Reviewerly/exHarmony huggingface dataset:
ROBDEKON: Robotic Systems for Decontamination in Hazardous Environments

Contamination with radioactive or chemical substances can have serious consequences for humans and the environment. For this reason, thorough decontamination is of great importance in the decommissioning of nuclear power plants and the remediation of contaminated sites. People who have to carry out such tasks are working under physically demanding conditions and may be exposed to high health risks. To get humans out of those hazardous environments, the ROBDEKON project was launched with the aim of establishing a national competence center for “Robotic Systems for Decontamination in Hazardous Environments”. In ROBDEKON, four research institutions work together with partners from industry to develop practical robot systems for decontamination tasks. The research activities include the development of mobile robots for rough terrain, autonomous construction machines, and robot manipulators as well as decontamination concepts, planning algorithms, multi-sensory 3D mapping of the environment and teleoperation using virtual reality. The focus is on three applications: the decommissioning of nuclear facilities, the decontamination of plant components and handling of waste, and the remediation of landfills and contaminated sites. This paper gives a high-level overview of the goal and the ongoing research activities in ROBDEKON, provides an insight into the infrastructure of the involved laboratories, and presents first project results.
Variational-Based Mixed Noise Removal With CNN Deep Learning Regularization

In this paper, the traditional model based variational methods and deep learning based algorithms are naturally integrated to address mixed noise removal, specially for Gaussian mixture noise and Gaussian-impulse noise removal problem. To be different from single type noise (e.g. Gaussian) removal, it is a challenge problem to accurately discriminate noise types and levels for each pixel. We propose a variational method to iteratively estimate the noise parameters, and then the algorithm can automatically classify the noise according to the different statistical parameters. The proposed variational problem can be separated into regularization, synthesis, parameters estimation and noise classification four steps with the operator splitting scheme. Each step is related to an optimization subproblem. To enforce the regularization, the deep learning method is employed to learn the natural images prior. Compared with some model based regularizations, the CNN regularizer can significantly improve the quality of the restored images. Compared with some learning based methods, the synthesis step can produce better reconstructions by analyzing the types and levels of the recognized noise. In our method, the convolution neutral network (CNN) can be regarded as an operator which associated to a variational functional. From this viewpoint, the proposed method can be extended to many image reconstruction and inverse problems. Numerical experiments in the paper show that our method can achieve some state-of-the-art results for Gaussian mixture noise and Gaussian-impulse noise removal.
Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks

Point-of-Interest (POI) recommendation is one of the most essential tasks in LBSNs to help users discover new interesting locations, especially when users travel out of town or to unfamiliar areas. Current studies on POI recommendation in LBSNs mainly focus on modeling multiple factors extracted from users' profiles and checking-in records. Data sparsity and incompleteness of user-POI interaction matrix are very common problems in POI recommendation, especially for the out-of-town scenario. Another challenge is that most information in the LBSNs is unreliable due to users' different backgrounds or preferences. Because of the close relationship between users, information from trustable friends on Communication-Based Social Networks (CBSNs) is more valuable than that in LBSNs, which can give a preferable suggestion instead of trustless reviews in LBSNs. In this study, we propose a latent probabilistic generative model called HI-LDA (Heterogeneous Information based LDA), which can accurately capture users' words on CBSNs by taking into full consideration the information on LBSNs including geographical effect as well as the abundant information including social relationship, users' interactive behaviors and comment content. In particular, the parameters of the HI-LDA model can be inferred by the Gibbs sampling method in an effective fashion. Beyond these proposed techniques, we introduce an POI recommendation framework integrating geographical clustering approach considering the locations and popularity of POIs simultaneously. Extensive experiments were conducted to evaluate the performance of the proposed framework on two real heterogeneous LBSN-CBSN networks. The experimental results demonstrate the superiority of HI-LDA on effective and efficient POI recommendation in both home-town and out-of-town scenarios, when compared with the state-of-the-art baseline approaches.
Algorithmic Censorship by Social Platforms: Power and Resistance

Abstract Effective content moderation by social platforms is both important and difficult; numerous issues arise from the volume of information, the culturally sensitive and contextual nature of that information, and the nuances of human communication. Attempting to scale moderation, social platforms are increasingly adopting automated approaches to suppressing communications that they deem undesirable. However, this brings its own concerns. This paper examines the structural effects of algorithmic censorship by social platforms to assist in developing a fuller understanding of the risks of such approaches to content moderation. This analysis shows that algorithmic censorship is distinctive for two reasons: (1) in potentially bringing all communications carried out on social platforms within reach and (2) in potentially allowing those platforms to take a more active, interventionist approach to moderating those communications. Consequently, algorithmic censorship could allow social platforms to exercise an unprecedented degree of control over both public and private communications. Moreover, commercial priorities would be inserted further into the everyday communications of billions of people. Due to the dominance of the web by a few social platforms, this may be difficult or impractical to escape for many people, although opportunities for resistance do exist.
Universal Randomized Guessing With Application to Asynchronous Decentralized Brute–Force Attacks

Consider the problem of guessing the realization of a random vector X by repeatedly submitting queries (guesses) of the form “Is X equal to x?” until an affirmative answer is obtained. In this setup, a key figure of merit is the number of queries required until the right vector is identified, a number that is termed the guesswork. Typically, one wishes to devise a guessing strategy which minimizes a certain guesswork moment. In this work, we study a universal, decentralized scenario where the guesser does not know the distribution of X, and is not allowed to use a strategy which prepares a list of words to be guessed in advance, or even remember which words were already used. Such a scenario is useful, for example, if bots within a Botnet carry out a brute-force attack in order to guess a password or decrypt a message, yet cannot coordinate the guesses between them or even know how many bots actually participate in the attack. We devise universal decentralized guessing strategies, first, for memoryless sources, and then generalize them for finite-state sources. In each case, we derive the guessing exponent, and then prove its asymptotic optimality by deriving a compatible converse bound. The strategies are based on randomized guessing using a universal distribution. We also extend the results to guessing with side information. Finally, for all above scenarios, we design efficient algorithms in order to sample from the universal distributions, resulting in strategies which do not depend on the source distribution, are efficient to implement, and can be used asynchronously by multiple agents.
Hybrid Water Cycle Optimization Algorithm With Simulated Annealing for Spam E-mail Detection

Spam is defined as junk and unwanted e-mail.The implementation of a reliable spam email filter becomes more and more important for e-mail users since they have to face with the growing amount of uninvited e-mails.The faults of spam classifiers are characterized by being more and more insufficient to handle huge volumes of relevant emails and to identify and detect the new spam email as example with high performance.The problem in spam classifiers is a huge number of features.Feature selection is an important task in keyword content classification for being among the most popular and effective methods for feature reduction.Accordingly, irrelevant and redundant features that can impede performance would be eliminated.Meta-heuristic optimization is to choose the optimal solution between possible multi-solutions, which respect the aim of this research that is the performance.The other problem is related to ambiguity of the effect of optimization feature selection on multiple classifiers algorithm which are popular used by previous work namely; K-nearest Neighbor, Naïve Bayesian and Support Vector Machine.Therefore, the aim of this research is to improve the accuracy of feature selection by applying hybrid Water Cycle and Simulated Annealing to optimize results and to evaluate the proposed Spam Detection.The methodology used in this study which consists of groundwork, induction, improvement, evaluation and comparison quality.The cross-validation was used for training and validation dataset and seven datasets were employed in testing the spam classification proposed.The results demonstrate that the meta-heuristic namely water cycle feature selection (WCFS) was employed and three ways of hybridization with Simulated Annealing as a feature selection employed.In comparison with other feature selection algorithms such as Harmony Search, Genetic Algorithm, and Particle Swarm, the hybridization interleaved hybridization outperformed other feature selection algorithms with accuracy 96.3%, on the other side the effect of using three classifier algorithms, the SVM was better than other of classifier algorithms with f-measurement 96.3%.The number of features using interleaved water cycle and Simulated Annealing the number of features has decreased to more than 50%.
Improving Question Answering by Commonsense-Based Pre-training

Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge. We believe the main reason is the lack of commonsense connections between concepts. To remedy this, we provide a simple and effective method that leverages external commonsense knowledge base such as ConceptNet. We pre-train direct and indirect relational functions between concepts, and show that these pre-trained functions could be easily added to existing neural network models. Results show that incorporating commonsense-based function improves the state-of-the-art on three question answering tasks that require commonsense reasoning. Further analysis shows that our system discovers and leverages useful evidence from an external commonsense knowledge base, which is missing in existing neural network models and help derive the correct answer.
On a Generalization of Substitution-Permutation Networks: The HADES Design Strategy

Keyed and unkeyed cryptographic permutations often iterate simple round functions. Substitution-permutation networks (SPNs) are an approach that is popular since the mid 1990s. One of the new directions in the design of these round functions is to reduce the substitution (S-Box) layer from a full one to a partial one, uniformly distributed over all the rounds. LowMC and Zorro are examples of this approach. A relevant freedom in the design space is to allow for a highly non-uniform distribution of S-Boxes. However, choosing rounds that are so different from each other is very rarely done, as it makes security analysis and implementation much harder. We develop the design strategy Hades and an analysis framework for it, which despite this increased complexity allows for security arguments against many classes of attacks, similar to earlier simpler SPNs. The framework builds upon the wide trail design strategy, and it additionally allows for security arguments against algebraic attacks, which are much more of a concern when algebraically simple S-Boxes are used. Subsequently, this is put into practice by concrete instances and benchmarks for a use case that generally benefits from a smaller number of S-Boxes and showcases the diversity of design options we support: A candidate cipher natively working with objects in $$\text {GF}(p)$$ , for securing data transfers with distributed databases using secure multiparty computation (MPC). Compared to the currently fastest design MiMC, we observe significant improvements in online bandwidth requirements and throughput with a simultaneous reduction of preprocessing effort, while having a comparable online latency.
Policing's ‘meme strategy’: understanding the rise of police social media engagement work

In 2017, the New South Wales Police Force in Australia embarked on a bold new social media strategy, harnessing humorous Internet memes and cute images of police animals to increase 'user engagement' with their posts. Provoked by changes to Facebook's News Feed Algorithm, this self-described 'meme-strategy' generated a surge of new followers for the organisation's social media accounts, with NSW Police's Facebook page reaching one million followers in August 2017 – a record for an official police Facebook page. This article examines the social media logics underpinning NSW Police's 'meme strategy' and similar police PR strategies that have employed humour and cute content to increase social media engagement. Through analysing the content and 'active' engagement metrics of NSW Police's 'meme strategy', in this article I critically examine this approach to police public relations, focusing in particular on its weaponisation of cute content to generate engagement. NSW Police's meme strategy, I argue, exemplifies what we might term social media 'engagement work': strategies intended to increase the reach of police messaging on social media and promote horizontal engagement spillover and vertical legitimacy spillover.
Compression failure characterization of cancellous bone combining experimental testing, digital image correlation and finite element modeling

Cancellous bone yield strain has been reported in the literature to be relatively constant and independent from microstructure and apparent density, while fracture strain shows higher scattering. The objective of this work is to assess this hypothesis, characterizing the compression fracture in cancellous bone from a numerical approach and relating it to morphological parameters. Quasi-static compression fractures of cancellous bone samples are modeled using high-resolution image-based finite elements, correlating the numerical models and experimental results. The yield strain and the strain at fracture are inferred from the micro-CT-based finite element models by inverse analysis. The validation of the fracture models is carried out through digital image correlation (DIC). To develop this work, cancellous bone parallelepiped-shaped specimens were prepared and micro-CT scanned at 22 μm spatial resolution. A morphometric analysis was carried out for each specimen in order to characterize its microstructure. Quasi-static compression tests were conducted, recording the force-displacement response and a sequence of images during testing for the application of the DIC technique. This was applied without the need of a speckle pattern benefiting from the irregular microstructure of cancellous bone. The finite element models are also used to simulate the local fracture of trabeculae at the micro level using a combination of continuum damage mechanics and the element deletion technique. Equivalent strain, computed both from DIC and micro-FE, was the best predictor of the compression fracture pattern. The procedure followed in this work permits the estimation of failure parameters that are difficult to measure experimentally, which can be used in numerical models.
A Model of Social Explanations for a Conversational Movie Recommendation System

A critical aspect of any recommendation process is explaining the reasoning behind each recommendation. These explanations can not only improve users' experiences, but also change their perception of the recommendation quality. This work describes our human-centered design for our conversational movie recommendation agent, which explains its decisions as humans would. After exploring and analyzing a corpus of dyadic interactions, we developed a computational model of explanations. We then incorporated this model in the architecture of a conversational agent and evaluated the resulting system via a user experiment. Our results show that social explanations can improve the perceived quality of both the system and the interaction, regardless of the intrinsic quality of the recommendations.
VWA: Hardware Efficient Vectorwise Accelerator for Convolutional Neural Network

Hardware accelerators for convolution neural networks (CNNs) enable real-time applications of artificial intelligence technology. However, most of the existing designs suffer from low hardware utilization or high area cost due to complex dataflow. This paper proposes a hardware efficient vectorwise CNN accelerator that adopts a 3$\times$3 filter optimized systolic array using 1-D broadcast dataflow to generate partial sum. This enables easy reconfiguration for different kinds of kernels with interleaved input or elementwise input dataflow. This simple and regular data flow results in low area cost while attains high hardware utilization. The presented design achieves 99\%, 97\%, 93.7\%, 94\% hardware utilization for VGG-16, ResNet-34, GoogLeNet, and Mobilenet, respectively. Hardware implementation with TSMC 40nm technology takes 266.9K NAND gate count and 191KB SRAM to support 168GOPS throughput and consumes only 154.98mW when running at 500MHz operating frequency, which has superior area and power efficiency than other designs.
Inverse Discriminative Networks for Handwritten Signature Verification

Handwritten signature verification is an important technique for many financial, commercial, and forensic applications. In this paper, we propose an inverse discriminative network (IDN) for writer-independent handwritten signature verification, which aims to determine whether a test signature is genuine or forged compared to the reference signature. The IDN model contains four weight-shared neural network streams, of which two receiving the original signature images are the discriminative streams and the other two addressing the gray-inverted images form the inverse streams. Multiple paths of attention modules connect the discriminative streams and the inverse streams to propagate messages. With the inverse streams and the multi-path attention modules, the IDN model intensifies the effective information of signature verification. Since there was no proper Chinese signature dataset in the community, we collected a large-scale Chinese signature dataset with approximately 29,000 images of 749 individuals’ signatures. We test our method on the Chinese signature dataset and other three signature datasets of different languages: CEDAR, BHSig-B, and BHSig-H. Experiments prove the strength and potential of our method.
Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs

Finding the root cause of a bug requires a significant effort from developers. Automated fault localization techniques seek to reduce this cost by computing the suspiciousness scores (i.e., the likelihood of program entities being faulty). Existing techniques have been developed by utilizing input features of specific types for the computation of suspiciousness scores, such as program spectrum or mutation analysis results. This article presents a novel learn-to-rank fault localization technique called PRecise machINe-learning-based fault loCalization tEchnique (PRINCE). PRINCE uses genetic programming (GP) to combine multiple sets of localization input features that have been studied separately until now. For dynamic features, PRINCE encompasses both Spectrum Based Fault Localization (SBFL) and Mutation Based Fault Localization (MBFL) techniques. It also uses static features, such as dependency information and structural complexity of program entities. All such information is used by GP to train a ranking model for fault localization. The empirical evaluation on 65 real-world faults from CoREBench, 84 artificial faults from SIR, and 310 real-world faults from Defects4J shows that PRINCE outperforms the state-of-the-art SBFL, MBFL, and learn-to-rank techniques significantly. PRINCE localizes a fault after reviewing 2.4% of the executed statements on average (4.2 and 3.0 times more precise than the best of the compared SBFL and MBFL techniques, respectively). Also, PRINCE ranks 52.9% of the target faults within the top ten suspicious statements.
ExpliClas: Automatic Generation of Explanations in Natural Language for Weka Classifiers

ExpliClas is a web service aimed at providing users with multimodal (textual + graphical) explanations related to Weka classifiers. In ExpliClas, two types of explanations are automatically generated. On the one hand, global explanations pay attention to the behavior of the classifier as a whole, i.e., they refer to a list of structural properties (number of classes, features, etc.) along with quality indicators such as accuracy or confusion matrix. On the other hand, local explanations go in depth with how the classifier deals with single instances. Current version of ExpliClas already explains classifications made by three different decision tree Weka implementations (J48, RepTree, and RandomTree) and one fuzzy algorithm (FURIA). In this paper, we describe ExpliClas in detail and illustrate its use with the Bank telemarketing dataset.