nils-herrmann/clean_scirepeval huggingface dataset:
Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations

Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have b ecome prominent techniques for blind sources separation (BSS), analys is of image databases, data mining and other information retrieval and clustering applications. In this paper we propose a family of e fficient algorithms for NMF/NTF, as well as sparse nonnegative coding and representatio n, that has many potential applications in computational neur oscience, multisensory processing, compressed sensing and multidimensio nal data analysis. We have developed a class of optimized local algorithm s which are referred to as Hierarchical Alternating Least Squares (HAL S) algorithms. For these purposes, we have performed sequential constrain ed minimization on a set of squared Euclidean distances. We then extend t his approach to robust cost functions using the Alpha and Beta divergence s and derive flexible update rules. Our algorithms are locally stable and work well for NMF-based blind source separation (BSS) not only for the ove r-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are su fficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, these algorit hms can be tuned to different noise statistics by adjusting a single parameter. Ext ensive experimental results confirm the accuracy and computational p erformance of the developed algorithms, especially, with usage of multilayer hierarchical NMF approach . key words: Nonnegative matrix factorization (NMF), nonnegative tensor factorizations (NTF), nonnegative PARAFAC, model reduction, feature extraction, compression, denoising, multiplicative local learning (adaptive) algorithms, Alpha and Beta divergences.
FREE AND OPEN-SOURCE SIMULATION SOFTWARE "URURAU"

Ururau is a free and open-source software written in the Java programming language. The use of the software allows the construction of discrete simulation models on the three layers that constitute the structure of the software. It means that the models can be developed in the upper layer of the graphic interface faster and of simple programming in the core of the software (new commands for example) or in the lower layer of the software libraries. The use of the Ururau has made the accomplishment of research of new functions and algorithms in the field of discrete event systems simulation possible.
IMI -- A Multilingual Semantic Annotation Environment

Semantic annotated parallel corpora, though rare, play an increasingly important role in natural language processing. These corpora provide valuable data for computational tasks like sense-based machine translation and word sense disambiguation, but also to contrastive linguistics and translation studies. In this paper we present the ongoing development of a web-based corpus semantic annotation environment that uses the Open Multilingual Wordnet (Bond and Foster, 2013) as a sense inventory. The system includes interfaces to help coordinating the annotation project and a corpus browsing interface designed specifically to meet the needs of a semantically annotated corpus. The tool was designed to build the NTU-Multilingual Corpus (Tan and Bond, 2012). For the past six years, our tools have been tested and developed in parallel with the semantic annotation of a portion of this corpus in Chinese, English, Japanese and Indonesian. The annotation system is released under an open source license (MIT).
Efficient frontier detection for robot exploration

Frontier-based exploration is the most common approach to exploration, a fundamental problem in robotics. In frontier-based exploration, robots explore by repeatedly detecting (and moving towards) frontiers, the segments which separate the known regions from those unknown. A frontier detection sub-process examines map and/or sensor readings to identify frontiers for exploration. However, most frontier detection algorithms process the entire map data. This can be a time consuming process, which affects the exploration decisions. In this work, we present several novel frontier detection algorithms that do not process the entire map data, and explore them in depth. We begin by investigating algorithms that represent two approaches: WFD , a graph search based algorithm which examines only known areas, and FFD , which examines only new laser readings data. We analytically examine the complexity of both algorithms, and discuss their correctness. We then improve by combining elements of both, to create two additional algorithms, called WFD-INC and WFD-IP . We empirically evaluate all algorithms, and show that they are all faster than a state-of-the-art frontier detector implementation (by several orders of magnitude). We additionally contrast them with each other and demonstrate the FFD and WFD-IP are faster than the others by one additional order of magnitude.
Sparse event detection in wireless sensor networks using compressive sensing

Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. In this paper, the first contribution is to formulate the problem for sparse event detection in wireless sensor networks as a compressive sensing problem. The number of (wake-up) sensors can be greatly reduced to the similar level of the number of sparse events, which is much smaller than the total number of sources. Second, we suppose the event has the binary nature, and employ the Bayesian detection using this prior information. Finally, we analyze the performance of the compressive sensing algorithms under the Gaussian noise. From the simulation results, we show that the sampling rate can reduce to 25% without sacrificing performance. With further decreasing the sampling rate, the performance is gradually reduced until 10% of sampling rate. Our proposed detection algorithm has much better performance than the l1-magic algorithm proposed in the literature.
High sensitive RF-DC rectifier and ultra low power DC sensing circuit for waking up wireless system

A 2.4 GHz band high-efficiency RF rectifier and high sensitive dc voltage sensing circuit is implemented. A passive RF to DC rectifier of multiplier voltage type has no current consumption. This rectifier is using native threshold voltage diode-connected NMOS transistors to avoid the power loss due to the threshold voltage. It consumes only 900nA with 1.5V supply voltage adopting ultra low power DC sensing circuit using subthreshold current reference. These block incorporates a digital demodulation logic blocks. It can recognize OOK digital information and existence of RF input signal above sensitivity level or not. A low power RF rectifier and DC sensing circuit was fabricated in 0.18um CMOS technology with native threshold voltage NMOS; This RF wake up receiver has -28dBm sensitivity at 2.4 GHz band.
Multilinear Discriminant Analysis for Face Recognition

There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we present a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction. Then, a novel approach, called k-mode optimization, is presented to iteratively learn these subspaces by unfolding the tensor along different tensor directions. We call this algorithm multilinear discriminant analysis (MDA), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes, 2) for classification problems involving higher order tensors, the MDA algorithm can avoid the curse of dimensionality dilemma and alleviate the small sample size problem, and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in k-mode optimization. We provide extensive experiments on ORL, CMU PIE, and FERET databases by encoding face images as second- or third-order tensors to demonstrate that the proposed MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes
A novel driving scheme for synchronous rectifier in MHz CRM flyback converter with GaN devices

Synchronous rectifier (SR) is widely used in flyback converter to reduce the output side conduction loss in order to meet the system conversion efficiency requirement. The conventional SR driving methods are not suitable for high frequency (>500kHz) application. This paper proposes a novel SR driving method for MHz flyback converter operating at critical conduction mode (CRM). A RC network is in parallel with SR to emulate the magnetizing current of flyback converter. The sensing signal is clean and immune to the parasitic ringing caused by the leakage inductance and parasitic capacitors. SR gate signal is accurately generated based on a simple signal processing circuit and the conduction loss of SR body diode is minimized. The theoretical analysis is validated by simulation and experiment.
A Steady State Model for Graph Power Law

Power law distribution seems to be an important characteristic of web graphs. Several existing web graph models generate power law graphs by adding new vertices and non-uniform edge connectivities to existing graphs. Researchers have conjectured that preferential connectivity and incremental growth are both required for the power law distribution. In this paper, we propose a different web graph model with power law distribution that does not require incremental growth. We also provide a comparison of our model with several others in their ability to predict web graph clustering behavior.
Retrieval-Based Learning : Active Retrieval Promotes Meaningful Learning

Retrieval is the key process for understanding learning and for promoting learning, yet retrieval is not often granted the central role it deserves. Learning is typically identified with the encoding or construction of knowledge, and retrieval is considered merely the assessment of learning that occurred in a prior experience. The retrieval-based learning perspective outlined here is grounded in the fact that all expressions of knowledge involve retrieval and depend on the retrieval cues available in a given context. Further, every time a person retrieves knowledge, that knowledge is changed, because retrieving knowledge improves one’s ability to retrieve it again in the future. Practicing retrieval does not merely produce rote, transient learning; it produces meaningful, long-term learning. Yet retrieval practice is a tool many students lack metacognitive awareness of and do not use as often as they should. Active retrieval is an effective but undervalued strategy for promoting meaningful learning.
Automatic Chatbot Knowledge Acquisition from Online Forum via Rough Set and Ensemble Learning

Existing chatbot knowledge bases are mostly hand-constructed, which is time consuming and difficult to adapt to new domains. Automatic chatbot knowledge acquisition method from online forums is presented in this paper. It includes a classification model based on rough set, and the theory of ensemble learning is combined to make a decision. Given a forum, multiple rough set classifiers are constructed and trained first. Then all replies are classified with these classifiers. The final recognition results are drawn by voting to the output of these classifiers. Finally, the related replies are selected as chatbot knowledge. Relevant experiments on a child-care forum prove that the method based on rough set has high recognition efficiency to related replies and the combination of ensemble learning improves the results.
Efficiently Verifiable Computation on Encrypted Data

We study the task of verifiable delegation of computation on encrypted data. We improve previous definitions in order to tolerate adversaries that learn whether or not clients accept the result of a delegated computation. In this strong model, we construct a scheme for arbitrary computations and highly efficient schemes for delegation of various classes of functions, such as linear combinations, high-degree univariate polynomials, and multivariate quadratic polynomials. Notably, the latter class includes many useful statistics. Using our solution, a client can store a large encrypted dataset on a server, query statistics over this data, and receive encrypted results that can be efficiently verified and decrypted.
 As a key contribution for the efficiency of our schemes, we develop a novel homomorphic hashing technique that allows us to efficiently authenticate computations, at the same cost as if the data were in the clear, avoiding a $10^4$ overhead which would occur with a naive approach. We support our theoretical constructions with extensive implementation tests that show the practical feasibility of our schemes.
Illustration2Vec: a semantic vector representation of illustrations

Referring to existing illustrations helps novice drawers to realize their ideas. To find such helpful references from a large image collection, we first build a semantic vector representation of illustrations by training convolutional neural networks. As the proposed vector space correctly reflects the semantic meanings of illustrations, users can efficiently search for references with similar attributes. Besides the search with a single query, a semantic morphing algorithm that searches the intermediate illustrations that gradually connect two queries is proposed. Several experiments were conducted to demonstrate the effectiveness of our methods.
Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change

This research demonstrates the application of association rule mining to spatio-temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A → B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio-temporal association rule occurs when there is a spatio-temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)-based data pre-processing is used to integrate diverse data sets, extract spatio-temporal relationships, classify numeric data into ordinal categories, and encode spatio-temporal relationship data in tabular format for use by conventional (non-spatio-temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatiotemporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.
Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation

Massive graphs, such as online social networks and communication networks, have become common today. To efficiently analyze such large graphs, many distributed graph computing systems have been developed. These systems employ the "think like a vertex" programming paradigm, where a program proceeds in iterations and at each iteration, vertices exchange messages with each other. However, using Pregel's simple message passing mechanism, some vertices may send/receive significantly more messages than others due to either the high degree of these vertices or the logic of the algorithm used. This forms the communication bottleneck and leads to imbalanced workload among machines in the cluster. In this paper, we propose two effective message reduction techniques: (1)vertex mirroring with message combining, and (2)an additional request-respond API. These techniques not only reduce the total number of messages exchanged through the network, but also bound the number of messages sent/received by any single vertex. We theoretically analyze the effectiveness of our techniques, and implement them on top of our open-source Pregel implementation called Pregel+. Our experiments on various large real graphs demonstrate that our message reduction techniques significantly improve the performance of distributed graph computation.
Clustering of streaming time series is meaningless

Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it's own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention.In this work we make a surprising claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature.We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work. Although the primary contribution of our work is to draw attention to the fact that an apparent solution to an important problem is incorrect and should no longer be used, we also introduce a novel method which, based on the concept of time series motifs, is able to meaningfully cluster some streaming time series datasets.
NaLIR: an interactive natural language interface for querying relational databases

In this demo, we present NaLIR, a generic interactive natural language interface for querying relational databases. NaLIR can accept a logically complex English language sentence as query input. This query is first translated into a SQL query, which may include aggregation, nesting, and various types of joins, among other things, and then evaluated against an RDBMS. In this demonstration, we show that NaLIR, while far from being able to pass the Turing test, is perfectly usable in practice, and able to handle even quite complex queries in a variety of application domains. In addition, we also demonstrate how carefully designed interactive communication can avoid misinterpretation with minimum user burden.
MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce

Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.
Change Management for Distributed Ontologies

Preface A writer keeps surprising himself... he doesn't know what he is saying until he sees it on the page. It is often said that the preface is the most read part of a thesis. If this is true, what is the contribution of distributing a thesis to the progress of science? Why does NWO 1 pay for printing it? Let us first try to answer the question why the preface is thought to the most read part of a thesis. A possible answer is that the preface is the only part of thesis that is understandable for outsiders. If we assume that a preface normally doesn't describe the scientific contribution, this would be a bad sign for scientists about their ability to explain their achievements. However, I doubt whether this is the genuine reason. In this thesis, for example, the summary (page 181) is probably easier to understand than the preface with its numerous references to people that many the readers will not know. My hypothesis is that the preface is among the most read parts of a thesis because it gives a glimpse of the real process behind the nicely shaped-up story in the book. It hints about the moments of disappointment and desperateness, the time lost in mastering LaTeX, the writer-blocks, the procrastinating, the " essential " philosophical discussions with colleagues, and the oh-so-nice but not very relevant programming tasks. In the end, a preface shows that doing research is not the well-structured process that it seems to be, but that it is as humble as all other activities. It may very well be that this message is the essence of what scientists have to say to the world... Therefore, for the sake of science, continue reading the preface and don't feel obliged to read further. The research described in this thesis has been performed within the KIRMIT project. This thesis is probably not the outcome that the people who wrote the proposal for the KIRMIT project had in mind. When the project started, we expected to work with two PhD students on multimedia indexing and retrieval, but because of various reasons things went different. I would like to thank Bob Wielinga, Bert Bredeweg and Hans Akkermans for the freedom and the time they gave me to find my own way. Hans has been very inspiring during the whole process, with his clear ideas about research methodologies …
Impeding Malware Analysis Using Conditional Code Obfuscation

Malware programs that incorporate trigger-based behavior initiate malicious activities based on conditions satisfied only by specific inputs. State-of-the-art malware analyzers discover code guarded by triggers via multiple path exploration, symbolic execution, or forced conditional execution, all without knowing the trigger inputs. We present a malware obfuscation technique that automatically conceals specific trigger-based behavior from these malware analyzers. Our technique automatically transforms a program by encrypting code that is conditionally dependent on an input value with a key derived from the input and then removing the key from the program. We have implemented a compiler-level tool that takes a malware source program and automatically generates an obfuscated binary. Experiments on various existing malware samples show that our tool can hide a significant portion of trigger based code. We provide insight into the strengths, weaknesses, and possible ways to strengthen current analysis approaches in order to defeat this malware obfuscation technique.