nils-herrmann/clean_scirepeval huggingface dataset:
3D generic object categorization, localization and pose estimation

We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotations and scale changes. Our approach is to capture a compact model of an object category by linking together diagnostic parts of the objects from different viewing points. We emphasize on the fact that our "parts" are large and discriminative regions of the objects that are composed of many local invariant features. Instead of recovering a full 3D geometry, we connect these parts through their mutual homographic transformation. The resulting model is a compact summarization of both the appearance and geometry information of the object class. We propose a framework in which learning is done via minimal supervision compared to previous works. Our results on categorization show superior performances to state-of-the-art algorithms such as (Thomas et al., 2006). Furthermore, we have compiled a new 3D object dataset that consists of 10 different object categories. We have tested our algorithm on this dataset and have obtained highly promising results.
Conformance Checking Using Cost-Based Fitness Analysis

The growing complexity of processes in many organizations stimulates the adoption of business process analysis techniques. Typically, such techniques are based on process models and assume that the operational processes in reality conform to these models. However, experience shows that reality often deviates from hand-made models. Therefore, the problem of checking to what extent the operational process conforms to the process model is important for process management, process improvement, and compliance. In this paper, we present a robust replay analysis technique that is able to measure the conformance of an event log for a given process model. The approach quantifies conformance and provides intuitive diagnostics (skipped and inserted activities). Our technique has been implemented in the ProM 6framework. Comparative evaluations show that the approach overcomes many of the limitations of existing conformance checking techniques.
BitBlaze: A New Approach to Computer Security via Binary Analysis

In this paper, we give an overview of the BitBlaze project, a new approach to computer security via binary analysis. In particular, BitBlaze focuses on building a unified binary analysis platform and using it to provide novel solutions to a broad spectrum of different security problems. The binary analysis platform is designed to enable accurate analysis, provide an extensible architecture, and combines static and dynamic analysis as well as program verification techniques to satisfy the common needs of security applications. By extracting security-related properties from binary programs directly, BitBlaze enables a principled, root-cause based approach to computer security, offering novel and effective solutions, as demonstrated with over a dozen different security applications.
SlashBurn: Graph Compression and Mining beyond Caveman Communities

Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the cavemen graph', and compress them. We show that the block-diagonal mental image of the cavemen graph' is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the SLASHBURN method to recursively split a graph into hubs and spokes connected only by the hubs. We also propose techniques to select the hubs and give an ordering to the spokes, in addition to the basic SLASHBURN. We give theoretical analysis of the proposed hub selection methods. Our view point has several advantages: (a) it avoids the no good cuts' problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Through experiments, we show that SLASHBURN consistently outperforms other methods for all data sets, resulting in better compression and faster running time. Moreover, we show that SLASHBURN with the appropriate spokes ordering can further improve compression while hardly sacrificing the running time.
Design, Analysis, and Prototyping of a Novel-Structured Solid-Rotor-Ringed Line-Start Axial-Flux Permanent-Magnet Motor

This paper presents the design process, detailed analysis, and prototyping of a novel-structured line-start solid-rotor-based axial-flux permanent-magnet (AFPM) motor capable of autostarting with solid-rotor rings. The preliminary design is a slotless double-sided AFPM motor with four poles for high torque density and stable operation. Two concentric unilevel-spaced raised rings are added to the inner and outer radii of the rotor discs for smooth line-start of the motor. The design allows the motor to operate at both starting and synchronous speeds. The basic equations for the solid rings of the rotor of the proposed AFPM motor are discussed. Nonsymmetry of the designed motor led to its 3-D time-stepping finite-element analysis (FEA) via Vector Field Opera 14.0, which evaluates the design parameters and predicts the transient performance. To verify the design, a prototype 1-hp four-pole three-phase line-start AFPM synchronous motor is built and is used to test the performance in real time. There is a good agreement between experimental and FEA-based computed results. It is found that the prototype motor maintains high starting torque and good synchronization.
Developing a Teacher Dashboard For Use with Intelligent Tutoring Systems

Many dashboards display analytics generated by educational technologies, but few of them work with intelligent tutoring systems (ITSs). We are creating a teacher dashboard for use with ITSs built and used within our CTAT/Tutorshop infrastructure: an environment for authoring and deploying ITSs. The dashboard will take advantage of the fine-grained interaction data and derived analytics that CTAT-built ITSs produce. We are taking a user-centered design approach in which we target two usage scenarios for the dashboard. In one scenario, a teacher uses the dashboard while helping a class of students working with the tutoring software in the school’s computer lab. In the other, the teacher uses the dashboard to prepare for an upcoming class session. So far, we have completed a Contextual Inquiry, ideation, Speed Dating sessions in which teachers evaluated story boards, usability testing, and a classroom study with a mocked up version of the dashboard with real data from the teacher’s current classes and students. We are currently analyzing the data produced in these activities, iterating on the design of the dashboard, and implementing a full version of the dashboard. Unique characteristics of this dashboard may be that it leverages finegrained interaction data produced by an ITS and that it will be fully integrated with an ITS development and deployment environment, and therefore available for use with many ITSs.
Ahaa --agile, hybrid assessment method for automotive, safety critical smes

The need for software is increasingly growing in the automotive industry. Software development projects are, however, often troubled by time and budget overruns, resulting in systems that do not fulfill customer requirements. Both research and industry lack strategies to combine reducing the long software development lifecycles (as required by time-to-market demands) with increasing the quality of the software developed. Software process improvement (SPI) provides the first step in the move towards software quality, and assessments are a vital part of this process. Unfortunately, software process assessments are often expensive and time consuming. Additionally, they often provide companies with a long list of issues without providing realistic suggestions. The goal of this paper is to describe a new low-overhead assessment method that has been designed specifically for small-to-medium-sized (SMEs) organisations wishing to be automotive software suppliers. This assessment method integrates the structured-ness of the plan-driven SPI models of Capability Maturity Model Integration (CMMI) and Automotive SPICE with the flexibleness of agile practices.
Frequent pattern mining: current status and future directions

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.
OctNet: Learning Deep 3D Representations at High Resolutions

We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.
Encoder-decoder with focus-mechanism for sequence labelling based spoken language understanding

This paper investigates the framework of encoder-decoder with attention for sequence labelling based spoken language understanding. We introduce Bidirectional Long Short Term Memory - Long Short Term Memory networks (BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep learning. In the sequence labelling task, the input and output sequences are aligned word by word, while the attention mechanism cannot provide the exact alignment. To address this limitation, we propose a novel focus mechanism for encoder-decoder framework. Experiments on the standard ATIS dataset showed that BLSTM-LSTM with focus mechanism defined the new state-of-the-art by outperforming standard BLSTM and attention based encoder-decoder. Further experiments also show that the proposed model is more robust to speech recognition errors.
Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
NETWORK INTRUSION DETECTION USING NAÏVE BAYES

With the tremendous growth of network-based services and sensitive information on networks, network security is getting more and more importance than ever. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types posses a serious problem for their detection. The human labelling of the available network audit data instances is usually tedious, time consuming and expensive. In this paper, we apply one of the efficient data mining algorithms called naïve bayes for anomaly based network intrusion detection. Experimental results on the KDD cup’99 data set show the novelty of our approach in detecting network intrusion. It is observed that the proposed technique performs better in terms of false positive rate, cost, and computational time when applied to KDD’99 data sets compared to a back propagation neural network based approach.
Audio-visual speech recognition using deep bottleneck features and high-performance lipreading

This paper develops an Audio-Visual Speech Recognition (AVSR) method, by (1) exploring high-performance visual features, (2) applying audio and visual deep bottleneck features to improve AVSR performance, and (3) investigating effectiveness of voice activity detection in a visual modality. In our approach, many kinds of visual features are incorporated, subsequently converted into bottleneck features by deep learning technology. By using proposed features, we successfully achieved 73.66% lipreading accuracy in speaker-independent open condition, and about 90% AVSR accuracy on average in noisy environments. In addition, we extracted speech segments from visual features, resulting 77.80% lipreading accuracy. It is found VAD is useful in both audio and visual modalities, for better lipreading and AVSR.
Choosing effective colours for data visualization

We describe a technique for choosing multiple colours for use during data visualization. Our goal is a systematic method for maximizing the total number of colours available for use, while still allowing an observer to rapidly and accurately search a display for any one of the given colours. Previous research suggests that we need to consider three separate effects during colour selection: colour distance, linear separation, and colour category. We describe a simple method for measuring and controlling all of these effects. Our method was tested by performing a set of target identification studies; we analysed the ability of thirty eight observers to find a colour target in displays that contained differently coloured background elements. Results showed our method can be used to select a group of colours that will provide good differentiation between data elements during data visualization.
Phrase Localization and Visual Relationship Detection with Comprehensive Linguistic Cues

This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual  We model the appearance, size, and position of entity bounding boxes, adjectives that contain attribute information, and spatial relationships between pairs of entities connected by verbs or prepositions. We pay special attention to relationships between people and clothing or body part mentions, as they are useful for distinguishing individuals. We automatically learn weights for combining these cues and at test time, perform joint inference over all phrases in a caption. The resulting system produces a 4% improvement in accuracy over the state of the art on phrase localization on the Flickr30k Entities dataset  and a 4-10% improvement for visual relationship detection on the Stanford VRD dataset [20].
A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot

We address the problem of online path planning for optimal sensing with a mobile robot. The objective of the robot is to learn the most about its pose and the environment given time constraints. We use a POMDP with a utility function that depends on the belief state to model th finite horizon planning problem. We replan as the robot progresses throughout the environment. The POMDP is highdimensional, continuous, non-differentiable, nonlinear , nonGaussian and must be solved in real-time. Most existing techniques for stochastic planning and reinforcement lear ning are therefore inapplicable. To solve this extremely com plex problem, we propose a Bayesian optimization method that dynamically trades off exploration (minimizing uncer tainty in unknown parts of the policy space) and exploitation (capitalizing on the current best solution). We demonstrate our approach with a visually-guide mobile robot. The solution proposed here is also applicable to other closelyrelated domains, including active vision, sequential expe rimental design, dynamic sensing and calibration with mobile sensors.
Bandwidth Enhancement of Small-Size Planar Tablet Computer Antenna Using a Parallel-Resonant Spiral Slit

An internal planar tablet computer antenna having a small size of 12 × 35 mm2 printed on a 0.8-mm thick FR4 substrate for the WWAN operation in the 824-960 and 1710-2170 MHz bands is presented. The antenna comprises a driven strip, a parasitic shorted strip and a ground pad, all printed on the small-size FR4 substrate. For bandwidth enhancement of the antenna's lower band, the antenna applies a parallel-resonant spiral slit embedded in the ground pad, which generates a parallel resonance at about 1.2 GHz and in turn results in a new resonance occurred nearby the quarter-wavelength mode of the parasitic shorted strip. This feature leads to a dual-resonance characteristic obtained for the antenna's lower band, making it capable of wideband operation to cover the desired 824-960 MHz with a small antenna size. The antenna's upper band is formed by the higher-order resonant mode contributed by the parasitic shorted strip and the quarter-wavelength resonant mode of the driven strip and can cover the desired 1710-2170 MHz band. Details of the proposed antenna and the operating principle of the parallel-resonant spiral slit are presented.
Discovering informative content blocks from Web documents

In this paper, we propose a new approach to discover informative contents from a set of tabular documents (or Web pages) of a Web site. Our system, InfoDiscoverer, first partitions a page into several content blocks according to HTML tag <TABLE> in a Web page. Based on the occurrence of the features (terms) in the set of pages, it calculates entropy value of each feature. According to the entropy value of each feature in a content block, the entropy value of the block is defined. By analyzing the information measure, we propose a method to dynamically select the entropy-threshold that partitions blocks into either informative or redundant. Informative content blocks are distinguished parts of the page, whereas redundant content blocks are common parts. Based on the answer set generated from 13 manually tagged news Web sites with a total of 26,518 Web pages, experiments show that both recall and precision rates are greater than 0.956. That is, using the approach, informative blocks (news articles) of these sites can be automatically separated from semantically redundant contents such as advertisements, banners, navigation panels, news categories, etc. By adopting InfoDiscoverer as the preprocessor of information retrieval and extraction applications, the retrieval and extracting precision will be increased, and the indexing size and extracting complexity will also be reduced.
A Survey on Performance Analysis of DES , AES and RSA Algorithm along with LSB Substitution Technique

Now a days sharing the information over internet is becoming a critical issue due to security problems. Hence more techniques are needed to protect the shared data in an unsecured channel. The present work focus on combination of cryptography and steganography to secure the data while transmitting in the network. Firstly the data which is to be transmitted from sender to receiver in the network must be encrypted using the encrypted algorithm in cryptography .Secondly the encrypted data must be hidden in an image or video or an audio file with help of steganographic algorithm. Thirdly by using decryption technique the receiver can view the original data from the hidden image or video or audio file. Transmitting data or document can be done through these ways will be secured. In this paper we implemented three encrypt techniques like DES, AES and RSA algorithm along with steganographic algorithm like LSB substitution technique and compared their performance of encrypt techniques based on the analysis of its stimulated time at the time of encryption and decryption process and also its buffer size experimentally. The entire process has done in C#.
Can you see what i hear?: the design and evaluation of a peripheral sound display for the deaf

We developed two visual displays for providing awareness of environmental audio to deaf individuals. Based on fieldwork with deaf and hearing participants, we focused on supporting awareness of non-speech audio sounds such as ringing phones and knocking in a work environment. Unlike past work, our designs support both monitoring and notification of sounds, support discovery of new sounds, and do not require a priori knowledge of sounds to be detected. Our Spectrograph design shows pitch and amplitude, while our Positional Ripples design shows amplitude and location of sounds. A controlled experiment involving deaf participants found neither display to be significantly distracting. However, users preferred the Positional Ripples display and found that display easier to monitor (notification sounds were detected with 90% success in a laboratory setting). The Spectrograph display also supported successful detection in most cases, and was well received when deployed in the field.
Design and Experimental Validation of a Cooperative Driving System in the Grand Cooperative Driving Challenge

In this paper, we present the Cooperative Adaptive Cruise Control (CACC) architecture, which was proposed and implemented by the team from Chalmers University of Technology, Göteborg, Sweden, that joined the Grand Cooperative Driving Challenge (GCDC) in 2011. The proposed CACC architecture consists of the following three main components, which are described in detail: 1) communication; 2) sensor fusion; and 3) control. Both simulation and experimental results are provided, demonstrating that the proposed CACC system can drive within a vehicle platoon while minimizing the inter-vehicle spacing within the allowed range of safety distances, tracking a desired speed profile, and attenuating acceleration shockwaves.
Learning the Parameters of Determinantal Point Process Kernels

Determinantal point processes (DPPs) are wellsuited for modeling repulsion and have proven useful in applications where diversity is desired. While DPPs have many appealing properties, learning the parameters of a DPP is difficult, as the likelihood is non-convex and is infeasible to compute in many scenarios. Here we propose Bayesian methods for learning the DPP kernel parameters. These methods are applicable in largescale discrete and continuous DPP settings, even when the likelihood can only be bounded. We demonstrate the utility of our DPP learning methods in studying the progression of diabetic neuropathy based on the spatial distribution of nerve fibers, and in studying human perception of diversity in images.