nils-herrmann/clean_scirepeval huggingface dataset:
GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics

Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines the scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation.
Structure Mapping in Analogy and Similarity

Analogy and similarity are often assumed to be distinct psychological processes. In contrast to this position, the authors suggest that both similarity and analogy involve a process of structural alignment and mapping, that is, that similarity is like analogy. In this article, the authors first describe the structure-mapping process as it has been worked out for analogy. Then, this view is extended to similarity, where it is used to generate new predictions. Finally, the authors explore broader implications of structural alignment for psychological processing.
Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

In on-line search and display advertising, the click-trough rate (CTR) has been traditionally a key measure of ad/campaign effectiveness. More recently, the market has gained interest in more direct measures of profitability, one early alternative is the conversion rate (CVR). CVRs measure the proportion of certain users who take a predefined, desirable action, such as a purchase, registration, download, etc.; as compared to simply page browsing. We provide a detailed analysis of conversion rates in the context of non-guaranteed delivery targeted advertising. In particular we focus on the post-click conversion (PCC) problem or the analysis of conversions after a user click on a referring ad. The key elements we study are the probability of a conversion given a click in a user/page context, P(conversion | click, context). We provide various fundamental properties of this process based on contextual information, formalize the problem of predicting PCC, and propose an approach for measuring attribute relevance when the underlying attribute distribution is non-stationary. We provide experimental analyses based on logged events from a large-scale advertising platform.
Capacitive fingerprinting: exploring user differentiation by sensing electrical properties of the human body

At present, touchscreens can differentiate multiple points of contact, but not who is touching the device. In this work, we consider how the electrical properties of humans and their attire can be used to support user differentiation on touchscreens. We propose a novel sensing approach based on Swept Frequency Capacitive Sensing, which measures the impedance of a user to the environment (i.e., ground) across a range of AC frequencies. Different people have different bone densities and muscle mass, wear different footwear, and so on. This, in turn, yields different impedance profiles, which allows for touch events, including multitouch gestures, to be attributed to a particular user. This has many interesting implications for interactive design. We describe and evaluate our sensing approach, demonstrating that the technique has considerable promise. We also discuss limitations, how these might be overcome, and next steps.
Learning and Inference with Constraints

Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. However, incorporating nonlocal dependencies in a probabilistic model can lead to intractable training and inference. This paper presents Constraints Conditional Models (CCMs), a framework that augments probabilistic models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. We further show that declarative constraints can be used to take advantage of unlabeled data when training the probabilistic model.
The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence

Internet of Things (IoT) has given rise to the fourth industrial revolution (Industrie 4.0), and it brings great benefits by connecting people, processes and data. However, cybersecurity has become a critical challenge in the IoT enabled cyber physical systems, from connected supply chain, Big Data produced by huge amount of IoT devices, to industry control systems. Evolutionary computation combining with other computational intelligence will play an important role for cybersecurity, such as artificial immune mechanism for IoT security architecture, data mining/fusion in IoT enabled cyber physical systems, and data driven cybersecurity. This paper provides an overview of security challenges in IoT enabled cyber-physical systems and what evolutionary computation and other computational intelligence technology could contribute for the challenges. The overview could provide clues and guidance for research in IoT security with computational intelligence.
Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.
Investigation of friction characteristics of a tendon driven wearable robotic hand

Wearable robots can assist people with disabilities to perform their daily tasks. However, the size, weight and wearability are important factors in the design because it is worn by the person controlling it. Various disabilities can be assisted with wearable robot technology, from the lower to upper body. The hand is an important part of the body for the disabled to perform their daily tasks. However, compared to the arms or legs, the degree of freedom is much higher, which makes it difficult to fabricate a compact wearable robot. We propose a frameless structure and modified differential mechanism to make the wearable robot compact. For the evaluation and control, it is necessary to analyze the friction force because the mechanism we proposed delivers power through more tube than the previous tendon tube transmission. Different from the previous friction model, we consider the friction at the edge of tube ends. This paper contains the design concept of the developed wearable robotic hand and its friction characteristics.
IFCIoT: Integrated Fog Cloud IoT Architectural Paradigm for Future Internet of Things

We propose a novel integrated fog cloud IoT (IFCIoT) architectural paradigm that promises increased performance, energy efficiency, reduced latency, quicker response time, scalability, and better localized accuracy for future IoT applications. The fog nodes (e.g., edge servers, smart routers, base stations) receive computation offloading requests and sensed data from various IoT devices. To enhance performance, energy efficiency, and real-time responsiveness of applications, we propose a reconfigurable and layered fog node (edge server) architecture that analyzes the applications’ characteristics and reconfigure the architectural resources to better meet the peak workload demands. The layers of the proposed fog node architecture include application layer, analytics layer, virtualization layer, reconfiguration layer, and hardware layer. The layered architecture facilitates abstraction and implementation for fog computing paradigm that is distributed in nature and where multiple vendors (e.g., applications, services, data and content providers) are involved. We also elaborate the potential applications of IFCIoT architecture, such as smart cities, intelligent transportation systems, localized weather maps and environmental monitoring, and real-time agricultural data analytics and control. Index Terms —Fog computing, edge computing, Internet of things, reconfigurable architecture, radio access network
A Block Cipher Generation Using Color Substitution

The most powerful and common approach to countering the threats to network / information security is encryption . Even though it is very powerful, the cryptanalysts are very intelligent and they were working day and night to break the ciphers. To make a stronger cipher it is recommended that to use: More stronger and complicated encryption algorithms, Keys with more number of bits (Longer keys), larger block size as input to process, use authentication and confidentiality and secure transmission of keys. It is for sure that if we follow all the mentioned principles we can make a very stronger cipher. With this we have the following problems: It is a time consuming process for both encryption and decryption, It is difficult for the crypt analyzer to analyze the problem. Also suffers with the problems in the existing system. The main objective of this paper is to solve all these problems and to bring the revolution in the Network security with a new substitution technique [3] is ‘color substitution technique’ and named as a “Play color cipher”.
A Performance Comparison of Contemporary DRAM Architectures

In response to the growing gap between memory access time and processor speed, DRAM manufacturers have created several new DRAM architectures. This paper presents a simulation-based performance study of a representative group, each evaluated in a small system organization. These small-system organizations correspond to workstation-class computers and use on the order of 10 DRAM chips. The study covers Fast Page Mode, Extended Data Out, Synchronous, Enhanced Synchronous, Synchronous Link, Rambus, and Direct Rambus designs. Our simulations reveal several things: (a) current advanced DRAM technologies are attacking the memory bandwidth problem but not the latency problem; (b) bus transmission speed will soon become a primary factor limiting memory-system performance; (c) the post-L2 address stream still contains significant locality, though it varies from application to application; and (d) as we move to wider buses, row access time becomes more prominent, making it important to investigate techniques to exploit the available locality to decrease access time.
Efficient Methods for Overlapping Group Lasso

The group Lasso is an extension of the Lasso for feature selection on (predefined) nonoverlapping groups of features. The nonoverlapping group structure limits its applicability in practice. There have been several recent attempts to study a more general formulation where groups of features are given, potentially with overlaps between the groups. The resulting optimization is, however, much more challenging to solve due to the group overlaps. In this paper, we consider the efficient optimization of the overlapping group Lasso penalized problem. We reveal several key properties of the proximal operator associated with the overlapping group Lasso, and compute the proximal operator by solving the smooth and convex dual problem, which allows the use of the gradient descent type of algorithms for the optimization. Our methods and theoretical results are then generalized to tackle the general overlapping group Lasso formulation based on the eq norm. We further extend our algorithm to solve a nonconvex overlapping group Lasso formulation based on the capped norm regularization, which reduces the estimation bias introduced by the convex penalty. We have performed empirical evaluations using both a synthetic and the breast cancer gene expression dataset, which consists of 8,141 genes organized into (overlapping) gene sets. Experimental results show that the proposed algorithm is more efficient than existing state-of-the-art algorithms. Results also demonstrate the effectiveness of the nonconvex formulation for overlapping group Lasso.
A document-level sentiment analysis approach using artificial neural network and sentiment lexicons

The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social media can be a rich source of sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining has become an open research domain that involves classifying text documents based on the opinion expressed, about a given topic, being positive or negative. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain, and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with intrinsic subjectivity knowledge available in the sentiment lexicons. The results obtained from experiments on the movie and hotel review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate results for sentiment based classification of text.
Performance Analysis of Online Anticipatory Algorithms for Large Multistage Stochastic Integer Programs

Despite significant algorithmic advances in recent years, finding optimal policies for large-scale, multistage stochastic combinatorial optimization problems remains far beyond the reach of existing methods. This paper studies a complementary approach, online anticipatory algorithms, that make decisions at each step by solving the anticipatory relaxation for a polynomial number of scenarios. Online anticipatory algorithms have exhibited surprisingly good results on a variety of applications and this paper aims at understanding their success. In particular, the paper derives sufficient conditions under which online anticipatory algorithms achieve good expected utility and studies the various types of errors arising in the algorithms including the anticipativity and sampling errors. The sampling error is shown to be negligible with a logarithmic number of scenarios. The anticipativity error is harder to bound and is shown to be low, both theoretically and experimentally, for the existing applications.
Turning CARTwheels: an alternating algorithm for mining redescriptions

We present an unusual algorithm involving classification trees---CARTwheels---where two trees are grown in opposite directions so that they are joined at their leaves. This approach finds application in a new data mining task we formulate, called redescription mining. A redescription is a shift-of-vocabulary, or a different way of communicating information about a given subset of data; the goal of redescription mining is to find subsets of data that afford multiple descriptions. We highlight the importance of this problem in domains such as bioinformatics, which exhibit an underlying richness and diversity of data descriptors (e.g., genes can be studied in a variety of ways). CARTwheels exploits the duality between class partitions and path partitions in an induced classification tree to model and mine redescriptions. It helps integrate multiple forms of characterizing datasets, situates the knowledge gained from one dataset in the context of others, and harnesses high-level abstractions for uncovering cryptic and subtle features of data. Algorithm design decisions, implementation details, and experimental results are presented.
Dataset for the First Evaluation on Chinese Machine Reading Comprehension

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, existing reading comprehension datasets are mostly in English. To add diversity in reading comprehension datasets, in this paper we propose a new Chinese reading comprehension dataset for accelerating related research in the community. The proposed dataset contains two different types: cloze-style reading comprehension and user query reading comprehension, associated with large-scale training data as well as human-annotated validation and hidden test set. Along with this dataset, we also hosted the first Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) and successfully attracted tens of participants, which suggest the potential impact of this dataset.
Software metrics: roadmap

Software metrics as a subject area is over 30 years old, but it has barely penetrated into mainstream software engineering. A key reason for this is that most software metrics activities have not addressed their most important requirement: to provide information to support quantitative managerial decision-making during the software lifecycle. Good support for decision-making implies support for risk assessment and reduction. Yet traditional metrics approaches, often driven by regression-based models for cost estimation and defects prediction, provide little support for managers wishing to use measurement to analyse and minimise risk. The future for software metrics lies in using relatively simple existing metrics to build management decision-support tools that combine different aspects of software development and testing and enable managers to make many kinds of predictions, assessments and trade-offs during the software life-cycle. Our recommended approach is to handle the key factors largely missing from the usual metrics approaches, namely: causality, uncertainty, and combining different (often subjective) evidence. Thus the way forward for software metrics research lies in causal modelling (we propose using Bayesian nets), empirical software engineering, and multi-criteria decision aids.
SemFix: Program repair via semantic analysis

Debugging consumes significant time and effort in any major software development project. Moreover, even after the root cause of a bug is identified, fixing the bug is non-trivial. Given this situation, automated program repair methods are of value. In this paper, we present an automated repair method based on symbolic execution, constraint solving and program synthesis. In our approach, the requirement on the repaired code to pass a given set of tests is formulated as a constraint. Such a constraint is then solved by iterating over a layered space of repair expressions, layered by the complexity of the repair code. We compare our method with recently proposed genetic programming based repair on SIR programs with seeded bugs, as well as fragments of GNU Coreutils with real bugs. On these subjects, our approach reports a higher success-rate than genetic programming based repair, and produces a repair faster.
Fighting for my space: coping mechanisms for sns boundary regulation

Sharing information online via social network sites (SNSs) is at an all-time high, yet research shows that users often exhibit a marked dissatisfaction in using such sites. A compelling explanation for this dichotomy is that users are struggling against their SNS environment in an effort to achieve their preferred levels of privacy for regulating social interactions. Our research investigates users' SNS boundary regulation behavior. This paper presents results from a qualitative interview-based study to identify "coping mechanisms" that users devise outside explicit boundary-regulation interface features in order to manage interpersonal boundaries. Our categorization of such mechanisms provides insight into interaction design issues and opportunities for new SNS features.
Bringing physics to the surface

This paper explores the intersection of emerging surface technologies, capable of sensing multiple contacts and of-ten shape information, and advanced games physics engines. We define a technique for modeling the data sensed from such surfaces as input within a physics simulation. This affords the user the ability to interact with digital objects in ways analogous to manipulation of real objects. Our technique is capable of modeling both multiple contact points and more sophisticated shape information, such as the entire hand or other physical objects, and of mapping this user input to contact forces due to friction and collisions within the physics simulation. This enables a variety of fine-grained and casual interactions, supporting finger-based, whole-hand, and tangible input. We demonstrate how our technique can be used to add real-world dynamics to interactive surfaces such as a vision-based tabletop, creating a fluid and natural experience. Our approach hides from application developers many of the complexities inherent in using physics engines, allowing the creation of applications without preprogrammed interaction behavior or gesture recognition.
Automatic Document Metadata Extraction Using Support Vector Machines

Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task. The method first classifies each line of the header into one or more of 15 classes. An iterative convergence procedure is then used to improve the line classification by using the predicted class labels of its neighbor lines in the previous round. Further metadata extraction is done by seeking the best chunk boundaries of each line. We found that discovery and use of the structural patterns of the data and domain based word clustering can improve the metadata extraction performance. An appropriate feature normalization also greatly improves the classification performance. Our metadata extraction method was originally designed to improve the metadata extraction quality of the digital libraries Citeseer [17] and EbizSearch[24]. We believe it can be generalized to other digital libraries.