Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing General Orthogonal Fractional Factorial Split-Plot Designs

While the orthogonal design of split-plot fractional factorial experiments has received much attention already, there are still major voids in the literature. First, designs with one or more factors acting at more than two levels have not yet been considered. Second, published work on nonregular fractional factorial split-plot designs was either based only on Plackett–Burman designs, or on small nonregular designs with limited numbers of factors. In this article, we present a novel approach to designing general orthogonal fractional factorial split-plot designs. One key feature of our approach is that it can be used to construct two-level designs as well as designs involving one or more factors with more than two levels. Moreover, the approach can be used to create two-level designs that match or outperform alternative designs in the literature, and to create two-level designs that cannot be constructed using existing methodology. Our new approach involves the use of integer linear programming and mixed integer linear programming, and, for large design problems, it combines integer linear programming with variable neighborhood search. We demonstrate the usefulness of our approach by constructing two-level split-plot designs of 16–96 runs, an 81-run three-level split-plot design, and a 48-run mixed-level split-plot design. Supplementary materials for this article are available online.
Localized reticular pseudodrusen and their topographic relation to choroidal watershed zones and changes in choroidal volumes.

PURPOSE We identified a topographic relation of localized reticular pseudodrusen (RPD) to choroidal watershed zones (CWZ) and to changes in choroidal volumes (CV). METHODS We included 30 eyes of 30 patients with RPD in an area <10 mm(2) and no other retinal alteration (76.7 ± 6.9 years). Patients underwent spectral-domain optical coherence tomography (SD-OCT), enhanced depth imaging (EDI) OCT, fluorescein video-angiography (vFA), indocyanine green video-angiography (vICG), and confocal scanning laser ophthalmoscopy (cSLO). vICG was evaluated for the presence, localization, and configuration of CWZ. Retinal areas affected by RPD were measured, and their localization was determined in relation to CWZ. CV was measured using a choroidal thickness map of the posterior pole and the Early Treatment of Diabetic Retinoscopy Study (ETDRS) grid. RESULTS In all study eyes, RPD could be demonstrated clearly in SD-OCT, EDI-OCT, FA, ICG, and cSLO. CWZ were identified in 25 eyes (83.3%). The area affected by RPD was 7.45 ± 2.25 mm(2). RPD area was located fully or partly within the CWZ in 22 eyes (88.0%). Mean CV in the full ETDRS grid area was reduced significantly (4.49 ± 1.44 mm(3)). Foveal CV and CV in the grid sector affected mostly by RPD were significantly diminished to 0.14 ± 0.05 mm(3) and 0.85 ± 0.38 mm(3), respectively. CONCLUSIONS The site of localized RPD seems to be related to presence and site of CWZ, suggesting that choroidal hypoxia may have a role in RPD pathogenesis. Reduced CV in eyes with localized RPD could be demonstrated and may be cause or consequence of RPD development.
The Behavior Analysis and Achievement Prediction Research of College Students Based on XGBoost Gradient Lifting Decision Tree Algorithm

Student behavior data is a reflection of students' learning styles and habits in campus. Quantitative evaluation of various behavior indicators of students' college learning life can reflect students' learning status and laws, and the academic performance is the most important and basic index to evaluate a student's learning situation. Based on the data of students' behavior under the "Four PIN" education system of Beihang Shoue College, this paper adopts XGBoost gradient upgrade decision tree algorithm to fully mine and analyze the situation of college students' study life and participation in social work, and to study the potential behavior patterns with strong correlation between students' behavior data and students' performance. The performance prediction model is established through data mining technology and the prediction accuracy is 73%. The study finds that "Pinjian" program records the growth of students. Students' overall evaluation and student performance shows a positive correlation. "Pinxue" program promotes academic development. Academic guidance in college plays an important role in improving students' academic performance. "Pinzhi" program enhances comprehensive literacy. Cultural and artistic activities and serving as student cadre show a positive correlation with academic performance.
Co-designing Inclusive Multisensory Story Mapping with Children with Mixed Visual Abilities

Story mapping is used in schools to promote children's understanding of stories and narrative structure. As a collaborative activity, it can support creativity and facilitate group interaction. However, most techniques used in primary schools rely on visual materials, which creates a barrier to learning for children with visual impairments (VI). To address this, we set out to design a collaborative story mapping tool with a group of children with mixed visual abilities and their teaching assistants. Using co-design approaches over ten workshops, we designed and prototyped different ideas for engaging children in storytelling and design. We present our co-design process and findings, and the resulting story mapping system. We outline how using multisensory elements can facilitate creativity and collaboration to help children with mixed visual abilities create and share stories together, and support learning and social inclusion of VI children in mainstream classrooms.
Effects of Early and Late Clamping of Umbilical Cord on Infant's Haemoglobin Level

premedication failed to prevent a slowing of the heart rate on the subsequent injection of suxamethonium. Similarly, the intravenous injection of 0.6 mg. of atropine also failed to afford protection. In this investigation, however, the intravenous administration of I mg. of atropine sulphate prevented the subsequent development of bradycardia or arrhythmia in every case, even when large doses of suxamethonium were used.
Parser-directed fuzzing

To be effective, software test generation needs to well cover the space of possible inputs. Traditional fuzzing generates large numbers of random inputs, which however are unlikely to contain keywords and other specific inputs of non-trivial input languages. Constraint-based test generation solves conditions of paths leading to uncovered code, but fails on programs with complex input conditions because of path explosion. In this paper, we present a test generation technique specifically directed at input parsers. We systematically produce inputs for the parser and track comparisons made; after every rejection, we satisfy the comparisons leading to rejection. This approach effectively covers the input space: Evaluated on five subjects, from CSV files to JavaScript, our pFuzzer prototype covers more tokens than both random-based and constraint-based approaches, while requiring no symbolic analysis and far fewer tests than random fuzzers.
Webchain: Verifiable Citations and References for the World Wide Web

Readers' capability to consider and assess sources is imperative. Digital preservation efforts, however, mostly neglected citation provenance, which is a necessity for transparent source verification. We therefore present Webchain, a new system enabling verifiable citations and references on the World Wide Web. Its architecture combines a distributed ledger with secure timestamping to ensure history of creation, ownership, and referential integrity of online resources. With Webchain, readers can independently detect content manipulation by verifying authenticity, integrity, and time consistency. At the same time, authors gain a proof of existence for referenced articles. Web-chain extends a well-known distributed timestamping scheme to handle an open and dynamic network topology by providing a solution for membership management. We examine the security of our approach, particularly regarding forging attacks. Our results show that we are able to render such attacks infeasible, even in the face of a powerful attacker.
ECPUB: An Energy Efficiency Evaluation Metric in Wireless Sensor Network

Improving the energy efficiency and balancing the energy distribution are two important aspects to prolong the lifetime of a wireless sensor network. This paper proposes the energy-cost-per-useful-bit (ECPUB) metric for evaluating the aforementioned aspects. It takes advantage of the concept “utility” in economics, and the energy consumption of transmitter and receiver, residual energy, the data quantity that sink needs and receives are taken into consideration. The impact of data quantity and the imbalance of node energy are discussed in this paper. To verify ECPUB, we apply it to LEACH protocol and the network lifetime is extended. This metric could be used to adjust the data size when doing data aggregation and to decide the appropriate next hop, path, or cluster heads.
Laws of large numbers for stochastic orders

We establish laws of large numbers for comparing sums of i.i. d. random variables in terms of stochastic dominance. Our results shed new light on a classic question, raised first by Samuelson (1963), on the relation between expected utility, risk aversion, and the aggregation of independent risks. In the context of statistical experiments, we answer a long-standing open question posed by Blackwell (1951): we show that generically, an experiment is more informative than another in large samples if and only if it has higher Renyi divergences.
On the number of signals whose directions can be estimated by an array

The dependence of direction finding accuracy and sensitivity on the number of signals and their separation is investigated. Using the Cramer-Rao lower bound on the direction-of-arrival estimates, it is shown that accuracy degrades rapidly with the number of signals if the signals are separated by less than a beamwidth. Using results on the sensitivity of the direction estimates to calibration errors, it is demonstrated that the sensitivity to calibration errors increases rapidly with the number of signals if the signals are separated by less than a beamwidth. >
STDP: Secure Privacy-Preserving Trajectory Data Publishing

As the smart devices and cloud services are rapidly expanding, a large amount of location information can easily be gathered. However, there is a conflict between collecting location information and protecting personal information since obtaining and utilizing the information may be restricted due to privacy concerns. In fact, various methods which use K-anonymity for original location data have been studied, but these methods have excessively reduced data utility while stressing highly on privacy preservation. In this research, we suggest a novel model to overcome this fundamental dilemma. Compared to the existing approaches, our study shows a new theoretical advancement in privacy protection and outstanding performance in terms of time complexity and data utility.
Real-Time Optimal-Route Computation: a Heuristic Approach

Optimal-route computation is an important function that is needed in many applications, especially in transportation-related activities. In certain applications, the real-time processing of this computation is required. The conventional algorithms for computing optimal routes do not handle this real-time requirement very efficiently, especially when large networks are involved. Therefore, alternative algorithms are needed. Of the possible alternatives, heuristic algorithms are considered to treat the real-time constraint much more effectively than nonheuristic algorithms. This paper discusses a heuristic algorithm for real-time computation of routes and compares it with other techniques and approaches.
Efficient and Privacy-Preserving Online Face Recognition Over Encrypted Outsourced Data

With the development of image processing technology and the pervasiveness of mobile devices, face recognition, which can be used to offer convenient and efficient individual authentication service, has attracted considerable interest in recent years. However, people's concern about their face data being leaked during the face recognition process impedes the flourish of face recognition. To address this problem, we present a novel privacy-preserving online face recognition scheme over encrypted outsourced data, named EPFR. With EPFR, a user can achieve secure, accurate and efficient authentication service without disclosing her/his face data. Specifically, an improved homomorphic encryption technology is introduced to provide an efficient online face recognition service based on the Eigenface algorithm. Through extensive analysis, we show that users' face data are kept confidential during the online face recognition process. In addition, we implement the scheme with a real face database, and simulation results demonstrate that the scheme can be used to provide efficient and accurate online face recognition service.
An Overview on the Applications and Security Issues of Fog Computing

Fog computing extends cloud computing paradigms to the edge of the network, enabling a new generation of applications and services. This paper mainly elaborates the related background and characteristics of fog computing, discusses the concept and hierarchical structure of fog computing services, compares the advantages of cloud computing and fog computing, analyzes the existing techniques and applications of fog computing, and analyzes possible security issues in fog computing and solutions to some of the security issues.
Flexible Virtual Energy Sharing by Distributed Task Reallocation in IoT Edge Networks

The convergence of Internet of Things (IoT) and edge computing provides a promising solution to many distributed IoT applications, which often involve real-time information gathering and complex processing. However, energy consumption of computational-intensive processing at edge devices becomes a main constraint due to limited battery capacity. In addressing this, computation offloading to a remote server has been utilized but leading to potentially increased latency due to network delay. In this paper, we propose a virtual energy sharing among edge IoT devices through a collaborative computing mechanism by a flexible and situation-dependent computation task reallocation coordinated by a smart gateway. Our key objective is to achieve energy aware task executions and energy sharing among collaborative edge devices by flexibly adjusting task volumes and CPU frequency based on the workload conditions. To implement this, trade-off between energy consumption and computation time of two collaborative edge devices is formulated to achieve a flexible and situation-dependent decision-making considering the time-varying resource conditions and application delay tolerance. Simulation results show that the proposed scheme could achieve a flexible virtual energy sharing by managing the trade-off between resource utilization and latency performance of tasks.
A Blockchain Model for Industrial Internet

Blockchain is one type of distributed ledger technology, which comes from Bitcoin, a peer to peer electronic cash system. Its characteristics like decentralization and immutability make it possible to resolve data barrier and security problems in centralized system. In this paper, we discussed the existing problems of advanced manufacturing and several advantages while applying blockchain. Then we proposed a blockchain model for industrial internet based on well-known blockchain system such as bitcoin ethereum and hyperledger fabric. After that, we gave the workflow for a personalized service in this blockchain model.
DoOC: A Platform for Domain-Oriented Online Community System Construction

Many online community systems, such as Twitter and Facebook, have rapidly expanded the social network in which people live. However, constructing such a system is not easy for developers. Thus, it is essential to provide a platform allowing developers to quickly construct an online community system. In this paper, we present a platform, DoOC, which can automatically and quickly construct an online community system. Given the information of a domain and some configuration information, DoOC extracts a label set of the domain and then constructs the online community system based on this label set. DoOC also applies a dynamic community detection algorithm to the community system, which considers both the label attributes of user nodes and the topological structure of social networks. DoOC is applied through the movie scenario to show its usability and efficiency.
FREE: A Fast and Robust Key Extraction Mechanism via Inaudible Acoustic Signal

To build a secure wireless networking system, it is essential that the cryptographic key is known only to the two (or more) communicating parties. Existing key extraction schemes put the devices into the physical proximity, and utilize the common inherent randomness between the devices to agree on a secret key, but they often rely on custom devices and have low bit rate. In this paper, we seek a key extraction approach that only leverages off-the-shelf mobile devices, while achieving significantly higher key generation efficiency. The core idea of our approach is to exploit fast varying inaudible acoustic channel for generating enough randomness and wireless parallel communication for exchanging reconciliation information to improve the key generation rate. We have carefully studied and validated the feasibility of our approach through both theoretical analysis and a variety of measurements. We implement our approach on different mobile devices and conduct extensive experiments in different real scenarios. The experiment results show that our approach achieves high efficiency and satisfactory robustness. Compared with the state of art methods, our approach improves the key generation rate by 38.46% and reduces the bit mismatch ratio by 42.34%.
Performance Optimisation of the Parallel CFD Code MGLET across Different HPC Platforms

This paper presents the optimisation techniques implemented to run across four HPC platforms the finite-volume computational fluid dynamics (CFD) code MGLET (Multi Grid Large Eddy Turbulence). We analysed and applied refactoring to the parallel communication routines, and reduced the memory footprint significantly, resulting in a substantial improvement of the parallel-scaling capability and in an increase of the maximum number of degrees of freedom for applications. Data structures and files layout were redesigned for implementing parallel I/O in HDF5. The new parallel I/O strategy results in a considerable increase in the average data transfer rate compared with the former serial implementation. An I/O pattern analysis and detailed I/O profiling of the new implementation were then conducted and further performance improvement was achieved by increasing the size of I/O requests and reducing the number of I/O processes. We compare the improved parallel-scaling capability of MGLET on different architectures using representative CFD application test cases.
Window Based BFT Blockchain Consensus

There is surge of interest to the blockchain technology not only in the scientific community but in the business community as well. Proof of Work (PoW) and Byzantine Fault Tolerant (BFT) are the two main classes of consensus protocols that are used in the blockchain consensus layer. PoW is highly scalable but very slow with about 7 (transactions/second) performance. BFT based protocols are highly efficient but their scalability are limited to only tens of nodes. One of the main reasons for the BFT limitation is the quadratic $O(n^{2})$ communication complexity of BFT based protocols for $n$ nodes that requires $n \times n$ broadcasting. In this paper, we present the Musch protocol which is BFT based and provides communication complexity $O(fn+n)$ for $f$ failures and $n$ nodes, where $f < n/3$, without compromising the latency. Hence, the performance adjusts to $f$ such that for constant $f$ the communication complexity is linear. Musch achieves this by introducing the notion of exponentially increasing windows of nodes to which complains are reported, instead of broadcasting to all the nodes. To our knowledge, this is the first BFT-based blockchain protocol which efficiently addresses simultaneously the issues of communication complexity and latency under the presence of failures.
Prediction of Time-to-Solution in Material Science Simulations Using Deep Learning

Predicting the time to solution for massively parallel scientific codes is a complex task. The reason for this is the presence of multiple, strongly interconnected algorithms that possibly react differently to the changes in compute power, vectorization length, memory and network bandwidth and latency and I/O throughput. A reliable prediction of execution time is however of great importance to the user who wants to plan on large scale simulations or virtual screening procedures characteristic of high throughput computing. In this article we present a practical approach based on machine learning techniques to achieve very accurate predictions of the time to solution for a DFT-based material science code. We compare our results with the predictions provided by a parametrized analytical performance model showing that deep learning solutions allow for a greater accuracy without the need of domain knowledge to introduce an explicit description of the algorithms implemented in the code.
Behavior Modification

This article describes how retail electricity demand can be made price-responsive through either dynamic, time-based retail pricing or DR programs offered by utilities and/or regional ISOs. PRD can provide the crucial link between wholesale and retail electricity markets that is missing under traditional fixed retail rates. Parties generally agree on the need for greater PRD to improve the efficiency of wholesale power markets. There is no mechanical formula for determining how much consumers will alter their usage patterns for any given event. Price response varies considerably across customers and it tends to be greater for customers with relatively high usage levels. Consumers can choose to save money by reducing consumption during periods of high prices or "buy through" at prices that reflect wholesale market conditions.
Blockchain for Civil Engineering Practices in Smart Cities

Many civil engineering malpractices can be avoided if there are timely, transparent, and unalterable records of these activities. This paper describes an approach to achieve this purpose with blockchain technology. A novel blockchain system designed to avoid high volatility in token price and encourage public participation in maintaining the ledger is introduced. Civil engineering and construction are the first testing fields for the system. It is anticipated that the system, after successfully implemented, can extend its scope to other areas as well.
Dialogue between Satellite and Cellular Networks: Pricing Game for Data Offloading Assisted by Ultra-dense LEO Constellations

Due to the limited backhaul capacity of traditional small cells (TSC), it is non-trivial for the traditional operator (TO) to satisfy the increasing data demand of users. To relieve this issue, we propose a data offloading scheme, where the TO offloads TSC users to LEO-based small cells owned by the satellite operator for satellite-backhauled network access. An iterative optimal algorithm based on the Stackelberg game is developed to maximize both operators' revenues. Simulation results show the effectiveness of our scheme.