Miracle-dz/UniFAR-FTUs huggingface dataset:
Eunomia: A Permissionless Parallel Chain Protocol Based on Logical Clock

The emerging parallel chain protocols represent a breakthrough to address the scalability of blockchain. By composing multiple parallel chain instances, the whole systems' throughput can approach the network capacity. How to coordinate different chains' blocks and to construct them into a global ordering is critical to the performance of parallel chain protocol. However, the existed solutions use either the global synchronization clock with the single-chain bottleneck or pre-defined ordering sequences with distortion of blocks' causality to order blocks. In addition, the prior ordering methods rely on that honest participants faithfully follow the ordering protocol, but remain silent for any denial of ordering (DoR) attack. On the other hand, the conflicting transactions included into the global block sequence will make Simple Payment Verification (SPV) difficult. Clients usually need to store a full record of transactions to distinguish the conflictions and tell whether transactions are confirmed. However, the requirement for a full record will greatly hinder blockchains' application, especially for mobile scenarios. In this technical report, we propose Eunomia, which leverages logical clock and fine-grained UTXO sharding to realize a simple, efficient, secure and permissionless parallel chain protocol. By observing the characteristics of the parallel chain, we find the blocks ordering issue in parallel chain has many similarities with the event ordering in the distributed system. Eunomia thus adopts "virtual" logical clock, which is optimized to have the minimum protocol overhead and runs in a distributed way. In addition, Eunomia combines the mining incentive with block ordering, providing incentive compatibility against DoR attack. What's more, the fine-grained UTXO sharding does well solve the conflicting transactions in parallel chain and is shown to be SPV-friendly.
Clustering Malware's Network Behavior using Simple Sequential Features

Developing malware variants is extremely cheap for attackers because of the availability of various obfuscation tools. These variants can be grouped in malware families, based on information retrieved from their static and dynamic analysis. Dynamic, network-level analysis of malware shows its core behavior since it captures the interaction with its developer. On the other hand, increasingly more emphasis is given to using Deep Packet Inspection (DPI) in order to cluster malware’s network behavior. However, DPI has severe privacy implications, as it involves inspecting payloads of the network traffic. This thesis presents an exploratory study, the aim of which is to characterize and cluster malware behavior using high-level, non-privacy-invasive, sequential features extracted from its network activity. The key intuition behind the proposed solution is that if the underlying infrastructure of distinct malware samples is similar, the order in which they perform certain actions should also be similar. The results of this research show that sequence clustering allows flexible and robust clusters, as opposed to using non-sequential features. The clusters themselves reveal interesting attacking capabilities, such as port scans, and the same Command and Control server responding to different malware families. Lastly, a comparison with clusters obtained from static analysis reveals that network-based clustering is far more qualified to determine the many behaviors exhibited by a single malware family, as well as behaviors common across multiple malware families.
PCRNet: Point Cloud Registration Network using PointNet Encoding

PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion. However, recent works in literature have shown the sensitivity of the PointNet representation to pose misalignment. This paper presents a novel framework that uses the PointNet representation to align point clouds and perform registration for applications such as tracking, 3D reconstruction and pose estimation. We develop a framework that compares PointNet features of template and source point clouds to find the transformation that aligns them accurately. Depending on the prior information about the shape of the object formed by the point clouds, our framework can produce approaches that are shape specific or general to unseen shapes. The shape specific approach uses a Siamese architecture with fully connected (FC) layers and is robust to noise and initial misalignment in data. We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.
Using Convolutional Neural Networks to perform content based image retrieval system

— Despite being invented close to sixty years ago, artificial neural networks (ANN) remain an area of active research and a powerful tool. Their resurgence in the context of deep learning has led to dramatic improvements in various domains from computer vision. The quantity of available data and the computing power are always increasing, which is desirable to train high capacity models such as Convolutional Neural Networks (CNN). It has been shown that CNN provide a high-level descriptor of the visual content of the image. In this paper, we investigate the use of such descriptors (convolutional neural codes) within the content-based image retrieval (CBIR) application.
A new method of product-service system design: product-based, participatory service design method

With the change of business model, market competition has gradually changed from product competition to service competition, and service design has been paid more and more attention by various industries. The ultimate goal of service design is tantamount to bring users a better experience and maximize the interests of stakeholders. Therefore, this paper will create a service design method based on service dominant logic and more emphasis on user experience. This paper sorts and summarizes the concept, content and characteristics of service design and product service system, and then find out the future development direction of service design and product service system. In this paper, various design methods are analyzed and compared, and one of the best design methods is associated with the product service system to form a new design method. After that, this new design method will be applied to the service design of children’s design thinking education as a practice.
Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce the performances of GNNs. It is very challenging to design robust graph neural networks against poisoning attack and several efforts have been taken. Existing work aims at reducing the negative impact from adversarial edges only with the poisoned graph, which is sub-optimal since they fail to discriminate adversarial edges from normal ones. On the other hand, clean graphs from similar domains as the target poisoned graph are usually available in the real world. By perturbing these clean graphs, we create supervised knowledge to train the ability to detect adversarial edges so that the robustness of GNNs is elevated. However, such potential for clean graphs is neglected by existing work. To this end, we investigate a novel problem of improving the robustness of GNNs against poisoning attacks by exploring clean graphs. Specifically, we propose PA-GNN, which relies on a penalized aggregation mechanism that directly restrict the negative impact of adversarial edges by assigning them lower attention coefficients. To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph. Experimental results on four real-world datasets demonstrate the robustness of PA-GNN against poisoning attacks on graphs. Code and data are available here: this https URL.
Business practitioners’ Perspective on Value of Mobile Technology

No existing research maps and discusses holistically values deriving from mobile technology use capturing both strategic and operational opportunities which are best to emerge in the B2B context. This empirical paper addresses this gap. Adapted grounded theory approach is applied to collect and analyse in-depth interviews with 28 B2B practitioners from advertising and marketing firms. Whether mobile technology is a simple mean to advanced communication with no physical boundaries of time and location or a business tool to boost creative thinking, this study concludes that mobile technology represents a novel and unique category of technology because of its core distinctive feature, ‘being mobile’. B2B practitioners argue that the true nature of mobile technology lies in seeing it as a source of value that derives from using mobile technology. B2B practitioners view mobile technology not only as a purely technical tool (functional value) enabling effective communication (social value) but as a strategic tool driving balanced and flexible ways in managing business (emotional value) and enabling creative thinking (creative value).
A Hybrid Fingerprint Quality Evaluation Model for WiFi Localization

The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user’s location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.
Detecting Inter-Component Vulnerabilities in Event-based Systems

Event-based system (EBS) has become popular because of its high flexibility, scalability, and adaptability. These advantages are enabled by its communication mechanism—implicit invocation and implicit concurrency between components. The communication mechanism is based on non-determinism in event processing, which can introduce inherent security vulnerabilities into a system referred to as event attacks. Event attack is a particular type of attack that can abuse, incapacitate, and damage a target system by exploiting the system's event-based communication model. It is hard to prevent event attacks because they are administered in a way that does not differ from ordinary event-based communication in general. While a number of techniques have focused on security threats in EBS, they do not appropriately resolve the event attack issues or suffer from inaccuracy in detecting and preventing event attacks. To address the risk of event attacks, I present a novel vulnerability detection technique for EBSs that are implemented by using message-oriented middleware platform. My technique has been evaluated on 25 open-source benchmark apps and eight real-world EBSs. The evaluation exhibited my technique's higher accuracy in detecting vulnerabilities on event attacks than existing techniques as well as its applicability to real-world EBSs.
Misfire fault diagnosis of diesel engine based on VMD and XWT

For diesel engine misfire fault diagnosis under strong noise, a new method based on variational mode decomposition (VMD) and cross wavelet transform (XWT) is proposed. Firstly, the vibration signal of cylinder head is processed by stages according to the working cycle of diesel engine, and then the vibration signal of each working cycle is re-sampled according to the same angle. Then the vibration signal of cylinder head is decomposed by VMD to denoise adaptively and reconstruct the signal. Next, XWT is used to analyze the time-frequency correlation of any two continuous working cycle signals, and the non-periodic components and random noise in the vibration signals are further eliminated to extract the combustion characteristics of diesel engines. Finally, the misfire fault of diesel engine is diagnosed by calculating the energy proportion of each cylinder in time-frequency space. The effectiveness of the proposed method is verified by simulation and experiment.
Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking

Dialogue state tracking (DST) is an essential component in task-oriented dialogue systems, which estimates user goals at every dialogue turn. However, most previous approaches usually suffer from the following problems. Many discriminative models, especially end-to-end (E2E) models, are difficult to extract unknown values that are not in the candidate ontology; previous generative models, which can extract unknown values from utterances, degrade the performance due to ignoring the semantic information of pre-defined ontology. Besides, previous generative models usually need a hand-crafted list to normalize the generated values. How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge. In this paper, we propose a Copy-Enhanced Heterogeneous Information Learning model with multiple encoder-decoder for DST (CEDST), which can effectively generate all possible values including unknown values by copying values from heterogeneous texts. Meanwhile, CEDST can effectively decompose the large state space into several small state spaces through multi-encoder, and employ multi-decoder to make full use of the reduced spaces to generate values. Multi-encoder-decoder architecture can significantly improve performance. Experiments show that CEDST can achieve state-of-the-art results on two datasets and our constructed datasets with many unknown values.
Mission Design and Modeling of Space Manipulators for In-Orbit Servicing Missions

In near future, in-orbit operations will change the vision of space as a "static" place, where each spacecraft’s lifetime is programmed from launch to retirement. Thanks to its capabilities, when need arises, operators could amend the initial planned mission of a satellite, by repairing or upgrading its payload, or simply extending its life expenctancy by refuelling, or, at least, have it deorbited. This topic is also part of a bigger initiative, aimed to foster suistainability in space, to grant future manned and unmanned mission a safe and clean environment to operate in. In this context, this thesis is focused on pointing out a possible in-orbit servicing mission, on a ESA owned satellite, in order to accomplish goals such as deorbiting or refuelling. The targets, selected through a trade-off, resulted to be the best in terms of ∆V budgets and mission time. Secondly, is developed, at an early stage, the model of a spaceraft-mounted robotic arm, capable of operating on spacecrafts. The manipulator is modeled using Simulink and following Lagrangian formulation, aiming to highlight and evaluate the reactions on the base induced by the manipulator’s motion. The results showed that this effects does not impact partcularly on the base dynamics and that they could be easily balanced by an ordinary control system. The thesis was developed during an internship at TAS-I (Thales Alenia Space Italia) in Turin in cooperation with the GNC (Guidance, Navigation and Control) group.
A switch‐controlled multimode narrowband receiver RF front‐end

This work illustrates a flexible and convenient method to build a multimode narrowband receiver RF front‐end by means of controlled switches, switched capacitors, and switched inductors. The front‐end comprises a dual‐gain‐mode narrowband low‐noise amplifier (LNA) and a dual‐linearity‐mode mixer. A four‐mode receiver RF front‐end constructed with the dual‐gain‐mode LNA and the dual‐linearity‐mode mixer operating in frequency band range from 1800 to 2050 MHz was demonstrated with an IBM 90‐nm CMOS process. The front‐end achieves a 1/1.6 dB noise figure, 30/20 dB power gain, and 16/−10 dBm third‐order input intercept point while draws a 5.9/3.6 mA current from a 1.8‐V supply voltage at the low noise mode and high linearity mode, respectively. The proposed technique can be employed to build an intelligent mobile system.
Making a Case for Using Effective Reading Programs: A Program Evaluation of Wilson Fundations.

xv Chapter 1: Introduction . 2 Background . 2 Program Description . 5 Context . 6 Description of the Program . 9 Overview of the Evaluation Approach . 17 Program Evaluation Model . 17 Purpose of the Evaluation . 19 Focus of the Evaluation . 19 Evaluation Questions . 21 Definition of Terms.22 Chapter 2: Review of Literature . 25 Phonemic Awareness . 24 Phonics . 27 Fluency . 28 Vocabulary . 29 Comprehension . 31 Wilson Language Training Corporation . 32
Optimizing All-to-All Data Transmission in WANs

All-to-all data transmission is a typical data transmission pattern in both consensus protocols and blockchain systems. Developing an optimization scheme that provides high throughput and low latency data transmission can significantly benefit the performance of those systems. This paper investigates the problem of optimizing all-to-all data transmission in a wide area network (WAN) using overlay multicast. We prove that in a hose network model, using shallow tree overlays with height up to two is sufficient for all-to-all data transmission to achieve the optimal throughput allowed by the available network resources. Upon this foundation, we build ShallowForest, a data plane optimization for consensus protocols and blockchain systems. The goal of ShallowForest is to improve consensus protocols’ resilience to skewed client load distribution. Experiments with skewed client load across replicas in the Amazon cloud demonstrate that ShallowForest can improve the commit throughput of the EPaxos consensus protocol by up to 100% with up to 60% reduction in commit latency.
Indication Specific Multi-Criteria Optimization in Radiotherapy Treatment Planning using Knowledge from previously delivered Treatment Plans

A multi-criteria optimization (MCO) approach for volumetric-modulated-arc (VMAT) radiotherapy treatment plan optimization with a low number of composite optimization objectives is presented. The solution space for a number of cranial and spinal patient geometries is explored and an approximation method of the solution space using trilinear dose interpolation is investigated. A machine learning (ML) driven approach for prediction of the characteristics of the solution space with respect to the patient geometry is developed and evaluated, followed by an outlook on how to combine the approximation method and the ML approach.
A Lifecycle Cost-Driven System Dynamics Approach for Considering Additive Re-Manufacturing or Repair in Aero-Engine Component Design

Aero-engine component design decisions should consider re-manufacturing and/or repair strategies and their impact on lifecycle cost. Existing design approaches do not account for alternative production technologies such as the use of additive manufacturing in life extension processes. This paper presents a modeling and optimization methodology for examining the impact of design decisions in the early development stage on component lifecycle cost during the in-service phase while considering the potential use of additive manufacturing in life extension strategies. Specifically, a system dynamics model is developed to assess different end-of-life scenarios. Finally, an optimization problem is formulated and solved to minimize lifecycle cost with respect to design variables related to re-manufacturing.
SIMPLER MAGIC: Synthesis and Mapping of In-Memory Logic Executed in a Single Row to Improve Throughput

In-memory processing can dramatically improve the latency and energy consumption of computing systems by minimizing the data transfer between the memory and the processor. Efficient execution of processing operations within the memory is therefore, a highly motivated objective in modern computer architecture. This article presents a novel automatic framework for efficient implementation of arbitrary combinational logic functions within a memristive memory. Using tools from logic design, graph theory and compiler register allocation technology, we developed synthesis and in-memory mapping of logic execution in a single row (SIMPLER), a tool that optimizes the execution of in-memory logic operations in terms of throughput and area. Given a logical function, SIMPLER automatically generates a sequence of atomic memristor-aided logic (MAGIC) NOR operations and efficiently locates them within a single size-limited memory row, reusing cells to save area when needed. This approach fully exploits the parallelism offered by the MAGIC NOR gates. It allows multiple instances of the logic function to be performed concurrently, each compressed into a single row of the memory. This virtue makes SIMPLER an attractive candidate for designing in-memory single instruction, multiple data (SIMD) operations. Compared to the previous work (that optimizes latency rather than throughput for a single function), SIMPLER achieves an average throughput improvement of <inline-formula> <tex-math notation="LaTeX">$435\times $ </tex-math></inline-formula>. When the previous tools are parallelized similarly to SIMPLER, SIMPLER achieves higher throughput of at least <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula>, with <inline-formula> <tex-math notation="LaTeX">$23\times $ </tex-math></inline-formula> improvement in area and <inline-formula> <tex-math notation="LaTeX">$20\times $ </tex-math></inline-formula> improvement in area efficiency. These improvements more than fully compensate for the increase (up to 17% on average) in latency.