Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression verification

Proving the equivalence of successive, closely related versions of a program has the potential of being easier in practice than functional verification, although both problems are undecidable. There are two main reasons for this claim: it circumvents the problem of specifying what the program should do, and in many cases it is computationally easier. We study theoretical and practical aspects of this problem, which we call regression verification.
Transcriptional Adaptation of Mycobacterium tuberculosis within Macrophages

Little is known about the biochemical environment in phagosomes harboring an infectious agent. To assess the state of this organelle we captured the transcriptional responses of Mycobacterium tuberculosis (MTB) in macrophages from wild-type and nitric oxide (NO) synthase 2–deficient mice before and after immunologic activation. The intraphagosomal transcriptome was compared with the transcriptome of MTB in standard broth culture and during growth in diverse conditions designed to simulate features of the phagosomal environment. Genes expressed differentially as a consequence of intraphagosomal residence included an interferon γ– and NO-induced response that intensifies an iron-scavenging program, converts the microbe from aerobic to anaerobic respiration, and induces a dormancy regulon. Induction of genes involved in the activation and β-oxidation of fatty acids indicated that fatty acids furnish carbon and energy. Induction of σE-dependent, sodium dodecyl sulfate–regulated genes and genes involved in mycolic acid modification pointed to damage and repair of the cell envelope. Sentinel genes within the intraphagosomal transcriptome were induced similarly by MTB in the lungs of mice. The microbial transcriptome thus served as a bioprobe of the MTB phagosomal environment, showing it to be nitrosative, oxidative, functionally hypoxic, carbohydrate poor, and capable of perturbing the pathogen's cell envelope.
The Relationship between Adaptive-Maladaptive Perfectionism and Life Satisfaction among Secondary School Students

The aim of this study was to investigate the relationship between adaptive-maladaptive perfectionism and life satisfaction among secondary school students. The participants of the study consists of 334 secondary school students (160 girls and 174 boys). A correlational research model was used in this study. In the study, some scales are used as data collection tools as follows: Positive and Negative Perfectionism Scale developed by Kirdok (2002) and Multidimensional Students’ Life Satisfaction Scale developed by Huebner (1994) and adapted to Turkish by Civitci (2007). Data analysis were conducted with SPSS 23.0 statistical package program in . 01and . 05 significance levels. All data were analyzed by correlation and regression. The findings of the study showed that adaptive perfectionism was positively associated with friend, school, living environment, family, self and total life satisfaction; maladaptive perfectionism was negatively associated with living environment and total life satisfaction. Also, there is no relationship between maladaptive perfectionism and friend, school, family, and self satisfactions. Implications of these findings are discussed and suggestions are presented within the context of literature.
Active Systems Management : The Evolution of Firewalls

For over twenty-five years, the security community has focused on improving security, and yet information systems remain as vulnerable as ever (perhaps more so). A perfect example of this is the (first intrusion occurred on July 10, 2001 and peaked on July 19, 2001) code red worm which automatically exploits a vulnerability in Microsoft’s Internet Information Server (IIS). Following a successful exploitation of a host, the worm propagates by scanning for other vulnerable hosts to infect. Nine days later, approximately 250,000 hosts were reported as infected. The unfortunate truth about the code red incident is that ALL of the intrusions should have been prevented. The incidents should have been prevented because Microsoft released a patch/fix for the vulnerability on June 18, 2001– almost a month before the beginning of the code red worm’s first release. The fact that information systems remain as vulnerable (or more so) than they were over 25 years ago when research began implies that a paradigm shift is in order. In this paper, I will motivate the need for a paradigm shift by describing current problems, and the trends which will only amplify those problems. I will then introduce the notion of Active Systems Management where both management and protection are provided on a per host basis.
Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences

Fairness-aware learning is a novel framework for classification tasks. Like regular empirical risk minimization (ERM), it aims to learn a classifier with a low error rate, and at the same time, for the predictions of the classifier to be independent of sensitive features, such as gender, religion, race, and ethnicity. Existing methods can achieve low dependencies on given samples, but this is not guaranteed on unseen samples. The existing fairness-aware learning algorithms employ different dependency measures, and each algorithm is specifically designed for a particular one. Such diversity makes it difficult to theoretically analyze and compare them. In this paper, we propose a general framework for fairness-aware learning that uses f-divergences and that covers most of the dependency measures employed in the existing methods. We introduce a way to estimate the f-divergences that allows us to give a unified analysis for the upper bound of the estimation error; this bound is tighter than that of the existing convergence rate analysis of the divergence estimation. With our divergence estimate, we propose a fairness-aware learning algorithm, and perform a theoretical analysis of its generalization error. Our analysis reveals that, under mild assumptions and even with enforcement of fairness, the generalization error of our method is $O(\sqrt{1/n})$, which is the same as that of the regular ERM. In addition, and more importantly, we show that, for any f-divergence, the upper bound of the estimation error of the divergence is $O(\sqrt{1/n})$. This indicates that our fairness-aware learning algorithm guarantees low dependencies on unseen samples for any dependency measure represented by an f-divergence.
Isoperibolic Calorimetric Measurements of the Fleischmann-Pons Effect

Important advantages exist for selecting a Dewar type isoperibolic calorimeter for measurements of anomalous excess enthalpy produced by the Fleischmann-Pons Effect (FPE). These advantages include a wide dynamic range for both the cell temperature and cell input power, direct visual observations inside the cell during calorimetric experiments, relative low cost, selfpurification of the system, the safety of an open system, and heat transfer mainly by electromagnetic radiation. Various generations of this calorimetry are described along with the mathematical modeling. The use of control or “blank” experiments, such as replacing palladium by platinum, show that anomalous excess power is measurable to within ±0.1 mW using this electrochemical calorimetry. The application of this Dewar isoperibolic calorimetry at other laboratories such as NHE (Japan), Grenoble (France) and Harwell (U.K.) is discussed. Variations of isoperibolic calorimetry used by China Lake, Caltech, and M.I. T. are also examined where the main heat transfer pathway is by conduction. An improved version of the China Lake isoperibolic calorimeter is capable of measuring the small excess power (6.5 mW) produced at the beginning of an experiment by the exothermic absorption of deuterium into palladium.
The Relationship between Subjective and Objective Company Performance Measures in Market Orientation Research: Further Empirical Evidence

Subjective performance measures have been widely used in research on market orientation and its presumed link to company performance. However, only a small number of studies have examined the link between subjective performance measures and objective ones. This study replicates earlier research and extends previous findings using a broader sample of firms than in most previous studies, and uses slightly different measurement scales. It finds that there is a strong correlation between objective and subjective performance measures. However, this correlation is far from perfect and the article concludes that researchers should attempt to validate their results by using both types of measures.
Scarlet – Real Time Mobile Augmented Reality Library

Since the beginning of the new century, augmented reality (AR) applications became very popular among smart phone users. There is also an emerging trend of users becoming the producers of the visual content in the AR environments. In this paper, we present Scarlet – a novel library for Android developers of AR applications. The key problem of AR is the registration of a virtual and the real world. Scarlet can recognize different visual markers (images, logos, black-and-white markers) very fast using the stateof-the-art local feature detection methods. Different local features were tested and are presented in the paper. Scarlet library also provides several functions for designing AR application. The main advantage in comparison to OpenCV4Android is that it is faster and the creation of AR application is easier for the user. Scarlet is nearly 11.5 times faster then OpenCV4Android 2.3. 1. Also, the number of the native calls is minimized.
A robust framework for short text categorization based on topic model and integrated classifier

In this paper, we propose a method for short text categorization using topic model and integrated classifier. To enrich the representation of short text, the Latent Dirichlet Allocation (LDA) model is used to extract latent topic information. While for classification, we combine two classifiers for achieving high reliability. Particularly, we train LDA models with variable number of topics using the Wikipedia corpus as external knowledge base, and extend labeled Web snippets by potential topics extracted by LDA. Then, the enriched representation of snippets are used to learn Maximum Entropy (MaxEnt) and support vector machine (SVM) classifiers separately. Finally, viewing that the most possible predicted result will appear in the top two candidates selected by MaxEnt classifier, we develop a novel scheme that if the gap between these candidates is large enough, the predicted result is considered to be reliable; otherwise, the SVM classifier will be integrated with MaxEnt classifier to make a comprehensive prediction. Experimental results show that our framework is effective and can outperform the state-of-the-art techniques.
On the solutions of the minimum energy problem in one dimensional sensor networks

We discuss solutions of the minimum energy problem in one dimensional wireless sensor networks for the data transmission cost function $E(x_i,x_j) = d(x_i,x_j)^a + \lambda \;d(x_i,x_j)^b$ with any exponent $a,b\in R$ and $\lambda \geq 0$, where $d(x_i,x_j)$ is a distance between transmitter and receiver. We define the minimum energy problem in terms of sensors signal power, transmission time and capacities of a transmission channels. We prove, that for the point-to-point data transmission method utilized by the sensors in the physical layer, when the transmitter adjust the power of its radio signal to the distance to the receiver, the solutions of the minimum energy problem written in terms of data transmission cost function and in terms of the sensors signal power coincide.
FEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES U . K . World War I and Interwar Data for Business Cycle and Growth Analysis

This article contributes new time series for studying the U.K. economy during World War I and the interwar period. The time series are per capita hours worked and average tax rates of capital income, labor income, and consumption. Uninterrupted time series of these variables are provided for an annual sample that runs from 1913 to 1938. We highlight the usefulness of these time series with several empirical applications. We use per capita hours worked in a growth accounting exercise to measure the contributions of capital, labor, and productivity to output growth. The average tax rates are employed in a Bayesian model averaging experiment to reevaluate the Benjamin and Kochin (1979) regression. JEL classification: E32, E62, N14, N34, N44
Structure of Tacit Knowledge and the Mechanism of Tacit Knowledge Promoting Knowledge Innovation Inside Enterprise

All the tacit knowledge inside enterprise is outlined into three knowledge dimensions: individual's skill knowledge, organization knowledge, and management system knowledge. The position and function of the knowledge inside tacit knowledge system is different from each other, and three knowledge dimensions influencing knowledge innovation also differs from each other. To order to manage tacit knowledge inside enterprise, accelerate the knowledge innovation, improve the competition level, the thesis provides a clear tacit knowledge system framework.
SNAP23 is selectively expressed in airway secretory cells and mediates baseline and stimulated mucin secretion

SNAP23 (23-kDa paralogue of synaptosome-associated protein of 25 kDa) is expressed in secretory but not ciliated cells of airway epithelium, suggesting that it mediates regulated but not constitutive secretion in polarized epithelia. Baseline but not stimulated mucin secretion in heterozygous mutant mice is fully compensated by increased intracellular stores.
How disorder impacts routing in human-centric disruption tolerant networks

Human-Centric DTNs exhibit some degree of regularity on their temporal contact patterns. The impact of this regularity on network performances has not been well studied and analyzed. In this paper, we study this temporal dimension of Human-Centric DTNs and its impacts on routing performances. We propose a simple parametric network model which covers the full spectrum of contact patterns from strictly periodic to fully random ones. Based on this model, we study the impact of contact patterns regularity on routing performances and we show how to exploit the temporal structure to navigate with a good resource/performance tradeoff in Human-Centric DTNs. Simulation and analytical analysis show that efficient routing with respect to their degree of regularity emerge within a subset of dynamic networks. Moreover, we show there is a specific degree of regularity where routing performance achieves its optimum.
Statistical Natural Language Generation from Tabular Non-textual Data

Most of the existing natural language generation (NLG) techniques employing statistical methods are typically resource and time intensive. On the other hand, handcrafted rule-based and template-based NLG systems typically require signiﬁcant human/designer efforts. In this paper, we proposed a statistical NLG technique which does not require any semantic relational knowledge and takes much less time to generate output text. The sys-tem can be used in those cases where source non-textual data are in the form of tuple in some tabular dataset. We carried out our experiments on the Prodigy-METEO wind forecasting dataset. For the evaluation purpose, we used both human evaluation and automatic evaluation. From the evaluation results we found that the linguistic quality and correctness of the texts generated by the system are better than many existing NLG systems.
Improving Dermoscopy Image Classification Using Color Constancy

Robustness is one of the most important characteristics of computer-aided diagnosis systems designed for dermoscopy images. However, it is difficult to ensure this characteristic if the systems operate with multisource images acquired under different setups. Changes in the illumination and acquisition devices alter the color of images and often reduce the performance of the systems. Thus, it is important to normalize the colors of dermoscopy images before training and testing any system. In this paper, we investigate four color constancy algorithms: Gray World, max-RGB, Shades of Gray, and General Gray World. Our results show that color constancy improves the classification of multisource images, increasing the sensitivity of a bag-of-features system from 71.0% to 79.7% and the specificity from 55.2% to 76% using only 1-D RGB histograms as features.
On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).
Consistency mechanisms for a distributed lookup service supporting mobile applications

This paper presents a general-purpose distributed lookup service, denoted Passive Distributed Indexing (PDI). PDI stores entries in form of (key, value) pairs in index caches located in each mobile device. Index caches are filled by epidemic dissemination of popular index entries. By exploiting node mobility, PDI can resolve most queries locally without sending messages outside the radio coverage of the inquiring node. Thus, PDI reduces network traffic for the resolution of keys to values. For keeping index caches coherent, configurable value timeouts implementing implicit invalidation and lazy invalidation caches implementing explicit invalidation are introduced. Inconsistency in index caches due to weak connectivity or node failure is handled by value timeouts. Lazy invalidation caches reduce the fraction of stale index entries due to modified data at the origin node. Similar to index caches, invalidation caches are filled by epidemic distributions of invalidation messages. Simulation results show that with the suitable integration of both invalidation mechanisms, more than 95% of results delivered by PDI index caches are up-to-date for the considered scenario.
Fauda television series and the turning of asymmetrical conflict into television entertainment

Since its release in February 2015, television drama/action series Fauda has attracted enormous public attention and debate, first in Israel and then abroad, following its global release on Netflix. The story of an Israeli undercover unit chasing an infamous Palestinian terrorist, the series was praised for its ‘realistic’ depiction of the conflict and empathetic portrayal of the opposing sides, as well as for the frenetic rhythm and suspense of the plot. On the other hand, the series has faced criticism for downplaying and obscuring the realities of the Israeli occupation, focusing on a cat and mouse thriller rather than the hardships incurred by a civilian population under military control. Based on a qualitative analysis of media coverage in Israel and abroad, this article analyzes the complex relationship between an ongoing asymmetrical conflict and its representation in fictional television entertainment.
Treating Illness Distress in Chronic Illness: Integrating Mental Health Approaches With Illness Self-Management

Cognitive-behavioral therapy (CBT) is an evidence-based treatment for depression and anxiety recommended for those with and without physical long-term conditions (LTCs). However, the cognitive-behavioral mechanisms targeted in CBT protocols are based on empirical cognitive-behavioral models of depression and anxiety. In these models, emotions are conceptualized as primary mental health disorders rather than a reaction to the challenges of living with a LTC commonly referred to as illness distress. This raises important clinical questions with theoretical implications. These include: Is the experience of illness distress conceptually distinct from primary mental health diagnoses of anxiety and mood disorder? Are there unique cognitive-behavioral mechanisms related to illness self-management, which should be incorporated into CBT for illness distress? How can illness self-management interventions be embedded within existing CBT protocols for depression and anxiety? To address these questions, we distinguish between primary mental health disorders and illness distress conceptually and explore the impact of this on tailored treatment planning and engagement. Second, we review how health psychology theoretical models can help to inform modifications of existing cognitive-behavioral treatments for anxiety and depression to better support the needs of individuals experiencing illness distress. Third, we provide examples of how to embed processes important for illness self-management including, illness cognitions and adherence, alongside existing CBT techniques. The mechanisms and intervention techniques discussed may help to inform the development of integrated CBT treatments for illness distress for future hypothesis testing in comparative effectiveness trials.
Predictive scheme for proximate interactions

Development and projections of wireless PDAs enable to envisage the design of new application classes that exploit short range and direct communications (e.g. one-hop communications). In the near future we may imagine mobile users dynamically and spontaneously exchanging information as soon as they are close enough. In such a context, a connection between two nodes may be broken at any time because of the unconstrained mobility of the nodes and the short communication range. Therefore transmitting data in the most efficient and reliable way becomes a real challenge. To give a first solution to this problem, we propose a mobility-aware software architecture that estimates the duration of a communication link before processing information exchanges.
The Nature of the Muscle-Relaxing Factor

High speed centrifugal fractionation of homogenates of rabbit skeletal muscle has led to the discovery of a soluble muscle-relaxing factor in the homogenate. Assay of the relaxing activity with deoxycholate-treated myofibrils and reconstituted actomyosin systems has established that the activity is not produced by the presence of contaminants. Relaxing activity could be removed or destroyed by charcoal, dialysis, prolonged heating, and treatment with the chelating resin, chelex-100, making it improbable that the effect is due simply to calcium deficiency. Many of the characteristics of this muscle-relaxing factor suggest that it is very similar to or the same as the factor formed by the incubation of muscle granule fractions and ATP. Evidence is presented that some soluble protein component is involved in the stabilization of the factor. The relaxing activity could be separated from the high molecular weight material in the supernatant by the technique of gel filtration. On the basis of the gel used, the molecular weight of the active agent should be less than 4000.
Is the Us Stock Market Myopic?

This paper tests whether the US stock market is myopic, in the sense that it places less than the appropriate weight on expected long-run earnings. The tests are made possible through reliance on a valuation model used by Ohlson [1995] that permits us, using only minimal assumptions, to make precise statements about how prices should relate to earnings expected at different points on the forecast horizon, under the null of market efficiency. We detect some anomalous stock price behavior, but find no support for the claim that stock prices exhibit myopic behavior as we have defined it here.