nils-herrmann/clean_scirepeval huggingface dataset:
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network

With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. So, it is important to study how face recognition networks are subject to attacks. In this paper, we focus on a novel way to do attacks against face recognition network that misleads the network to identify someone as the target person not misclassify inconspicuously. Simultaneously, for this purpose, we introduce a specific attentional adversarial attack generative network (AGN ) to generate fake face images. For capturing the semantic information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces face recognition networks as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person.
Process Mining Based on Regions of Languages

In this paper we give an overview, how to apply region based me thods for the synthesis of Petri nets from languages to process min ing. The research domain of process mining aims at constructing a process model from an event log, such that the process model can reproduce t he log, and does not allow for much more behaviour than shown in the log. We her e consider Petri nets to represent process models. Event logs can be int erpreted as finite languages. Region based synthesis methods can be used to constr u t a Petri net from a language generating the minimal net behaviour including t he given language. Therefore, it seems natural to apply such methods in the proc ess mining domain. There are several different region based methods in literat ure yielding different Petri nets. We adapt these methods to the process mining doma in and compare them concerning efficiency and usefulness of the resulting P etri net.
Refining Duplicate Detection for Improved Data Quality

Detecting duplicates is a pervasive data quality challenge that hinders organizations from extracting value from their data sooner. The increased complexity and heterogeneity of modern datasets has lead to the presence of varying record formats, missing values, and evolving data semantics. As data is integrated, duplicates inevitably occur in the integrated instance. One of the challenges in deduplication is determining whether two values are sufficiently close to be considered equal. Existing similarity functions often rely on counting the number of required edits to transform one value to the other. This is insufficient in attribute domains, such as time, where small syntactic differences do not always translate to ’closeness’. In this paper, we propose a duplication detection framework, which adapts metric functional dependencies (MFDs) to improve the detection accuracy by relaxing the matching condition on numeric values to allow a permitted tolerance. We evaluate our techniques against two existing approaches using three real data collections, and show that we achieve an average 25% and 34% improvement in precision and recall, respectively, over non-MFD versions.
Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI

In this paper we describe a method to perform sequencediscriminative training of neural network acoustic models without the need for frame-level cross-entropy pre-training. We use the lattice-free version of the maximum mutual information (MMI) criterion: LF-MMI. To make its computation feasible we use a phone n-gram language model, in place of the word language model. To further reduce its space and time complexity we compute the objective function using neural network outputs at one third the standard frame rate. These changes enable us to perform the computation for the forward-backward algorithm on GPUs. Further the reduced output frame-rate also provides a significant speed-up during decoding. We present results on 5 different LVCSR tasks with training data ranging from 100 to 2100 hours. Models trained with LFMMI provide a relative word error rate reduction of ∼11.5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions. A further reduction of ∼2.5%, relative, can be obtained by fine tuning these models with the word-lattice based sMBR objective function.
PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification

We present a new release of the Paraphrase Database. PPDB 2.0 includes a discriminatively re-ranked set of paraphrases that achieve a higher correlation with human judgments than PPDB 1.0’s heuristic rankings. Each paraphrase pair in the database now also includes finegrained entailment relations, word embedding similarities, and style annotations.
Adoption and Impact of IT Governance and Management Practices: A COBIT 5 Perspective

This paper empirically investigates how adoption of IT governance and management processes, as identified in the IT governance framework COBIT 5, relates to the level of IT-related goals achievement, which in turn associates to the level of enterprise goals achievement. Simultaneously, this research project provides an international benchmark on how organizations are currently adopting the governance and management processes as identified in COBIT 5. The findings suggest that organizations are best in adopting the “IT factory” related management processes and that implementation scores drop in management and governance processes when more business and board involvement is required. Additionally, there are significant differences in perceived implementation maturity of COBIT 5 processes between SMEs and larger organizations. Also, the data offers empirical evidence that the COBIT 5 processes have a positive association with enterprise value creation. KeywORdS COBIT 5, Enterprise Goals, Enterprise Governance and Management of IT, IT Governance, SMEs
The impact of visual attributes on online image diffusion

Little is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable fraction of all content shared. Existing art on image sharing focuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predictive power of different features on popularity (number of reshares). We found that visual properties have low predictive power compared that of social cues. However, after factoring-out social influence, visual features show considerable predictive power, especially for images with higher exposure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular.
Ecology, sexual selection, and the evolution of mating systems.

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Multifactorial uncertainty assessment for monitoring population abundance using computer vision

Computer vision enables in-situ monitoring of animal populations at a lower cost and with less ecosystem disturbance than with human observers. However, computer vision uncertainty may not be fully understood by end-users, and the uncertainty assessments performed by technology experts may not fully address end-user needs. This knowledge gap can yield misinterpretations of computer vision data, and trust issues impeding the transfer of valuable technologies. We bridge this gap with a user-centered analysis of the uncertainty issues. Key uncertainty factors, and their interactions, are identified from the perspective of a core task in ecology research and beyond: counting individuals from different classes. We highlight factors for which uncertainty assessment methods are currently unavailable. The remaining uncertainty assessment methods are not interoperable. Hence it is currently difficult to assess the combined results of multiple uncertainty factors, and their impact on end-user counting tasks. We propose a framework for assessing the multifactorial uncertainty propagation along the data processing pipeline. It integrates methods from both computer vision and ecology domains, and aims at supporting the statistical analysis of abundance trends for population monitoring. Our typology of uncertainty factors and our assessment methods were drawn from interviews with marine ecology and computer vision experts, and from prior work for a fish monitoring application. Our findings contribute to enabling scientific research based on computer vision.
Dermal fillers and combinations of fillers for facial rejuvenation.

Until recently, the use of dermal fillers was limited in the United States by the small number of products approved by the Food and Drug Administration. The products now approved for use in the United States have opened up the range of possibilities for combinations of products that are synergistic in their effects. Combinations of products may be discussed in temporal or anatomic relationships. Temporal combinations refer to the use of different fillers at different times, whereas anatomic combinations refer to the use of different fillers in different parts of the face. Before discussing how the various fillers may be used in combination, it is worthwhile to consider their use in isolation. Soft-tissue augmentation products under consideration in the present article include the hyaluronic acids (HA), poly L lactic acid (PLLA), calcium hydroxylapatite (CAHA), porcine collagen, and silicone.
Bonding-wire triangular spiral inductor for on-chip switching power converters

This work presents the first design and modelling of bonding-wire-based triangular spiral inductors , targeting their application to on-chip switching power converters. It is demonstrated that the equilateral triangular shape compared to other polygonal shapes best balances the inductive density as well as the total Equivalent Series Resistance (ESR). Afterwards, a design procedure is presented in order to optimize the inductor design, in terms of ESR and occupied area reduction. Finally, finite-elements simulation results of an optimized design (27nH, 1 Ω) are presented to validate the proposed expressions.
Efficient solutions for Mastermind using genetic algorithms

We present a new genetic algorithm for playing the game of Mastermind. The algorithm requires low running times and results in a low expected number of guesses. Its performance is comparable to that of other meta-heuristics for the standard setting with four positions and six colors, while it outperforms the existing algorithms when more colors and positions are examined. The central idea underlying the algorithm is the creation of a large set of eligible guesses collected throughout the different generations of the genetic algorithm, the quality of each of which is subsequently determined based on a comparison with a selection of elements of the set.
Differential neuronal plasticity in mouse hippocampus associated with various periods of enriched environment during postnatal development

Enriched environment (EE) is characterized by improved conditions for enhanced exploration, cognitive activity, social interaction and physical exercise. It has been shown that EE positively regulates the remodeling of neural circuits, memory consolidation, long-term changes in synaptic strength and neurogenesis. However, the fine mechanisms by which environment shapes the brain at different postnatal developmental stages and the duration required to induce such changes are still a matter of debate. In EE, large groups of mice were housed in bigger cages and were given toys, nesting materials and other equipment that promote physical activity to provide a stimulating environment. Weaned mice were housed in EE for 4, 6 or 8 weeks and compared with matched control mice that were raised in a standard environment. To investigate the differential effects of EE on immature and mature brains, we also housed young adult mice (8 weeks old) for 4 weeks in EE. We studied the influence of onset and duration of EE housing on the structure and function of hippocampal neurons. We found that: (1) EE enhances neurogenesis in juvenile, but not young adult mice; (2) EE increases the number of synaptic contacts at every stage; (3) long-term potentiation (LTP) and spontaneous and miniature activity at the glutamatergic synapses are affected differently by EE depending on its onset and duration. Our study provides an integrative view of the role of EE during postnatal development in various mechanisms of plasticity in the hippocampus including neurogenesis, synaptic morphology and electrophysiological parameters of synaptic connectivity. This work provides an explanation for discrepancies found in the literature about the effects of EE on LTP and emphasizes the importance of environment on hippocampal plasticity.
Tracking multiple vehicles using foreground, background and motion models

In this paper a vehicle tracking algorithm is presented based on the combination of a per pixel background model (an extension of work by Stauffer and Grimson ) and a set of single hypothesis foreground models based on a general model of object size, position, velocity, and colour distribution. Each pixel in the scene is thus 'explained' as either background, belonging to one of the foreground objects or as noise. Calibrated ground-plane information is used within the foreground model to strengthen the object size and velocity consistency assumptions. A learned a priori model of typical road travel direction and speed is used to provide a prior estimate of object velocity which used to initialise the velocity model of each of the foreground object models. This model is typically an Extended Kalman filter but other models are possible within the algorithm. The system runs at near video frame rate (20fps) on modest hardware and is robust assuming sufficient image resolution is available and vehicle sizes do not greatly exceed the priors on object size used in object initialisation.
Implementation of an inexpensive EEG headset for the pattern recognition purpose

There are many types of bio-signals with various control application prospects. In this work possible control application domain of electroencephalographic signal obtained from an easily available, inexpensive EEG headset - Emotiv EPOC was presented. This work also involved application of an embedded system platform. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. Potential implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. In this work traditional, statistical methods were neither used nor described.
Classifying sanctions and designing a conceptual sanctioning process model for socio-technical systems

We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions. We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.
Intelligent classification of electronic music

Electronic Dance Music (EDM) is a class of music produced primarily using electronic instruments and computers comprising several sub-genres. Disc jockeys (DJs) mix EDM songs from similar sub-genres for live performances in concerts and festivals. Given a large database of electronic music, if a system can classify songs into their respective sub-genres, it can greatly help producers and DJs while making mixes. We describe an approach to classify four major sub-genres of EDM using artificial neural networks. We extracted several rhythmic and statistical features from a database of 400 songs, evenly distributed among the four sub-genres, and used these features to train a Back Propagation Network (BPN) and a Probabilistic Neural Network (PNN). In addition to classification, we also employed the RELIEFF feature selection algorithm to reduce the number of features and improve our results. Comparing the performances of the two networks on the basis of their correct classifications, we observed that BPN combined with the features proposed yielded a better result of 94.16%.
Analysis of clickjacking attacks and an effective defense scheme for Android devices

Smartphones bring users lots of convenience by integrating all useful functions people may need. While users are spending more time on their phones, have they ever questioned of being spoofed by the phone they are interacting with? This paper conducts a thorough study of the mobile clickjacking attacks. We first present how the clickjacking attack works and the key points to remain undiscovered. Then, we evaluate its potential threats by exploring the feasibility of launching clickjacking attacks on various UIs, including system app windows, 3rd-party app windows, and other system UIs. Finally, we propose a system-level defense scheme against clickjacking attacks on Android platform, which requires no user or developer effort and is compatible with existing apps. The performance of the countermeasure is evaluated with extensive experiments. The results show that our scheme can effectively prevent clickjacking attacks with only a minor impact to the system.
Deep Dictionary Learning

Two popular representation learning paradigms are dictionary learning and deep learning. While dictionary learning focuses on learning “basis” and “features” by matrix factorization, deep learning focuses on extracting features via learning “weights” or “filter” in a greedy layer by layer fashion. This paper focuses on combining the concepts of these two paradigms by proposing deep dictionary learning and show how deeper architectures can be built using the layers of dictionary learning. The proposed technique is compared with other deep learning approaches, such as stacked autoencoder, deep belief network, and convolutional neural network. Experiments on benchmark data sets show that the proposed technique achieves higher classification and clustering accuracies. On a real-world problem of electrical appliance classification, we show that deep dictionary learning excels where others do not yield at-par performance. We postulate that the proposed formulation can pave the path for a new class of deep learning tools.
PurTreeClust: A Clustering Algorithm for Customer Segmentation from Massive Customer Transaction Data

Clustering of customer transaction data is an important procedure to analyze customer behaviors in retail and e-commerce companies. Note that products from companies are often organized as a product tree, in which the leaf nodes are goods to sell, and the internal nodes (except root node) could be multiple product categories. Based on this tree, we propose the “personalized product tree”, named purchase tree, to represent a customer’s transaction records. So the customers’ transaction data set can be compressed into a set of purchase trees. We propose a partitional clustering algorithm, named PurTreeClust, for fast clustering of purchase trees. A new distance metric is proposed to effectively compute the distance between two purchase trees. To cluster the purchase tree data, we first rank the purchase trees as candidate representative trees with a novel separate density, and then select the top <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="chen-ieq1-2763620.gif"/></alternatives></inline-formula> customers as the representatives of <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="chen-ieq2-2763620.gif"/></alternatives></inline-formula> customer groups. Finally, the clustering results are obtained by assigning each customer to the nearest representative. We also propose a gap statistic based method to evaluate the number of clusters. A series of experiments were conducted on ten real-life transaction data sets, and experimental results show the superior performance of the proposed method.
Physical Discipline and Behavior Problems in African American , European American , and Hispanic Children : Emotional Support as a Moderator

Using data collected over a 6-year period on a sample of 1,039 European American children, 550 African American children, and 401 Hispanic children from the children of the National Longitudinal Survey of Youth, this study assessed whether maternal emotional support of the child moderates the relation between spanking and behavior problems. Children were 4–5 years of age in the first of 4 waves of data used (1988, 1990, 1992, 1994). At each wave, mothers reported their use of spanking and rated their children’s behavior problems. Maternal emotional support of the child was based on interviewer observations conducted as part of the Home Observation for Measurement of the Environment. For each of the 3 racial-ethnic groups, spanking predicted an increase in the level of problem behavior over time, controlling for income-needs ratio and maternal emotional support. Maternal emotional support moderated the link between spanking and problem behavior. Spanking was associated with an increase in behavior problems over time in the context of low levels of emotional support, but not in the context of high levels of emotional support. This pattern held for all 3 racial-ethnic groups.