Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic RF Chain Selection for Energy Efficient and Low Complexity Hybrid Beamforming in Millimeter Wave MIMO Systems

This paper proposes a novel architecture with a framework that dynamically activates the optimal number of radio frequency (RF) chains used to implement hybrid beamforming in a millimeter wave (mmWave) multiple-input and multiple-output (MIMO) system. We use fractional programming to solve an energy efficiency maximization problem and exploit the Dinkelbach method (DM)-based framework to optimize the number of active RF chains and data streams. This solution is updated dynamically based on the current channel conditions, where the analog/digital (A/D) hybrid precoder and combiner matrices at the transmitter and the receiver, respectively, are designed using a codebook-based fast approximation solution called gradient pursuit (GP). The GP algorithm shows less run time and complexity while compared to the state-of-the-art orthogonal matching pursuit (OMP) solution. The energy and spectral efficiency performance of the proposed framework is compared with the existing state-of-the-art solutions, such as the brute force (BF), the digital beamformer, and the analog beamformer. The codebook-free approaches to design the precoders and combiners, such as alternating direction method of multipliers (ADMMs) and singular value decomposition (SVD)-based solution are also shown to be incorporated into the proposed framework to achieve better energy efficiency performance.
Improving Dialog Systems Using Knowledge Graph Embeddings

Dialog systems are systems or applications intended to converse with a human user. Recent dialog systems have employed the sequence-to-sequence framework to treat conversation as a translation problem, translating from question to answer in an open-domain. Knowledge graph embedding started as a way to scale question answering to a large, open-domain dataset without the use of hand-crafted rules. This thesis seeks to connect the two, by converting knowledge graph embeddings to word embeddings and evaluating the resulting dialog models.
Reliable Parkinson’s Disease Detection by Analyzing Handwritten Drawings: Construction of an Unbiased Cascaded Learning System Based on Feature Selection and Adaptive Boosting Model

Parkinson’s disease (PD) is the second most common neurodegenerative disease of central nervous system (CNS). Till now, there is no definitive clinical examination that can diagnose a PD patient. However, it has been reported that PD patients face deterioration in handwriting. Hence, different computer vision and machine learning researchers have proposed micrography and computer vision based methods. But, these methods possess two main problems. The first problem is biasedness in models caused by imbalanced data i.e. machine learning models show good performance on majority class but poor performance on minority class. Unfortunately, previous studies neither discussed this problem nor took any measures to avoid it. In order to highlight the biasedness in the constructed models and practically demonstrate it, we develop four different machine learning models. To alleviate the problem of biasedness, we propose to use random undersampling method to balance the training process. The second problem is low rate of classification accuracy which has limited clinical significance. To improve the PD detection accuracy, we propose a cascaded learning system that cascades a Chi2 model with adaptive boosting (Adaboost) model. The Chi2 model ranks and selects a subset of relevant features from the feature space while Adaboost model is used to predict PD based on the subset of features. Experimental results confirm that the proposed cascaded system shows better performance than other six similar cascaded systems that used six different state of the art machine learning models. Moreover, it was also observed that the proposed cascaded system improves the strength of conventional Adaboost model by 3.3% and reduces its complexity. Additionally, the cascaded system achieved classification accuracy of 76.44%, sensitivity of 70.94% and specificity of 81.94%.
Commercial partnerships and collaborative innovation in China: the moderating effect of technological uncertainty and dynamic capabilities

Purpose The purpose of this study is to empirically investigate the extent to which two types of commercial partnerships (business partner and non-business partner) affect the collaborative innovation of firms in emerging economies. Specifically, the roles of two commercial partnerships are investigated. Additionally, the study explores the moderating effect of external technological uncertainty and internal dynamic capabilities on the relationship between two commercial partnerships and on collaborative innovation. Design/methodology/approach Using a sample of 370 high-tech firms in China, the authors applied the partial least squares structural equation modeling approach to model these relationships. Findings The findings reveal opportunities and challenges for companies according to two intensities of commercial partnership for collaborative innovation. The partnership contribution to innovation and competiveness is different within the two routes and ranges. The findings indicate that (1) intense commercial relationships with business partners have a stronger positive significant impact on collaborative innovation than those with non-business partners and (2) non-business partners have a weaker positive impact on collaborative innovation at high external technological uncertainty. It was also found that (3) the positive impact of business partners on collaborative innovation is weakened when a firm has high dynamic capabilities, whereas the positive impact of non-business partners is strengthened. Research limitations/implications Insight into the roles of two commercial partnerships in achieving collaborative innovation facilitates the advancement of the theoretical understanding of the circumstances under which cooperative innovation can be more effective under different partnerships. Originality/value A key strategic question is whether comprehensiveness enables firms to make better strategic decisions in various environments. In the process of innovation, companies must choose different types and quantities of partners, and they must regulate their partners’ innovative behavior by establishing a corresponding network structure and relationship rules. The current study focuses on analysis of how different intensities of commercial partnerships affect collaborative innovation. This research provides a theoretical framework that creates a new classification of commercial relations with regard to collaborative innovation, and it highlights the difference between the two types of partnerships. This study finds that there are many problems in the selection of innovative partners in China’s high-tech companies. Therefore, companies should strengthen their understanding of cooperative innovation, and they should build and manage highly efficient innovation networks. This study helps companies, high-tech industry associations, academia and government to take enhanced, informed actions.
PALE: Time Bounded Practical Agile Leader Election

Many tasks executed in dynamic distributed systems, such as sensor networks or enterprise environments with bring-your-own-device policy, require central coordination by a leader node. In the past it has been proven that distributed leader election in dynamic environments with constant changes and asynchronous communication is not possible. Thus, state-of-the-art leader election algorithms are not applicable in asynchronous environments with constant network changes. Some algorithms converge only after the network stabilizes (an unrealistic requirement in many dynamic environments). Other algorithms reach consensus in the presence of network changes but require a global clock or some level of communication synchrony. Determining the weakest assumptions, under which bounded leader election is possible, remains an unresolved problem. In this study we present a leader election algorithm that operates in the presence of changes and under weak (realistic) assumptions regarding message delays and regarding the clock drifts of the distributed nodes. The proposed algorithm is self-sufficient, easy to implement and can be extended to support multiple regions, self-stabilization, and mobile ad-hoc networks. We prove the algorithm's correctness and provide a complexity analysis of the time, space, and number of messages required to elect a leader.
Recall What You See Continually Using GridLSTM in Image Captioning

The goal of image captioning is to automatically describe an image with a sentence, and the task has attracted research attention from both the computer vision and natural-language processing research communities. The existing encoder–decoder model and its variants, which are the most popular models for image captioning, use the image features in three ways: first, they inject the encoded image features into the decoder only once at the initial step, which does not enable the rich image content to be explored sufficiently while gradually generating a text caption; second, they concatenate the encoded image features with text as extra inputs at every step, which introduces unnecessary noise; and, third, they using an attention mechanism, which increases the computational complexity due to the introduction of extra neural nets to identify the attention regions. Different from the existing methods, in this paper, we propose a novel network, Recall Network, for generating captions that are consistent with the images. The recall network selectively involves the visual features by using a GridLSTM and, thus, is able to recall image contents while generating each word. By importing the visual information as the latent memory along the depth dimension LSTM, the decoder is able to admit the visual features dynamically through the inherent LSTM structure without adding any extra neural nets or parameters. The Recall Network efficiently prevents the decoder from deviating from the original image content. To verify the efficiency of our model, we conducted exhaustive experiments on full and dense image captioning. The experimental results clearly demonstrate that our recall network outperforms the conventional encoder–decoder model by a large margin and that it performs comparably to the state-of-the-art methods.
Collision Detection and Isolation on a Robot using Joint Torque Sensing

As robotic systems become more flexible and intelligent, they must be able to move into environments with a high degree of uncertainty or clutter, such as our homes, workplaces, and the outdoors. In these unstructured scenarios, it is possible that the body of the robot collides with its surroundings. As such, it would be desirable to characterise these contacts in terms of their location and interaction forces. This paper addresses the problem of detecting and isolating collisions between a robotic manipulator and its environment, using only on-board joint torque and position sensing. The algorithm is based on a particle filter and, under some assumptions, is able to identify the contact location anywhere on the robot body. It requires the robot to perform small exploratory movements, progressively integrating the new sensing information through a Bayesian framework. The approach was tested and benchmarked in simulation, with respect to its accuracy and robustness. Validation using a robot with joint torque sensing in a real environment demonstrated the applicability of the method to real-world scenarios.
Sparse, hierarchical and shared-factors priors for representation learning

Feature representation is a central concern of today’s machine learning systems. A proper representation can facilitate a complex learning task. This is the case when for instance the representation has low dimensionality and consists of high-level characteristics. But how can we determine if a representation is adequate for a learning task? Recent work suggests that it is better to see the choice of representation as a learning problem in itself. This is called Representation Learning. This thesis presents a series of contributions aimed at improving the quality of the learned representations. The first contribution elaborates a comparative study of Sparse Dictionary Learning (SDL) approaches on the problem of grasp detection (for robotic grasping) and provides an empirical analysis of their advantages and disadvantages. The second contribution proposes a Convolutional Neural Network (CNN) architecture for grasp detection and compares it to SDL. Then, the third contribution elaborates a new parametric activation function and validates it experimentally. Finally, the fourth contribution details a new soft parameter sharing mechanism for multitasking learning.
Low Complexity Regularization of Inverse Problems

This thesis is concerned with recovery guarantees and sensitivity analysis of variational regularization for noisy linear inverse problems. This is cast as aconvex optimization problem by combining a data fidelity and a regularizing functional promoting solutions conforming to some notion of low complexity related to their non-Smoothness points. Our approach, based on partial smoothness, handles a variety of regularizers including analysis/structured sparsity, antisparsity and low-Rank structure. We first give an analysis of thenoise robustness guarantees, both in terms of the distance of the recovered solutions to the original object, as well as the stability of the promoted modelspace. We then turn to sensivity analysis of these optimization problems to observation perturbations. With random observations, we build un biased estimator of the risk which provides a parameter selection scheme.
Improving the dependability of the temperature build-up sensor system in the city of Enschede

This graduation is concerned with the development of a sensor system which is used for long-term registration of the temperature development of different locations in Enschede in order to see if and where urban heat islands come into existence. Urban heat islands are urban areas which are significantly hotter than similar rural areas. Since urban areas are often heavily populated, the high temperatures are a cause of heat stress. The municipality of Enschede is therefore interested in the size and severity of these urban heat islands in Enschede. Previous graduation projects already proved that a sensor system can be developed to register the temperature development in the city of Enschede. However, the sensor system needs to be dependable to some degree for it to operate on a long-term base. The goal of this graduation project is to improve the design of the sensor system, so it is able to operate dependably on a long-term base. The end result of this graduation project is an autonomous sensor system which is able to measure the temperature of the air, the relative humidity and the speed of the wind. The sensor system does so in a dependable way and is able to label the data with a quality mark. The sensor system provides its own power and utilizes a LoRaWAN network to store the gathered data in a database.
Identification of Topics and Their Evolution in Management Science: Replicating and Extending an Expert Analysis Using Semi-Automated Methods

Latent Dirichlet allocation (LDA) is a popular generative probabilistic model that enables researchers to analyze large semantic datasets; however, few open-source software tools with Graphical User Interfaces (GUIs) are available to researchers. This study identifies an open-source software tool that, in conjunction with a popular electronic spreadsheet software application, can be used to perform topic modeling. A process is developed and evaluated against a pre-existing expert review that examines work published in Management Science on the topics of technological innovation, product development, and entrepreneurship between 1954 and 2004 (Shane and Ulrich, 2004). The process is then replicated using an expanded corpus that includes all articles published in Management Science between 2005 and 2015. The discussion includes an analysis of the process and insights generated by using topic modeling. A replicable process for researchers and suggestions for practitioners are provided.
Extraction and selection of statistical harmonics features for electrical appliances identification using k-NN classifier combined with voting rules method

In this paper, we propose a novel framework for electrical appliances identification using statistical harmonic features of current signals and the use of the k-NN classifier combined with a voting rule strategy. Harmonic coefficients are computed over time using short-time Fourier series of the current signals. From these sequences of coefficients, the mean, standard deviation, skewness, and kurtosis are computed, which provide the statistical harmonic features. This framework has three novelties: (i) selecting the best combination of statistical measures in the sense of classification rate (CR); (ii) combining the k-NN classifier with the voting rule method in order to search for the best number of voting vectors; and (iii) selecting relevant features for the task of appliances identification by using one of the relevant feature selection algorithms based on mutual information. Results evaluated on the Plaid dataset clearly show that the mean and standard deviation statistics combination gives the best CR of 92% with 500 features and gives the minimal computing time compared to the system based on HMM models. Moreover, combining the k-NN classifier with the voting rule using the above features increases the CR up to 95%. Using this combination, the results also show that an increase of the training dataset size further improves identification performance results in terms of precision, sensitivity, and F-score. A feature selection procedure based on joint mutual information strategy shows that using a selected subset of five features is sufficient, giving similar CR results to those obtained using the total number of features, whatever the training dataset size.
Digital Heritage as a Scholarly Field—Topics, Researchers, and Perspectives from a Bibliometric Point of View

Digital heritage comprises a broad variety of approaches and topics and involves researchers from multiple disciplines. Against this background, this article presents a four-stage investigation on standards, publications, disciplinary cultures, as well as scholars in the field of digital heritage and particularly tangible objects as monuments and sites, carried out in 2016 and 2017. It includes results of (1) the inquiry of nearly 4,000 publications from major conferences, (2) a workshop-based survey involving 44 researchers, (3) 15 qualitative interviews, as well as (4) two online surveys with 1,000 and 700 participants, respectively. As an overall finding, the community is driven by researchers from European countries, especially Italy, with a background in humanities. Cross-national co-authorships are promoted by cultural and spatial closeness and—probably due to funding policy—EU membership. A discourse is primarily driven by technologies, and the most common keywords refer to the technologies used. Most prominent research areas are data acquisition and management, visualization, and analysis. Recent topics are, for instance, unmanned airborne vehicle (UAV)-based 3D surveying technologies, augmented and virtual reality visualization, metadata and paradata standards for documentation, and virtual museums. Since a lack of money is named as the biggest obstacle nowadays, competency and human resources are most frequently named as demand. An epistemic culture in the scholarly field of digital heritage is closer to engineering than to humanities. Moreover, conference series are most relevant for a scientific discourse, and especially EU projects set pace as most important research endeavors.
Blockage Aware Fair Scheduling with Differentiated Service Support in mmWave WPANs/WLANs

Millimeter Wave (mmWave) communications are evolving as a potential and promising technology to address the ever increasing mobile data rate requirements. This paper addresses fair scheduling in directional antenna based mmWave wireless personal and local area networks with non-uniform traffic demand. Due to their small wavelength, mmWave signals are susceptible to blockage. The proposed fair schedulers handle the link blockage problem through relaying, and are capable of providing end-to-end fair bandwidth allocation to relay flows. To achieve differentiated and fair service allocation to various regions of the network while using only limited flow related information, a service tag based scheduler is proposed in this paper. Then, the performance bounds on the minimum throughput and unfairness of this scheduler are obtained. To approximate the performance of the service tag based scheduler while minimizing the control overhead, a heuristic fair scheduler is also proposed. Results from extensive simulations conducted in a mmWave WPAN deployed to support high data rate applications show the performance advantages of the proposed schedulers, compared to existing fair schedulers, in terms of throughput and fairness.
Optimizing Fixation Prediction Using Recurrent Neural Networks for 360$^{\circ }$ Video Streaming in Head-Mounted Virtual Reality

We study the problem of predicting the viewing probability of different parts of <inline-formula><tex-math notation="LaTeX">$3\text{60}^{\circ }$</tex-math></inline-formula> videos when streaming them to head-mounted displays. We propose a fixation prediction network based on recurrent neural network, which leverages sensor and content features. The content features are derived by computer vision (CV) algorithms, which may suffer from inferior performance due to various types of distortion caused by diverse <inline-formula><tex-math notation="LaTeX">$\text{360}^{\circ }$</tex-math></inline-formula> video projection models. We propose a unified approach with overlapping virtual viewports to eliminate such negative effects, and we evaluate our proposed solution using several CV algorithms, such as saliency detection, face detection, and object detection. We find that overlapping virtual viewports increase the performance of these existing CV algorithms that were not trained for <inline-formula><tex-math notation="LaTeX">$\text{360}^{\circ }$</tex-math></inline-formula> videos. We next fine-tune our fixation prediction network with diverse design options, including: 1) with or without overlapping virtual viewports, 2) with or without future content features, and 3) different feature sampling rates. We empirically choose the best fixation prediction network and use it in a <inline-formula><tex-math notation="LaTeX">$\text{360}^{\circ }$</tex-math></inline-formula> video streaming system. We conduct extensive trace-driven simulations with a large-scale dataset to quantify the performance of the <inline-formula><tex-math notation="LaTeX">$\text{360}^{\circ }$</tex-math></inline-formula> video streaming system with different fixation prediction algorithms. The results show that our proposed fixation prediction network outperforms other algorithms in several aspects, such as: 1) achieving comparable video quality (average gaps between −0.05 and 0.92 dB), 2) consuming much less bandwidth (average bandwidth reduction by up to 8 Mb/s), 3) reducing the rebuffering time (on average 40 s in bandwidth-limited 4G cellular networks), and 4) running in real-time (at most 124 ms).