nils-herrmann/clean_scirepeval huggingface dataset:
A survey of data mining techniques for analyzing crime patterns

In recent years the data mining is data analyzing techniques that used to analyze crime data previously stored from various sources to find patterns and trends in crimes. In additional, it can be applied to increase efficiency in solving the crimes faster and also can be applied to automatically notify the crimes. However, there are many data mining techniques. In order to increase efficiency of crime detection, it is necessary to select the data mining techniques suitably. This paper reviews the literatures on various data mining applications, especially applications that applied to solve the crimes. Survey also throws light on research gaps and challenges of crime data mining. In additional to that, this paper provides insight about the data mining for finding the patterns and trends in crime to be used appropriately and to be a help for beginners in the research of crime data mining.
An Empirical Study of Web Cookies

Web cookies are used widely by publishers and 3rd parties to track users and their behaviors. Despite the ubiquitous use of cookies, there is little prior work on their characteristics such as standard attributes, placement policies, and the knowledge that can be amassed via 3rd party cookies. In this paper, we present an empirical study of web cookie characteristics, placement practices and information transmission. To conduct this study, we implemented a lightweight web crawler that tracks and stores the cookies as it navigates to websites. We use this crawler to collect over 3.2M cookies from the two crawls, separated by 18 months, of the top 100K Alexa web sites. We report on the general cookie characteristics and add context via a cookie category index and website genre labels. We consider privacy implications by examining specific cookie attributes and placement behavior of 3rd party cookies. We find that 3rd party cookies outnumber 1st party cookies by a factor of two, and we illuminate the connection between domain genres and cookie attributes. We find that less than 1% of the entities that place cookies can aggregate information across 75% of web sites. Finally, we consider the issue of information transmission and aggregation by domains via 3rd party cookies. We develop a mathematical framework to quantify user information leakage for a broad class of users, and present findings using real world domains. In particular, we demonstrate the interplay between a domain’s footprint across the Internet and the browsing behavior of users, which has significant impact on information transmission.
Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach

In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals are presented based on image and volumetric coding. Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of “lossy plus residual coding,” consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different sampling rate and resolution. The proposed multichannel compression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately.
Strategic and Operational Benefits of Electronic Integration in B2B Procurement Processes

Our goal is to assess the strategic and operational benefits of electronic integration for industrial procurement. We conduct a field study with an industrial supplier and examine the drivers of performance of the procurement process. Our research quantifies both the operational and strategic impacts of electronic integration in a B2B procurement environment for a supplier. Additionally, we show that the customer also obtains substantial benefits from efficient procurement transaction processing. We isolate the performance impact of technology choice and ordering processes on both the trading partners. A significant finding is that the supplier derives large strategic benefits when the customer initiates the system and the supplier enhances the system’s capabilities. With respect to operational benefits, we find that when suppliers have advanced electronic linkages, the order-processing system significantly increases benefits to both parties. (Business Value of IT; Empirical Assessment; Electronic Integration; Electronic Procurement; B2B; Strategic IT Impact; Operational IT Impact)
Parameter-Free Geometric Document Layout Analysis

ÐAutomatic transformation of paper documents into electronic documents requires geometric document layout analysis at the first stage. However, variations in character font sizes, text line spacing, and document layout structures have made it difficult to design a general-purpose document layout analysis algorithm for many years. The use of some parameters has therefore been unavoidable in previous methods. In this paper, we propose a parameter-free method for segmenting the document images into maximal homogeneous regions and identifying them as texts, images, tables, and ruling lines. A pyramidal quadtree structure is constructed for multiscale analysis and a periodicity measure is suggested to find a periodical attribute of text regions for page segmentation. To obtain robust page segmentation results, a confirmation procedure using texture analysis is applied to only ambiguous regions. Based on the proposed periodicity measure, multiscale analysis, and confirmation procedure, we could develop a robust method for geometric document layout analysis independent of character font sizes, text line spacing, and document layout structures. The proposed method was experimented with the document database from the University of Washington and the MediaTeam Document Database. The results of these tests have shown that the proposed method provides more accurate results than the previous ones. Index TermsÐGeometric document layout analysis, parameter-free method, periodicity estimation, multiscale analysis, page segmentation.
Sentence Correction Incorporating Relative Position and Parse Template Language Models

Sentence correction has been an important emerging issue in computer-assisted language learning. However, existing techniques based on grammar rules or statistical machine translation are still not robust enough to tackle the common errors in sentences produced by second language learners. In this paper, a relative position language model and a parse template language model are proposed to complement traditional language modeling techniques in addressing this problem. A corpus of erroneous English-Chinese language transfer sentences along with their corrected counterparts is created and manually judged by human annotators. Experimental results show that compared to a state-of-the-art phrase-based statistical machine translation system, the error correction performance of the proposed approach achieves a significant improvement using human evaluation.
Preventing drive-by download via inter-module communication monitoring

Drive-by download attack is one of the most severe threats to Internet users. Typically, only visiting a malicious page will result in compromise of the client and infection of malware. By the end of 2008, drive-by download had already become the number one infection vector of malware . The downloaded malware may steal the users' personal identification and password. They may also join botnet to send spams, host phishing site or launch distributed denial of service attacks.
 Generally, these attacks rely on successful exploits of the vulnerabilities in web browsers or their plug-ins. Therefore, we proposed an inter-module communication monitoring based technique to detect malicious exploitation of vulnerable components thus preventing the vulnerability being exploited. We have implemented a prototype system that was integrated into the most popular web browser Microsoft Internet Explorer. Experimental results demonstrate that, on our test set, by using vulnerability-based signature, our system could accurately detect all attacks targeting at vulnerabilities in our definitions and produced no false positive. The evaluation also shows the performance penalty is kept low.
Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.

This article experimentally investigates the potential of using flexible, inductance-based contraction sensors in the closed-loop motion control of soft robots. Accurate motion control remains a highly challenging task for soft robotic systems. Precise models of the actuation dynamics and environmental interactions are often unavailable. This renders open-loop control impossible, while closed-loop control suffers from a lack of suitable feedback. Conventional motion sensors, such as linear or rotary encoders, are difficult to adapt to robots that lack discrete mechanical joints. The rigid nature of these sensors runs contrary to the aspirational benefits of soft systems. As truly soft sensor solutions are still in their infancy, motion control of soft robots has so far relied on laboratory-based sensing systems such as motion capture, electromagnetic (EM) tracking, or Fiber Bragg Gratings. In this article, we used embedded flexible sensors known as Smart Braids to sense the contraction of McKibben muscles through changes in inductance. We evaluated closed-loop control on two systems: a revolute joint and a planar, one degree of freedom continuum manipulator. In the revolute joint, our proposed controller compensated for elasticity in the actuator connections. The Smart Braid feedback allowed motion control with a steady-state root-mean-square (RMS) error of [1.5]°. In the continuum manipulator, Smart Braid feedback enabled tracking of the desired tip angle with a steady-state RMS error of [1.25]°. This work demonstrates that Smart Braid sensors can provide accurate position feedback in closed-loop motion control suitable for field applications of soft robotic systems.
Architectures and Control of Submodule Integrated DC–DC Converters for Photovoltaic Applications

This paper describes photovoltaic (PV) module architectures with parallel-connected submodule-integrated dc-dc converters (subMICs) that improve efficiency of energy capture in the presence of partial shading or other mismatch conditions. The subMICs are bidirectional isolated dc-dc converters capable of injecting or subtracting currents to balance the module substring voltages. When no mismatches are present, the subMICs are simply shut down, resulting in zero insertion losses. It is shown that the objective of minimum subMIC power processing can be solved as a linear programming problem. A simple close-to-optimal distributed control approach is presented that allows autonomous subMIC control without the need for a central controller or any communication among the subMICs. Furthermore, the proposed control approach is well suited for an isolated-port architecture, which yields additional practical advantages including reduced subMIC power and voltage ratings. The architectures and the control approach are validated by simulations and experimental results using three bidirectional flyback subMICs attached to a standard 180-W, 72-cell PV module, yielding greater than 98% module-level power processing efficiency for a mismatch less than 25%.
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state-of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors.
Automatic verification of the TLS handshake protocol

E-commerce is based on transactions between client and server agents. These transactions require a protocol that provides privacy and reliability between these two agents. A widely used protocol on e-commerce is Transport Layer Security (TLS). In this paper we present a way to use Formal Methods to ensure the e-commerce properties of this protocol. Specifically we use a known tool for Model Checking (UPPAAL) to describe and analyze the behaviour of the protocol (by means of timed automata). Thus, with this tool we can make an automatic verification of TLS.
An auditory brain–computer interface (BCI)

Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.
Interpreting CNNs via Decision Trees

This paper presents a method to learn a decision tree to quantitatively explain the logic of each prediction of a pretrained convolutional neural networks (CNNs). Our method boosts the following two aspects of network interpretability. 1) In the CNN, each filter in a high conv-layer must represent a specific object part, instead of describing mixed patterns without clear meanings. 2) People can explain each specific prediction made by the CNN at the semantic level using a decision tree, i.e. which filters (or object parts) are used for prediction and how much they contribute in the prediction. To conduct such a quantitative explanation of a CNN, our method learns explicit representations of object parts in high conv-layers of the CNN and mines potential decision modes memorized in fully-connected layers. The decision tree organizes these potential decision modes in a coarse-to-fine manner. Experiments have demonstrated the effectiveness of the proposed method.
To BLOB or Not To BLOB: Large Object Storage in a Database or a Filesystem?

Application designers must decide whether to store large objects (BLOBs) in a filesystem or in a database. Generally, this decision is based on factors such as application simplicity or manageability. Often, system performance affects these factors. Folklore tells us that databases efficiently handle large numbers of small objects, while filesystems are more efficient for large objects. Where is the break-even point? When is accessing a BLOB stored as a file cheaper than accessing a BLOB stored as a database record? Of course, this depends on the particular filesystem, database system, and workload in question. This study shows that when comparing the NTFS file system and SQL Server 2005 database system on a create, {read, replace}* delete workload, BLOBs smaller than 256KB are more efficiently handled by SQL Server, while NTFS is more efficient BLOBS larger than 1MB. Of course, this break-even point will vary among different database systems, filesystems, and workloads. By measuring the performance of a storage server workload typical of web applications which use get/put protocols such as WebDAV [WebDAV], we found that the break-even point depends on many factors. However, our experiments suggest that storage age, the ratio of bytes in deleted or replaced objects to bytes in live objects, is dominant. As storage age increases, fragmentation tends to increase. The filesystem we study has better fragmentation control than the database we used, suggesting the database system would benefit from incorporating ideas from filesystem architecture. Conversely, filesystem performance may be improved by using database techniques to handle small files. Surprisingly, for these studies, when average object size is held constant, the distribution of object sizes did not significantly affect performance. We also found that, in addition to low percentage free space, a low ratio of free space to average object size leads to fragmentation and performance degradation.
Responsiveness in instant messaging: predictive models supporting inter-personal communication

For the majority of us, inter-personal communication is an essential part of our daily lives. Instant Messaging, or IM, has been growing in popularity for personal and work-related communication. The low cost of sending a message, combined with the limited awareness provided by current IM systems result in messages often arriving at inconvenient or disruptive times. In a step towards solving this problem, we created statistical models that successfully predict responsiveness to incoming instant messages -- simply put: whether the receiver is likely to respond to a message within a certain time period. These models were constructed using a large corpus of real IM interaction collected from 16 participants, including over 90,000 messages. The models we present can predict, with accuracy as high as 90.1%, whether a message sent to begin a new session of communication would get a response within 30 seconds, 1, 2, 5, and 10 minutes. This type of prediction can be used, for example, to drive online-status indicators, or in services aimed at finding potential communicators.
High Speed Pattern Matching for Network IDS/IPS

The phenomenal growth of the Internet in the last decade and society's increasing dependence on it has brought along, a flood of security attacks on the networking and computing infrastructure. Intrusion detection/prevention systems provide defenses against these attacks by monitoring headers and payload of packets flowing through the network. Multiple string matching that can compare hundreds of string patterns simultaneously is a critical component of these systems, and is a well-studied problem. Most of the string matching solutions today are based on the classic Aho-Corasick algorithm, which has an inherent limitation; they can process only one input character in one cycle. As memory speed is not growing at the same pace as network speed, this limitation has become a bottleneck in the current network, having speeds of tens of gigabits per second. In this paper, we propose a novel multiple string matching algorithm that can process multiple characters at a time thus achieving multi-gigabit rate search speeds. We also propose an architecture for an efficient implementation on TCAM-based hardware. We additionally propose novel optimizations by making use of the properties of TCAMs to significantly reduce the memory requirements of the proposed algorithm. We finally present extensive simulation results of network-based virus/worm detection using real signature databases to illustrate the effectiveness of the proposed scheme.
Analysis of critical success factors in IT project management

The success of IT project related to numerous factors. It had an important significance to find the critical factors for the success of project. Based on the general analysis of IT project management, this paper analyzed some factors of project management for successful IT project from the angle of modern project management. These factors include project participators, project communication, collaboration, and information sharing mechanism as well as project management process. In the end, it analyzed the function of each factor for a successful IT project. On behalf of the collective goal, by the use of the favorable project communication and collaboration, the project participants carry out successfully to the management of the process, which is significant to the project, and make project achieve success eventually.
Analysis and Testing of Web Applications

The economic relevance of Web applications increases the importance of controlling and improving their quality. Moreover, the new available technologies for their development allow the insertion of sophisticated functions, but often leave the developers responsible for their organization and evolution. As a consequence, a high demand is emerging for methodologies and tools for quality assurance of Web based systems.
In this paper, a UML model of Web applications is proposed for their high level representation. Such a model is the starting point for several analyses, which can help in the assessment of the static site structure. Moreover, it drives Web application testing, in that it can be exploited to define white box testing criteria and to semi-automatically generate the associated test cases.
The proposed techniques were applied to several real world Web applications. Results suggest that an automatic support to the verification and validation activities can be extremely beneficial. In fact, it guarantees that all paths in the site which satisfy a selected criterion are properly exercised before delivery. The high level of automation that is achieved in test case generation and execution increases the number of tests that are conducted and simplifies the regression checks.
A Generalization of Dung's Abstract Framework for Argumentation: Arguing with Sets of Attacking Arguments

One of the most widely studied systems of argumentation is the one described by Dung in a paper from 1995. Unfortunately, this framework does not allow for joint attacks on arguments, which we argue must be required of any truly abstract argumentation framework. A few frameworks can be said to allow for such interactions among arguments, but for various reasons we believe that these are inadequate for modelling argumentation systems with joint attacks. In this paper we propose a generalization of the framework of Dung, which allows for sets of arguments to attack other arguments. We extend the semantics associated with the original framework to this generalization, and prove that all results in the paper by Dung have an equivalent in this more abstract framework.