nils-herrmann/clean_scirepeval huggingface dataset:
Direct marketing decision support through predictive customer response modeling

a r t i c l e i n f o Keywords: Customer response predictive model Knowledge-based marketing RFM Neural networks Decision tree models Logistic regression Decision support techniques and models for marketing decisions are critical to retail success. Among different marketing domains, customer segmentation or profiling is recognized as an important area in research and industry practice. Various data mining techniques can be useful for efficient customer segmentation and targeted marketing. One such technique is the RFM method. Recency, frequency, and monetary methods provide a simple means to categorize retail customers. We identify two sets of data involving catalog sales and donor contributions. Variants of RFM-based predictive models are constructed and compared to classical data mining techniques of logistic regression, decision trees, and neural networks. The spectrum of tradeoffs is analyzed. RFM methods are simpler, but less accurate. The effect of balancing cells, of the value function, and classical data mining algorithms (decision tree, logistic regression, neural networks) are also applied to the data. Both balancing expected cell densities and compressing RFM variables into a value function were found to provide models similar in accuracy to the basic RFM model, with slight improvement obtained by increasing the cutoff rate for classification. Classical data mining algorithms were found to yield better prediction , as expected, in terms of both prediction accuracy and cumulative gains. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are presented. Finally we discuss practical implications based on the empirical results. The role of decision support techniques and models for marketing decisions has been important since the inception of decision support systems (DSSs) . Diverse techniques and models (e.g., optimization, knowledge-based systems, simulation) have emerged over the last five decades. Many marketing domains, including pricing, new product development , and advertising, have benefited from these techniques and models [16]. Among these marketing domains, customer segmentation or profiling is recognized as an important area [18,19,26,43]. There are at least two reasons for this. First, the marketing paradigm is becoming customer-centric [41] and targeted marketing and service are suitable. Second, unsolicited marketing is costly and ineffective (e.g., low response rate) [15,30]. Along with these reasons, there are increasing efforts on collecting and analyzing customer data for better marketing decisions [9,26,30]. The advancement of online shopping technologies and database systems has accelerated this trend. Data mining has been a valuable tool in this regard. Various …
3GPP LTE Versus IEEE 802.11p/WAVE: Which Technology is Able to Support Cooperative Vehicular Safety Applications?

The concept of vehicular ad-hoc networks enables the design of emergent automotive safety applications, which are based on the awareness among vehicles. Recently, a suite of 802.11p/WAVE protocols aimed at supporting car-to-car communications was approved by IEEE. Existing cellular infrastructure and, above all 3GPP LTE, is being considered as another communication technology appropriate for vehicular applications. This letter provides a theoretical framework which compares the basic patterns of both the technologies in the context of safety-of-life vehicular scenarios. We present mathematical models for the evaluation of the considered protocols in terms of successful beacon delivery probability.
Addressing Transient and Permanent Faults in NoC With Efficient Fault-Tolerant Deflection Router

Continuing decrease in the feature size of integrated circuits leads to increases in susceptibility to transient and permanent faults. This paper proposes a fault-tolerant solution for a bufferless network-on-chip, including an on-line fault-diagnosis mechanism to detect both transient and permanent faults, a hybrid automatic repeat request, and forward error correction link-level error control scheme to handle transient faults and a reinforcement-learning-based fault-tolerant deflection routing (FTDR) algorithm to tolerate permanent faults without deadlock and livelock. A hierarchical-routing-table-based algorithm (FTDR-H) is also presented to reduce the area overhead of the FTDR router. Synthesized results show that, compared with the FTDR router, the FTDR-H router can reduce the area by 27% in an 88 network. Simulation results demonstrate that under synthetic workloads, in the presence of permanent link faults, the throughput of an 8 8 network with FTDR and FTDR-H algorithms are 14% and 23% higher on average than that with the fault-on-neighbor (FoN) aware deflection routing algorithm and the cost-based deflection routing algorithm, respectively. Under real application workloads, the FTDR-H algorithm achieves 20% less hop counts on average than that of the FoN algorithm. For transient faults, the performance of the FTDR router can achieve graceful degradation even at a high fault rate. We also implement the fault-tolerant deflection router which can achieve 400 MHz in TSMC 65-nm technology.
Wiener filtering based speech enhancement with Weighted Denoising Auto-encoder and noise classification

A novel speech enhancement method based on Weighted Denoising Auto-encoder (WDA) and noise classification is proposed in this paper. A weighted reconstruction loss function is introduced into the conventional Denoising Auto-encoder (DA), and the relationship between the power spectra of clean speech and noisy observation is described by WDA model. First, the sub-band power spectrum of clean speech is estimated by WDA model from the noisy observation. Then, the a priori SNR is estimated by the a Posteriori SNR Controlled Recursive Averaging (PCRA) approach. Finally, the clean speech is obtained by Wiener filter in frequency domain. In addition, in order to make the proposed method suitable for various kinds of noise conditions, a Gaussian Mixture Model (GMM) based noise classification method is employed. And the corresponding WDA model is used in the enhancement process. From the test results under ITUT G.160, it is shown that, in comparison with the reference method which is the Wiener filtering method with decision-directed approach for SNR estimation, the WDA-based speech enhancement methods could achieve better objective speech quality, no matter whether the noise conditions are included in the training set or not. And the similar amount of noise reduction and SNR improvement can be obtained with smaller distortion on speech level.
Learning Feature Pyramids for Human Pose Estimation

Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multibranch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.
SHPF: Enhancing HTTP(S) Session Security with Browser Fingerprinting

Session hijacking has become a major problem in today's Web services, especially with the availability of free off-the-shelf tools. As major websites like Facebook, You tube and Yahoo still do not use HTTPS for all users by default, new methods are needed to protect the users' sessions if session tokens are transmitted in the clear. In this paper we propose the use of browser fingerprinting for enhancing current state-of-the-art HTTP(S) session management. Monitoring a wide set of features of the user's current browser makes session hijacking detectable at the server and raises the bar for attackers considerably. This paper furthermore identifies HTML5 and CSS features that can be used for browser fingerprinting and to identify or verify a browser without the need to rely on the User Agent string. We implemented our approach in a framework that is highly configurable and can be added to existing Web applications and server-side session management with ease.
Agnostic Estimation of Mean and Covariance

We consider the problem of estimating the mean and covariance of a distribution from i.i.d. samples in the presence of a fraction of malicious noise. This is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when a fraction of data is adversarially corrupted, agnostically learning mixtures, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.
Nonlinear canonical correlation analysis by neural networks

Canonical correlation analysis (CCA) is widely used to extract the correlated patterns between two sets of variables. A nonlinear canonical correlation analysis (NLCCA) method is formulated here using three feedforward neural networks. The first network has a double-barreled architecture, and an unconventional cost function, which maximizes the correlation between the two output neurons (the canonical variates). The remaining two networks map from the canonical variates back to the original two sets of variables. Tested on data sets with correlated nonlinear structures, NLCCA showed that the underlying nonlinear structures could be retrieved accurately under moderately noisy conditions. After a mode had been retrieved, NLCCA was applied to the residual to successfully retrieve the next mode. When tested for prediction skills, the NLCCA outperformed the CCA when the two sets of variables contained correlated nonlinear structures.
Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo

Recently there have been conceptually new developments in Monte Carlo methods through the introduction of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally new Monte Carlo algorithms which can be used to sample from, say, a posterior distribution. Interestingly, continuous-time algorithms seem particularly well suited to Bayesian analysis in big-data settings as they need only access a small sub-set of data points at each iteration, and yet are still guaranteed to target the true posterior distribution. Whilst continuous-time MCMC and SMC methods have been developed independently we show here that they are related by the fact that both involve simulating a piecewise deterministic Markov process. Furthermore we show that the methods developed to date are just specific cases of a potentially much wider class of continuous-time Monte Carlo algorithms. We give an informal introduction to piecewise deterministic Markov processes, covering the aspects relevant to these new Monte Carlo algorithms, with a view to making the development of new continuous-time Monte Carlo more accessible. We focus on how and why sub-sampling ideas can be used with these algorithms, and aim to give insight into how these new algorithms can be implemented, and what are some of the issues that affect their efficiency.
Ability and skill graphs for system modeling, online monitoring, and decision support for vehicle guidance systems

In this paper, the ability and skill graphs are introduced for modeling vehicle guidance systems in the concept phase of the development process (abilities), for online monitoring of system operation (skills), and to support driving decisions (skill levels) of automated road vehicles and advanced driver assistance systems. Both graphs rely on a decomposition of the human driving task. An ability is the entirety of conditions which are necessary to provide a certain part of the driving task. The ability graph can be developed in parallel to the item definition according to the ISO 26262 standard in the concept phase of the development process and can be used for supporting further development steps. A skill is defined as an abstract representation of a part of the driving task including information about the skills current performance. The skill graph is used to monitor the current system performance during operation and skill levels are input to driving decisions. Abilities and skills cover all aspects of the driving task including environment and self perception, data processing, decision making, and behavior execution. During operation of the developed item, the skill graph is instantiated as a (distributed) software component to process online information for assessing current skill levels. Each skill uses one or more performance metrics, which represent its current performance capability in relation to the maximum (inherent) ability level. The resulting information could replace the monitoring of the system by a human driver and can be used as an input to driving decisions of the vehicle to support appropriate and safe decisions.
Side-channel attacks on the bitstream encryption mechanism of Altera Stratix II: facilitating black-box analysis using software reverse-engineering

In order to protect FPGA designs against IP theft and related issues such as product cloning, all major FPGA manufacturers offer a mechanism to encrypt the bitstream used to configure the FPGA. From a mathematical point of view, the employed encryption algorithms, e.g., AES or 3DES, are highly secure. However, recently it has been shown that the bitstream encryption feature of several FPGA product lines is susceptible to side-channel attacks that monitor the power consumption of the cryptographic module. In this paper, we present the first successful attack on the bitstream encryption of the Altera Stratix II FPGA. To this end, we reverse-engineered the details of the proprietary and unpublished Stratix II bitstream encryption scheme from the Quartus II software. Using this knowledge, we demonstrate that the full 128-bit AES key of a Stratix II can be recovered by means of side-channel analysis with 30,000 measurements, which can be acquired in less than three hours. The complete bitstream of a Stratix II that is (seemingly) protected by the bitstream encryption feature can hence fall into the hands of a competitor or criminal - possibly implying system-wide damage if confidential information such as proprietary encryption schemes or keys programmed into the FPGA are extracted. In addition to lost IP, reprogramming the attacked FPGA with modified code, for instance, to secretly plant a hardware trojan, is a particularly dangerous scenario for many security-critical applications.
SnipSuggest: Context-Aware Autocompletion for SQL

In this paper, we present SnipSuggest, a system that provides onthe-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest’s current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7% average precision, at interactive speed. We also show that SnipSuggest outperforms naı̈ve approaches, such as recommending popular snippets.
Experiments on deep learning for speech denoising

In this paper we present some experiments using a deep learning model for speech denoising. We propose a very lightweight procedure that can predict clean speech spectra when presented with noisy speech inputs, and we show how various parameter choices impact the quality of the denoised signal. Through our experiments we conclude that such a structure can perform better than some comparable single-channel approaches and that it is able to generalize well across various speakers, noise types and signal-to-noise ratios.
Reconstruction and representation of 3D objects with radial basis functions

We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs allow us to model large data sets, consisting of millions of surface points, by a single RBF — previously an impossible task. A greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational advantages. The energy-minimisation characterisation of polyharmonic splines result in a “smoothest” interpolant. This scale-independent characterisation is well-suited to reconstructing surfaces from non-uniformly sampled data. Holes are smoothly filled and surfaces smoothly extrapolated. We use a non-interpolating approximation when the data is noisy. The functional representation is in effect a solid model, which means that gradients and surface normals can be determined analytically. This helps generate uniform meshes and we show that the RBF representation has advantages for mesh simplification and remeshing applications. Results are presented for real-world rangefinder data.
Precision and Recall for Regression

Cost sensitive prediction is a key task in many real world applications. Most existing research in this area deals with classification problems. This paper addresses a related regression problem: the prediction of rare extreme values of a continuous variable. These values are often regarded as outliers and removed from posterior analysis. However, for many applications (e.g. in finance, meteorology, biology, etc.) these are the key values that we want to accurately predict. Any learning method obtains models by optimizing some preference criteria. In this paper we propose new evaluation criteria that are more adequate for these applications. We describe a generalization for regression of the concepts of precision and recall often used in classification. Using these new evaluation metrics we are able to focus the evaluation of predictive models on the cases that really matter for these applications. Our experiments indicate the advantages of the use of these new measures when comparing predictive models in the context of our target applications.
Practical Evasion of a Learning-Based Classifier: A Case Study

Learning-based classifiers are increasingly used for detection of various forms of malicious data. However, if they are deployed online, an attacker may attempt to evade them by manipulating the data. Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier. In practice, such assumptions rarely hold, especially for systems deployed online. A significant amount of information about a deployed classifier system can be obtained from various sources. In this paper, we experimentally investigate the effectiveness of classifier evasion using a real, deployed system, PDFrate, as a test case. We develop a taxonomy for practical evasion strategies and adapt known evasion algorithms to implement specific scenarios in our taxonomy. Our experimental results reveal a substantial drop of PDFrate's classification scores and detection accuracy after it is exposed even to simple attacks. We further study potential defense mechanisms against classifier evasion. Our experiments reveal that the original technique proposed for PDFrate is only effective if the executed attack exactly matches the anticipated one. In the discussion of the findings of our study, we analyze some potential techniques for increasing robustness of learning-based systems against adversarial manipulation of data.
History and State of the Art in Commercial Electric Ship Propulsion, Integrated Power Systems, and Future Trends

Electric propulsion has emerged as one of the most efficient propulsion arrangements for several vessel types over the last decades. Even though examples can be found in the history at the end of 19th century, and further into the 20th century, the modern use of electric propulsion started in the 1980s along with the development of semiconductor switching devices to be used in high power drives (dc drives and later ac-to-ac drives). This development opened up for full rpm control of propellers and thrusters, and thereby enabling a simplification of the mechanical structure. However, the main reason for using electric propulsion in commercial ship applications is the potential for fuel savings compared to equivalent mechanical alternatives, except for icebreakers where the performance of an electric powered propeller is superior to a combustion engine powered propeller. The fuel saving potential lies within the fact that the applicable vessels have a highly varying operation profile and are seldom run at full power. This favors the power plant principle in which electric power can be produced at any time with optimum running of prime movers, e.g., diesel engines, by turning on and off units depending on the power demand for propulsion and other vessel loads. Icebreakers were among the first vessels to take advantage of this technology later followed by cruise vessel, and the offshore drilling vessels operating with dynamic positioning (DP). The converter technology was rapidly developing and soon the dc drives were replaced with ac drives. In the same period electric propulsion emerged as basic standard for large cruise liners, and DP operated drilling vessels, but also found its way into other segments as shuttle tankers, ferries, and other special vessels. At the same time podded propulsion were introduced, where the electric motor was mounted directly on the propeller shaft in a submerged 360 $^{\circ}$ steerable pod, adding better efficiency, improved maneuvering, and reduced installation space/cost to the benefits of electric propulsion. The future trends are now focusing on further optimization of efficiency by allowing multiple energy sources, independent operation of individual power producers, and energy storage for various applications, such as power back up, peak shaving, or emission free operation (short voyages).