Miracle-dz/UniFAR-FTUs huggingface dataset:
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling

This paper takes a new look at two sampling schemes commonly used to adapt machine learning algorithms to imbalanced classes and misclassification costs. It uses a performance analysis technique called cost curves to explore the interaction of over and undersampling with the decision tree learner C4.5. C4.5 was chosen as, when combined with one of the sampling schemes, it is quickly becoming the community standard when evaluating new cost sensitive learning algorithms. This paper shows that using C4.5 with undersampling establishes a reasonable standard for algorithmic comparison. But it is recommended that the cheapest class classifier be part of that standard as it can be better than under-sampling for relatively modest costs. Over-sampling, however, shows little sensitivity, there is often little dierence in performance when misclassification costs are changed.
Simulation of Mooring Lines Based on Position-Based Dynamics Method

The Position-Based Dynamics (PBD) method considers the rope as a series of particles in rope simulation. This paper introduces the PBD method and simulates mooring line in ship mooring simulation training system with four constraints, which are distance constraint, bending constraint, Long Range Attachment (LRA) constraint and pin constraint. In this paper, the LRA constraint method is applied to solve the overstretch problem of mooring line simulation caused by distance constraint and bending constraint. A pin constraint which combines two particles into one is put forward to achieve the connection effect between cable and the eye splice as well as the interaction between anchor and chain. Moreover, the method of Continuous Collision Detection (CCD) constraint is proposed to realize the collision detection between mooring lines and other objects. Compared with Newton’s method, our adopted method is more efficient and robustness. Since it has sufficient visual plausibility and can realize real-time visualization, the simulation system developed by PBD method has a good training effect for crew.
Propagation history ranking in social networks: A causality-based approach

: Information diffusion is one of the most important issues in social network analysis. Unlike most existing works, which either rely on network topology or node proﬁles, this study focuses on the diffusion itself, i.e., the recorded propagation histories. These histories are the evidence of diffusion and can be used to explain to users what happened in their networks. However, these histories can quickly grow in size and complexity, limiting their capacity to be intuitively understood. To reduce this information overload, in this paper we present the problem of propagation history ranking. The goal is to rank participant edges/nodes by their contribution to the diffusion. We ﬁrst discuss and adapt a causal measure, Difference of Causal Effects (DCE), as the ranking criterion. Then, to avoid the complex calculation of DCE, we propose two integrated ranking strategies by adopting two indicators. One is responsibility, which captures the necessity aspect of causal effects. We further give an approximate algorithm, which could guarantee a feasible solution, for this indicator. The other is capability, which captures the sufﬁciency aspect of causal effects. Finally, promising experimental results are presented to verify the feasibility of the proposed ranking strategies.
Coverage Analysis of Cooperative NOMA in Millimeter Wave Networks

This letter analyzes the coverage probability of a cooperative non-orthogonal multiple access (NOMA) millimeter wave (mmWave) network consisting of source (<inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>) and destination (<inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>) pair without a direct link. A cooperative relay (<inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>), which is selected from a set of active users, helps <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> to receive its data. The transmission from <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> is based on NOMA, by which <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> can receive its own data and <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>’s data simultaneously. Two relay selection schemes are developed. For each scheme, the favorable users that meet a rate threshold are selected to be in the decoding set. Then from the decoding set, a relay is selected that is nearest to <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> (in the 1st selection scheme), or nearest to <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> (in the 2nd selection scheme). For both schemes, we characterize the spatial density (location dependent) of decoding sets and derive the coverage probability. Both schemes outperform orthogonal multiple access. We also derive the coverage of randomly picking a relay (i.e., no selection) to quantify the benefits of relay selection in mmWave NOMA.
Universal Reversible Combinational Circuit Design using Quantum Dot Cellular Automata

— Quantum-dot cellular automaton (QCA) is a novel step towards the VLSI design by replacing the traditional CMOS Technology. It might bypass the transistor paradigm to molecular –scale or nano scale computing. In QCA paradigm, information is represented with the polarization of Quantum dots in a QCA cell. A very low power stimulus such as photon or light or very small voltage can change the polarity of QCA cell. In case of reversible logic circuit like combinational and sequential circuit, the total computation done with less power dissipation. Reversible logic synthesis in QCA had been proved to be very efficient Low power design for nanotechnology. In this paper, we proposed Reversible Logic Gate which is proved to be universal reversible logic gate by means of implementing all basic reversible logic (AND, OR, NOT, NOR, NAND, XOR and XNOR) gates. An important metric for evaluating reversible circuit is Garbage count. Hence Garbage minimization issue had also been addressed in our proposal. Here we had shown that our URLG implements all logic gates with reduced garbage output compared to previous proposal. Logic synthesis of 4*4 RLG using URLG being discussed and compared with previously reported HNG gate.
Restructuring Textual Information for Online Retrieval

Title of Thesis: Restructuring Textual Information for Online Retrieval Lawrence Koved, Master of Science, 1985 Thesis directed by: Ben Shneiderman, Associate Professor, Department of Computer Science Two experiments were conducted to evaluate two styles of online documents. The first experiment compared paper manuals to online manuals using two different database structuring techniques a sequential (linear) structure and a tree structure. People using the paper manuals were faster at solving problems than the people using the computer manuals. No differences were found between the linear and tree structures, or in accuracy of problem solutions. In a subjective evaluation of user preferences, the computer manuals were rated as better and more organized than the paper manuals. The second experiment compared two methods of retrieving online information that allowed the reader to specify the attributes needed to guide the information retrieval process. The first manual recorded the attributes entered by the reader via menus, and material in the manuals not relevant to the current search was pruned from the search space. The second manual did not record the menu selections, and the readers repeatedly entered the attributes several times in order to complete the task. The manual that recorded the attributes allowed the readers to work over twice as fast and was pref erred over the other manual. t f A theore lical found ation is presented for the underlying online documentation used in the experiments. The user's traversal through the da tabase is presented as a graph search process, using a production system. The results of the experiments and their theoretical foundations are evaluated in terms of the impact they might have on future online document storage and retrieval systems.
Content Delivery Through Hybrid Architecture in Video on Demand System

Peer-to-Peer (P2P) network needs architectural modification for smooth and fast transportation of video content. The viewer imports chunk video objects through the proxy server. The enormous growth of user requests in a small session of time creates huge load on the VOD system. The situation requires either the proxy server streamed video-content fully or partly to the viewers. The missing chunk at the proxy server is imported from the connected peer nodes. Peers exchange chunks among themselves according to some chunk selection policy. Peer node randomly contacts another peer to download a missing chunk from the buffers during each time slot. In video streaming, when the relevant frame is required at the viewer ends that should be available at the respective proxy server. The video watcher also initiates various types of interactive operations like a move forward or skips some finite number of frames that create congestion inside the VOD system. To elevate the situation it needs an effective content delivery mechanism for smooth transportation of content. The proposed hybrid architecture is composed of P2P and mesh architecture that effectively enhances the search mechanism and content transportation in the VOD system.
Accurate Estimation of Battery SOH and RUL Based on a Progressive LSTM with a Time Compensated Entropy Index

The accurate estimation of the State of Health (SOH) and Remaining Useful Life (RUL) has been a subject of keen interest due to its impact on safety and condition-based maintenance services. A number of approaches have been proposed to tackle this problem based either on a model-driven or on a data-driven framework. Due to the electro-chemical complexity involved in battery aging, they are yet to achieve the accuracy required, especially, for real-world applications. This is because of the difficulty either in identifying the time-varying nature of model parameters and in collecting the real-world training dataset from widely varying modes of battery usage. In this paper, we propose a method of estimating SOH and RUL simultaneously in such a way as to contribute to its real-world applicability. First, noticing that battery aging causes the time sequence of charging and discharging voltage and current in a cycle to be shortened and dispersed, we define an aging index, referred to here as the time compensated entropy, for SOH and RUL. Second, for LSTM-based RUL prediction, we optimize the number of SOH input and the RUL prediction sequences for the minimum prediction error associated with a sequence of cycles. Third, we adopt a progressive framework of LSTMs such that whatever learned from the prior predictions are transferred to the subsequent prediction, starting with learned SOH. For experimental verification, we train the proposed progressive LSTM network based on CALCE datasets and apply to various cases of charging and discharging cycles. With SOH estimated online, we achieve less than 10 cycles of accuracy in RUL prediction, moving closer to real-world applicability.
Health Monitoring Framework for Aircraft Engine System using Deep Neural Network

A real-time monitoring framework is developed to detect operational anomalies in aircraft engine performance. A historical flight dataset recorded from commercial aircraft is utilized to perform the proposed method. Sampling frequency synchronization and denoise are performed on the flight dataset using signal processing techniques. A robust detection algorithm using the deep neural network is developed to capture flight performance anomalies that show significant off-nominal behavior in engine related and flight dynamic features. The accuracy and efficiency of the proposed monitoring method are validated through a demonstration of anomaly detection in the aircraft engine system associated with dynamic flight behavior.
Creating Electrical Load Profiles Through Time Series Clustering

Challenged by new problems ranging from new renewable production methods to novel sources of loads, the Swedish electrical system is facing a time of change. To keep up with this process, having knowledge about what trends are present in electrical consumer behavior will be key. At the same time a new tool is emerging in the form of more available electrical load data. Motivated by this development, this thesis comprises the work of finding a data driven time series clustering method that can provide meaningful and intuitive profiles to describe the behaviors of consumers in the local electrical grid level. Part one of this work consists of a broad survey where five clustering methods from literature as well as one novel method based on ordinary least square modeling are applied to real electrical load data with the goal of finding the most useful one. The data consists of hourly measured consumption from varied consumer types in the local grid in southern Sweden. Applying the methods to a small set of consumers, the linear model method is deemed to give the profiles that provide the most intuitive and useful information. With models based on time of day and outdoor temperature, the model generates profiles showing the consumers most typical load patterns. In part two of this work the linear model method is presented with improvements where most importantly consumers that are poorly modelled by the time of day and temperature are excluded from the clustering process. To test the performance of the improved method it is applied to two large data sets containing varied consumer types and only small house consumers respectively. The method is shown to generate intuitive profiles that even in a homogeneous data set consisting of only housing consumers can distinguish different types of daily routines. As a method that is quick, simple and easily understandable, the method is suggested as a tool both in decision making processes as well as an exploratory tool for more advanced consumer modeling.
Supporting children’s outdoor science learning with mobile computers: integrating learning on-the-move strategies with context-sensitive computing

ABSTRACT Two cases of context-sensitive mobile computing curricula for children are shown to develop analytical understandings of how science-related sense-making talk can be supported through physical and digital environment interaction, which supports play and learning through movement. Our work investigates how proximity-based computing mediates children’s engagement in scientific sense-making conversations in a botanical garden. We draw upon sociocultural perspectives on sense-making talk as well as learning on-the-move to inform our study design. Data are from children in summer camp, including field notes, photographs, and video records (26 children in Case A, 24 in Case B) in an Arboretum. Our findings illustrate that children engaged in both playful and scientific sense-making talk in two distinct curricular tasks. We provide insights into science sense-making talk patterns and pedagogical practices of integrating learning on-the-move strategies into informal education with design principles related to augmenting gardens with digital content. Our results suggest that learning on-the-move strategies influence sense-making talk and that embodied interactions within the gardens support science sense-making and role-play. We posit that designs for learner-centered mobile computing can create digital-physical hybrid spaces where learners engage each other and natural objects as they walk through community spaces.
A High Throughput B+tree for SIMD Architectures

B+tree is one of the most important data structures and has been widely used in different fields. With the increase of concurrent queries and data-scale in storage, designing an efficient B+tree structure has become critical. Due to abundant computation resources, SIMD architectures provide potential opportunities to achieve high query throughput for B+tree. However, prior methods cannot achieve satisfactory performance results due to low resource utilization and poor memory performance. In this paper, we first identify the gaps between B+tree and SIMD architectures. Concurrent B+tree queries involve many global memory accesses and different divergences, which mismatch with SIMD architecture features. Based on this observation, we propose Harmonia, a novel B+tree structure to bridge the gaps. In Harmonia, a B+tree structure is divided into a key region and a child region. The key region stores the nodes with its keys in a breadth-first order. The child region is organized as a prefix-sum array, which only stores each node's first child index in the key region. Since the prefix-sum child region is small and the children's index can be retrieved through index computations, most of it can be stored in on-chip caches, which can achieve good cache locality. To make it more efficient, Harmonia also includes two optimizations: partially-sorted aggregation and narrowed thread-group traversal, which can mitigate memory and execution divergence and improve resource utilization. Evaluations on a 28-core INTEL CPU show that Harmonia can achieve up to 207 million queries per second, which is about 1.7X faster than that of CPU-based HB+Tree [1] , a recent state-of-the-art solution. And on a Volta TITAN V GPU, it can achieve up to 3.6 billion queries per second, which is about 3.4X faster than that of GPU-based HB+Tree.
Mechanisms and classifications of Chronic Obstructive Pulmonary Disease: A literature review

Abstract COPD is a prevalent chronic health condition secondary to only heart disease and stroke. A review of the literature reveals whilst smoking is the most common aetiological factor, environmental and occupational pollutions, genetic predisposition, hyper-responsive airways and respiratory infections are additional mechanisms to the development of COPD in susceptible individuals. Assessment of the severity of COPD via spirometry is considered the gold standard in quantifying the degree of respiratory impairment experienced by the client with the condition. However, there are inconsistent parameters of disease severity between each of the international respiratory bodies, which must influence reporting of outcomes. This paper reviews the literature reports of the mechanisms; systemic effects, disease-staging systems used in COPD and the relationship between disease severity and health outcomes.
Probabilistic Online Learning of Appearance and Structure for Robotics

A robotic system can be characterized by its interactions with environments. With growing demand for robots deployed in various scenarios, the ability to perform physical interaction in uncontrolled environments has become of great interest. While a robot performs interactive tasks, its visual and spatial sensing plays a critical role. Being a major source of learning, vision not only guides immediate actions, but also indirectly improves future actions and decisions. How visual information is gathered and represented will significantly influence how a robot can plan and act. Although recent advances in machine perception have presented unprecedented performance in some areas, there still exist challenges in various aspects. In this dissertation, I will address two such issues and suggest an online probabilistic approach to each problem. Most successful approaches in visual learning depend on fragments of exemplars prepared by humans. It is simply unaffordable to provide constant human supervision to a robotic system that would receive tens of new image frames per second. Ideally, a robotic system is required to gather information from its unique experience and keep growing knowledge on the fly without such external aids. One way to implement the self-learning is to take advantage of the naturally correlated sensations of different sensory modalities. The first part of this talk presents a probabilistic online self-learning framework to alleviate the dependency in robotic visual learning by leveraging structural priors. Another challenge in robotics is its spatial understanding. Aside from planning and performing actions, spatial representation itself still largely requires more research. While point or grid-based representations are currently being employed for practical conveniences, these methods suffer from discretization and disconnected spatial information. On the other hand, Gaussian Processes (GP) have recently gained attention as an alternative to represent the distance field of structures continuously and probabilistically. It is not only the seamless expression of structures, but also direct access to the distance and direction to obstacles that make the representation invaluable. The second part of the talk presents an online framework for continuous spatial mapping using GP. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Electrical & Systems Engineering First Advisor Daniel Lee
MDiET: Malware Detection in Encrypted Traffic

With the increasing adoption of end-to-end encryption in industrial systems, the risk of distributing hidden malware by exploiting encrypted channels gradually turns to a major concern. Due to encryption, the stateof-the-art, signature-based mechanisms might fail to detect malware sufficiently, thus new approaches are required. In this work, a method for malware detection in encrypted traffic based on Machine Learning is presented. A supervised learning approach is adopted and the efficiency of the solution is demonstrated by a set of exhaustive simulations. Further considerations for incorporating the proposed method in a reference industrial network are also discussed.
Computational Technology for Global Search Based on the Modified Algorithm of the Univariate Nonlocal Optimization

The paper presents an approach to the numerical study of the problems of finding a global extremum of multiextremal functions, based on the use of a parabolas algorithm. As local methods of onedimensional search, the methods of parabolic interpolation and the golden section are used. The numerical testing of modifications of the implemented approach using known non-convex functions has been carried out. The proposed technique has been applied to investigate a more complex optimization problem of a controlled dynamical power system. The obtained numerical results allowed us to demonstrate the efficiency of the proposed computational technology. Keywords-global optimization problem, parabolas algorithm, computational technology, power system.