Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatically generating malicious disks using symbolic execution

Many current systems allow data produced by potentially malicious sources to be mounted as a file system. File system code must check this data for dangerous values or invariant violations before using it. Because file system code typically runs inside the operating system kernel, even a single unchecked value can crash the machine or lead to an exploit. Unfortunately, validating file system images is complex: they form DAGs with complex dependency relationships across massive amounts of data bound together with intricate, undocumented assumptions. This paper shows how to automatically find bugs in such code using symbolic execution. Rather than running the code on manually-constructed concrete input, we instead run it on symbolic input that is initially allowed to be "anything. " As the code runs, it observes (tests) this input and thus constrains its possible values. We generate test cases by solving these constraints for concrete values. The approach works well in practice: we checked the disk mounting code of three widely-used Linux file systems: ext2, ext3, and JFS and found bugs in all of them where malicious data could either cause a kernel panic or form the basis of a buffer overflow attack
Adolescent Nicotine Dependence Measured by the Modified Fagerström Tolerance Questionnaire at Two Time Points

ABSTRACT Within the framework of a computer-assisted smoking pevention and cessation program, high school students completed a modified version of the Fagerstrom Tolerance Questionnaire (FTQ) as part of two in-class computerized survey sessions (Sessions I and II) spaced two months apart. Seventy-eight 10th, 11th and 12th grade smokers (at least 1 cigarette/week) who have completed both computer sessions were included in the analysis. Individuals who changed the number of cigarettes smoked between Sessions I and II were considered to have shifted their “true score” on nicotine dependence. No proxy item for nicotine dependence was available in the survey, so the item “How many cigarettes do you smoke a day? ” was extracted from the modified FTQ to serve as the proxy item; thus, the analysis of test-retest reliability was based on 6 FTQ items. The test-retest analysis was restricted to students whose number of cigarettes per day did not vary across time points (n = 51). Principal components analysis on the 7.
Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction

Regression under the "small n$, large p" condition, of small sample size n and large number of features p in the learning data set, is a recurring setting in which learning from data is difficult. With prior knowledge about relationships of the features, p can effectively be reduced, but explicating such prior knowledge is difficult for experts. In this paper we introduce a new method for eliciting expert prior knowledge about the similarity of the roles of features in the prediction task. The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters. Specifically, for learning to predict a target variable with Bayesian linear regression, the feature relationships are used to construct a Gaussian prior with a full covariance matrix for the regression coefficients. Evaluation of our method in experiments with simulated and real users on text data confirm that prior elicitation of feature similarities improves prediction accuracy. Furthermore, elicitation with an interactive scatterplot display outperforms straightforward elicitation where the users choose feature pairs from a feature list.
RecMap: Rectangular Map Approximations

In many application domains, data is collected and referenced by its geospatial location. Nowadays, different kinds of maps are used to emphasize the spatial distribution of one or more geospatial attributes. The nature of geospatial statistical data is the highly nonuniform distribution in the real world data sets. This has several impacts on the resulting map visualizations. Classical area maps tend to highlight patterns in large areas, which may, however, be of low importance. Cartographers and geographers used cartograms or value-by-area maps to address this problem long before computers were available. Although many automatic techniques have been developed, most of the value-by-area cartograms are generated manually via human interaction. In this paper, we propose a novel visualization technique for geospatial data sets called RecMap. Our technique approximates a rectangular partition of the (rectangular) display area into a number of map regions preserving important geospatial constraints. It is a fully automatic technique with explicit user control over all exploration constraints within the exploration process. Experiments show that our technique produces visualizations of geospatial data sets, which enhance the discovery of global and local correlations, and demonstrate its performance in a variety of applications
Review of selected algorithms in the method energy evening algorithm in wireless sensor network

In this paper compared the algorithms proposed in the method in wireless sensor network. Energy consumption is very important for self-powered radio nodes. But in some applications energy balancing is more important. Wireless sensor networks used in large areas like farmland or warehouses consist of hundreds of nodes. In classic method routing is directed to short time of transmission or low energy consumption. But unbalanced energy consumption can often cause unpredictable failures because of lack energy in frequently used nodes. Energy balancing prevents this behavior by dynamic bypassing the used nodes. The paper describes examples of algorithms that can be used in the method in wireless sensor network. The goal of this method is the prolongation of the network through a selection of routes data to minimize the scattering of energy in the nodes of the network.
Strategic alliances in emerging markets: an investigation of the effects of culture and emerging market characteristics on the performance of acquisitions in Eastern Europe

The objective of this study is to investigate the effects of culture - national, organisational, and regional - on the performance of strategic alliances (mergers, acquisitions, or joint ventures) in Eastern Europe. From a research perspective Eastern Europe is very much `virgin territory', in which the effect of Western European management techniques and processes should be uniquely detectable as few have previously existed. There are schools of thought that suggest that because of the years of Soviet domination there may be homogeneity of organisational cultures in the region, which will make the effect of national cultural differences easy to detect. Further, freedom from communism will result in the newly liberated economies exhibiting similar `emerging market' characteristics as the economies evolve, hence pointing towards some homogeneity of regional influences. Alternatively, many observers suggest that Eastern Europe, having been freed from Soviet domination, is re-establishing latent forms of free market systems that have been dormant during the Soviet era, and consequently the markets' development can be thought of as 'reemergence'. These issues of cultural differences and emerging market characteristics have not been previously researched at the organisational level in this environment. Hitherto international strategic alliances have largely been unsuccessful from the viewpoints of either management or investors. The reasons for this are thought to lie in the implementation phase of the management of the alliance, as it is now recognised that value can only be added after the deal or agreement has been signed. Within the implementation phase, cultural integration is thought to be the major challenge. Cultural integration may have different challenges in Eastern Europe because of the issues outlined above. An exploratory study based on inductive methods has established that there is nohomogeneity of organisational cultures in the region. The findings are in accordance with the small group of researchers who suggest that is the manner in which different cultures are managed, rather than the cultural differences themselves, which creates a basis for successful integration. Additionally, two factors, the effects of experience and reputation, not prominent in the management literature, are identified as important in the integration process. It is postulated that success in managing the integration process is a function of organisational capability as much as the adoption of a formulaic process based on best practice or theoretical considerations, and a model for assessing such capability is proposed. The findings suggest that the markets of Eastern Europe are following patterns of `reemergence' rather than `emergence', and the study questions whether in fact emerging markets display common characteristics which are observable at the level of the firm.
Efficient instance annotation in multi-instance learning

The cost associated with manually labeling every individual instance in large datasets is prohibitive. Significant labeling efforts can be saved by assigning a collective label to a group of instances (a bag). This setup prompts the need for algorithms that allow labeling individual instances (instance annotation) based on bag-level labels. Probabilistic models in which instance-level labels are latent variables can be used for instance annotation. Brute-force computation of instance-level label probabilities is exponential in the number of instances per bag due to marginalization over all possible combinations. Existing solutions for addressing this issue include approximate methods such as sampling or variational inference. This paper proposes a discriminative probability model and an expectation maximization procedure for inference to address the instance annotation problem. A key contribution is a dynamic programming solution for exact computation of instance probabilities in quadratic time. Experiments on bird song, image annotation, and two synthetic datasets show a significant accuracy improvement by 4%-14% over a recent state-of-the-art rank loss SIM method.
TBuffer: Constructing a Tail Buffer in DRAM for Flash-Based Main Memory System

The Ultra-DIMM constituted by DRAM and Flash memory is a promising solution used to tackle the challenges existing in traditional DRAM in terms of energy consuming and scalability. In this hybrid memory system, DRAM is used as the data buffer of Flash memory due to the performance and endurance gaps between main memory and Flash. However, the inconsistency of access granularity between the main memory and Flash makes the DRAM-based buffer complex. On the one hand, the basic access unit of Flash is page. However, buffering pages of Flash in DRAM without distinguishing the hot cache lines from cold cache lines within each page leads to a waste of cache capacity. On the other hand, general-purpose replacement schemes focus on high hit rate and do not consider the peculiarities of Flash, thus leads to performance and lifespan overhead. In this paper, we propose TBuffer, an additional buffer in DRAM enhanced by history-aware identification and LazyFlush. History-aware identification can increase hit rate by evicting cold cache lines and keeping more hot cache lines in DRAM, while LazyFlush can further improve performance and lifespan by delaying flushing dirty objects and reducing writes to Flash. We evaluate the TBuffer via trace-driven simulations. Experimental results have shown that it outperforms other existing schemes, increases hit rate by up to 12%, reduces the access latency by a factor of up to 50.8% with an average of 19.7%, and achieves 16.6% lifespan improvement on average.
Learning Sparse Functional Factors for Large-Scale Service Clustering

The past decade has witnessed a fast growth of web-based services, making discovery of user desired services from a large and diverse service space a fundamental challenge. Service clustering has been demonstrated as a promising solution by automatically detecting functionally similar services so that they can be searched and discovered together. In this way, both the efficiency and accuracy of service discovery can be improved. However, the autonomous nature of service providers leads to highly diverse usage of terms in their respective service descriptions. Furthermore, a typical service description is comprised of very limited terms due to the small number of (and focused) functionalities offered by the service. These unique characteristics make service descriptions different from regular text documents, which poses additional challenges when clustering large-scale services. Recent works show that service clustering can benefit from discovery and use of functionality-related latent factors to represent services as opposed to a large and diverse set of terms. Nonetheless, how to determine the total number of latent functional factors and sparsely assign them to each service description arises as a central challenge, especially for a large service space where there is no easy way to enumerate the types of different functionalities. In this paper, we propose a machine learning method that automatically learns the number of latent functional factors in a service space. It also enforces the sparsity constraint, which allows each service to be represented by a small number of latent functional factors. The sparsity constraint is in line with the fact that most real-world services only provide limited functionalities. We conduct extensive experiments on two sets of real-world service data to demonstrate the effectiveness of the proposed service clustering approach.
Area-Efficient Evaluation of a Class of Arithmetic Expressions Using Deeply Pipelined Floating-Point Cores

Due to technological advances,it has becomepossible to implement floating-point coreson FPGAs in an effort to provide hardware acceleration for the myriad applications that require high performance floating-point arithmetic. However, in order to achieve a high clock rate, these floating-point cores must be deeply pipelined. Due to this deep pipelining and the complexity of floating-point arithmetic, floating-point cores use a great deal of the FPGA’s area. It is thus important to use as few floating-point cores in an architecture as possible.However, the deep pipelining makes it difficult to reusethe samefloatingpoint core for a series of floating-point computations that are dependent upon one another. In this paper, we describe an area-efficient architecture and algorithm for the evaluation of arithmetic expressions.This design effectively hides the pipeline latencyof the floating-point coresand usesonly onefloating-point core for each type of operator in the expression.The design is applicable to a wide variety of fields suchasscientific computing, cognition, and graph theory. We analyzethe performance of this design when implemented on a Xilinx Virtex-II Pro FPGA.
Civilian gunshot wounds to the spinal cord and cauda equina.

We evaluated 42 patients with neurological deficits after civilian gunshot wounds to the spine. Thirty-five of these patients (the study population presented here) received their initial and follow-up care at Louisiana State University Medical Center in Shreveport over a 4-year period. Each patient had incurred a single gunshot wound to the spinal cord or cauda equina with an accompanying neurological deficit. The patient population was divided into three groups. Group 1 patients had incurred a complete motor and sensory loss below the injury (20 patients (57%]. Group 2 patients had incurred incomplete spinal cord injuries (9 patients (26%], whereas Group 3 patients had cauda equina injuries (6 patients (17%]. Myelography was performed for all Group 2 and 3 patients as well as Group 1 patients in whom the trajectory of the bullet did not explain a higher level of neurological injury (3 patients (15%]. A decompressive operation was performed in the patients whose myelography showed neural compression. Three patients in Group 1 (15%), 5 patients in Group 2 (56%), and 5 Group 3 patients (83%) underwent operation. All 3 Group 1 patients who underwent operation had some improvement of nerve root function postoperatively. All operated Group 2 and 3 patients had improvement of myelopathic or radicular function postoperatively. All began improving within several days of operation, implying a cause and effect relationship. None of the 17 nonoperated Group 1 patients improved neurologically, whereas 3 of the 4 nonoperated Group 2 patients improved. The single nonoperated Group 3 patient improved neurologically. (ABSTRACT TRUNCATED AT 250 WORDS)
Measurement of intrinsic rise times for various L(Y)SO and LuAG scintillators with a general study of prompt photons to achieve 10 ps in TOF-PET

The coincidence time resolution (CTR) of scintillator based detectors commonly used in positron emission tomography is well known to be dependent on the scintillation decay time (τd ? >) and the number of photons detected (n′ ? >), i.e. CTR∝τd/n′ ? >. However, it is still an open question to what extent the scintillation rise time (τr ? >) and other fast or prompt photons, e.g. Cherenkov photons, at the beginning of the scintillation process influence the CTR. This paper presents measurements of the scintillation emission rate for different LSO type crystals, i.e. LSO:Ce, LYSO:Ce, LSO:Ce codoped Ca and LGSO:Ce. For the various LSO-type samples measured we find an average value of 70 ps for the scintillation rise time, although some crystals like LSO:Ce codoped Ca seem to have a much faster rise time in the order of 20 ps. Additional measurements for LuAG:Ce and LuAG:Pr show a rise time of 535 ps and 251 ps, respectively. For these crystals, prompt photons (Cherenkov) can be observed at the beginning of the scintillation event. Furthermore a significantly lower rise time value is observed when codoping with calcium. To quantitatively investigate the influence of the rise time to the time resolution we measured the CTR with the same L(Y)SO samples and compared the values to Monte Carlo simulations. Using the measured relative light yields, rise- and decay times of the scintillators we are able to quantitatively understand the measured CTRs in our simulations. Although the rise time is important to fully explain the CTR variation for the different samples tested we determined its influence on the CTR to be in the order of a few percent only. This result is surprising because, if only photonstatistics of the scintillation process is considered, the CTR would be proportional to the square root of the rise time. The unexpected small rise time influence on the CTR can be explained by the convolution of the scintillation rate with the single photon time resolution (SPTR) of the photodetector and the photon travel spread (PTS) in the crystal. The timing benefits of prompt photons at the beginning of the scintillation process (Cherenkov etc) are further studied, which leads to the conclusion that the scintillation rise time, SPTR and PTS have to be lowered simultaneously to fully profit from these fast photons in order to improve the CTR significantly.
An extended spreading activation technique for hashtag recommendation in microblogging platforms

Microblogging platforms such as Twitter have become immensely popular. Users of these platforms use hashtags to gather similar posts under one umbrella. However, the percentage of tweets containing hashtags is still small, and hashtag use is very heterogeneous because users are free to create their own hashtags. Therefore, users might spend considerable time searching for appropriate hashtags for their posts in real time. Thus, there is a need for a powerful and effective hashtag recommendation system to assist users. Recent publications have described methods to address this need from different viewpoints, but these methods are based on traditional approaches to classification and tag recommendation that are successful for long texts but not for short texts, such as tweets. The existing methods use a syntactic mechanism, which has limitations; for example, they do not consider blogger preferences in the choice of hashtags. In this paper, we introduce an approach to hashtag recommendation in microblogging platforms that is based on our extended version of the spreading activation technique and uses several semantic knowledge bases to fill the gap between the semantic context of tweets and the semantic meaning of hashtags. The proposed approach semantically annotates the current tweet and the most popular hashtags in a recent period, after which it constructs a semantic network using DBpedia. We propose the use of an extended version of the spreading activation technique to calculate the semantic similarities of tweets and hashtags and then recommend the top k suitable hashtags to the blogger. The experimental results obtained from two Twitter datasets collected in 2012 and 2016 demonstrate the effectiveness of our approach.
Global Shapes of F-actin Depolymerization-competent Minimal Gelsolins

Background: Shape-function studies are necessary to design better therapeutic alternatives of the plasma gelsolin. Results: N-terminal fragment 30–161 is the smallest segment with F-actin depolymerization potential, and G1-G3 can function independent of Ca2+ ions or low pH. Conclusion: The g2-g3 linker plays a role in imparting pH/Ca2+ insensitivity to G1-G3. Significance: We provide the first evidence that g2-g3 linker regulates mobility of the G1 domain. Because of its ability to rapidly depolymerize F-actin, plasma gelsolin has emerged as a therapeutic molecule in different disease conditions. High amounts of exogenous gelsolin are, however, required to treat animal models of different diseases. Knowing that the F-actin depolymerizing property of gelsolin resides in its N terminus, we made several truncated versions of plasma gelsolin. The smaller versions, particularly the one composed of the first 28–161 residues, depolymerized the F-actin much faster than the native gelsolin and other truncates at the same molar ratios. Although G1-G3 loses its dependence on Ca2+ or low pH for the actin depolymerization function, interestingly, G1-G2 and its smaller versions were found to regain this requirement. Small angle x-ray scattering-based shape reconstructions revealed that G1-G3 adopts an open shape in both the presence and the absence of Ca2+ as well as low pH, whereas G1-G2 and residues 28–161 prefer collapsed states in Ca2+-free conditions at pH 8. The mutations in the g2-g3 linker resulted in the calcium sensitivity of the mutant G1-G3 for F-actin depolymerization activity, although the F-actin-binding sites remained exposed in the mutant G1-G3 as well as in the smaller truncates even in the Ca2+-free conditions at pH 8. Furthermore, unlike wild type G1-G3, calcium-sensitive mutants of G1-G3 acquired closed shapes in the absence of free calcium, implying a role of g2-g3 linker in determining the open F-actin depolymerizing-competent shape of G1-G3 in this condition. We demonstrate that the mobility of the G1 domain, essential for F-actin depolymerization, is indirectly regulated by the gelsolin-like sequence of g2-g3 linker.