Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.
Hardware-in-the-loop simulation of fuel control actuator of a turboshaft gas turbine engine

The turboshaft engine is the major component in the propulsion system of most marine vehicles, and proper control of its function as a sub-system in the propulsion system has a direct impact on the performance of the vehicle’s propulsion control system. The engine performance control is performed through the fuel control system. The fuel control system of a turboshaft gas turbine engine consists of two parts: electronic control unit and fuel control unit which is the actuator of the fuel control system. In this article, a hardware-in-the-loop simulation is presented for testing and verifying the performance of the fuel control unit. In the hardware-in-the-loop simulation, the fuel control unit in hardware form is tested in connection with the numerically simulated model of engine and electronic control unit. In this simulation, a Wiener model for the turboshaft engine is developed which is validated with the experimental data. Subsequently, a multi-loop fuel controller algorithm is designed for the engine and the parameters are optimized so that the time response and physical constraints are satisfied. In the next step, a state-of-the-art hydraulic test setup is built and implemented to perform the hardware-in-the-loop test. The test system contains personal and industrial computer, sensors, hydraulic components, and data acquisition cards to connect software and hardware parts to each other. In this hardware-in-the-loop simulator, a host–target structure is used for real-time simulation of the software models. The results show the effectiveness of hardware-in-the-loop simulation in fuel control unit evaluation and verify the steady and transient performance of the designed actuator.
A new privacy‐preserving authentication protocol for anonymous web browsing

Anonymous authentication technique receives wide attention in recent years since it can protect users' privacy. Anonymous web browsing refers to utilization of the World Wide Web that hides a user's personally identifiable information from the websites visited. Even if a user can hide the IP address and other physical information with anonymity programs such as Tor, the web server can always monitor the user on the basis of the identity. In this paper, we firstly give an overview and cryptanalysis on the protocol of Yang et al and point out the security weaknesses of their protocol. Then, we propose a new authentication protocol for anonymous web browsing. In the proposed protocol, we take the advantages of a pseudo identity mechanism and an identity‐based elliptic curve cryptography algorithm to achieve user anonymity, robust security, and high efficiency. The result of security analysis and performance evaluation indicate the feasibility and practicality of our proposed anonymous authentication protocol.
Information acquisition from SEC filings and earnings management

The literature suggests that investors’ information acquisition may have two opposite effects on earnings management. First, greater information acquisition enables investors to better ascertain a firm’s operational and financial conditions and thereby to better monitor earnings management. Second, extensive information acquisition, especially the acquisition of interim reports, can induce manager myopia if investors focus on short-term performance. Using the number of downloads of 10-K, 10-Q and 8-K filings to proxy for information acquisition, I find that downloads are negatively related to earnings management, consistent with the monitoring view. In further analyses, I find that this negative relation is stronger when downloads are concentrated among fewer IP addresses, consistent with concentrated information acquisition having a stronger monitoring effect. I also find that the relation is stronger for firms that are more likely to experience agency problems, e.g., firms with weaker external and internal monitoring and firms with incentives to manage earnings. I contribute to the extant research by providing evidence of the influence that investors’ information acquisition has on corporate reporting behavior.
The Effect Employee Commitment on Service Performance through a Mediating Function of Organizational Citizenship Behaviour Using Servqual and Collaborative Filtering Modeling: Evidence From China’s Hospitality Industry

This article Centralizes on exploring the potential the effect of employee commitment and service quality, which are the core factors that affect competitiveness in the hospitality industry. This article analyzes the impact of employee commitment on the quality of service through the intermediate function of OCB in empirical research based on integrated models and theories. This study gathered information using surveys and calculated the reliability of the Cronbach-Alpha and used questionnaires are "Working Commitment with "0.766" reliability, Podsakoff OCB "0.846" reliability, SERVQUAL quality of service "0.960" reliability. The samples used in this study were 223 employees in Guangdong hospitality industry. According to the effects of research on the attitudes and role clarity can be positive as it will extend employee engagement and effective politics, but conflict is not and cannot communicate with a commitment to politics through political commitment to extend or decrease. Component examined five unique dimensions of organizational citizenship behaviour by Oregon, including altruism, deontology, magnanimity, civility, Virtue of citizens" as a positive influence on the quality of their services and this can be improved. It is significant to develop and raise the competitiveness of the Chinese hospitality industry.
Fine-Grained Named Entity Recognition in Question Answering with DBpedia

Named Entity Recognition in Question Answer tasks can help the language understanding. Traditional NER task usually solved by a supervised model, which need a large number of annotated corpora and extract context information as feature for training, however questions are usually short sentences, with little context information, little public annotated corpora. On the other hand coarse-grained named entities’ types cannot offer enough information for question understanding. In this paper, we propose a novel model to address both problems, using a distant supervised method. Firstly, we use the web search to obtain more relevant information. Secondly, we present a greedy n-grams algorithm to extract the entity mentions. Finally, we use the kNN to classification and get fine-gained entity types combining with the entity mentions which can link with DBpedia. Experimental results show that our model outperforms various state-of-art systems in public dataset--TREC.
Crane Safety System with Monocular and Controlled Zoom Cameras

In this paper, we propose an approach and workflow in order to detect humans in the environment around a crane with Monocular Images. The considered area is split up into a zone around the crane truck and one around the load. The load will be monitored with an optical zoom camera where we can control the zoom. We discretize the zoom levels and a Convolutional Neural Network for each zoom level is trained. Afterwards a Meta Convolutional Neural Network is trained in order to select the next zoom level. Since there are no public datasets available for this kind of task we propose to generate the needed data with a photorealistic
Nationalismus, Fremdenhaß und Antisemitismus. Psychoanalytische Überlegungen

Verbreitete soziale Krisenerscheinungen wie Arbeitslosigkeit, Wohnungsnot und fehlende Lebensperspektiven reichen nicht aus, um einen aggressiven Nationalismus und die neuerliche Fremdenfeindlichkeit in Deutschland zu verstehen. Wahrend Fremdenhas und Antisemitismus psychoanalytisch gut erforscht sind, harrt das Phantasma der »Nation« immer noch eines fundierten psychoanalytischen Verstandnisses. Anhand einer Fallvignette demonstriert der Autor, das die Ubernahme nationalistischer Ideologien, die vor allem in Deutschland an die alte Vorstellung von der Nation als einem biologischen Organismus anknupfen konnen, geeignet ist, dem Ausbruch einer individuellen Neurose vorzubeugen und im Sinne einer »Schiefheilung« (Freud) von Neurosen wirksam zu werden. Das Phantasma der »Nation« offnet psychologisch einen Raum, in dem der Wunsch nach praambivalenter Verschmelzung mit einem Objekt, das alles Heterogene, Fremde und Autonome abgestosen hat, an sein Ziel kommt.
Factors influencing the non-disclosure of sustainability information on Malaysian district councils' websites within an institutional theory framework / Corina Joseph

This study examined why Malaysian district councils did not report sustainability information on their websites. It is a case study approach using semi-structured interviews. Results indicated that internal and external organizational factors influenced the non-reporting of sustainability information on councils' websites. In institutional theory, the lack of commitment shown by the top management in implementing sustainable development program appears to discourage the promotion of normative isomorphic pressure for disclosing sustainability information on websites. There is evidence of coerciveisomorphism relating to website bureaucratic procedures, but, not relating to disclosure of sustainability information. The overall findings may assist in formulating policiesto encourage district councils to report sustainability information to stakeholders.
Hedging Structured Concepts

We develop an online algorithm called Component Hedge for learning structured concept classes when the loss of a structured concept sums over its components. Example classes include paths through a graph (composed of edges) and partial permutations (composed of assignments). The algorithm maintains a parameter vector with one non-negative weight per component, which always lies in the convex hull of the structured concept class. The algorithm predicts by decomposing the current parameter vector into a convex combination of concepts and choosing one of those concepts at random. The parameters are updated by first performing a multiplicative update and then projecting back into the convex hull. We show that Component Hedge has optimal regret bounds for a large variety of structured concept classes.
Cross-Media Retrieval based on Pseudo-Label Learning and Semantic Consistency Algorithm

To retrieve heterogeneous multimodal data with the same semantics, many algorithms for retrieval over multimodal data have been suggested. The organization and analysis of heterogeneous data have become the focus of intensive research. Here, a new and efficient algorithm for cross-media retrieval is proposed based on pseudo-label learning and semantic consistency (PLSC). In this algorithm, an adaptive learning projection matrix optimization method is proposed, and in the process of learning the projection matrices, the method fully considers the semantic information of the labeled and unlabeled samples. Thus, the PLSC algorithm can utilize more useful information than other methods and can learn the more efficient projection matrices. Firstly, the class centers of labeled text are computed. We use median feature vectors as the class center vectors. Next, unlabeled images are projected onto the text space and are assigned pseudo-labels by comparing with the class center vectors of the text data. Finally, a new training dataset, which includes labeled and unlabeled data, is generated for training the projection matrix. Using the projection matrix to project image or text data onto the same feature space, the data can be compared with each other for similarity, and the distance between data points can be calculated using the Euclidean metric. Validation experiments suggest that the PLSC outperforms other state-of-the-art algorithms.
Towards Better Customer Experience: Models for Automatic Detection of Potential Misuse or Mistakes in Customer Care Process

In this study we explore AI methods and models for intelligent customer support solutions that could help automatically supervise the support case handling process and detect potential mistakes or misuse in case handling that may lead to an unwanted customer experience. We developed different types of proof of concept classification models for automatic prediction of case status based on its conversation/activity history. We also conducted LDA topic modelling analysis to help obtain insights into the characteristics of different types of customer care cases.
An Imputation model by Dirichlet Process Mixture of Elliptical Copulas for Data of Mixed Type

Copula-based methods provide a flexible approach to build missing data imputation models of multivariate data of mixed types. However, the choice of copula function is an open question. We consider a Bayesian nonparametric approach by using an infinite mixture of elliptical copulas induced by a Dirichlet process mixture to build a flexible copula function. A slice sampling algorithm is used to sample from the infinite dimensional parameter space. We extend the work on prior parallel tempering used in finite mixture models to the Dirichlet process mixture model to overcome the mixing issue in multimodal distributions. Using simulations, we demonstrate that the infinite mixture copula model provides a better overall fit compared to their single component counterparts, and performs better at capturing tail dependence features of the data. Simulations further show that our proposed model achieves more accurate imputation especially for continuous variables and better inferential results in some analytic models. The proposed model is applied to a medical data set of acute stroke patients in Australia.
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interference-aware routing protocol is developed that allows nodes to avoid communication holes created by jamming attacks. Then, a distributed cooperation framework, based on deep reinforcement learning, is proposed that allows nodes to assess network conditions and make deep learning-driven, distributed, and real-time decisions on whether to participate in data communications, defend the network against jamming and eavesdropping attacks, or jam other transmissions. The objective is to maximize the network performance that incorporates throughput, energy efficiency, delay, and security metrics. Simulation results show that the proposed jamming-aware routing approach is robust against jamming and when throughput is prioritized, the proposed deep reinforcement learning approach can achieve significant (measured as three-fold) increase in throughput, compared to a benchmark policy with fixed roles assigned to nodes.
Blockchain 3.0 Smart Contracts in E-Government 3.0 Applications

The adoption of Information Communication Technologies (ICT) and Web 3.0 contributes to the e-government sector by transforming how public administrations provide advanced and innovative services to interact with citizens. Blockchain (BC) and Artificial Intelligence (AI) disruptive technologies will reshape how we live, work, and interact with government sectors and industries. This paper presents how Blockchain 3.0 and Artificial Intelligence enhance robust, secure, scalable, and authenticity provenance solutions. Two validation scenarios are analyzed to present how blockchain smart contracts and AI agents support energy and health-oriented e-government services.
Fast and Robust Spectrally Sparse Signal Recovery: A Provable Non-Convex Approach via Robust Low-Rank Hankel Matrix Reconstruction

Consider a spectrally sparse signal $\boldsymbol{x}$ that consists of $r$ complex sinusoids with or without damping. We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering $\boldsymbol{x}$ and a sparse corruption vector $\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$. In this paper, we exploit the low-rank property of the Hankel matrix constructed from $\boldsymbol{x}$, and develop an efficient non-convex algorithm, coined Accelerated Alternating Projections for Robust Low-Rank Hankel Matrix Reconstruction (AAP-Hankel). The high computational efficiency and low space complexity of AAP-Hankel are achieved by fast computations involving structured matrices, and a subspace projection method for accelerated low-rank approximation. Theoretical recovery guarantee with a linear convergence rate has been established for AAP-Hankel. Empirical performance comparisons on synthetic and real-world datasets demonstrate the computational advantages of AAP-Hankel, in both efficiency and robustness aspects.
VAIS ASR: Building a conversational speech recognition system using language model combination

Automatic Speech Recognition (ASR) systems have been evolving quickly and reaching human parity in certain cases. The systems usually perform pretty well on reading style and clean speech, however, most of the available systems suffer from situation where the speaking style is conversation and in noisy environments. It is not straight-forward to tackle such problems due to difficulties in data collection for both speech and text. In this paper, we attempt to mitigate the problems using language models combination techniques that allows us to utilize both large amount of writing style text and small number of conversation text data. Evaluation on the VLSP 2019 ASR challenges showed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER on the VLSP 2019 data sets.
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method

Author(s): Chen, Tian; Li, Lingge; Elias, Gabriel; Fortin, Norbert; Shahbaba, Babak | Abstract: It is well established that temporal organization is critical to memory, and that the ability to temporally organize information is fundamental to many perceptual, cognitive, and motor processes. While our understanding of how the brain processes the spatial context of memories has advanced considerably, our understanding of their temporal organization lags far behind. In this paper, we propose a new approach for elucidating the neural basis of complex behaviors and temporal organization of memories. More specifically, we focus on neural decoding - the prediction of behavioral or experimental conditions based on observed neural data. In general, this is a challenging classification problem, which is of immense interest in neuroscience. Our goal is to develop a new framework that not only improves the overall accuracy of decoding, but also provides a clear latent representation of the decoding process. To accomplish this, our approach uses a Variational Auto-encoder (VAE) model with a diversity-encouraging prior based on determinantal point processes (DPP) to improve latent representation learning by avoiding redundancy in the latent space. We apply our method to data collected from a novel rat experiment that involves presenting repeated sequences of odors at a single port and testing the rats' ability to identify each odor. We show that our method leads to substantially higher accuracy rate for neural decoding and allows to discover novel biological phenomena by providing a clear latent representation of the decoding process.
Phase Space Reconstruction from a Biological Time Series: A Photoplethysmographic Signal Case Study

In the analysis of biological time series, the state space comprises a framework for the study of systems with presumably deterministic properties. However, a physiological experiment typically captures an observable, or, in other words, a series of scalar measurements that characterize the temporal response of the physiological system under study; the dynamic variables that make up the state of the system at any time are not available. Therefore, only from the acquired observations should state vectors reconstructed to emulate the different states of the underlying system. It is what is known as the reconstruction of the state space, called phase space in real-world signals, for now only satisfactorily resolved using the method of delays. Each state vector consists of m components, extracted from successive observations delayed a time t. The morphology of the geometric structure described by the state vectors, as well as their properties, depends on the chosen parameters t and m. The real dynamics of the system under study is subject to the correct determination of the parameters t and m. Only in this way can be deduced characteristics with true physical meaning, revealing aspects that reliably identify the dynamic complexity of the physiological system. The biological signal presented in this work, as a case study, is the PhotoPlethysmoGraphic (PPG) signal. We find that m is five for all the subjects analyzed and that t depends on the time interval in which it evaluates. The Henon map and the Lorenz flow are used to facilitate a more intuitive understanding of applied techniques.
ICPS-net: an end-to-end RGB-based indoor camera positioning system using deep convolutional neural networks

Indoor positioning and navigation inside an area with no GPS-data availability is a challenging problem. There are applications such as augmented reality, autonomous driving, navigation of drones inside tunnels, in which indoor positioning gets crucial. In this paper, a tandem architecture of deep network-based systems, for the first time to our knowledge, is developed to address this problem. This structure is trained on the scene images being obtained through scanning of the desired area segments using photogrammetry. A CNN structure based on EfficientNet is trained as a classifier of the scenes, followed by a MobileNet CNN structure which is trained to perform as a regressor. The proposed system achieves amazingly fine precisions for both Cartesian position and quaternion information of the camera.