Reviewerly/exHarmony huggingface dataset:
A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance

We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring language specific knowledge, but no direct supervision.Our technique involves creating a small grammar of closed-class affixes which can be written in a few hours.The grammar over generates analyses for word forms attested in a raw corpus which are disambiguated based on features of the linguistic base proposed for each form.Extending the grammar to cover orthographic, morphosyntactic or lexical variation is simple, making it an ideal solution for challenging corpora with noisy, dialect-inconsistent, or otherwise non-standard content.We demonstrate the utility of de-lexical segmentation on several dialects of Arabic.We consistently outperform competitive unsupervised baselines and approach the performance of state-of-the-art supervised models trained on large amounts of data, providing evidence for the value of linguistic input during preprocessing.
Variational low rank multinomials for collaborative filtering with side-information

We are interested in Bayesian models for collaborative filtering that incorporate side-information or metadata about items in addition to user-item interaction data. We present a simple and flexible framework to build models for this task that exploit the low-rank structure in user-item interaction datasets. Although the resulting models are non-conjugate, we develop an efficient technique for approximating posteriors over model parameters using variational inference. We borrow the "re-parameterization trick" from Bayesian deep learning literature to enable variational inference in our models. The resulting approximate Bayesian inference algorithm is scalable and can handle large scale datasets. We demonstrate our ideas on three real world datasets where we show competitive performance against widely used baselines.
A Data-Driven Approach to Cyber Risk Assessment

Cyber risk assessment requires defined and objective methodologies; otherwise, its results cannot be considered reliable. The lack of quantitative data can be dangerous: if the assessment is entirely qualitative, subjectivity will loom large in the process. Too much subjectivity in the risk assessment process can weaken the credibility of the assessment results and compromise risk management programs. On the other hand, obtaining a sufficiently large amount of quantitative data allowing reliable extrapolations and previsions is often hard or even unfeasible. In this paper, we propose and study a quantitative methodology to assess a potential annualized economic loss risk of a company. In particular, our approach only relies on aggregated empirical data, which can be obtained from several sources. We also describe how the method can be applied to real companies, in order to customize the initial data and obtain reliable and specific risk assessments.
Geometric Graph Convolutional Neural Networks.

Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the graph vertices that provides an order based on their spatial positions. To remedy this issue, we propose Spatial Graph Convolutional Network (SGCN) which uses spatial features to efficiently learn from graphs that can be naturally located in space. Our contribution is threefold: we propose a GCN-inspired architecture which (i) leverages node positions, (ii) is a proper generalization of both GCNs and Convolutional Neural Networks (CNNs), (iii) benefits from augmentation which further improves the performance and assures invariance with respect to the desired properties. Empirically, SGCN outperforms state-of-the-art graph-based methods on image classification and chemical tasks.
Parsing Meaning Representations: Is Easier Always Better?

The parsing accuracy varies a great deal for different meaning representations.In this paper, we compare the parsing performances between Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS), and provide an in-depth analysis of what factors contributed to the discrepancy in their parsing accuracy.By crystalizing the trade-off between representation expressiveness and ease of automatic parsing, we hope our results can help inform the design of the next-generation meaning representations.
A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”

The concept presented in this paper is based on previous dynamical methods to realize a time-varying matrix inversion. It is essentially a set of coupled ordinary differential equations (ODEs) which does indeed constitute a recurrent neural network (RNN) model. The coupled ODEs constitute a universal modeling framework for realizing a matrix inversion provided the matrix is invertible. The proposed model does converge to the inverted matrix if the matrix is invertible, otherwise it converges to an approximated inverse. Although various methods exist to solve a matrix inversion in various areas of science and engineering, most of them do assume that either the time-varying matrix inversion is free of noise or they involve a denoising module before starting the matrix inversion computation. However, in the practice, the noise presence issue is a very serious problem. Also, the denoising process is computationally expensive and can lead to a violation of the real-time property of the system. Hence, the search for a new ‘matrix inversion’ solving method inherently integrating noise-cancelling is highly demanded. In this paper, a new combined/extended method for time-varying matrix inversion is proposed and investigated. The proposed method is extending both the gradient neural network (GNN) and the Zhang neural network (ZNN) concepts. Our new model has proven that it has exponential stability according to Lyapunov theory. Furthermore, when compared to the other previous related methods (namely GNN, ZNN, Chen neural network, and integration-enhanced Zhang neural network or IEZNN) it has a much better theoretical convergence speed. To finish, all named models (the new one versus the old ones) are compared through practical examples and both their respective convergence and error rates are measured. It is shown/observed that the novel/proposed method has a better practical convergence rate when compared to the other models. Regarding the amount of noise, it is proven that there is a very good approximation of the matrix inverse even in the presence of noise.
Policy Preference Detection in Parliamentary Debate Motions

Debate motions (proposals) tabled in the UK Parliament contain information about the stated policy preferences of the Members of Parliament who propose them, and are key to the analysis of all subsequent speeches given in response to them.We attempt to automatically label debate motions with codes from a pre-existing coding scheme developed by political scientists for the annotation and analysis of political parties' manifestos.We develop annotation guidelines for the task of applying these codes to debate motions at two levels of granularity and produce a dataset of manually labelled examples.We evaluate the annotation process and the reliability and utility of the labelling scheme, finding that inter-annotator agreement is comparable with that of other studies conducted on manifesto data.Moreover, we test a variety of ways of automatically labelling motions with the codes, ranging from similarity matching to neural classification methods, and evaluate them against the gold standard labels.From these experiments, we note that established supervised baselines are not always able to improve over simple lexical heuristics.At the same time, we detect a clear and evident benefit when employing BERT, a state-of-the-art deep language representation model, even in classification scenarios with over 30 different labels and limited amounts of training data.
A new feature selection method based on task environments for controlling robots

This paper proposes a task-based feature selection as a part of the Supervised Deep Learning (SDL) framework. The main challenge of control tasks is choosing appropriate inputs for a controller, especially when a camera prepares the sensory data. Since the captured images have high dimensions, it is important to extract proper task-based features to reduce the dimension of controller input while preserving its performance. In this paper, deep learning is utilized to do this and the appropriate features representing the depth images are acquired. These features besides some expert and memory-based features form a candidate feature set. A feature selection method based on state transition probability is proposed. This method exploits the clustering technique and genetic algorithm. It chooses a subset of candidate feature set to maximize the certainty of transition from the current state of the task environment to the next one. The suggested method named Transition Certainty based Feature Selection (TCFS) is examined for a wheeled robot navigation task in uneven terrains with many obstacles. Wall following and obstacle avoidance tasks are considered as robot missions. Depth images captured by a vision system (Kinect camera) are the only sensory data of the robot controller. The proposed architecture is evaluated in WEBOTS© simulator and on a real robot. The experiments demonstrate that TCFS improves robot performance in comparison to SDL architecture according to defined indexes.
Steganalysis of AMR Speech Based on Multiple Classifiers Combination

In this paper, we focus on steganalysis in adaptive multi-rate (AMR) speech streams, whose goal is to detect covert communication behaviors effectively to prevent illegal uses of AMR steganography. Differing from the existing methods for speech steganalysis, we present a novel steganalysis scheme based on multiple classifiers combination. Specifically, we design a new combined classifier model, whose main idea is as follows. First of all, steganalysis features are respectively inputted into two different classifier sets to obtain the first and second types of prediction results. Then, the second type of prediction results are viewed as a special type of features and fed into another classifier set to obtain the third type of prediction results. Finally, all the three types of prediction results are fused to achieve a final detection result. The presented steganalysis scheme is comprehensively evaluated using two types of state-of-the-art features for fixed codebook (FCB) parameters, namely, pulse-pair characteristics-based features and pulse-correlation based features, and compared with the existing methods based on support vector machines (SVM). The experimental results demonstrate that the proposed scheme is feasible and effective, and significantly outperforms the previous SVM-based methods for detecting steganography in FCB domains in every case.
The ICT Used by the English Lecturers for Non English Study Program Students at STAIN Curup

This research is done by the researcher to investigate kinds of ICT used by English lecturers for non-English Study Program students at IAIN Curup and to investigate the roles of ICT for the English lecturer at IAIN Curup. This research used descriptive quantitative Because the questioner's results were evaluated and explained in the explanatory form.The questionnaire used as the instrument to collect the data. Questionnaire about the ICT used by the English lecturers for non-English Study Program students at IAIN Curup and the role of ICT for English Lecturer. Five processes is used to analyze the data from questionnaires in this research. They are (1) data managing of research result, (2) reading/memoing of result, (3) description of result; (4) classifying of research result and (5) interpreting of research result. The result showed that the lecturers always used three types of ICR in teaching English in the classroom, they were the speaker, educational games, and website resources. The reason for them using three types of ICT because it was easy to use and cheap. In using the ICT, the lecturers have a different opinion about About ICT's role in promoting its learning operations. The use of ICT in the teaching and learning system had 7 (seven) roles. It is about the role of ICT in motivating, attracting, and enhancing the success of learners in studying English.
Security of continuous-variable measurement-device-independent quantum key distribution with imperfect state preparation

The state preparation operation of continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) protocol may become imperfect in practical applications. We address the security of the CV-MDI-QKD protocol based on imperfect preparation of the coherent state under realistic conditions of lossy and noisy quantum channel. Specifically, we assume that the imperfection of Alice's and Bob's practical state preparations equal to the amplification of ideal modulators and lasers at both Alice's and Bob's sides by untrusted third-parties Fred and Gray employing phase-insensitive amplifiers (PIAs), respectively. The equivalent excess noise introduced by the imperfect state preparation is comprehensively and quantitatively calculated by adopting the gains of PIAs. Security analysis shows that CV-MDI-QKD is quite sensitive to the imperfection of practical state preparation, which inevitably deteriorates the performance and security of CV-MDI-QKD system. Moreover, a lower bound of the secret key rate is derived under arbitrary collective attacks, and the upper threshold of this imperfection tolerated by the system is obtained in the form of the specific gains of PIAs. In addition, the methods presented will improve and perfect the practical security of CV-MDI-QKD protocol.
Winglets: Visualizing Association with Uncertainty in Multi-class Scatterplots

This work proposes Winglets, an enhancement to the classic scatterplot to better perceptually pronounce multiple classes by improving the perception of association and uncertainty of points to their related cluster. Designed as a pair of dual-sided strokes belonging to a data point, Winglets leverage the Gestalt principle of Closure to shape the perception of the form of the clusters, rather than use an explicit divisive encoding. Through a subtle design of two dominant attributes, length and orientation, Winglets enable viewers to perform a mental completion of the clusters. A controlled user study was conducted to examine the efficiency of Winglets in perceiving the cluster association and the uncertainty of certain points. The results show Winglets form a more prominent association of points into clusters and improve the perception of associating uncertainty.
Optimal Resource Allocation Model and Algorithm for Elastic Enterprise Applications Migration to the Cloud

Cloud computing has been widely used in various industries in recent years. However, when migrating enterprise applications into the cloud, enterprise users face a problem with minimizing migration time and cloud resource providers face a dilemma of resource allocation problem, with the objective of maximizing the migration utility of enterprise users while minimizing the cost of cloud resource providers. In order to achieve them, this paper considered cloud migration objectives including cloud migration time, cloud migration utility, and cloud data center cost, and proposed a resource allocation model for enterprise applications migration into the cloud. The model is divided into two stages: the bandwidth allocation for enterprise applications migration to the cloud and the physical resource allocation of cloud resource providers for enterprise applications deployment into the cloud. In the first stage, we aim to minimize the cloud migration time for enterprise applications, and propose a scheme of bandwidth allocation for each component of applications. In the second stage, we present the resource allocation of cloud resource providers and propose a gradient-based algorithm which can achieve optimal resource allocation. Finally, we give some numerical simulation results to illustrate the performance of the proposed algorithm.
Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.
FPR—Fast Path Risk Algorithm to Evaluate Collision Probability

As mobile robots and autonomous vehicles become increasingly prevalent in human-centred environments, there is a need to control the risk of collision. Perceptual modules, for example machine vision, provide uncertain estimates of object location. In that context, the frequently made assumption of an exactly known free-space is invalid. Clearly, no paths can be guaranteed to be collision free. Instead, it is necessary to compute the probabilistic risk of collision on any proposed path. The FPR algorithm, proposed here, efficiently calculates an upper bound on the risk of collision for a robot moving on the plane. That computation orders candidate trajectories according to (the bound on) their degree of risk. Then paths within a user-defined threshold of primary risk could be selected according to secondary criteria such as comfort and efficiency. The key contribution of this letter is the FPR algorithm and its `convolution trick' to factor the integrals used to bound the risk of collision. As a consequence of the convolution trick, given K obstacles and N candidate paths, the computational load is reduced from the naive O(NK), to the qualitatively faster O(N + K).
Tailoring Agile-Based Software Development Processes

Software development process tailoring is a standard and regular practice of software development companies. Without realizing it as a regular and well-defined standard approach, companies perform it on an ad-hoc basis. Due to which, process tailoring could not be developed into a formal process and approach to managing software development, processes and projects. Software development process paradigm shift from conventional software development approaches to the agile methodologies left many companies struggling with the reusability of the existing processes and defining new processes from scratch. Limited work on process tailoring and lack of a formal approach, particularly for overwhelmingly used agile methodologies, affected the acknowledgment of this process. Addressing this limitation, present research work formulates a process tailoring framework to tailor agile-based software development processes. The proposed framework recommends tailoring three key processes of agile methodologies based on the project state considering the client’s perspective and requirements. The existing literature have been reviewed to develop a theoretical framework which is verified and validated through structured interviews and case study of real projects. The framework provides a formal and a structured approach to tailor agile-based processes and methodologies.
A Pervasive Healthcare System for COPD Patients

Chronic obstructive pulmonary disease (COPD) is one of the most severe public health problems worldwide. Pervasive computing technology creates a new opportunity to redesign the traditional pattern of medical system. While many pervasive healthcare systems are currently found in the literature, there is little published research on the effectiveness of these paradigms in the medical context. This paper designs and validates a rule-based ontology framework for COPD patients. Unlike conventional systems, this work presents a new vision of telemedicine and remote care solutions that will promote individual self-management and autonomy for COPD patients through an advanced decision-making technique. Rules accuracy estimates were 89% for monitoring vital signs, and environmental factors, and 87% for nutrition facts, and physical activities.
A Robust Color Image Watermarking Algorithm Based on APDCBT and SSVD

With the wide application of color images, watermarking for the copyright protection of color images has become a research hotspot. In this paper, a robust color image watermarking algorithm based on all phase discrete cosine biorthogonal transform (APDCBT) and shuffled singular value decomposition (SSVD) is proposed. The host image is transformed by the 8 × 8 APDCBT to obtain the direct current (DC) coefficient matrix, and then, the singular value decomposition (SVD) is performed on the DC matrix to embed the watermark. The SSVD and Fibonacci transform are mainly used at the watermark preprocessing stage to improve the security and robustness of the algorithm. The watermarks are color images, and a color quick response (QR) code with error correction mechanism is introduced to be a watermark to further improve the robustness. The watermark embedding and extraction processes are symmetrical. The experimental results show that the algorithm can effectively resist common image processing attacks, such as JPEG compression, Gaussian noise, salt and pepper noise, average filter, median filter, Gaussian filter, sharpening, scaling attacks, and a certain degree of rotation attacks. Compared with the color image watermarking algorithms considered in this paper, the proposed algorithm has better performance in robustness and imperceptibility.
ADPR: An Attention-based Deep Learning Point-of-Interest Recommendation Framework

With the development of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has attracted lots of attention. Most of the existing studies focus on recommending POIs to users based on their recent check-ins. However, the recent check-ins may contain some daily check-ins that users are not really interested in. If a model treats the recent check-ins equally, it is non-trivial to capture the actual preference of users. To address the issue of mining the actual preferences of users in the POI recommendation, we propose an attention-based deep learning POI recommendation framework (ADPR), which consists of a latent representation method and an attention-based deep convolutional neural network. To learn the embedding of users and POIs, we propose a latent representation method, which incorporates the geographical influence and the categories of POIs to capture the relationships between POIs better. Further, we propose an attention-based deep convolutional neural network, which employs the attention mechanism to filter the important information in the recent check-ins, to recommend POIs to users based on the latent representations of users and the recent check-ins. We conduct experiments on a real-world LBSN dataset to evaluate our framework, and the experimental results show the effectiveness of our framework.