Miracle-dz/UniFAR-FTUs huggingface dataset:
Centralized resource reduction and target setting under DEA control

Data envelopment analysis (DEA) is a powerful tool for measuring the relative efficiencies of a set of decision making units (DMUs) such as schools and bank branches that transform multiple inputs to multiple outputs. In centralized decision-making systems, management normally imposes common resource constraints such as fixed capital, budgets for operating capital and staff count. In consequence, the profit or net value added of the units subject to resource reductions will decrease. In terms of performance evaluation combined with resource allocation, the interest of central management is to restore the general efficiency value of the DMUs. The paper makes four contributions to the literature: (1) we take into consideration the performance evaluation of the centralized budgeting of hierarchical organizations along with sales and market allocation within manufacturing and distribution organizations; (2) we address the evaluation problems that the central decision maker does not desire to deteriorate the efficiency score of the DMUs after input and/or output reduction; (3) we develop a common set of weights (CSW) method based on the goal program (GP) concept to control the total weight flexibility in the conventional DEA models; (4) we extend a new approach to optimize the inputs and/or outputs contraction such that the efficiency of all DMUs will get bigger than or equal to the efficiency of previous change. We ultimately present a numerical example involving with three inputs and two outputs to illustrate the applicability and efficacy of the proposed approach.
Random key-assignment for secure Wireless Sensor Networks

A distributed Wireless Sensor Network (WSN) is a collection of n sensors with limited hardware resources. Sensors can exchange messages via Radio Frequency (RF), whose range usually covers only a limited number of other sensors. An interesting problem is how to implement secure pair-wise communications among any pair of sensors in a WSN. A WSN requires completely distributed solutions which are particularly challenging due to the limited resources and the size of the network. Moreover, WSNs can be subject to several security threats, including the physical compromising of a sensor. Hence, any solution for secure pairwise communications should tolerate the collusion of a set of corrupted sensors. This paper describes a probabilistic model and two protocols to establish a secure pair-wise communication channel between any pair of sensors in the WSN, by assigning a small set of random keys to each sensor. We build, based on the first Direct Protocol, a second Co-operative Protocol. The Co-operative Protocol is adaptive: its security properties can be dynamically changed during the life-time of the WSN. Both protocols also guarantee implicit and probabilistic mutual authentication without any additional overhead and without the presence of a base station. The performance of the Direct Protocol is analytically characterized while, for the Co-operative Protocol, we provide both analytical evaluations and extensive simulations. For example, the results show that, assuming each sensor stores 120 keys, in a WSN composed of 1024 sensors with 32 corrupted sensors the probability of a channel corruption is negligible in the case of the Co-operative Protocol.
A framework for data base semantic integrity

A structured framework is provided for describing the semantic integrity requirements of a data base. The semantic integrity of a data base is said to be violated when the data base ceases to represent a legitimate configuration of the application domain it is intended to model. In the context of the relational data model, it is possible to identify multiple levels of semantic integrity information. Relation constraints comprise one such level; they express the semantic information not contained in the structure of the relations nor in the identity of their underlying domains. The three components of a relation constraint are considered: (1) the assertion (a predicate on the state of the data base or on transitions between data base states), (2) the validity requirement (the occasion(s) at which the assertion must hold), and (3) the violation-action (the action that is to occur if the assertion does not hold at a time when it should). A framework for relation constraints is presented. Details of a structured classification scheme are outlined. This scheme is intended to form a basis for a high level, well-directed, and disciplined methodology for the design of relational data bases. Emphasis is placed on the assertion component of relation constraints. Assertions are viewed as more than expressions of some relationship among different values in a data base; assertions single out the data that is constrained, and state the properties that this data must possess. A classification is provided of the various predicate types used to identify constrained data and to state the properties which they are to possess. Approaches to the relation constraint assertion specification process are discussed.
A New Approach of Data Aggregation in Wireless Sensor Networks

In this paper we discuss algorithms that allow the concealed data aggregation (CDA) in wireless sensor networks. We describe and evaluate three algorithms that were reported to suit to the WSN scenario. As result of the evaluation, where we emphasize the awareness to potential attack scenarios, we present a brief overview of strengths and weaknesses of the algorithms. Since no algorithm provides all desirable goals, we propose two approaches to cope with the problems. The first is the successive combination of two algorithms. It increases security, while the additional efforts can be minimized by carefully selected parameters. For the second approach we face specific weaknesses and engineer mechanisms that solve the particular issues. With the considered homomorphic message authentication code and a discussion of the id-issue we exemplary evaluate the two biggest issues of the very promising CMT algorithm.
x-Hour Outdoor Photometric Stereo

While Photometric Stereo (PS) has long been confined to the lab, there has been a recent interest in applying this technique to reconstruct outdoor objects and scenes. Un-fortunately, the most successful outdoor PS techniques typically require gathering either months of data, or waiting for a particular time of the year. In this paper, we analyze the illumination requirements for single-day outdoor PS to yield stable normal reconstructions, and determine that these requirements are often available in much less than a full day. In particular, we show that the right set of conditions for stable PS solutions may be observed in the sky within short time intervals of just above one hour. This work provides, for the first time, a detailed analysis of the factors affecting the performance of x-hour outdoor photometric stereo.
jMIR: Tools for Automatic Music Classification

jMIR is a free and open-source software suite designed for applications related to automatic music classification. jMIR includes the jAudio, jSymbolic and jWebMiner feature extractors, the ACE meta-learning framework, the ACE XML information exchange file formats, the jMusicMetaManager musical dataset management software and the Codaich, Bodhidharma MIDI and SAC musical datasets. The primary goals underlying jMIR are the provision of powerful ready-to-use software tools to music researchers with diverse ranges of technical backgrounds, the encouragement of research combining features derived from audio, symbolic and cultural sources of data, and the provision of a framework for iteratively and collaboratively developing further music information retrieval (MIR) tools and performing original music classification research.
Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation

In neural machine translation, the attention mechanism facilitates the translation process by producing a soft alignment between the source sentence and the target sentence. However, without dedicated distortion and fertility models seen in traditional SMT systems, the learned alignment may not be accurate, which can lead to low translation quality. In this paper, we propose two novel models to improve attention-based neural machine translation. We propose a recurrent attention mechanism as an implicit distortion model, and a fertility conditioned decoder as an implicit fertility model. We conduct experiments on large-scale Chinese–English translation tasks. The results show that our models significantly improve both the alignment and translation quality compared to the original attention mechanism and several other variations.
Angiotensin II Induces c‐fos Expression in Smooth Muscle Via Transcriptional Control

Angiotensin II (Ang II) has been shown to cause hypertrophy of cultured quiescent rat aortic smooth muscle cells. This observation along with the recent demonstration of angiotensinogen messenger RNA (mRNA) in the vessel wall has led us to postulate a role for Ang II in hypertensive smooth muscle hypertrophy. One of the earliest responses in a wide variety of cells in response to a growth-promoting agent is the induction of the proto-oncogene c-fos. To investigate the mechanism of the action of Ang II, we investigated the effect of Ang II on the expression of the c-fos gene in rat aortic smooth muscle cells that were made quiescent by being grown in a defined serum-free media for 48 hours. Ang II (10−6-10−10 M) resulted in a dose-dependent increase in c-fos mRNA expression. This induction was angiotensin-receptor specific since it was completely abolished by the competitive inhibitor saralasin. Inhibition of protein synthesis did not block the rise in c-fos mRNA expression; it resulted in a superinduction and stabilization of the c-fos mRNA. Using a nuclear runoff transcription assay, we demonstrated that Ang II stimulated the transcription rate of the c-fos gene. This activation of c-fos gene expression may be an important mechanism in the angiotensin-induced smooth muscle hypertrophy.
An algorithm for the facility location problems

Variants of the facility location problem have been studied extensively in the operations research and management science literatures. In this paper, we present a new analysis method of a simple greedy algorithm for the uncapacitated facility location problem. We achieve an approximation guarantee of 1.5749 better than 1.853, which is the approximation ratio provided by the combinatorial approximation algorithm due to Charikar and Guha.
A Computational Interpretation of Parametricity

Reynolds' abstraction theorem has recently been extended to lambda-calculi with dependent types. In this paper, we show how this theorem can be internalized. More precisely, we describe an extension of the Pure Type Systems with a special parametricity rule (with computational content), and prove fundamental properties such as Church-Rosser's and strong normalization. All instances of the abstraction theorem can be both expressed and proved in the calculus itself. Moreover, one can apply parametricity to the parametricity rule: parametricity is itself parametric.
Pcons: A neural‐network–based consensus predictor that improves fold recognition

During recent years many protein fold recognition methods have been developed, based on different algorithms and using various kinds of information. To examine the performance of these methods several evaluation experiments have been conducted. These include blind tests in CASP/CAFASP, large scale benchmarks, and long‐term, continuous assessment with newly solved protein structures. These studies confirm the expectation that for different targets different methods produce the best predictions, and the final prediction accuracy could be improved if the available methods were combined in a perfect manner. In this article a neural‐network‐based consensus predictor, Pcons, is presented that attempts this task. Pcons attempts to select the best model out of those produced by six prediction servers, each using different methods. Pcons translates the confidence scores reported by each server into uniformly scaled values corresponding to the expected accuracy of each model. The translated scores as well as the similarity between models produced by different servers is used in the final selection. According to the analysis based on two unrelated sets of newly solved proteins, Pcons outperforms any single server by generating ∼8%–10% more correct predictions. Furthermore, the specificity of Pcons is significantly higher than for any individual server. From analyzing different input data to Pcons it can be shown that the improvement is mainly attributable to measurement of the similarity between the different models. Pcons is freely accessible for the academic community through the protein structure‐prediction metaserver at http://bioinfo.pl/meta/.
Advances in Brief The Oncogeneqin Codesfor a TranscriptionalRepressor '

The retroviral oncogene qin codes for a protein that belongs to the winged helix family of transcriptional regulators. The Qin protein is localized in the nucleus and binds to the same DNA consensus sequence as rat brain factor 1 (BF-1). Cellular Qin shows greater affinity to DNA than does viral Qin. Alone or fused to the DNA-binding domain of the yeast GAlA protein, both Qin proteins act as transcriptional repressors. The major transcriptional repression domain maps to the region of amino acids 252â€”395 of viral Qin.
Joint Models for Chinese POS Tagging and Dependency Parsing

Part-of-speech (POS) is an indispensable feature in dependency parsing. Current research usually models POS tagging and dependency parsing independently. This may suffer from error propagation problem. Our experiments show that parsing accuracy drops by about 6% when using automatic POS tags instead of gold ones. To solve this issue, this paper proposes a solution by jointly optimizing POS tagging and dependency parsing in a unique model. We design several joint models and their corresponding decoding algorithms to incorporate different feature sets. We further present an effective pruning strategy to reduce the search space of candidate POS tags, leading to significant improvement of parsing speed. Experimental results on Chinese Penn Treebank 5 show that our joint models significantly improve the state-of-the-art parsing accuracy by about 1.5%. Detailed analysis shows that the joint method is able to choose such POS tags that are more helpful and discriminative from parsing viewpoint. This is the fundamental reason of parsing accuracy improvement.
How to Prevent Type-Flaw Guessing Attacks on Password Protocols

Abstract : A message in a protocol is said to have a type-flaw if it was created with an intended type, but is later received and treated as a different type. A type-flaw guessing attack is an attack in which a type-flaw is induced in a protocol to enable a password guessing attack to occur. Heather, Lowe, and Schneider in "How to Prevent Type Flaw Attacks on Security Protocols" (July 2000) prove that attacks that use type-flaws can be prevented if honest agents tag messages with their intended types. However, their tagging scheme cannot be used in a password protocol since it allows a guess to be directly verified using the tags inside password encryptions. In this paper, the authors prove that following a modification of Heather et al.'s scheme, most type-flaw guessing attacks can still be prevented.
Effectiveness of Simple Linguistic Processing in Automatic Sentiment Classification of Product Reviews

This paper reports a study in automatic sentiment classification, i.e., automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In the first part of this study, several different approaches, unigrams (individual words), selected words (such as verb, adjective, and adverb), and words labeled with part-of-speech tags were investigated. A sample of 1,800 various product reviews was retrieved from Review Centre (www.reviewcentre.com) for the study. 1,200 reviews were used for training, and 600 for testing. Using SVM, the baseline unigram approach obtained an accuracy rate of around 76%. The use of selected words obtained a marginally better result of 77.33%. Error analysis suggests various approaches for improving classification accuracy: use of negation phrase, making inference from superficial words, and solving the problem of comments on parts. The second part of the study that is in progress investigates the use of negation phrase through simple linguistic processing to improve classification accuracy. This approach increased the accuracy rate up to 79.33%.
Can photo-ionisation explain the decreasing fraction of X-ray obscured AGN with luminosity?

Chandra and XMM-Newton surveys show that the fraction of obscured AGN decreases rapidly with increasing luminosity. Although this is usually explained by assuming that the covering factor of the central engine is much smaller at luminous QSOs, the exact origin of this effect remains unknown. We perform toy simulations to test whether photo-ionisation of the obscuring screen in the presence of a strong radiation field can reproduce this effect. In particular, we create X-ray spectral simulations using a warm absorber model assuming a range of input column densities and ionisation parameters. We fit instead the simulated spectra with a simple cold absorption power-law model that is the standard practice in X-ray surveys. We find that the fraction of absorbed AGN should fall with luminosity as L −0.16±0.03 in rough agreement with the observations. Furthermore, this apparent decrease in the obscuring material is consistent with the dependence of the FeKα narrow-line equivalent width on luminosity, i.e. the X-ray Baldwin effect.
Modelling and control of automotive clutch systems

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Application of a novel and automated branched DNA in situ hybridization method for the rapid and sensitive localization of mRNA molecules in plant tissues

Premise of the study: A novel branched DNA detection technology, RNAscope in situ hybridization (ISH), originally developed for use on human clinical and animal tissues, was adapted for use in plant tissue in an attempt to overcome some of the limitations associated with traditional ISH assays. Methods and Results: Zea mays leaf tissue was formaldehyde fixed and paraffin embedded (FFPE) and then probed with the RNAscope ISH assay for two endogenous genes, phosphoenolpyruvate carboxylase (PEPC) and phosphoenolpyruvate carboxykinase (PEPCK). Results from both manual and automated methods showed tissue- and cell-specific mRNA localization patterns expected from these well-studied genes. Conclusions: RNAscope ISH is a sensitive method that generates high-quality, easily interpretable results from FFPE plant tissues. Automation of the RNAscope method on the Ventana Discovery Ultra platform allows significant advantages for repeatability, reduction in variability, and flexibility of workflow processes.
A Role for the Ventral Visual Stream in Reporting Movements

In this experiment we contrast the neural activity associated with reporting a stimulus attribute with the activity that occurs when the same stimulus attribute is used to guide behavior. Reporting the characteristics of a stimulus differs from simply tracking that stimulus since reporting requires that a stimulus is explicitly recognized and associated with an arbitrary response. In one condition the subject used his right finger to follow a square that moved randomly on a screen. In a second condition he had to indicate changes in the direction of the square's movements by touching one of two report buttons with his right finger. Two other conditions were added to control for the differences in the form of movement between the two primary conditions. When the reporting condition was contrasted with the tracking condition (controlling for the differences in the form of movement), areas in the ventral visual system (the left ventral prefrontal cortex and the left inferior temporal cortex) were activated. This study shows that contrasting a manual task which involves a report with a manual task which does not activates the ventral visual system. However, the observation of additional activity in other areas suggests that, while activity in the ventral stream is necessary for reporting, it is not sufficient.
The high-energy polarization-limiting radius of neutron star magnetospheres - I. Slowly rotating neutron stars

In the presence of strong magnetic fields, the vacuum becomes a birefringent medium. We show that this quantum electrodynamics effect decouples the polarization modes of photons leaving the neutron star (NS) surface. Both the total intensity and the intensity in each of the two modes are preserved along the path of a ray through the NS magnetosphere. We analyse the consequences that this effect has on aligning the observed polarization vectors across the image of the stellar surface to generate large net polarizations. In contrast to previous predictions, we show that the thermal radiation of NSs should be highly polarized even in the optical. When detected, this polarization will be the first demonstration of vacuum birefringence. It could be used as a tool to prove the high magnetic field nature of anomalous X-ray pulsars (AXPs) and it could also be used to constrain physical NS parameters, such as R/M, to which the net polarization is sensitive.
Performance Analysis of Dual-Hop OFDM Relay System with Subcarrier Mapping in Nakagami-m Fading

Subcarrier mapping (SCM) is considered to be crucial for capacity-maximization in orthogonal frequency division multiplexing (OFDM) relaying networks and has been investigated extensively. However, no study has examined its exact or approximate close-form analysis under Nakagami-m fading. This paper considers the ordered subcarrier pairing schemes, i.e., worst-to-best (WTB) SCM and best-to-best (BTB) SCM, for the analysis of bit error rate (BER) and capacity of a dual-hop OFDM amplify-and-forward (AF) relay system. The system-analysis is presented for Nakagami-m fading with emphasis on two special cases: onesided Gaussian fading (m = 2 ) and Rayleigh fading (m = 1). Close-form expressions for the probability density function (PDF) and moment generating function (MGF) of end-to-end SNR are derived while considering fixed gain AF relays. The classical MGF and PDF based approaches are used to compare the BER performance of the system with WTB SCM and BTB SCM schemes. Close-form expressions for the upper bound on ergodic capacity are derived by analyzing Jensen’s inequality. Accurate analysis is presented for integer m while the non-integer m values allow the derivations of approximate expressions. The accuracy of the suggested approximation is verified analytically as well as numerically. The simulation results validate the analysis in Nakagami-m fading channel. key words: OFDM, SCM, Nakagami-m, dual-hop