nils-herrmann/clean_scirepeval huggingface dataset:
A fingerprint pattern classification approach based on the coordinate geometry of singularities

The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% is obtained on the five-class problem. This performance evaluation is done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002).
Negative Deceptive Opinion Spam

The rising influence of user-generated online reviews (Cone, 2011) has led to growing incentive for businesses to solicit and manufacture DECEPTIVE OPINION SPAM—fictitious reviews that have been deliberately written to sound authentic and deceive the reader. Recently, Ott et al. (2011) have introduced an opinion spam dataset containing gold standard deceptive positive hotel reviews. However, the complementary problem of negative deceptive opinion spam, intended to slander competitive offerings, remains largely unstudied. Following an approach similar to Ott et al. (2011), in this work we create and study the first dataset of deceptive opinion spam with negative sentiment reviews. Based on this dataset, we find that standard n-gram text categorization techniques can detect negative deceptive opinion spam with performance far surpassing that of human judges. Finally, in conjunction with the aforementioned positive review dataset, we consider the possible interactions between sentiment and deception, and present initial results that encourage further exploration of this relationship.
Non-rigid registration of 3D surfaces by deformable 2D triangular meshes

Non-rigid surface registration, particularly registration of human faces, finds a wide variety of applications in computer vision and graphics. We present a new automatic surface registration method which utilizes both attraction forces originating from geometrical and textural similarities, and stresses due to non-linear elasticity of the surfaces. Reference and target surfaces are first mapped onto their feature image planes, then these images are registered by subjecting them to local deformations, and finally 3D correspondences are established. Surfaces are assumed to be elastic sheets and are represented by triangular meshes. The internal elastic forces act as a regularizer in this ill-posed problem. Furthermore, the non-linear elasticity model allows us to handle large deformations, which can be essential, for instance, for facial expressions. The method has been tested successfully on 3D scanned human faces, with and without expressions. The algorithm runs quite efficiently using a multiresolution approach.
The Behavior Grid: 35 ways behavior can change

This paper presents a new way of categorizing behavior change in a framework called the Behavior Grid. This preliminary work shows 35 types of behavior along two categorical dimensions. To demonstrate the analytical potential for the Behavior Grid, this paper maps behavior goals from Facebook onto the framework, revealing potential patterns of intent. To show the potential for designers of persuasive technology, this paper uses the Behavior Grid to show what types of behavior change might most easily be achieved through mobile technology. The Behavior Grid needs further development, but this early version can still be useful for designers and researchers in thinking more clearly about behavior change and persuasive technology.
Recurrent neural network language model adaptation for multi-genre broadcast speech recognition

Recurrent neural network language models (RNNLMs) have recently become increasingly popular for many applications i ncluding speech recognition. In previous research RNNLMs have normally been trained on well-matched in-domain data. The adaptation of RNNLMs remains an open research area to be explored. In this paper, genre and topic based RNNLM adaptation techniques are investigated for a multi-genre broad cast transcription task. A number of techniques including Proba bilistic Latent Semantic Analysis, Latent Dirichlet Alloc ation and Hierarchical Dirichlet Processes are used to extract sh ow level topic information. These were then used as additional input to the RNNLM during training, which can facilitate unsupervised test time adaptation. Experiments using a state-o f-theart LVCSR system trained on 1000 hours of speech and more than 1 billion words of text showed adaptation could yield pe rplexity reductions of 8% relatively over the baseline RNNLM and small but consistent word error rate reductions.
Learning to Estimate 3D Human Pose and Shape from a Single Color Image

This work addresses the problem of estimating the full body 3D human pose and shape from a single color image. This is a task where iterative optimization-based solutions have typically prevailed, while Convolutional Networks (ConvNets) have suffered because of the lack of training data and their low resolution 3D predictions. Our work aims to bridge this gap and proposes an efficient and effective direct prediction method based on ConvNets. Central part to our approach is the incorporation of a parametric statistical body shape model (SMPL) within our end-to-end framework. This allows us to get very detailed 3D mesh results, while requiring estimation only of a small number of parameters, making it friendly for direct network prediction. Interestingly, we demonstrate that these parameters can be predicted reliably only from 2D keypoints and masks. These are typical outputs of generic 2D human analysis ConvNets, allowing us to relax the massive requirement that images with 3D shape ground truth are available for training. Simultaneously, by maintaining differentiability, at training time we generate the 3D mesh from the estimated parameters and optimize explicitly for the surface using a 3D per-vertex loss. Finally, a differentiable renderer is employed to project the 3D mesh to the image, which enables further refinement of the network, by optimizing for the consistency of the projection with 2D annotations (i.e., 2D keypoints or masks). The proposed approach outperforms previous baselines on this task and offers an attractive solution for direct prediction of3D shape from a single color image.
Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students’ performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student’s performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student’s data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.  Educational data mining; Decision tree; C4.5 algorithm; ID3 algorithm; CART algorithm.
Chronos: predictable low latency for data center applications

In data center applications, predictability in service time and controlled latency, especially tail latency, are essential for building performant applications. This is especially true for applications or services built by accessing data across thousands of servers to generate a user response. Current practice has been to run such services at low utilization to rein in latency outliers, which decreases efficiency and limits the number of service invocations developers can issue while still meeting tight latency budgets.
 In this paper, we analyze three data center applications, Memcached, OpenFlow, and Web search, to measure the effect of 1) kernel socket handling, NIC interaction, and the network stack, 2) application locks contested in the kernel, and 3) application-layer queueing due to requests being stalled behind straggler threads on tail latency. We propose Chronos, a framework to deliver predictable, low latency in data center applications. Chronos uses a combination of existing and new techniques to achieve this end, for example by supporting Memcached at 200,000 requests per second per server at mean latency of 10 μs with a 99th percentile latency of only 30 μs, a factor of 20 lower than baseline Memcached.
Three-Phase Soft-Switching Inverter With Minimum Components

In this paper, a novel three-phase soft-switching inverter is presented. The inverter-switch turn on and turn off are performed under zero-voltage switching condition. This inverter has only one auxiliary switch, which is also soft switched. Having one auxiliary switch simplifies the control circuit considerably. The proposed inverter is analyzed, and its operating modes are explained in details. The design considerations of the proposed inverter are presented. The experimental results of the prototype inverter confirm the theoretical analysis.
Building an internet of school things ecosystem: a national collaborative experience

Over the course of the next 10 years, the Internet of Things (IoT) is set to have a transformational effect on the everyday technologies which surround us. Access to the data produced by these devices opens an interesting space to practice discovery based learning. This paper outlines a participatory design approach taken to develop an IoTbased ecosystem which was deployed in 8 schools across England. In particular, we describe how we designed and developed the system and reflect on some of the early experiences of students and teachers. We found that schools were willing to adopt the IoT technology within certain bounds and we outline best practices uncovered when introducing technologies to schools.
Batch normalized recurrent neural networks

Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to train and difficult to parallelize. Recent work has shown that normalizing intermediate representations of neural networks can significantly improve convergence rates in feed-forward neural networks . In particular, batch normalization, which uses mini-batch statistics to standardize features, was shown to significantly reduce training time. In this paper, we investigate how batch normalization can be applied to RNNs. We show for both a speech recognition task and language modeling that the way we apply batch normalization leads to a faster convergence of the training criterion but doesn't seem to improve the generalization performance.
Where Should Saliency Models Look Next?

Recently, large breakthroughs have been observed in saliency modeling. The top scores on saliency benchmarks have become dominated by neural network models of saliency, and some evaluation scores have begun to saturate. Large jumps in performance relative to previous models can be found across datasets, image types, and evaluation metrics. Have saliency models begun to converge on human performance? In this paper, we re-examine the current state-of-the-art using a finegrained analysis on image types, individual images, and image regions. Using experiments to gather annotations for high-density regions of human eye fixations on images in two established saliency datasets, MIT300 and CAT2000, we quantify up to 60% of the remaining errors of saliency models. We argue that to continue to approach human-level performance, saliency models will need to discover higher-level concepts in images: text, objects of gaze and action, locations of motion, and expected locations of people in images. Moreover, they will need to reason about the relative importance of image regions, such as focusing on the most important person in the room or the most informative sign on the road. More accurately tracking performance will require finer-grained evaluations and metrics. Pushing performance further will require higher-level image understanding.
A Flexible Hybrid Method for IT Project Management

Project management (PM) landscaping is continually changing in the IT industry. Working with the small teams and often with the limited budgets, while facing frequent changes in the business requirements, project managers are under continuous pressure to deliver fast turnarounds. Following the demands of the IT project management, leaders in this industry are optimizing and adopting different and new more effective styles and strategies. This paper proposes a new hybrid way of managing IT projects, flexibly combining the traditional and the Agile method. Also, it investigates what is the necessary organizational transition in an IT company, required before converting from the traditional to the proposed new hybrid method.
Roofline Model Toolkit: A Practical Tool for Architectural and Program Analysis

We present preliminary results of the Roofline Toolkit for multicore, manycore, and accelerated architectures. This paper focuses on the processor architecture characterization engine, a collection of portable instrumented micro benchmarks implemented with Message Passing Interface (MPI), and OpenMP used to express thread-level parallelism. These benchmarks are specialized to quantify the behavior of different architectural features. Compared to previous work on performance characterization, these microbenchmarks focus on capturing the performance of each level of the memory hierarchy, along with thread-level parallelism, instruction-level parallelism and explicit SIMD parallelism, measured in the context of the compilers and run-time environments. We also measure sustained PCIe throughput with four GPU memory managed mechanisms. By combining results from the architecture characterization with the Roofline model based solely on architectural specifications, this work offers insights for performance prediction of current and future architectures and their software systems. To that end, we instrument three applications and plot their resultant performance on the corresponding Roofline model when run on a Blue Gene/Q architecture.
The evolution of jealousy

If a single deontic reasoning system handles deontic social contracts and deontic precautions, then performance on both should be impaired by neural damage to that system. But if they are handled by two distinct neurocognitive systems, as predicted by SCT and HMT, then neural trauma could cause a dissociation. Buller’s hypothesis fails again: focal brain damage can selectively impair social contract reasoning while leaving precautionary reasoning intact . This dissociation within the domain of deontic rules has recently been replicated using neuroimaging [6]. Interpretations of social contract rules track SCT’s domain-specialized inference procedures: in , we refuted Buller-style logic explanations of social contract results for perspective change, switched rules, and ‘wants’ problems – facts he fails to mention, let alone discuss. Buller’s systematic inattention to large bodies of findings that conflict with his assertions is not due to lack of space in TICS – the pretence that these findings do not exist pervades his book, and its treatment of many areas of evolutionary psychology. (For further analysis, see www. psych.ucsb.edu/research/cep/buller.htm)
Machine Learning and Data Mining

Over the past decade , many organizations have begun to routinely capture huge volumes of historical data describing their operations, products, and customers. At the same time, scientists and engineers in many fields have been capturing increasingly complex experimental data sets, such as gigabytes of functional magnetic resonance imaging (MRI) data describing brain activity in humans. The field of data mining addresses the question of how best to use this historical data to discover general regularities and improve the process of making decisions. Machine Learning and Data Mining
Handbook of Information and Communication Security

Part A: Fundamentals and Cryptography Chapter 1: A Framework for System Security Chapter 1 aims to describe a conceptual framework for the design and analysis of secure systems with the goal of defining a common language to express “concepts”. Since it is designed both for theoreticians and for practitioners, there are two kinds of applicability. On the one hand a meta-model is proposed to theoreticians, enabling them to express arbitrary axioms of other security models in this special framework. On the other hand the framework provides a language for describing the requirements, designs, and evaluations of secure systems. This information is given to the reader in the introduction and as a consequence he wants to get the specification of the framework. Unfortunately, the framework itself is not described! However, the contents cover first some surrounding concepts like “systems, owners, security and functionality”. These are described sometimes in a confusing way, so that it remains unclear, what the author really wants to focus on. The following comparison of “Qualitative and Quantitative Security” is done 1For example: if the reader is told, that “every system has an owner, and every owner is a system”, there obviously seems to be no difference between these entities (cp. p. 4).
Design Procedures for Three-Stage CMOS OTAs With Nested-Miller Compensation

Design procedures for three-stage CMOS operational transconductance amplifiers employing nested-Miller frequency compensation are presented in this paper. After describing the basic methodology on a Class-A topology, some modifications, to increase swing, slew-rate and current drive capability, are subsequently discussed for a Class-AB solution. The approaches developed are simple as they do not introduce unnecessary circuit constraints and yield accurate results. They are hence suited for a pencil-and-paper design, but can be easily integrated into an analog knowledge-based computer-aided design tool. Experimental prototypes, designed in a 0.35-mum technology by following the proposed procedures, were fabricated and tested. Measurement results were found in close agreement with the target specifications
Multi-scale approach from mechatronic to Cyber-Physical Systems for the design of manufacturing systems

Recent advances in manufacturing industry, and notably in the Industry 4.0 context, promote the development of CPSs and consequently give rise to a number of issues to be solved. The present paper describes the context of the extension of mechatronic systems to cyber-physical ones, firstly by highlighting their similarities and differences, and then by underlining the current needs for CPSs in the manufacturing sector. Then, the paper presents the main research issues related to CPS design and, in particular, the needs for an integrated and multi-scale designing approach to prevent conflicts across different design domains early enough within the CPS development process. To this aim, the impact of the extension from mechatronic to Cyber-Physical Systems on their design is examined through a set of existing related modelling techniques. The multi-scalability requirement of these techniques is firstly described, concerning external/internal interactions, process control, behaviour simulation, representation of topological relationships and interoperability through a multi-agent platform, and then applied to the case study of a tablets manufacturing process. Finally, the proposed holistic description of such a multi-scale manufacturing CPS allows to outline the main characteristics of a modelling-simulation platform, able notably to bridge the semantic gaps existing between the different designing disciplines and specialised domains.
The social brain hypothesis

The concensus view has traditionally been that brains evolved to process information of ecological relevance. This view, however, ignores an important consideration: Brains are exceedingly expensive both to evolve and to maintain. The adult human brain weighs about 2% of body weight but consumes about 20% of total energy intake.2 In the light of this, it is difficult to justify the claim that primates, and especially humans, need larger brains than other species merely to do the same ecological job. Claims that primate ecological strategies involve more complex problem-solving3,4 are plausible when applied to the behaviors of particular species, such as termite-extraction by chimpanzees and nut-cracking by Cebus monkeys, but fail to explain why all primates, including those that are conventional folivores, require larger brains than those of all other mammals. An alternative hypothesis offered during the late 1980s was that primates’ large brains reflect the computational demands of the complex social systems that characterize the order.5,6 Prima facie, this suggestion seems plausible: There is ample evidence that primate social systems are more complex than those of other species. These systems can be shown to involve processes such as tactical deception5 and coalition-formation,7,8 which are rare or occur only in simpler forms in other taxonomic groups. Because of this, the suggestion was rapidly dubbed the Machiavellian intelligence hypothesis, although there is a growing preference to call it the social brain hypothesis.9,10 Plausible as it seems, the social brain hypothesis faced a problem that was recognized at an early date. Specifically, what quantitative empirical evidence there was tended to favor one or the other of the ecological hypotheses,1 whereas the evidence adduced in favor of the social brain hypothesis was, at best,  In this article, I shall first show how we can test between the competing hypotheses more conclusively and then consider some of the implications of the social brain hypothesis for humans. Finally, I shall briefly consider some of the underlying cognitive mechanisms that might be involved.