nils-herrmann/clean_scirepeval huggingface dataset:
DeepMem: Learning Graph Neural Network Models for Fast and Robust Memory Forensic Analysis

Kernel data structure detection is an important task in memory forensics that aims at identifying semantically important kernel data structures from raw memory dumps. It is primarily used to collect evidence of malicious or criminal behaviors. Existing approaches have several limitations: 1) list-traversal approaches are vulnerable to DKOM attacks, 2) robust signature-based approaches are not scalable or efficient, because it needs to search the entire memory snapshot for one kind of objects using one signature, and 3) both list-traversal and signature-based approaches all heavily rely on domain knowledge of operating system. Based on the limitations, we propose DeepMem, a graph-based deep learning approach to automatically generate abstract representations for kernel objects, with which we could recognize the objects from raw memory dumps in a fast and robust way. Specifically, we implement 1) a novel memory graph model that reconstructs the content and topology information of memory dumps, 2) a graph neural network architecture to embed the nodes in the memory graph, and 3) an object detection method that cross-validates the evidence collected from different parts of objects. Experiments show that DeepMem achieves high precision and recall rate in identify kernel objects from raw memory dumps. Also, the detection strategy is fast and scalable by using the intermediate memory graph representation. Moreover, DeepMem is robust against attack scenarios, like pool tag manipulation and DKOM process hiding.
Genetic algorithms and Machine Learning

There is no a priori reason why machine learning must borrow from nature. A field could exist, complete with well-defined algorithms, data structures, and theories of learning, without once referring to organisms, cognitive or genetic structures, and psychological or evolutionary theories. Yet at the end of the day, with the position papers written, the computers plugged in, and the programs debugged, a learning edifice devoid of natural metaphor would lack something. It would ignore the fact that all these creations have become possible only after three billion years of evolution on this planet. It would miss the point that the very ideas of adaptation and learning are concepts invented by the most recent representatives of the species Homo sapiens from the careful observation of themselves and life around them. It would miss the point that natural examples of learning and adaptation are treasure troves of robust procedures and structures.
Visual cortex inspired features for object detection in X-ray images

Visual cortex inspired features mimic what we know of the brain's visual cortex, which is still the best existing object detection system regarding speed and accuracy. For this paper we benchmarked two prominent implementations of these features, Mutch and Lowe's SLF-HMAX and Pinto et al.'s V1-like, against the popular local invariant features SIFT and PHOW in combination with the bag of visual words approach. The benchmark task was the detection of illicit objects in X-ray images of luggage. X-ray inspection is one of the main means of preventing the transport of illicit objects into sensitive areas. The visual cortex inspired features performed superior to the conventional features, probably owing to the textureless nature of X-ray images and the encoding of geometric information.
Optimal trajectory for swim-out acoustic decoy to countermeasure torpedo

The optimal trajectory of a swim-out acoustic decoy defending against an acoustic homing torpedo is studied. First, the dynamic models of the torpedo and submarine, and several trajectory models of the decoy are established. The factors that affect the countermeasure effectiveness are analyzed. Then based on the assessment criterion, genetic algorithm is used to obtain the optimal parameters of the trajectory models under different cases. Simulation results show that the helical and circular models of decoy trajectory are dominated by three other models, i.e., straight line, second turn-angle and snake trajectory.
SPAM E-MAIL DETECTION USING CLASSIFIERS AND ADABOOST TECHNIQUE

The Internet has turned our existence upside down and has dramatically revolutionalized many different fields. Emails, social networking sites, surfing all have become a part and parcel of our lives. Emails are the fastest way to send information from one place to another. Whatever may be the work on the Internet, emails are involved everywhere. But, nowadays, emails are getting more prone to exploitation due to malicious attacks which include spam mails. Spam is flooding the whole internet with many copies of the same messages so as to force people who would not otherwise choose to receive such messages. So, in this study, Analyzing the performance of certain classifiers like Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Decision Tree with the help of a dataset is done. These classifiers help in detecting spam emails with the help of keywords. Boosting grants power to these classifiers to improve their accuracy of prediction in most of the cases. Adaptive boosting also known as Adaboosting is used after the result of every classifier. This is implemented with the help of WEKA interface. The results have shown a comparison of all the three algorithms with boosting on the same dataset.
Analysis and design of compact two-way Wilkinson power dividers using coupled lines

This paper analyzes the two-way Wilkinson power dividers using coupled lines as λ/4 impedance transformer to have more compact layout. The analysis shows that the input port matching is only influenced by the even mode impedance of the coupled lines. Once the even mode impedance is fixed, all other specifications of the power divider are determined by the odd mode impedance of the coupled lines. Design tradeoffs among the output matching, isolation and the odd mode impedance are shown. By using smaller odd mode impedance, layout constraint can be relaxed. To validate the analysis, Wilkinson power dividers using coupled lines with areas of 1.5 cm2 and 1.25 cm2 are designed together with a conventional divider with an area of 3.8 cm2. The experimental results of the three designs are comparable. When coupled lines are used, the layout is more compact with a reduction in size of more than 50% compared to the conventional design.
Sensors and systems for fruit detection and localization: A review

This paper reviews the research and development of machine vision systems for fruit detection and localization for robotic harvesting and/or crop-load estimation of specialty tree crops including apples, pears, and citrus. Variable lighting condition, occlusions, and clustering are some of the important issues needed to be addressed for accurate detection and localization of fruit in orchard environment. To address these issues, various techniques have been investigated using different types of sensors and their combinations as well as with different image processing techniques. This paper summarizes various techniques and their advantages and disadvantages in detecting fruit in plant or tree canopies. The paper also summarizes the sensors and systems developed and used by researchers to localize fruit as well as the potential and limitations of those systems. Finally, major challenges for the successful application of machine vision system for robotic fruit harvesting and crop-load estimation, and potential future directions for research and development are discussed.
WLAN location determination via clustering and probability distributions

We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.
E-book Reading and its Impact on Academic Status of Students at Payame Noor University, Iran

The purpose of this study is to explore the effect of taking advantage of electronic books on the academic progress of students at Payame Noor University, Iran. This research is of descriptive and survey methodology. The statistical population includes the students of public administration in Shiraz Payame Noor University, who are studying in the academic year 2013-2014. 142 students have been chosen by simple random sampling. Results indicated that use of electronic resources causes a meaningful difference to be created in several contexts such as academic progress, variety of learning resources, flexibility while learning, and learning effectiveness between the students of virtual and conventional programs.
Long term video segmentation through pixel level spectral clustering on GPUs

We introduce a new technique for performing video segmentation combining the state-of-the-art image segmentation and optical flow algorithms on GPUs. We avoid pre-clustering into superpixels and probabilistic reasoning, and instead view the problem as a generalization of image segmentation techniques. Utilizing spectral clustering techniques at the pixel level (as opposed to 2D/3D superpixels), we demonstrate video segmentation over hundreds of frames - far beyond what has been achieved through pixel level spectral segmentation techniques before. Our algorithm achieves comparable accuracy as other sparse motion clustering techniques while still maintaining 100% density in segmentation over long time periods. We achieve better accuracy with lower oversegmentation compared to dense video segmentation techniques. We exploit increased computational power made available through parallelism in GPUs and efficient numerical algorithms to achieve these results. We show our results on the motion segmentation dataset . Our technique can also be used to provide good quality 3D superpixels and extended to tasks where the ability to track 3D volumes over time is useful.
Road Surface 3D Reconstruction Based on Dense Subpixel Disparity Map Estimation

Various 3D reconstruction methods have enabled civil engineers to detect damage on a road surface. To achieve the millimeter accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used. However, none of the existing stereo matching algorithms are specially suitable for the reconstruction of the road surface. Hence in this paper, we propose a novel dense subpixel disparity estimation algorithm with high computational efficiency and robustness. This is achieved by first transforming the perspective view of the target frame into the reference view, which not only increases the accuracy of the block matching for the road surface but also improves the processing speed. The disparities are then estimated iteratively using our previously published algorithm, where the search range is propagated from three estimated neighboring disparities. Since the search range is obtained from the previous iteration, errors may occur when the propagated search range is not sufficient. Therefore, a correlation maxima verification is performed to rectify this issue, and the subpixel resolution is achieved by conducting a parabola interpolation enhancement. Furthermore, a novel disparity global refinement approach developed from the Markov random fields and fast bilateral stereo is introduced to further improve the accuracy of the estimated disparity map, where disparities are updated iteratively by minimizing the energy function that is related to their interpolated correlation polynomials. The algorithm is implemented in C language with a near real-time performance. The experimental results illustrate that the absolute error of the reconstruction varies from 0.1 to 3 mm.
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds

Kernel k-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear k-means clustering algorithm. However, kernel kmeans clustering is computationally expensive when the non-linear feature map is highdimensional and there are many input points. Kernel approximation, e.g., the Nyström method, has been applied in previous works to approximately solve kernel learning problems when both of the above conditions are present. This work analyzes the application of this paradigm to kernel k-means clustering, and shows for the first time that applying the linear k-means clustering algorithm to O( ) features constructed using a so-called rank-restricted Nyström approximation results in cluster assignments that satisfy a 1 + approximation ratio in terms of the kernel k-means cost function, relative to the guarantee provided by the same algorithm without the use of the Nyström method. As part of the analysis, this work establishes the first 1 + relativeerror trace norm guarantee for low-rank approximation using the rank-restricted Nyström approximation. Empirical evaluations on the 8.1 million instance MNIST8M dataset demonstrate the scalability and usefulness of kernel k-means clustering with Nyström approximation. This work argues that spectral clustering using Nyström approximation—a popular and computationally efficient, but theoretically unsound approach to non-linear clustering— should be replaced with the efficient and theoretically sound combination of kernel k-means clustering with Nyström approximation. The superior performance of the latter approach is empirically verified.
Database Dependency Discovery: A Machine Learning Approach

Database dependencies, such as functional and multivalued dependencies, express the presence of structure in databas e relations, that can be utilised in the database design proce ss. The discovery of database dependencies can be viewed as an induction problem, in which general rules (dependencies) a re obtained from specific facts (the relation). This viewpoint has the advantage of abstracting away as much as possible from the particulars of the dependencies. The algorithms in this paper are designed such that they can easily be generalised to other kinds of dependencies. Like in current approaches to computational induction such as inductive logic programming, we distinguish between top down algorithms and bottom-up algorithms. In a top-down approach, hypotheses are generated in a systematic way and then tested against the given relation. In a bottom-up approach, the relation is inspected in order to see what dependencies it may satisfy or violate. We give algorithms for bot h approaches.
A Path-Tracking Criterion for an LHD Articulated Vehicle

A path-tracking criterion for the so-called LHD (load-haul-dump) truck used in underground mining is proposed in this paper. It exploits the particular configuration of this vehicle, composed of two units connected by an actuated articulation. The task is to follow the path represented by the middle of the tunnel, maintaining the whole vehicle at a reduced distance from the path itself, to decrease the risk of crashes against the walls of the tunnel. This is accomplished via feedback through the synthesis of an appropriate path-tracking criterion. The criterion is based on monitoring the distances of the midpoints of both axles of the vehicle from their orthogonal projections on the path, using two different moving frames simultaneously. Local asymptotic stability to paths of constant curvature is achieved by means of linear-state feedback. mobile robots, articulated vehicles, mining trucks, path tracking, off-tracking, feedback stabilization
FI-CAP: Robust Framework to Benchmark Head Pose Estimation in Challenging Environments

Head pose estimation is challenging in a naturalistic environment. To effectively train machine-learning algorithms, we need datasets with reliable ground truth labels from diverse environments. We present Fi-Cap, a helmet with fiducial markers designed for head pose estimation. The relative position and orientation of the tags from a reference camera can be automatically obtained from a subset of the tags. Placed at the back of the head, it provides a reference system without interfering with sensors that record frontal face. We quantify the performance of the Fi-Cap by (1) rendering the 3D model of the design, evaluating its accuracy under various rotation, image resolution and illumination conditions, and (2) comparing the predicted head pose with the location of the projected beam of a laser mounted on glasses worn by the subjects in controlled experiments conducted in our laboratory. Fi-Cap provides ideal benchmark information to evaluate automatic algorithms and alternative sensors for head pose estimation in a variety of challenging environments, including our target application for advanced driver assistance systems (ADAS).
A direct fingerprint minutiae extraction approach based on convolutional neural networks

Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of minutiae. In this paper, a novel minutiae extraction approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks. Minutiae can be effectively extracted due to the well designed architectures. Furthermore, the accuracy is guaranteed in that the comprehensive estimate is made to eliminate spurious minutiae. Moreover, a number of implement skills are employed both to avoid overfitting and to improve the robustness. This approach makes a good performance because it not only makes all use of information in fingerprint images but also learns the minutiae patterns from large amounts of data. Comparisons are made with previous works and a widely applied commercial fingerprint identification system. Results show that our approach performs better both in accuracy and robustness.
6-DOF hovering controller design of the Quad Tiltrotor aircraft: Simulations and experiments

This paper presents a particular class of a convertible micro aerial vehicle (MAV) with fixed wings, the so-called Quad Tiltrotor aircraft. This aircraft is able to change its flight configuration from hover to level flight and vice versa by means of a transition maneuver. In this first part of the research, the hover dynamics of the Quad Tiltrotor is investigated. Dynamical model and nonlinear control based on Lyapunov design are studied. The presented approach focuses on the problem of finding a control law capable of stabilizing the aircraft's position. Some simulations results are given, which demonstrate the effectiveness of the controller. Further, some experimental results are presented tested on the Quad Tiltrotor experimental platform.
GraRep: Learning Graph Representations with Global Structural Information

In this paper, we present {GraRep}, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al.
 We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.
A Survey of Evaluation Methods and Measures for Interpretable Machine Learning

The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life. Explainable artificial intelligence systems can be a solution by self-explaining the reasoning behind the decisions and predictions of the intelligent system. Researchers from different disciplines work together to define, design and evaluate interpretable intelligent systems for the user. Our work supports the different evaluation goals in interpretable machine learning research by a thorough review of evaluation methodologies used in machine-explanation research across the fields of human-computer interaction, visual analytics, and machine learning. We present a 2D categorization of interpretable machine learning evaluation methods and show a mapping between user groups and evaluation measures. Further, we address the essential factors and steps for a right evaluation plan by proposing a nested model for design and evaluation of explainable artificial intelligence systems.
Gamut : Sifting through Images to Detect Android Malware

Mobile phones are omnipresent in present-day life. Everything can be controlled using this pocket-computer which typically runs on Android or iOS. However, the risk of phones breaches is greater than ever. In the first half of 2016, on average 9,468 Android malware samples are found every day . Malware detection systems in the wild cannot keep up with this growing number. In this work, I propose Gamut, a tool to generate different images out of an Android app without loss of information. The user can pick a level of semantic when generating the image. Datasets of images are created to train convolutional neural networks on malware detection. In this way, feature engineering is done by the neural network after adding domain knowledge. Hence, this ensures us an automatic feature space while achieving a high performance. Different levels of semantics have different results, peaking at 92% accuracy by combining domain-expert knowledge with the feature engineering by the neural network.
Reusing Ambient Light to Recognize Hand Gestures Prepared by Mahina -

In this paper, we explore the feasibility of reusing ambient light to recognize human gestures. We present GestureLite, a system that provides hand gesture detection and classification using the pre-existing light in a room. We observe that in an environment with a reasonably consistent lighting scheme, a given gesture will block some light rays and leave others unobstructed, resulting in the user casting a unique shadow pattern for that movement. GestureLite captures these unique shadow patterns using a small array of light sensors. Using standard machine learning techniques, GestureLite can learn these patterns and recognize new instances of specific gestures when the user performs them. We tested GestureLite using a 10-gesture dictionary in several real-world environments and found it achieves, on average, a gesture recognition accuracy of 98%.  2016
Essentials of fuzzy modeling and control

Essentials of Fuzzy Modeling and Control, on its back cover, presents itself as the only comprehensive guide in the field: it "gives you all the concepts, tools, and techniques for fuzzy control and modeling in one volume. The book follows a logical, pedagogically consistent format designed to fully acquaint readers with the entire range of fuzzy concepts, tools, and modeling techniques. While it is an excellent general introduction for students, the authors' ultimate goal is to give researchers an opportunity to acquire everything they need to build the kinds of fuzzy rule-based models used in the development of the controllers now being used for the next generation of intelligent systems."