Miracle-dz/UniFAR-FTUs huggingface dataset:
Security Protection of Software Programs by Information Sharing and Authentication Techniques Using Invisible ASCII Control Code

A new method for software program protection by information sharing and authentication techniques using invisible ASCII control codes is proposed. A scheme for sharing a secret source program written in Visual C++ among a group of participants, each holding a camouflage program to hide a share, is first proposed for safe keeping of the secret program. Only when all the shares hidden in the camouflage programs are collected can the secret program be recovered. The secret program, after being exclusive-ORed with all the camouflage programs, is divided into shares. Each share is encoded next into a sequence of special ASCII control codes which are invisible when the codes are inserted in the comment of the Visual C ++ program and viewed in the window of the Microsoft VC ++ editor. These invisible codes then are hidden in the camouflage program, resulting in a stegoprogram for a participant to keep. Each stego-program can still be compiled and executed to perform the original function of the camouflage program. A secret program recovery scheme is also proposed. To enhance security under the assumption that the sharing and recovery algorithms are known to the public, three security measures via the use of a secret random key are also proposed, which not only can prevent the secret program from being recovered illegally without providing the secret key, but also can authenticate the stego-program provided by each participant, during the recovery process, by checking whether the share or the camouflage program content in the stego-program have been tampered with incidentally or intentionally. Experimental results show the feasibility of the proposed method.
Dosimetric quantification of the incidental irradiation of the ‘true’ (deep) ano-inguinal lymphatic drainage of anal cancer patients not described in conventional contouring guidelines

Abstract Introduction: The ano-inguinal lymphatic drainage (AILD) is located in the subcutaneous adipose tissue of the proximal medial thigh. Findings from fluorescence methods give us new information about anatomical conditions of the AILD. Current contouring guidelines do not advise the inclusion of the ‘true’ AILD into the clinical target volume (CTV). Aim of this work was the retrospective analysis of the incidental dose to the AILD in an anal cancer (AC) patient cohort who underwent definitive chemoradiation (CRT) therapy with Volumetric Arc Therapy – Intensity Modulated Radiation Therapy (VMAT-IMRT). Methods: VMAT-IMRT plans of 15 AC patients were analyzed. Based on findings from new fluorescence methods we created a new volume, the expected AILD. The examined dosimetric parameters were the minimal, maximal and mean dose and V10–V50 that were delivered to the AILD, respectively. Results: The median volume of AILD was 1047 cm³. Mean Dmin, Dmax and Dmean were 7.5 Gy, 58.9 Gy and 40.8 Gy for AILD. The clinical relevant dose of 30.0 Gray covered in mean 76% of the volume of the AILD, respectively. Conclusions: Only 76% of the AILD-volume received at least an expected required treatment dose of 30 Gy incidentally. Concerning the low number of loco-regional relapses in AC patients after definitive CRT one has to balance increased side effects against a rigid oncological–anatomical interpretation of the local lymphatic drainage by including the AILD into the standard CTV.
Infrared Thermal Sensing of Positive and Negative Affective States

Useful information about affects and affective states can be extracted form the physiological signals, even under difficult lighting and pose conditions. Little work has been done on using physiological signals in automated affect recognition systems. We employed measurements of facial skin temperature variations for developing a non-intrusive automated facial expression classification system. Variances in thermal intensity values recorded at thermally significant locations on human faces were used to discern between normal, pretended happy and pretended sad facial expression of affective states. A three-step algorithmic approach was used to construct the classifier. Employed approach involved derivation of principal components, stepwise selection of optimal and most discriminating features, and linear discriminant analysis within the reduced optimal eigenspace. The resulting classifier performed at an impressively low (16.2%) error rate
Wavelet transform-based frequency tuning ILC

A discrete wavelet transform-based cutoff frequency tuning method is proposed and experimental investigation is reported. In the method, discrete wavelet packet algorithm, as a time-frequency analysis tool, is employed to decompose the tracking error into different frequency regions so that the maximal error component can be identified at any time step. At each time step, the passband of the filter is from zero to the upper limit of frequency region where the maximal error component resides. Hence, the filter is a function of time as well as index of cycle. The experimental results show that this method can suppress higher frequency error components at proper time steps. While at the time steps where the major tracking error falls into lower frequency range, the cutoff frequency of the filter is set lower to reduce the influence of noises and uncertainties. This way, learning transient and long-term stability can be improved.
Effects of 180 mT static magnetic fields on diabetic wound healing in rats

Diabetic wound (DW) problems are becoming a formidable clinical challenge due to the sharp increase in the diabetic population and the high incidence of DW. Static magnetic field (SMF) therapy, an inexpensive and accessible noninvasive method, has been proven to be effective on various tissue repairs. However, the issue of the therapeutic effect of SMF on DW healing has never been investigated. The objective of this study was to systematically evaluate the effect of a 180 mT moderate‐intensity gradient SMF on DW healing in streptozotocin‐induced diabetic rats. Forty‐eight 3‐month‐old male Sprague–Dawley rats (32 diabetic and 16 non‐diabetic rats) were assigned to three equal groups: normal wound, DW, and DW + SMF groups. An open circular wound with 1.5 cm diameter was created in the dorsum. The wound was covered with a dressing and the magnet was fixed on top of the dressing. On days 5, 12, and 19, four rats of each group were euthanized and gross wound area, histology and tensile strength were evaluated. The wound area determination suggested that SMF significantly increased the healing rate and reduced the gross healing time. This result was further confirmed by histological observations. The wound tensile strength, reflecting the amount and quality of collagen deposition, increased to a larger extent in the DW + SMF group on days 12 and 19 compared with the DW group. The results indicated that 180 mT SMF presented a beneficial effect on DW healing, and implied the clinical potential of SMF therapy in accelerating DW repair and releasing the psychological and physical burdens of diabetic patients. Bioelectromagnetics 31:640–648, 2010. © 2010 Wiley‐Liss, Inc.
LMAP : A Real Lightweight Mutual Authentication Protocol for Low-cost RFID tags

Low-cost Radio Frequency Identification (RFID) tags are devices of very limited computational capabilities, where only 250-3K logic gates can be devoted to security-related tasks. Many proposals have recently appeared, but all of them are based on RFID tags using classical cryptographic primitives such as PRNGs, hash functions, block ciphers, etc. We believe this assumption to be fairly unrealistic, as classical cryptographic constructions lie well beyond the computational reach of these constrained systems. A new approach is necessary to tackle this problem, so we propose a real lightweight mutual authentication protocol for low-cost RFID tags that offers an adequate security level and can be implemented even in the most limited systems, as it only needs around 300 gates.
Use of Decision Trees and Attributional Rules in Incremental Learning of an Intrusion Detection Model

Current intrusion detection systems are mostly based on typical data mining techniques. The growing prevalence of new network attacks represents a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a Learnable Model for Anomaly Detection (LMAD), as an ensemble real-time intrusion detection model using incremental supervised machine learning techniques. Such techniques are utilized to detect new attacks. The proposed model is based on making use of two different machine learning techniques, namely, decision trees and attributional rules classifiers. These classifiers comprise an ensemble that provides bagging for decision making. Our experimental results showed that, the model automatically learns new rules from continuous network stream, such that it can efficiently discriminate between anomaly and normal connections, offering the advantage of being deployed on any environment. The model is intensively tested online and its evaluation showed promising results.
New features for complex Arabic fonts in cascading recognition system

We propose in this work an approach for automatic recognition of printed Arabic text in open vocabulary mode and ultra low resolution (72 dpi). This system is based on Hidden Markov Models using the HTK toolkit. The novelty of our work is in the analysis of three complex fonts presenting strong ligatures: DiwaniLetter, DecoTypeNaskh and DecoTypeThuluth. We propose a feature extraction based on statistical and structural primitives allowing a robust description of the different morphological variability of the considered fonts. The system is benchmarked on the Arabic Printed Text Image (APTI) database.
Towards systematic, comprehensive trace generation for behavioral pattern detection through symbolic execution

In reverse engineering, dynamic pattern detection is accomplished by collecting execution traces and comparing them to expected behavioral patterns. The traces are collected by manually executing the program under study and therefore represent only part of all relevant program behavior. This can lead to false conclusions about the detected patterns. In this paper, we propose to generate all relevant program traces by using symbolic execution. In order to reduce the created trace data, we allow to limit the trace collection to a user-selectable subset of the statically detected pattern candidates.
DejaVu: Transparent User-Level Checkpointing, Migration, and Recovery for Distributed Systems

In this paper, we present a new fault tolerance system called DejaVu for transparent and automatic checkpointing, migration, and recovery of parallel and distributed applications. DejaVu provides a transparent parallel checkpointing and recovery mechanism that recovers from any combination of systems failures without any modification to parallel applications or the OS. It uses a new runtime mechanism for transparent incremental checkpointing that captures the least amount of state needed to maintain global consistency and provides a novel communication architecture that enables transparent migration of existing MPI codes, without source-code modifications. Performance results from the production-ready implementation show less than 5% overhead in real-world parallel applications with large memory footprints.
Clinical differentiation of acute necrotizing from chronic nonsuppurative pancreatitis in cats: 63 cases (1996-2001).

OBJECTIVE To characterize clinical, clinicopathologic, radiographic, and ultrasonographic findings in cats with histologically confirmed acute necrotizing pancreatitis (ANP) or chronic nonsuppurative pancreatitis (CP) and identify features that may be useful in the antemortem differentiation of these disorders. DESIGN Retrospective study. ANIMALS 63 cats with histologically confirmed ANP (n = 30) or CP (33). PROCEDURE Medical records were reviewed for signalment, clinical signs, concurrent diseases, clinicopathologic findings, and results of radiography and ultrasonography. RESULTS Cats in both groups had similar nonspecific clinical signs, physical examination findings, and radiographic and ultrasonographic abnormalities. Abdominal ultrasonographic abnormalities, including hypoechoic pancreas, hyperechoic mesentery, and abdominal effusion, were found in cats in both groups and, therefore, were not specific for ANP. Cats with CP were significantly more likely to have concurrent diseases than were cats with ANP (100 and 83%, respectively). Clinicopathologic abnormalities were similar between groups; however, serum alanine aminotransferase and alkaline phosphatase activities were significantly higher in cats with CP. CONCLUSIONS AND CLINICAL RELEVANCE Results suggest that ANP and CP in cats cannot be distinguished from each other solely on the basis of history, physical examination findings, results of clinicopathologic testing, radiographic abnormalities, or ultrasonographic abnormalities.
Cache efficient implementation for block matrix operations

Efficiently manipulating and operating on block matrices can be beneficial in many applications, among others those involving iteratively solving nonlinear systems. These types of problems consist of repeatedly assembling and solving sparse linear systems. In the case of very large systems, without a careful manipulation of the corresponding matrices, solving can become very time consuming. This paper proposes a memory storage scheme convenient for both, numeric and structural matrix modification and, at the same time, allowing efficient arithmetic operation. This scheme was used in the implementation of a simple BLAS-like library. The advantage of the new scheme is demonstrated through exhaustive tests on the popular University of Florida Sparse Matrix Collection. Furthermore, this library was used in solving several nonlinear graph optimization problems.
HYDROGEN PEROXIDE FORMATION AND CATALASE ACTIVITY IN THE LACTIC ACID BACTERIA.

Some lactic acid bacteria formed detectable H2O2 and some did not, regardless of their preference or requirement for aerobic or anaerobic conditions. Whether or not H2O2 was formed depended in some instances on the substrate used as energy source. Two H2O2-splitting activities were encountered though never in the same organism. One, named pseudocatalase activity, was insensitive to 0·01 m-azide or 0·01 m-cyanide and appeared to be the action of an acid-sensitive non-haem-containing enzyme detectable in some leuconostocs and pediococci when grown in media containing a low concentration of glucose. The second, named catalase activity, was detected in a number of lactobacilli, leuconostocs, streptococci and pediococci grown on media containing haematin or heated blood; presumably these organisms are able to synthesize the apoenzyme but not the prosthetic group of catalase. This activity was inhibited by 0·01 m-azide or 0·01 m-cyanide; it was not acid-sensitive. There was little correlation between H2O2-splitting activity and the preference or requirement of the organisms for aerobic or anaerobic conditions, or between H2O2-splitting activity and H2O2 formation. Of a few organisms examined, some appeared capable of forming cytochromes when grown in media containing heated blood. One showed traces of a cytochrome whether grown in the presence or absence of heated blood.
Superstring on Symmetric Product

The string theory on symmetric product describes the second-quantized string theory. The development for the bosonic open string was discussed in the previous work. [1] In this paper, we consider the open superstring theory on the symmetric product and examine the nature of the second quantization. The fermionic partition functions are obtained from the consistent fermionic extension of the twisted boundary conditions for the non-abelian orbifold, and they can be interpreted in terms of the long string language naturally. In the closed string sector, the boundary/crosscap states are also constructed. These boundary states are classified into three types in terms of the long string language, and explain the change of the topology of the world-sheet. To obtain the anomaly-free theory, the dilaton tadpole must be cancelled. This condition gives SO(32) Chan-Paton group as ordinary superstring theory. ∗ e-mail address : fuji@hep-th.phys.s.u-tokyo.ac. jp
Product Line Approach to Role-Based Middleware Development for Ubiquitous Sensor Network

Currently sensor network middlewares are developed so that they include all functionalities for various nodes. Since sensor network nodes usually operate with limited resources, it is desirable for them to have middlewares that have only the functionalities necessary to perform their roles. This paper proposes a systematic method for developing sensor network middleware using the product line approach In this method, functionalities of the sensor network middleware common to all nodes and functionalities specific to the different roles of nodes are carefully separated and grouped so that nodes with different roles have different middlewares that are specialized to their roles but minimally consumes the resources.
Compressive Wideband Power Spectrum Estimation

In several applications, such as wideband spectrum sensing for cognitive radio, only the power spectrum (a.k.a. the power spectral density) is of interest and there is no need to recover the original signal itself. In addition, high-rate analog-to-digital converters (ADCs) are too power hungry for direct wideband spectrum sensing. These two facts have motivated us to investigate compressive wideband power spectrum sensing, which consists of a compressive sampling procedure and a reconstruction method that is able to recover the unknown power spectrum of a wide-sense stationary signal from the obtained sub-Nyquist rate samples. It is different from spectrum blind sampling (SBS), which aims at reconstructing the original signal instead of the power spectrum. In this paper, a solution is first presented based on a periodic sampling procedure and a simple least-squares reconstruction method. We evaluate the reconstruction process both in the time and frequency domain. Then, we examine two possible implementations for the compressive sampling procedure, namely complex Gaussian sampling and multicoset sampling, although we mainly focus on the latter. A new type of multicoset sampling is introduced based on the so-called minimal sparse ruler problem. Next, we analyze the statistical properties of the estimated power spectrum. The computation of the mean and the covariance of the estimates allows us to calculate the analytical normalized mean squared error (NMSE) of the estimated power spectrum. Further, when the received signal is assumed to contain only circular complex zero-mean Gaussian i.i. d. noise, the computed mean and covariance can be used to derive a suitable detection threshold. Simulation results underline the promising performance of our proposed approach. Note that all benefits of our method arise without putting any sparsity constraints on the power spectrum.
Multi-function Modification of cotton fabrics for improving utilization of reactive dyes

The cotton fabrics was chemically modified by carboxymethyl chitosan (CMCS) which has good antibacterial properties. CMCS was synthesis by the reaction of chitosan with monochlororacetic acid in the presence of sodium hydroxide via carboxymethylation reaction. CMCS was characterized by Fourier transform infrared (FTIR) spectroscopy, nitrogen content, carboxyl content. The structure of both untreated and treated cotton fabrics were compared and investigated by FTIR and scanning electronic microscopy (SEM). The dyeing properties of the treated cotton fabrics were discussed It was found that the total dye utilization of treated cotton by the salt-free dyeing was much higher than that of untreated one by the conventional dyeing. The color fastness properties, the dyeing levelness and the tear strength of treated cotton could meet application demand. Antibacterial properties of untreated and treated cotton fabrics were evaluated. The treated fabrics show good antibacterial and enhance the dyeability of cotton properties.
Evaluation of sampling strategies for modeling survival of uveal malignant melanoma.

PURPOSE To evaluate sampling strategies used to estimate survival after uveal malignant melanoma that exclude some patients who would be censored from the analysis. METHODS Simulation was performed on a population-based data set of 133 patients who had an eye enucleated because of uveal melanoma. One thousand bootstrap samples of 80 patients were drawn, without replacement, according to three sampling strategies: a random draw (conventional strategy), a draw limited to patients who died of the tumor or survived at least 10 years without metastasis ("late-censoring" strategy), and a draw modified so that 40 patients died of melanoma and others survived at least 10 years without metastasis ("fifty-fifty" strategy). The bias in the Kaplan-Meier analysis and Cox proportional hazards regression was quantified. RESULTS The late-censoring strategy decreased the proportion of censored patients from 53% to 42%, whereas the fifty-fifty strategy assigned 50% of patients to this group. The former strategy overestimated mortality, the excess being 5.2% and 3.7% at 10 and 20 years, respectively. The latter strategy underestimated mortality, the bias being 1.6% and 4.6% at 10 and 20 years, respectively. The bias differed according to categories of explanatory variables so that the log-rank test statistic was inflated a median of 1.08 times (range, 0.73-1.87) and 1.14 times (range, 0.87-1.84), and the Wald chi(2) statistic of the Cox regression was inflated a median of 1.18 times (range, 0.79-2.13) and 1.16 times (range, 0.71-2.02), respectively, when the late-censoring and fifty-fifty strategies were applied. CONCLUSIONS Sampling strategies that exclude on purpose a proportion of patients who would be censored produce biased statistics, because they violate assumptions of survival analysis. Only random sampling from an underlying population produces unbiased survival estimates.
Performance Comparison of the Structure Learning Bayesian Network Algorithms Using Gene Expression Data

Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to fully understand disease ontology and to reduce the cost of drug development, gene regulatory networks (GRN) have to be constructed. During the last decade, many GRN inference algorithms like ‘Bayesian network’ that are based on genome-wide data have been developed to unravel the complexity of gene regulation. Recently, many of structure learning algorithms were used to learn Bayesian network that have shown promise in gene regulatory network reconstruction. In this paper we apply different structure learning algorithms on actual microarray data to obtain a better understanding of their relative strengths and weaknesses on the system biology community and we evaluate their outputs from different perspectives.