Reviewerly/exHarmony huggingface dataset:
Black-box inter-application traffic monitoring for adaptive container placement

A key issue in the performance of modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, is the placement of each container, or container pod, in virtual and physical servers. Although it has been shown that inter-application traffic is an important factor in placement decisions, as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way, thus putting it out of reach of cloud platforms.
Evaluating semantic textual similarity in clinical sentences using deep learning and sentence embeddings

The wide adoption of electronic health records (EHRs) has fostered an improvement in healthcare quality, with EHRs currently representing a major source of medical information. Nevertheless, this process has also brought new challenges to the medical environment since the facilitated replication of information (e.g. using copy-paste) has resulted in less concise and sometimes incorrect information, which hinders the understandability of this data and can compromise the quality of medical decisions drawn from it. Due to the high volume and redundancy in medical data, it is imperative to develop solutions that can condense information whilst retaining its value, with a possible methodology involving the assessment of the semantic similarity between clinical text excerpts.
UniformAugment: A Search-free Probabilistic Data Augmentation Approach

Augmenting training datasets has been shown to improve the learning effectiveness for several computer vision tasks. A good augmentation produces an augmented dataset that adds variability while retaining the statistical properties of the original dataset. Some techniques, such as AutoAugment and Fast AutoAugment, have introduced a search phase to find a set of suitable augmentation policies for a given model and dataset. This comes at the cost of great computational overhead, adding up to several thousand GPU hours. More recently RandAugment was proposed to substantially speedup the search phase by approximating the search space by a couple of hyperparameters, but still incurring non-negligible cost for tuning those. In this paper we show that, under the assumption that the augmentation space is approximately distribution invariant, a uniform sampling over the continuous space of augmentation transformations is sufficient to train highly effective models. Based on that result we propose UniformAugment, an automated data augmentation approach that completely avoids a search phase. In addition to discussing the theoretical underpinning supporting our approach, we also use the standard datasets, as well as established models for image classification, to show that UniformAugment's effectiveness is comparable to the aforementioned methods, while still being highly efficient by virtue of not requiring any search.
Business Rationale for Linked Data at Governments: A Case Study at the Netherlands’ Kadaster Data Platform

Linked Data is an innovative approach for publishing heterogeneous data sources on the web. As such, it can transcend the traditional confines of separate databases, as well as the confines of separate institutions. At the same time, businesses and governmental organizations alike are trying to cope with ever-increasing quantities of heterogeneous data that must be used across multiple departments, manufacturing locations, and governmental bodies. Linked Data would, therefore, be a great technological solution for today's organizational problems. However, we observe that a serious gap exists between Linked Data research and business research. While Linked Data research is almost exclusively technologically oriented, the business research literature has not devoted much attention to the use of Linked Data solutions yet. In this paper, we seek to bridge this gap, by introducing a real-world use case where Linked Data technologies are applied in large-scale government settings. We argue in detail that Linked Data provides a major contribution to the business vision of a modern governmental institution based on the experience of the Netherlands' Cadastre Land Registry and Mapping Agency (Kadaster), so far, the largest implementation of Linked Data in the governments of the Netherlands.
Estimating and Influencing User Mental Models of a Robot's Perceptual Capabilities

People develop mental models of robots to improve their interactions with them, but predictions from these models are not always accurate. Robots often fail to communicate their capabilities, especially perceptual capabilities---i.e., what they can sense and understand about the world. This paper describes ongoing preliminary work [8] towards enabling robots to autonomously estimate and influence human beliefs about robot perceptual capabilities. A custom-designed, web-based game is being used to establish feasibility and guide ongoing work. Our approach is discussed along with results from a pilot study.
A Deep Neural Network-Based Approach to Finding Similar Code Segments

This paper presents a Siamese architecture model with two identical Convolutional Neural Networks (CNNs) to identify code clones; two code fragments are represented as Abstract Syntax Trees (ASTs), CNN-based subnetworks extract feature vectors from the ASTs of pairwise code fragments, and the output layer produces how similar or dissimilar they are. Experimental results demonstrate that CNN-based feature extraction is effective in detecting code clones at source code or bytecode levels.
Development Features of the FSES for Tiered and Continuous Higher Education

The article discusses the dynamics of the development of federal state educational standards (FSES) for the preparation of bachelors, masters and specialists in the field of photonics, instrumentation, optical and biotechnological systems and technologies over the past twenty-five years. The authors analyze the features, advantages and disadvantages of tiered and continuous higher education. Particular attention is paid to the development and improvement of state educational standards of higher education in the specialty “Electronic and Optical-electronic Devices and Special-purpose Systems.” The article substantiates the expansion of the scope of professional use of a specialist in the field of electronic and optoelectronic devices and systems and the need to introduce a new – information-analytical type of activity. based on the developed competency models of the results of mastering the educational programs of graduate, undergraduate and specialty studies, a comparative analysis of their orientation in various fields of activity is carried out. One of the main trends in the development of state regulation of training students in an enlarged group of specialties and areas of training “Photonics, Instrumentation, Optical and Biotechnical Systems and Technologies” is providing universities with greater freedom in the development of educational programs and the selection of applied educational technologies. It is shown that the main strategic task of the developer of the main professional educational program is to form a set of competencies that will allow the graduate to work in one of the areas of professional activity. At the same time, this set of competencies should be correlated with some labor functions formally described by professional standards and qualification requirements. It is concluded that the main result of the evolutionary development of educational programs of tiered and continuous higher education was the transition from their academic orientation to a practice-oriented character providing focus on certain professional qualifications and requirements of the employer.
Learning difficulties in comprehending virus and bacteria material for senior high schools

Previous research only focuses on the difficulties and factors causing learning difficulties without any solution. This study aimed to identify learning difficulties, factors, and alternative solutions overcoming the problems of learning difficulties about viruses and bacteria. Data collection was carried out through the provision of a concept mastery test involving 195 students (8 schools) and in-depth interviews conducted by involving 37 students (4 schools) who received the lowest test scores. Data analysis was performed using a percentage formula to determine the proportion of difficulties for each competency. The results showed that students had difficulty with the highest percentage of virus material, those were the competence of analyzing the role of viruses in life (85.64%) and virus replication process (83.21%). The competence in bacterial material which has the highest percentage of difficulty (77.26%) in identify bacterial classification. The factors causing students to have difficulty learning virus and bacteria consisted of the nature of the topic (77.03%), attitudes and learning habits (60.81%), teacher’s teaching skills (37.84%), and school facilities (27.03%). Alternative solutions are: teachers reduce the content that is complex and detailed, carry out an interactive learning process (audiovisual/animation), providing more learning experiences through practical activities, and teaching through interesting ways.
Igbo-English Machine Translation: An Evaluation Benchmark

Although researchers and practitioners are pushing the boundaries and enhancing the capacities of NLP tools and methods, works on African languages are lagging. A lot of focus on well resourced languages such as English, Japanese, German, French, Russian, Mandarin Chinese etc. Over 97% of the world's 7000 languages, including African languages, are low resourced for NLP i.e. they have little or no data, tools, and techniques for NLP research. For instance, only 5 out of 2965, 0.19% authors of full text papers in the ACL Anthology extracted from the 5 major conferences in 2018 ACL, NAACL, EMNLP, COLING and CoNLL, are affiliated to African institutions. In this work, we discuss our effort toward building a standard machine translation benchmark dataset for Igbo, one of the 3 major Nigerian languages. Igbo is spoken by more than 50 million people globally with over 50% of the speakers are in southeastern Nigeria. Igbo is low resourced although there have been some efforts toward developing IgboNLP such as part of speech tagging and diacritic restoration
MANAJEMEN KEUANGAN SEKOLAH DALAM PEMENUHAN SARANA PRASARANA PENDIDIKAN (Studi kasus di SD Muhammadiyah 1 Krian, Sidoarjo)

This study aims to analyze and understand school financial management in fulfilling educational infrastructure facilities at SD Muhammadiyah 1 Krian, Sidoarjo, East Java. This research uses a qualitative approach with case study. Informants in this study consisted of leader, secretary, and treasurer. Data collection techniques are carried out using interviews, participant observation, and documentation. The data analysis technique uses data reduction, data display, and conclusion drawing / verification. Checking the validity of the data is done through; credibility, transferability, dependability, and confirmability. The results showed that; school financial management in fulfilling educational infrastructure facilities at SD Muhammadiyah 1 Krian was carried out through; school budget planning, school budget absorption, accounting for school finances, school financial accountability. This research has implications for the importance of financial management in the fulfillment of educational facilities and infrastructures, through involving stakeholders, both internal and external, to evaluate, analyze, supervise and meet various school needs.
On Tractable Representations of Binary Neural Networks

We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs). Obtaining this function as an OBDD/SDD facilitates the explanation and formal verification of a neural network's behavior. First, we consider the task of verifying the robustness of a neural network, and show how we can compute the expected robustness of a neural network, given an OBDD/SDD representation of it. Next, we consider a more efficient approach for compiling neural networks, based on a pseudo-polynomial time algorithm for compiling a neuron. We then provide a case study in a handwritten digits dataset, highlighting how two neural networks trained from the same dataset can have very high accuracies, yet have very different levels of robustness. Finally, in experiments, we show that it is feasible to obtain compact representations of neural networks as SDDs.
Improving 3D Object Detection through Progressive Population Based Augmentation

Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentation methods for individual architectures. In this work, we present the first attempt to automate the design of data augmentation policies for 3D object detection. We introduce the Progressive Population Based Augmentation (PPBA) algorithm, which learns to optimize augmentation strategies by narrowing down the search space and adopting the best parameters discovered in previous iterations. On the KITTI 3D detection test set, PPBA improves the StarNet detector by substantial margins on the moderate difficulty category of cars, pedestrians, and cyclists, outperforming all current state-of-the-art single-stage detection models. Additional experiments on the Waymo Open Dataset indicate that PPBA continues to effectively improve the StarNet and PointPillars detectors on a 20x larger dataset compared to KITTI. The magnitude of the improvements may be comparable to advances in 3D perception architectures and the gains come without an incurred cost at inference time. In subsequent experiments, we find that PPBA may be up to 10x more data efficient than baseline 3D detection models without augmentation, highlighting that 3D detection models may achieve competitive accuracy with far fewer labeled examples.
Vision/Position Hybrid Control for a Hexa Robot Using Bacterial Foraging Optimization in Real-time Pose Adjustment

This paper presents a novel architecture of the vision/position hybrid control for a Hexa parallel robot. The 3D vision system is combined with the Proportional-Integral-Derivative (PID) position controller to form a two-level closed-loop controller of the robot. The 3D vision system measures the pose of the end-effector after the PID control. The measurement of the 3D vision system is used as a feedback of the second closed-loop control. The 3D vision system has a simple structure using two fixed symmetric cameras at the top of the robot and four planar colored markers on the surface of the end-effector. The 3D vision system detects and reconstructs the 3D coordinates of colored markers. Based on the distance and coplanarity constraints of the colored markers, the optimization problem is modeled for the real-time adjustment, which is implemented during the operation of the robot to minimize the measurement error of the 3D vision system due to both the initial calibration of the stereo camera and the external noise affecting image processing. The bacterial foraging optimization is appropriately configured to solve the optimization problem. The experiment is performed on a specific Hexa parallel robot to assess the effectiveness and feasibility of the proposed real-time adjustment using the bacterial foraging optimization. The experimental result shows that it has high accuracy and fast computation time although the experiment is conducted on a laptop with an average hardware configuration. An experimental comparison of the performance between the proposed method and another control method is also implemented. The results show the superiority and application potential of the proposed method.
Construction of a mathematical model to describe the dynamics of marine technical systems with elastic links in order to improve the process of their design

A mathematical model (MM) has been developed to describe the dynamics of the MTS EL element using an underwater towed system (UTS) as an example, as well as the MM of MTS with EL.The MM of the EL element dynamics makes it possible to take into consideration:1) the movement of a carrier vessel (CV);2) features of the EL design, which affect the functional characteristics MTS; 3) the movement of an underwater vehicle (UV); 4) the impact of obstacles along the path of UV and EL;5) large movements of EL as part of MTS.The mathematical model of MTS with EL makes it possible to solve the following tasks:1) to determine a change in the shape of EL and the forces of its tension in the process of maneuvering of CV and UV taking into consideration sea waves, wind loads on CV, the sea depth and its change in the assigned water area, the mass and elastic properties of EL; 2) to determine the relative position of CV and UV in the process of their maneuvering;3) to determine the maximum loads on EL necessary to assess its strength during the maneuvering of CV and UV.Analysis of design tasks in the construction of marine tethered systems (MTdS) as a variety of MTS reveals that the calculation of MtdS EL is associated with significant theoretical complexity and is science-intensive. The proposed procedure for improving the design of MTS with EL, based on the MM that describes the dynamics of MTS EL (as well as MTS with EL), makes it possible to investigate the different modes of operation of almost all classes of MTdS. Using it could improve existing methods of calculating and designing MTdSs with EL thereby bringing them to the level of an engineering application
Brief communication: Three errors and two problems in a recent paper: gazeNet: End-to-end eye-movement event detection with deep neural networks (Zemblys, Niehorster, and Holmqvist, 2019)

Zemblys et al. (Behavior Research Methods, 51(2), 840–864, 2019) reported on a method for the classification of eye-movements (“gazeNet”). I have found three errors and two problems with that paper that are explained herein. Error 1: The gazeNet classification method was built assuming that a hand-scored dataset from Lund University was all collected at 500 Hz, but in fact, six of the 34 recording files were actually collected at 200 Hz. Of the six datasets that were used as the training set for the gazeNet algorithm, two were actually collected at 200 Hz. Problem 1 has to do with the fact that even among the 500 Hz data, the inter-timestamp intervals varied widely. Problem 2 is that there are many unusual discontinuities in the saccade trajectories from the Lund University dataset that make it a very poor choice for the construction of an automatic classification method. Error 2 The gazeNet algorithm was trained on the Lund dataset, and then compared to other methods, not trained on this dataset, in terms of performance on this dataset. This is an inherently unfair comparison, and yet nowhere in the gazeNet paper is this unfairness mentioned. Error 3 arises out of the novel event-related agreement analysis employed by the gazeNet authors. Although the authors intended to classify unmatched events as either false positives or false negatives, many are actually being classified as true negatives. True negatives are not errors, and any unmatched event misclassified as a true negative is actually driving kappa higher, whereas unmatched events should be driving kappa lower.
Orthant Based Proximal Stochastic Gradient Method for $\ell_1$-Regularized Optimization

Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel stochastic method -- Orthant Based Proximal Stochastic Gradient Method (OBProx-SG) -- to solve perhaps the most popular instance, i.e., the l1-regularized problem. The OBProx-SG method contains two steps: (i) a proximal stochastic gradient step to predict a support cover of the solution; and (ii) an orthant step to aggressively enhance the sparsity level via orthant face projection. Compared to the state-of-the-art methods, e.g., Prox-SG, RDA and Prox-SVRG, the OBProx-SG not only converges to the global optimal solutions (in convex scenario) or the stationary points (in non-convex scenario), but also promotes the sparsity of the solutions substantially. Particularly, on a large number of convex problems, OBProx-SG outperforms the existing methods comprehensively in the aspect of sparsity exploration and objective values. Moreover, the experiments on non-convex deep neural networks, e.g., MobileNetV1 and ResNet18, further demonstrate its superiority by achieving the solutions of much higher sparsity without sacrificing generalization accuracy.
Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution

Recent developments in the miniaturization of hardware have facilitated the use of robots or mobile sensory agents in many applications such as exploration of GPS-denied, hardly accessible unknown environments. This includes underground resource exploration and water pollution monitoring. One problem in scaling-down robots is that it puts significant emphasis on power consumption due to the limited energy available online. Furthermore, the design of adequate controllers for such agents is challenging as representing the system mathematically is difficult due to complexity. In that regard, Evolutionary Algorithms (EA) is a suitable choice for developing the controllers. However, the solution space for evolving those controllers is relatively large because of the wide range of the possible tunable parameters available on the hardware, in addition to the numerous number of objectives which appear on different design levels. A recently-proposed method, dubbed as Instinct Evolution Scheme (IES), offered a way to limit the solution space in these cases. This scheme uses Behavior Trees (BTs) to represent the robot behaviour in a modular, re-usable and intelligible fashion. In this paper, we improve upon the original IES by using Grammatical evolution (GE) to implement a full BT evolution model integratable with IES. A special emphasis is put on minimizing the complexity of the BT generated by GE. To test the scheme, we consider an environment exploration task on a virtual environment. Results show 85% correct reactions to environment stimuli and a decrease in relative complexity to 4.7%. Finally, the evolved BT is represented in an if-else on-chip compatible format.