nils-herrmann/clean_scirepeval huggingface dataset:
EvoloPy: An Open-source Nature-inspired Optimization Framework in Python

EvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent nature-inspired metaheuristic algorithms. The goal of this framework is to facilitate the use of metaheuristic algorithms by non-specialists coming from different domains. With a simple interface and minimal dependencies, it is easier for researchers and practitioners to utilize EvoloPy for optimizing and benchmarking their own defined problems using the most powerful metaheuristic optimizers in the literature. This framework facilitates designing new algorithms or improving, hybridizing and analyzing the current ones. The source code of EvoloPy is publicly available at GitHub (https://github.com/7ossam81/EvoloPy).
Robust Nonrigid Registration by Convex Optimization

We present an approach to nonrigid registration of 3D surfaces. We cast isometric embedding as MRF optimization and apply efficient global optimization algorithms based on linear programming relaxations. The Markov random field perspective suggests a natural connection with robust statistics and motivates robust forms of the intrinsic distortion functional. Our approach outperforms a large body of prior work by a significant margin, increasing registration precision on real data by a factor of 3.
Improving energy efficiency of location sensing on smartphones

Location-based applications have become increasingly popular on smartphones over the past years. The active use of these applications can however cause device battery drain owing to their power-intensive location-sensing operations. This paper presents an adaptive location sensing framework that significantly improves the energy efficiency of smartphones running location-based applications. The underlying design principles of the proposed framework involve substitution, suppression, piggybacking, and adaptation of applications' location-sensing requests to conserve energy. We implement these design principles on Android-based smartphones as a middleware. Our evaluation results show that the design principles reduce the usage of the power-intensive GPS (Global Positioning System) by up to 98% and improve battery life by up to 75%.
A p53-mediated DNA damage response limits reprogramming to ensure iPS cell genomic integrity

The reprogramming of differentiated cells to pluripotent cells (induced pluripotent stem (iPS) cells) is known to be an inefficient process. We recently reported that cells with short telomeres cannot be reprogrammed to iPS cells despite their normal proliferation rates, probably reflecting the existence of ‘reprogramming barriers’ that abort the reprogramming of cells with uncapped telomeres. Here we show that p53 (also known as Trp53 in mice and TP53 in humans) is critically involved in preventing the reprogramming of cells carrying various types of DNA damage, including short telomeres, DNA repair deficiencies, or exogenously inflicted DNA damage. Reprogramming in the presence of pre-existing, but tolerated, DNA damage is aborted by the activation of a DNA damage response and p53-dependent apoptosis. Abrogation of p53 allows efficient reprogramming in the face of DNA damage and the generation of iPS cells carrying persistent DNA damage and chromosomal aberrations. These observations indicate that during reprogramming cells increase their intolerance to different types of DNA damage and that p53 is critical in preventing the generation of human and mouse pluripotent cells from suboptimal parental cells.
Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces

Sphere tracing is a new technique for rendering implicit surfaces using geometric distance. Distance-based models are common in computer-aided geometric design and in the modeling of articulated figures. Given a function returning the distance to an object, sphere tracing marches along the ray toward its first intersection in steps guaranteed not to penetrate the implicit surface. Sphere tracing is particularly adept at rendering pathological surfaces. Creased and rough implicit surfaces are defined by functions with discontinuous or undefined derivatives. Current root finding techniques such as L-G surfaces and interval analysis require periodic evaluation of the derivative, and their behavior is dependent on the behavior of the derivative. Sphere tracing requires only a bound on the magnitude of the derivative, robustly avoiding problems Manuscript, July 1994. Recommended for publication: The Visual Computer.
A network in a laptop: rapid prototyping for software-defined networks

Mininet is a system for rapidly prototyping large networks on the constrained resources of a single laptop. The lightweight approach of using OS-level virtualization features, including processes and network namespaces, allows it to scale to hundreds of nodes. Experiences with our initial implementation suggest that the ability to run, poke, and debug in real time represents a qualitative change in workflow. We share supporting case studies culled from over 100 users, at 18 institutions, who have developed Software-Defined Networks (SDN). Ultimately, we think the greatest value of Mininet will be supporting collaborative network research, by enabling self-contained SDN prototypes which anyone with a PC can download, run, evaluate, explore, tweak, and build upon.
Coordination Avoidance in Database Systems

Concurrent access to shared state results in a fundamental trade-off between coordination—surfacing in the form of reduced availability, decreased scalability, and increased latency—and applicationlevel consistency, or semantic guarantees for end users. Traditional mechanisms such as serializable transactions are sufficient to ensure application-level consistency but require synchronous coordination, while weaker mechanisms may sacrifice consistency for less coordination and greater scalability. In this paper, we identify a necessary and sufficient condition for achieving coordination-free execution without violating application-level consistency: invariant confluence. By explicitly considering application-level invariants, invariant confluence analysis allows databases to coordinate between operations only when anomalies that might violate invariants are possible. This provides a formal basis for coordinationavoiding database systems, which coordinate only when it is necessary to do so. We demonstrate the utility of invariant confluence analysis on a subset of SQL and via a coordination-avoiding proofof-concept database prototype that scales linearly to over a million TPC-C New-Order transactions per second on 100 servers.
Spectral Network Embedding: A Fast and Scalable Method via Sparsity

Network embedding aims to learn low-dimensional representations of nodes in a network, while the network structure and inherent properties are preserved. It has attracted tremendous attention recently due to significant progress in downstream network learning tasks, such as node classification, link prediction, and visualization. However, most existing network embedding methods suffer from the expensive computations due to the large volume of networks. In this paper, we propose a 10× ∼ 100× faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis. In Progle, we first construct a sparse proximity matrix and train the network embedding efficiently via sparse matrix decomposition. Then we introduce a network propagation pattern via spectral analysis to incorporate local and global structure information into the embedding. Besides, this model can be generalized to integrate network information into other insufficiently trained embeddings at speed. Benefiting from sparse spectral network embedding, our experiment on four different datasets shows that Progle outperforms or is comparable to state-of-the-art unsupervised comparison approaches—DeepWalk, LINE, node2vec, GraRep, and HOPE, regarding accuracy, while is 10× faster than the fastest word2vec-based method. Finally, we validate the scalability of Progle both in real large-scale networks and multiple scales of synthetic networks.
Effect of rule weights in fuzzy rule-based classification systems

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility grade with the new pattern. When we use fuzzy IF–THEN rules with certainty grades (i.e., rule weights), the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy IF–THEN rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy IF–THEN rules with certainty grades.
Constrained Arc-Eager Dependency Parsing

Arc-eager dependency parsers process sentences in a single left-to-right pass over the input and have linear time complexity with greedy decoding or beam search. We show how such parsers can be constrained to respect two different types of conditions on the output dependency graph: span constraints, which require certain spans to correspond to subtrees of the graph, and arc constraints, which require certain arcs to be present in the graph. The constraints are incorporated into the arc-eager transition system as a set of preconditions for each transition and preserve the linear time complexity of the parser.
A Capacitive Coupled Patch Antenna Array With High Gain and Wide Coverage for 5G Smartphone Applications

In this paper, a novel capacitive coupled patch antenna array capable of providing high gain and 360° coverage in the elevation plane is proposed. The proposed antenna element is studied in detail using the theory of characteristic modes to enhance the bandwidth. It exhibits a uniform radiation pattern with a stable gain when integrated with the mobile phone chassis. These elements are positioned in the mobile phone chassis as a set of four sub-arrays each with 12 antenna elements to provide high realized gain around 16.5 dBi with each sub-array providing 90° coverage. The antenna array is fabricated, and the beam steering performance of the array is evaluated using a beam-steering test bed. The results show that it covers the frequency range of 24–28 GHz, which is a promising band for future 5G-based smartphone services as predicted and could be a strong candidate for 5G applications.
How do fixes become bugs?

Software bugs affect system reliability. When a bug is exposed in the field, developers need to fix them. Unfortunately, the bug-fixing process can also introduce errors, which leads to buggy patches that further aggravate the damage to end users and erode software vendors' reputation.
 This paper presents a comprehensive characteristic study on incorrect bug-fixes from large operating system code bases including Linux, OpenSolaris, FreeBSD and also a mature commercial OS developed and evolved over the last 12 years, investigating not only themistake patterns during bug-fixing but also the possible human reasons in the development process when these incorrect bug-fixes were introduced. Our major findings include: (1) at least 14.8%--24.4% of sampled fixes for post-release bugs in these large OSes are incorrect and have made impacts to end users. (2) Among several common bug types, concurrency bugs are the most difficult to fix correctly: 39% of concurrency bug fixes are incorrect. (3) Developers and reviewers for incorrect fixes usually do not have enough knowledge about the involved code. For example, 27% of the incorrect fixes are made by developers who have never touched the source code files associated with the fix. Our results provide useful guidelines to design new tools and also to improve the development process. Based on our findings, the commercial software vendor whose OS code we evaluated is building a tool to improve the bug fixing and code reviewing process.
What is ‘ multi ’ in multi-agent learning ?

Learning in multi -agent environments constitutes a research and application area whose importance is broadly acknowledged in artificial intelli gence. Although there is a rapidly growing body of literature on multi -agent learning, almost nothing is known about the intrinsic nature of and requirements for this kind of learning. This observation is the starting point of this chapter which aims at providing a more general characterization of multi -agent learning. This is done in an interdisciplinary way from two different perspectives: the perspective of single-agent learning (the ‘machine learning perspective’) and the perspective of human-human collaborative learning (the ‘psychological perspective’) . The former leads to a ‘positive’ characterization: three types of learning mechanisms multiplication, division, and interaction are identified and ill ustrated that can occur in multi -agent but not in single-agent settings. The latter leads to a ‘negative’ characterization: several cognitive processes like conflict resolution, mutual regulation, and explanation are identified and discussed that are most essential to human-human collaborative learning, but have been largely ignored so far in the available multi -agent learning approaches. Misunderstanding among humans is identified as a major source of these processes, and its important role in the context of multiagent systems is stressed. This chapter also offers a brief guide to agents and multi -agent systems as studied in artificial intelli gence, and suggests directions for future research on multi-agent learning.
Designing Social Networks to Combat Fear of Missing Out

Fear of missing out, (hereafter referred to as FoMO), is increasingly becoming an issue of concern in relation to the use of Social Network Sites (SNSs). Despite its importance, the effects of FoMO continue to receive limited attention, while guidance on how SNSs design is responsible for developing should and combatting it, remains inadequate. In this position paper, we argue that dual responsibility of SNSs design. We report on initial results of a multiphase empirical study which was undertaken to examine the features of social networks that may contribute to triggering FoMO, and to explore how future SNSs can be designed to aid people manage their FoMO. The study involved three focus group sessions and a diary study. We argue that future SNSs shall support interaction styles and protocols and their agreement and adherence processes to enable people prevent and combat FoMO and present styles for doing that.
A Survey on Image Matting Techniques

With advanced digital camera, the matting techniques are highly used to create an innovative composite and to facilitate other editing tasks that has gained increasing benefits from both professionals as well as consumers. Image Matting techniques are key step of image and video editing, image translation and in film production to track the object in scene. Image matting methods categories in to three types, sampling based, propagation based, and learning based. A hybrid of sampling based and propagation-based matting uses to improve the result of alpha matte. Various image matting techniques and systems have been proposed to efficiently extract high quality mattes from image. In this paper a comprehensive review of existing image matting techniques along with parametric evaluation of these schemes are discussed.
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models.
Feature Generation Using General Constructor Functions

Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creating an accurate, succinct and comprehensible representation of the target concept. To overcome this problem, researchers have proposed algorithms for automatic construction of features. The majority of these algorithms use a limited predefined set of operators for building new features. In this paper we propose a generalized and flexible framework that is capable of generating features from any given set of constructor functions. These can be domain-independent functions such as arithmetic and logic operators, or domain-dependent operators that rely on partial knowledge on the part of the user. The paper describes an algorithm which receives as input a set of classified objects, a set of attributes, and a specification for a set of constructor functions that contains their domains, ranges and properties. The algorithm produces as output a set of generated features that can be used by standard concept learners to create improved classifiers. The algorithm maintains a set of its best generated features and improves this set iteratively. During each iteration, the algorithm performs a beam search over its defined feature space and constructs new features by applying constructor functions to the members of its current feature set. The search is guided by general heuristic measures that are not confined to a specific feature representation. The algorithm was applied to a variety of classification problems and was able to generate features that were strongly related to the underlying target concepts. These features also significantly improved the accuracy achieved by standard concept learners, for a variety of classification problems.
New Algorithms for Learning Incoherent and Overcomplete Dictionaries

In sparse recovery we are given a matrix A ∈ R (“the dictionary”) and a vector of the form AX where X is sparse, and the goal is to recover X . This is a central notion in signal processing, statistics and machine learning. But in applications such as sparse coding, edge detection, compression and super resolution, the dictionary A is unknown and has to be learned from random examples of the form Y = AX where X is drawn from an appropriate distribution — this is the dictionary learning problem. In most settings, A is overcomplete: it has more columns than rows. This paper presents a polynomial-time algorithm for learning overcomplete dictionaries; the only previously known algorithm with provable guarantees is the recent work of [48] who gave an algorithm for the undercomplete case, which is rarely the case in applications. Our algorithm applies to incoherent dictionaries which have been a central object of study since they were introduced in seminal work of [18]. In particular, a dictionary is μ-incoherent if each pair of columns has inner product at most μ/ √ n. The algorithm makes natural stochastic assumptions about the unknown sparse vector X , which can contain k ≤ cmin(√n/μ logn,m) non-zero entries (for any η > 0). This is close to the best k allowable by the best sparse recovery algorithms even if one knows the dictionary A exactly. Moreover, both the running time and sample complexity depend on log 1/ǫ, where ǫ is the target accuracy, and so our algorithms converge very quickly to the true dictionary. Our algorithm can also tolerate substantial amounts of noise provided it is incoherent with respect to the dictionary (e.g., Gaussian). In the noisy setting, our running time and sample complexity depend polynomially on 1/ǫ, and this is necessary.
Software traceability: trends and future directions

Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.
The Application of the Accelerated Stability Assessment Program (ASAP) to Quality by Design (QbD) for Drug Product Stability

An isoconversion paradigm, where times in different temperature and humidity-controlled stability chambers are set to provide a fixed degradant level, is shown to compensate for the complex, non-single order kinetics of solid drug products. A humidity-corrected Arrhenius equation provides reliable estimates for temperature and relative humidity effects on degradation rates. A statistical protocol is employed to determine best fits for chemical stability data, which in turn allows for accurate estimations of shelf life (with appropriate confidence intervals) at any storage condition including inside packaging (based on the moisture vapor transmission rate of the packaging and moisture sorption isotherms of the internal components). These methodologies provide both faster results and far better predictions of chemical stability limited shelf life (expiry) than previously possible. Precise shelf-life estimations are generally determined using a 2-week, product-specific protocol. Once the model for a product is developed, it can play a critical role in providing the product understanding necessary for a quality by design (QbD) filing for product approval and enable rational control strategies to assure product stability. Moreover, this Accelerated Stability Assessment Program (ASAP) enables the coupling of product attributes (e.g., moisture content, packaging options) to allow for flexibility in how control strategies are implemented to provide a balance of cost, speed, and other factors while maintaining adequate stability.