nils-herrmann/clean_scirepeval huggingface dataset:
Fairness in Machine Learning: Lessons from Political Philosophy

What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise ‘fairness’ in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.
Action Research

Create a short summary of your paper (200 words), double-spaced. Your summary will say something like: In this action research study of my classroom of 7 grade mathematics, I investigated ______. I discovered that ____________. As a result of this research, I plan to ___________. You now begin your paper. Pages should be numbered, with the first page of text following the abstract as page one.  We should be able to identity the following sections and you may use these headings (headings should be bold, centered, and capitalized). Consider the page length to be a minimum.
Rotation Equivariant CNNs for Digital Pathology

We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysis showing improved stability on predictions, and demonstrate that exploiting rotation equivariance significantly improves tumor detection performance on a challenging lymph node metastases dataset. We further present a novel derived dataset to enable principled comparison of machine learning models, in combination with an initial benchmark. Through this dataset, the task of histopathology diagnosis becomes accessible as a challenging benchmark for fundamental machine learning research.
A 0.3–1.4 GHz All-Digital Fractional-N PLL With Adaptive Loop Gain Controller

A 0.3-1.4 GHz all-digital phase locked loop (ADPLL) with an adaptive loop gain controller (ALGC), a 1/8-resolution fractional divider and a frequency search block is presented. The ALGC reduces the nonlinearity of the bang-bang phase-frequency detector (BBPFD), reducing output jitter. The fractional divider partially compensates for the large input phase error caused by fractional-N frequency synthesis. A fast frequency search unit using the false position method achieves frequency lock in 6 iterations that correspond to 192 reference clock cycles. A prototype ADPLL using a BBPFD with a dead-zone-free retimer, an ALGC, a fractional divider, and a digital logic implementation of a frequency search algorithm was fabricated in a 0.13-μm CMOS logic process. The core occupies 0.2 mm2 and consumes 16.5 mW with a 1.2-V supply at 1.35-GHz. Measured RMS and peak-to-peak jitter with activating the ALGC are 3.7 ps and 32 ps respectively.
Two-Stage Cost-Sensitive Learning for Software Defect Prediction

Software defect prediction (SDP), which classifies software modules into defect-prone and not-defect-prone categories, provides an effective way to maintain high quality software systems. Most existing SDP models attempt to attain lower classification error rates other than lower misclassification costs. However, in many real-world applications, misclassifying defect-prone modules as not-defect-prone ones usually leads to higher costs than misclassifying not-defect-prone modules as defect-prone ones. In this paper, we first propose a new two-stage cost-sensitive learning (TSCS) method for SDP, by utilizing cost information not only in the classification stage but also in the feature selection stage. Then, specifically for the feature selection stage, we develop three novel cost-sensitive feature selection algorithms, namely, Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplacian Score (CSLS), and Cost-Sensitive Constraint Score (CSCS), by incorporating cost information into traditional feature selection algorithms. The proposed methods are evaluated on seven real data sets from NASA projects. Experimental results suggest that our TSCS method achieves better performance in software defect prediction compared to existing single-stage cost-sensitive classifiers. Also, our experiments show that the proposed cost-sensitive feature selection methods outperform traditional cost-blind feature selection methods, validating the efficacy of using cost information in the feature selection stage.
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores

Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brain's function and efficiency. Judiciously balancing the dual objectives of functional capability and implementation/operational cost, we develop a simple, digital, reconfigurable, versatile spiking neuron model that supports one-to-one equivalence between hardware and simulation and is implementable using only 1272 ASIC gates. Starting with the classic leaky integrate-and-fire neuron, we add: (a) configurable and reproducible stochasticity to the input, the state, and the output; (b) four leak modes that bias the internal state dynamics; (c) deterministic and stochastic thresholds; and (d) six reset modes for rich finite-state behavior. The model supports a wide variety of computational functions and neural codes. We capture 50+ neuron behaviors in a library for hierarchical composition of complex computations and behaviors. Although designed with cognitive algorithms and applications in mind, serendipitously, the neuron model can qualitatively replicate the 20 biologically-relevant behaviors of a dynamical neuron model.
Software-defined networking security: pros and cons

Software-defined networking (SDN) is a new networking paradigm that decouples the forwarding and control planes, traditionally coupled with one another, while adopting a logically centralized architecture aiming to increase network agility and programability. While many efforts are currently being made to standardize this emerging paradigm, careful attention needs to be paid to security at this early design stage too, rather than waiting until the technology becomes mature, thereby potentially avoiding previous pitfalls made when designing the Internet in the 1980s. This article focuses on the security aspects of SDN networks. We begin by discussing the new security advantages that SDN brings and by showing how some of the long-lasting issues in network security can be addressed by exploiting SDN capabilities. Then we describe the new security threats that SDN is faced with and discuss possible techniques that can be used to prevent and mitigate such threats.
Global Localization from Monocular SLAM on a Mobile Phone

We propose the combination of a keyframe-based monocular SLAM system and a global localization method. The SLAM system runs locally on a camera-equipped mobile client and provides continuous, relative 6DoF pose estimation as well as keyframe images with computed camera locations. As the local map expands, a server process localizes the keyframes with a pre-made, globally-registered map and returns the global registration correction to the mobile client. The localization result is updated each time a keyframe is added, and observations of global anchor points are added to the client-side bundle adjustment process to further refine the SLAM map registration and limit drift. The end result is a 6DoF tracking and mapping system which provides globally registered tracking in real-time on a mobile device, overcomes the difficulties of localization with a narrow field-of-view mobile phone camera, and is not limited to tracking only in areas covered by the offline reconstruction.
Measuring Information Security Awareness - A West Africa Gold Mining Environment Case

AngloGold Ashanti is an international gold mining company that has recently implemented an information security awareness program worldwide at all of their operations. Following the implementation, there was a normal business need to evaluate and measure the success and effectiveness of the program. A measuring tool that can be applied globally and that addressed AngloGold Ashanti’s unique requirements was developed and applied at the mining sites located in the West Africa region. The objective of this paper is, firstly, to give a brief overview on the measuring tool developed and, secondly to report on the application and results in the West Africa region.
From Aardvark to Zorro: A Benchmark for Mammal Image Classification

Current object recognition systems aim at recognizing numerous object classes under limited supervision conditions. This paper provides a benchmark for evaluating progress on this fundamental task. Several methods have recently proposed to utilize the commonalities between object classes in order to improve generalization accuracy. Such methods can be termed interclass transfer techniques. However, it is currently difficult to asses which of the proposed methods maximally utilizes the shared structure of related classes. In order to facilitate the development, as well as the assessment of methods for dealing with multiple related classes, a new dataset including images of several hundred mammal classes, is provided, together with preliminary results of its use. The images in this dataset are organized into five levels of variability, and their labels include information on the objects’ identity, location and pose. From this dataset, a classification benchmark has been derived, requiring fine distinctions between 72 mammal classes. It is then demonstrated that a recognition method which is highly successful on the Caltech101, attains limited accuracy on the current benchmark (36.5%). Since this method does not utilize the shared structure between classes, the question remains as to whether interclass transfer methods can increase the accuracy to the level of human performance (90%). We suggest that a labeled benchmark of the type provided, containing a large number of related classes is crucial for the development and evaluation of classification methods which make efficient use of interclass transfer.
20 Years of Four HCI Conferences: A Visual Exploration

We present a visual exploration of the field of human–computer interaction (HCI) through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology, and Advanced Visual Interfaces and the IEEE Symposium on Information Visualization. This article describes many global and local patterns we discovered in this data set, together with the exploration process that produced them. Some expected patterns emerged, such as that—like most social networks— coauthorship and citation networks exhibit a power-law degree distribution, with a few widely collaborating authors and highly cited articles. Also, the prestigious and long-established CHI conference has the highest impact (citations by the others). Unexpected insights included that the years when a given conference was most selective are not correlated with those that produced its most highly referenced articles and that influential authors have distinct patterns of collaboration. An interesting sidelight is that methods from the HCI field—exploratory data analysis by information visualization and direct-manipulation interaction—proved useful for this analysis. They allowed us to take an open-ended, exploratory approach, guided by the data itself. As we answered our original questions, new ones arose; as we confirmed patterns we expected, we discovered refinements, exceptions, and fascinating new ones.
Near-optimal Reinforcement Learning in Factored MDPs

Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will su er ( Ô SAT ) regret on some MDP, where T is the elapsed time and S and A are the cardinalities of the state and action spaces. This implies T = (SA) time to guarantee a near-optimal policy. In many settings of practical interest, due to the curse of dimensionality, S and A can be so enormous that this learning time is unacceptable. We establish that, if the system is known to be a factored MDP, it is possible to achieve regret that scales polynomially in the number of parameters encoding the factored MDP, which may be exponentially smaller than S or A. We provide two algorithms that satisfy near-optimal regret bounds in this context: posterior sampling reinforcement learning (PSRL) and an upper confidence bound algorithm (UCRL-Factored).
On optimal multiple changepoint algorithms for large data

Many common approaches to detecting changepoints, for example based on statistical criteria such as penalised likelihood or minimum description length, can be formulated in terms ofminimising a cost over segmentations. We focus on a class of dynamic programming algorithms that can solve the resulting minimisation problem exactly, and thus find the optimal segmentation under the given statistical criteria. The standard implementation of these dynamic programming methods have a computational cost that scales at least quadratically in the length of the time-series. Recently pruning ideas have been suggested that can speed up the dynamic programming algorithms, whilst still being guaranteed to be optimal, in that they find the true minimum of the cost function. Herewe extend these pruningmethods, and introduce twonewalgorithms for segmenting data: FPOPand SNIP.Empirical results show that FPOP is substantially faster than existing dynamic programming methods, and unlike the existing methods its computational efficiency is robust to the number of changepoints in the data. We evaluate the method Electronic supplementary material The online version of this article (doi:10.1007/s11222-016-9636-3) contains supplementary material, which is available to authorized users. B Robert Maidstone r.maidstone@lancs.ac.uk; r.maidstone@lancaster.ac.uk 1 STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, UK 2 McGill University and Genome Quebec Innovation Center, Quebec, Canada 3 Institute of Plant Sciences Paris-Saclay, UMR 9213/UMR1403, CNRS, INRA, Université Paris-Sud, Université d’Evry, Université Paris-Diderot, Sorbonne Paris-Cité, Paris, France 4 Department of Mathematics and Statistics, Lancaster University, Lancaster, UK for detecting copy number variations and observe that FPOP has a computational cost that is even competitive with that of binary segmentation, but can give much more accurate segmentations.
Understanding the coverage and scalability of place-centric crowdsensing

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.
TAG Parsing with Neural Networks and Vector Representations of Supertags

We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely, and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is “almost parsing.” We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distributional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences.
Personalized speech recognition on mobile devices

We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-time on a Nexus 5 Android smartphone. We employ a quantized Long Short-Term Memory (LSTM) acoustic model trained with connectionist temporal classification (CTC) to directly predict phoneme targets, and further reduce its memory footprint using an SVD-based compression scheme. Additionally, we minimize our memory footprint by using a single language model for both dictation and voice command domains, constructed using Bayesian interpolation. Finally, in order to properly handle device-specific information, such as proper names and other context-dependent information, we inject vocabulary items into the decoder graph and bias the language model on-the-fly. Our system achieves 13.5% word error rate on an open-ended dictation task, running with a median speed that is seven times faster than real-time.
StackGhost: Hardware Facilitated Stack Protection

Conventional security exploits have relied on overwriting the saved return pointer on the stack to hijack the path of execution. Under Sun Microsystem’s Sparc processor architecture, we were able to implement a kernel modification to transparently and automatically guard applications’ return pointers. Our implementation called StackGhost under OpenBSD 2.8 acts as a ghost in the machine. StackGhost advances exploit prevention in that it protects every application run on the system without their knowledge nor does it require their source or binary modification. We will document several of the methods devised to preserve the sanctity of the system and will explore the performance ramifications of StackGhost.
Business Intelligence and Analytics: Research Directions

Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.
An improved Sobel edge detection

This paper proposes a method which combines Sobel edge detection operator and soft-threshold wavelet de-noising to do edge detection on images which include White Gaussian noises. In recent years, a lot of edge detection methods are proposed. The commonly used methods which combine mean de-noising and Sobel operator or median filtering and Sobel operator can not remove salt and pepper noise very well. In this paper, we firstly use soft-threshold wavelet to remove noise, then use Sobel edge detection operator to do edge detection on the image. This method is mainly used on the images which includes White Gaussian noises. Through the pictures obtained by the experiment, we can see very clearly that, compared to the traditional edge detection methods, the method proposed in this paper has a more obvious effect on edge detection.
Keys for Graphs

Keys for graphs aim to uniquely identify entities represented by vertices in a graph. We propose a class of keys that are recursively defined in terms of graph patterns, and are interpreted with subgraph isomorphism. Extending conventional keys for relations and XML, these keys find applications in object identification, knowledge fusion and social network reconciliation. As an application, we study the entity matching problem that, given a graph G and a set Σ of keys, is to find all pairs of entities (vertices) in G that are identified by keys in Σ. We show that the problem is intractable, and cannot be parallelized in logarithmic rounds. Nonetheless, we provide two parallel scalable algorithms for entity matching, in MapReduce and a vertex-centric asynchronous model. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.
Leveraging big data technology for small and medium-sized enterprises (SMEs)

Wisdom aligns with technology is the key factor for sustainable business development. By increasing amount of public and private data, organizations need to find new solutions to manage data and information which lead to knowledge, better decision making, and value. In the big data-bang, smart organization surfing on-line technology and start planning big data strategy. However, many organizations do not yet have a big data strategy. A challenge facing SMEs is that they may not have the same capacity as large companies to analysis new data sets. Also, traditional data processing tools are not capable for SMEs decision making because of volume, velocity and variety if data. For address this problem we need new leveraging technology, tools and talent. SMEs which have risen to leveraging the value of big data are using advantage of cloud computing and open-source software to realize various goals. The main goal of this investment is about value as a new concept in a big data era. In this study, we focus on emerging trends and future requirement: technology and tools for SMEs.
A self-powered bidirectional DC solid state circuit breaker using two normally-on SiC JFETs

This paper reports self-powered, autonomously operated bidirectional solid state circuit breakers (SSCB) with two back-to-back connected normally-on SiC JFETs as the main static switch for DC power systems. The SSCBs detect short circuit faults by sensing the sudden voltage rise between its two power terminals in either direction, and draws power from the fault condition itself to turn and hold off the SiC JFETs. The two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or extra wiring. A low-power, fast-starting, isolated DC/DC converter is designed and optimized to activate the SSCB in response to a short circuit fault. The SSCB prototypes are experimentally demonstrated to interrupt fault currents up to 150 amperes at a DC bus voltage of 400 volts within 0.7 microseconds.