Miracle-dz/UniFAR-FTUs huggingface dataset:
Dyad : a system for using physically secure coprocessors

T h e D y a d pro jec t a t Carnegie Mellon Universi ty is us ing physical ly secure coprocessor t o achieve new protocols and sys tems address ing a n u m b e r of perplexing secur i ty p rob lems . These coprocessors can b e p roduced as boa rds or i n t eg ra t ed circui t chips a n d can be direct ly inser ted in s t a n d a r d works ta t ion or PC-s ty l e c o m p u t e r s . Th i s p a p e r presents a set of secur i ty p rob lems a n d easily imp lemen tab le solut ions t h a t exploit t h e power of physical ly secure coprocessors : (1) p ro tec t ing t h e in tegr i ty of publ ic ly accessible works ta t ions (2) t amper -p roo f a c c o u n t i n g / a u d i t t rai ls (3) copy p ro tec t ion (4) e lectronic currency wi thou t centra l ized servers We out l ine t h e a rch i tec tu ra l requ i rements for t h e use of secure coprocessors.
A framework for the design of usable electronic text

This thesis examines the human issues underlying the design and usability of electronic text systems. In so doing it develops a framework for the conceptualisation of these issues that aims to guide designers of electronic texts in their attempts to produce usable systems. The thesis commences with a review of the traditional human factors literature on electronic text according to three basic themes: its concern with perceptual, manipulatory and structural issues. From this examination it is concluded that shortcomings in translating this work into design result from the adoption of overly narrow uni-disciplinary views of reading taken from cognitive psychology and information science which are inappropriate to serve the needs of electronic text designers. In an attempt to provide a more relevant description of the reading process a series of studies examining readers and their views as well as uses of texts is reported. In the first, a repertory grid based investigation revealed that all texts can be described in reader-relvant terms according to three criteria: why a text is read, what a text contains and how it is read. These criteria then form the basis of two investigations of reader-text interaction using academic journals and user manuals. The results of these studies highlighted the need to consider readers' models of a document's structure in discussing text usability. Subsequent experimental work on readers' models of academic articles demonstrated not only that such models are important aspects of reader-text interaction but that data of this form could usefully be employed in the design of an electronic text system. The proposed framework provides a broad, qualitative model of the important issues for designers to consider when developing a product It consists of four interactive elements that focus attention on aspects of reading that have been identified as central to usability. Simple tests of the utility and validity of the framework are reported and it is shown that the framework both supports reasoned analysis and subsequent prediction of reader behaviour as well as providing a parsimonious account of their verbal utterances while reading. The thesis concludes with an analysis of the likely uses of such a framework and the potential for electronic text systems in an increasingly information-hungry world.
On the Capacity Region of the Random-Multiple Access Channel

The Random-Multiple Access Channel (R-MAC) is defined. A coding theorem that establishes an achievable rate region for reliable communication is presented. For the Binary Adder Channel (BAC), it is shown that the region given in the theorem is indeed the- capacity region. The asynchronous case is also discussed. Under mild asynchronism conditions the achievable rate region remains the same as for the synchronous case.
Test Quality for Use in Curricular and Instructional Decision Making in Reading. Reading Research Report No. 28.

Three expert panels reviewed the 1991 Maryland School Performance Assessment Program (MSPAP) reading test. This was the first year of an assessment program designed to measure school progress toward, among other content areas, three reading outcomes: reading for literary experience, reading to become informed, and reading to perform a task. The MSPAP, given throughout the state, is a nontraditional, criterion-referenced performance assessment, which in 1991 required 9 hours of testing time over an 8-day period. The three panels, one consisting (JE experts with an instructional perspective, one with a curricular perspective, and one a psychometric perspective, independently addressed a variety of test quality issues after review of the test materials and a presentation by a test developer. Results indicate that a test such as the 1991 MSPAP is adequate to assess school progress in reading but may be confounded with writing and may not adequately measure progress in basic reading skills. Findings suggest that a test such as the MSPAP is useful for making curricular and instructional decisions, but that use of the test for making decisions about individual students was not supported. (Contains 24 references.) (Author) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** Test Quality for Use in Curricular and Instructional Decision Making in Reading William D. Schafer John T. Guthrie University of Maryland College Park Janice F. Almasi State University of New York at Buffalo Peter P. Afflerbach University of Maryland College Park U S DEPARTMENT OF EDUCATION 011ce LeuCiiiOildi Aesen " and EDUCATIONAL. RESOURCES INFORMATION CENTER (ERIC) t! d This docurnnt has been reproduced as ecerved Iron the person or organ3:ra originating d 0 Minor changi. , nave been made to improve reproduction quality Points of view or opinions staled in this document do not necessarily represent official OERI epsilon or policy NRRC National Reading Research Center READING RESEARCH REPORT NO. 28
Automated Cell Phenotyping for Imaging Mass Cytometry

Imaging mass cytometry (IMC) is a new advancement in tissue imaging that is quickly gaining wider usage since its recent launch. It improves upon current tissue imaging methods by allowing for a significantly higher number of proteins to be imaged at once on a single tissue slide. For most analyses of IMC data, determining the phenotype of each cell is a crucial step. Current methods of phenotyping require sufficient biological knowledge regarding the protein expression profile of the various cell types. Here, we develop a deep convolutional autoencoder-classifier to automate the cell phenotyping process into four basic cell types. Biopsy tissue from bladder cancer patients is used to evaluate the efficacy of the classification. The model is evaluated and validated through feature importance, confirming that the significant features are biologically relevant. Our results demonstrate the potential of deep learning to automate the task of cell phenotyping for high-dimensional IMC data.
ARQUITECTURA DE ALTO RENDIMIENTO PARA PROCESADORES DIFUSOS

Fuzzy logic has become a very good choice to represent uncertain models of complex systems that cannot be easily represented in terms of conventional mathematics. Specifically fuzzy hardware has turned to be the choice to reach high speed inference rates, which has been presented on high performance applications such as water treatment, robotics, pattern recognition, among other efficient applications of fuzzy systems. There are two forms to represent membership values universe: first one is when floating point numbers are used, second form is when integer universe is used. In the first case result of operations belongs to [0, 1], in the second case results belong to interval of integers [0, 1 2 − m ], where m is the number of bits used according to application resolution demands. This case is fully compatible with digital computers because floating point operations consume much more time and resources than integer operations. This work presents a method to implement fuzzy logic inference engine for discrete implementations which includes a new method for defuzzification, with the objective of reducing the number of instructions to be executed, having as consequence fewer processing time and less resources consumed. Simulation results for new methods are considered. For tuning fuzzy systems there are various methods that require adjustment of membership functions, another methods for tuning fuzzy systems are based on parametric operations for conjunction and disjunction. The problem of construction of parametric classes of fuzzy conjunction and disjunction operations suitable for effective digital hardware implementation is studied. The new method of generation of fuzzy parametric conjunctions by means of other fuzzy conjunctions, disjunctions and generators is proposed. The approach is based on the use of simple parametric generators together with min-max, Lukasiewicz and drastic t-norms and t-conorms that have effective digital implementation. Several simple parametric classes of generators are proposed and examples of parametric conjunctions generated by means of these generators and basic t-norms and tnorms are considered. Circuits for basic construction blocks are implemented on VLSI hardware using FPGA programmable devices, simulation results are presented.
Design of a trainable fuzzy autopilot for ship maneuvering

A trainable fuzzy controller for ship maneuvering is demonstrated in this paper. A trainable fuzzy controller can model the control actions of an expert helmsman, that is, it can transform them into fuzzy IF-THEN rules. The training is achieved by a set of recorded data of an expert helmsman. The performance of the fuzzy controller can then be further improved by choosing scaling factors properly.
Endüstri 4.0 ve Girişimcilikte Yeni Yaklaşımlar

Dunyanin 18. yy da I. Sanayi Devrimi ile baslayan endustrilesme yolculugu bugun yeni bir doneme girmis gozukuyor. Su ve buhar enerjili mekanik uretim sistemlerinin ortaya cikmasiyla baslayan endustrilesme sureci, 20. yy baslangicinda elektrigin ve kitle uretim sistemlerinin kullanilmasiyla ikinci asamaya gecmis, 1970’lerden itibaren uretim otomasyonunu daha ust duzeye tasiyan elektronik ve bilgi teknolojilerinin devreye girmesiyle de ucuncu asama baslamistir. 2011 yilina gelindiginde ise ilk kez Almanya’da kullanilan Endustri 4.0 kavrami yeni bir donemin baslangicini tum dunyaya ilan etmis oldu. Endustri 4.0’i tanimlayan temel konular ise siber- fiziksel sistemlere ve dinamik veri islemeye dayali uretim sistemlerinin ortaya cikmasidir. Endustri 4.0; cok hizli bir dijital donusum yasanan dunyada, bilisim, iletisim ve internet teknolojilerinin uretim sureclerini yogun bicimde etkiledigi bir ortami ifade etmektedir. Bu durumun uretimde verimlilik, maliyet avantaji, karlilik gibi firma duzeyindeki etkilerinin yani sira, buyume, istihdam, insan kaynaklari, egitim, yatirim, girisimcilik gibi makro duzeydeki etkileri de soz konusudur. Bu calismanin amaci kavramsal duzeyde Endustri 4.0’i inceleyerek, gunumuz girisimcilik anlayisina muhtemel etkilerini incelemek ve olasi bir “Yeni Girisimcilik” konseptinin temel paradigmalarini tartismaktir.
Multiple-Imputation Inferences with Uncongenial Sources of Input

Conducting sample surveys, imputing incomplete observa- tions, and analyzing the resulting data are three indispensable phases of modern practice with public-use data files and with many other statistical applications. Each phase inherits different input, including the information preceding it and the intellectual assessments available, and aims to provide output that is one step closer to arriving at statistical infer- ences with scientific relevance. However, the role of the imputation phase has often been viewed as merely providing computational convenience for users of data. Although facilitating computation is very important, such a viewpoint ignores the imputer's assessments and information inaccessible to the users. This view underlies the recent controversy over the validity of multiple-imputation inference when a procedure for analyzing multi- ply imputed data sets cannot be derived from (is "uncongenial" to) the model adopted for multiple imputation. Given sensible imputations and complete-data analysis procedures, inferences from standard multiple- imputation combining rules are typically superior to, and thus different from, users' incomplete-data analyses. The latter may suffer from serious nonresponse biases because such analyses often must rely on convenient but unrealistic assumptions about the nonresponse mechanism. When it is desirable to conduct inferences under models for nonresponse other than the original imputation model, a possible alternative to recreating impu- tations is to incorporate appropriate importance weights into the standard combining rules. These points are reviewed and explored by simple exam- ples and general theory, from both Bayesian and frequentist perspectives, particularly from the randomization perspective. Some convenient terms are suggested for facilitating communication among researchers from dif- ferent perspectives when evaluating multiple-imputation inferences with uncongenial sources of input.
SimpleView++: Neighborhood Views for Point Cloud Classification

Existing multi- view-based point cloud classification methods only utilize multiple views of point clouds and discard the point clouds from further processing. Among these methods, the Simple View model demonstrates that features from six orthogonal perspective projections of a point cloud achieved comparable 3D classification. However, points on the local structures overlap in these projections resulting in the loss of structural information. Also, we found that the performance of Simple View degrades at lower projection resolutions. We propose the use of neighbor projections along with object projections to learn finer local structural information. In this paper, we introduce SimpleView++ to concatenate features from the combined orthogonal per-spective projections at object and neighbor levels with encoded features from the point cloud. We evaluated Simple-View++ using ModelNet40 and ScanObjectNN benchmark datasets. Our idea to use neighbor views broadly applies to other existing view-based methods. For example, neighbor views improve the results of MVTN by 2% on the hardest variant of ScanObjectNN. Our experiments also validate that neighbor view features considerably enhance classification accuracy at lower resolutions.
Model checking, abstraction, and compositional verification

Because of the difficulty of adequately simulating large digital designs, there has been a recent surge of interest in formal verification, in which a mathematical model of the design is proved to satisfy a precise specification. Model checking is one formal verification technique. It consists of checking that a finite-state model of the design satisfies a specification given in temporal logic, which is a logic that can express properties involving the sequencing of events in time. One of the main drawbacks of model checking is the state explosion problem. This problem occurs in systems composed of multiple processes executing in parallel; the size of the state space generally grows exponentially with the number of components. This thesis considers two methods for avoiding the state explosion problem in the context of model checking: compositional verification and abstraction. In compositional verification, our goal is to check local properties of the components in the design, deduce that these hold in the global system, and then use them to prove the overall specification. With abstraction, we can hide internal state, replace complex data types with simpler abstract ones, or simplify some of the timing behavior of the components. Using a connection between the abstracted and unabstracted systems, we deduce that whatever properties we prove at the abstract level also hold in the original system. We develop the necessary framework for using these two techniques with model checking, and demonstrate via a number of examples how they can be applied to realistic systems. Our largest example is the cache coherence protocol described in the IEEE Futurebus+ standard. In the course of the verification, we found errors in the standard, and proposed fixes for the protocol.
The VLSI kernel of neural algorithms

A unified description of neural algorithms by means of general objective functions is shown to be the key to economic design of software and hardware. The compute-intensive algorithmic strings present in the dynamical equations corresponding to an objective function are to be executed by dedicated VLSI circuits. Cellular neural networks are recovered as a special case, and a corresponding general learning rule is derived. <<ETX>>
The Architecture of Pipelined Computers

A wrench having a fixed jaw and a movable jaw slidably mounted thereon, a cylindrical screw member rotatably mounted in an aperture formed on the base of the fixed jaw engaged with a rack on the movable jaw, the screw member having a bore forming socket means at each end and the base having sockets formed therein at said aperture, a first ball member interposed between one socket of said base and the socket means on one end of said screw member and a second ball member interposed between one socket of said base and the socket means on the other end of said screw member and spring means urging each of said balls to the sockets of said base.
Tracks for Learning: Metacognition and Learning Technologies.

Research into metacognition suggests that learners need to become aware of the processes of their learning as distinct from the content of learning to improve their learning outcomes. Information technologists have the opportunity to take theoretical views of learning and develop design models to investigate and demonstrate these theoretical views. One way of doing this is by providing interactive multimedia programs that support student centred learning. There are too many examples of educational multimedia that fail to exploit its capability to offer interactive practice of a subject (Laurillard, 1993). This paper outlines an investigation into the nature of metacognition and its relation to the learning process in a constructivist interactive multimedia learning environment.
Position tracking control of an induction motor via partial state feedback

This paper presents a position tracking controller for an induction motor driving a robotic load. Using nonlinear models of the induction motor and a nonlinear observer, a focal exponential stability (ES) result for motor position tracking error is obtained. The control only requires measurements of rotor position and stator currents while utilizing exact knowledge of the system parameters. Simulation results are given to show the effectiveness of the proposed controller. <<ETX>>
Reinforcement Learning for Vulnerability Assessment in Peer-to-Peer Networks

Proactive assessment of computer-network vulnerability to unknown future attacks is an important but unsolved computer security problem where AI techniques have significant impact potential. In this paper, we investigate the use of reinforcement learning (RL) for proactive security in the context of denial-of-service (DoS) attacks in peer-to-peer (P2P) networks. Such a tool would be useful for network administrators and designers to assess and compare the vulnerability of various network configurations and security measures in order to optimize those choices for maximum security. We first discuss the various dimensions of the problem and how to formulate it as RL. Next we introduce compact parametric policy representations for both single attacker and botnets and derive a policy-gradient RL algorithm. We evaluate these algorithms under a variety of network configurations that employ recent fair-use DoS security mechanisms. The results show that nur RL-based approach is able to significantly outperform a number of heuristic strategies in terms of the severity of the attacks discovered. The results also suggest some possible network design lessons for reducing the attack potential of an intelligent attacker.
MINIO : an I/O benchmark for investigating high level parallel libraries

Input/output (I/O) operations are one of the biggest challenges facing scientific computing as it transitions to exascale. The traditional software stack – com- prising of parallel file systems, middlewares and high level libraries – has evolved to enable applications to better cope with the demands of enormous datasets. This software stack makes high performance parallel I/O easily accessible to application engineers, however it is important to ensure best performance is not compromised through attempts to enrich these libraries. We present MINIO, a benchmark for the investigation of I/O behaviour focusing on understanding overheads and inefficiencies in high level library usage. MINIO uses HDF5 and TyphonIO to explore I/O at scale using different application behavioural pat- terns. A case study is performed using MINIO to identify performance limiting characteristics present in the TyphonIO library as an example of performance discrepancies in the I/O stack.
Hyperspectral Remote Sensing Images Classification Method Based on Learned Dictionary

A novel hyperspectral image classification method based on learned dictionary is presented in this paper. Firstly, the sampled image pixel and its classification vector are combined as sample pair. Secondly, defined as a sample vector, the sample pair are used for sparse coding and dictionaries learning. Then, the sparse association between sample pairs is established efficiently. Finally, defined as prior knowledge, the sparse association is used to guide the classification of input image. The whole dictionary learning process can be achieved offline, and improve the speed of the algorithm. Several experiments show that the method can get good classification results.
Backpropagation: the basic theory

Since the publication of the PDP volumes in 1986, learning by backpropagation has become the most popular method of training neural networks. The reason for the popularity is the underlying simplicity and relative power of the algorithm. Its power derives from the fact that, unlike its precursors, the perceptron learning rule and the Widrow-Hoff learning rule, it can be employed for training nonlinear networks of arbitrary connectivity. Since such networks are often required for real-world applications, such a learning procedure is critical. Nearly as important as its power in explaining its popularity is its simplicity. The basic idea is old and simple; namely define an error function and use hill climbing (or gradient descent if you prefer going downhill) to find a set of weights which optimize performance on a particular task. The algorithm is so simple that it can be implemented in a few lines of code, and there have been no doubt many thousands of implementations of the algorithm by now. The name back propagation actually comes from the term employed by Rosenblatt (1962) for his attempt to generalize the perceptron learning algorithm to the multilayer case. There were many attempts to generalize the perceptron learning procedure to multiple layers during the 1960s and 1970s, but none of them were especially successful. There appear to have been at least three independent inventions of the modern version of the back-propagation algorithm: Paul Werbos developed the basic idea in 1974 in a Ph.D. dissertation entitled