Reviewerly/exHarmony huggingface dataset:
Introduction – FAIR data, systems and analysis

The FAIR principles outline key attributes to make digital resources more Findable, Accessible, Interoperable, and Reusable. Globally endorsed and widely adopted, there is now a pressing need to enable the establishment of an Internet of FAIR Data an
R\'{e}nyi Generative Adversarial Networks

We propose a loss function for generative adversarial networks (GANs) using Renyi information measures with parameter $\alpha$. More specifically, we formulate GAN's generator loss function in terms of Renyi cross-entropy functionals. We demonstrate that for any $\alpha$, this generalized loss function preserves the equilibrium point satisfied by the original GAN loss based on the Jensen-Renyi divergence, a natural extension of the Jensen-Shannon divergence. We also prove that the Renyi-centric loss function reduces to the original GAN loss function as $\alpha \to 1$. We show empirically that the proposed loss function, when implemented on both DCGAN (with $L_1$ normalization) and StyleGAN architectures, confers performance benefits by virtue of the extra degree of freedom provided by the parameter $\alpha$. More specifically, we show improvements with regard to: (a) the quality of the generated images as measured via the Frechet Inception Distance (FID) score (e.g., best FID=8.33 for RenyiStyleGAN vs 9.7 for StyleGAN when evaluated over 64$\times$64 CelebA images) and (b) training stability. While it was applied to GANs in this study, the proposed approach is generic and can be used in other applications of information theory to deep learning, e.g., AI bias or privacy.
CONCEPTUALIZING MOBILE DIGITAL LITERACY SKILLS FOR EDUCATORS

With mobile phones as a near pervasive technology amongst the youth, their use in classroom practice are often considered particularly daunting by the teaching fraternity.Many teachers qualified in the so-called 'pre PC' era, and do not consider themselves on technology par with their students.Their students on the other hand are growing up in an era of technology immersion.In order to enact the 'loco parentis' role often required, these educators need to be equipped with mobile digital literacy skills; often neglected in, amongst other, Teacher Professional Development interventions.The purpose of this paper is conceptualize mobile digital literacy skills for educators in using mobile technology in formal education.The research exapts the 'best-fit' framework synthesis of Carroll et al. ( 2013) from the health domain, towards this end.The strategy is operationalized through two phases.Phase 1 identifies the Ng (2013) digital skills model as a priory framework.Phase 2 utilizes the categories presented in Phase 1 to guide the classing of purposefully selected literature towards conceptualizing mobile digital literacy skills for educators using mobile technology in formal education.The literature considered was identified in accordance with Grant et al. ( 2014) description of a systematized scoping review.The contribution of this paper lies in its outcomes to identify and classify literacy skills for mobile technologies.These identified skills can further guide interventions aimed at educational mobile phone use.
Ultracold Lattice Gas Automata for Single Trapped Ion Interacting with a Laser Field

Abstract In the same context of lattice gauge theory, a notion of cellular automaton (CA) is considered as a description of the underlying dynamics of fields evaluated on a discrete lattice. The explorative approach of this work is initiated by taking the automata evolution as an elementary description of fields dynamics on one side and pivotal role of locality on other side to connect the notion of states and transformations to density operators in quantum electrodynamics. In order to parameterize the definition of proposed model in the context of lattice field theory the nonlinear interaction is introduced with neighboring cells. In this paper, analytical and explicit expression of atomic density matrix for single trapped ion interacting with laser field based on cellular automata dynamics are discussed. The definition is further explored by introducing some probabilistic theories as the theoretical framework then it is concluded that the graph of cellular automata in continuum time limit of the CA dynamics leads to Maxwell–Bloch equation.
Image Object Extraction Based on Semantic Segmentation and Label Loss

Object extraction refers to the operation of obtaining an object area from an image based on a small amount of mark information given by users, which is a key step in image processing. In order to obtain a complete object profile, current methods usually require a large number of manual annotations, especially for objects with irregular contours. Since traditional algorithms rely on low-level pixel features without semantic information, and are based on obvious mathematical assumptions (ie, strong inductive bias), it is difficult to completely identify objects. At present, in order to improve the integrity of object extraction, semantic segmentation-based methods increase the complexity and latancy by adding more pre-processing and post-processing steps. In this paper, we propose a novel model named IOEBSS, which includes a fast binary plane pre-processing, an improved Deeplab v3+ semantic segmentation model, and an auxiliary loss function named Label Loss. Through the fast binary plane pre-processing, the model can accelerate the transformation of interactive inputs. The improved semantic segmentation model makes the extracted results more semantically complete, and Label Loss is more conducive to gradient flow and accelerates training convergence. For the above reasons, IOEBSS can accurately and quickly identify objects with complex contours and colors. On Pascal VOC and COCO datasets, compared to current methods, IOEBSS has a significant improvement in accuracy, inference speed, and convergence speed.
Android game implementation in health learning for early childhood education

Abstract The challenge as a teacher in adopting the 2013 Indonesian Education Curriculum for early age education is finding an effective learning media to support learning material. This study aims to design, and evaluate an education game about the importance of maintaining health for children. This puzzle game is developed for Android operating system. Ten elementary school students participated in this study. Questionnaire with Likert scale is used to measure the usability of the games. The result indicates that about 60% of respondents strongly agree in terms of ease of use, content and design.
Almost Rerere: An Approach for Automating Conflict Resolution from Similar Resolved Conflicts

Concurrent development requires the ability of reconciling conflicting updates to the code made independently. A specific case occurs when long living feature branches are integrated to a rapid changing code base. In this scenario, every integration test will require to manually resolve the same conflicts at every iteration. In this paper we propose a framework for automating the detection and resolution of conflicts in the code updated by distinct developers, one of which may be a code generator. The tool learns how to solve conflicts from past experience and applies resolutions, encoded as replacement regular expressions, to conflicts not seen before. Experiments show that the number of automatically resolved conflicts and the quality of the solution increase as the system acquires experience.
T-GD: Transferable GAN-generated Images Detection Framework

Recent advancements in Generative Adversarial Networks (GANs) enable the generation of highly realistic images, raising concerns about their misuse for malicious purposes. Detecting these GAN-generated images (GAN-images) becomes increasingly challenging due to the significant reduction of underlying artifacts and specific patterns. The absence of such traces can hinder detection algorithms from identifying GAN-images and transferring knowledge to identify other types of GAN-images as well. In this work, we present the Transferable GAN-images Detection framework T-GD, a robust transferable framework for an effective detection of GAN-images. T-GD is composed of a teacher and a student model that can iteratively teach and evaluate each other to improve the detection performance. First, we train the teacher model on the source dataset and use it as a starting point for learning the target dataset. To train the student model, we inject noise by mixing up the source and target datasets, while constraining the weight variation to preserve the starting point. Our approach is a self-training method, but distinguishes itself from prior approaches by focusing on improving the transferability of GAN-image detection. T-GD achieves high performance on the source dataset by overcoming catastrophic forgetting and effectively detecting state-of-the-art GAN-images with only a small volume of data without any metadata information.
Cause-Effect Association between Event Pairs in Event Datasets

Causal discovery from observational data has been intensely studied across fields of study. In this paper, we consider datasets involving irregular occurrences of various types of events over the timeline. We propose a suite of scores and related algorithms for estimating the cause-effect association between pairs of events from such large event datasets. In particular, we introduce a general framework and the use of conditional intensity rates to characterize pairwise associations between events. Discovering such potential causal relationships is critical in several domains, including health, politics and financial analysis. We conduct an experimental investigation with synthetic data and two real-world event datasets, where we evaluate and compare our proposed scores using assessments from human raters as ground truth. For a political event dataset involving interaction between actors, we show how performance could be enhanced by enforcing additional knowledge pertaining to actor identities.
Scientific Approaches to the Digitalization of Drugs Assortment Monitoring Using Artificial Neural Networks

The periodic lack of essential medicines on the market is a global public health problem. The possibility of using machine learning technology and artificial neural networks to monitor drug shortages has not been previously investigated. The aim of this work is to assess the appropriateness of using digital systems to collect primary data and machine learning systems for monitoring drug deficiency. The analysis of the problem area of the shortage of drugs and the capabilities of machine learning technologies and neural systems was performed. As a result of the obtained information, a scientific synthesis of promising possibilities for combining these areas was carried out. A neural network model has been proposed. The deep integration of existing information databases using a normative reference system, the application of scientific approaches to the monitoring of drug supply, including drug shortages, using machine learning and neural networks will improve the quality and continuity of the drug supply process for the population and, accordingly, medical care in general. In the context of piloting models for implementing insurance principles for reimbursing the population for the purchase of medicines, these decisions are most relevant.
Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning

Cost partitioning is a method for admissibly combining admissible heuristics. In this work, we extend this concept to merge-and-shrink (M&amp;S) abstractions that may use labels that do not directly correspond to operators. We investigate how optimal and saturated cost partitioning (SCP) interact with M&amp;S transformations and develop a method to compute SCPs during the computation of M&amp;S. Experiments show that SCP significantly improves M&amp;S on standard planning benchmarks.
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification

Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling. While models such as neural attention-based aspect extraction (ABAE) have been successfully applied to user-generated texts, they are less coherent when applied to traditional data sources such as news articles and newsgroup documents. In this work, we introduce a simple approach based on sentence filtering in order to improve topical aspects learned from newsgroups-based content without modifying the basic mechanism of ABAE. We train a probabilistic classifier to distinguish between out-of-domain texts (outer dataset) and in-domain texts (target dataset). Then, during data preparation we filter out sentences that have a low probability of being in-domain and train the neural model on the remaining sentences. The positive effect of sentence filtering on topic coherence is demonstrated in comparison to aspect extraction models trained on unfiltered texts.
Virtual application technology of citizen journalism based on mobile user experience

Abstract At present, the development of citizen journalism in the era of globalization and information technology is increasingly developing because of the presence of the internet and application technology that are in demand by readers and writers to develop their potential. The technology that is developing at this time is to use an Android technology mobile application that is very supportive of its operation. However, Citizen Journalism must be careful in using and filtering information from irresponsible blogs so that they can deceive the informant and easily incite an information that has not been proven by the truth of the news. Therefore, this research will make a citizen journalism application based on mobile technology in the platform of an Android and AJAX programming mobile. The results of the research show that the Mobile Technology Citizen Journalism application can be tested by sending or accessing news or information content on the server using a GPRS connection. Tests carried out include features, reliability, suitability, easy to use, and perception of the quality of this Mobile application.
Learning to Search for Objects in Images from Human Gaze Sequences

Human vision relies on saccades to extract high quality information on small areas of the field of view, pointing the high resolution region of the retina (i.e. fovea) to the regions of interest. The eye motions are guided by top-down information provided by the task, which in our case is the search for a given object. In this work we propose a Recurrent Neural Network (RNN) model that learns from human demonstrations how to explore an image. The exploration samples are obtained from eye tracking data acquired while subjects inspect images. The proposed model extracts visual features from Convolutional Neural Networks (CNNs), which correspond to the input of the RNN. The contribution of this work is to consider the visual features along with the object label in a new model that is able to search for a given object in an image. We make a comparative study on the importance of context during object search tasks, showing that foveated images perform better than uniform image region crops.
Blind Quantitative Steganalysis Using CNN–Long Short-Term Memory Architecture

Covered communication is termed as steganography. And unveiling details about this camouflaged communication is steganalysis. In the current era, steganalysis helps forensics examiners to analyze and detect malicious communication. Feature extraction, training phase and testing phase are three steps used by traditional machine learning algorithms for learning complex characteristics of images before using them for analysis. Deep learning models, on the other hand, comprise a large number of hidden network layers which are used to capture various statistical information of digital images and learn them automatically. This paper proposes a novel deep neural network technique using convolutional neural network (CNN) with long short-term memory (LSTM) for quantitative steganalysis, prediction of embedded message length in given stego-object. Experiments are performed using Keras Python library. Statistical measure MAE is used to compare proposed quantitative steganalysis. Experimental results show the good performance of the proposed model.
MapReduce-Based Framework For Blockchain Scalability

The proliferation of blockchain technology into the wide spectrum of industries has been stymied by its inability to scale. With the augmenting popularity of blockchains, scalability of blockchain is hindering it from attaining the meteoric transaction rate present in the existing solutions like MasterCard and VISA. Presently, the scalability solutions of blockchains use several off-chain and on-chain mechanisms. This paper proposes an on-chain solution backed by big data technologies. We aim to provide a real-time scalable transaction processing by using big data framework to overcome the roadblocks of scalability. Our framework has horizontal and vertical scaling to augment the sluggish blockchain transaction rate using sharding frameworks of big data.
Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time.

Emergency Department (ED) overcrowding is a major global healthcare issue. In this paper, we used Long Short-Term Memory (LSTM) recurrent neural networks to build a model to predict ED wait time in the next 2 hours using a randomly generated patient timestamp dataset of a typical patient hospital journey. Compared with Linear Regression model, the average mean absolute error for the LSTM model is decreased by 15% (3 minutes) (p<0.001). The LSTM model statistically outperforms the LR model, however, both models could be practically useful in ED wait time prediction.
Reputation Based Fault Tolerance in Blockchain Networks

The Blockchain technology brings a rapid growth in the industry, It emphasizes the service to lead the complexity of software and malicious attack in the network. This technology is used to monitor the highly vulnerable services and it is used to increase the complexity of the warehouse data. It assures the security and consistency of data, The warehouse data has been replicated the availability and the enhancement of security in the services. This technology originated from internet sector as a decentralized, distributed ledger for data transaction. Nowadays, it is visualised as a backbone or frame work for decentralized data processing in open source network. Blockchain uses variety of consensus protocol which is reliable for nodes and communication resources that is used for data consistency. Byzantine fault tolerance algorithm has been proposed for computational cost and security also for consensus efficiency. This paper deals by proposing practical byzantine fault tolerance on edge computing networks paves away for reducing storage overhead also security purpose on edge devices. The proposed model is simulated in the constrain environment and the results are discussed. It shows that the proposed method has increase the availability and security of the stored data.
Electronic Polling Agent Using Blockchain: A New Approach

The blockchain supports all kinds of potential for improving digital systems. Frequently, we hear about all the potential of blockchain in reference to economic services and finance systems. But the digital system that could possibly be most enhanced by blockchain is e-voting. Over the progression of the former election cycle, fraud, fake votes, and “system rigging” were widespread. This makes it clear that both conventional paper-based ballot system and the centralized electronic-based voting system have definite apertures creating vulnerabilities and vote manipulation. The blockchain is an immutable ledger, and integrating blockchain technology with a voting system can give the solution to numerous problems faced by modern systems. The motive for e-voting system using blockchain is to facilitate transparency, fairness, and audibility. While the number of voters increases, the time taken to cast vote, i.e., time taken to generate block also increases due to which the throughput of the system decreases. A new system is proposed in this paper, which aims to provide a fast, secure, and transparent voting approach using blockchain.
H-Accuracy, an Alternative Metric to Assess Classification Models in Medicine.

As widely known, regular accuracy is a misleading and shallow indicator of the performance of a predictive model, especially in real-life domains like medicine, where decisions affect health or life. In this paper we present and discuss a new accuracy measure, the H-accuracy, as a more conservative alternative to regular accuracy, which we claim is more informative in the medical domain (and others of similar needs) for the elements it encompasses. In particular, the proposed measure takes into account important information such as the complexity of the cases and the case prevalance in the population. We also provide proof that the H-accuracy is a generalization of the balanced accuracy and illustrate the descriptive power of this score.
Target Tracking Based on Camshift Algorithm and Multi-feature Fusion

Abstract This paper proposes a new algorithm on the basics of Camshift algorithm and multi-feature fusion. First, the target area is weighted by the Gaussian function. Then LBP is used to extract local texture features, next fuse the texture features with the color features to obtain the color and texture histogram. Finally, for the purpose of tracking the target accurately and preventing occlusion, the Kalman filter is used to forecast the position of the moving target. From the comparison of the experimental results, the conclusion can be drawn that the new algorithm can effectively overcome the interference of objects and track the target accurately in a more complex environment.
On the Design of Bit Permutation Based Ciphers

Bit permutation based block ciphers, like PRESENT and GIFT, are well-known for their extreme lightweightness in hardware implementation. However, designing such ciphers comes with one major challenge – to ensure strong cryptographic properties simply depending on the combination of three components, namely S-box, a bit permutation and a key addition function. Having a wrong combination of components could lead to weaknesses. In this article, we studied the interaction between these components, improved the theoretical security bound of GIFT and highlighted the potential pitfalls associated with a bit permutation based primitive design. We also conducted analysis on TRIFLE, a first-round candidate for the NIST lightweight cryptography competition, where our findings influenced the elimination of TRIFLE from second-round of the NIST competition. In particular, we showed that internal state bits of TRIFLE can be partially decrypted for a few rounds even without any knowledge of the key.
Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract)

We consider the challenges of learning multi-agent/robot macro-action-based deep Q-nets including how to properly update each macro-action value and accurately maintain macro-action-observation trajectories. We address these challenges by first proposing two fundamental frameworks for learning macro-action-value function and joint macro-action-value function. Furthermore, we present two new approaches of learning decentralized macro-action-based policies, which involve a new double Q-update rule that facilitates the learning of decentralized Q-nets by using a centralized Q-net for action selection. Our approaches are evaluated both in simulation and on real robots.