nils-herrmann/clean_scirepeval huggingface dataset:
Classification of Intrusion Detection Dataset using machine learning Approaches

The paper describes about a method of int rusion detection that uses machine learning algorit hms. Here we discuss about the combinational use of two machine l arning algorithms called Principal Component Anal ysis and Naive Bayes classifier. The dimensionality of the dataset is reduced by using the principal component analys i and the classification of the dataset in to normal and atta ck classes is done by using Naïve Bayes Classifier. The experiments were conducted on the intrusion detection dataset called KDD’99 cup dataset. The comparison of the results with and without dimensionality reduction is also done.
Facial expression recognition based on geometric and optical flow features in colour image sequences

Facial expression recognition is a useful feature in modern human computer interaction (HCI). In order to build efficient and reliable recognition systems, face detection, feature extraction and classification have to be robustly realised. Addressing the latter two issues, this work proposes a new method based on geometric and transient optical flow features and illustrates their comparison and integration for facial expression recognition. In the authors’ method, photogrammetric techniques are used to extract three-dimensional (3-D) features from every image frame, which is regarded as a geometric feature vector. Additionally, optical flow-based motion detection is carried out between consecutive images, what leads to the transient features. Artificial neural network and support vector machine classification results demonstrate the high performance of the proposed method. In particular, through the use of 3-D normalisation and colour information, the proposed method achieves an advanced feature representation for the accurate and robust classification of facial expressions.
Colored Maximum Variance Unfolding

Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while preserving the local distances of the original data. We show that MVU also optimizes a statistical dependence measure which aims to retain the identity of individual observations under the distancepreserving constraints. This general view allows us to design “colored” variants of MVU, which produce low-dimensional representations for a given task, e.g. subject to class labels or other side information.
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning

Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining. We introduce a novel method called ARM-HSTRL (Association Rule Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning). It extracts temporal and structural relationships of sub-goals in RL, and multi-task RL. In particular,it finds sub-goals and relationship among them. It is shown the significant efficiency and performance of the proposed method in two main topics of RL.
A Tri-Modal 20-Gbps/Link Differential/DDR3/GDDR5 Memory Interface

This paper describes a tri-modal asymmetric bidirectional differential memory interface that supports data rates of up to 20 Gbps over 3" FR4 PCB channels while achieving power efficiency of 6.1 mW/Gbps at full speed. The interface also accommodates single-ended standard DDR3 and GDDR5 signaling at 1.6-Gbps and 6.4-Gbps operations, respectively, without package change. The compact, low-power and high-speed tri-modal interface is enabled by substantial reuse of the circuit elements among various signaling modes, particularly in the wide-band clock generation and distribution system and the multi-modal driver output stage, as well as the use of fast equalization for post-cursor intersymbol interference (ISI) mitigation. In the high-speed differential mode, the system utilizes a 1-tap transmit equalizer during a WRITE operation to the memory. In contrast, during a memory READ operation, it employs a linear equalizer (LEQ) with 3 dB of peaking as well as a calibrated high-speed 1-tap predictive decision feedback equalizer (prDFE), while no transmitter equalization is assumed for the memory. The prototype tri-modal interface implemented in a 40-nm CMOS process, consists of 16 data links and achieves more than 2.5 × energy-efficient memory transactions at 16 Gbps compared to a previous single-mode generation.
Immersive and collaborative data visualization using virtual reality platforms

Effective data visualization is a key part of the discovery process in the era of “big data”. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding. Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis. However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle. How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces? A better understanding of how we can perceive and interact with multidimensional information poses some deep questions in the field of cognition technology and human-computer interaction. To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware. These potentially powerful and innovative tools for multi-dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space. Immersion provides benefits beyond the traditional “desktop” visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.
Achieving Perfect Location Privacy in Wireless Devices Using Anonymization

The popularity of mobile devices and location-based services (LBSs) has raised significant concerns regarding the location privacy of their users. A popular approach to protect location privacy is anonymizing the users of LBS systems. In this paper, we introduce an information-theoretic notion for location privacy, which we call perfect location privacy. We then demonstrate how anonymization should be used by LBS systems to achieve the defined perfect location privacy. We study perfect location privacy under two models for user movements. First, we assume that a user’s current location is independent from her past locations. Using this independent identically distributed (i.i.d.) model, we show that if the pseudonym of the user is changed before <inline-formula> <tex-math notation="LaTeX">$O\left({n^{\frac {2}{r-1}}}\right)$ </tex-math></inline-formula> observations are made by the adversary for that user, then the user has perfect location privacy. Here, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of the users in the network and <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is the number of all possible locations. Next, we model users’ movements using Markov chains to better model real-world movement patterns. We show that perfect location privacy is achievable for a user if the user’s pseudonym is changed before <inline-formula> <tex-math notation="LaTeX">$O\left({n^{\frac {2}{|E|-r}}}\right)$ </tex-math></inline-formula> observations are collected by the adversary for that user, where <inline-formula> <tex-math notation="LaTeX">$|E|$ </tex-math></inline-formula> is the number of edges in the user’s Markov chain model.
AI Approaches to Fraud Detection and Risk Management

researchers and practitioners doing work in these three related areas. Risk management, fraud detection, and intrusion detection all involve monitoring the behavior of populations of users (or their accounts) to estimate, plan for, avoid, or detect risk. In his paper, Til Schuermann (Oliver, Wyman, and Company) categorizes risk into market risk, credit risk, and operating risk (or fraud). Similarly, Barry Glasgow (Metropolitan Life Insurance Co.) discusses inherent risk versus fraud. This workshop focused primarily on what might loosely be termed “improper behavior,” which includes fraud, intrusion, delinquency, and account defaulting. However, Glasgow does discuss the estimation of “inherent risk,” which is the bread and butter of insurance firms. Problems of predicting, preventing, and detecting improper behavior share characteristics that complicate the application of existing AI and machine-learning technologies. In particular, these problems often have or require more than one of the following that complicate the technical problem of automatically learning predictive models: large volumes of (historical) data, highly skewed distributions (“improper behavior” occurs far less frequently than “proper behavior”), changing distributions (behaviors change over time), widely varying error costs (in certain contexts, false positive errors are far more costly than false negatives), costs that change over time, adaptation of undesirable behavior to detection techniques, changing patterns of legitimate behavior, the trad■ The 1997 AAAI Workshop on AI Approaches to Fraud Detection and Risk Management brought together over 50 researchers and practitioners to discuss problems of fraud detection, computer intrusion detection, and risk scoring. This article presents highlights, including discussions of problematic issues that are common to these application domains, and proposed solutions that apply a variety of AI techniques.
Personalization of web-search using short-term browsing context

Search and browsing activity is known to be a valuable source of information about user's search intent. It is extensively utilized by most of modern search engines to improve ranking by constructing certain ranking features as well as by personalizing search. Personalization aims at two major goals: extraction of stable preferences of a user and specification and disambiguation of the current query. The common way to approach these problems is to extract information from user's search and browsing long-term history and to utilize short-term history to determine the context of a given query. Personalization of the web search for the first queries in new search sessions of new users is more difficult due to the lack of both long- and short-term data.
 In this paper we study the problem of short-term personalization. To be more precise, we restrict our attention to the set of initial queries of search sessions. These, with the lack of contextual information, are known to be the most challenging for short-term personalization and are not covered by previous studies on the subject. To approach this problem in the absence of the search context, we employ short-term browsing context. We apply a widespread framework for personalization of search results based on the re-ranking approach and evaluate our methods on the large scale data. The proposed methods are shown to significantly improve non-personalized ranking of one of the major commercial search engines. To the best of our knowledge this is the first study addressing the problem of short-term personalization based on recent browsing history. We find that performance of this re-ranking approach can be reasonably predicted given a query. When we restrict the use of our method to the queries with largest expected gain, the resulting benefit of personalization increases significantly
Tripartite graph clustering for dynamic sentiment analysis on social media

The growing popularity of social media (e.g., Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised tri-clustering framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the proposed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California ballot Twitter data.
Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic

We present Spectrogram, a machine learning based statistical anomaly detection (AD) sensor for defense against web-layer code-injection attacks. These attacks include PHP file inclusion, SQL-injection and cross-sitescripting; memory-layer exploits such as buffer overflows are addressed as well. Statistical AD sensors offer the advantage of being driven by the data that is being protected and not by malcode samples captured in the wild. While models using higher order statistics can often improve accuracy, trade-offs with false-positive rates and model efficiency remain a limiting usability factor. This paper presents a new model and sensor framework that offers a favorable balance under this constraint and demonstrates improvement over some existing approaches. Spectrogram is a network situated sensor that dynamically assembles packets to reconstruct content flows and learns to recognize legitimate web-layer script input. We describe an efficient model for this task in the form of a mixture of Markovchains and derive the corresponding training algorithm. Our evaluations show significant detection results on an array of real world web layer attacks, comparing favorably against other AD approaches.
Contextual Bandits with Linear Payoff Functions

In this paper, we study the contextual bandit problem (also known as the multi-armed bandit problem with expert advice) for linear payoff functions. For T rounds, K actions, and d dimensional feature vectors, we prove an O ( √ Td ln(KT ln(T )/δ) ) regret bound that holds with probability 1− δ for the simplest known (both conceptually and computationally) efficient upper confidence bound algorithm for this problem. We also prove a lower bound of Ω( √ Td) for this setting, matching the upper bound up to logarithmic factors.
Adaptive and Quality Quadrilateral/Hexahedral Meshing from Volumetric Data.

This paper describes an algorithm to extract adaptive and quality quadrilateral/hexahedral meshes directly from volumetric data. First, a bottom-up surface topology preserving octree-based algorithm is applied to select a starting octree level. Then the dual contouring method is used to extract a preliminary uniform quad/hex mesh, which is decomposed into finer quads/hexes adaptively without introducing any hanging nodes. The positions of all boundary vertices are recalculated to approximate the boundary surface more accurately. Mesh adaptivity can be controlled by a feature sensitive error function, the regions that users are interested in, or finite element calculation results. Finally, a relaxation based technique is deployed to improve mesh quality. Several demonstration examples are provided from a wide variety of application domains. Some extracted meshes have been extensively used in finite element simulations.
Digital Control of a Low-Frequency Square-Wave Electronic Ballast With Resonant Ignition

This paper proposes a two-stage low-frequency square-wave (LFSW) electronic ballast with digital control. The first stage of the ballast is a power factor correction (PFC) stage, and the second is a full-bridge (FB) converter used for both lamp ignition and LFSW drive. As a novelty for LFSW ballasts, ignition is achieved without an additional igniter circuit by operating the FB during start-up as a high-frequency resonant inverter. After ignition, the converter operates as an LFSW inverter to avoid exciting acoustic resonances by controlling the FB as a buck converter and regulating alternately positive or negative current to the lamp. Lamp power is regulated by adjusting the average current supplied by the PFC stage. Another contribution of this paper is to utilize digital control as a simple solution to achieve multimode control, including resonant lamp ignition, LFSW transitions, and lamp current and power regulation.
Sphere-meshes for real-time hand modeling and tracking

Modern systems for real-time hand tracking rely on a combination of discriminative and generative approaches to robustly recover hand poses. Generative approaches require the specification of a geometric model. In this paper, we propose a the use of sphere-meshes as a novel geometric representation for real-time generative hand tracking. How tightly this model fits a specific user heavily affects tracking precision. We derive an optimization to non-rigidly deform a template model to fit the user data in a number of poses. This optimization jointly captures the user's static and dynamic hand geometry, thus facilitating high-precision registration. At the same time, the limited number of primitives in the tracking template allows us to retain excellent computational performance. We confirm this by embedding our models in an open source real-time registration algorithm to obtain a tracker steadily running at 60Hz. We demonstrate the effectiveness of our solution by qualitatively and quantitatively evaluating tracking precision on a variety of complex motions. We show that the improved tracking accuracy at high frame-rate enables stable tracking of extended and complex motion sequences without the need for per-frame re-initialization. To enable further research in the area of high-precision hand tracking, we publicly release source code and evaluation datasets.
100-GHz transistors from wafer-scale epitaxial graphene.

The high carrier mobility of graphene has been exploited in field-effect transistors that operate at high frequencies. Transistors were fabricated on epitaxial graphene synthesized on the silicon face of a silicon carbide wafer, achieving a cutoff frequency of 100 gigahertz for a gate length of 240 nanometers. The high-frequency performance of these epitaxial graphene transistors exceeds that of state-of-the-art silicon transistors of the same gate length.
CORPORATE SOCIAL RESPONSIBILITY , CUSTOMER SATISFACTION , AND MARKET VALUE

Although prior research has addressed the influence of corporate social responsibility (CSR) on perceived customer responses, it is not clear whether CSR affects market value of the firm. This study develops and tests a conceptual framework, which predicts that (1) customer satisfaction partially mediates the relationship between CSR and firm market value (i.e., Tobin’s q and stock return), (2) corporate abilities (innovativeness capability and product quality) moderate the financial returns to CSR, and (3) these moderated relationships are mediated by customer satisfaction. Based on a large-scale secondary dataset, the results show support for this framework. Interestingly, it is found that in firms with low innovativeness capability, CSR actually reduces customer satisfaction levels and, through the lowered satisfaction, harms market value. The uncovered mediated and asymmetrically moderated results offer important implications for marketing theory and practice. In today’s competitive market environment, corporate social responsibility (CSR) represents a high-profile notion that has strategic importance to many companies. As many as 90% of the Fortune 500 companies now have explicit CSR initiatives (Kotler and Lee 2004; Lichtenstein et al. 2004). According to a recent special report by BusinessWeek (2005a, p.72), large companies disclosed substantial investments in CSR initiatives (i.e., Target’s donation of $107.8 million in CSR represents 3.6% of its pretax profits, with GM $51.2 million at 2.7%, General Mills $60.3 million at 3.2%, Merck $921million at 11.3%, HCA $926 million at 43.3%). By dedicating everincreasing amounts to cash donations, in-kind contributions, cause marketing, and employee volunteerism programs, companies are acting on the premise that CSR is not merely the “right thing to do,” but also “the smart thing to do” (Smith 2003). Importantly, along with increasing media coverage of CSR issues, companies themselves are also taking direct and visible steps to communicate their CSR initiatives to various stakeholders including consumers. A decade ago, Drumwright (1996) observed that advertising with a social dimension was on the rise. The trend seems to continue. Many companies, including the likes of Target and Walmart, have funded large national ad campaigns promoting their good works. The October 2005 issue of In Style magazine alone carried more than 25 “cause” ads. Indeed, consumers seem to be taking notice: whereas in 1993 only 26% of individuals surveyed by Cone Communications could name a company as a strong corporate citizen, by 2004, the percentage surged to as high as 80% (BusinessWeek 2005a). Motivated, in part, by this mounting importance of CSR in practice, several marketing studies have found that social responsibility programs have a significant influence on a number of customer-related outcomes (Bhattacharya and Sen 2004). More specifically, based on lab experiments, CSR is reported to directly or indirectly impact consumer product responses
Personalized Web search for improving retrieval effectiveness

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.