Reviewerly/exHarmony huggingface dataset:
The English Language Scientific Literature Classification Based on Abstract Using Rocchio Algorithm

Abstract The need for the documents in the form of journals or scientific articles is currently increasing which means the need of documents available also increasing, in another side it makes difficult to find and present journals. Therefore, a method is needed to classify journals automatically according to the categories of the journal. One method that can help organize documents according to their categories is classification. In this study, Rocchio algorithm is used as a method to classify journals. The data used comes from Google Scholar. Journal consists of 4 (four) categories: Computer Vision and Pattern Recognition, Artificial Intelligent, Data Mining and Analysis, and Computer System. System testing is carried out by taking 160 journals for training data and 48 journals for testing data. The results of this study produced a web-based journal classification system and showed the method used was able to classify journals with accuracy result is 93%.
Vanishing Ideals over Finite Fields

Let $$\mathbb{F}_{q}$$ be a finite field, let $$\mathbb{X}$$ be a subset of the projective space ℙs−1 over $$\mathbb{F}_{q}$$ parametrized by rational functions, and let I( $$(\mathbb{X})$$ ) be the vanishing ideal of $$\mathbb{X}$$ . The main result of this paper is a formula for I( $$(\mathbb{X})$$ ) that will allow us to compute (i) the algebraic invariants of I( $$(\mathbb{X})$$ ) and (ii) the basic parameters of the corresponding Reed–Muller-type code.
OnboardDepth: Depth Prediction for Onboard Systems

Depth sensing is important for robotics systems for both navigation and manipulation tasks. We here present a learning-based system which predicts accurate scene depth and can take advantage of many types of sensor supervision. We develop an algorithm which combines both supervised and unsupervised constraints to produce high quality depth and which is robust to the presence of noise, sparse sensing, and missing information. Our system is running onboard in realtime, is easy to deploy, and is applicable to a variety of robot platforms.
Analysis of Tuberculosis Disease Using Association Rule Mining

Tuberculosis (TB) is a chronic infectious disease and remains a serious explanation for death globally. Most of the infected people are from poverty-stricken communities with low healthcare infrastructure. Machine learning (ML) shows a unique way to facilitate the diagnosis of TB. ML can provide deep insights into large biomedical datasets, as well as it can uncover new biomedical and healthcare knowledge. ML techniques have to be utilized to successfully recognize TB occurrence at an early stage or re-occurrence. Association rule mining and decision tree can be used for prediction of the occurrence and re-occurrence of TB. Association rules and decision tree give a meaningful and efficient way to define and present certain dependencies among the attributes in a dataset ML algorithms help to identify, discover hidden and valuable information, and have been massively used and explored in the medical domain. The main purpose of this study is to apply ML techniques for extracting hidden patterns, which are significant to predict TB at an early stage.
eHealth in Hospital Information Management

Solutions to the basic technological and methodological problems associated with the actual use of information technology systems in the healthcare area: security and privacy of medical information; social and professional acceptance of information systems; integration and use of medical knowledge; inclusion of healthcare professionals' expertise; lack of centralized control; communication standards and integration with various types of software and hardware devices are challenges that can be felt in many regions, specially when we implement applications custom-made to well defined requirements and specific circumstances.
A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling

In this paper, we consider a Bayesian mixture model that allows us to integrate out the weights of the mixture in order to obtain a procedure in which the number of clusters is an unknown quantity. To determine clusters and estimate parameters of interest, we develop an MCMC algorithm denominated by sequential data-driven allocation sampler. In this algorithm, a single observation has a non-null probability to create a new cluster and a set of observations may create a new cluster through the split-merge movements. The split-merge movements are developed using a sequential allocation procedure based in allocation probabilities that are calculated according to the Kullback–Leibler divergence between the posterior distribution using the observations previously allocated and the posterior distribution including a ‘new’ observation. We verified the performance of the proposed algorithm on the simulated data and then we illustrate its use on three publicly available real data sets.
Energy, Latency, and Reliability Tradeoffs in Coding Circuits

Using the Thompson circuit complexity model, it is shown that fully parallel encoding and decoding schemes with asymptotic block error probability that scales as O( f (n)) have energy that scales as Ω(n- ln f (n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> ). In addition, it is shown that the number of clock cycles [T(n)] required for any encoding or decoding scheme that reaches this bound must scale as T(n) ≥ - ln f (n) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> . Similar scaling results are extended to serialized computation. A similar approach is extended to three dimensions by generalizing the Grover information-friction energy model. Within this model, it is shown that encoding and decoding schemes with probability of block error Pe(n) consume at least Ω(n(- ln Pe(n)) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1/3)</sup> ) energy.
Pulse-Net: Dynamic Compression of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are used in a range of computer vision tasks, with state-of-the-art CNNs such as AlexNet and VGG16 constructed using a large number of parameter and multiply-add operations (MACs). These tasks require high computational power and high energy requirements to run the CNNs, making them unsuitable for deployment on In-ternet of Things devices. To overcome this issue and facilitate the use of CNNs on these resource-constrained devices, compression technology through pruning research has gained momentum and is an important tool for improving performance during inference. Our work focuses on pruning unwanted filters and nodes in all layers of a network. The network is pruned iteratively during training via a novel approach we call Pulse-Net, and a significant number of filters/nodes are removed while ensuring any loss in accuracy is within a predetermined range. The unpruned network can be extracted from the original structure for the inference stage. This novel method has an easy-to-set parameter to control the trade-off between accuracy and compression. Pulse-Net gives greater compression, while maintaining competitive accuracy loss, than other reported methods like, efficient convnets, ThiNet and Cross-Entropy Pruning. It also has better robustness against adversarial attacks than other compression and pruning techniques.
Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization

This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs. We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basisfunctions. We compare our truncated prior to the untruncated Laplacian based prior in simulated and real data examples to illustrate the improved scalability in terms of the size of the underlying graph.
Fast hierarchical wavelet-domain motion estimation for arbitrarily shaped visual objects

Motion estimation is widely used for video compression as it reduces the memory requirements of any video file while maintaining high visual quality. But the computational cost consumed by motion estimation/compensation usually accounts for more than 50%, even to 80%, of the total cost of the encoder. So researchers try to develop fast motion estimation algorithms to lower the computational complexity. Moreover, few studies have been concentrated on the efficient motion estimation of arbitrarily shaped visual objects, which particularly pays insufficient attention to boundary continuity of the motion-compensated objects as well. In this research, we present a hierarchical wavelet-domain approach to motion estimation of visual objects with arbitrary shapes. We explore the guiding role of binary alpha-plane in assisting motion estimation of visual objects, and bring forward a binary alpha-plane matching scheme. It constrains the intensity-based motion estimation into a small subset of initial candidate vectors in the search window. This both accelerates search speed and helps to produce coherent object boundary. Moreover, we employ the low-band-shift technique to eliminate the wavelet’s shift-variance effect. And based on the observation that high frequency subbands of wavelet contribute little to calculating motion vectors, a new matching function is derived for fast wavelet-domain motion estimation. Coupled with the alpha-plane matching and the devised matching function, a hierarchical low-band-based motion estimation approach is further proposed. Through executing a refined search around the initial candidate vectors using only the low frequency subband at each scale, our approach achieves a satisfying balance between prediction accuracy and computational efficiency. Experimental results show that by using the standard accuracy measurements and the benchmark video sequences, the performance of the proposed approach is close to the full search with a matching quality over 99%, and with 95.80% reduction in complexity.
Structure Preserving Convolutional Attention for Image Captioning

In the task of image captioning, learning the attentive image regions is necessary to adaptively and precisely focus on the object semantics relevant to each decoded word. In this paper, we propose a convolutional attention module that can preserve the spatial structure of the image by performing the convolution operation directly on the 2D feature maps. The proposed attention mechanism contains two components: convolutional spatial attention and cross-channel attention, aiming to determine the intended regions to describe the image along the spatial and channel dimensions, respectively. Both of the two attentions are calculated at each decoding step. In order to preserve the spatial structure, instead of operating on the vector representation of each image grid, the two attention components are both computed directly on the entire feature maps with convolution operations. Experiments on two large-scale datasets (MSCOCO and Flickr30K) demonstrate the outstanding performance of our proposed method.
Toward Imitating Visual Attention of Experts in Software Development Tasks

Expert programmers' eye-movements during source code reading are valuable sources that are considered to be associated with their domain expertise. We advocate a vision of new intelligent systems incorporating expertise of experts for software development tasks, such as issue localization, comment generation, and code generation. We present a conceptual framework of neural autonomous agents based on imitation learning (IL), which enables agents to mimic the visual attention of an expert via his/her eye movement. In this framework, an autonomous agent is constructed as a context-based attention model that consists of encoder/decoder network and trained with state-action sequences generated by an experts' demonstration. Challenges to implement an IL-based autonomous agent specialized for software development task are discussed in this paper.
Designing a full-body customizable haptic interface using two-dimensional signal transmission

The concept of spatial computing has a high potential to augment our lives. We can imagine the expansion of the concept of spatial computing in the future as the expanded visual space will blend into our daily lives. In such a future, how do we feel and interact with another reality that overlaps with real space? We have researched this question from an approach using a haptic interface. We previously developed the Synesthesia Suit[Konishi et al. 2016]. This is a full body haptic suit that extends the VR experience of the game "Rez Infinite". It delivers the visual and musical experience of "Rez" to the whole body, and provides haptic feedback. However, the Synesthesia Suit is difficult to walk around in a physical space, due to the thick cables required for its operation. Basically it is especially suited for VR gaming and it was difficult to apply to spatial computing experience.
VLSI Design and Synthesis of Reduced Power and High Speed ALU Using Reversible Gates and Vedic Multiplier

In the recent times most of the advanced circuits designing projects are using reversible logic since it is helping to decrease the configuration dependent warmth scattering. This allows vitality free calculation, high range of circuit density and better testing provisions. A novel structure for Reversible 16-bit ALU is demonstrated in this paper using fast Vedic multiplier which is planned by falling 1-bit ALUs. Control unit and snake unit are having a significant role to be played in the 1-bit ALU. Peres door was used in the control unit and Haghparast and Navi Gate (HNG) were used for viper unit. The hugest part of this paper is that when contrasted with different papers, this ALU configuration has diminished door check, and transistor tally. A usage depends on Field Programmable Gate Arrays (FPGA) spartan3E XC3S500E which gives client adaptability to a programmable arrangements and bringing down the expense. Power also reduced by 6% when compared with the best existing.
Super-isolated abelian varieties

We call an abelian variety over a finite field Fq super-isolated if its (Fq-rational) isogeny class contains a single isomorphism class. In this paper, we use the Honda-Tate theorem to characterize super-isolated ordinary simple abelian varieties by certain algebraic integers. Our main result is that for a fixed dimension g≥3, there are finitely many such varieties over all finite fields.
Evolving Reversible Fault-Tolerant Adder Architectures and Their Power Estimation

Fault tolerance property is incorporated in a circuit to increase its reliability. Error at the output side can be prevented by Fault-tolerant circuits. Its design enables the circuit to continue operation, at an error-free state, rather than failing completely, when some part of the circuit fails. Here, reversible adder fault-tolerant architectures of ripple carry, carry look-ahead adder (CLA) and carry skip adder (CSA) are implemented and their corresponding powers are estimated. Power efficiency and reliability of the implemented adders make them effective.
Detecting illegal diving and other suspicious activities in the North Sea: Tale of a successful trial

While the volume of data available to surveillance systems is steadily increasing, the assessment of a complex situation is still mainly done by humans. This might result in wrong assessments or even in entirely missing crucial situations. An automated system to assess incoming data and highlight situations of interest could greatly support human experts to perform their tasks. This work describes an approach to model expert and domain knowledge in order to reason about situations of interest. The model is a Multi-Source Dynamic Bayesian Network (DBN), which encodes different situations and their relationships at different levels of abstraction. Through the use of Bayesian inference, the existence probability for a situation can be estimated. This paper reports on a successful implementation of a multi-source DBN to detect illegal diving activities, tested during live sea trial in the North Sea under the EU project MARISA. During the trial the Netherlands Coastguard performed different manoeuvres simulating anomalous behaviours, including illegal diving. The multi-source DBN Behavior Analysis service processed real data streams of AIS and radar contacts and positively detected the unique event, early, without false alarm.
PSMECS: A provably secure ID‐based communication in mobile edge computing

Summary Human‐centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real‐time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real‐time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads.
Independent natural extension for infinite spaces

Sets of desirable gambles Conditional lower previsionsWe define and study the independent natural extension of two local uncertainty models for the general case of infinite spaces, using the frameworks of sets of desirable gambles and conditional lower previsions.In contrast to Miranda and Zaffalon [16], we adopt Williamscoherence instead of Walley-coherence.We show that our notion of independent natural extension always exists-whereas theirs does not-and that it satisfies various convenient properties, including factorisation and external additivity.The strength of these properties depends on the specific type of epistemic independence that is adopted.In particular, epistemic event-independence is shown to outperform epistemic atom-independence.Finally, the cases of lower expectations, expectations, lower probabilities and probabilities are obtained as special instances of our general definition.By applying our results to these instances, we demonstrate that epistemic independence is indeed epistemic, and that it includes the conventional notion of independence as a special case.
Analysis of File Security with Three-Pass Protocol Scheme Using Massey-Omura Algorithm In Android

Abstract Today’s data transactions using the android device is commonly done anytime. Cryptography is needed to secure data to be transmitted to ensure data security from the manipulation of others. To secure the data, one of cryptography scheme is Three-Pass Protocol, this scheme allows data transmission without key exchange, so this scheme is more secure and efficient. A Massey-Omura algorithm is an asymmetric key cryptography system that will be used in this research. For primality testing in key and prime number generator, the Agrawal-Biswas algorithm will be used. The strength of Massey-Omura is the difficulty of calculating enormous discrete logarithmic and Three-Pass Protocol with his no key exchange capabilities make this method more secure. In this research, the Mean Squared Error value obtained is 0 and the value of Peak Signal to Noise Ratio obtained is infinite. The complexity of the Massey-Omura algorithm and the Agrawal-Biswas algorithm is Θ (n). Graph of file extension against processing time shows a stable graph that means file extension is not affecting processing time. Graph of file size against processing time shows a linear graph that means a processing time would increase along with file size.
A Dynamic Approach for Combining Abstract Argumentation Semantics

Abstract argumentation semantics provide a direct relation from an argumentation framework to corresponding sets of acceptable arguments, or equivalently to labeling functions. Instead, we study stepwise update relations on argumentation frameworks whose fixpoints represent the labeling functions on the arguments. We make use of this dynamic approach in order to study novel ways of combining abstract argumentation semantics. In particular, we introduce the notion of a merge of two argumentation semantics, which is defined in such a way that the merge of the preferred and the grounded semantics is the complete semantics. Finally, we consider how to define new semantics using the merge operator, in particular, how meaningfully combine features of naive-based and complete-based semantics.
Initial data generating bounded solutions of systems of linear discrete equations

In the paper, a system of two linear discrete equations is considered. Numerous sufficient conditions are derived for the existence of solutions to such systems with prescribed asymptotic behavior of solutions. In the literature, not many recommendations are given as to how to detect the initial data generating such solutions. For the case of a system of two linear equations, this gap is filled in this paper. Using the limits of special convergent sequences, sets of initial data are constructed generating the desired solutions.
A Convolutional Autoencoder Approach To Learn Volumetric Shape Representations For Brain Structures

We propose a novel machine learning strategy for studying neuroanatomical shape variation. Our model works with volumetric binary segmentation images, and requires no pre-processing such as the extraction of surface points or a mesh. The learned shape descriptor is invariant to affine transformations, including shifts, rotations and scaling. Thanks to the adopted autoencoder framework, inter-subject differences are automatically enhanced in the learned representation, while intra-subject variances are minimized. Our experimental results on a shape retrieval task showed that the proposed representation outperforms a state-of-the-art benchmark for brain structures extracted from MRI scans.
Development and Evaluation on Night Forest Virtual Reality as Innovative Nyctophobia Treatment

Abstract Nyctophobia is an exaggerated fear that arises when a person is in the dark or at nightime. In psychology, cognitive behavioral therapy is commonly used to treat a person who is accustomed to face his/her fears by giving positive suggestions of the fears, then confront the fears until he/she no longer feels anxious. This paper presents the storyboard and prototype development of Virtual Reality (VR) Night Forest application which aims to provide an alternative tool for Nyctophobia treatment. The adventure game which is located in a forest at nightime is used as a new means to allow the user to become familiar with darkness. In the game, the user is made aware of the wonderful experience of hearing natural sounds such as footsteps, bird and door sounds. The user, as a boy scout, is then assigned to look for logs in the forest with different lighting levels. The storyboards are implemented on Mobile VR Apps. The experimental study shows that fear level of female users remains high compared with male users. Nevertheless, the repeated use of the game can positively motivate users to train themselves to get comfortable in darkness. In the experiment, the usability assessment concerning virtual reality user interfaces is presented using Sutcliffe approach.