Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitations and challenges of multigigabit DRAM chip design

This paper describes the limitations and challenges involved in designing gigabit DRAM chips in terms of high-density devices, high-performance circuits, and low-power/low-voltage circuits. The key results obtained are as follows. 1) For formation of a MOSFET shallow junction, which suppresses threshold voltage (V/sub T/) variation and offset voltage of sense amplifiers, reduction in ion-implantation energy and process temperature is essential. Also, the keys in terms of area, speed, stable cell operation, and ease of fabrication are use of low-resistivity multilevel metal wiring and high permittivity materials and three-dimensional memory cells to reduce a difference in height between the memory cell array and the surrounding peripheral circuits. 2) For creation of a high speed, the keys are memory-subsystem technology such as pipeline operation, wide-bit I/O, low-voltage interfaces, and high-density packaging. Embedded DRAM further enhances the speed and throughput by using massively parallel processing of signals on a large number of data-lines and reducing internal bus capacitances. 3) For power reduction, the key continues to be reduction of the data-line dissipating charge through both partial activation of multidivided data-lines and lowering of the data-line voltage. Ultralow-voltage operation, essential to drastic power reduction, can be achieved by subthreshold-current reduction circuits such as source-gate backbiasing, multi-V/sub T/, dynamic V/sub T/, and node-boosting schemes.
Bayesian classification by data augmentation

A typical remote sensing data clustering is the maximum likelihood supervised procedure. It consists of the estimation of a suitable mixture of distributions, based on training samples only, and in the subsequent pixel-by-pixel classification, performed by maximizing the likelihood ratio. In this way all the information on the parameters of the distributions, contained in the unsurveyed samples, is lost. In the paper it is proposed to apply a suitable Bayesian method, known as a data augmentation algorithm, to fully exploit the information contained in the data. The method is presented in detail and applied to an elementary simulated example proving its capability of achieving almost the theoretical limit for the classification error. Comparisons with current classification methods as well as an application to a real dataset are reported.
Protecting Browser Extensions from JavaScript Injection Attacks with Runtime Protection and Static Analysis

With the rapid proliferation of the internet, web browsers have evolved from singlepurpose remote document viewers into multifaceted systems for executing dynamic, interactive web applications. In order to enhance the web browsing experience of users and to facilitate on-demand customizability, most web browsers now can be fitted with extensions : pieces of software that utilize the underlying web platform of a browser and provide a wide range of features such as advertisement blocking, safety ratings of websites, in-browser web development, and many more. Extensible web browsers provide access to their powerful privileged components in order to facilitate the development of feature-rich extensions. This exposure comes at a price, though, as a vulnerable extension can introduce a security hole through which an attacker can access the privileged components and penetrate a victim user’s browser, steal the user’s sensitive information, and even execute arbitrary code in the user’s computer. The current browser security model is inadequate for preventing attacks via such vulnerable extensions. Therefore, an effective protection mechanism is required that would provide web browsers adequate security while still allowing them to be extended. In this thesis, we propose a runtime protection mechanism for JavaScript-based browser extensions. Our protection mechanism performs offline randomization of an i extension’s source code and augments the corresponding browser with appropriate modifications. The protection from malicious injection attacks is enforced at runtime by distinguishing attack code from the randomized extension code. Furthermore, for maximum backward compatibility with existing extensions, we propose a complementary static points-to analysis technique that can be invoked on-demand for assessing the security of dynamic code generation functions present in the source code of extensions. Our combined approach of runtime protection and static analysis is independent of the existing extension platforms, thus obviating the need of radically changing the platforms and requiring developers to rewrite their extensions. We implement our protection mechanism in the popular Mozilla Firefox browser and evaluate our approach on a set of vulnerable and non-vulnerable Mozilla Firefox extensions. The evaluation results indicate that our approach can be a viable solution for preventing attacks on JavaScript-based browser extensions while incurring negligible performance overhead and maintaining backward compatibility with existing extensions.
Decentralized Gain Adaptation for Optimal Pinning Controllability of Complex Networks

Coordinating ensembles of dynamical systems in a decentralized manner is of central importance when controlling complex networks. Pinning control is a much used technique where only a small fraction of the network nodes is directly controlled, with control gains being typically selected uniformly across the control layer. In this letter, we tackle the problem of optimally selecting the control gains of a pinning control strategy. Indeed, in networks of nonlinear dynamical systems fulfilling the so-called QUAD assumption, pinning controllability improves as the smallest eigenvalue $\lambda _{1}$ of an extended Laplacian matrix increases. Based on this observation, we pose a constrained optimization problem on the network. Rather than solving it in a centralized fashion, we propose a fully decentralized multilayer approach. Specifically, one layer is used to evaluate the sensitivity of $\lambda _{1}$ to the variation of the gains, while the second layer uses such an estimate to dynamically tune the control gains. The effectiveness of the approach is demonstrated via a representative example.
Spreading properties of time hopping codes in ultra wideband systems

We analyse the power spectral density of time hopping (TH) coded impulse trains in ultra-wideband (UWB) systems, where the codes are described by their period and probability distribution. Thereby a number of significant code parameters are derived, which provide appropriate spectral smoothing properties. Furthermore the power spectrum of UWB impulse trains applying three different coders for time hopping are compared.
Dis-Dyn Crawler:A Distributed Crawler for Dynamic Web Page

Nowadays, it has became a widespread approach for achieving rich information in modern web applications using AJAX , which cause two serious problems for web crawler. One is the incomplete information getting from web page due to the inability to parse dynamic web page. Another is the efficiency of the crawler. In order to solve the above problems, this paper proposes a distributed dynamic web crawler naming Dis-Dyn Crawler. This system uses HtmlUnit to page dynamic pages and choose Redis and ZMQ (Message Queue Zero) to realize the distribution feature, which improve the efficiency of the crawler. The experimental results show that Dis-Dyn Crawler has better performance than Nutch-a distributed crawler system, and the dynamic page parsing efficiency is also improved.
Multitask Representation Learning for Multimodal Estimation of Depression Level

In this article, we propose a novel multitask learning attention -based deep neural network model, which facilitates the fusion of various modalities. In particular, we use this network to both regress and classify the level of depression. Acoustic, textual, and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus -a Wizard of Oz. From the results, we empirically justify that a) multitask learning networks cotrained over regression and classification have better performance compared to single -task networks, and b) the fusion of all the modalities helps in giving the most accurate estimation of depression with respect to regression.
ABE : Un protocole de réservation de bande passante pour les réseaux ad hoc basés sur IEEE 802.11

Les recherches menees dans le domaine de la QoS pour les reseaux ad hoc ont connu un essor ces dernieres annees. La principale difficulte lors de la mise en place d'un protocole de QoS, reside dans l'estimation precise des ressources disponibles. Dans cet article, nous proposons une methode fiable permettant d'estimer la bande passante residuelle se basant sur une estimation probabiliste de la synchronisation des periodes de temps libre entre les mobiles emetteur et recepteur et une estimation de la probabilite de collision au niveau des liens radio. Par des simulations, nous comparons les performances de notre methode avec d'autres protocoles de QoS bases sur d'autres techniques d'evaluation de la bande passante residuelle.
Unsupervised learning models of invariant features in images: Recent developments in multistage architecture approach for object detection

Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta-heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition.
Multiphoton transitions in Josephson-junction qubits (Review Article)

Two basic physical models, a two-level system and a harmonic oscillator, are realized on the mesoscopic scale as coupled qubit and resonator. The realistic system includes moreover the electronics for controlling the distance between the qubit energy levels and their populations and to read out the resonator’s state, as well as the unavoidable dissipative environment. Such rich system is interesting both for the study of fundamental quantum phenomena on the mesoscopic scale and as a promising system for future electronic devices. We present recent results for the driven superconducting qubit–resonator system, where the resonator can be realized as an LC circuit or a nanomechanical resonator. Most of the results can be described by the semiclassical theory, where a qubit is treated as a quantum two-level system coupled to the classical driving field and the classical resonator. Application of this theory allows to describe many phenomena for the single and two coupled superconducting qubits, among which ar.
Practical Implications of Differences Betzveen the American and Canadian Norms for WISC-III and a Short Form for Children with Learning Disabilities

The WISC-III protocols of 66 Canadian children with learning disabilities were scored using both the American and the Canadian norms to make comparisons of the resulting profiles. The Canadian WISC-III scores were compared with both the Canadian study of WISC-III and the American WISC-III scores for these children. The Canadian WISC-III scores were found to be lower than the American WISC-III scores in Verbal IQ, Full Scale IQ, Verbal Comprehension, Perceptual Organization, and Freedom from Distractibility. Lower scores resulted in changes in Full Scale IQ classifications, but use of the confidence interval around the Full Scale IQ avoids misclassifications of children. Fewer children had a scatter of 10 points or more when scored using the Canadian in contrast to the American norms. The Dumont/Faro short form for WISC-III was validated using Canadian normed scores. These results will assist prartitioners and researchers in the interpretation of scores using the Canadian norms. Practitioners are urged to use the Canadian norms when assessing the intellectual capabilities of canadian children.
Protecting data privacy with decentralized self-emerging data release systems

In the age of Big Data, releasing private data at a future point in time is critical for various applications. Such self-emerging data release requires the data to be protected until a prescribed data release time and be automatically released to the target recipient at the release time. While straight-forward centralized approaches such as cloud storage services may provide a simple way to implement self-emerging data release, unfortunately, they are limited to a single point of trust and involves a single point of control. This dissertation proposes new decentralized designs of self-emerging data release systems using large-scale peer-to-peer (P2P) networks as the underlying infrastructure to eliminate a single point of trust or control. The first part of the dissertation presents the design of decentralized self-emerging data release systems using two different P2P network infrastructures, namely Distributed Hash Table (DHT) and blockchain. The second part of this dissertation proposes new mechanisms for supporting two key functionalities of self-emerging data release, namely (i) enabling the release of self-emerging data to blockchain-based smart contracts for facilitating a wide range of decentralized applications and (ii) supporting a cost-effective gradual release of self-emerging data in the decentralized infrastructure. We believe that the outcome of this dissertation would contribute to the development of decentralized security primitives and protocols in the context of timed release of private data.
STM/AFM nano-oxidation process to room-temperature-operated single-electron transistor and other devices

Application of a scanning tunneling microscopy (STM) and an atomic force microscopy (AFM) to electron devices and an optical device are introduced in this paper. Using STM tip/AFM cantilever as a cathode, surfaces of a metal or a semiconductor are oxidized to form a few tens of nanometers-wide oxidized metal line or an oxidized semiconductor line, which works as an energy barrier for an electron. A single-electron transistor (SET), a photoconductive switch, and a high-electron mobility transistor (HEMT) are fabricated using this fabrication process. The fabricated SET operates even at high room temperatures and shows the large Coulomb gap and staircase of 200-mV periods and the large Coulomb oscillation periods of 406 mV. The fabricated photoconductive switch shows a ultra-fast response time, i.e., a full-width at half-maximum response of 380 fs at a bias voltage of 10 V. The drain current of HEMT was controlled by the oxidized semiconductor wire on the channel region formed by this fabrication process.
Convex Multi-Objective Filter Optimization for Output Feedback L1-Adaptive Controller

This paper proposes a systematic analysis and synthesis method for optimal design of filter for L1 adaptive output feedback controllers. In the L1 adaptive feedback structure, the low-pass filter plays an important role in the stability and transient performance of the closed-loop system. The filter design is also the key to the trade-off between the performance and robustness of the closed-loop system. Although, the filter design for the L1 adaptive architecture has been studied in previous papers, the need for a numerically tractable synthesis method is yet to be fulfilled. The key challenge in establishing a satisfactory trade-off scheme between performance and robustness in L1-adaptive control architecture is the non-convex nature of the underlying optimization problem for filter design. We notice that the closed-loop L1 adaptive system converges to its corresponding linear reference system both in transient and steady-state in the presence of fast estimation rates. Taking advantage of this property, we discretize the closed-loop (linear) reference system of L1 adaptive controller and formulate the filter optimization in terms of robustness to time delay and performance both in transient and steady state. Illustrative examples are provided to validate the filter design methodology of this paper.
Extended Object Tracking and Stream Control Model Based on Predictive Evaluation Metric of Multiple-Angled Streams

Paper describes the design of extended model for tracking and locating object in multi-cameras environment with controlling the streams from multiple angles in real time. Proposed and experimentally implemented solution is attempting to deliver via streaming technology of the only specific shot from scene that has the best view on tracked object in real time. Measurement of the best shot for the tracking object is based on proposed evaluation metric, consisting of dimensional, locational and reliability parameters gathered from object tracking methods. For latency optimization and accuracy of evaluation the prediction of evaluation metric was outlined. Solution is experimentally tested in standard and mobile environment as well. Approach described in this paper is innovative regarding the content related video stream delivery.
Title Optimal sensor pairing for TDOA based source localization and tracking in sensor networks

Source localization based on time-difference-ofarrival (TDOA) measurements from spatially separated sensors is an important problem in sensor networks. While extensive research has been performed on algorithm development, limited attention has been paid to sensor geometry design. In this paper, we study the optimal sensor pair geometry for the TDOA based source localization problem. Analytic solutions to the optimal sensor pair geometries, for both static and movable source cases, are derived when there exist no communication constraints. Furthermore, in many applications, sensor platforms such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are movable, and their movements and the communications between sensors are constrained. The problem becomes how to optimize the trajectories for the moving platforms such that optimal source localization and tracking can be achieved. We extend our work to optimal sensor path planning and cast it as a constrained nonlinear optimization problem. The sequential quadratic programming (SQP) method is adopted for a solution. Computer simulations demonstrate good localization performance.
Providing Off-Campus Library Services by “Team”

Abstract In contrast to many large academic libraries, Kansas State University (K-State) does not have a distance librarian. As a result, the Library Services Project Team (Team) was formed to take a fresh look at current library services for distance learners. Although the Team has been in place for over two years, and has implemented many changes, there was no mechanism for receiving formal feedback from students and faculty about these services. Because it is important to know whether services are being used, two librarians on the Team developed a Web-based survey targeted at distance faculty and students. This paper discusses the assessment project including development of the survey, the university approval process, use of an electronic in-house survey system, administration of the survey, and compilation and analysis of results.
A Technology Platform for a Novel Home Care Delivery Service to Patients with Dementia

Chronic illnesses impose a great burden on the lives of citizens worldwide. In modern health-care, decentralisation, dehospitalisation and self management of diseases at home are crucial factors for improving the every-day life of the patients and the people close to them. People in general tend to dislike obtrusive monitoring on their daily activities, so the challenge for home care solutions is to implement systems that provide clinicians with adequate and concise information on their patients’ health status while at the same time be unobtrusive and easy to use. Moreover, such systems must ensure that they produce high impact warnings on the patient’s status only when it is needed, in order to relieve clinicians from unnecessary workload and become a real tool for decision making and efficient patient follow-up. ALADDIN’s objective is to develop a trustworthy and reliable system supporting patients with dementia and their informal carers in the management of the disease from home. Based on a set of monitoring parameters and measuring scales feeding a reconfigurable Event Detection mechanism used for Risk Assessment and Analysis, the system aims to early detect symptoms that predict decline, avoid emergencies and secondary effects and, ultimately, prolong the period that patients can remain safely cared at home. Informal carers are also closely monitored by the system whereas additional features supporting networking, education and cognitive stimulation are also integrated along with decision support and patient management tools for the treating clinicians. The platform has been built based on credible methodologies for efficient patient follow-up, risk detection and adaptive care. It is an open, secure, interoperable, integrated IT-solution designed according to Service Oriented Architecture principles. The benefits of this platform are expected to lie in the prevention of emergencies, in reduction of carer burden and in maintenance of the patient’s and carer’s quality of life.
Selecting frequency feature for license plate detection based on AdaBoost

In this paper, a new method for license plate detection based on AdaBoost is proposed. In the new method, character frequency feature, which is powerful feature for detecting license plate character, are introduced to feature pool. The frequency features obtained from the FFT of horizontal projection of binary image are selected by AdaBoost. Then, Haar-like features selected by AdaBoost are used to capture subtle structure of license plate. Furthermore, considering the characteristic of Chinese license plate that there are two types of license plate: deeper background-lighter character and lighter background-deeper character license plates, two detectors are designed to extract different license plates respectively. Experimental results show the efficiency of the proposed method.
Modeling of antennas for mobile communication

Recent expansion of applications of mobile communication presents new research opportunities and challenges in the design of new antennas and modeling their performance in realistic communication environments. The FDTD method provides an efficient tool for some aspects of design and for modeling of antenna performance in realistic situations. We model the performance of two types of representative antennas, namely: a dual-band strip-sleeve monopole on a laptop computer and a diversity patch antenna on a handset.