Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Encrypted Scheme Over Outsourcing Data for Query on Cloud Platform

With the proliferation of cloud platforms, users are inclined to outsource their encrypted data to cloud platforms. However, the existing encryption schemes only provide users with limited query types over encrypted data. Meanwhile, if users encrypt their data by deterministic encryption, their encrypted data will be easily subject to the frequency attack. Besides, users’ data privacy is likely to be disclosed to the cloud server when their encrypted data is updated on cloud platforms. To address these problems, in this paper, we propose an effective encrypted query scheme over outsourcing data on cloud platforms. In our scheme, users’ data is encrypted based on all possible queries to meet users’ diverse query demands. Furthermore, a double AES encryption method is adopted to cope with the frequency attack existing in deterministic encryption. To protect users’ privacy when their data is updated, a neighbor rows exchange method is designed in our scheme. The theoretical analysis and comparative experiments demonstrate the effectiveness of our scheme.
Security Analysis of Device Binding for IP-based IoT Devices

As one of the fastest growing technologies today, the Internet of Things has profoundly changed the ways people interact with the physical world. With a mobile application on a smartphone, a user can conveniently control an IoT device and acquire the sensor data of the external environment. To enable such convenience, a critical step is to bind the user's smartphone with the IoT device and then establish a secure communication channel between them. Although various techniques have already been adopted, however, little has been done so far to systematically evaluate the security implications of those binding mechanisms in IoT. In this paper, we report the first systematic study on device binding mechanisms of IoT, in an attempt to understand the security implications. For this purpose, we defined a practical adversary model and systematically investigated 24 popular IoT products on the consumer market. Our investigation reveals the fact that IoT developers often mistrust the environment and do not follow best practices in device binding. As a result, we were able to launch several types of real-world attacks against the device binding process. Our research brings the insecure designs of device binding to the spotlight and shows that the threat to IoT device binding is realistic and serious.
Finger-Vein Recognition Based on Deep DenseNet Using Composite Image

Finger-vein recognition has the advantages of high immutability, as finger veins are located under the skin, high user convenience, as a non-invasive and contactless capture device, is used, and high readability even when one of the fingers is damaged or not available for recognition. However, there is an issue of recognition performance degradation caused by finger positional variation, misalignment, and shading from uneven illumination. The existing hand-crafted feature-based methods have exhibited varied performance depending on how these issues were handled by pre-processing. To overcome this shortcoming of hand-crafted feature-based methods, convolutional neural network (CNN)-based recognition methods have been researched. The existing systems based on a CNN use two methods: using a difference image as the input to the network and calculating the distance between feature vectors extracted from the CNN. Difference images can be susceptible to noise as they are generated by differences in pixel values. Also, the method for calculating the distance between feature vectors cannot employ all layers of the trained network and has less accuracy than the method employing difference images. To address these issues, this paper examined a method less susceptible to noise and which uses the entire network; a composite image of two finger-vein images was used as the input to a deep, densely-connected convolutional network (DenseNet). Two open databases, namely Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein database and The Hong Kong Polytechnic University finger image database (version 1), were used for experiments and the results show that the proposed method has greater performance than the existing methods.
Logan: A Distributed Online Log Parser

Logs serve as a critical tool for debugging and monitoring applications. However, gaining insights from unstructured logs is difficult. Hence, many log management and analysis applications first parse logs into structured templates. In this paper, we train a data-driven log parser on our new Apache Spark dataset, the largest application log dataset yet. We implement a distributed online algorithm to accommodate for the large volume of data. We also devise a new metric for evaluation of parsers when labeled data is unavailable. We show that our method generalizes over diverse datasets without any parameter tuning or domain-specific inputs from the user. When evaluated on publicly available HDFS dataset our method performs 13x faster than the previous state-of-the-art.
Determination of Phase Flux-Linkage of Flux Switching Motor with Spatial Magnetic System

This work considers the designs of flux switching motors used in a gearless traction drive of rail transport. Their feature is spatial magnetic system. It is shown that the basis for mathematical models of such electric motors can be relied on the information about the flux-linkage of the phase. A method for the determination of the phase flux-linkage of switching motors was proposed.
AppUsage2Vec: Modeling Smartphone App Usage for Prediction

App usage prediction, i.e. which apps will be used next, is very useful for smartphone system optimization, such as operating system resource management, battery energy consumption optimization, and user experience improvement as well. However, it is still challenging to achieve usage prediction of high accuracy. In this paper, we propose a novel framework for app usage prediction, called AppUsage2Vec, inspired by Doc2Vec. It models app usage records by considering the contribution of different apps, user personalized characteristics, and temporal context. We measure the contribution of each app to the target app by introducing an app-attention mechanism. The user personalized characteristics in app usage are learned by a module of dual-DNN. Furthermore, we encode the top-k supervised information in loss function for training the model to predict the app most likely to be used next. The AppUsage2Vec was evaluated on a dataset of 10,360 users and 46,434,380 records in three months. The results demonstrate the state-of-the-art performance.
An Anonymous Routing Scheme for Preserving Location Privacy in Wireless Sensor Networks

Wireless sensor networks consist of various sensors that are deployed to monitor the physical world. And many existing security schemes use traditional cryptography theory to protect message content and contextual information. However, we are concerned about location security of nodes. In this paper, we propose an anonymous routing strategy for preserving location privacy (ARPLP), which sets a proxy source node to hide the location of real source node. And the real source node randomly selects several neighbors as receivers until the packets are transmitted to the proxy source. And the proxy source is randomly selected so that the adversary finds it difficult to obtain the location information of the real source node. Meanwhile, our scheme sets a branch area around the sink, which can disturb the adversary by increasing the routing branch. According to the analysis and simulation experiments, our scheme can reduce traffic consumption and communication delay, and improve the security of source node and base station.
Development of smart networks for navigation in dynamic underwater environments

This work describes the development of a network of Autonomous Underwater Vehicles (AUVs) for ocean monitoring and surveillance in dynamic environments. The key component of the system is the ecoSUB vehicle, a small and low cost AUV that acts as a mobile node of the network. Although limited when compared to more traditional assets such as ships or larger and more expensive AUVs, once networked these vehicles can obtain a gain that goes beyond the capabilities of each individual platform, representing a step change in the accessibility to autonomous system use in science and commercial applications. This paper reports results towards the implementation of such a network, describing the communication and localisation infrastructure that it has been built to support it. Experimental results from an initial trail undertaken in Vobster Quay, UK in July 2018 are reported showing the gain in localisation performance that the ecoSUBs can achieve.
Frequency- and Bandwidth-Tunable Absorptive Bandpass Filter

In this paper, an analytic design method for a frequency- and bandwidth-tunable absorptive bandpass filter is introduced. The presented design method relies on the transformation from the normalized absorptive low-pass filter prototype to a bandpass filter structure with parallel resonators and admittance inverters. The inverter values for producing a predefined frequency response are given in closed-form expressions so that a filter design needs no heuristic approach and can be carried out in an analytic manner. This work also presents a new tunable stub resonator structure capable of changing not only its resonant frequency but also the slope parameter. As the slope parameter can be adjusted, it is allowed to use static inverters in designing a filter whose bandwidth can be tuned. For demonstrating our design method for a reconfigurable absorptive bandpass filter, a third-order filter having the target to have the frequency tuning range from 1.35 to 1.65 GHz and the bandwidth tuning range from 5% to 10% has been designed and measured.
Why Does this App Need this Data? Automatic Tightening of Resource Access

On mobile operating systems, apps may access resources that are not be needed for their primary functionality. Which are the resources an app actually needs for its core functionality? And what happens if we deny access to other resources? Using a test generator for user interaction, we systematically explore app behavior under varied resource constraints and determine the impact of access restrictions, yielding a minimal set of required privileges for each app and functionality. In our proof of concept on Android apps, our TIARA prototype could block up to 69% of resource accesses while retaining all previously explored functionality.
Interpolated Compressed Sensing for Calibrationless Parallel MRI Reconstruction

Parallel magnetic resonance imaging (pMRI) in clinical study are commonly acquired in multiple slices; parallely along different channels. Since, MRI traditionally suffers from slow data acquisition, reconstruction of images in clinical pMRI would be further slower. Compressed sensing MRI (CS-MRI) has successfully demonstrated its potential in reducing the scan time of pMRI by manifolds. Due to high correlation of adjacent slices in multislice sequence, interpolation of multi-slice data may be carried out to support non-uniform undersampling based CS reconstruction of slices in k-space. Exploiting intra/inter slice as well as multichannel data redundancy of multi-slice pMRI, it is possible to accelerate the scan time further. These correlations can be well modeled by introducing multidimensional wavelet forest sparsity and joint total variation regularization during the CS reconstruction. To validate our claim, a number of experiments are carried out with real pMRI datasets and results are compared with the state-of-the-art.
A New Evolutionary Multiobjective Model for Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most classical NP-hard problems in the combinatorial optimization, as many practical problems, such as scheduling problems and vehicle-routing cost allocation problems can be abstracted. The introduction of multiobjective in the TSP is a very important research topic, which brings serious challenges to the TSP. Currently, genetic algorithms (GAs) are one of the most effective methods to solve the multiobjective traveling salesman problem (MOTSP). However, GA-based algorithms suffer the premature convergence, the insufficient diversity, and nonuniform distribution of solutions when solving the MOTSP, which further restrict the wide application of GA-based algorithms. In order to overcome these problems, this paper proposes an improved method for GAs based on a novel evolutionary computational model, named the Physarum-inspired computational model (PCM). Based on the prior knowledge of the PCM, the initialization of the population in the proposed method is first optimized to enhance the distribution of solutions. Then, the hill climbing (HC) method is used to improve the diversity of individuals and avert running into the local optimum. Compared to the other MOTSP solving algorithms, a series of experimental results demonstrate that our proposed method achieves a better performance.
The New Methodology to Control the Speed of e – Vehicles by using Brushless DC Motor

The e – Vehicle are the future of the automobile industry. The e - Vehicle are immediate power that is delivered seamlessly, silently and efficiently. In this paper we practically implemented the speed control of e – Vehicle by using combination of four switch three phase inverter connected to the ATMEGA328P microcontroller. When accelerating, energy optimization during the drive, the electric motor engine work in allocate to optimize power for maximum efficiency.The e – Vehicles speed control method in this method using BLDC motor to controlled the speed by using FSTPI and motor have controller which control and connected to microcontroller for speed control and also there is constant torque operation and smoothly controlling speed. In this paper practical reading, result and the output waveform as given in this This methodology can help to saving of time and allow to operate switching action in Nano seconds and easy gear changing system which reduces the weight of the e – vehicles and allow to complete atomized system.
Miniaturized Underwater Gliders as Payload Transfer Units

Due to their high energetic efficiency and therefore ability to cover long distances, underwater gliders have become a common tool for ocean sampling missions. In this work, the application scenario involves multiple underwater gliders which function as payload transfer units to build up an underwater navigation system. Since the gliders are intended to be transported inside of a tubular shuttle system, extensive miniaturization of the vehicles is necessary. Furthermore a low cost design is preferred for inexpensive reproduction. To meet these particular requirements the Micro Glider V2 was developed as a prototype.
Designing Fulfilling Test Cases with Test Aspect Model

UML Testing Profile 2.0 (UTP 2) has improved the possibilities of various modeling related to test architecture compared to past UTP. On the other hand, UTP 2 does not mention much about the derivation of test requirements, which is one of important activities in testing. The test aspect helps test engineers to derive test requirements. In this paper, we show concrete examples that test aspects extract test requirements and test cases not specified in the development model. Then, we show that test engineers can expand test requirements by visualizing the test aspect and can design test case without oversight.
Smart Water Management in Agricultural Land Using IoT

The proposed system is focused on water management in agricultural land. Water is the primary and predominant supplementary for agricultural land and over 69% of the world's fresh water are committed to agricultural purposes. Therefore, it’s high time to Conserve and utilize water resources efficiently with the assistance of ingenious technology like IoT and Automation. In this regard, we have come up with a solution of conserving water by IoT based Water recycling and Irrigation system as "Smart Water management in Agricultural land".
PerRD: A System for Personalized Route Description

Nowadays, mobile devices are already seen everywhere in life, which makes the application of vehicle navigation more and more widely. The traditional turn-by-turn navigation does the path planning just based on the characteristics of the roads themselves, and then gives mechanized steering instructions at each corner. For those roads people are familiar with in this route, path descriptions which provide detailed route description information, will become redundant and verbose. In this paper, we study a Personalized Route Description system dubbed PerRD - with which the goal is to generate more customized and intuitive route descriptions based on user generated content. The goal is to optimize a given route description with paths which users know well, which makes the route more consistent with users’ driving habits, and to create a concise and meaningful route descriptions with POIs and street names.
Location Inference for Non-Geotagged Tweets in User Timelines [Extended Abstract]

This study explores the problem of inferring locations for individual tweets. We scrutinize Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. Extensive experiments on a large set of real Twitter data suggest that our models are effective at inferring locations for non-geotagged tweets and outperform the state-of-the-art approaches significantly in terms of inference accuracy.
Scene-Aware Deep Networks for Semantic Segmentation of Images

Scene classification and semantic segmentation are two important research directions in computer vision. They are widely used in the research of automatic driving and human–computer interaction. The purpose of the scene classification is to use the image classification to determine the category of the scene in an image by analyzing the background and the target object, while semantic segmentation aims to classify the image at the pixel level and mark the position and semantic information of the scene unit. In this paper, we aimed to train the semantic segmentation neural network in different scenarios to obtain the models with the same number of scene categories, which they are used to process the images. During the process of the actual test, the semantic segmentation dataset was firstly divided into three categories based on the scene classification algorithm. Then the semantic segmentation neural network is trained under three scenarios, and three semantic segmentation network models are obtained accordingly. To test the property of our methods, the semantic segmentation models we got were selected to treat other pictures, and the results obtained from the performance of scene-aware semantic segmentation were much better than semantic segmentation without considering categories. Our study provided an essential improvement of semantic segmentation by adding category information into consideration, which will be helpful to obtain more precise models for further picture analysis.
The Predictive Accuracy of Credit Ratings: Measurement and Statistical Inference

Credit ratings are ordinal predictions for the default risk of an obligor. To evaluate the accuracy of such predictions commonly used measures are the Accuracy Ratio or, equivalently, the Area under the ROC curve. The disadvantage of these measures is that they treat default as a binary variable thereby neglecting the timing of the default events and also not using the full information from censored observations. We present an alternative measure that is related to the Accuracy Ratio but does not suffer from these drawbacks. As a second contribution, we study statistical inference for the Accuracy Ratio and the proposed measure in the case of multiple cohorts of obligors with overlapping lifetimes. We derive methods that use more sample information and lead to more powerful tests than alternatives that filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S\&P Long Term Credit Ratings.
Kinetic and Theoretical Investigation of the Gas-Phase Ozonolysis of Isoprene: Carbonyl Oxides as an Important Source for OH Radicals in the Atmosphere

Kinetic measurements as well as B3LYP/ and MP2/6-31G(d,p) calculations provide evidence that carbonyl oxides formed in the gas-phase ozonolysis of alkylated alkenes are an important source of OH radicals. In the gas-phase ozonolysis of propene, cis-2-butene, trans-2-butene, tetramethylethene, and isoprene, 18, 17, 24, 36, and 19% OH radicals (relative to reacted ozone, error margin ≤4%) are measured using CO as a scavenger for OH. The quantum chemical calculations show that OH radical production depends on syn positioned methyl (alkyl) groups and their interaction with the terminal O atom of a carbonyl oxide. For example, in the gas-phase ozonolysis of ethene only 5% OH radicals are measured while for a carbonyl oxide with syn-positioned methyl (alkyl) group, a much larger amount of OH radicals is formed. This is due to the fact that 1,4 H migration and the formation of an intermediate hydroperoxy alkene, that is prone to undergo OO bond cleavage, is energetically more favorable than isomerization to diox.
Glucose‐6‐Phosphate Dehydrogenase Deficiency in Female Heterozygoses and the X‐Inactivation Hypothesis

The levels of G‐6‐PD activity in 75 female heterozygotes detected by pedigree analysis are reported, together with biochemical and genetic data of a pedigree in which a wide variation in G‐6‐PD activity was revealed in heterozygotes. The results strongly favour the hypothesis that one X‐chromosome is inactivated during early embryonic development in the human female, at least as far as the locus of G‐6‐PD is concerned.
The Swiss Catalogue of Learning Objectives

PBL philosophy may challenge the need for explicit and specific educational objectives in medical education. From a practical point of view, however, such objectives are essential to achieve a close overlap between learning, teaching and assessment. Since the 1970s medical licensing in Switzerland has been based, among other things, on passing a uniform, centrally prepared MCQ exam for the graduates of all five Swiss medical schools. The need for a set of jointly developed learning, teaching and assessment objectives has become apparent. The Joint Conference of Swiss Medical Schools has therefore charged a small taskforce with the development of such a catalogue. This paper describes the background, process and results of this work.