nils-herrmann/clean_scirepeval huggingface dataset:
A Bayesian game approach for intrusion detection in wireless ad hoc networks

In wireless ad hoc networks, although defense strategies such as intrusion detection systems (IDSs) can be deployed at each mobile node, significant constraints are imposed in terms of the energy expenditure of such systems. In this paper, we propose a game theoretic framework to analyze the interactions between pairs of attacking/defending nodes using a Bayesian formulation. We study the achievable Nash equilibrium for the attacker/defender game in both static and dynamic scenarios. The dynamic Bayesian game is a more realistic model, since it allows the defender to consistently update his belief on his opponent's maliciousness as the game evolves. A new Bayesian hybrid detection approach is suggested for the defender, in which a lightweight monitoring system is used to estimate his opponent's actions, and a heavyweight monitoring system acts as a last resort of defense. We show that the dynamic game produces energy-efficient monitoring strategies for the defender, while improving the overall hybrid detection power.
Pyrolysis and dehalogenation of plastics from waste electrical and electronic equipment (WEEE): a review.

Plastics from waste electrical and electronic equipment (WEEE) have been an important environmental problem because these plastics commonly contain toxic halogenated flame retardants which may cause serious environmental pollution, especially the formation of carcinogenic substances polybrominated dibenzo dioxins/furans (PBDD/Fs), during treat process of these plastics. Pyrolysis has been proposed as a viable processing route for recycling the organic compounds in WEEE plastics into fuels and chemical feedstock. However, dehalogenation procedures are also necessary during treat process, because the oils collected in single pyrolysis process may contain numerous halogenated organic compounds, which would detrimentally impact the reuse of these pyrolysis oils. Currently, dehalogenation has become a significant topic in recycling of WEEE plastics by pyrolysis. In order to fulfill the better resource utilization of the WEEE plastics, the compositions, characteristics and dehalogenation methods during the pyrolysis recycling process of WEEE plastics were reviewed in this paper. Dehalogenation and the decomposition or pyrolysis of WEEE plastics can be carried out simultaneously or successively. It could be 'dehalogenating prior to pyrolysing plastics', 'performing dehalogenation and pyrolysis at the same time' or 'pyrolysing plastics first then upgrading pyrolysis oils'. The first strategy essentially is the two-stage pyrolysis with the release of halogen hydrides at low pyrolysis temperature region which is separate from the decomposition of polymer matrixes, thus obtaining halogenated free oil products. The second strategy is the most common method. Zeolite or other type of catalyst can be used in the pyrolysis process for removing organohalogens. The third strategy separate pyrolysis and dehalogenation of WEEE plastics, which can, to some degree, avoid the problem of oil value decline due to the use of catalyst, but obviously, this strategy may increase the cost of whole recycling process.
ONDUX: on-demand unsupervised learning for information extraction

Information extraction by text segmentation (IETS) applies to cases in which data values of interest are organized in implicit semi-structured records available in textual sources (e.g. postal addresses, bibliographic information, ads). It is an important practical problem that has been frequently addressed in the recent literature. In this paper we introduce ONDUX (On Demand Unsupervised Information Extraction), a new unsupervised probabilistic approach for IETS. As other unsupervised IETS approaches, ONDUX relies on information available on pre-existing data to associate segments in the input string with attributes of a given domain. Unlike other approaches, we rely on very effective matching strategies instead of explicit learning strategies. The effectiveness of this matching strategy is also exploited to disambiguate the extraction of certain attributes through a reinforcement step that explores sequencing and positioning of attribute values directly learned on-demand from test data, with no previous human-driven training, a feature unique to ONDUX. This assigns to ONDUX a high degree of flexibility and results in superior effectiveness, as demonstrated by the experimental evaluation we report with textual sources from different domains, in which ONDUX is compared with a state-of-art IETS approach.
Algorithmic Statistics

While Kolmogorov complexity is the accepted absolute measure of information content of an individual finite object, a similarly absolute notion is needed for the relation between an individual data sample and an individual model summarizing the information in the data, for example, a finite set (or probability distribution) where the data sample typically came from. The statistical theory based on such relations between individual objects can be called algorithmic statistics, in contrast to ordinary statistical theory that deals with relations between probabilistic ensembles. Since the algorithmic theory deals with individual objects and not with averages over ensembles of objects it is surprising that similar properties hold, albeit sometimes in weaker form. We first recall the notion of algorithmic mutual information between individual objects and show that this information cannot be increased by algorithmic or probabilistic means (as is the case with probabilistic mutual information). We develop the algorithmic theory of typical statistic, sufficient statistic, and minimal sufficient statistic. This theory is based on two-part codes consisting of the code for the statistic (the model embodying the regularities, the meaningful information, in the data) and the model-to-data code. In contrast to the situation in probabilistic statistical theory, the algorithmic relation of (minimal) sufficiency is an absolute relation between the individual model and the individual data sample. We distinguish implicit and explicit descriptions of the models. We give characterizations of algorithmic (a.k.a. Kolmogorov) minimal sufficient statistics for all data samples for both description modes—in the explicit mode under some constraints. We also strengthen and elaborate some earlier results by Shen on the “Kolmogorov structure function” and “absolutely non-stochastic objects”—objects that have no simpler algorithmic (explicit) sufficient statistics and are literally their own algorithmic (explicit) minimal sufficient statistics. We discuss the implication of the results for potential applications.
Concept learning as motor program induction: A large-scale empirical study

Human concept learning is particularly impressive in two respects: the internal structure of concepts can be representationally rich, and yet the very same concepts can also be learned from just a few examples. Several decades of research have dramatically advanced our understanding of these two aspects of concepts. While the richness and speed of concept learning are most often studied in isolation, the power of human concepts may be best explained through their synthesis. This paper presents a large-scale empirical study of one-shot concept learning, suggesting that rich generative knowledge in the form of a motor program can be induced from just a single example of a novel concept. Participants were asked to draw novel handwritten characters given a reference form, and we recorded the motor data used for production. Multiple drawers of the same character not only produced visually similar drawings, but they also showed a striking correspondence in their strokes, as measured by their number, shape, order, and direction. This suggests that participants can infer a rich motorbased concept from a single example. We also show that the motor programs induced by individual subjects provide a powerful basis for one-shot classification, yielding far higher accuracy than state-of-the-art pattern recognition methods based on just the visual form.
The Cost of Digital Advertisement: Comparing User and Advertiser Views

Digital advertisements are delivered in the form of static images, animations or videos, with the goal to promote a product, a service or an idea to desktop or mobile users. Thus, the advertiser pays a monetary cost to buy ad-space in a content provider’s medium (e.g., website) to place their advertisement in the consumer’s display. However, is it only the advertiser who pays for the ad delivery? Unlike traditional advertisements in mediums such as newspapers, TV or radio, in the digital world, the end-users are also paying a cost for the advertisement delivery. Whilst the cost on the advertiser’s side is clearly monetary, on the end-user, it includes both quantifiable costs, such as network requests and transferred bytes, and qualitative costs such as privacy loss to the ad ecosystem. In this study, we aim to increase user awareness regarding the hidden costs of digital advertisement in mobile devices, and compare the user and advertiser views. Specifically, we built OpenDAMP, a transparency tool that passively analyzes users’ web traffic and estimates the costs in both sides. We use a year-long dataset of 1270 real mobile users and by juxtaposing the costs of both sides, we identify a clear imbalance: the advertisers pay several times less to deliver ads, than the cost paid by the users to download them. In addition, the majority of users experience a significant privacy loss, through the personalized ad delivery mechanics.
Bag-of-visual-words and spatial extensions for land-use classification

We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data.
 The methods are evaluated using a large ground truth image dataset of 21 land-use classes. In addition to comparisons with standard approaches, we perform extensive evaluation of different configurations such as the size of the visual dictionaries used to derive the BOVW representations and the scale at which the spatial relationships are considered.
 We show that even though BOVW approaches do not necessarily perform better than the best standard approaches overall, they represent a robust alternative that is more effective for certain land-use classes. We also show that extending the BOVW approach with our proposed spatial co-occurrence kernel consistently improves performance.
A Domain-independent Rule-based Framework for Event Extraction

We describe the design, development, and API of ODIN (Open Domain INformer), a domainindependent, rule-based event extraction (EE) framework. The proposed EE approach is: simple (most events are captured with simple lexico-syntactic patterns), powerful (the language can capture complex constructs, such as events taking other events as arguments, and regular expressions over syntactic graphs), robust (to recover from syntactic parsing errors, syntactic patterns can be freely mixed with surface, token-based patterns), and fast (the runtime environment processes 110 sentences/second in a real-world domain with a grammar of over 200 rules). We used this framework to develop a grammar for the biochemical domain, which approached human performance. Our EE framework is accompanied by a web-based user interface for the rapid development of event grammars and visualization of matches. The ODIN framework and the domain-specific grammars are available as open-source code.
A Probabilistic Theory of Deep Learning

A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks such as visual object and speech recognition. The key factor complicating such tasks is the presence of numerous nuisance variables, for instance, the unknown object position, orientation, and scale in object recognition or the unknown voice pronunciation, pitch, and speed in speech recognition. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks; they are constructed from many layers of alternating linear and nonlinear processing units and are trained using large-scale algorithms and massive amounts of training data. The recent success of deep learning systems is impressive — they now routinely yield pattern recognition systems with nearor super-human capabilities — but a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive.
Scalable Multi-query Optimization for SPARQL

This paper revisits the classical problem of multi-query optimization in the context of RDF/SPARQL. We show that the techniques developed for relational and semi-structured data/query languages are hard, if not impossible, to be extended to account for RDF data model and graph query patterns expressed in SPARQL. In light of the NP-hardness of the multi-query optimization for SPARQL, we propose heuristic algorithms that partition the input batch of queries into groups such that each group of queries can be optimized together. An essential component of the optimization incorporates an efficient algorithm to discover the common sub-structures of multiple SPARQL queries and an effective cost model to compare candidate execution plans. Since our optimization techniques do not make any assumption about the underlying SPARQL query engine, they have the advantage of being portable across different RDF stores. The extensive experimental studies, performed on three popular RDF stores, show that the proposed techniques are effective, efficient and scalable.
Convolutional Neural Network Based Automatic Object Detection on Aerial Images

We are witnessing daily acquisition of large amounts of aerial and satellite imagery. Analysis of such large quantities of data can be helpful for many practical applications. In this letter, we present an automatic content-based analysis of aerial imagery in order to detect and mark arbitrary objects or regions in high-resolution images. For that purpose, we proposed a method for automatic object detection based on a convolutional neural network. A novel two-stage approach for network training is implemented and verified in the tasks of aerial image classification and object detection. First, we tested the proposed training approach using UCMerced data set of aerial images and achieved accuracy of approximately 98.6%. Second, the method for automatic object detection was implemented and verified. For implementation on GPGPU, a required processing time for one aerial image of size 5000 × 5000 pixels was around 30 s.
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure

For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual’s disease, which can in turn enable clinicians to optimize treatments. We represent an individual’s disease trajectory as a continuous-valued continuous-time function describing the severity of the disease over time. We propose a hierarchical latent variable model that individualizes predictions of disease trajectories. This model shares statistical strength across observations at different resolutions–the population, subpopulation and the individual level. We describe an algorithm for learning population and subpopulation parameters offline, and an online procedure for dynamically learning individual-specific parameters. Finally, we validate our model on the task of predicting the course of interstitial lung disease, a leading cause of death among patients with the autoimmune disease scleroderma. We compare our approach against state-of-the-art and demonstrate significant improvements in predictive accuracy.
Reverse-Engineering a Cryptographic RFID Tag

The security of embedded devices often relies on the secrecy of proprietary cryptographic algorithms. These algorithms and their weaknesses are frequently disclosed through reverse-engineering software, but it is commonly thought to be too expensive to reconstruct designs from a hardware implementation alone. This paper challenges that belief by presenting an approach to reverse-engineering a cipher from a silicon implementation. Using this mostly automated approach, we reveal a cipher from an RFID tag that is not known to have a software or micro-code implementation. We reconstruct the cipher from the widely used Mifare Classic RFID tag by using a combination of image analysis of circuits and protocol analysis. Our analysis reveals that the security of the tag is even below the level that its 48-bit key length suggests due to a number of design flaws. Weak random numbers and a weakness in the authentication protocol allow for pre-computed rainbow tables to be used to find any key in a matter of seconds. Our approach of deducing functionality from circuit images is mostly automated, hence it is also feasible for large chips. The assumption that algorithms can be kept secret should therefore to be avoided for any type of silicon chip. Il faut qu’il n’exige pas le secret, et qu’il puisse sans inconvénient tomber entre les mains de l’ennemi. ([A cipher] must not depend on secrecy, and it must not matter if it falls into enemy hands.) August Kerckhoffs, La Cryptographie Militaire, January 1883 [13]
Adaptive Grids for Clustering Massive Data Sets

Clustering is a key data mining problem. Density and grid based technique is a popular way to mine clusters in a large multi-dimensional space wherein clusters are regarded as dense regions than their surroundings. The attribute values and ranges of these attributes characterize the clusters. Fine grid sizes lead to a huge amount of computation while coarse grid sizes result in loss in quality of clusters found. Also, varied grid sizes result in discovering clusters with different cluster descriptions. The technique of Adaptive grids enables to use grids based on the data distribution and does not require the user to specify any parameters like the grid size or the density thresholds. Further, clusters could be embedded in a subspace of a high dimensional space. We propose a modified bottom-up subspace clustering algorithm to discover clusters in all possible subspaces. Our method scales linearly with the data dimensionality and the size of the data set. Experimental results on a wide variety of synthetic and real data sets demonstrate the effectiveness of Adaptive grids and the effect of the modified subspace clustering algorithm. Our algorithm explores at-least an order of magnitude more number of subspaces than the original algorithm and the use of adaptive grids yields on an average of two orders of magnitude speedup as compared to the method with user specified grid size and threshold.
Social Contagion Theory: Examining Dynamic Social Networks and Human Behavior

Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a 'three degrees of influence' property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations.
Extreme gigantomastia in pregnancy: case report and review of literature

We present an extreme case of Gigantomastia in pregnancy in a 24-year old woman, gravida 2, in a 28 weeks’ of gestation, with a total breast weight of 33 kg, complicated by infection, ulcerations and subsequent hemorrhage. Thorough laboratory analyses did not reveal any hint as to the cause of this enormous breast enlargement. Gynecological examinations and ultrasound revealed a viable, progressive normal fetus. The severity of the problem is further emphasized by the patients’ breathing problems and even big difficulty in standing and walking. We performed bilateral simple mastectomy as a life-saving procedure to prevent fatal complications. The procedure finished without any complications or large amount of blood loss. There are less than 100 cases of gravid gigantomastia reported, but never to such extreme breast weight. Etiology remains uncertain, and controversy exists in therapeutic modality. According to the literature the most reliable conservative treatment is bromocriptine therapy, but if the condition progresses surgical intervention, in the form of reduction mammoplasty or simple mastectomy, is the treatment of choice.
Search-based software maintenance

The high cost of software maintenance could potentially be greatly reduced by the automatic refactoring of object-oriented programs to increase their understandability, adaptability and extensibility. This paper describes a novel approach in providing automated refactoring support for software maintenance; the formulation of the task as a search problem in the space of alternative designs. Such a search is guided by a quality evaluation function that must accurately reflect refactoring goals. We have constructed a search-based software maintenance tool and report here the results of experimental refactoring of two Java programs, which yielded improvements in terms of the quality functions used. We also discuss the comparative merits of the three quality functions employed and the actual effect on program design that resulted from their use
A Probabilistic Analysis of the Efficiency of Automated Software Testing

We study the relative efficiencies of the random and systematic approaches to automated software testing. Using a simple but realistic set of assumptions, we propose a general model for software testing and define sampling strategies for random (R) and systematic (S0) testing, where each sampling is associated with a sampling cost: 1 and c units of time, respectively. The two most important goals of software testing are: (i) achieving in minimal time a given degree of confidence x in a program's correctness and (ii) discovering a maximal number of errors within a given time bound n̂. For both (i) and (ii), we show that there exists a bound on c beyond which R performs better than S0 on the average. Moreover for (i), this bound depends asymptotically only on x. We also show that the efficiency of R can be fitted to the exponential curve. Using these results we design a hybrid strategy H that starts with R and switches to S0 when S0 is expected to discover more errors per unit time. In our experiments we find that H performs similarly or better than the most efficient of both and that S0 may need to be significantly faster than our bounds suggest to retain efficiency over R.