Reviewerly/exHarmony huggingface dataset:
M to 1 Joint Source-Channel Coding of Gaussian Sources via Dichotomy of the Input Space Based on Deep Learning

In this paper, we propose a deep neural network framework for Joint Source-Channel Coding of an m dimensional i.i.d. Gaussian source for transmission over a single additive white Gaussian noise channel with no delay. The framework employs two neural encoder-decoder pairs that learn to split the input signal space into two disjoint support sets. The encoder and the decoder are jointly trained to minimize the mean square error subject to a power constraint on the signal transmitted across the channel. The proposed method achieves results as good as the state of the art for m=3,4 and is easily extendable to higher dimensions. The trained model, we discovered, assigns almost equal probability to the disjoint support sets. The results show that the scheme performance is within 1.9dB of the Shannon optimal limit over a wide range of Channel Signal to Noise Ratios (CSNR) from 0dB to 30dB for various values of m. The method is also robust, i.e. employing a model trained at CSNR+/-5dB is only 0.6dB worse than a model trained specifically for that CSNR.
ReinforcedRimJump: Tangent-Based Shortest-Path Planning for Two-Dimensional Maps

Path planning under two-dimensional maps is a fundamental problem in mobile robotics and other real-world applications (unmanned vehicles, navigation applications for mobile phones, and so forth). However, traditional algorithms (graph searching, artificial potential field, genetic, and so forth) rely on grid-by-grid searching. Thus, these methods generally do not find the global optimal path, and as the map scale increases, their time cost increase sharply, except artificial potential field. A few algorithms that do not rely on grid-by-grid searching (rapidly-exploring random tree, visibility graph, and tangent graph) have special requirements for maps. Considering that the shortest path is composed of tangents between obstacles, in this paper, we propose a method called ReinforcedRimJump (RRJ) that does not rely on the point-by-point traversal but rather obtains the shortest path by finding the tangent multiple times between obstacles. The first improvement of this method is the precomputation of tangents, which causes the method to have a lower time cost than traditional methods. The second improvement of RRJ is edge segmentation, which allows RRJ to be used when the target is in the depression of the obstacle. To verify the theoretical advantages of RRJ, some comparative experiments under various maps are performed. The experimental results show that RRJ can always find the shortest path in the shortest time. Furthermore, the time cost of RRJ is insensitive to the map size compared to other methods. The experimental results presented herein demonstrate that RRJ meets the theoretical expectations.
Issues of Human Activity Recognition in Healthcare

Human activity recognition substantiates itself as a useful tool applicable in many smart assistive technologies. Gathering, evaluating, and analyzing data in order to recognize human activities poses many issues, especially in healthcare applications. This paper seeks to answer the question what are the issues regarding the human activity recognition process in healthcare. After conducting a review of approaches used to implement each stage of a specific activity recognition process, the results indicated that issues mostly arise in data collection stage. Based on that, the applicability of related approaches (i.e., wearables, smartphones, and non-contact sensing) is discussed. Underlying issues are identified in order to serve as a starting point for future research activities.
Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGs

In this paper, we present a quantum algorithm for dynamic programming approach for problems on directed acyclic graphs (DAGs). The running time of the algorithm is $$O(\sqrt{\hat{n}m}\log \hat{n})$$ , and the running time of the best known deterministic algorithm is $$O(n+m)$$ , where n is the number of vertices, $$\hat{n}$$ is the number of vertices with at least one outgoing edge; m is the number of edges. We show that we can solve problems that use OR, AND, NAND, MAX and MIN functions as the main transition steps. The approach is useful for a couple of problems. One of them is computing a Boolean formula that is represented by Zhegalkin polynomial, a Boolean circuit with shared input and non-constant depth evaluating. Another two are the single source longest paths search for weighted DAGs and the diameter search problem for unweighted DAGs.
Investigating motion sickness techniques for immersive virtual environments

Motion sickness is one of important issues in immersive virtual environments. In some cases it may last for hours after participation in the virtual experience. Reducing the amount of motion sickness in healthcare applications is of great importance. This paper is examining how motion sickness can be reduced in immersive virtual environments. Two visual methods were designed to assess how they could help to alleviate motion sickness. The first method is the presence of a frame of reference (in form of a cockpit and a radial) and the second method is the visible path (in form of waypoints in the virtual environment). Four testing groups were formed: two for each individual method, one combining both methods and one control group. Each group consisted of 15 healthy subjects. Results show that there is a pattern in the data favouring visual path as a better method against motion sickness compared to the frame of reference.
Regularized Urdu Speech Recognition with Semi-Supervised Deep Learning

Automatic Speech Recognition, (ASR) has achieved the best results for English, with end-to-end neural network based supervised models. These supervised models need huge amounts of labeled speech data for good generalization, which can be quite a challenge to obtain for low-resource languages like Urdu. Most models proposed for Urdu ASR are based on Hidden Markov Models (HMMs). This paper proposes an end-to-end neural network model, for Urdu ASR, regularized with dropout, ensemble averaging and Maxout units. Dropout and ensembles are averaging techniques over multiple neural network models while Maxout are units in a neural network which adapt their activation functions. Due to limited labeled data, Semi Supervised Learning (SSL) techniques are also incorporated to improve model generalization. Speech features are transformed into a lower dimensional manifold using an unsupervised dimensionality-reduction technique called Locally Linear Embedding (LLE). Transformed data along with higher dimensional features is used to train neural networks. The proposed model also utilizes label propagation-based self-training of initially trained models and achieves a Word Error Rate (WER) of 4% less than that reported as the benchmark on the same Urdu corpus using HMM. The decrease in WER after incorporating SSL is more significant with an increased validation data size.
Classification of Occupancy Sensor Anomalies in Connected Indoor Lighting Systems

We consider the problem of classifying anomalous occupancy sensor behavior in connected indoor lighting systems. Anomalous occupancy sensor behavior may occur in the form of either a high number of false alarms (type-1 anomalies) or missed detections (type-2 anomalies). Two anomaly discovery scenarios are considered: one, in which no anomalies exist post-deployment, and two, in which both anomaly types are found together with normally functional sensors. We address the problem of classifying anomalies that may occur subsequently using a machine learning approach. Under scenario 1, we consider a one class random forest classifier to determine whether an occupancy signal is normal or not. In scenario 2, we consider a supervised random forest classifier to determine whether the detection signal of an occupancy sensor is normal, or exhibits type-1 or type-2 anomalies. We devise occupancy signal features in time and frequency domains to perform 2-class classification in scenario 1, and 3-class classification in scenario 2. The proposed method is evaluated using motion sensor data from an office building, and is shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k -means method and a random forest classifier with a single signal energy feature.
Attention-based Autoencoder Topic Model for Short Texts

Due to sparse word co-occurrences, traditional topic models work poorly on short texts. Recent work aggregates short texts to augment word co-occurrence. However, aggregation has a constraint on topic distribution over a document. Thus, we propose an Attention-based Autoencoder Topic Model (AATM) in this paper. The attention mechanism of AATM emphasizes relevant information and improves topic coherence. We also incorporate externally well-trained word embeddings to introduce contextual semantic information. Moreover, AATM consists of a phrase model and a knowledge-based ranking model to discover phrase-level topics and inter-topic rankings, respectively. Experimental results on real-world data validate the efficacy of AATM.
Unsupervised-learning-based keyphrase extraction from a single document by the effective combination of the graph-based model and the modified C-value method

Keyphrases of a given document represent its main topic and they are used as a simple method to represent the document. Statistical and graph-based models as unsupervised approaches have been mainly studied. The statistical models have some difficulty in extracting keyphrases from a single document because most statistical ones generally require statistical information from a large corpus. On the other hand, graph-based models can extract keyphrases by only using the information from a single document; nevertheless, they have some drawbacks. The scores of the edges can be biased because a single document does not contain sufficient information to score the edges of a graph and this influences the score of the nodes. In this paper, we propose an effective combination method of a statistical model, C-value method, and a graph-based model to overcome the drawbacks of each model. A new scoring method for keyphrase candidates is developed by the graph-based model and the scores calculated by the new method are applied to the modified C-value method to estimate the final importance scores of the keyphrase candidates. Subsequently, the proposed model is evaluated using two datasets, SemEval 2010 and Inspec, and its results outperformed the state-of-the-art model among unsupervised models and the existing graph-based ranking models.
An optimized RGA supporting selective undo for collaborative text editing systems

Collaboration plays a key role in distributed applications. As a fundamental vehicle for collaboration, collaborative text editing systems have been an important field within CSCW. More recently, with the increasing popularity of cloud computing, collaborative text editing systems move towards large-scale collaborations based on the cloud computing/cloud platform. The computing performance is the key factor of success for large-scale collaborations. CRDT algorithms have been proved to outperform traditional algorithms in publications. However, how to support selective undo has been a challenging issue for existing CRDT algorithms. This paper proposes an efficient CRDT algorithm called ORGAU that provides integrated do and selective undo efficiently. The correctness and operation intentions preserving of the proposed algorithm under an integrated do/undo framework are formally proved. Compared with the typical CRDT algorithms, the proposed algorithm has better computing performance both in theoretical analysis and experimental evaluation while keeping the same space complexity.
IoTPatchPool: Incentivized delivery network of IoT software updates based on proofs-of-distribution

In this paper we introduce IoTPatchPool — a novel scheme that enables an incentivized distributed delivery network of IoT software updates. We use a decentralized storage network for reducing the load on the vendor when distributing patches toward IoT devices, and eliminating central points of failure. Unlike existing peer-to-peer file sharing networks which have a fundamental availability problem for unpopular files, our protocol utilizes blockchain-based smart contracts to incentivize independent peers, termed distributors, by means of cryptocurrency payments. A vendor of IoT devices deploys a smart contract with a deposit, which acts as a publicly verifiable binding bid for delivery of patches to a specific set of IoT endpoints. Distributors will compete for delivering the updates to the desired IoT devices and present proofs-of-distribution in exchange for cryptocurrency payments. We address the fair exchange problem between distributors and the consuming IoT devices by utilizing a variation of the Zero-Knowledge Contingent Payments (ZKCP) protocol, ensuring that the process is trustless. This can encourage the participation of a large number of distributors, and thus facilitate a rapid scale-out of the system. Finally, we present and evaluate a prototype implementation combining the BitTorrent network with the cryptocurrency Ethereum.
Multi-pattern correlation tracking

In this paper, we propose a novel multi-pattern correlation tracker (MPCT) which deeply models the appearance of the target object for robust tracking. Specifically, multiple correlation filters are learned to capture different appearance patterns of the target object during the tracking process and each filter represents one specific appearance pattern. With the proposed reliable and matching score, a two stage selection algorithm is developed to select a suitable correlation filter to localize the target object. To effectively obtain different filters, we design an online evaluation algorithm to generate filters for different appearance patterns. By taking advantage of multiple filters to model different appearance patterns, the proposed MPCT tracker can not only capture dynamic appearance changes under complex scenes but also deal with severe occlusion and model drift problems to achieve better tracking performance. Extensive experimental results prove that the proposed tracking algorithm performs superiorly against several state-of-the-art tracking methods on challenging tracking benchmarks.
An effective dimension reduction algorithm for clustering Arabic text

Text clustering is a challenging task in natural language processing due to the very high dimensional space produced by this process (i.e. curse of dimensionality problem). Since these texts contain considerable amounts of ambiguities and redundancies, they produce different noise effects. For an efficient and accurate clustering algorithm, we need to extract the main concepts of the text by eliminating the noise and reducing the high dimensionality of the data. This paper compares among three of the famous dimension reduction algorithms for text clustering to show the pros and cons of each one, namely Principal Component Analysis (PCA), Nonnegative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). It presents an effective dimension reduction algorithm for Arabic text clustering using PCA. For that purpose, a series of the experiments has been conducted using two linguistic corpora for both English and Arabic and analyzed the results from a clustering quality point of view. The experiments have shown that PCA improves the quality of the clustering process and that it gives more interpretable results with less time needed for the clustering process for both Arabic and English documents.
Moodle Mobile Plugin for Problem-Based Learning (PBL) in Engineering Education

Modular Object-Oriented Dynamic Learning Environment (MOODLE) is the name of an application program that can transform any instructional media into a web form. Problem-based learning (PBL) is a process that uses identified issues within a scenario to increase knowledge and understanding. Moodle provides support for a number of group-based activities for collaborative learning. Unfortunately, there are presently no modules designed for PBL. Moodle has now introduced its mobile app that incorporates the majority of these activities, but as in the case of the web environment, it lacks support for PBL. The PBL plugin for Moodle's mobile App was designed to facilitate the uploading and delivery of PBL lesson plans in engineering education. An education design research methodology was used to explore PBL lesson plan for engineering students. Results show that students were happy to work with mobile PBL environment and engage in their learning activities to achieve the course objectives.
Automated Interpretation and Integration of Security Tools Using Semantic Knowledge

A security orchestration platform aims at integrating the activities performed by multi-vendor security tools to streamline the required incident response process. To make such a platform useful in practice in a Security Operation Center (SOC), we need to address three key challenges: interpretability, interoperability, and automation. In this paper, we proposed a novel semantic integration approach to automatically select and integrate security tools with essential capability for auto-execution of an incident response process in a security orchestration platform. The capability of security tools and the activities of the incident response process are formalized using ontologies, which have been used for NLP based approach to classify the activities for the emerging incident response processes. The developed ontologies and NLP approaches have been used for an interoperability model for selection and integration of security tools at runtime for the successful execution of an incident response process. Experimental results demonstrate the feasibility of the classifier and interoperability model for achieving interpretability, interoperability, and automation of security tools integrated into a security orchestration platform.
Impact of Feature selection on content-based recommendation system

As a variety of foods and recipes have an impact on our daily lives and our choices, more and more recipes are being served in many restaurants around the world to cater the needs of customers and satisfy their different tastes. In order for customers to be able to decide where to eat, we present the Restaurant Content-based Recommender System. It classifies restaurants, with priority of order, according to their numerous features. We use Content based recommender system because the traditional collaborative filtering algorithms doesn't show satisfying performance as regards data sparsity, and also when having a contextual data. In this article, we will study the impact of feature selection on the performance of content based recommender system and we will use and show the performance of another method for calculation similarity between items in order to move away from classical cosine method.
Smart University: Assessment Models for Resources and Economic Potential

The current development trend of any business entity, both an educational institution and a manufacturing company, or a commercial organization and a financial company, regardless of its scale, should set business goals focused on increasing its profits. Smart goal setting—specific, measurable, achievable, realistic, and timely—is recognized as the best management practice. Smart philosophy in setting goals is clarity and clarity of the task, the basis for discussion, and cooperation between business units, a powerful motivational tool. Customizing tasks on the principle of smart is one of the most effective and frequently used tools in the business. Models for assessing the resource and economic potential of Smart University are currently quite debatable, which makes the scientific article relevant. The article reveals the content and structure of the resource and economic potential of a business entity. The technique of deterministic factor analysis using the method of chain substitutions is considered. The formula for calculating the utilization of the resource potential of the organization is presented. A method of analyzing the influence of factors by the method of chain substitutions in the factor model of the utilization factor of the organization’s resource potential is given. The result of the research is the formation of a new algorithm for assessing the economic potential of a smart system. The results were tested in assessing the resource potential of the smart unit of the university on the example of the department.
Feature assessment and ranking for classification with nonlinear sparse representation and approximate dependence analysis

Feature selection has received significant attention in knowledge management and decision support systems in the past decades. In this study, kernel-based sparse representation and feature dependence analysis are integrated into a feature assessment and ranking framework. The proposed method utilizes the advantages of the kernel-based sparse representation technique and of the information theoretic metric to iteratively obtain the salient feature cluster. Then, a novel approximate dependence analysis is applied to further maintain complementarity while eliminating redundancy among the features selected by nonlinear orthogonal matching pursuit (NOMP). This can effectively prevent the significant bias caused by the pairwise correlation analysis for a large-scale feature set. To illustrate the effectiveness of the proposed method, classification experiments are conducted with three representative classifiers, on nine well-known datasets. The experimental results show the superiority of the proposed method compared with the representative information theoretic and model-based methods in classification for data-driven decision support systems.
Rule-driven inconsistency resolution for knowledge graph generation rules

Knowledge graphs, which contain annotated descriptions of entities and their interrelations, are often generated using rules that apply semantic annotations to certain data sources. (Re)using ontology terms without adhering to the axioms defined by their ontologies results in inconsistencies in the se graphs, affecting their quality. Methods and tools were proposed to detect and resolve inconsistencies, the root causes of which include rules and ontologies. However, these either require access to the complete knowledge graph, which is not always available in a time-constrained situation, or assume that only generation rules can be refined but not ontologies. In the past, we proposed a rule-driven method for detecting and resolving inconsistencies without complete knowledge graph access, but it requires a predefined set of refinements to the rules and does not guide users with respect to the order the rules should be inspected. We extend our previous work with a rule-driven method, called Resglass, that considers refinements for generation rules as well as ontologies. In this article, we describe Resglass, which includes a ranking to determine the order with which rules and ontology elements should be inspected, and its implementation. The ranking is evaluated by comparing the manual ranking of experts to our automatic ranking. The evaluation shows that our automatic ranking achieves an overlap of 80% with experts ranking, reducing this way the effort required during the resolution of inconsistencies in both rules and ontologies.