Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance analysis of cooperative NOMA with a shared AF relay

Non-orthogonal multiple access (NOMA) has been proposed as a promising technique for the fifth generation wireless networks. In this study, the authors investigate the performance of a cooperative relaying scheme using NOMA (termed NOMA-AF-CRS), where two sources communicate with their corresponding destinations over the same frequency simultaneously through a shared amplify-and-forward (AF) relay. First, the closed-form approximations are derived for the outage probability. The analytical results are further evaluated in the high signal-to-noise ratio region to characterise the diversity order achieved by the system. Then, the ergodic sum capacity of NOMA-AF-CRS is analysed and an upper bound to the ergodic sum capacity is derived. Finally, simulation results are presented to verify the proposed analysis and demonstrate the advantages of NOMA-AF-CRS over the conventional orthogonal multiple access techniques.
Air Pollutant Severity Prediction Using Bi-Directional LSTM Network

Air pollution has emerged as a universal concern across the globe affecting human health. This increasing danger motivates the study of systems for predicting air pollutant severities ahead of time. In this paper, we have proposed the use of a bi-directional LSTM model to predict air pollutant severity levels ahead of time. We have shown that the predictions can be significantly improved using an ensemble of three Bi-Directional LSTMs (BiLSTM) that model the long-term, short-term and immediate effects of PM2.5 (the key air pollutant) severity levels. Further, weather information data has been taken into account while modelling, since they are found to boost prediction accuracies. Experimental results for multiple locations in New Delhi, India are presented to demonstrate model superiority over earlier techniques.
Interference alignment based hybrid cooperative transmission strategy with limited backhaul

The performance of coordinated multi-point (CoMP) transmission system depends heavily on the quality of information exchange via backhaul links. However, the limited capacity of backhaul leads to quantisation error and delay of shared channel state information. In this study, the authors propose a novel hybrid CoMP transmission strategy to mitigate the impacts of the backhaul delay for data sharing and switch the transmission modes adaptively based on the current arrivals of the shared user data. Furthermore, they present analytical expressions for the average sum rate in an interference alignment network, in which the combining effects of quantisation from limited capacity and connection uncertainty introduced by finite delay are considered. With the analytical results, they evaluate the sum rate performance under limited backhaul for the conventional CoMP transmission modes and the proposed strategy. The simulation results demonstrate that the proposed strategy functions better on overcoming the impacts of capacity limited backhaul than the conventional ones.
Towards 3D Neural Style Transfer

Neural Style Transfer was first unveiled by (Gatys, Ecker, and Bethge 2015), and since then has produced fantastic results working with 2D images. One logical extension of this field would be to move from 2D images into 3D models, and be able to transfer a notion of 3D style from one model to another. Here, we provide steps towards both understanding what style transfer in a 3D setting would look like, as well as demonstrating our own attempts towards one possible imple-
Equidistant Nodes Clustering: a Soft Clustering Algorithm Applied for Synset Induction

In this paper, we present an algorithm for fuzzy graph clustering called Equidistant Nodes Clustering (ENC). In the core of the algorithm there is an assumption that the nodes in a community can reach each other within a limited number of steps. We describe the algorithm and apply it to a natural language processing task of synset induction. The experimental results show that ENC outperforms popular clustering algorithms as according to a gold standard evaluation.
OP-Map Based Next Generation Frequency Sharing System

As the under 6 GHz band suitable for mobile communication is saturated, it is very difficult to secure a new frequency. Also, a method of retrieving and rearranging a low-utilization frequency band is difficult to be practiced due to a cost limit. To solve this problem, research on frequency sharing is actively in underway overseas. The frequency sharing model which has recently been drawing attention is citizens broadband radio service (CBRS) in U.S. and licensed shared access (LSA) in Europe. The ultimate goal of these frequency sharing models is to increase the spectral efficiency by using the band utilized for other uses in common with mobile communication. In this paper, we propose a four-layer next generation frequency sharing model from a conceptual point of view larger than CBRS and LSA. The proposed frequency sharing model is not limited to managing only specific bands, but covers the entire wireless communication spectrum and numerous systems as a whole. For the four-layer system, we diversify the license type to mitigate the protection from interference from other layers against the two-layer like LSA or three-layer such as CBRS and increase the coexistence probability. We discuss the architecture for four-layer frequency sharing systems and the requirement of the main function of the spectrum management system (SMS), the main server of the proposed frequency sharing model. We also discuss nonexclusive license types, system operations, and the use case suitable for the actual situation of domestic frequency usage.
Analysis of Applicability of Deep Learning Methods in Compressor Fault Diagnosis

The paper presents the results of work carried out on the applicability of deep learning techniques for the purpose of diagnostics of industrial rotary compressors. The paper focuses on the possibility of using the library TensorFlow by Google to build classiﬁers typical for this library, e.g. convolutional neural networks, as well as classical ones used in diagnostics, e.g. multilayer perceptron (MLP) or support vector machine (SVM). To provide a complete diagnostic tool was not the aim of the paper. Thus, only selected examplary faults were considered - dips of the power supply voltage and surge. At the beginning, a description of test stand, from which the test data were collected, is given. The main part of the work contains a description of the implementation of classiﬁers, and the results of their tests conducted on the actual measurement data. The data, registered during the experiments on site, represented both, the fault free state, as well the state with selected faults. Finally, the concept of the software (functionality and structure) dedicated for using considered techniques for both off-line tasks of building clas-siﬁers, as well as on-line monitoring in the cloud is discussed.
Automated Identification of Type-Specific Dependencies between Requirements

Requirements Engineering is one of the most important phases in a software project. The elicitation of requirements and the identification of dependencies between these requirements appears to be a challenging task. In this paper, we present an approach to automatically identify requirement dependencies of type requires by using supervised classification techniques. Our results indicate that the implemented approach can detect potential requires dependencies between requirements (formulated on a textual level). We evaluated our approach on a test dataset and figured out that it is possible to identify requirement dependencies with a high prediction quality. We trained and tested our system with different classifiers such as Naive Bayes, Linear SVM, k-Nearest Neighbors, and Random Forest. The results show that Random Forest classifiers correctly predict dependencies with a F1 score of ~82%.
An Efficient Subsequence Similarity Search on Modern Intel Many-core Processors for Data Intensive Applications

Many time series analytical problems arising in a wide spectrum of data intensive applications require subsequence similarity search as a subtask. Currently, Dynamic Time Warping (DTW) is considered as the best similarity measure in most domains. Since DTW is computationally expensive there are parallel algorithms have been developed for FPGA, GPU, and Intel Xeon Phi. In our previous work, we accelerated subsequence similarity search with Intel Xeon Phi’s Knights Corner generation by CPU+Phi computational scheme. Such an approach needs significant changes for the Phi’s second-generation product, Knights Landing (KNL), which is an independent bootable device supporting only native applications. In this paper, we present a novel parallel algorithm for subsequence similarity search in time series for the Intel Xeon Phi KNL many-core processor. In order to efficiently exploit vectorization capabilities of Phi KNL, the algorithm provides the sophisticated data layout and computational scheme. We performed experiments on synthetic and real-word datasets, which showed good scalability of the algorithm.
Optimal Sensor Placement Problem for an Electro-pneumatic Actuator

In this paper, a method for formulating and solving the optimal sensor placement problem for an electro–pneumatic actuator is presented. The approach minimizes the number of additional sensors while maintaining maximum possible diagnosability and isolability. The proposed strategy is based on a Binary Diagnostic Matrix. Proposed isolability measure distinguish weak and strong isolability. It uses the branch-and-cut algorithm to find a solution.
Context and Embeddings in Language Modelling - an Exploration

Embeddings are a natural way to map text to a latent space, commonly consumed in downstream language tasks, e.g., questionanswering, named-entity recognition or neural machine translations. Embeddings typically capture syntactical relations between parts of a sequence and solve semantic problems connected with word-sense-disambiguation (WSD) well. As a result of WSD, the curse of dimensionality, out-of-vocabulary words, overfitting to a domain and missing real world knowledge, infering meaning without context is hard. Thus we require two things. First, we need techniques to actively overcome syntactical problems dealing with WSD and semantically correlating words/sentences. Second, we require context to reconstruct the intentions and settings of a given text such that it can be understood. This work explores different embedding models, data augmentation techniques and context selection strategies (subsampling on the input space) for real world language problems.
Deformation-Based Abnormal Motion Detection using 3D Skeletons

In this paper, we propose a system for abnormal motion detection using 3D skeleton information, where the abnormal motion is not known a priori. To that end, we present a curve-based representation of a sequence, based on few joints of a 3D skeleton, and a deformation-based distance function. We further introduce a time-variation model that is specifically designed for assessing the quality of a motion; we refer to a distance function that is based on such a model as motion quality distance. The overall advantages of the proposed approach are 1) lower dimensional yet representative sequence representation and 2) a distance function that emphasizes time variation, the motion quality distance, which is a particularly important property for quality assessment. We validate our approach using a publicly available dataset, SPHERE-StairCase2014 dataset. Qualitative and quantitative results show promising performance.
Engineering an Aligned Gold-Standard Corpus of Human to Machine Oriented Controlled Natural Language

Knowledge base creation and population are an essential formal backbone for a variety of intelligent applications, decision support and expert systems and intelligent search. While the abundance of unstructured text helps in easing the knowledge acquisition gap, the ambiguous nature of language tends to impact accuracy when engaging in more complex semantic analysis. Controlled Natural Languages (CNLs) are subsets of natural language that are restricted grammatically in order to reduce or eliminate ambiguity for the purposes of machine processability, or unambiguous human communication within a domain or industry context, such as Simplified English. This type of human-oriented CNL is under-researched despite having found favor within industry over many years. We describe a novel dataset which aligns a representative sample of Simplified English Wikipedia sentences with a well known machine-oriented CNL. This linguistic resource is both human-readable and semantically machine interpretable and can benefit a variety of NLP and knowledge based applications.
Single-image Super-resolution via De-biased Sparse Representation

Sparse representation and dictionary learning of image patches are well-known methods for single-image super-resolution. However, due to the regularization term of sparse-inducing penalties, the solution is usually biased. In this study, we present a de-biasing framework by adding a de-biasing step after sparse representation. Two de-biasing methods with sign consistency and feature consistency are further proposed under this framework. Using a unified proximal gradient method, we can solve the proposed de-biasing methods efficiently. Experiments on real super-resolution datasets validate the effectiveness and robustness of the proposed de-biasing methods.
Dependency Parsing and Bidirectional LSTM-CRF for Aspect-level Sentiment Analysis of Chinese

Aspect-level sentiment analysis of Chinese is to extract, aggregate a21 apply fine-grained aspect-level sentiment information from text for sentiment understanding, and it is useful in various application domains. It usually first extracts aspect terms and sentiment words simultaneously, then pairs the aspect terms with sentiment words, and lastly classifies the aspect-level sentiment. In this sense, we formulate this problem as a pipeline of aspect terms and sentiment words extraction through sequence labeling, aspect-sentiment word pairing, and aspect-level sentiment classification. In this paper, we use a bidirectional LSTM-CRF model to extract aspect terms and sentiment words, some syntax rules based on dependency parsing to pair aspect and sentiment words, and mainstream classifiers to determine the sentiment polarities.
Dynamic power allocation scheme with clustering based on physical layer security

Achieving large confidential capacity under the wiretap channel model is a challenge due to the narrow modulation bandwidth and total transmission power constraints. The confidential capacity of a system can be improved through a non-orthogonal multiple access technique that can obtain the highest transmission power in a downlink network. A clustering method is applied to network users who require data with similar contents. Based on the channel gain of each user, cluster heads are selected as agents for the corresponding clusters; then, the total transmission power is shared among the cluster heads. Before the power allocation process, the signal-to-interference-plus-noise ratio of the cluster heads is derived by considering clipping noise to ensure fairness. On this basis, an optimal power allocation scheme is proposed using Lagrangian dual theory. A case is presented to validate the performance of the proposed power allocation scheme. The comparison of the numerical results with those of other schemes shows that the proposed method achieves better performance regarding both secrecy sum capacity and outage probability.
Clustering Multilingual Aspect Phrases for Sentiment Analysis

The area of sentiment analysis has experienced significant developments in the last few years. More specifically, there has been growing interest in aspect-based sentiment analysis in which the goal is to extract, group, and rate the overall opinion about the features of the entity being evaluated. Techniques for aspect extraction can produce an undesirably large number of aspects - with many of those relating to the same product feature. This problem is aggravated when the reviews are written in many languages. In this paper, we address the novel task of multilingual aspect clustering which aims at grouping together the aspects extracted from reviews written in several languages. We contribute with a proposal of techniques to tackle this problem and test them on reviews written in five languages. Our experiments show that our unsupervised clustering technique achieves results that outperform a semi-supervised baseline in many cases.
Improving the Classification of Drunk Texting in Tweets Using Semantic Enrichment

Excessive alcohol consumption is a worldwide problem, and social networks such as Twitter can provide valuable data that help understanding factors related to alcoholism, particularly among youngsters. The identification of drunk tweets (i.e. posted under the influence of alcohol) is complex because tweets are short, sparse and written with diverse and internet specific vocabulary, possibly with errors due to alcohol influence. In this paper, we propose an enriching framework that integrates conceptual and semantic features that expand and generalize the vocabulary, providing context to tweet terms. It also handles misspellings and the selection of discriminative features resulting from contextual enrichment. We outperformed the baseline, achieving improvements of 13.79 percentage points in recall, with no significant harm to precision. We illustrate the value of drunk tweets classification by developing an exploratory analysis that reveals drunk tweeters demographics and tweet properties.
Secure Performance of an Untrusted AF Relay System with a Friendly Wireless Powered Jammer

We propose a jam-then-harvest protocol for dual-hop cooperative networks with an untrusted relay, where an external friendly jammer helps keep information secret from the untrusted relay by transmitting a priori jamming signal for the destination. In particular, the wireless powered jammer scavenges energy from the received forwarded signal and recovers its initial energy to perform jamming in next time slot. We analytically derive an exact expression of the probability of nonzero secrecy rate (PNSR) for the proposed jam-then-harvest protocol. For performance comparison, cooperative jamming with the constant power supply is provided as a lower bound benchmark. Our results show that the proposed protocol not only can achieve the secure communication but also can harvest the enough energy without a loss of performance in the low jamming power region.
Development of an Access Method to Menu Ontology Data through Converting Natural Language into SPARQL Queries

In recent years, vast amount of knowledge in various fields is open to the public on the Web. This knowledge will be helpful for understanding natural language if it is used in a natural language processing system. With this background, we propose a method to generate SPARQL queries by reference to the knowledge iteratively. Besides, as an experiment to evaluate our proposed method, we plan to introduce our work to a service interaction at a robot cafeteria.
Utility-Based Multi-Stakeholder Recommendations by Multi-Objective Optimization

In the recommender systems, the receiver of the recommendations may not be the only stakeholder in the system, while others may come into play. For example, job positions cannot be simply recommended to a user according to his or her tastes only without considering the expectations of the recruiters. In this paper, we propose a utility-based recommendation model which produces recommendations by optimizing the utilities of multiple stakeholders. Particularly, we take advantage of the multi-criteria ratings that are associated with user expectations and evaluations. And we propose to learn the user expectations by the learning-to-rank approaches if they are unknown in the data. We also propose to seek the optimal solutions by using the multi-objective optimization techniques. Our experiments based on a speed-dating data set demonstrate the effectiveness of the proposed methods in which we are able to keep the balance between multiple utilities and the recommendation performance by adopting the multi-objective optimization.
Deep Dilated Convolutional Network for Material Recognition

Material is actually one of the intrinsic features for objects, consequently material recognition plays an important role in image understanding. For the same material, it may have various shapes and appearances, but keeps the same physical characteristic, which brings great challenges for material recognition. Most recent material recognition methods are based on image patches, and cannot give accurate segmentation results for each specific material. In this paper, we propose a deep learning based method to do pixel level material segmentation for whole images directly. In classical convolutional network, the spacial size of features becomes smaller and smaller with the increasing of convolutional layers, which loses the details for pixel-wise segmentation. Therefore we propose to use dilated convolutional layers to keep the details of features. In addition, the dilated convolutional features are combined with traditional convolutional features to remove the artifacts that are brough by dilated convolution. In the experiments, the proposed dilated network showed its effectiveness on the popular MINC dataset and its extended version.
Capacity and costs for 5G networks in dense urban areas

A techno-economic analysis of the 5G enhanced mobile broadband scenario in dense urban areas has been accomplished by radio capacity modelling of probable 5G technologies within a 1 km 2 grid representing central London. Different density networks were modelled at 700 MHz (macro network), 3.5 GHz (micro network) and 24-27.5 GHz (hot spots) - together with 802.11ac access points. Using published data on network costs various deployment options have been evaluated for capacity, headline rate and capital expenditure/operating expense. It has been shown that reaching headline rates of 64-100 Mbps everywhere is possible with a number of different technology options. Massive increases in capacity (in excess of 100 Gbps/km 2 ), however, can only be realistically achieved with a millimetre wave (outdoor) and 802.11ac (internally). The cost of deploying such capacity, however, will be several times that of LTE - the authors estimate a 4-5 times increase in costs for a 100 Mbps everywhere network that has ×100 capacity increase over existing LTE networks. The cost of rolling out 5G is becoming an important issue and this work provides one of the few published estimates of the economics of ultra-high capacity networks.
Nurture: assisted pediatrics for indian parents

Early detection of developmental and growth deviations enables early intervention so that children and parents suffer minimal irreversible physical and financial damage. In India however, pediatricians operate within acute time constraints that keep them from performing comprehensive developmental assessments for their patients. Parents' lack of awareness regarding growth and development milestones further hinders early detection. Our paper contributes the design and evaluation of a probe that seeks to understand the needs of variously resource-constrained middle-class parents in India for monitoring their children's growth, development, and immunization milestones. Our findings highlight the potential of mobile information-based applications to engage, assist, and empower Indian parents. In addition, we describe the affordances of a design process that originated amidst a richly resourced hackerspace organized to address the needs of under-resourced settings, and discuss the consequences of this process on our design.