Reviewerly/exHarmony huggingface dataset:
Adaptive Image-Space Regulation for Robotic Systems

This brief investigates the image-space regulation problem for visually servoed robotic systems by considering the uncertainties in the camera model and the manipulator kinematics and dynamics. By exploiting the depth-related passivity of the overall system kinematics, we first propose an adaptive dynamic controller that avoids the image-space velocity measurement by introducing a filter. In addition, we further show how to utilize the passivity property to design an adaptive kinematic control scheme, which simplifies the controller structure of the visually servoed robotic systems with the assumption that the low-level control loop can guarantee the square-integrability and boundedness of the joint velocity tracking error. We prove that both the proposed adaptive controllers can ensure the asymptotic convergence of the image-space position error. The results from a numerical simulation and extensive experimental studies illustrate the performance of the proposed adaptive image-space regulation control schemes.
Grey Wolf Optimization Algorithm with Improved Convergence Factor and Position Update Strategy

For a high search accuracy and overcoming the problem of tangling the local optimum of grey wolf optimization (GWO) algorithm, a nonlinear convergence factor combining tangent and logarithmic functions is proposed to dynamically adjust the global search ability of the algorithm. An adaptive position updating strategy is also introduced to accelerate the convergence speed of the algorithm in the process of convergence. The experimental results on benchmark functions show that the improved algorithms outperform the standard grey wolf algorithm in convergence speed, stability and optimization accuracy.
A Semi-Markov Decision Model With Inverse Reinforcement Learning for Recognizing the Destination of a Maneuvering Agent in Real Time Strategy Games

Recognizing the destination of a maneuvering agent is important to create intelligent AI players in Real Time Strategy (RTS) games. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-the-shelf planners. However, the common problem in these frameworks is that they usually lack of the modeling of the action duration as in real-world scenarios the agent may take several steps to transit between grids. To solve this problem, a semi-Markov decision model (SMDM), which explicitly models the duration of an action, is proposed in this paper. Besides, most of the current works do not establish a behavioral model of the identified person, and there is almost no work modeling individual behavioral preference, which limits the accuracy of the recognition results. In this paper, the Inverse Reinforcement Learning (IRL) method is adopted in opponent behavior learning for the destination recognition problem. To adapt to the dynamic environment, the Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) method is transformed by defining a Fitness index to measure the effect of weight and use the Nelder-Mead polyhedron search to find the optimal weight. In experiments, we build the game scenario in the Unreal Engine 4 environment and collect the moving trajectories from the human players in several different tasks for evaluating the performance of our methods. The results show that the recognizer using IRL can recognize the destination effectively even if the intention changes during the midway, and it performs better than other models in terms of several most frequently-used metrics.
Self-Efficacy in Social Science

Abstract In order to manage the challenges of the 21 st century, the assessment of individual self-efficacy constitutes an important factor in the educational context; both on the personal as well as on the social and organisational level. This fact justifies the consideration of self-efficacy as a basic competence, whose development must be started on the primary level. Pupils and teachers realise the resources that contribute to the development of a successful self-concept: increased motivation, a courageous selection of tasks to be fulfilled, and the possible influence on future social and economic developments. During their formation pedagogues are learners and teachers at the same time. In their lectures they deal with Social Entrepreneurship Education and competences connected with it. They reflect their own potential to exert influence and develop strategies to support pupils/students in the development of their own self-concept. In accordance with current findings the re-formulation of the curriculum of primary and secondary level demonstrates a development towards conceptual learning in larger contexts, following the principles of self-guidance and activity-orientation.
Assessing Hyper Parameter Optimization and Speedup for Convolutional Neural Networks

The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures.
The Image-Based Inertial Release (IBIR) Test: A New High Strain Rate Test for Stiffness Strain-Rate Sensitivity Identification

Abstract A key limitation of current moderate and high strain rate test methods is the need for external force measurement. For high loading rate hydraulic machines, ringing in the load cell corrupts the force measurement. Similarly, the analysis of split-Hopkinson bar tests requires the assumption that the specimen is in a state of quasi-static equilibrium. Recently, image-based inertial test methods have shown that external force measurement is not required if full-field measurements are available and inertial effects are significant enough. In this case the load information is provided by the acceleration fields which are derived from full-field displacement measurements. This article describes a new image-based inertial test method that can be used for simultaneous quasi-static and high strain rate stiffness identification on the same test sample. An experimental validation of the new test method is provided using PMMA samples. A major advantage of this new test method is that it utilises a standard tensile test machine and the only specialist equipment that is required is an ultra-high speed camera.
Towards Scrum Based Agile Framework for Global Software Development Teams

Organizations are moving their workforce to guarantee matters of trade and profit where accounts are expected to be less expensive and provide better quality products with the accessibility of skilled labor. With the evident benefits, distributed teams often face challenges with reliable and efficient communication coordination patterns that lead towards project failure. In the rapidly growing software industry, it becomes critical for any business to have better management activities to acquire right processes and practices. Agile methods are considered as a remedy for mitigating distributed teams’ challenges, with their consistent focus on communication. Here, we intend to propose a tailored Scrum based solution for distributed teams that empowers anticipated communication and coordination throughout the development process. In the first phase of framework development we have evaluated the state of agile practices from both literature and industry; based on collected evidence and suggestive guidelines a scrum based agile framework was formulated. The Scrum is a project management method in Agile Methods family. Scrum is an iterative process that asks for incremental development of the product, includes cross functional development and testing teams. Evaluation results show that the proposed framework contributes positively to improve development process quality. We have performed theoretical as well as empirical evaluation to validate proposed framework. We have eight project managing rising aspects in DSD (Distributed Software Development) to evaluate the proposed framework.
Deep Learning for Recognizing the Anatomy of Tables on Datasheets

With the growth of information flow through supply chains, we address the issue of semantically segmenting tabular data from document flows. This challenge is abstract in definition as there is no guideline to what defines a table, therefore we primarily applied deep learning methods to compare performances, as well as a morphology-based method to compare several methods for the task of table structure detection. Taking advantage of transfer learning, we found that Mask-RCNN was the most capable network at abstracting to segmentation of tables. Due to the large variation in sizes between columns and rows, most networks failed to detect both with equal effectiveness. However with Mask-RCNN 97.01% precision and 98.28% recall were attained, which put it far ahead of other models in row detection, cementing Mask-RCNN as the most effective choice in this task.
E-Book Information Behaviors and Formats among Graduate Students in Information Sciences

This study reports on a survey of students in information sciences designed to test differences related to e-book formats and other factors. The results from 161 respondents suggest that strategic decision-making shapes use of different e-book and print format options for users who already have significant exposure to e-books. These format options include use in a browser, a downloaded pdf, an e-pub, or a printout. While distance student status related to reported use of library e-books, disciplinary background did not. On the other hand, experience in various fields of scholarship correlated to specific tasks pursued when using e-books, but distance status largely did not. These results suggest ways to tailor local and cooperative e-book collection strategies and related services for academic users.
Experimental demonstration of intensity-modulation/direct-detection secret key distribution

Quantum key distribution, that transmits single-photons or strongly attenuated coherent light, has been studied for secret-key cryptography. This paper experimentally demonstrates a novel key distribution scheme, where a transmitter sends slightly intensity-modulated light with a power level used in conventional fiber communication systems and a receiver directly detects it with a conventional photodiode followed by threshold processing. Its security relies on that the slight intensity modulation is masked by quantum noise. Experiments are presented, which demonstrate that such slight modulation is feasible in practice and a secret key is actually created. The present key distribution scheme employs a setup similar to that in conventional optical communication, featuring practicality.
Insincere Question Classification on Question Answering Forum

Insincerity is defined as a word or action that is genuinely not felt by humans, has no meaning, or not based on sincere feelings. In the internet forum, especially question answering forum, insincerity is one of the severe problems because it can affect the quality of internet forums. Frequently found content (i.e., a message or post) which is not appropriate with the rules of the forum in general and can interfere with other users. Machine learning model will be implemented to detect the questions posed by users in the question answering forum. We examine six baseline machine learning models with n-gram, POS tag and word embedding features. The result of our experiment shows that Multilayer Perceptron model using POS tag features give us highest F1-Score, which is 87.81%.
Enterprise Resource Planning System Implementation With Purchase Management Module In Lembaga Amil Zakat Nasional

Zakat is one of the obligatory worship for every Muslim. In 2017, Rumah Yatim was granted permission as Lembaga Amil Zakat Nasional (LAZNAS) and has the legality to collect and manage zakat. Currently, the departments of Rumah Yatim are using a system on their own department and not yet integrated with each other. With a system that is not yet integrated, the purchasing process is also not yet effective. The process of requesting goods from the inventory department, ordering goods to the vendors and requesting payment to the accounting department requires excess time to be completed. Therefore, they need to implement an information system technology that focuses on the purchasing management that can automate and integrate with other departments. This study used the Quickstart method to help run the ERP system. To find out the current purchasing business process, an interview with the purchasing staff was held at Rumah Yatim. The result of this paper is the ERP Purchase Management module design using the Odoo software.
Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction

Event prediction plays an important role in financial risk assessment and disaster warning, which can help government decision-making and economic investment. Previous works are mainly based on time series for event prediction such as statistical language model and recurrent neural network, while ignoring the impact of prior knowledge on event prediction. This makes the direction of event prediction often biased or wrong. In this paper, we propose a hierarchical event prediction model based on time series and prior knowledge. To ensure the accuracy of the event prediction, the model obtains the time-based event information and prior knowledge of events by Gated Recurrent Unit and Associated Link Network respectively. The semantic selective attention mechanism is used to fuse the time-based event information and prior knowledge, and finally generate predicted events. Experimental results on Chinese News datasets demonstrate that our model significantly outperforms the state-of-the-art methods, and increases the accuracy by 2.8%.
Text Classification via Network Topology: A Case Study on the Holy Quran

Due to the growth in the number of texts and documents available online, machine learning based text classification systems are getting more popular recently. Feature extraction, converting unstructured text into a structured feature space, is one of the essential tasks for text classification. In this paper, we propose a novel feature extraction approach for text classification using the network representation of text, network topology, and machine learning techniques. We present experimental results on classifying the Holy Quran chapters based on the place each chapter was revealed to illustrate the effectiveness of the approach.
Synthetic Image and Strange Attractor: Two Folded Encryption Approach for Secure Image Communication

Chaotic attractors are used in variety of applications especially in image encryption due to their sensitivity, confusion and diffusion properties. Such attractors are continuous form of chaos which can be described using differential equations. In this work, an image encryption technique employing Chen attractor and FPGA generated synthetic image has been discussed. Altera Cyclone II FPGA was used to generate synthetic image that consumed 438 logic elements and 34.03 mW of power consumption. The simulation was carried out on LabVIEW platform. The performance of the algorithm was evaluated through statistical (Entropy, Histogram, Correlation) and differential analyses (NPCR and UACI) for grayscale images of various sizes 128 × 128, 256 × 256 and 512 × 512 which yielded good figures.
Effective Mutation and Recombination for Evolving Convolutional Networks

The major part of the success that we have had in deep learning is attributed to the proper design of the architecture of the network model. With all the hardware resources and mathematical foundation at hand, it is the clever assembling of the pieces that define the performance of that deep learning model. Almost all of the research in this field till date use the models that are designed by a human researcher using their past experiences or applying the iterative process of adding components to the model to see which one performs the best. As we strive towards the general Artificial Intelligence, this process of manually tuning the network to make it work on a specific task does not add up well for the cause. We are concerned with automating the task of network architecture design where the learning algorithm tries to discover the best performing model on itself. To do this, we employ an evolving algorithm in addition to the gradient descent to evolve and train the model. Specifically, we use a form of genetic algorithm with mutation and recombination operators to constantly change the architecture, while the commonly used gradient descent is used as a learning algorithm for each of the genetically procreated models. We propose a series of mutation operators and a method of recombination called Highest Varying k-Features Recombination(HVk-FR) to evolve the CNN models. Results show using our method of recombination on top of mutation yields the best accuracy.
Audio Codec Simulation based Data Augmentation for Telephony Speech Recognition

Real telephony speech recognition task is challenging due to 1) diversified channel distortions and 2) limited access to the real data because of the data privacy consideration. In this paper, assuming no real telephony data are available, we employ diversified audio codecs simulation based data augmentation method to train telephony speech recognition system. Specifically, we assume only wide-band 16 kHz data are available, and we first down-sample the 16 kHz data to the 8 kHz data; we then pass the down-sampled data through various categories of audio codecs to simulate the real channel distortion. As a result, we train our speech recognition with such distorted data. To analyze the effectiveness of different audio codec simulation methods, we classify them into three main categories according to their distortion severity, in terms of their spectrogram analysis. We conduct experiments on various real telephony test sets to show the effectiveness of the proposed data augmentation method. The result shows that the real data is more close with highly distorted simulation data, since the model with highly distorted data reduce the Word-Error-Rate 7.28%-12.78% compared to the baseline.
Data Block and Tuple Identification Using Master Index

Relational databases are still very often used as a data storage, even for the sensor oriented data. Each data tuple is logically stored in the table referenced by relationships between individual tables. From the physical point of view, data are stored in the data files delimited by the tablespaces. Files are block-oriented. When retrieving data, particular blocks must be identified and transferred into the memory for the evaluation and processing. This paper deals with storage principles and proposes own methods for effective data block location and identification if no suitable index for the query is present in the system. Thanks to that, the performance of the whole system is optimized, and the processing time and costs are significantly lowered. The proposed solution is based on the master index, which points just to the blocks with relevant data. Thus, no sequential block scanning is necessary for consuming many system resources. The paper analyzes the impact of block size, which can have a significant impact on sensor oriented data, as well.
A quantum algorithm to efficiently sample from interfering binary trees

Author(s): Provasoli, D; Nachman, B; Bauer, C; De Jong, WA | Abstract: Quantum computers provide an opportunity to efficiently sample from probability distributions that include non-trivial interference effects between amplitudes. Using a simple process wherein all possible state histories can be specified by a binary tree, we construct an explicit quantum algorithm for an important three-dimensional subspace of the parameter space that runs in polynomial time to sample from the process once. The corresponding naive Markov chain algorithm does not produce the correct probability distribution and an explicit classical calculation of the full distribution requires exponentially many operations. However, the problem can be reduced to a system of two qubits with repeated measurements, shedding light on a quantum-inspired efficient classical algorithm.
Solving Vertex Cover in Polynomial Time on Hyperbolic Random Graphs.

The VertexCover problem is proven to be computationally hard in different ways: It is NP-complete to find an optimal solution and even NP-hard to find an approximation with reasonable factors. In contrast, recent experiments suggest that on many real-world networks the run time to solve VertexCover is way smaller than even the best known FPT-approaches can explain. Similarly, greedy algorithms deliver very good approximations to the optimal solution in practice.We link these observations to two properties that are observed in many real-world networks, namely a heterogeneous degree distribution and high clustering. To formalize these properties and explain the observed behavior, we analyze how a branch-and-reduce algorithm performs on hyperbolic random graphs, which have become increasingly popular for modeling real-world networks. In fact, we are able to show that the VertexCover problem on hyperbolic random graphs can be solved in polynomial time, with high probability.The proof relies on interesting structural properties of hyperbolic random graphs. Since these predictions of the model are interesting in their own right, we conducted experiments on real-world networks showing that these properties are also observed in practice. When utilizing the same structural properties in an adaptive greedy algorithm, further experiments suggest that, on real instances, this leads to better approximations than the standard greedy approach within reasonable time.
Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character's roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.