nils-herrmann/clean_scirepeval huggingface dataset:
Automated procedure clustering for reverse engineering PL/SQL programs

PL/SQL programs are mainly composed of procedures that interact with a database. These procedures are grouped into packages at the architecture design level for increasing modularity and maintainability. However, the intended modular structure is subject to architectural drift especially for large scale programs that are maintained for a long duration. Existing reverse engineering and refactoring techniques cannot be directly applied for these programs. Unlike other programs, PL/SQL programs involve procedures that are in most cases indirectly coupled due to their database operations. In this paper, we provide an approach and a tool that support reverse engineering and refactoring of PL/SQL programs. We analyze dependencies among PL/SQL procedures based on the accessed database elements. We represent these dependencies in the form of a design structure matrix. Then, we cluster the procedures and propose a packaging structure to the designer. We performed an industrial case study from the telecommunications domain. We observed promising results in which several unexisted packages were proposed by our tool and the accuracy of the results were approved by domain experts.
HosTaGe : a Mobile Honeypot for Collaborative Defense [ Tool Demo ]

The continuous growth of the number of cyber attacks along with the massive increase of mobile devices creates a highly heterogeneous landscape in terms of security challenges. We argue that in order for security researchers to cope with both the massive amount and the complexity of attacks, a more pro-active approach has to be taken into account. In addition, distributed attacks that are carried out by interconnected attackers require a collaborative defense. Diverging from traditional security defenses, honeypots are systems whose value lies on in being attacked and compromised. In this paper, we extend the idea of HosTaGe, i.e., a low interaction honeypot for mobile devices. Our system is specifically designed in a user-centric manner and runs out-ofthe-box in the Android operating system. We present the design rational and discuss the different attack surfaces that HosTaGe is able to handle. The main contribution of this paper is the introduction of the collaborative capabilities of HosTaGe.
Learning the parts of objects by non-negative matrix factorization

Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.
Segmentation of Liver Lesions with Reduced Complexity Deep Models

We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver. The proposed architecture uses bilinear interpolation with subpixel convolution at the last layer to upscale the course feature in bottle neck architecture. Since bilinear interpolation and sub-pixel convolution do not have any learnable parameter, our overall model is faster and occupies less memory footprint than the traditional U-net. We evaluate our proposed architecture on the highly competitive dataset of 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method achieves competitive results while reducing the number of learnable parameters roughly by a factor of 13.8 compared to the original UNet model.
Eyewear Computing - Augmenting the Human with Head-mounted Wearable Assistants (Dagstuhl Seminar 16042)

The seminar was composed of workshops and tutorials on head-mounted eye tracking, egocentric vision, optics, and head-mounted displays. The seminar welcomed 30 academic and industry researchers from Europe, the US, and Asia with a diverse background, including wearable and ubiquitous computing, computer vision, developmental psychology, optics, and human-computer interaction. In contrast to several previous Dagstuhl seminars, we used an ignite talk format to reduce the time of talks to one half-day and to leave the rest of the week for hands-on sessions, group work, general discussions, and socialising. The key results of this seminar are 1) the identification of key research challenges and summaries of breakout groups on multimodal eyewear computing, egocentric vision, security and privacy issues, skill augmentation and task guidance, eyewear computing for gaming, as well as prototyping of VR applications, 2) a list of datasets and research tools for eyewear computing, 3) three small-scale datasets recorded during the seminar, 4) an article in ACM Interactions entitled “Eyewear Computers for Human-Computer Interaction”, as well as 5) two follow-up workshops on “Egocentric Perception, Interaction, and Computing” at the European Conference on Computer Vision (ECCV) as well as “Eyewear Computing” at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). Seminar January 24–29, 2016 – http://www.dagstuhl.de/16042 1998
Best Keyword Cover Search

It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.
Android taint flow analysis for app sets

One approach to defending against malicious Android applications has been to analyze them to detect potential information leaks. This paper describes a new static taint analysis for Android that combines and augments the FlowDroid and Epicc analyses to precisely track both inter-component and intra-component data flow in a set of Android applications. The analysis takes place in two phases: given a set of applications, we first determine the data flows enabled individually by each application, and the conditions under which these are possible; we then build on these results to enumerate the potentially dangerous data flows enabled by the set of applications as a whole. This paper describes our analysis method, implementation, and experimental results.
Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines

We propose a content-based soft annotation (CBSA) procedure for providing images with semantical labels. The annotation procedure starts with labeling a small set of training images, each with one single semantical label (e.g., forest, animal, or sky). An ensemble of binary classifiers is then trained for predicting label membership for images. The trained ensemble is applied to each individual image to give the image multiple soft labels, and each label is associated with a label membership factor. To select a base binary-classifier for CBSA, we experiment with two learning methods, Support Vector Machines (SVMs) and Bayes Point Machines (BPMs, and compare their class-prediction accuracy. Our empirical study on a 116-category 25K-image set shows that the BPM-based ensemble provides better annotation quality than the SVM-based ensemble for supporting multimodal image retrievals.
Movement, posture and equilibrium: Interaction and coordination

CONTENTS I. Introduction 2. Why arc anticipatory adjustments of posture associated with movements? 2.1. Definition of anticipatory postural adjustments 2.2. "Internal" disturbance of posture and equilibrium 2.3. Modular approach to posture versus global approach to equilibrium 2.4. Anticipatory postural adjustments associated with equilibrium maintenance 2.4.1. Axial movements 2.4.2. Arm movements 2.4.3. Leg movements and equilibrium control 2.5. Stabilization of the position of given segments 2.6. Coordination between posture and equilibrium 3. How are the anticipatory postural adjustments organized? 3.
Contrastive Estimation: Training Log-Linear Models on Unlabeled Data

Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on u labeled data, we requireunsupervisedestimation methods for log-linear models; few exist. We describe a novel approach,contrastive estimation . We show that the new technique can be intuitively understood as exploiting implicit negative evidence and is computationally efficient. Applied to a sequence labeling problem—POS tagging given a tagging dictionary and unlabeled text—contrastive estimation outperforms EM (with the same feature set), is more robust to degradations of the dictionary, and can largely recover by modeling additional features.
Optimization Criteria, Sensitivity and Robustness of Motion and Structure Estimation

The prevailing efforts to study the standard formulation of motion and structure recovery have been recently focused on issues of sensitivity and and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues we study the main aspects of the problem: the choice of objective functions, optimization techniques and the sensitivity and robustness issues in the presence of noise. We clearly reveal the relationship among different objective functions, such as “(normalized) epipolar constraints”, “reprojection error” or “triangulation”, which can all be be unified in a new “ optimal triangulation” procedure formulated as a constrained optimization problem. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so called “essential manifold”, making the new optimization techniques on Riemanian manifolds directly applicable. Using these analytical results we provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points of a function defined on essential manifold and the simulation results clarify the difference between the effect of bas relief ambiguity and other types of local minima leading to a consistent interpretations of simulation results over large range of signal-to-noise ratio and variety of configurations. Further more we justify the choice of the linear techniques for (re)initialization and for detection of incorrect local minima.
A Survey Paper : Areas , Techniques and Challenges of Opinion Mining

Mining is a promising discipline, defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The field aims at solving the problems related to opinions about products, Politian in newsgroup posts, review sites, comments on Facebook posts and twitter etc. This paper is about to covers the techniques, applications, long and short future research areas, Research gaps and future challenges in opinion mining. Further, an attempt has been made to discuss in detail the use of supervised, unsupervised, machine learning and case based reasoning techniques in opinion mining to perform computational treatment of sentiments.
AN EFFICIENT HEURISTIC BASED TREE CLASSIFIER MODEL FOR MEDICAL DISEASE DIAGNOSIS

The explosive growth of medical data and distributed sources augments the demand for efficient disease diagnosis and identify the cause of disease. The varying filtering concept used on the existing system performs the decision making process to exploit the contextual information. But filtering was not developed on medical expert support system for predicting drug effectiveness. Data publishing using slicing partitioned high dimensional data into parallel and perpendicular vectors. Slicing technique with column generalization was not examined thoroughly with classification implications. To thoroughly predict the drug effectiveness on medical expert support system, Smoothing Heuristic Divider Method of Classification tree (SHDC) is proposed in this paper. The main objective behind SHDC is to examine and classify thoroughly drug effectiveness using classification tree on the varying set of medical data. Initially, Support Vector Machine predictive model is developed in SHDC method to predict data quality and to perform mapping operation. Data quality in SVM is measured using double exponential smoothing technique to predict the medical data changes for varied intervals. SVM in SHDC method maps the input data vectors into high dimensional space to obtain optimal hyper plane support vectors on medical data support system. Secondly, classification is performed using Heuristic Divider Method to split (i.e.,) classify the medical data hyper planes for improving the searching process. Finally, the classifier is provided to support medical expert support system using Support Vector Machine which constructs separating hyper plane. With this, the separating hyper plane maximizes classifier margin and performs experimental evaluation on factors such as true rating rate, classification accuracy and finally percentage reduction in prediction time.
CASIA@V2: A MLN-based Question Answering System over Linked Data

We present a question answering system (CASIA@V2) over Linked Data (DBpedia), which translates natural language questions into structured queries automatically. Existing systems usually adopt a pipeline framework, which contains four major steps: 1) Decomposing the question and detecting candidate phrases; 2) mapping the detected phrases into semantic items of Linked Data; 3) grouping the mapped semantic items into semantic triples; and 4) generating the rightful SPARQL query. We present a jointly learning framework using Markov Logic Network(MLN) for phrase detection, phrases mapping to semantic items and semantic items grouping. We formulate the knowledge for resolving the ambiguities in three steps of QALD as first-order logic clauses in a MLN. We evaluate our approach on QALD-4 test dataset and achieve an F-measure score of 0.36, an average precision of 0.32 and an average recall of 0.40 over 50 questions.
Location Systems for Ubiquitous Computing

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General transfer-matrix method for optical multilayer systems with coherent, partially coherent, and incoherent interference.

The optical response of coherent thin-film multilayers is often represented with Fresnel coefficients in a 2 x 2 matrix configuration. Here the usual transfer matrix was modified to a generic form, with the ability to use the absolute squares of the Fresnel coefficients, so as to include incoherent (thick layers) and partially coherent (rough surface or interfaces) reflection and transmission. The method is integrated by use of models for refractive-index depth profiling. The utility of the method is illustrated with various multilayer structures formed by ion implantation into Si, including buried insulating and conducting layers, and multilayers with a thick incoherent layer in an arbitrary position.
Does the application of constraint-induced movement therapy during acute rehabilitation reduce arm impairment after ischemic stroke?

Motor dysfunction after unilateral deafferentation in primates can be overcome by restraining the unaffected limb. We asked whether a constraint-induced movement (CIM) program could be implemented within 2 weeks after stroke and whether CIM is more effective than traditional upper-extremity (UE) therapies during this period.



Twenty-three persons were enrolled in a pilot randomized, controlled trial that compared CIM with traditional therapies. A blinded observer rated the primary end point, the Action Research Arm Test (ARA). Inclusion criteria were the following: ischemic stroke within 14 days, persistent hemiparesis, evidence of preserved cognitive function, and presence of a protective motor response. Differences between the groups were compared by using Student's t tests, ANCOVA, and Mann-Whitney U: tests.



Twenty subjects completed the 14-day treatment. Two adverse outcomes, a recurrent stroke and a death, occurred in the traditional group; 1 CIM subject met rehabilitation goals and was discharged before completing 14 inpatient days. The CIM treatment group had significantly higher scores on total ARA and pinch subscale scores (P:<0.05). Differences in the mean ARA grip, grasp, and gross movement subscale scores did not reach statistical significance. UE activities of daily living performance was not significantly different between groups, and no subject withdrew because of pain or frustration.



A clinical trial of CIM therapy during acute rehabilitation is feasible. CIM was associated with less arm impairment at the end of treatment. Long-term studies are needed to determine whether CIM early after stroke is superior to traditional therapies.
Real Time Intrusion and Wormhole Attack Detection in Internet of Things

There are currently more objects connected to the Internet than people in the world. This gap will continue to grow, as more objects gain the ability to directly interface with the Internet. Providing security in IoT is challenging as the devices are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL and 6LoWPAN. Due to this it is easy to attack in IoT network. The proposed system is a novel intrusion detection system for the IoT, which is capable of detecting Wormhole attack and attacker. The proposed methods uses the location information of node and neighbor information to identify the Wormhole attack and received signal strength to identify attacker nodes. Design of such system will help in securing the IoT network and may prevents such attacks. This method is very energy efficient and only takes fixed number of UDP packets for attack detection, hence it is beneficial for resource constrained environment.
Catching Whales and Minnows Using WiFiNet: Deconstructing Non-WiFi Interference Using WiFi Hardware

We present WiFiNet— a system to detect, localize, and quantify the interference impact of various non-WiFi interference sources on WiFi traffic using commodity WiFi hardware alone. While there are numerous specialized solutions today that can detect the presence of non-WiFi devices in the unlicensed spectrum, the unique aspects of WiFiNet are four-fold: First, WiFiNet quantifies the actual interference impact of each non-WiFi device on specific WLAN traffic in real-time, which can vary from being a whale — a device that currently causes a significant reduction in WiFi throughput — to being a minnow — a device that currently has minimal impact. WiFiNet continuously monitors changes in a device’s impact that depend on many spatio-temporal factors. Second, it can accurately discern an individual device’s impact in presence of multiple and simultaneously operating non-WiFi devices, even if the devices are of the exact same type. Third, it can pin-point the location of these non-WiFi interference sources in the physical space. Finally, and most importantly, WiFiNet meets all these objectives not by using sophisticated and high resolution spectrum sensors, but by using emerging off-the-shelf WiFi cards that provide coarse-grained energy samples per sub-carrier. Our deployment and evaluation of WiFiNet demonstrates its high accuracy — interference estimates are within±10% of the ground truth and the median localization error is ≤ 4 meters. We believe a system such as WiFiNet can empower existing WiFi clients and APs to adapt against non-WiFi interference in ways that have not been possible before.
Eye images increase generosity , but not for long : the limited effect of a false cue

⁎ Corresponding author. E-mail address: asparks@uoguelph.ca (A. Sparks). 1090-5138/$ – see front matter  Al http://dx.doi.org/10.1016/j.evolhumbehav.2013.05.001 Article history: Initial receipt 5 November 2012 Final revision received 3 May 2013