nils-herrmann/clean_scirepeval huggingface dataset:
Cascaded H-Bridge Multilevel PV Topology for Alleviation of Per-Phase Power Imbalances and Reduction of Second Harmonic Voltage Ripple

The cascaded H-bridge (CHB) topology is ideal for implementing large-scale converters for photovoltaic (PV) applications. The improved quality of output voltage waveforms, high efficiency due to transformer-less connection, and ability to employ multiple instances of a maximum power point tracking (MPPT) algorithm are just some advantages. An important disadvantage is the required over-rating to ensure balanced three-phase currents at times of unequal PV generation. Unequal generation occurs due to shading, temperature inhomogeneity, faulty H-bridges, etc. Capacitor voltage balancing under such conditions requires zero-sequence voltage injection which increases the required number of series connected H-bridges. However, leakage current and safety requirements often dictate a need for isolation between PV arrays and the cascaded converter. Therefore, this paper proposes a converter topology that avoids the cost of extra series connected H-bridges by extending the function of dc-dc converters that provide isolation. Second harmonic power oscillations seen in typical cascaded topologies can also be eliminated or reduced through use of the proposed topology. Simulation and experimental results are presented that confirm correct operation of the proposed approach.
Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.
The quest for Ubiquity: A roadmap for software and systems traceability research

Traceability underlies many important software and systems engineering activities, such as change impact analysis and regression testing. Despite important research advances, as in the automated creation and maintenance of trace links, traceability implementation and use is still not pervasive in industry. A community of traceability researchers and practitioners has been collaborating to understand the hurdles to making traceability ubiquitous. Over a series of years, workshops have been held to elicit and enhance research challenges and related tasks to address these shortcomings. A continuing discussion of the community has resulted in the research roadmap of this paper. We present a brief view of the state of the art in traceability, the grand challenge for traceability and future directions for the field.
An innovative web-based collaborative platform for video annotation

Large scale labeled datasets are of key importance for the development of automatic video analysis tools as they, from one hand, allow multi-class classifiers training and, from the other hand, support the algorithms’ evaluation phase. This is widely recognized by the multimedia and computer vision communities, as witnessed by the growing number of available datasets; however, the research still lacks in annotation tools able to meet user needs, since a lot of human concentration is necessary to generate high quality ground truth data. Nevertheless, it is not feasible to collect large video ground truths, covering as much scenarios and object categories as possible, by exploiting only the effort of isolated research groups. In this paper we present a collaborative web-based platform for video ground truth annotation. It features an easy and intuitive user interface that allows plain video annotation and instant sharing/integration of the generated ground truths, in order to not only alleviate a large part of the effort and time needed, but also to increase the quality of the generated annotations. The tool has been on-line in the last four months and, at the current date, we have collected about 70,000 annotations. A comparative performance evaluation has also shown that our system outperforms existing state of the art methods in terms of annotation time, annotation quality and system’s usability.
A Critique of Structure-from-Motion Algorithms

I review current approaches to structure from motion (SFM) and suggest a framework for designing new algorithms. The discussion focuses on reconstruction rather than on correspondence and on algorithms reconstructing from many images. I argue that it is important to base experiments and algorithm design on theoretical analyses of algorithm behavior and on an understanding of the intrinsic, algorithm-independent properties of SFM optimal estimation. I propose new theoretical analyses as examples, which suggest a range of experimental questions about current algorithms as well as new types of algorithms. The paper begins with a review of several of the important multi-image-based approaches to SFM, including optimization, fusing (e.g., Kalman filtering), projective methods, and invariant-based algorithms. I suggest that optimization by means of general minimization techniques needs to be supplemented by a theoretical understanding of the SFM least-squares error surface. I argue that fusing approaches are essentially no more robust than algorithms reconstructing from a small number of images and advocate experiments to determine the limitations of fusing. I also propose that fusing may be one of the best reconstruction strategies in situations where few-image algorithms give reasonable results, and suggest that an experimental understanding of the properties of few-image algorithms is important for designing good fusing methods. I emphasize the advantages of an approach based on fusing image-pair reconstructions. With regard to the projective approach, I argue that its trade-off of simplicity versus accuracy/robustness needs more careful experimental examination, and I advocate more research on the effects of calibration error on Euclidean reconstruction. I point out the relative lack of research on adapting Euclidean approaches to deal with incomplete knowledge of the calibration. I argue that invariant-based algorithms could be more nonrobust and inaccurate, and not necessarily much faster, than an approach fusing two-image optimizations. Based on recent results showing that two-image reconstructions are nearly as accurate as multi-image ones, I suggest that the authors of invariants methods conduct careful comparisons of their algorithms to two-image-based results. The remainder of the paper discusses the issues involved in designing a generally applicable SFM algorithm. I argue that current SFM algorithms perform well only in restricted domains, and that different types of algorithms do well on quite different types of sequences. I present examples of three domains that are important in applications and describe three types of
Graph mining approach to suspicious transaction detection

Suspicious transaction detection is used to report banking transactions that may be connected with criminal activities. Obviously, perpetrators of criminal acts strive to make the transactions as innocent-looking as possible. Because activities such as money laundering may involve complex organizational schemes, machine learning techniques based on individual transactions analysis may perform poorly when applied to suspicious transaction detection. In this paper, we propose a new machine learning method for mining transaction graphs. The method proposed in this paper builds a model of subgraphs that may contain suspicious transactions. The model used in our method is parametrized using fuzzy numbers which represent parameters of transactions and of the transaction subgraphs to be detected. Because money laundering may involve transferring money through a variable number of accounts the model representing transaction subgraphs is also parametrized with respect to some structural features. In contrast to some other graph mining methods in which graph isomorphisms are used to match data to the model, in our method we perform a fuzzy matching of graph structures.
Selection of eLECTRIC mOTOR dRIVES for electric vehicles

In this study, six kinds of the drivetrain systems of electric motor drives for EVs are discussed. Furthermore, the requirements of EVs on electric motor drives are presented. The comparative investigation on the efficiency, weight, cost, cooling, maximum speed, and fault-tolerance, safety, and reliability is carried out for switched reluctance motor, induction motor, permanent magnet blushless DC motor, and brushed DC motor drives, in order to find most appropriate electric motor drives for electric vehicle applications. The study shows that switched reluctance motor drives are the prior choice for electric vehicles.
Clustering and Resource Allocation for Dense Femtocells in a Two-Tier Cellular OFDMA Network

Small cells such as femtocells overlaying the macrocells can enhance the coverage and capacity of cellular wireless networks and increase the spectrum efficiency by reusing the frequency spectrum assigned to the macrocells in a universal frequency reuse fashion. However, management of both the cross-tier and co-tier interferences is one of the most critical issues for such a two-tier cellular network. Centralized solutions for interference management in a two-tier cellular network with orthogonal frequency-division multiple access (OFDMA), which yield optimal/near-optimal performance, are impractical due to the computational complexity. Distributed solutions, on the other hand, lack the superiority of centralized schemes. In this paper, we propose a semi-distributed (hierarchical) interference management scheme based on joint clustering and resource allocation for femtocells. The problem is formulated as a mixed integer non-linear program (MINLP). The solution is obtained by dividing the problem into two sub-problems, where the related tasks are shared between the femto gateway (FGW) and femtocells. The FGW is responsible for clustering, where correlation clustering is used as a method for femtocell grouping. In this context, a low-complexity approach for solving the clustering problem is used based on semi-definite programming (SDP). In addition, an algorithm is proposed to reduce the search range for the best cluster configuration. For a given cluster configuration, within each cluster, one femto access point (FAP) is elected as a cluster head (CH) that is responsible for resource allocation among the femtocells in that cluster. The CH performs sub-channel and power allocation in two steps iteratively, where a low-complexity heuristic is proposed for the sub-channel allocation phase. Numerical results show the performance gains due to clustering in comparison to other related schemes. Also, the proposed correlation clustering scheme offers performance, which is close to that of the optimal clustering, with a lower complexity.
Secure and Private Collaborative Linear Programming

The growth of the Internet has created tremendous opportunities for online collaborations. These often involve collaborative optimizations where the two parties are, for example, jointly minimizing costs without violating their own particular constraints (e.g., one party may have too much inventory, another too little inventory but too much production capacity, etc). Many of these optimizations can be formulated as linear programming problems, or, rather, as collaborative linear programming, in which two parties need to jointly optimize based on their own private inputs. It is often important to have online collaboration techniques and protocols that carry this out without either party revealing to the other anything about their own private inputs to the optimization (other than, unavoidably, what can be deduced from the collaboratively computed optimal solution). For example, two organizations who jointly invest in a project may want to minimize some linear objective function while satisfying both organizations' private and confidential constraints. Constraints are usually private when they reveal too much about the organizations' financial health, its future business strategy, etc. Linear programming problems have been widely studied in the literature. However, the existing solutions (e.g., the simplex method) do not extend to the above-mentioned framework in which the linear constraints are shared by the two parties, who do not want to disclose their own to the other party. In this paper, we give an efficient protocol for solving linear programming problems in the honest-but-curious model, such that neither party reveals anything about their private input to the other party (other than what can be inferred from the result). The amount of communication and computation done by our protocol is proportional to the time complexity of the simplex method, a widely used linear programming algorithm. We also provide a practical solution that prevents certain malicious behavior of the participants. The use of the known general circuit-simulation solutions to secure function evaluation is unacceptable for the simplex method, as it implies an exponential size circuit
Security Issues in Internet of Things ( IoT ) : A Survey

In the recent years, people need to use Internet at anytime and anywhere. Internet of Things (IOT) allows people and things to be connected Anytime, Anyplace, with Anything and Anyone, ideally using Any path/network and Any service. IOT can be distinguished by various technologies, which provide the creative services in different application domains. This implies that there are various challenges present while deploying IOT. The traditional security services are not directly applied on IOT due to different communication stacks and various standards. So flexible security mechanisms are need to be invented, which deal with the security threats in such dynamic environment of IOT. In this survey we present the various research challenges with their respective solutions. Also, some open issues are discovered and some hints for further research direction are advocated.  Internet-of-Things; Sensor Networks; Smart objects; Sensors; Actuators; ubiquitous; Security
Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning

The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical non-parametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space. Both the neural parameters and Bayesian priors are learned jointly using tailored variational inference. The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances. We apply our model in video representation learning. Our method is able to discover highly interpretable activity hierarchies, and obtain improved clustering accuracy and generalization capacity based on the learned rich representations.
Emotion analysis of Arabic articles and its impact on identifying the author's gender

The Gender Identification (GI) problem is concerned with determining the gender of the author of a given text based on its contents. The GI problem is one of the authorship profiling problems which have a wide range of applications in various fields such as marketing and security. Due to its importance, extensive research efforts have been invested in the GI problem for different languages. Unfortunately, the same cannot be said about the Arabic language despite its strategic importance and widespread. In this work, we explore the GI problem for Arabic text as a supervised learning problem. Specifically, we consider and compare two approaches for feature extraction. The first one is the Bag-Of-Words (BOW) approach while the second one is based on computing features related to sentiments and emotions. One goal of this work is to confirm the validity of the common stereotype that female authors tend to write in a more emotional way than male authors. Our results show that there is no conclusive evidence that this is true for our dataset.
Ranking and retrieval of image sequences from multiple paragraph queries

We propose a method to rank and retrieve image sequences from a natural language text query, consisting of multiple sentences or paragraphs. One of the method's key applications is to visualize visitors' text-only reviews on TRIPADVISOR or YELP, by automatically retrieving the most illustrative image sequences. While most previous work has dealt with the relations between a natural language sentence and an image or a video, our work extends to the relations between paragraphs and image sequences. Our approach leverages the vast user-generated resource of blog posts and photo streams on the Web. We use blog posts as text-image parallel training data that co-locate informative text with representative images that are carefully selected by users. We exploit large-scale photo streams to augment the image samples for retrieval. We design a latent structural SVM framework to learn the semantic relevance relations between text and image sequences. We present both quantitative and qualitative results on the newly created DISNEYLAND dataset.
Learning Programs: A Hierarchical Bayesian Approach

We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, we introduce a nonparametric hierarchical Bayesian prior over programs which shares statistical strength across multiple tasks. The key challenge is to parametrize this multi-task sharing. For this, we introduce a new representation of programs based on combinatory logic and provide an MCMC algorithm that can perform safe program transformations on this representation to reveal shared inter-program substructures.
A brief survey on anonymization techniques for privacy preserving publishing of social network data

Nowadays, partly driven by many Web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. Privacy preserving publishing of social network data becomes a more and more important concern. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the new challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case, and examine the possible problem formulation in three important dimensions: privacy, background knowledge, and data utility. We survey the existing anonymization methods for privacy preservation in two categories: clustering-based approaches and graph modification approaches.
(alpha, K)-anonymity: an Enhanced K-anonymity Model for Privacy Preserving Data Publishing

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.
Ontolingua: a mechanism to support portable ontologies

An ontology is a set of deenitions of content-speciic knowledge representation prim-itives: classes, relations, functions, and object constants. Ontolingua is mechanism for writing ontologies in a canonical format, such that they can be easily translated into a variety of representation and reasoning systems. This allows one to maintain the ontol-ogy in a single, machine-readable form while using it in systems with diierent syntax and reasoning capabilities. The syntax and semantics are based on the KIF knowledge interchange format 11]. Ontolingua extends KIF with standard primitives for deening classes and relations, and organizing knowledge in object-centered hierarchies with inheritance. The Ontolingua software provides an architecture for translating from KIF-level sentences into forms that can be eeciently stored and reasoned about by target representation systems. Currently, there are translators into LOOM, Epikit, and Algernon, as well as a canonical form of KIF. This paper describes the basic approach of Ontolingua to the ontology sharing problem, introduces the syntax, and describes the semantics of a few ontological commitments made in the software. Those commitments, which are reeected in the on-tolingua syntax and the primitive vocabulary of the frame ontology, include: a distinction between deenitional and nondeenitional assertions; the organization of knowledge with classes, instances, sets, and second-order relations; and assertions whose meaning depends on the contents of the knowledge base. Limitations of Ontolingua's \conser-vative" approach to sharing ontologies and alternative approaches to the problem are discussed.
Heterogeneous hardware/software acceleration of the BWA-MEM DNA alignment algorithm

The fast decrease in cost of DNA sequencing has resulted in an enormous growth in available genome data, and hence led to an increasing demand for fast DNA analysis algorithms used for diagnostics of genetic disorders, such as cancer. One of the most computationally intensive steps in the analysis is represented by the DNA read alignment. In this paper, we present an accelerated version of BWA-MEM, one of the most popular read alignment algorithms, by implementing a heterogeneous hardware/software optimized version on the Convey HC2ex platform. A challenging factor of the BWA-MEM algorithm is the fact that it consists of not one, but three computationally intensive kernels: SMEM generation, suffix array lookup and local Smith-Waterman. Obtaining substantial speedup is hence contingent on accelerating all of these three kernels at once. The paper shows an architecture containing two hardware-accelerated kernels and one kernel optimized in software. The two hardware kernels of suffix array lookup and local Smith-Waterman are able to reach speedups of 2.8x and 5.7x, respectively. The software optimization of the SMEM generation kernel is able to achieve a speedup of 1.7x. This enables a total application acceleration of 2.6x compared to the original software version.
Design Guidelines for Domain Specific Languages

Designing a new domain specific language is as any other complex task sometimes error-prone and usually time consuming, especially if the language shall be of high-quality and comfortably usable. Existing tool support focuses on the simplification of technical aspects but lacks support for an enforcement of principles for a good language design. In this paper we investigate guidelines that are useful for designing domain specific languages, largely based on our experience in developing languages as well as relying on existing guidelines on general purpose (GPLs) and modeling languages. We defined guidelines to support a DSL developer to achieve better quality of the language design and a better acceptance among its users.