Reviewerly/exHarmony huggingface dataset:
A Framework for Automatic Analysis of Essays Based on Idea Mining

Assessing essays in online and open learning environment can be problematic as the number of submission increases with the increase of enrolment. A tool to automatically analyse those essays is required to assist instructor in the assessment process. The aim of this study is to propose a framework for automatic analysis of essays by identifying idea within the text in order to assist instructor in the assessment. The framework incorporates an idea mining model to identify and measure idea from text and proposes analysis method based on the calculated scores. A simulation of potential application of the framework is provided to show how a tool implemented based on that can be used for automatic and interactive assessment of essays.
The Online Sales Application Of Black And White Print Based On Yii Framework On Higher Education E-Commerce Website

Raharja Internet Cafe as a facility in Raharja College which is provided for Personal Raharja in helping provide the need for lecture activities. Raharja Internet Cafe has a problem that the system of selling black and white print products that apply consumers must come directly to the RIC room at LV-002 at Raharja College, but Raharja Internet Cafe cannot accommodate many consumers because of the limited area. These problems are the background for the establishment of an electronic sales system (e-commerce) based on Yii framework with the aim of facilitating the sale of black and white print products for consumers and staff of Raharja Internet Cafe. E-commerce website at Raharja Internet Cafe is a web-based application with a structured programming concept. The e-commerce application development Shop Copy Nicky uses 4 (four) stages according to the steps that exist in software development, including the stages of observation, analysis, literature study consisting of 10 (ten) literature and implementation. The conclusion of the e-commerce website development on Raharja Internet Cafe is that the website built can make it easy for consumers to make transactions, and Raharja Internet Cafe can get comprehensive and real time reports about sales data, and payment systems for consumers that are easier because they use payment system that is done online
Reducing the variance in online optimization by transporting past gradients

Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting. One issue is the staleness due to using past gradients. We propose to correct this staleness using the idea of {\em implicit gradient transport} (IGT) which transforms gradients computed at previous iterates into gradients evaluated at the current iterate without using the Hessian explicitly. In addition to reducing the variance and bias of our updates over time, IGT can be used as a drop-in replacement for the gradient estimate in a number of well-understood methods such as heavy ball or Adam. We show experimentally that it achieves state-of-the-art results on a wide range of architectures and benchmarks. Additionally, the IGT gradient estimator yields the optimal asymptotic convergence rate for online stochastic optimization in the restricted setting where the Hessians of all component functions are equal.
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks.In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning.The optimized hashcode representations are then fed to a supervised classifier following the prior work.This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set.We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t.stateof-the-art supervised and semi-supervised approaches.
An Argumentative Characterization of Disjunctive Logic Programming

This paper extends the result of Caminada and Schulz [6, 7] by showing that assumption-based argumentation can represent not only normal logic programs, but also disjunctive logic programs. For this, we incorporate a previous work of ours (see [19, 20]), in which reasoning with assumption-based argumentation frameworks is based on certain core logics and the strict/defeasible assumptions may be arbitrary formulas in those logics. In our case, the core logic respects some inference rules for disjunction, which allows disjunctions in the heads of the programs’ rules to be handled properly.
Using Recurrent Neural Networks for Semantic Role Labeling in Portuguese

Semantic Role Labeling is the task of automatically detecting the semantic role played by words or phrases in a sentence. There is a small number of studies dedicated to Semantic Role Labeling in the Portuguese language, and the obtained performance is far from that of the English language. In this article, we propose an end-to-end semantic role labeler for the Portuguese language, which leans on a deep bidirectional long short-term memory neural network architecture. The predictions are used as inputs to an inference stage that employs a global recursive neural parsing algorithm, tailored for the task. We also provide a detailed analysis of the effects of word embedding dimensionality and network depth on the overall performance of the proposed model. The proposed approach outperforms the state-of-the-art approach on the PropBank-Br corpus, while reducing the relative error in approximately 8.74%.
Flipped learning and interactive methods with smartphones in modern physics at secondary schools

We present an example of an active learning activity in modern physics at secondary school developed by us in the framework of flipped learning. The activity dealing with the black-body radiation was a part of our experimental course of modern physics at one of Slovak grammar schools. Using an inquiry-based form of the activity with an interactive demonstration as its introduction, students actively learned modern physics with a GeoGebra simulation as a fundamental tool of visualization, experimentation and measurement. Our results show that thanks to interactive simulations the interactive methods in the framework of flipped modern physics can be really viable, even in classical classrooms with currently widely available digital technologies – smartphones and a Wi-Fi connection.
Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing

Ontology-based visual query formulation is a viable alternative to textual query editors in the Semantic Web domain for extracting data from structured data sources in terms of the skills and knowledge required. A visual query system is at any moment responsible for providing the user with query extension suggestions; however, suggestions leading to empty results are often not useful. To this end, in this article, we first present an approach for projecting OWL 2 ontologies into navigation graphs to be used for query formulation and then a solution where an efficient finite index is used to calculate non-ranked approximated extension suggestions for ontology-based visual query systems using navigation graphs. The results of our experiments suggest that one can efficiently project an ontology into a navigation graph, query it for running an interactive user interface, and suggest query extensions that do not lead to dead-ends.
The Large-Scale Symmetry Learning Applying Pavlov Principle

Symmetry detection task in the domain of 100-dimension binary vectors is considered. This task is characterized by practically infinite number of training samples. We train an artificial neural network with binary neurons to solve the symmetry detection task. Weight changing of hidden neurons is performed according to Pavlov Principle. In the presence of error, synaptic weights are adjusted considering a matrix of random weights. After training on a relatively small number of data samples our network obtained generalization ability and detects symmetry on data not present at the training set. The obtained averaged percentage of correct recognition of our network is better than those of classic perceptron with fixed weights of synapses of neurons of hidden layer. We also compare performance of different modifications of the architecture including different number of hidden layers, different number of neurons in hidden layer, different number of neurons’ synapses.
Interpretability of Gradual Semantics in Abstract Argumentation

Argumentation, in the field of Artificial Intelligence, is a formalism allowing to reason with contradictory information as well as to model an exchange of arguments between one or several agents. For this purpose, many semantics have been defined with, amongst them, gradual semantics aiming to assign an acceptability degree to each argument. Although the number of these semantics continues to increase, there is currently no method allowing to explain the results returned by these semantics. In this paper, we study the interpretability of these semantics by measuring, for each argument, the impact of the other arguments on its acceptability degree. We define a new property and show that the score of an argument returned by a gradual semantics which satisfies this property can also be computed by aggregating the impact of the other arguments on it. This result allows to provide, for each argument in an argumentation framework, a ranking between arguments from the most to the least impacting ones w.r.t. a given gradual semantics.
An Empirical Study of the Usefulness of State-Dependent Action Costs in Planning

The vast majority of work in planning to date has focused on state-independent action costs. However, if a planning task features state-dependent costs, using a cost model with state-independent costs means either introducing a modeling error, or potentially sacrificing compactness of the model. In this paper, we investigate the conflicting priorities of modeling accuracy and compactness empirically, with a particular focus on the extent of the negative impact of reduced modeling accuracy on (a) the quality of the resulting plans, and (b) the search guidance provided by heuristics that are fed with inaccurate cost models. Our empirical results show that the plan suboptimality introduced by ignoring state-dependent costs can range, depending on the domain, from inexistent to several orders of magnitude. Furthermore, our results show that the impact on heuristic guidance additionally depends strongly on the heuristic that is used, the specifics of how exactly the costs are represented, and whether one is interested in heuristic accuracy, node expansions, or overall runtime savings.
Secure Protocol for Identity-based Provable Data Possession in Cloud Storage

Remote data possession is becoming an increasingly important issue in cloud storage. It enables users to verify if their outsourced data have remained intact while in cloud storage. The existing remote data audit (RDA) protocols were designed with the public key infrastructure (PKI) system. However, this incurs considerable costs when users need to frequently access data from the cloud service provider with PKI. This study proposes a protocol, called identity-based RDA (ID-RDA) that addresses this problem without the need for users' certificates. This study outperforms existing RDA protocols in computation and communication.
Comparative Analysis of Prosodic Characteristics Using WaveNet Embeddings

We present a methodology for assessing similarities and differences between language varieties and dialects in terms of prosodic characteristics.A multi-speaker, multi-dialect WaveNet network is trained on low sample-rate signal retaining only prosodic characteristics of the original speech.The network is conditioned on labels related to speakers' region or dialect.The resulting conditioning embeddings are subsequently used as a multi-dimensional characteristics of different language varieties, with results consistent with dialectological studies.The method and results are illustrated on a Swedia 2000 corpus of Swedish dialectal variation.
Complete Coverage Path Planning of UUV for Marine Mine Countermeasure Using Grid Division and Spanning Tree

In recent years, with the continuous improvement of the Unmanned Underwater Vehicle (UUV) systems, naval mine detection missions carried out by UUVs have proliferated and made significant progress. During the process of mine detection, the completeness and efficiency of path planning are critical to the effectiveness of the mission. This paper addresses an off-line and on-line path planning algorithm for UUV in a marine area with obstacles based on the rectangular grid division and spanning tree circumnavigation. In this algorithm, area to detect is equally divided into (2*M)*(2*N) rectangular cell and the UUV circumnavigates the spanning tree of the graph induced from the 2*2 mega cell division while visiting every 1*1 grid with minor backtracking. Simulation results show that the path generated by the algorithm is complete and efficient.
Multispectral video acquisition using spectral sweep camera

This paper proposes a novel multispectral video acquisition method for dynamic scenes by using the Spectral-Sweep camera. To fully utilize the redundancies of multispectral videos in the spatial, temporal and spectral dimensions, we propose a Complex Optical Flow (COF) method that could extract the spatial and spectral signal variations between adjacent spectral-sweep frames. A complex L1-norm constrained optimization algorithm is proposed to compute the COF maps, with which we recover the entire multispectral video by temporally propagating the captured spectral-sweep frames under the guidance of reconstructed COF maps. We demonstrate the promising accuracy of reconstructing full spatial and temporal sensor resolution multispectral videos with our method both quantitatively and qualitatively. Compared with state-of-the-art multispectral imagers, our computational multispectral imaging system can significantly reduce the hardware complexities, while achieves comparable or even better performance.
Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor Independent of Multi-values

In this paper, we propose a chaotic complex-valued associative memory with adaptive scaling factor independent from multi-values. This model is based on the conventional chaotic complex-valued associative memory with adaptive scaling factor that can realize dynamic associations of multi-valued patterns. In the conventional chaotic complex-valued associative memory with adaptive scaling factor, parameters of the chaotic complex-valued neuron model are automatically adjusted according to the internal state of neurons. In the conventional model, a multi-value pattern is expressed by assigning points at positions obtained by equally dividing a unit circle of the complex plane into S multiple values. It has been confirmed that almost same recall ability can be obtained as in the case of performing manual adjustment in the model for $$S=4,\ 6,\ 8$$ , but no study has been conducted for other cases. In addition, it is known that the optimum method of automatically adjusting parameters also differs depending on the value of S. In this study, we also conduct experiments at $$ S = 10,\ 12,\ 14$$ and 16, and propose a method to automatically adjust the parameters of the chaotic complex-valued neuron model independently from the value of S. Computer experiments were carried out and it was confirmed that automatic adjustment of parameters can be performed in the proposed model without depending on multi-values.
Evaluating the Vector Supercomputer SX-Aurora TSUBASA as a Co-Processor for In-Memory Database Systems

In-memory column-store database systems are state of the art for the efficient processing of analytical workloads. In these systems, data compression as well as vectorization play an important role. Currently, the vectorized processing is done using regular SIMD (Single Instruction Multiple Data) extensions of modern processors. For example, Intel’s latest SIMD extension supports 512-bit vector registers which allows the parallel processing of 8× 64-bit values. From a database system perspective, this vectorization technique is not only very interesting for compression and decompression to reduce the computational overhead, but also for all database operators like joins, scan, as well as groupings. In contrast to these SIMD extensions, NEC Corporation has recently introduced a novel pure vector engine (supercomputer) as a co-processor called SX-Aurora TSUBASA. This vector engine features a vector length of 16.384 bits with the world’s highest bandwidth of up to 1.2 TB/s, which perfectly fits to data-intensive applications like in-memory database systems. Therefore, we describe the unique architecture and properties of this novel vector engine in this paper. Moreover, we present selected in-memory column-store-specific evaluation results to show the benefits of this vector engine compared to regular SIMD extensions. Finally, we conclude the paper with an outlook on our ongoing research activities in this direction.
Construction of a device that automatically generates various touch interactions

This paper proposes a device that automatically generates various touch interactions including multi-touch to realize touch interactions at high speed, continuously, and hands free. The proposed device does not require additional software installations to the touch panel, and it can be expected to improve efficiency and realize automation of work using the touch panel. The proposed device consists of an electrode sheet printed with conductive ink and a voltage control circuit, and generates touch interactions by changing the capacitance of the touch panel temporally and spatially.
A Cultural Probes Study on the (Non-)Use of Digital Artefacts

The field of Human-Computer Interaction (HCI) is primarily concerned with the design and use of digital artefacts. However, the increased penetration of digital technology into public and private life has also increased the need to better understand negative aspects of use. Studies on technology 'non-use' have the potential to challenge the 'digital imperative' and normative assumptions in HCI, but they are currently limited to the classification of individual non-use and the identification of non-users' strategies. This paper applies cultural probes to gain new perspectives on the role of active non-use and selective use in HCI design. Probe packages were designed for both users and non-users of smartphones to prompt their reflection on everyday smartphone use. The results suggest valuable differences in the way both groups perceive and reflect on the use of smartphones in everyday situations. Capturing, understanding and communicating such different perceptions and reflections is important for balanced design and appropriation processes of digital artefacts.
A Power-Efficient Architecture for On-Chip Reservoir Computing

Reservoir computing is a neuromorphic computing paradigm which is well suited for hardware implementations. In this work, an enhanced reservoir architecture is introduced as to lower the losses and improve mixing behaviour in silicon photonic reservoir computing designs.
Color Normalization-Based Nuclei Detection in Images of Hematoxylin and Eosin-Stained Multi Organ Tissues

This article presents an adaptation of a color transfer method for binarization of cell nuclei in Hematoxylin- and Eosin-stained microscopy images. The aim is to check the ability and accuracy of nuclei detection for multi-organ cases using a public dataset. The results are obtained using the Monte Carlo method and then compared to the ground truth segmentations. Some cases are presented in detail and discussed. This method seems to be promising for the further development of classic and deterministic algorithms for H&E nuclei detection.
Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality

Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user’s motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user’s motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data.
Filter Method Ensemble with Neural Networks

The main concept behind designing a multiple classifier system is to combine a number of classifiers such that the resulting system succeeds to topple the individual classifiers by pooling together the decisions of all classifiers. Uniting relatively simple pattern recognition models with limited performance is commonly found in the literature. It performs better when each learner be trained well, but different learners have different working principles which adds diversity in the ensemble. In this paper, we first select three optimal subsets of features using three different filter methods namely Mutual Information (MI), Chi-square, and Anova F-Test. Then with the selected features we build three learning models using Multi-layer Perceptron (MLP) based classifier. Class membership values provided by these three classifiers for each sample are concatenated which is then fed to next MLP based classifier. Experimentation performed on five UCI Machine Learning Repository, namely Arrhythmia, Ionosphere, Hill-Valley, Waveform, Horse Colic shows the effectiveness of the proposed ensemble model.
Appropriating 3D Printers in Augmented Reality

Digital fabrication technologies, such as 3D printers, are receiving more and more attention, not only from professionals but also from hobbyists. However, even though people have easier access to these devices, 3D printers remain a black box for many users. To support the appropriation of 3D printers, this demonstration presents a system which extends a physical printer to include virtual components using augmented reality (AR). With these components, we try to explain how the printer works and allow the user to operate the printer through an AR application. We extend existing software with a custom solution to create a unique user interface and user experience. Our user interface provides a new way of inspecting models in AR before they are printed.