memory systems in terms of the reconstruction error and compression rate.