Reviewerly/exHarmony huggingface dataset:
Volumetric Feature Learning for Query-by-Example in Medical Imaging Archives

The increasing challenges and requirements of medical image retrieval systems are leading the scientific community towards exploring modern representation methods as a means to improve clinical information retrieval as we know it. While current research tackles medical image retrieval through text-based, visual-based, or mixed approaches, representation learning can play an important role in improving retrieval capabilities by encoding medical image content into compact representations, addressing the problem of dimensionality. This paper introduces the potential of representation learning for the retrieval of high dimensionality imaging studies through automatically learned representations for regions of interest. Preliminary results are presented for feature learning through adversarial auto-encoding, based on the VISCERAL medical image retrieval benchmark.
SmarTmem: Intelligent Management of Transcendent Memory in a Virtualized Server

Managing memory capacity in virtualized environments is still a challenging problem. Many solutions have been proposed and implemented, including memory ballooning and memory hotplug. But these mechanisms are slow to respond to changes in virtual machine (VM) memory demands. Transcendent Memory (tmem) was introduced to improve responsiveness in memory provisioning, by pooling idle and fallow memory in the hypervisor, and making these physical pages available as additional memory for the VMs through a key-value store. However, tmem presents some limitations of its own. State-of-the-art hypervisors do not implement any efficient way to manage tmem capacity, letting VMs compete for it in a greedy way by default, regardless of their actual memory demand. In this paper, we demonstrate the need for intelligent memory capacity management for tmem, and we present the design and implementation of SmarTmem, a mechanism that integrates coarse-grained user-space memory management with fine-grain allocation and enforcement at the virtualization layer. Our results show that our solution can improve the running time of applications from the Cloudsuite benchmarks by up to 35% compared to the default tmem allocation mechanism.
Evaluating Pre-trained Predictor Models of Pedestrian Destinations for a Voice Guidance Robot

In recent year, there has been increasing interest in communication robots, and a variety of services including voice guidance are expected for such robots. For providing those services, state estimation of robot users is required. From the background, we have been studying a method to predict the walking direction of a pedestrian who heads toward a robot in order to provide suitable voice guidance to him/her by a communication robot installed at the reception desk of a public facility. In this paper, we verify the effectiveness of the proposed method by using actual observed data. Here, we measure pedestrian trajectories using a laser range scanner installed on a tripod and predict the branching direction using pre-trained predictor models by a machine learning algorithm. In this paper, we generated two predictor models using an open dataset of pedestrian trajectories in a shopping mall. By conducting evaluation experiments using the models, we found out that one model can predict the direction with practical accuracy but the accuracy of another one is not sufficient. The result shows that using robust and adequate predictor models are important for our target system.
Irradiance forecast model for PV generation based on cloudiness web service

Abstract Among renewable energies that play a critically important role to achieve a sustainable development in the future, solar photovoltaic (PV) power have been growing dramatically recently. Despite the enormous potential of solar PV, its drawback, which is intermittent in generation, imposes a significant issue on electrical system, such as stability or operation planning of the system. One of the solutions for the problem is PV generation forecast that have been researched in various studies. This paper presents a methodology to obtain an indirect PV production forecast model. Processes of model formation, validation and test are performed in the study using real data from a Campbell Scientific weather station Grenoble, France.
PENDIDIKAN DI ERA DIGITAL

Nowadays the increasing on information of technology can not be depated wit education. The education must be follow the information technology and communication. The education in digital era can be able to integrated with information technology and communication into the whole lessons. The increasing education in digital era thestudent can be understand many skills easily. The impact of inovation is learning technology method of study using experiment of technology. The change of the duty of the education needs inovation and creativities in learning process. The new technology meanly multimedia is the important thing and learning activities. With multimedia can be variation in the situation of lesson and the student enjoy the lesson. So that the teacher as a teacher must be integrated the information technology and communication into learning well and every day can be increasingly the education quality in Indonesia.
TALEM(The Advanced Learning and Education Management) System With OBE(Outcome-based Education)

Being a developing country and one where the population is growing almost exponentially, all these sorts of problem like socio-economic problems present in Pakistan and also the underlying cause of these problems is the quality of education, with the help of quality education we can be able to break those strong bridges of social cycles of poverty. “The Advanced Learning and Education Management System” (TALEM System) is amid to bridge the gap between our and international standard of education by providing an easy implementation of Outcome-Based Education (OBE) system and to accelerate integration of IT in the educational system of Pakistan by providing a modern campus management solution. Digitizing the attendance process, implementation of Augmented Reality to create better understanding of topics and real time bus tracking to ensure safety all these factors will improve the educational experience. We hope that efforts like TALEM System contribute in the development of the country.
M-fuzzifying basic inquisitive semantics

The basic system of inquisitive semantics (InqB) established by Groenendijk et al. is a general inquisitive semantic theory which doesn’t concern fuzziness. To explain the fuzzy phenomena in natural languages, this paper extends InqB into the framework of M-fuzzifying setting and establishes a basi c system of M-fuzzifying inquisitive semantics. To begin with, the notion of M-fuzzifying supporting mapping is defined, where M is a completely distributive lattice with an involution operator and each subset of the universal set of all possible worlds can be regarded as a support of any well-formed formula to some degree. Then the notions of M-fuzzifying entailment order, M-fuzzifying truth mappings, M-fuzzifying informative content mappings and M-fuzzifying inquisitive content mappings are introduced and their properties are discussed. Further, the degrees of assertiveness, informativeness, inquisitiveness and questioning of a well-formed formula are defined, by which the M-fuzzifying assertive projection operator and the M-fuzzifying questioning projection operator are introduced and characterized. Finally, a necessary and sufficient condition is obtained, where a well-formed formula is exactly the disjunction of its unique M-fuzzifying assertive projection and unique M-fuzzifying questioning projection.
Stochastic Video Generation with Disentangled Representations

Frame-to-frame uncertainty is a major challenge in video prediction. The use of the deterministic models always leads to averaging of future states. Some methods draw samples from a prior at each time step to deal with the uncertainty of the future states, such as the SVG model [1]. However, these models always use only one set of latent variables to represent the whole stochastic part in a video clip whereas sequential data often involves multiple independent factors. In this paper, we exploit the complex representation of information in video sequences by formulating it explicitly with a disentangled-representation stochastic video generation (DR-SVG) model that imposes sequence-dependent prior and sequence-independent prior to different sets of latent variables. Through a variational lower-bound and adversarial objective functions in latent space, our model can produce crisper frames with clear content and pose which indicate the sequence-dependent and sequence-independent component respectively.
Sequence Time Expression Recognition in the Spanish Clinical Narrative

Time expression recognition is one of the open issues in Natural Language Processing. These expressions are relevant to determine temporal aspects of the text as well as to establish relationships among facts described in said text. In the clinical domain, the temporal aspects are relevant to determine, for example, a sequence of facts in a clinical history. This paper presents research on the recognition of time expressions in Spanish according to the TIMEX3 standard. First, we establish HeidelTime, a well-known state of the art rule-based system, as a reference. Next, a hybrid model (a combination of bidirectional LSTM, CNN and CRF) is introduced to try to improve the results for the Spanish language. Both architectures have been tested with a Timex3 annotated Spanish corpus (TIMEBANK 1.0) to compare them. First, the results obtained show that the neural architecture obtains better results in Spanish. Finally, the neural architecture has been tested on a corpus of Clinical Notes (English and Spanish) in order to determine the results on this domain.
A Hybrid Similarity Measure Based on Binary and Decimal Data for Data Mining

We suggest a new similarity measure to improve the quality of data mining, especially for recommender system. A similarity measure is widely used for classification, clustering, anomaly detection and so on. Many recommender systems predict unrated score through clustering similar users. This method is so called collaborative filtering(CF), which is being widely used. In CF, how to define a similarity measure is a major concern. Conventional measures based on Pearson Correlation Coefficient(PCC) are hard to reflect the implicit and explicit information at the same time. We propose a hybrid similarity measure, named BD PCC, which is a type of PCC, named after the first letter of 'Binary' and 'Decimal' types respectively. As we suggest from its name, BD PCC is defined by concatenating two PCCs on two different types of data. Although other hybrid measures need some processes to concatenate, BD PCC is free from scale issue. Because it consists of both PCCs unlike other hybrid measures consisting of values in different ranges. Since PCC for binary data can be defined if the user bought at least one item, BD PCC relieves the sparsity of data. We tested the proposed similarity measure in recommender systems and the prediction accuracy has been improved for real data sets, MovieLens 100K[8], MovieLens 1M[8], MovieLens latest small[8], and FilmTrust 35K[9].
VM Availability in Presence of Malicious Attacks in Open-Source Cloud

The performance of Computer system did not increase rapidly despite growing computing power. It depends on many other factors, but often it found rapidly degraded due to improper handling of resources like resource leakage, the flaw in programming, resource exhaustion or malicious attacks. These vulnerabilities became solemn when it affected hues number of users in the open-source cloud. This paper exhibits the degradations of virtual machines (VMs) availability in cloud-computing due to malicious attacks. Simulation result shows how orphaned VMs accumulate over time due to VM leakage and effected pool of resources. The paper describe some techniques applicable at multiple components of the cloud to identify and eliminate the orphaned VMs. Finally the experimental results show that how orphan control technique improved the availability of VMs even during various malicious attacks.
Modelling of the Processes of the Small Hydroelectric Power Station on the Basis of Interval Input and Output Data Analysis

When designing any technical and technological processes or systems, the need to take into account the inaccuracy of measurement or measurement errors is an important point. However, in most works, to account for possible deviations of parameter values from nominal, they impose limits only on system output, avoiding the input, considering that the measured values are objectively accurate. This decision is false. The paper proposes to develop a method with allowance for these deviations and to test it.
Deep Real-world and Real-time Face Identification System

Face identification has been one of the most challenging and attractive areas, and it has become a popular research area in the computer vision community because of its impact in application areas, such as surveillance, security systems, and biometrics. With the introduction deep learning technique, face recognition accuracy has skyrocketed. The state-of-the-art has achieved close to 100% recognition rates by applying convolutional neural networks (CNN), as these networks are more robust in regards to image variation than other methods are. However, in an uncontrolled environment with illumination, extreme poses, and variations in facial expression, the face recognition problem is far from being solved. This paper has two contributions, the first one is to discuss real-world challenges in face identification and suggest solutions for them. To overcome one these challenges, we train an MLP model to validate the output of CNN which reduce the false positive rate. The second contribution is to implement an OpenVX computational graph model to achieve real-time performance and reduce the run-time.
Deep Morphological Neural Networks

Mathematical morphology intends to extract object features such as geometric and topological structures in digital images. Given a set of target images and original images, it is cumbersome and time-consuming to determine the suitable morphological operations and structuring elements. In this paper, we propose deep morphological neural networks, which include a nonlinear feature extraction layer to learn the structuring element correctly and an adaptive layer to select appropriate morphological operations automatically. We demonstrate the applications of object recognition, including hand-written digits, geometric shapes, traffic signs, and brain tumor. Experimental results show the higher computational efficiency and higher accuracy of our developed model as compared against existing convolutional neural network models.
Adaptive kernel fuzzy C-Means clustering algorithm based on cluster structure1

The well-known Fuzzy C-Means (FCM) algorithm and its modified clustering derivatives have been widely applied in various fields. However, previous studies have focused on the yield of correctly clustered data, and few have addressed the alignment of extracted influential areas of clusters to natura l cluster structure. Various clustering algorithms present diverse characteristics in cluster structure detection due to the different clustering principles involved. For example, Mahalanobis distance-based FCM algorithms effectively detect the influential direction of each cluster, while kernel-based FCM algorithms provide an interface for adjusting the influential range. Combining the advantages of these previous algorithms, the Adaptive Kernel Fuzzy C-Means (AKFCM) algorithm based on cluster structure is proposed in this paper. The AKFCM algorithm can effectively detect the influential direction and adjust the influential range of each cluster with adaptive kernelization. By applying the previous and AKFCM algorithms to both synthetic and real-world datasets, the proposed algorithm is proven to achieve better performance not only in clustering accuracy but also in the extraction of reasonable influential areas. The proposed algorithm could be helpful for clustering datasets composed of clusters with different directions and ranges in structure.
Digital Image Enhancement Gray Scale Images In Frequency Domain

Abstract The aim of the research is to improve the distorted digital images using one of the methods of frequency domain. Where The Transform Cosine Discrete (DCT) function was used as one methods of frequency domain. Good results have been obtained using this method. standard gray scale images, which have distortions, have been improved and get improved image results.
Face recognition algorithm based on improved kernel sparse representation

When the face recognition technology captures the sample image, the recognition performance is rapidly reduced due to the change of the shooting angle and distance, the different illumination brightness, and the variability of the facial posture. In order to solve the problem of insufficient computational complexity of SRC algorithm and insufficient number of training samples, this paper proposes a face recognition algorithm based on discriminative low rank decomposition and fast kernel sparse representation. On the basis of low rank decomposition, the discriminability of the low rank matrix is improved, and the discriminative low rank approximation matrix and the sparse error matrix are combined to form a dictionary for testing. In addition, the kernel coordinate descent method is used to solve the sparse representation matrix. The experimental results on the AR face database show that the proposed algorithm guarantees the recognition accuracy and improves the recognition efficiency when the training samples are insufficient.
Equivalence Between Non-malleability Against Replayable CCA and Other RCCA-Security Notions

Replayable chosen ciphertext (RCCA) security was introduced by Canetti, Krawczyk, and Nielsen (CRYPTO 03) in order to handle an encryption scheme that is “non-malleable except tampering which preserves the plaintext”. RCCA security is a relaxation of CCA security and a useful security notion for many practical applications such as authentication and key exchange. Canetti et al. defined non-malleability against RCCA (NM-RCCA), indistinguishability against RCCA (IND-RCCA), and universal composability against RCCA (UC-RCCA). Moreover, they proved that these three security notions are equivalent when considering a PKE scheme whose plaintext space is super-polynomially large. Among these three security notions, NM-RCCA seems to play the central role since RCCA security was introduced in order to capture “non-malleability except tampering which preserves the plaintext.” However, their definition of NM-RCCA is not a natural extension of that of classical non-malleability, and it is not clear whether their NM-RCCA captures the requirement of classical non-malleability. In this paper, we propose definitions of indistinguishability-based and simulation-based non-malleability against RCCA by extending definitions of classical non-malleability. We then prove that these two notions of non-malleability and IND-RCCA are equivalent regardless of the size of plaintext space of PKE schemes.
Self-Attentive Networks for one-shot Image Recognition

Despite recent breakthroughs in applications of deep neural networks, learning from a limited number of examples remains a key challenge in academia and industry. A prototypical example is one-shot learning, in which we must make predictions when only given one sample of each unseen class. Most previous works either categorize instances by the pair similarities or use Long-Short Term Memory (LSTM) structure based method, however, they usually suffer from inter-and intra-class variation problem or high computation cost. In this paper, we propose self-attentive networks to address the above issues in one-shot image recognition problem. Deep neural features and attention mechanism especially self-attention are employed in the proposed self-attentive networks. Experiments on Omniglot and MiniImagenet datasets show that our proposed self-attentive networks achieve state-of-the-art accuracy on one-shot image recognition. In addition, owing to low time complexity and parallelizable architecture, self-attentive networks require significantly less time to train and infer.
Digital Reality and Perspective of the Management of Educational Organizations

The study considers the reality and prospect of the digital transformation of the economy and society using the example of the digital transformation of the management of educational organizations. The authors of the study clarify the essential features and possibilities of the digital transformation of the management of educational organizations, and identify the natural links between the reality of the digital transformation of the management of educational organizations and the formation of digital competences of managerial staff. Recommendations to improve the training of managerial staff for the digital transformation of the management of educational organizations are given. The methodological basis of the study is systemic and competence-based approaches; methodological basis - analysis and synthesis, statistical methods, self-assessment and expert assessment, questioning; the empirical basis is the data of the federal statistical observation of the activities of Russian educational organizations, the results of the qualitative assessment of managerial staff of educational organizations (N = 300).
Factors that Influence the Adoption of Information Security on Requirement Phase for Custom-Made Software at SMEs

This paper reports security factors to custom-made software at SMEs from development perspectives. The primary purpose of this work is to support the In-house software development companies to understand the security issues are being addressed to deal with customer demands. It targets the requirement phase from the overall Software Development Life Cycle (SDLC) by conducting a systematic snapshot mapping in the literature addressing the security requirements in the period from 2000 to 2018. The data extraction process resulted in gathering 40 relevant papers. Primary, reviewed papers addressed classification factors based on management, development, organizational, and environmental perspectives. Secondary, it addressed classification governance issues, and security services including authorization, authentication, availability, confidentiality, and integrity. Our results revealed that project management issues have studied significantly. A large number of the studies are conducted at security management factors and adopting the authorization security service. This study presents a future trend based on a gap research forum that revealed after conducting the systematic mapping approach.
Twice Opportunity Knocks Syntactic Ambiguity: A Visual Question Answering Model with yes/no Feedback

Visual Question Answering (VQA) is a joint task that aims to answer questions based on given images. During dialogs between humans, syntactic ambiguity is a common phenomenon and it also could be found in the questions of VQA systems. Generally, the existing methods for VQA utilize one-shot answering frameworks, which will face a great difficulty if syntactic ambiguity occurs in questions. In human dialogs, people often conquer the problem by feeding back questions for confirmation. Inspired by this observation, we propose a novel method to eliminate the syntactic ambiguity in VQA via the user's feedback. We compared our method with the existing methods on two benchmark datasets, CLEVR and CLEVR-CoGenT. We found that the accuracy of our method is close to 100% on the CLEVR dataset. On the CLEVR-CoGenT dataset, our method is also 21% higher than the state-of-the-art method.