Reviewerly/exHarmony huggingface dataset:
Egocentric visitors localization in natural sites

Localizing visitors in natural environments is challenging due to the unavailability of pre-installed cameras or other infrastructure such as WiFi networks. We propose to perform localization using egocentric images collected from the visitor’s point of view with a wearable camera. Localization can be useful to provide services to both the visitors (e.g., showing where they are or what to see next) and to the site manager (e.g., to understand what the visitors pay more attention to and what they miss during their visits). We collected and publicly released a dataset of egocentric videos asking 12 subjects to freely visit a natural site. Along with video, we collected GPS locations by means of a smartphone. Experiments comparing localization methods based on GPS and images highlight that image-based localization is much more reliable in the considered domain and small improvements can be achieved by combining GPS- and image-based predictions using late fusion.
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal

Abnormal activity recognition requires detection of occurrence of anomalous events that suffer from a severe imbalance in data. In a video, normal is used to describe activities that conform to usual events while the irregular events which do not conform to the normal are referred to as abnormal. It is far more common to observe normal data than to obtain abnormal data in visual surveillance. In this paper, we propose an approach where we can obtain abnormal data by transforming normal data. This is a challenging task that is solved through a multi-stage pipeline approach. We utilize a number of techniques from unsupervised segmentation in order to synthesize new samples of data that are transformed from an existing set of normal examples. Further, this synthesis approach has useful applications as a data augmentation technique. An incrementally trained Bayesian convolutional neural network (CNN) is used to carefully select the set of abnormal samples that can be added. Finally through this synthesis approach we obtain a comparable set of abnormal samples that can be used for training the CNN for the classification of normal vs abnormal samples. We show that this method generalizes to multiple settings by evaluating it on two real world datasets and achieves improved performance over other probabilistic techniques that have been used in the past for this task.
SSA: A More Humanized Automatic Evaluation Method for Open Dialogue Generation

Dialogue generation has been gaining ever-increasing attention, and various models have been proposed and adopted in many fields in recent years. How to evaluate their performance is critical. However, current evaluation metrics tend to be insufficient because of their simplicity and crudeness, resulting in weak correlation with human judgements. To solve this issue, we propose an automatic and comprehensive evaluation metric, which consists of three assessment criteria: Semantic Coherence, Syntactic Validity and Ability of Expression (SSA). The first two criteria are used to evaluate the generations from semantic and syntactic aspects respectively at the sentence level and the last one is to evaluate the overall performance at the model level. With two generative models, we conduct experiments on three datasets, including Twitter, Subtitle and Lenovo. Comparing with the previous metrics such as BLEU, METEOR and ROUGE, the correlation coefficient between SSA and human judgements is increased by 0.23-0.35, i.e. 324%-864% relative improvements. The experimental results demonstrate that SSA correlates more strongly with human judgements on the evaluation for open dialogue generation. Additionally, SSA is able to evaluate the semantic coherence and syntactic validity of generations exactly. More importantly, the evaluation models can be trained without human annotations. Thus, SSA is flexible and extensible to different datasets.
Curricular trends in the Universidad Francisco de Paula Santander academic program offerings

Abstract The objective of the research work, was to analyze the curriculum of the programs from “Universidad Francisco de Paula Santander” in San José de Cucuta, Colombia. The sample was participatory non-probability, with the participation of 200 students from Catatumbo and university student’s, both from the different semesters and documents curriculums. A mixed study of sequential explanatory design was applied. In the first quantitative phase, the results were obtained by applying a digital questionnaire to the students. The data were analyzed using the observation guide for their respective analysis. The results of both phases were merged by the contrasting method. The conclusions demonstrate the importance of curriculum for training in research and innovation and critical thinking, curriculum for the creation, adaptation and/or use of communication and information technologies, curriculum for social inclusion in the university.
SkiDNet: Skip Image Denoising Network for X-Rays

Medical imaging has evolved to become an essential tool for screening and diagnosing diseases, but they have certain limitations just like every other technology. X-rays, which is one of the most common radiological examinations, is not immune to imperfections. In this paper, we aim to tackle one such imperfection in X-rays, which is the presence of undesirable noises which causes aberrations in the output projections. This makes diagnosis and analysis difficult since such noises shroud the intricate details that these images contain. Distinctive denoising algorithms have been proposed in the past for a spectrum of vision datasets, but a very few of them are for X-rays. We introduce a new denoising network called SkiDNet, a deep learning approach using an encoder-decoder architecture with skip connections of varying length. The network has been trained on the NIH Chest X-Ray Dataset. With the unique properties injected by different types of connections, SkiDNet is able to surpass the performance of existing models. Furthermore, adopting a different approach to weight initialization and batch normalization makes the network more robust. Denoised X-rays obtained from the network were objectively evaluated using different metrics namely mean squared error, peak signal-to-noise ratio, and the structural similarity index.
Data-to-Text Generation with Attention Recurrent Unit

Recurrent Neural Networks (RNNs) have shown promising results in many text generation tasks with their ability in modeling complex data distribution. However, the text generation model in their encoder or decoder RNNs still can not use the context efficiently. In this paper, we propose a novel Attention Recurrent Unit (ARU) to generate short descriptive texts conditioned on database records. Different from conventional approaches Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), ARU allows the context information from the encoder to be aligned first inside the unit, which can improve the ability of content selection and surface realization for the model. And we also design a method called DoubleAtten to enhance the attention distribution when computing the generation probabilities. On the recently released ROTOWIRE dataset, extensive experimental results demonstrate that the ARU and DoubleAtten can efficiently improve the model performance for data-to-text generation task.
Subspace controllability of bipartite symmetric spin networks

We consider a class of spin networks where each spin in a certain set interacts, via Ising coupling, with a set of central spins, and the control acts simultaneously on all the spins. Due to the permutation symmetries of the network, the system is not globally controllable but it displays invariant subspaces of the underlying Hilbert space. The system is said to be subspace controllable if it is controllable on each of these subspaces. We characterize the given invariant subspaces and the dynamical Lie algebra of this class of systems and prove subspace controllability in every case.
Secure and Balanced Scheme for Non-Local Data Storage in Blockchain Network

Blockchain-based decentralized storage is one of the most typical and prevalence application. However, current works are not concerned about the storage imbalance caused by data leakage. This heavily impacts the availability and stability of the blockchain network. This paper proposes a double-chain scheme to improve the security of the blockchain network. The problem on storage unbalance is formalized, and a heuristic algorithm named FMA is proposed, followed by a customized genetic algorithm (GA), together with a tabu search algorithm (TSA), to deal with the problem for non-local data storage. Numerical results show that the proposed FMA can obtain the same storage fitness compared with GA and TSA for the case of the small number of nodes. GA is able to outperform the other two algorithms in terms of storage fitness, and the solution quality of TSA is bound by the qualities of FMA and GA.
Multi-Task Learning with Capsule Networks

Multi-task learning is a machine learning approach learning multiple tasks jointly while exploiting commonalities and differences across tasks. A shared representation is learned by multi-task learning, and what is learned for each task can help other tasks be learned better. Most of existing multi-task learning methods adopt deep neural network as the classifier of each task. However, a deep neural network can exploit its strong curve-fitting capability to achieve high accuracy in training data even when the learned representation is not good enough. This is contradictory to the purpose of multi-task learning. In this paper, we propose a framework named multi-task capsule (MT-Capsule) which improves multi-task learning with capsule network. Capsule network is a new architecture which can intelligently model part-whole relationships to constitute viewpoint invariant knowledge and automatically extend the learned knowledge to different new scenarios. The experimental results on large real-world datasets show MT-Capsule can significantly outperform the state-of-the-art methods.
Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

Artificial Neural networks (ANNs) are powerful computing systems employed for various applications due to their versatility to generalize and to respond to unexpected inputs/patterns. However, implementations of ANNs for safety-critical systems might lead to failures, which are scarcely predicted in the design phase as ANNs are highly parallel and their parameters are hardly interpretable. Here we develop and evaluate a novel symbolic software verification framework based on incremental bounded model checking (BMC) to check for adversarial cases and coverage methods in multi-layer perceptron (MLP). We developed and evaluated a novel symbolic software verification framework based on incremental bounded model checking (BMC) to check for adversarial cases and coverage methods in a multi-layer perceptron(MLP). Besides, we developed an efficient SMT-based Context-Bounded Model Checker for Graphical Processing Units (ESBMC-GPU), to ensure the reliability of specific safety properties in which safety-critical systems can fail and make incorrect decisions at the cost of material damage or even put lives in danger. This paper marks the first symbolic verification framework to reason over ANNs implemented in CUDA. Our experimental results show that our approach implemented in ESBMC-GPU can successfully verify safety properties and covering methods in ANNs and correctly generate 28 adversarial cases in MLPs.
Regression analysis for multivariate process data of counts using convolved Gaussian processes

Research on Poisson regression analysis for process data of counts has developed rapidly in the last decade. One of the difficult problems in the multivariate case is how to construct a cross-correlation structure whilst, at the same time, making sure that the covariance matrix is positive definite. To address this issue, we propose to use convolved Gaussian processes (CGP) in this paper. This approach provides a semi-parametric model and offers a natural framework for modelling the common mean structure and covariance structure simultaneously. The CGP enables the model to define a different covariance structure for each component of the response variables. This flexibility ensures the model can cope with data coming from different sources or having different structures, and thus to provide accurate estimation and prediction. In addition, the model is able to accommodate large-dimensional covariates. The definition of the model, the inference and the implementation, as well as its asymptotic properties, are discussed. Comprehensive numerical examples with both simulation studies and real data are presented.
Auditable Credential Anonymity Revocation Based on Privacy-Preserving Smart Contracts

Anonymity revocation is an essential component of credential issuing systems since unconditional anonymity is incompatible with pursuing and sanctioning credential misuse. However, current anonymity revocation approaches have shortcomings with respect to the auditability of the revocation process. In this paper, we propose a novel anonymity revocation approach based on privacy-preserving blockchain-based smart contracts, where the code self-execution property ensures availability and public ledger immutability provides auditability. We describe an instantiation of this approach, provide an implementation thereof and conduct a series of evaluations in terms of running time, gas cost and latency. The results show that our scheme is feasible and efficient.
Comparing Static and Dynamic Weighted Software Coupling Metrics

Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses.
Semi-Supervised Online Elastic Extreme Learning Machine with Forgetting Parameter to deal with concept drift in data streams

Concept drift is a common problem for online sequential algorithms that deal with data streams. Many supervised and unsupervised approaches to solve concept drifts were proposed recently, including some ELM-based algorithms. Due to its fast training, ELM-based algorithms can quickly adapt to dataset changes, detecting and preventing concept drift. SSOE-ELM is a semi-supervised online ELM-based algorithm with good accuracy and generalization ability, but as an online algorithm it is also affected by the concept drift problem. In this paper, a variation of SSOE-ELM algorithm with a semi-supervised concept drift detector and a forgetting parameter called SSOE-FP-ELM is proposed. This new approach is compared with standard SSOE-ELM and FP-ELM. Our experimental results show that SSOE-FP-ELM outperforms SSOE-ELM and FP-ELM in accuracy with two different concept drift types, without a considerable increase in training time.
Run-time Class Generation: Algorithms for Intersection of Homogeneous and Inhomogeneous Classes

The ability to react on and adapt to challenges of the working environment is important and desired characteristic of modern knowledge-based systems. Knowledge extraction from different data sources and representation of heterogeneous knowledge are illustrative examples of such challenges. For effective solving of these tasks knowledge-based systems should be able to generate (create) new classes of objects dynamically, when required. Therefore, the algorithms for dynamic creation of new classes of objects via computing the homogeneous and inhomogeneous intersection of homogeneous and inhomogeneous classes of objects are proposed in the paper. New classes of objects, established via proposed algorithms, define set of types, which determine heterogeneous collections of objects. Proposed approach provides an ability to check the existence of common parts for different classes of objects and to create new classes of objects dynamically, based on previously defined ones. Classes, created using proposed approach, could be integrated within the knowledge base and used for further construction of conceptual hierarchies.
Adjustment Factor for Use Case Point Software Effort Estimation (Study Case: Student Desk Portal)

With the growth of technology, the requirement for customized software to support business increases and the experts on software development also increases. The more Software Developers means the more competition in Software Development. Software metric for effort estimation is a strategy to use metrics related to effort estimation in software development becomes necessary to determine the effort required to develop the system and hence determine pricing (in the case of software house). Several methods exist to estimate effort. This research discusses the Use Case Point Method using a Student Desk Portal as the study case. The effort calculated based on Use Case method may differ from the actual case. In this paper, the differences are analyzed, and an adjustment factor is constructed to resolve the differences between the effort estimated by use case point and the actual effort placed in the actual software development. A new criterion is introduced by using data from the actual software development, be used as further adjustment to the effort obtained by using Use Case Point method. This new criteria, the simplicity of project is added to be a part of the environment factor. The result is positive, the effort calculated by Use Case Point for three different application is in accordance with the actual result.
Modified BB84 Quantum Key Distribution Protocol Using Legendre Symbol

Quantum key distribution is considered as one of the safe ways of distributing keys among the frames employed in communication. In 1984, BB84 appeared first as a protocol for QKD. In the present study paper, we proposed a new way of encoding a stream of bits into polarized photons using Legendre Symbol (a/p), where both of the sender and the receiver negotiate of using the function of Legendre symbol. Hence, in this case the proposed protocol uses only quantum channel instead of the classical channel, this leads to less time spent and less quantum error rate occurring comparing with the previous BB84 protocol. The experimental outcome confirms that the proposed protocol delivers efficient results comparing with the original BB84 protocol.
Secure and Efficient Query Processing Technique for Encrypted Databases in Cloud

Cloud computing is an attractive environment for both organizations and individual users, as it provides scalable computing and storage services at an affordable price. However, privacy and confidentiality are two challenges that trouble most users. Data encryption, using a powerful encryption algorithm such as the Advanced Encryption Standard (AES), is one solution that can allay users' concerns, but other challenges with searching over encrypted data have arisen. Researchers have proposed many different schemes to execute Standard Query Language (SQL) queries over encrypted data by encrypting the data with more than one encryption algorithm. However, other researchers have proposed systems based on the fragmentation of encrypted data. In this paper, we propose bit vector-based model (BVM), a secure database system that works as an intermediary between users and the cloud provider. In BVM, before the encryption and outsourcing processes, the query manager (QM) takes each record from the main table, parses it, builds a bit vector for it, and stores it. The BV stores bits, zero and one, and its length equals the total number of sub-columns for all sensitive columns. BVM aims to reduce the range of retrieved encrypted records that are related to a user's query from the cloud. In our model, the cloud provider cannot deduce information from the encrypted data nor can infer which encryption algorithm was used to encrypt data. We implement BVM and run different experiments to compare our model with the methods in which data are not encrypted in the cloud. Our evaluation shows that BVM reduces the range of the retrieved encrypted records from the cloud to less than 35 percent of encrypted records. As a result, our model avoids unnecessary decryption processes that affect delay times.
Long-Term Prediction of Small Time-Series Data Using Generalized Distillation

The recent increase of "big data" in our society has led to major impacts of machine learning and data mining technologies in various fields ranging from marketing to science. On the other hand, there still exist areas where only small-sized data are available for various reasons, for example, high data acquisition costs or the rarity of targets events. Machine learning tasks using such small data are usually difficult because of the lack of information available for training accurate prediction models. In particular, for long-term time-series prediction, the data size tends to be small because of the unavailability of the data between input and output times in training. Such limitations on the size of time-series data further make long-term prediction tasks quite difficult; in addition, the difficulty that the far future is more uncertain than the near future.In this paper, we propose a novel method for long-term prediction of small time-series data designed in the framework of generalized distillation. The key idea of the proposed method is to utilize the middle-time data between the input and output times as "privileged information," which is available only in the training phase and not in the test phase. We demonstrate the effectiveness of the proposed method on both synthetic data and real-world data. The experimental results show the proposed method performs well, particularly when the task is difficult and has high input dimensions.
Optimization of a Convolutional Neural Network Using a Hybrid Algorithm

In recent years, Convolutional Neural Networks (CNNs) have been widely used in image recognition due to their aptitude in large scale image processing. The CNN uses Back-propagation (BP) to train weights and biases, which in turn makes the error consistently smaller. The most common optimizers that uses a BP algorithm are Stochastic Gradient Decent (SGD), Adam, and Adadelta. These optimizers, however, have been proved to fall easily into the regional optimal solution. Little research has been conducted on the application of Soft Computing in CNN to fix the above problem, and most studies that have been conducted focus on Particle Swarm Optimization. Among them, the hybrid algorithm combined with SGD proposed by Albeahdili improves the image classification accuracy over that achieved by the original CNN. This study proposes the amalgamation of Improved Simplified Swarm Optimization (iSSO) with SGD, hence culminating in the iSSO-SGD which is intended train CNNs more efficiently to establish a better prediction model and improve the classification accuracy. The performance of the proposed iSSO-SGD can be affirmed through a comparison with the PSO-SGD, the Adam, Adadelta, rmsprop and momentum optimizers and their abilities in improving the accuracy of image classification.