nils-herrmann/clean_scirepeval huggingface dataset:
Energy-efficient magnetic circuits based on nanoelectronic devices

As CMOS technology scales down to the nanoscale, high leakage power consumption becomes the main problem and challenge of electronic circuits. To overcome this challenge, nano-emerging technologies and logic-in-memory structure are being studied. Magnetic tunnel junction (MTJ) is an emerging technology which has many advantages when used in logic in memory structures in conjunction with CMOS. In this paper, we present novel designs of hybrid MTJ/CMOS circuits; AND, XOR and 1-bit full adder. The proposed MTJ/CMOS full adder design has 71% lower Power-delay-product (PDP) compared to the previous MTJ/CMOS full adder. To further improve the energy efficiency we investigated the use of nanoelectronic devices (CNFET, FinFET) in the proposed circuits and compared them with the CMOS based designs. The hybrid MTJ/CNFET and MJT/FinFET full adders have about 18 and 11 times lower PDP, respectively, when compared to the MJT/CMOS design. Also, the MTJ/CNFET based full adder has 66% lower PDP than the MTJ/FinFET based design.
Parallel Multiscale Autoregressive Density Estimation

PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup O(log N) sampling instead of O(N) enabling the practical generation of 512× 512 images. We evaluate the model on class-conditional image generation, text-toimage synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling.
Near linear time algorithm to detect community structures in large-scale networks.

Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.
Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach

We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image. This novel patch decomposition approach benefits MEF in many aspects. First, as opposed to most pixel-wise MEF methods, the proposed algorithm does not require post-processing steps to improve visual quality or to reduce spatial artifacts. Second, it handles RGB color channels jointly, and thus produces fused images with more vivid color appearance. Third and most importantly, the direction of the signal structure component in the patch vector space provides ideal information for ghost removal. It allows us to reliably and efficiently reject inconsistent object motions with respect to a chosen reference image without performing computationally expensive motion estimation. We compare the proposed algorithm with 12 MEF methods on 21 static scenes and 12 deghosting schemes on 19 dynamic scenes (with camera and object motion). Extensive experimental results demonstrate that the proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes. Moreover, it maintains a lower computational cost compared with the state-of-the-art deghosting schemes.11The MATLAB code of the proposed algorithm will be made available online. Preliminary results of Section III-A  were presented at the IEEE International Conference on Image Processing, Canada, 2015.
Visualisation of Linked Data - Reprise

Linked Data promises to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to a Web of data. The ability for data consumers to adopt a follow your nose approach, traversing links defined within a dataset or across independently-curated datasets, is an essential feature of this new Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, inter-related datasets. But for the Web of Data to be successful, we must design novel ways of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data throughout its management cycle. The design of user interfaces for Linked Data, and more specifically interfaces that represent the data visually, play a central role in this respect. Contributions to this special issue on Linked Data visualisation investigate different approaches to harnessing visualisation as a tool for exploratory discovery and basic-to-advanced analysis. The papers in this volume illustrate the design and construction of intuitive means for end-users to obtain new insight and gather more knowledge, as they follow links defined across datasets over the Web of Data.
Gated Graph Sequence Neural Networks

This work on GGS-NN is motivated by the program verification application, where we need to analyze dynamic data structures created in the heap. On a very high level, in this application a machine learning model analyzes the heap states (a graph with memory nodes and pointers as edges) during the execution of a program and comes up with logical formulas that describes the heap. These logical formulas are then fed into a theorem prover to prove the correctness of the program. Problem-specific node annotations are used to initialize .
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.
E-Learning in Malaysia: Success Factors in Implementing E-Learning Program

The main objective of this study was to identify successful factors in implementing an e-learning program. Existing literature has identified several successful factors in implementing an e-learning program. These factors include program content, web page accessibility, learners’ participation and involvement, web site security and support, institution commitment, interactive learning environment, instructor competency, and presentation and design. All these factors were tested together with other related criteria which are important for e-learning program implementation. The samples were collected based on quantitative methods, specifically, self-administrated questionnaires. All the criteria that were tested to see if they were important in an e-learning program implementation.
Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs

We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory (LSTM) recurrent neural networks to learn representations of the parser state, we replace lookup-based word representations with representations constructed from the orthographic representations of the words, also using LSTMs. This allows statistical sharing across word forms that are similar on the surface. Experiments for morphologically rich languages show that the parsing model benefits from incorporating the character-based encodings of words.
Controlling a Wheelchair Indoors Using Thought

Amyotrophic lateral sclerosis, or ALS, is a degenerative disease of the motor neurons that eventually leads to complete paralysis. We are developing a wheelchair system that can help ALS patients, and others who can't use physical interfaces such as joysticks or gaze tracking, regain some autonomy. The system must be usable in hospitals and homes with minimal infrastructure modification. It must be safe and relatively low cost and must provide optimal interaction between the user and the wheelchair within the constraints of the brain-computer interface. To this end, we have built the first working prototype of a brain-controlled wheelchair that can navigate inside a typical office or hospital environment. This article describes the BCW, our control strategy, and the system's performance in a typical building environment. This brain-controlled wheelchair prototype uses a P300 EEG signal and a motion guidance strategy to navigate in a building safely and efficiently without complex sensors or sensor processing
Leveraging Visual Question Answering for Image-Caption Ranking

Visual Question Answering (VQA) is the task of taking as input an image and a free-form natural language question about the image, and producing an accurate answer. In this work we view VQA as a “feature extraction” module to extract image and caption representations. We employ these representations for the task of image-caption ranking. Each feature dimension captures (imagines) whether a fact (question-answer pair) could plausibly be true for the image and caption. This allows the model to interpret images and captions from a wide variety of perspectives. We propose score-level and representation-level fusion models to incorporate VQA knowledge in an existing state-of-the-art VQA-agnostic image-caption ranking model. We find that incorporating and reasoning about consistency between images and captions significantly improves performance. Concretely, our model improves state-of-the-art on caption retrieval by 7.1% and on image retrieval by 4.4% on the MSCOCO dataset.
The Duchenne smile: emotional expression and brain physiology. II.

Facial expression, EEG, and self-report of subjective emotional experience were recorded while subjects individually watched both pleasant and unpleasant films. Smiling in which the muscle that orbits the eye is active in addition to the muscle that pulls the lip corners up (the Duchenne smile) was compared with other smiling in which the muscle orbiting the eye was not active. As predicted, the Duchenne smile was related to enjoyment in terms of occurring more often during the pleasant than the unpleasant films, in measures of cerebral asymmetry, and in relation to subjective reports of positive emotions, and other smiling was not.
CUBIC: a new TCP-friendly high-speed TCP variant

CUBIC is a congestion control protocol for TCP (transmission control protocol) and the current default TCP algorithm in Linux. The protocol modifies the linear window growth function of existing TCP standards to be a cubic function in order to improve the scalability of TCP over fast and long distance networks. It also achieves more equitable bandwidth allocations among flows with different RTTs (round trip times) by making the window growth to be independent of RTT -- thus those flows grow their congestion window at the same rate. During steady state, CUBIC increases the window size aggressively when the window is far from the saturation point, and the slowly when it is close to the saturation point. This feature allows CUBIC to be very scalable when the bandwidth and delay product of the network is large, and at the same time, be highly stable and also fair to standard TCP flows. The implementation of CUBIC in Linux has gone through several upgrades. This paper documents its design, implementation, performance and evolution as the default TCP algorithm of Linux.
Usability, accessibility and ambient-assisted living: a systematic literature review

Ambient-assisted living (AAL) is, nowadays, an important research and development area, foreseen as an important instrument to face the demographic aging. The acceptance of the AAL paradigm is closely related to the quality of the available systems, namely in terms of intelligent functions for the user interaction. In that context, usability and accessibility are crucial issues to consider. This paper presents a systematic literature review of AAL technologies, products and services with the objective of establishing the current position regarding user interaction and how are end users involved in the AAL development and evaluation processes. For this purpose, a systematic review of the literature on AAL was undertaken. A total of 1,048 articles were analyzed, 111 of which were mainly related to user interaction and 132 of which described practical AAL systems applied in a specified context and with a well-defined aim. Those articles classified as user interaction and systems were further characterized in terms of objectives, target users, users’ involvement, usability and accessibility issues, settings to be applied, technologies used and development stages. The results show the need to improve the integration and interoperability of the existing technologies and to promote user-centric developments with a strong involvement of end users, namely in what concerns usability and accessibility issues.
The Geometry Of Kernel Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a classical multivariate method concerned with describing linear dependencies between sets of variables. After a short exposition of the linear sample CCA problem and its analytical solution, the article proceeds with a detailed characterization of its geometry. Projection operators are used to illustrate the relations between canonical vectors and variates. The article then addresses the problem of CCA between spaces spanned by objects mapped into kernel feature spaces. An exact solution for this kernel canonical correlation (KCCA) problem is derived from a geometric point of view. It shows that the expansion coefficients of the canonical vectors in their respective feature space can be found by linear CCA in the basis induced by kernel principal component analysis. The effect of mappings into higher dimensional feature spaces is considered critically since it simplifies the CCA problem in general. Then two regularized variants of KCCA are discussed. Relations to other methods are illustrated, e.g., multicategory kernel Fisher discriminant analysis, kernel principal component regression and possible applications thereof in blind source separation.
An Affordance-Based Methodology for Package Design

The term affordance describes an object’s utilitarian function or actionable possibilities. Product designers have taken great interest in the concept of affordances because of the bridge they provide relating to design, the interpretation of design and, ultimately, functionality in the hands of consumers. These concepts have been widely studied and applied in the field of psychology but have had limited formal application to packaging design and evaluation. We believe that the concepts related to affordances will reveal novel opportunities for packaging innovation. To catalyse this, presented work had the following objectives: (a) to propose a method by which packaging designers can purposefully consider affordances during the design process; (b) to explain this method in the context of a packaging-related case study; and (c) to measure the effect on package usability when an affordance-based design approach is employed.
Community Informatics and Sustainability: Why Social Capital Matters

This paper adopts a holistic approach to explain why social capital matters for effective implementation, widespread uptake, greater social inclusion, and the sustainability of CI initiatives. It describes a theoretical framework drawn from diffusion of innovation, community development and social capital theories. The framework emphasises the interplay between physical infrastructure (including hard technologies and their location in the community), soft technologies (including capacity building, education, training and awareness raising), social infrastructure (including local networks and community organisations) and social capital (including trust and reciprocity, strong sense of community, shared vision, and outcomes from participation in local and external networks).
Network Community Detection: A Review and Visual Survey

Community structure is an important area of research. It has received a considerable attention from the scientific community. Despite its importance, one of the key problems in locating information about community detection is the diverse spread of related articles across various disciplines. To the best of our knowledge, there is no current comprehensive review of recent literature which uses a scientometric analysis using complex networks analysis covering all relevant articles from the Web of Science (WoS). Here we present a visual survey of key literature using CiteSpace. The idea is to identify emerging trends besides using network techniques to examine the evolution of the domain. Towards that end, we identify the most influential, central, as well as active nodes using scientometric analyses. We examine authors, key articles, cited references, core subject categories, key journals, institutions, as well as countries. The exploration of the scientometric literature of the domain reveals that Yong Wang is a pivot node with the highest centrality. Additionally, we have observed that Mark Newman is the most highly cited author in the network. We have also identified that the journal, "Reviews of Modern Physics" has the strongest citation burst. In terms of cited documents, an article by Andrea Lancichinetti has the highest centrality score. We have also discovered that the origin of the key publications in this domain is from the United States. Whereas Scotland has the strongest and longest citation burst. Additionally, we have found that the categories of "Computer Science" and "Engineering" lead other categories based on frequency and centrality respectively.
'houston, we have a solution': a case study of the analysis of astronaut speech during NASA apollo 11 for long-term speaker modeling

Speech and language processing technology has the potential of playing an important role in future deep space missions. To be able to replicate the success of speech technologies from ground to space, it is important to understand how astronaut’s speech production mechanism changes when they are in space. In this study, we investigate the variations of astronaut’s voice characteristic during NASA Apollo 11 mission. While the focus is constrained to analysis of the three astronauts voices who participated in the Apollo 11 mission, it is the first step towards our long term objective of automating large components of space missions with speech and language technology. The result of this study is also significant from an historical point of view as it provides a new perspective of understanding the key moment of human history landing a man on the moon, as well as employed for future advancement in speech and language technology in “non-neutral”conditions.
Poster : Low-latency blockchain consensus

Blockchains are increasingly attractive due to their decentralization, yet inherent limitations of high latency, in the order of minutes, and attacks on consensus cap their practicality. We introduce Blinkchain, a Byzantine consensus protocol that relies on sharding and locality-preserving techniques from distributed systems to provide a bound on consensus latency, proportional to the network delay between the buyer and the seller nodes. Blinkchain selects a random pool of validators, some of which are legitimate with high probability, even when an attacker focuses its forces to crowd out legitimate validators in a small vicinity.
Feature Engineering for Supervised Link Prediction on Dynamic Social Networks

Link prediction is an important network science problem in many domains such as social networks, chem/bioinformatics, etc. Most of these networks are dynamic in nature with patterns evolving over time. In such cases, it is necessary to incorporate time in the mining process in a seamless manner to aid in better prediction performance. We propose a two-step solution strategy to the link prediction problem in dynamic networks in this work. The first step involves a novel yet simple feature construction approach using a combination of domain and topological attributes of the graph. In the second phase, we perform unconstrained edge selection to identify potential candidates for prediction by any generic two-class learner. We design various experiments on a real world collaboration network and show the effectiveness of our approach.
Map-Reduce for Machine Learning on Multicore

We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to manydifferent learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a singlealgorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model  can be written in a certain “summation form,” which allows them to be easily parallelized on multicore computers. We adapt Google’s map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.