nils-herrmann/clean_scirepeval huggingface dataset:
Mobility management for IoT: a survey

Internet of Thing (IoT) or also referred to as IP-enabled wireless sensor network (IP-WSN) has become a rich area of research. This is due to the rapid growth in a wide spectrum of critical application domains. However, the properties within these systems such as memory size, processing capacity, and power supply have led to imposing constraints on IP-WSN applications and its deployment in the real world. Consequently, IP-WSN is constantly faced with issues as the complexity further rises due to IP mobility. IP mobility management is utilized as a mechanism to resolve these issues. The management protocols introduced to support mobility has evolved from host-based to network-based mobility management protocols. The presence of both types of solutions is dominant but depended on the nature of systems being deployed. The mobile node (MN) is involved with the mobility-related signaling in host-based protocols, while network-based protocols shield the host by transferring the mobility-related signaling to the network entities. The features of the IoT are inclined towards the network-based solutions. The wide spectrum of strategies derived to achieve enhanced performance evidently displays superiority in performance and simultaneous issues such as long handover latency, intense signaling, and packet loss which affects the QoS for the real-time applications. This paper extensively reviews and discusses the algorithms developed to address the challenges and the techniques of integrating IP over WSNs, the attributes of mobility management within the IPv4 and IPv6, respectively, and special focus is given on a comprehensive review encompassing mechanisms, advantages, and disadvantages on related work within the IPv6 mobility management. The paper is concluded with the proposition of several pertinent open issues which are of high research value.
Transaction Repair for Multi-Version Concurrency Control

The optimistic variants of Multi-Version Concurrency Control (MVCC) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even more conflicts.
 In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, when encountering conflicts among transactions, our mechanism quickly detects the conflict locations in the program and partially re-executes the conflicting transactions. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput.
Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user's search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.
Terabit/sec-class board-level optical interconnects through polymer waveguides using 24-channel bidirectional transceiver modules

We report here on the design, fabrication and characterization of an integrated optical data bus designed for terabit/sec-class module-to-module on-board data transfer using integrated optical transceivers. The parallel optical transceiver is based on a through-silicon-via (TSV) silicon carrier as the platform for integration of 24-channel VCSEL and photodiode arrays with CMOS ICs. The Si carrier also includes optical vias (holes) for optical access to conventional surface-emitting 850-nm optoelectronic (OE) devices. The 48-channel transceiver is flip-chip soldered to an organic carrier forming the transceiver Optomodule. The optical printed circuit board (o-PCB) is a typical FR4 board with a polymer waveguide layer added on top. A 48-channel flex-waveguide is fabricated separately and attached to the FR4 board. Turning mirrors are fabricated into the waveguides and a lens array is attached to facilitate optical coupling. An assembly procedure has been developed to surface mount the Optomodule to the o-PCB using a ball grid array (BGA) process which provides both electrical and optical interconnections. Efficient optical coupling is achieved using a dual-lens optical system, with one lens array incorporated into the Optomodule and a second on the o-PCB. Fully functional Optomodules with 24 transmitter + 24 receiver channels were characterized with transmitters operating up to 20 Gb/s and receivers up to 15 Gb/s. Finally, two Optomodules were assembled onto an o-PCB and a full optical link demonstrated, achieving > 20 bidirectional links at 10 Gb/s. At 15 Gb/s, error-free operation was demonstrated for 15 channels in each direction, realizing a record o-PCB link with a 225 Gb/s bidirectional aggregate data rate.
A statistical model-based voice activity detection

In this letter, we develop a robust voice activity detector (VAD) for the application to variable-rate speech coding. The developed VAD employs the decision-directed parameter estimation method for the likelihood ratio test. In addition, we propose an effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences. According to our simulation results, the proposed VAD shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.
Read Empiricism And The Philosophy Of Mind Empiricism And The Philosophy Of Mind empiricism and the philosophy of mind

empiricism and the philosophy of mind. Book lovers, when you need a new book to read, find the book here. Never worry not to find what you need. Is the empiricism and the philosophy of mind your needed book now? That's true; you are really a good reader. This is a perfect book that comes from great author to share with you. The book offers the best experience and lesson to take, not only take, but also learn.
Summing up evidence: one answer is not always enough

Are meta-analyses the brave new world, or are the critics of such combined analyses right to say that the biases inherent in clinical trials make them uncombinable? Negative trials are often unreported, and hence can be missed by meta-analysts. And how much heterogeneity between trials is acceptable? A recent major criticism is that large randomised trials do not always agree with a prior meta-analysis. Neither individual trials nor meta-analyses, reporting as they do on population effects, tell how to treat the individual patient. Here we take a more rounded approach to meta-analyses, arguing that their strengths outweigh their weaknesses, although the latter must not be brushed aside.
Phoneme visem mapping for Marathi language using linguistic approach

Visual speech is transcribed using visems. Visem is a set of visibly different phonemes in a language. There is high correlation between Phoneme and visem and phoneme to visem mapping is many to one because of the fact that many phonemes cannot be distinguished visually. Since each language has its own phonetic set, visem classification becomes language dependant process. Most of the phoneme visem maps available in literature are developed for English language. This paper presents phoneme visem mapping of consonants and vowels of Marathi language based on linguistic approach. This is the first attempt towards Marathi phoneme visem mapping and will be useful for visual speech recognition in Marathi language.
Power System Transient Stability Assessment Based on Big Data and the Core Vector Machine

In this paper, an online power system transient stability assessment (TSA) problem is mapped as a two-class classification problem and a novel data mining algorithm the core vector machine (CVM) is proposed to solve the problem based on phasor measurement units (PMUs) big data. First of all, an offline training, online application framework is proposed, which contained four sub-steps, namely features selection, offline training, online application, and assessment evaluation. First, 24 features are selected to present the system status. Then in the offline training procedure, the PMU big data is generated by time domain simulation, and a CVM model is trained and tested. In the online application procedure, an interface between PMU data center and feature calculation program is set up to collect real time specific PMU big data and the CVM trained is applied to the TSA problem. Last but not least, the evaluation indices are calculated. Compared with other support vector machines, the proposed CVM based assessment algorithm has the higher precision, meanwhile, it has the least time consumption and space complexity. As long as online PMU big data are received, TSA can be done simultaneously. Case studies on the IEEE New England 39-bus system, and real systems in China and the U.S., exhibit the speed and effectiveness of the proposed algorithm.
"Preventative" vs. "Reactive": How Parental Mediation Influences Teens' Social Media Privacy Behaviors

Through an empirical, secondary analysis of 588 teens (ages 12 - 17) and one of their parents living in the USA, we present useful insights into how parental privacy concerns for their teens and different parental mediation strategies (direct intervention versus active mediation) influence teen privacy concerns and privacy risk-taking and risk-coping privacy behaviors in social media. Our results suggest that the use of direct intervention by itself may have a suppressive effect on teens, reducing their exposure to online risks but also their ability to engage with others online and to learn how to effectively cope with online risks. Therefore, it may be beneficial for parents to combine active mediation with direct intervention so that they can protect their teens from severe online risks while empowering teens to engage with others online and learn to make good online privacy choices.
Phishing detection based Associative Classification data mining

Website phishing is considered one of the crucial security challenges for the online community due to the massive numbers of online transactions performed on a daily basis. Website phishing can be described as mimicking a trusted website to obtain sensitive information from online users such as usernames and passwords. Black lists, white lists and the utilisation of search methods are examples of solutions to minimise the risk of this problem. One intelligent approach based on data mining called Associative Classification (AC) seems a potential solution that may effectively detect phishing websites with high accuracy. According to experimental studies, AC often extracts classifiers containing simple ‘‘If-Then’’ rules with a high degree of predictive accuracy. In this paper, we investigate the problem of website phishing using a developed AC method called Multi-label Classifier based Associative Classification (MCAC) to seek its applicability to the phishing problem. We also want to identify features that distinguish phishing websites from legitimate ones. In addition, we survey intelligent approaches used to handle the phishing problem. Experimental results using real data collected from different sources show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms. Further, MCAC generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance.
A Zero-Crossing-Based 8-bit 200 MS/s Pipelined ADC

Zero-crossing-based circuits (ZCBC) are introduced as a generalization of comparator-based switched-capacitor circuits (CBSC). To demonstrate this concept, an 8-bit, 200 MS/s, pipelined ADC is implemented in a 0.18 CMOS technology. A dynamic zero-crossing detector and current source replace the functionality of an opamp to realize a precision charge transfer. Furthermore, current source splitting improves linearity at high speeds and bit decision flip-flops replace traditional bit decision comparators for increased speed. The complete ADC draws no static current and consumes 8.5 mW of power. The corresponding FOM is 0.38 pJ/step at 100 MS/s and 0.51 pJ/step at 200 MS/s.
ICP-based pose-graph SLAM

Odometry-like localization solutions can be built upon Light Detection And Ranging (LIDAR) sensors, by sequentially registering the point clouds acquired along a robot trajectory. Yet such solutions inherently drift over time: we propose an approach that adds a graphical model layer on top of such LIDAR odometry layer, using the Iterative Closest Points (ICP) algorithm for registration. Reference frames called keyframes are defined along the robot trajectory, and ICP results are used to build a pose graph, that in turn is used to solve an optimization problem that provides updates for the keyframes upon loop closing, enabling the correction of the path of the robot and of the map of the environment. We present in details the configuration used to register data from the Velodyne High Definition LIDAR (HDL), and a strategy to build local maps upon which current frames are registered, either when discovering new areas or revisiting previously mapped areas. Experiments show that it is possible to build the graph using data from ICP and that the loop closings in the graph level reduces the overall drift of the system.
CloudSeer: Workflow Monitoring of Cloud Infrastructures via Interleaved Logs

Cloud infrastructures provide a rich set of management tasks that operate computing, storage, and networking resources in the cloud. Monitoring the executions of these tasks is crucial for cloud providers to promptly find and understand problems that compromise cloud availability. However, such monitoring is challenging because there are multiple distributed service components involved in the executions. CloudSeer enables effective workflow monitoring. It takes a lightweight non-intrusive approach that purely works on interleaved logs widely existing in cloud infrastructures. CloudSeer first builds an automaton for the workflow of each management task based on normal executions, and then it checks log messages against a set of automata for workflow divergences in a streaming manner. Divergences found during the checking process indicate potential execution problems, which may or may not be accompanied by error log messages. For each potential problem, CloudSeer outputs necessary context information including the affected task automaton and related log messages hinting where the problem occurs to help further diagnosis. Our experiments on OpenStack, a popular open-source cloud infrastructure, show that CloudSeer's efficiency and problem-detection capability are suitable for online monitoring.
S-CNN: Subcategory-Aware Convolutional Networks for Object Detection

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.
Dual-band feed systems for SATCOM antenna applications

Dual and multi-band feed systems that enable reflector antennas to operate over multiple frequency bands have been used for some time. With newer generations of satellites, there is an increased demand for these types of feeds in satellite communications. At the same time, the requirements for performance such as bandwidth and band separation are becoming increasingly challenging, leading to the development of new techniques to achieve multi-band operation. This paper gives an overview of dual and multi-band feed development at BAE Systems Australia, and shows examples of some of the different techniques used to achieve the required performance.
Unscented Kalman Filtering on Riemannian Manifolds

In recent years there has been a growing interest in problems, where either the observed data or hidden state variables are confined to a known Riemannian manifold. In sequential data analysis this interest has also been growing, but rather crude algorithms have been applied: either Monte Carlo filters or brute-force discretisations. These approaches scale poorly and clearly show a missing gap: no generic analogues to Kalman filters are currently available in non-Euclidean domains. In this paper, we remedy this issue by first generalising the unscented transform and then the unscented Kalman filter to Riemannian manifolds. As the Kalman filter can be viewed as an optimisation algorithm akin to the Gauss-Newton method, our algorithm also provides a general-purpose optimisation framework on manifolds. We illustrate the suggested method on synthetic data to study robustness and convergence, on a region tracking problem using covariance features, an articulated tracking problem, a mean value optimisation and a pose optimisation problem.
A Monolithic CMOS-MEMS 3-Axis Accelerometer With a Low-Noise, Low-Power Dual-Chopper Amplifier

This paper reports a monolithically integrated CMOS-MEMS three-axis capacitive accelerometer with a single proof mass. An improved DRIE post-CMOS MEMS process has been developed, which provides robust single-crystal silicon (SCS) structures in all three axes and greatly reduces undercut of comb fingers. The sensing electrodes are also composed of the thick SCS layer, resulting in high resolution and large sensing capacitance. Due to the high wiring flexibility provided by the fabrication process, fully differential capacitive sensing and common-centroid configurations are realized in all three axes. A low-noise, low- power dual-chopper amplifier is designed for each axis, which consumes only 1 mW power. With 44.5 dB on-chip amplification, the measured sensitivities of x-, y-, and z-axis accelerometers are 520 mV/g, 460 mV/g, and 320 mV/g, respectively, which can be tuned by simply changing the amplitude of the modulation signal. Accordingly, the overall noise floors of the x-, y-, and z-axis are 12 mug/radicHz , 14 mug/radicHz, and 110 mug/radicHz, respectively, when tested at around 200 Hz.
Finger Vein Authentication Technology and Its Future

In this paper, finger vein authentication, a new biometric method utilizing the vein patterns inside one's fingers for personal identification, is introduced. Vein patterns are different for each finger and for each person, and as they are hidden underneath the skin's surface, forgery is extremely difficult. These unique aspects of finger vein pattern recognition set it apart from previous forms of biometrics and have led to its adoption by the major Japanese financial institutions as their newest security technology. This paper discusses the technology and applications of finger vein authentication, as well as the importance of semiconductor devices in its future development
DASNet: Reducing Pixel-level Annotations for Instance and Semantic Segmentation

Pixel-level annotation demands expensive human efforts and limits the performance of deep networks that usually benefits from more such training data. In this work we aim to achieve high quality instance and semantic segmentation results over a small set of pixel-level mask annotations and a large set of box annotations. The basic idea is exploring detection models to simplify the pixel-level supervised learning task and thus reduce the required amount of mask annotations. Our architecture, named DASNet, consists of three modules: detection, attention, and segmentation. The detection module detects all classes of objects, the attention module generates multi-scale class-specific features, and the segmentation module recovers the binary masks. Our method demonstrates substantially improved performance compared to existing semi-supervised approaches on PASCAL VOC 2012 dataset.
Guidelines for conducting and reporting case study research in software engineering

Case study is a suitable research methodology for software engineering research since it studies contemporary phenomena in its natural context. However, the understanding of what constitutes a case study varies, and hence the quality of the resulting studies. This paper aims at providing an introduction to case study methodology and guidelines for researchers conducting case studies and readers studying reports of such studies. The content is based on the authors’ own experience from conducting and reading case studies. The terminology and guidelines are compiled from different methodology handbooks in other research domains, in particular social science and information systems, and adapted to the needs in software engineering. We present recommended practices for software engineering case studies as well as empirically derived and evaluated checklists for researchers and readers of case study research.
Periodically Controlled Hybrid Systems Verifying A Controller for An Autonomous Vehicle

This paper introduces Periodically Controlled Hybrid Automata (PCHA) for describing a class of hybrid control systems. In a PCHA, control actions occur roughly periodically while internal and input actions, may occur in the interim changing the discrete-state or the setpoint. Based on periodicity and subtangential conditions, a new sufficient condition for verifying invariance of PCHAs is presented. This technique is used in verifying safety of the planner-controller subsystem of an autonomous ground vehicle, and in deriving geometric properties of planner generated paths that can be followed safely by the controller under environmental uncertainties.