Miracle-dz/UniFAR-FTUs huggingface dataset:
AdaBoost Ensemble Correction Models for TDDFT Calculated Absorption Energies

Molecular excited states are important for molecular optical properties, which can be feasibly explored by quantum chemical calculations. However, the computation is highly demanding due to their complicated characteristic features. Therefore, high accuracy and unambiguous descriptions are strongly desired for excited state investigations. This paper proposes accurate, robust, and efficient ensemble correction models for absorption calculations with the most used quantum chemical method, time-dependent density functional theory (TDDFT). Models are built by AdaBoost framework with both weak machine learning: support vector machine (SVM), general regression neural network (GRNN), and an ensemble learning: the random forest (RF) regression method. With the models, the low accuracy calculations, TDDFT calculated absorption energies (<inline-formula> <tex-math notation="LaTeX">$\lambda _{\max }$ </tex-math></inline-formula>) for 433 organic molecules with the minimum basis set STO-3G, are significantly improved. The mean absolute error (MAE) and the root mean square error (RMSE) of the calculated <inline-formula> <tex-math notation="LaTeX">$\lambda _{\max }$ </tex-math></inline-formula> are reduced from 0.62 and 0.79 eV to 0.11 and 0.14 eV, respectively. The validation parameters of the proposed correction model can reach up to R<sup>2</sup>(0.97), Q<sup>2</sup>(0.98), and <inline-formula> <tex-math notation="LaTeX">$\text{Q}^{2}_{\text {cv}}$ </tex-math></inline-formula> (0.99), which suggests the great goodness-of-fit and predictability. This investigation illustrates that the proposed ensemble correction models by sophisticated algorithms are highly efficient and accurate. Therefore, it may serve as an alternative tool to establish good correction models for TDDFT absorption calculations, which could significantly improve the accuracy of TDDFT calculations and extend machine learning algorithms on other feature calculations of excited states.
IoT based on Wireless Power Transmission

In this paper, we have presented the concept of cable less transmission i.e. power without the usage of any kind of the electrical conductor or wires. We present an idea discussed here, how energy can be transmitted as microwaves, so as to reduce the transmission and allocation losses, known as Microwave Power transmission (MPT). We have also cited several aspects relating to history of wireless power transmission systems along with the present day scenario of Power transmission systems and also some of the developmental changes in Wireless Power Transmission (WPT). The Basic Design and Implementation of Wireless power System has also been given. We have also presented the Merits, Demerits and the applications of WPT with monitoring IoT based.
Bayesian Network Model for Learning Arithmetic Concepts

An object usually belongs to multiple concepts, but some concepts can be judged directly while other concepts need to be inferred indirectly. To learn some arithmetic concepts from positive integer number sets, we address an arithmetic concept Bayesian network (ACBN) model by taking advantage of Bayesian networks. Specifically, we first give an ACBN model to represent the arithmetic concept knowledge and their direct relationships, and then we design an ACBN model learning algorithm based on domain knowledge. Furthermore, to infer indirectly some arithmetic concepts, we design the learning method of evidence concepts based on the idea of k-nearest neighbors, and then we propose the inference algorithm of the ACBN model. Finally, the experimental results demonstrate that the ACBN model can effectively learn some daily arithmetic concepts.
Behavioral Biometric Authentication in Android Unlock Patterns through Machine Learning

The penetration of ICT in our everyday lives has introduced numerous automations, and the continuous need for communication has made mobile devices indispensable. As a result, there is an ever-increasing deployment of services for which users need to authenticate. While the use of plain passwords is the default, many applications require higher standards of security, such as drawn patterns and fingerprints, used mostly to authenticate users and unlock their smart devices. In this work we propose a biometrics-based machine learning approach that supports user authentication in Android to augment native user authentication mechanisms, making the process more seamless and secure. Our evaluation results show very high rates of success, both for authenticating the legitimate user and also for rejecting the false ones. Finally, we showcase how the proposed solution can be deployed in non-rooted devices.
Interpretable Cascade Classifiers with Abstention

In many prediction tasks such as medical diagnostics, sequential decisions are crucial to provide optimal individual treatment. Budget in real-life applications is always limited, and it can represent any limited resource such as time, money, or side effects of medications. In this contribution, we develop a POMDP-based framework to learn cost-sensitive heterogeneous cascading systems. We provide both the theoretical support for the introduced approach and the intuition behind it. We evaluate our novel method on some standard benchmarks, and we discuss how the learned models can be interpreted by human experts.
Secure Multi-UAV Collaborative Task Allocation

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields. However, they are unable to meet large-scale and complex missions with a limited energy reserve. Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied. In this paper, a new systematic framework is proposed to solve the problem of multi-UAV collaborative task allocation. It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm. The purpose is to enable multi-UAV to complete tasks with lower energy consumption. As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed. Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution. We have also proposed an improved method to improve the accuracy of task allocation further. In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems. Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation; moreover, compared with other task allocation methods, our method has great performance.
On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition

We study constrained nonconvex optimization problems in machine learning and signal processing. It is well-known that these problems can be rewritten to a min-max problem in a Lagrangian form. However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown. To bridge the gap, we study the landscape of the Lagrangian function. Further, we define a special class of Lagrangian functions. They enjoy the following two properties: 1. Equilibria are either stable or unstable (Formal definition in Section 2); 2.Stable equilibria correspond to the global optima of the original problem. We show that a generalized eigenvalue (GEV) problem, including canonical correlation analysis and other problems as special examples, belongs to the class. Specifically, we characterize its stable and unstable equilibria by leveraging an invariant group and symmetric property (more details in Section 3). Motivated by these neat geometric structures, we propose a simple, efficient, and stochastic primal-dual algorithm solving the online GEV problem. Theoretically, under sufficient conditions, we establish an asymptotic rate of convergence and obtain the first sample complexity result for the online GEV problem by diffusion approximations, which are widely used in applied probability. Numerical results are also provided to support our theory.
A 28-GHz CMOS Phased-Array Transceiver Based on LO Phase-Shifting Architecture With Gain Invariant Phase Tuning for 5G New Radio

This paper presents a 28-GHz CMOS four-element phased-array transceiver chip for the fifth-generation mobile network (5G) new radio (NR). The proposed transceiver is based on the local-oscillator (LO) phase-shifting architecture, and it achieves quasi-continuous phase tuning with less than 0.2-dB radio frequency (RF) gain variation and 0.3°C phase error. Accurate beam control with suppressed sidelobe level during beam steering could be supported by this work. At 28 GHz, a single-element transmitter-mode output ${{\mathrm {P}}_{\mathrm {1\,dB}}}$ of 15.7 dBm and a receiver-mode noise figure (NF) of 4.1 dB are achieved. The eight-element transceiver modules developed in this work are capable of scanning the beam from −50° to +50° with less than −9-dB sidelobe level. A saturated equivalent isotropic radiated power (EIRP) of 39.8 dBm is achieved at 0° scan. In a 5-m over-the-air measurement, the proposed module demonstrates the first 512 quadrature amplitude modulation (QAM) constellation in the 28-GHz band. A data stream of 6.4 Gb/s in 256-QAM could be supported within a beam angle of ±50°. The achieved maximum data rate is 15 Gb/s in 64-QAM. The proposed transceiver chip consumes 1.2 W/chip in transmitter mode and 0.59 W/chip in receiver mode.
An energy efficient gait for humanoid robots walking on even and uneven terrains

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CCRO: Citation’s Context & Reasons Ontology

Research papers can be visualized as a networked information space that contains a collection of information entities, inter-connected by directed links, commonly known as citation graph. There is a possibility to enrich the citation graph with meaningful relations using semantic tags. We have discovered and evaluated more than 150 citations’ reasons from the existing published literature to be represented as citation tags. Many of these reasons have overlapped and diffused meanings. Annotating such a large volume of citation graphs with citation’s reasons manually is nearly impossible, giving rise to a need to discover the citation’s reasons automatically with high accuracy. The first step towards this is developing a minimal set of citation’s context and reasons that are disjoint in nature. It would be a great help to the reasoning system if these reasons are represented in a formal way in the form of an ontology. By adopting a well-defined scientific methodology to formulate an ontology of citation reasons, we have reduced 150 reasons into only eight disjoint reasons, using an iterative process of sentiment analysis, collaborative meanings, and experts’ opinions. Based on our findings and experiments, we have proposed a citation’s context and reasons ontology (CCRO) that provides abstract conceptualization required to organize citations’ relations. CCRO has been verified, validated, and assessed by using the well-defined procedures and tools proposed in the literature for ontology evaluation. The results show that the proposed ontology is concise, complete, and consistent. For the instantiation and mapping of ontology classes on real data, we have developed a mapping graph among the verbs with predicative complements in the English Language, the verbs extracted from the selected corpus using the NLP and CCRO classes. Using this mapping graph, the mapping of ontology classes in each citation’s sentiment is explained with a complete mapping on the selected dataset.
PaaSword: A Data Privacy and Context-aware Security Framework for Developing Secure Cloud Applications - Technical and Scientific Contributions

Most industries worldwide have entered a period of reaping the benefits and opportunities cloud offers. At the same time, many efforts are made to address engineering challenges for the secure development of cloud systems and software.With the majority of software engineering projects today relying on the cloud, the task to structure end-to-end secure-by-design cloud systems becomes challenging but at the same time mandatory. The PaaSword project has been commissioned to address security and data privacy in a holistic way by proposing a context-aware security-by-design framework to support software developers in constructing secure applications for the cloud. This chapter presents an overview of the PaaSword project results, including the scientific achievements as well as the description of the technical solution. The benefits offered by the framework are validated through two pilot implementations and conclusions are drawn based on the future research challenges which are discussed in a research agenda.
Rule-based and Machine Learning Hybrid System for Patient Cohort Selection

Clinical trials play a critical role in medical studies. However, identifying and selecting cohorts for such trials can be a troublesome task since patients must match a set of complex pre-determined criteria. Patient selection requires a manual analysis of clinical narratives in patients’ records, which is a time-consuming task for medical researchers. In this work, natural language processing (NLP) techniques were used to perform automatic patient cohort selection. The approach herein presented was developed and tested on the 2018 n2c2 Track 1 Shared-Task dataset where each patient record is annotated with 13 selection criteria. The resulting hybrid approach is based on heuristics and machine learning and attained a micro-average and macro-average F1-score of 0.8844 and 0.7271, respectively, in the n2c2 test set. Part of the source code resultant from this work is available at https://github.com/ruiantunes/2018-n2c2-track-1/.
The Use of Technology and Social Media in Teaching Statistics

When teaching an applied subject such as Statistics it is important to be up-to-date with modern teaching methods and take advantage of any available teaching "tools". Technology can play an important role in this, with the use of social media, interactive online applications and videos. The use of these tools allows the teacher to take advantage of flipped-classroom approaches and for the student to grasp difficult statistical concepts (e.g. regression, simulation, confidence intervals, etc) easier through visualisation and a hands-on approach. In this paper, I will present how a lecturer can take advantage of some of the available “tools” that I used (e.g. Shiny, R Markdown, Screencast-O-Matic, Twitter, etc) in such a way that class time could be spent in stimulating debate and discussion.
Unsupervised Learning of Scene Categories on the Lunar Surface

Learning scene categories is a challenging task due to the high diversity of images. State-of-the-art methods are typically trained in a fully supervised manner, requiring manual labeling effort. In some cases, however, these manual labels are not available. In this work, an example of completely unlabeled scene images, where labels are hardly obtainable, is presented: orbital images of the lunar surface. A novel method that exploits feature representations derived from a CNN trained on a different data source is presented. These features are adapted to the lunar surface in an unsupervised manner, allowing for learning scene categories and detecting regions of interest. The experiments show that meaningful representatives and scene categories can be derived in a fully unsupervised fashion.
Mapping urban fingerprints of odonyms automatically extracted from French novels

ABSTRACT In this paper, we propose and discuss a methodology to map the spatial fingerprints of novels and authors based on all of the named urban roads (i.e., odonyms) extracted from novels. We present several ways to explore Parisian space and fictional landscapes by interactively and simultaneously browsing geographical space and literary text. Our project involves building a platform capable of retrieving, mapping and analyzing the occurrences of named urban roads in novels in which the action occurs wholly or partly in Paris. This platform will be used in several areas, such as cultural tourism, urban research, and literary analysis. The paper focuses on extracting named urban roads and mapping the results for a sample of 31 novels published between 1800 and 1914. Two approaches to the annotation of odonyms are compared. First, we describe a proof of concept using queries made via the TXM textual analysis platform. Then, we describe an automatic process using a natural language processing (NLP) method. Additionally, we mention how the geosemantic information annotated from the text (e.g., a structure combining verbs, spatial relations, named entities, adjectives and adverbs) can be used to automatically characterize the semantic content associated with named urban roads.
Fingerprint Image Segmentation based on Oriented Pattern Analysis

Segmentation is a crucial task in automatic fingerprint identification systems. This paper describes a novel segmentation approach which takes into account the directional information inherent in fingerprint ridges. The method considers a directional operator to feed a k-means unsupervised clustering algorithm that labels the image in non-overlapping regions. Morphological operations are performed to fill holes and properly separate foreground from background. Experiments conducted on Fingerprint Verification Competition (FVC) datasets demonstrate that the proposed method, denoted as Oriented Pattern-based Segmentation (OPS), achieves competitive results when compared to other well-known available fingerprint segmentation approaches.
An Efficient Privacy-Friendly Hop-by-Hop Data Aggregation Scheme for Smart Grids

The fine-grained and large number of measurements collected by smart meters can be used to reconstruct consumer behavior, and thus their widespread deployment for the modernization of electricity distribution networks has been associated with privacy concerns. This paper proposes an efficient and privacy-friendly hop-by-hop data aggregation scheme and a billing solution for smart grid systems. In our approach, hop-by-hop communication is utilized for transmitting usage reports of the smart meters. From the outcome of the security and performance analyses we can argue that our proposed scheme is secure and computationally more efficient, as compared to the other solutions.
Database-Based IP Network Routing

The Software-Defined Networking (SDN) approach has the goal of simplifying network management. SDN uses a logically centralized approach to enable simpler network programmability and simplify the network architecture. SDN is in general associated with the OpenFlow protocol, which standardizes communication between a controller and network devices. Alternatively, a database approach could be used to tackle data exchange between controller and network devices. This solution requires the installation of a database server inside each switch, and replicas of those local switches databases, in the controller. The database approach offers several potential advantages over OpenFlow such as higher level of abstraction, flexibility and the use of mature implementations of standardised database protocols to propagate information events and commands. The purpose of this work is to apply a Database-Based Control Plane (DBCP) for SDN networks on a wide area environment. The objective is to implement a replacement of the control plane of a wide area network, currently achieved using a link-state protocol such as OSPF or IS-IS, by an SDN approach based on similar techniques as the ones used in [4]. We conducted an experiment, which we called IP-DBCP, that consisted of the definition of data models and the construction of an SDN network with database replication as the means of communication between one controller and multiple switches. To this end, a switch was developed, using OpenSwitch software as a logical hardware layer, that is capable of executing a MySQL database management system, load it with its characteristics and collect data related to its network neighbourhood. A controller was also developed that executes a MySQL database management system with the replicated databases of all switches. The controller uses those replicated databases to construct routing rules, using a shortest-path algorithm. Ultimately we tested the correct functioning of the solution and evaluated the convergence time by performing network state changes and compared the results with the ones found in traditional link state protocols.
Deep feature representations and multi-instance multi-label learning of whole slide breast histopathology images (Tüm slayt meme histopatoloji görüntülerinin derin öznitelik gösterimleri ve çoklu-örnek çoklu-etiket öğrenimi)

DEEP FEATURE REPRESENTATIONS AND MULTI-INSTANCE MULTI-LABEL LEARNING OF WHOLE SLIDE BREAST HISTOPATHOLOGY IMAGES Caner Mercan Ph.D. in Computer Engineering Advisor: Selim Aksoy March 2019 The examination of a tissue sample has traditionally involved a pathologist investigating the case under a microscope. Whole slide imaging technology has recently been utilized for the digitization of biopsy slides, replicating the microscopic examination procedure with the computer screen. This technology made it possible to scan the slides at very high resolutions, reaching up to 100, 000 × 100, 000 pixels. The advancements in the imaging technology has allowed the development of automated tools that could help reduce the workload of pathologists during the diagnostic process by performing analysis on the whole slide histopathology images. One of the challenges of whole slide image analysis is the ambiguity of the correspondence between the diagnostically relevant regions in a slide and the slide-level diagnostic labels in the pathology forms provided by the pathologists. Another challenge is the lack of feature representation methods for the variable number of variable-sized regions of interest (ROIs) in breast histopathology images as the state-of-the-art deep convolutional networks can only operate on fixed-sized small patches which may cause structural and contextual information loss. The last and arguably the most important challenge involves the clinical significance of breast histopathology, for the misdiagnosis or the missed diagnoses of a case may lead to unnecessary surgery, radiation or hormonal therapy. We address these challenges with the following contributions. The first contribution introduces the formulation of the whole slide breast histopathology image analysis problem as a multi-instance multi-label learning (MIMLL) task where a slide corresponds to a bag that is associated with the slide-level diagnoses provided by the pathologists, and the ROIs inside the slide correspond to the instances in the bag. The second contribution involves a novel feature representation method for the variable number of variable-sized ROIs using the activations of deep convolutional networks. Our final contribution includes a more advanced MIMLL