Reviewerly/exHarmony huggingface dataset:
Order-Sensitive Keywords Based Response Generation in Open-Domain Conversational Systems

External keywords are crucial for response generation models to address the generic response problems in open-domain conversational systems. The occurrence of keywords in a response depends heavily on the order of the keywords as they are generated sequentially. Meanwhile, the order of keywords also affects the semantics of a response. Previous keywords based methods mainly focus on the composite of keywords, while the order of keywords has not been sufficiently discussed. In this work, we propose an order-sensitive keywords based model to explore the influence of the order of keywords in open-domain response generation. It automatically inferences the most suitable order that is optimized to generate a natural and relevant response, and subsequently generates the response using the ordered keywords as building blocks. We conducted experiments on a public Twitter dataset and the results show that our approach outperforms the state-of-the-art baselines in both automatic and human evaluations.
Sentiment Analysis of Chinese Tourism Review Based on Boosting and LSTM

Sentiment analysis is a hot topic in recent years' Natural Language Processing Research, traditional practices are through machine learning, current research is based on deep learning. Most models generally do not consider the characteristic of network data while in modeling procedure. Considering the tourism comments emotion distribution is not balanced, and adding distribution information of comments to the model will play an important role in the effect, this paper proposed a boosting based LSTM model that can well model the comment text. We have done the evaluation on the related data set, and the experimental results show that the model in this paper has achieved good results.
Towards Increasing the Efficiency of E-Learning Systems Using Recommendation System Approach

Data mining approaches have been tried in e-learning systems for information optimization and knowledge extraction to make decisions. In recent years, the recommendation system has gained popularity in every field be it e-commerce, entertainment, sports, healthcare, news, etc. However, in e-learning system, the recommender systems were not effectively utilized in comparison to other domains and thus emerged as a bottleneck for almost all e-learning systems for not offering flexible delivery of the learning resources. Current e-learning systems lack personalization features, and the information is presented in a static way despite their varying learning objectives and needs. The aim of recommender system is to personalize the information with respect to learner interest. The objective of this study is to highlight various algorithmic techniques that can be used to improve information retrieval process to provide effective recommendations to learners for improving their performance and satisfaction level.
Breaking Deadlocks in Multi-agent Reinforcement Learning with Sparse Interaction

Although multi-agent reinforcement learning (MARL) is a promising method for learning a collaborative action policy that will enable each agent to accomplish specific tasks, the state-action space increased exponentially. Coordinating Q-learning (CQ-learning) effectively reduces the state-action space by having each agent determine when it should consider the states of other agents on the basis of a comparison between the immediate rewards in a single-agent environment and those in a multi-agent environment. One way to improve the performance of CQ-learning is to have agents greedily select actions and switch between Q-value update equations in accordance with the state of each agent in the next step. Although this "GPCQ-learning" usually outperforms CQ-learning, a deadlock can occur if there is no difference in the immediate rewards between a single-agent environment and a multi-agent environment. A method has been developed to break such a deadlock by detecting its occurrence and augmenting the state of a deadlocked agent to include the state of the other agent. Evaluation of the method using pursuit games demonstrated that it improves the performance of GPCQ-learning.
Towards Decisional Natural Language Why-Question Recommendation Approach in Business Intelligence Context

Business Intelligence is the key technologies that ensures effective decision-making. It provides decisional information and adapted systems as Data Warehouses and OLAP tools. To access decisional information, decision makers should express their requirements in Natural Language. Question answering systems have been developed to handle these requirements. A Natural Language query can be formulated as WH-questions (“What, Who, Where, Why”), Keyword-like questions. In this paper, we focus on decisional Why-Questions. Expressing a requirement in Natural Language is not always obvious since the decision maker is unaware of the DW content. Consequently, we propose a grammar and an approach for Why-Questions recommendation system. For evaluation purposes, an experimental study is presented.
Toward a secure video watermarking in compressed domain

Collusion attack is a major concern in the field of video watermarking. To overcome this problem, a classic approach is exploiting a content-based key in order to select the embedding locations. This key should be chosen carefully especially in the blind embedding to prevent the possibility of any alteration after inserting the watermark. Extracting a robust content-based key is a more challenging issue in blind compressed domain watermarking since the syntactic elements such as DCT residuals may change after re-encoding even without inserting the watermark. Consequently, it is really desirable to derive a robust content-based key using just the syntactic elements of compressed video sequences. In this paper, we utilize the inter-mode values, as available information, to provide a robust content-based key. The key is evaluated and found to be robust against common compressed domain video watermarking attacks. In addition, the key generation mechanism is appropriate for real-time applications since the full decompression and recompression of the video stream are avoided.
The Influence of Artificial Intelligence Development on Patent Legislation

The artificial intelligence technology has brought unprecedented challenges to today's ethical standards, legal rules, social order and public management systems. In terms of Patent law, the main challenge is the patent eligibility of the artificial intelligence invention. Since inventions in the field of artificial intelligence include methods for implementing mental steps using computers and devices designed to automate mental steps, the implicit requirements for judging patent eligibility may be inconsistent with the nature of artificial intelligence technology. However, due to the importance of artificial intelligence to social development, many countries have begun to revise patent examination guidelines related patent-eligibility criteria to address the challenges of artificial intelligence.
Recognizing Arabic handwritten words using multiple features and classifier selection

In this paper, we present a new robust offline Arabic handwritten words recognition based on the holistic approach. We propose to use three descriptors namely Multi-Level Local Phase Quantization (ML-LPQ), Histogram of Gradient (HOG) and Gabor features to describe images. Each feature is fed to three well-known supervised classifiers K-Nearest Neighbors, Naïve Bayes and Support Vector Machine (SVM), and then for each feature, we retain and select the classifier that yielded the best results. Moreover, in order to improve recognition performance, we opt for using majority vote technic as a combination scheme. Experimental results are carried out using our own created database. The obtained results, given at the end of the paper, have demonstrated the efficiency of the proposed method where an average recognition of 97.84% has been achieved.
NACOD: A Naïve Associative Classifier for Online Data

Analyzing data in real time constitutes a challenge nowadays, due to the constant generation of data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making algorithm within the associative approach. The proposed algorithm, named Naïve Associative Classifier for Online Data (NACOD), is able to deal with hybrid as well as with incomplete data. In addition, NACOD is transparent and transportable, which makes it a very useful decision-maker in environments that require such properties. The numerical experiments carried out show the effectiveness of NACOD.
A climate zone approach to global solar radiation modelling using artificial neural networks

Abstract Information on solar availability is crucial in the study of both passive building designs and active solar energy systems. There are still many locations within different climate zones worldwide that do not have solar radiation measurements. Correlation between solar radiation and the more commonly measured meteorological variables such as temperature and sunshine hours is useful for locations with no measured solar radiation data. This is also useful for locations with measured solar radiation data, in that any missing data due to equipment breakdown or malfunction can be modelled. Global solar radiation (GSR) was modelled for 96 cities in different climate zones across China using artificial neural networks (ANNs). The novelty of this study is the climate zone approach, by which locations with similar climates were modelled together. Climate classification was based on both the traditional thermal climates and the solar climates. Two sets of models were developed based on measured diurnal temperatures and sunshine hours. Model performance in terms of the predictive power of ANN solar radiation models was evaluated through the Nash-Sutcliffe efficiency coefficient (NSEC). Error analysis of the predicted solar radiation as compared with the measured data was also conducted for each of the 96 cities.
Monocular depth estimation with geometrical guidance using a multi-level convolutional neural network

Depth estimation using monocular images is highly challenging but is a considerable topic in understanding scene structure. This paper proposed a multi-level convolutional neural network (CNN), in which the low-level and high-level features were well-integrated in order to estimate the depth values from a single image. To estimate the depth values, a fully convolutional architecture which used a structure improvement strategy was applied to correct the depth values using low-level features of the shallow layers. A hierarchical context aggregation scheme was proposed according to the dilated convolutional operators that integrate the global and local contexts in a progressive way to recover the local details. In addition, a rectifying block was used to subtract the existed fitting residuals of integrated multi-level features. In the second level, a modifier network was provided to improve the estimated depth values, in particular in the object boundaries. It is possible to determine the objective function in the modifier networks by considering the geometrical features directing the network to achieve the best results. The proposed framework was evaluated using the computer vision (Make3D, NYU, and SUN datasets) and remote sensing (Vaihingen and Potsdam datasets) datasets that specified the significant performance of the proposed framework.
A Filter-based Unsupervised Feature Selection Method via Improved Local Structure Preserving

In this paper, we propose a novel filter based unsupervised feature selection algorithm. We first extract the global level manifold structure using LLE on all features. We also extract the feature level manifold structure using LLE on each single feature. We then compute the feature-wise non-negative local linear reconstruction weight to capture the feature relationship. The true manifold structure of feature is then computed by the linear combination of its own Laplacian matrix and its neighbor's Laplacian matrices. The importance of feature is then evaluated by the difference between the global manifold structure and the combined feature level manifold structure. Extensive experimental results on benchmark data sets well demonstrate that the proposed method outperform state-of-the-art filter-based unsupervised feature selection methods.
Breast Cancer Prediction Using Ensemble Techniques

One of the most dangerous types of cancer affecting women across the world happens to be breast cancer. As per clinical experts, detecting this cancer in its first stage is crucial in saving lives. The variables like sample code number, clump thickness, uniformity of cell size, uniformity of cell shape etc., are some of the important risk factors providing information, allowing identification of the recurrence of breast cancer and cure it in the earliest stages.
PrivAnalyzer: Measuring the Efficacy of Linux Privilege Use

Operating systems such as Linux break the power of the root user into separate privileges (which Linux calls capabilities) and give processes the ability to enable privileges only when needed and to discard them permanently when the program no longer needs them. However, there is no method of measuring how well the use of such facilities reduces the risk of privilege escalation attacks if the program has a vulnerability. This paper presents PrivAnalyzer, an automated tool that measures how effectively programs use Linux privileges. PrivAnalyzer consists of three components: 1) AutoPriv, an existing LLVM-based C/C++ compiler which uses static analysis to transform a program that uses Linux privileges into a program that safely removes them when no longer needed, 2) ChronoPriv, a new LLVM C/C++ compiler pass that performs dynamic analysis to determine for how long a program retains various privileges, and 3) ROSA, a new bounded model checker that can model the damage a program can do at each program point if an attacker can exploit the program and abuse its privileges. We use PrivAnalyzer to determine how long five privileged open source programs retain the ability to cause serious damage to a system and find that merely transforming a program to drop privileges does not significantly improve security. However, we find that simple refactoring can considerably increase the efficacy of Linux privileges. In two programs that we refactored, we reduced the percentage of execution in which a device file can be read and written from 97% and 88% to 4% and 1%, respectively.
On the rotational variance of the differential evolution algorithm

In this study we examine the rotational (in)variance of the differential evolution (DE) algorithm. We show that the classic DE/rand/1/bin algorithm, which uses constant mutation and standard crossover, is rotationally variant. We then study a previously proposed rotationally invariant formulation in which the crossover operation takes place in an orthogonal base constructed using Gramm-Schmidt orthogonalization. We propose two new formulations by firstly considering a very simple rotationally invariant formulation using constant mutation and whole arithmetic crossover. This rudimentary formulation performs badly, due to lack of diversity. We introduce diversity into the formulation using two distinctly different strategies. The first adjusts the crossover step by perturbing the direction of the linear combination between the target vector and the mutant vector. This formulation is invariant in a stochastic sense only. The other formulation adds a self-scaling random vector with a standard normal distribution, sampled uniformly from the surface of an n-dimensional unit sphere to the unaltered whole arithmetic crossover vector. This formulation is strictly invariant, if in a stochastic sense only. We compare the four invariant formulations in terms of numerical efficiency for a modest set of test problems; the intention not being to propose yet another competitive and/or superior DE variant, but rather to present formulations that are both diverse and invariant, in the hope that this will stimulate additional future contributions, since rotational invariance in general is a desirable, salient feature for an optimization algorithm.
Design of a Cyber-Physical System using STEM: Chicken Egg Incubator

In this paper, STEM approach to study, design and implement chicken egg incubator as a cyber-physical system is demonstrated. In science viewpoint (S), bringing a life to earth can provide magnificent feeling that inspires students to learn more about life sciences. In views of technology (T), the students learn how to design and use micro-controllers, sensors and other components. Specifically the students also develop programming skills in controlling optimal ventilation, temperature and relative humidity for incubators in order to hatch chicken eggs. In Engineering and Mathematically (E&M), Kalman filter is adopted to estimate the states from a series of noisy measurements. According to our experiment, based on the survey questionnaire, the learning outcomes are satisfied.
User-Centered Design in Defining and Developing Health and Well-Being ICT Solutions

Implementing emerging technologies is a complex task which requires time, precision, and organization. The definition of a logical structure for classifying and organizing complex information during the design process of a technological solution provides flexibility while the process becomes more prescriptive. One of the most used methods in the ICT field is the user-centered design methodology (UCD). UCD methodology situates the final user as the cornerstone of the research and development process since the success or failure of a technological solution will depend on users’ acceptance. The following chapter presents experiences, best practices and lessons learned applying UCD methodology in different European projects from several years of work conducted at LifeSTech group from UPM, in areas such as: chronic diseases management, accessibility, and cognitive rehabilitation. Specifically, the chapter explains how the UCD methodology was applied during the different stages of the design and development process for different domains and use cases.
A Distributed Topic Model for Large-Scale Streaming Text

Learning topic information from large-scale unstructured text has attracted extensive attention from both the academia and industry. Topic models, such as LDA and its variants, are a popular machine learning technique to discover such latent structure. Among them, online variational hierarchical Dirichlet process (onlineHDP) is a promising candidate for dynamically processing streaming text. Instead of a static assignment in advance, the number of topics in onlineHDP is inferred from the corpus as the training process proceeds. However, when dealing with large scale streaming data it still suffers from the limited model capacity problem. To this end, we proposed a distributed version of the onlineHDP algorithm (named as DistHDP) in this paper, the training task is split into many sub-batch tasks and distributed across multiple worker nodes, such that the whole training process is accelerated. The model convergence is guaranteed through a distributed variation inference algorithm. Extensive experiments conducted on several real-world datasets demonstrate the usability and scalability of the proposed algorithm.
Deep green function convolution for improving saliency in convolutional neural networks

Current saliency methods require to learn large scale regional features using small convolutional kernels, which is not possible with a simple feed-forward network. Some methods solve this problem by using segmentation into superpixels while others downscale the image through the network and rescale it back to its original size. The objective of this paper is to show that saliency convolutional neural networks (CNN) can be improved by using a Green's function convolution (GFC) to extrapolate edges features into salient regions. The GFC acts as a gradient integrator, allowing to produce saliency features by filling thin edges directly inside the CNN. Hence, we propose the gradient integration and sum (GIS) layer that combines the edges features with the saliency features. Using the HED and DSS architecture, we demonstrated that adding a GIS layer near the network's output allows to reduce the sensitivity to the parameter initialization, to reduce the overfitting and to improve the repeatability of the training. By simply adding a GIS layer to the state-of-the-art DSS model, there is an absolute increase of 1.6% for the F-measure on the DUT-OMRON dataset, with only 10ms of additional computation time. The GIS layer further allows the network to perform significantly better in the case of highly noisy images or low-brightness images. In fact, we observed an F-measure improvement of 5.2% when noise was added to the dataset and 2.8% when the brightness was reduced. Since the GIS layer is model agnostic, it can be implemented into different fully convolutional networks. A major contribution of the current work is the first implementation of Green's function convolution inside a neural network, which allows the network to operate in the feature domain and in the gradient domain at the same time, thus improving the regional representation via edge filling.
Plaintext-related image encryption scheme using hyperchaotic system and DNA computing

We propose a hyperchaotic image encryption algorithm based on plaintext information and DNA computing. Compared to the traditional permutation-diffusion structure, the proposed method enhances the relationship between pixel position, gray value, and the plaintext image. First, the algorithm's hyperchaotic system iteratively generates multiple pseudorandom matrices and globally scrambles the image position by operating on the pseudorandom matrices and plaintext information. Then, the pseudorandom matrices and the scrambled image are encoded into DNA sequences for subsequent algebraic operations, and a new vector is constructed to modulate the sequence to obtain robust encryption performance. Finally, the processed DNA sequence is decoded and a XOR operation is applied to the binary pseudorandom sequence to obtain a final cipher image. Based on the experimental results and security analysis, the proposed algorithm not only demonstrates excellent encryption but also resists various typical attacks.