nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers

This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.
Drag and Drop: A Better Approach to CAPTCHA

A Completely Automated Public Turing test to tell Computers and Humans Apart is a test use to determine whether the user is a human or a malicious computer program. In simple terms CAPTCHA is a program that can generate and grade tests that most humans can pass, yet current computer programs can’t pass. They can be found as blurred or distorted random images, in free email signups, online polls or commenting on blogs; these images are difficult to recognize by computer programs but easy for most humans. They work as basic tool for preventing Denial of Service, brute force attacks and advertising spams. CAPTCHAs have evolved from simple text based to recent advancements like audio and visual tests. Text based CAPTCHAs have been broken successfully with the help of Optical Character Recognition and every approach is vulnerable against laundry attacks or redirection. The suggested approach ‘Drag and Drop CAPTCHA’ or ‘DnD’ is inclusive solution against OCR and laundry attacks. This HIP challenge uses conventional mouse events to recognize human intervention proof. If an attacker computer knows the answer of test but it can’t pass this test without human intervention. This technique can be considered as an Artificial Intelligence challenge. It creates win-win situation either this tough AI problem gets solved or DnD technique is distinction tool for human & computers.  CAPTCHA, drag and drop, Internet security, OCR, Human Intervention Proof, Artificial Intelligence
Service-oriented Cost Allocation for Business Intelligence and Analytics: Who pays for BI&A?

Quantifying and designing the cost pool generated by Business Intelligence and Analytics (BI&A) would improve cost transparency and invoicing processes, allowing a fairer, more exact allocation of costs to service consumers. Yet there is still no method for determining BI&A costs to provide a base for allocation purposes. While literature describes several methods for BI&A cost estimation on an ROI or resource-consumption level, none of these methods considers an overall approach for BI&A. To tackle this problem, we propose a service-oriented cost allocation model which calculates BI&A applications based on defined services, enabling a cost transfer to service consumers. This new approach specifies steps towards deriving a usable pricing scheme for an entire BI&A service portfolio – both for allocation purposes as well as improving cost evaluation of BI&A projects. Moreover, it prevents BI&A departments from being considered as the sole cost driver, increasing customer understanding and cost awareness.
Comparing ANN Based Models with ARIMA for Prediction of Forex Rates

In the dynamic global economy, the accuracy in forecasting the foreign currency exchange (Forex) rates or at least predicting the trend correctly is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. In this paper, we developed and investigated three Artificial Neural Network (ANN) based forecasting models using Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG) and Backpropagation with Baysian Regularization (BPR) for Australian Foreign Exchange to predict six different currencies against Australian dollar. Five moving average technical indicators are used to build the models. These models were evaluated using three performance metrics, and a comparison was made with the best known conventional forecasting model ARIMA. All the ANN based models outperform ARIMA model. It is found that SCG based model performs best when measured on the two most commonly used metrics and shows competitive results when compared with BPR based model on the third indicator. Experimental results demonstrate that ANN based model can closely forecast the forex market.
16 Personalization in E-Commerce Applications

This chapter is about personalization and adaptation in electronic commerce (e-commerce) applications. In the first part, we briefly introduce the challenges posed by e-commerce and we discuss how personalization strategies can help companies to face such challenges. Then, we describe the aspects of personalization, taken as a general technique for the customization of services to the user, which have been successfully employed in e-commerce Web sites. To conclude, we present some emerging trends and and we discuss future perspectives.
The impact of technology scaling on lifetime reliability

The relentless scaling of CMOS technology has provided a steady increase in processor performance for the past three decades. However, increased power densities (hence temperatures) and other scaling effects have an adverse impact on long-term processor lifetime reliability. This paper represents a first attempt at quantifying the impact of scaling on lifetime reliability due to intrinsic hard errors, taking workload characteristics into consideration. For our quantitative evaluation, we use RAMP (Srinivasan et al., 2004), a previously proposed industrial-strength model that provides reliability estimates for a workload, but for a given technology. We extend RAMP by adding scaling specific parameters to enable workload-dependent lifetime reliability evaluation at different technologies. We show that (1) scaling has a significant impact on processor hard failure rates - on average, with SPEC benchmarks, we find the failure rate of a scaled 65nm processor to be 316% higher than a similarly pipelined 180nm processor; (2) time-dependent dielectric breakdown and electromigration have the largest increases; and (3) with scaling, the difference in reliability from running at worst-case vs. typical workload operating conditions increases significantly, as does the difference from running different workloads. Our results imply that leveraging a single microarchitecture design for multiple remaps across a few technology generations will become increasingly difficult, and motivate a need for workload specific, microarchitectural lifetime reliability awareness at an early design stage.
User-centered security: stepping up to the grand challenge

User-centered security has been identified as a grand challenge in information security and assurance. It is on the brink of becoming an established subdomain of both security and human/computer interface (HCI) research, and an influence on the product development lifecycle. Both security and HCI rely on the reality of interactions with users to prove the utility and validity of their work. As practitioners and researchers in those areas, we still face major issues when applying even the most foundational tools used in either of these fields across both of them. This essay discusses the systemic roadblocks at the social, technical, and pragmatic levels that user-centered security must overcome to make substantial breakthroughs. Expert evaluation and user testing are producing effective usable security today. Principles such as safe staging, enumerating usability failure risks, integrated security, transparent security and reliance on trustworthy authorities can also form the basis of improved systems
A New Low Cost Leaky Wave Coplanar Waveguide Continuous Transverse Stub Antenna Array Using Metamaterial-Based Phase Shifters for Beam Steering

In this paper, we have proposed a new leaky-wave coplanar waveguide (CPW) continuous transverse stub (CTS) antenna array with metamaterial-based phase shifters for beam steering applications. The array integrates three CTS elements and two 6-stage negative reflective index (NRI) phase shifters, and is fed by CPW transmission line. Beam steering capabilities are achieved by tuning the values of the NRI phase shifters. The proposed CPW-CTS array is fabricated for 2.4-GHz wireless local area network (WLAN). The measured data, including parameters, radiation patterns and gain, agree well with the simulation results. A scan-angle range from 58 to 124 of unidirectional radiation pattern is measured in the -plane with good impedance matching (-1 dB ). Designs incorporating continuous beam steering using tunable NRI metamaterial phase shifters are also discussed.
An Algebra and Equivalences to Transform Graph Patterns in Neo4j

Modern query optimizers of relational database systems embody more than three decades of research and practice in the area of data management and processing. Key advances include algebraic query transformation, intelligent search space pruning, and modular optimizer architectures. Surprisingly, many of these contributions seem to have been overlooked in the emerging field of graph databases so far. In particular, we believe that query optimization based on a general graph algebra and its equivalences can greatly improve on the current state of the art. Although some graph algebras have already been proposed, they have often been developed in a context, in which a relational database system is used as a backend to process graph data. As a consequence, these algebras are typically tightly coupled to the relational algebra, making them unsuitable for native graph databases. While we support the approach of extending the relational algebra, we argue that graph-specific operations should be defined at a higher level, independent of the database backend. In this paper, we introduce such a general graph algebra and corresponding equivalences. We demonstrate how it can be used to optimize Cypher queries in the setting of the Neo4j native graph database.
CC Prediction in the Enron Corpus

Email is the most popular communication tool on the web. To improve the way we handle email messages, machine learning techniques have been proposed in different areas, from adaptive spam filtering to automated message foldering (i.e., predicting the correct folder to store a message). One of the ideas recently proposed is to automatically predict the recipients of an already composed message; a problem also referred to as CC Prediction problem. The goal of this problem is to predict the most likely recipients for a message, given its current text and given the email addresses already specified. If successfully automated, this idea can be a valuable addition to email clients, particularly in the large corporations. It can prevent a user from forgetting to add an important collaborator or manager as recipient. Also, it can be used to identify people in an organization that are working in a similar topic or project, or to find people with appropriate expertise or skills. In this work we investigate this problem using the textual contents and social network information features from a large real world collection of email messages, the Enron Email corpus [ 14]. Using a classification-based reranking scheme to combine the two different types of features, experiments indicate that we can correctly predict CC-recipients in more than 56% of the test cases.
Digital Filters for Fast Harmonic Sequence Component Separation of Unbalanced and Distorted Three-Phase Signals

In this paper, two methods for determining the fundamental frequency and harmonic positive- and negative-sequence components of three-phase signals are investigated. Many aspects of the space-vector discrete Fourier transform and generalized delayed signal cancellation (GDSC) such as response time for different possible implementations, frequency adaptation schemes, stability of recursive implementation, and rounding error effects are discussed. A new design procedure for GDSC transformations is presented. New indices for characterizing three-phase unbalanced and distorted signals are proposed. Simulations and experiments are included in order to verify the performances and illustrate the theoretical conclusions.
A miniaturized dual-frequency Wilkinson power divider using defected ground structure

In this paper, we apply a defected ground structure (DGS) to design a compact dual-frequency Wilkinson power divider (WPD). As the necessary area for dual-frequency Wilkinson power divider has a considerable increment toward single frequency WPD, we have to reduce its size especially in lower frequencies. With using dumbbell DGS, both designed operating frequencies are significantly lowered and consequently 49% size reduction for both operating frequencies is achieved. Simulated and measured results are presented and show good agreement.
medixain : Robust Blockchain Optimization Enabling Individual Medical Wallet Architecture

In todays rapidly aging societies further medical advancements more than ever rely on the automated analysis of vast amounts of data to receive more significant results of, e.g., medication effects. Particularly in medical research, it is evident how important such procedures are, keywords are, i.e., next generation sequencing or imaging techniques, yet, also medical practices strongly require such tooling. A major challenge in the health sector is demographic change, with an aging society and the resulting increased morbidity of the individual. At the same time, medicine controls from a reactive to a predictive and preventive medicine which, through early interventions, attempts to prevent illness or at least to treat it at an early stage as well as also highly individualized. This paradigm shift from a global medicine to an individual, stratified medicine should lead to the individual being able to enjoy good health. This is the only way to combat rising costs in the health system. To achieve this, it is necessary to collect and combine large amounts of heterogeneous personal data. These include, for example, clinical, epidemiological, imaging, molecular genetic, but also economic data. By analyzing such data using “data mining” techniques, new knowledge about disease development, prevention and individualized therapy can be generated. At the same time, economic data can also be used to develop models which, in addition to an optimization of medical care, can also show cost savings. Since the hospital information systems mostly include economic and clinical data, the prerequisites for the development of models are presented, which show an optimized diagnosis and therapy and at the same time improve the economic conditions without sacrificing medical quality. Moreover, not only medical institutions but also patients themselves suffer from a lack of sophisticated access possibilities to their medical records. Although we have the means of highly digitalized processes, patients would need to collect an entire suitcase
Adaptive sparse polynomial regression for camera lens simulation

Lens effects are crucial visual elements in the synthetic imagery, but rendering lens effects with complex full lens models is time-consuming. This paper proposes a polynomial regression-based approach for constructing a sparse and accurate polynomial lens model. Terms of a polynomial are built adaptively in a bottom-up approach. Depending on the distribution of aberrations, this approach partitions the light field and builds separate polynomial models for local light fields. A line pupil-based sampling method is presented to accelerate the generation of camera rays. In addition, a new Monte Carlo estimator is derived to support general Monte Carlo rendering. Experiments show that this approach significantly reduces the time cost of constructing a polynomial lens model in comparison to state-of-the-art methods, while achieving high imaging accuracy.
PFC efficency improvement and THD reduction at light loads with ZVS and valley switching

A PFC circuit's boost inductor and power MOSFET output capacitance resonate inevitably when the inductor current becomes discontinuous. The resonant current can be big enough to distort the PFC current significantly. Depending on the ac input voltage and the PFC output voltage, the MOSFET's Vds can resonate as low as zero volts. Proper control of the MOSFET to turn on at a Vds valley or zero voltage Vds can minimize switching loss and reduce PFC current distortion. A synchronous signal with a predicted noise blocking period is generated at the point where Vds rises from zero voltage. The synchronous signal is used to synchronize the PFC's PWM for reliable valley switching or ZVS. A 360W PFC circuit is used to validate the concept. Test results show at light loads a.c. current distortion is eliminated and overall efficiency is improved.
Automatically detecting problematic use of smartphones

Smartphone adoption has increased significantly and, with the increase in smartphone capabilities, this means that users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, there is growing evidence of problematic use of smartphones that impacts both social and heath aspects of users' lives. Currently, assessment of overuse or problematic use depends on one-time, self-reported behavioral information about phone use. Due to the known issues with self-reports in such types of assessments, we explore an automated, objective and repeatable approach for assessing problematic usage. We collect a wide range of phone usage data from smartphones, identify a number of usage features that are relevant to this assessment, and build detection models based on Adaboost with machine learning algorithms automatically detecting problematic use. We found that the number of apps used per day, the ratio of SMSs to calls, the number of event-initiated sessions, the number of apps used per event initiated session, and the length of non-event-initiated sessions are useful for detecting problematic usage. With these, a detection model can identify users with problematic usage with 89.6% accuracy (F-score of .707).
Unsupervised Profiling for Identifying Superimposed Fraud

Many fraud analysis applications try to detect “probably fraudulent” usage patterns, and to discover these patterns in historical data. This paper builds on a different detection concept; there are no fixed “probably fraudulent” patterns, but any significant deviation from the normal behavior indicates a potential fraud. In order to detect such deviations, a comprehensive representation of “customer behavior” must be used. This paper presents such representation, and discusses issues derived from it: a distance function and a clustering algorithm for probability distributions.
Effects of gratitude meditation on neural network functional connectivity and brain-heart coupling

A sense of gratitude is a powerful and positive experience that can promote a happier life, whereas resentment is associated with life dissatisfaction. To explore the effects of gratitude and resentment on mental well-being, we acquired functional magnetic resonance imaging and heart rate (HR) data before, during, and after the gratitude and resentment interventions. Functional connectivity (FC) analysis was conducted to identify the modulatory effects of gratitude on the default mode, emotion, and reward-motivation networks. The average HR was significantly lower during the gratitude intervention than during the resentment intervention. Temporostriatal FC showed a positive correlation with HR during the gratitude intervention, but not during the resentment intervention. Temporostriatal resting-state FC was significantly decreased after the gratitude intervention compared to the resentment intervention. After the gratitude intervention, resting-state FC of the amygdala with the right dorsomedial prefrontal cortex and left dorsal anterior cingulate cortex were positively correlated with anxiety scale and depression scale, respectively. Taken together, our findings shed light on the effect of gratitude meditation on an individual’s mental well-being, and indicate that it may be a means of improving both emotion regulation and self-motivation by modulating resting-state FC in emotion and motivation-related brain regions.
Analytics using R for predicting credit defaulters

Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims to extract useful knowledge from tremendous amount of complex data sets. This work aims to develop a model and construct a prototype for the same using a data set available in the UCI repository. The model is a decision tree based classification model that uses the functions available in the R Package. Prior to building the model, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The final model is used for prediction with the test dataset and the experimental results prove the efficiency of the built model.
Individuals’ Stress Assessment Using Human-Smartphone Interaction Analysis

The increasing presence of stress in people’ lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., ‘tap’, ‘scroll’, ‘swipe’ and ‘text writing’, and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance.