Reviewerly/exHarmony huggingface dataset:
Eugene: Towards Deep Intelligence as a Service

The paper discusses an emerging suite of machine intelligence services that are of increasing importance in the highly instrumented world of the Internet of Things (IoT). The suite, called Eugene, would offer a form of intelligent behavior (based on deep neural networks) to otherwise simple embedded devices; the clients of the service. These devices would benefit from service resources to learn from data and to perform intelligent inference, classification, prediction, and estimation tasks that they are too limited to carry out on their own. The paper discusses the taxonomy of such services and the state of implementation, as well as the various challenges entailed, including scheduling, caching (of intelligent functions), and cooperative learning.
Study on Sampling Based Discrete Nie-Tan Algorithms for Computing the Centroids of General Type-2 Fuzzy Sets

General type-2 fuzzy logic systems (GT2 FLSs) have become a hot topic in current academic field.Computing the centroids of general type-2 fuzzy sets (also called type-reduction) is a central block in GT2 FLSs.Recent studies prove the continuous Nie-Tan (CNT) algorithms to be actually an accurate approach to calculate the centroids of interval type-2 fuzzy sets (IT2 FSs).This paper compares the sum operation in discrete NT algorithms and the integral operation in CNT algorithms.According to the alphaplanes representation theory of general type-2 fuzzy sets (GT2 FSs), both the discrete and continuous NT algorithms can be extended to compute the centroids of GT2 FSs.Four computer simulation experiments indicate that, when the centroid type-reduced sets and defuzzified values of GT2 FSs are solved, to properly increase the number of sampling points of primary variable can make the results of discrete NT algorithms exactly approach to the accurate benchmark CNT algorithms.Furthermore, the computation efficiency of sampling based discrete NT algorithms is much higher than the CNT algorithms.
Scambaiting on the Spectrum of Digilantism

Digilantism is punishment through online exposure of supposed wrongdoing. Paedophile hunting is one example, and the practice is open to many of the classical objections to vigilantism. But it lies on a spectrum that contains many other kinds of digilantism. Scambaiting is among the other kinds. It consists of attracting online approaches from perpetrators of different kinds of online advance-fee fraud. Characteristically, it takes the form of protracted email exchanges between scammers and scambaiters. These exchanges are mainly down-to-earth and occasionally testy conversations about the details of fictitious money transfers or involved explanations of delays in payment. They succeed in their purpose if they waste a lot of their targets’ time, but they can also be pursued as a sort of comic art form. Scambaiting exchanges seem often, but not always, to be relatively harmless. They therefore help to make intelligible a region of morally permissible digilantism on the spectrum of digilantism. Not that scambaiters never go too far, but their typical weapons inflict and risk inflicting far less harm than those of other digilantes, and there are actual scambaiting norms that have been chosen because of their relative harmlessness.
Fashion-AttGAN: Attribute-Aware Fashion Editing With Multi-Objective GAN

In this paper, we introduce a novel task, namely attribute-aware fashion-editing, to the fashion domain. A first dataset is constructed for this task with 14,221 images and 22 attributes, which has been made publically available. We re-define the overall objectives in AttGAN and propose the Fashion-AttGAN model to tackle this new task. Experimental results show effectiveness of our Fashion-AttGAN on fashion editing over the original AttGAN.
SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning

A wide range of computer vision algorithms rely on identifying sparse interest points in images and establishing correspondences between them. However, only a subset of the initially identified interest points results in true correspondences (inliers). In this paper, we seek a detector that finds the minimum number of points that are likely to result in an application-dependent "sufficient" number of inliers k. To quantify this goal, we introduce the "k-succinctness" metric. Extracting a minimum number of interest points is attractive for many applications, because it can reduce computational load, memory, and data transmission. Alongside succinctness, we introduce an unsupervised training methodology for interest point detectors that is based on predicting the probability of a given pixel being an inlier. In comparison to previous learned detectors, our method requires the least amount of data pre-processing. Our detector and other state-of-the-art detectors are extensively evaluated with respect to succinctness on popular public datasets covering both indoor and outdoor scenes, and both wide and narrow baselines. In certain cases, our detector is able to obtain an equivalent amount of inliers with as little as 60% of the amount of points of other detectors. The code and trained networks are provided at https://github.com/uzh-rpg/sips2_open.
Quantum-computing with AI &amp; blockchain: modelling, fault tolerance and capacity scheduling

We model the hardware and software architecture for generalized Internet of Things (IoT) by quantum cloud-computing and blockchain. To reduce the measurement error and increase the efficiency of quantum entanglement (i.e. the capability of fault tolerance) in the current quantum computers and communications, we design a quantum-computing chip by modelling it as a multi-input multi-output (MIMO) quantum channel and obtain its channel capacity via our recently derived mutual information formula. To capture the internal qubit data flow dynamics of the channel, we model it via a deep convolutional neural network (DCNN) with generalized stochastic pooling in terms of resource-competition among different quantum eigenmodes or users. The pooling is corresponding to a resource allocation policy with two levels of competitions as in cognitive radio: the first one is on users’ selection in a ‘win–lose’ manner; the second one is on resourcesharing among selected users in a ‘win–win’ manner. To wit, our scheduling policy is the one by mixing a saddle point to a zero-sum game problem and a Pareto optimal Nash equilibrium point to a nonzero- sum game problem. The effectiveness of our policy is proved by diffusion modelling with theory and numerical examples.
Deep Active Shape Model for Robust Object Fitting

Object recognition and localization is still a very challenging problem, despite recent advances in deep learning (DL) approaches, especially for objects with varying shapes and appearances. Statistical models, such as an Active Shape Model (ASM), rely on a parametric model of the object, allowing an easy incorporation of prior knowledge about shape and appearance in a principled way. To take advantage of these benefits, this paper proposes a new ASM framework that addresses two tasks: (i) comparing the performance of several image features used to extract observations from an input image; and (ii) improving the performance of the model fitting by relying on a probabilistic framework that allows the use of multiple observations and is robust to the presence of outliers. The goal in (i) is to maximize the quality of the observations by exploring a wide set of handcrafted features (HOG, SIFT, and texture templates) and more recent DL-based features. Regarding (ii), we use the Generalized Expectation-Maximization algorithm to deal with outliers and to extend the fitting process to multiple observations. The proposed framework is evaluated in the context of facial landmark fitting and the segmentation of the endocardium of the left ventricle in cardiac magnetic resonance volumes. We experimentally observe that the proposed approach is robust not only to outliers, but also to adverse initialization conditions and to large search regions (from where the observations are extracted from the image). Furthermore, the results of the proposed combination of the ASM with DL-based features are competitive with more recent DL approaches (e.g. FCN [1], U-Net [2] and CNN Cascade [3]), showing that it is possible to combine the benefits of statistical models and DL into a new deep ASM probabilistic framework.
Secure multi-party functional dependency discovery

Data profiling is an important task to understand data semantics and is an essential pre-processing step in many tools. Due to privacy constraints, data is often partitioned into silos, with different access control. Discovering functional dependencies (FDs) usually requires access to all data partitions to find constraints that hold on the whole dataset. Simply applying general secure multi-party computation protocols incurs high computation and communication cost. This paper formulates the FD discovery problem in the secure multi-party scenario. We propose secure constructions for validating candidate FDs, and present efficient cryptographic protocols to discover FDs over distributed partitions. Experimental results show that solution is practically efficient over non-secure distributed FD discovery, and can significantly outperform general purpose multi-party computation frameworks. To the best of our knowledge, our work is the first one to tackle this problem.
Machine Learning approaches for Anomaly Detection in Multiphase Flow Meters

Multiphase Flow Meters (MPFM) are important metering tools in the oil and gas industry. A MPFM provides real-time measurements of gas, oil and water flows of a well without the need to separate the phases, a time-consuming procedure that has been classically adopted in the industry. Evaluating the composition of the flow is fundamental for the well management and productivity prediction; therefore, procedures for measuring quality assessment are of crucial importance. In this work we propose an Anomaly Detection approach to MPFM that is effectively able to hand the complexity and variability associated with MPFM data. The proposed approach is designed for embedded implementation and it exploits unsupervised Anomaly Detection approaches like Cluster Based Local Outlier Factor and Isolation Forest.
A method for outlier detection based on cluster analysis and visual expert criteria

Abstract Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering‐based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance‐related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.
Algorithms for estimating the partition function of restricted Boltzmann machines

Accurate estimates of the normalization constants (partition functions) of energy-based probabilistic models (Markov random fields) are highly important, for example, for assessing the performance of models, monitoring training progress, and conducting likelihood ratio tests. Several algorithms for estimating the partition function (in relation to a reference distribution) have been introduced, including Annealed Importance Sampling (AIS) and Bennett's Acceptance Ratio method (BAR). However, their conceptual similarities and differences have not been worked out so far and systematic comparisons of their behavior in practice have been missing. We devise a unifying theoretical framework for these algorithms, which comprises existing variants and suggests new approaches. It is based on a generalized form of Crooks' equality linking the expectation over a distribution of samples generated by a transition operator to the expectation over the distribution induced by the reversed operator. The framework covers different ways of generating samples, such as parallel tempering and path sampling. An empirical comparison revealed the differences between the methods when estimating the partition function of restricted Boltzmann machines and Ising models. In our experiments, BAR using parallel tempering worked well with a small number of bridging distributions, while path sampling based AIS performed best when many bridging distributions were available. Because BAR gave the overall best results, we favor it over AIS. Furthermore, the experiments showed the importance of choosing a proper reference distribution.
Exploring diverse and fine-grained caption for video by incorporating convolutional architecture into LSTM-based model

As a fundamental problem in visual understanding, video captioning has attracted much attention from both computer vision and natural language processing communities. Despite recent emergence of video captioning methods, how to generate diverse and fine-grained video description is far from being solved. To this end, this work makes the following contributions. First, a novel high-quality video captioning system featuring hierarchical long short-term memory structure and dual-stage loss is designed to translate videos to sentences. Second, we incorporate the convolutional architecture into our captioning system with the aim of generating diverse and fine-grained description. Third, we propose a novel performance evaluation metric named LTMS to assess the fine-grained captions. The experimental results on the benchmark datasets MSVD and MSR-VTT indicate the effectiveness of the proposed model, achieving superior performance over state-of-the-art methods.
Crypto assets: the role of ICO tokens within a well-diversified portfolio

This paper re-examines the discussion on blockchain technology, crypto assets and ICOs, providing also evidence that in crypto markets there are currently two classes of assets, namely standalone cryptocurrencies (or ‘coins’) and tokens, which result from an ICO and are intrinsically linked to the performance of the issuing company or venture. While the former have been arguments of various empirical studies regarding their price dynamics and their effect on the variance of a well-diversified portfolio, no such study has been done to analyze listed tokens, which in our sample are over 700 and with a backing of about $17.3Bn from their respective ICOs. Therefore, investors interested in optimizing their portfolios should first assess the diversifier, hedge or safe haven role of tokens vis-à-vis traditional assets, on top of ‘coins’, in order to sensibly use this new asset class. After constructing various indices to represent both the token asset class as a whole and its sub-classes, we model dynamic conditional correlations among all the assets classes in our sample to obtain time-varying correlations for each token-asset pair. We find that tokens are effective diversifiers but not a hedge nor a safe haven asset. We evidence that tokens retain important systematic differences with the two other asset classes to which they are most generally compared to, namely ‘coins’ and equities.
Curvature and height corrections of hyperspectral images using built-in 3D laser profilometry

Optical imaging systems use a light source that illuminates a sample and a photodetector that detects light reflected from or transmitted through the sample. The sample surface curvature, surface-to-camera distance, and illumination-source-to-surface distance significantly affect the measured signal, resulting in image artifacts. To correct the images, a three-dimensional (3D) profilometry system was used to obtain 3D surface information. The 3D information enables image correction using Lambert cosine law and height correction. In this study, the feasibility of the correction method for push-broom hyperspectral imaging of three different objects is presented. Results show a significant reduction of image artifacts, making further image analysis more accurate and robust. The presented 3D profilometry method is applicable to all push-broom imaging systems and the described correction procedure can be applied to all spectral or monochromatic imaging systems.
Enhanced Transformer Model for Data-to-Text Generation

Neural models have recently shown significant progress on data-to-text generation tasks in which descriptive texts are generated conditioned on database records.In this work, we present a new Transformer-based data-totext generation model which learns content selection and summary generation in an endto-end fashion.We introduce two extensions to the baseline transformer model: First, we modify the latent representation of the input, which helps to significantly improve the content correctness of the output summary; Second, we include an additional learning objective that accounts for content selection modelling.In addition, we propose two data augmentation methods that succeed to further improve performance of the resulting generation models.Evaluation experiments show that our final model outperforms current state-of-theart systems as measured by different metrics: BLEU, content selection precision and content ordering.We made publicly available the transformer extension presented in this paper 1 .
Enhancing the interoperability between deep learning frameworks by model conversion

Deep learning (DL) has become one of the most successful machine learning techniques. To achieve the optimal development result, there are emerging requirements on the interoperability between DL frameworks that the trained model files and training/serving programs can be re-utilized. Faithful model conversion is a promising technology to enhance the framework interoperability in which a source model is transformed into the semantic equivalent in another target framework format. However, several major challenges need to be addressed. First, there are apparent discrepancies between DL frameworks. Second, understanding the semantics of a source model could be difficult due to the framework scheme and optimization. Lastly, there exist a large number of DL frameworks, bringing potential significant engineering efforts.
How to (not) Share a Password

Bad choices of passwords were and are a pervasive problem. Users choosing weak passwords do not only compromise themselves, but the whole ecosystem. E.g, common and default passwords in IoT devices were exploited by hackers to create botnets and mount severe attacks on large Internet services, such as the Mirai botnet DDoS attack. We present a method to help protect the Internet from such large scale attacks. Our method enables a server to identify popular passwords (heavy hitters), and publish a list of over-popular passwords that must be avoided. This filter ensures that no single password can be used to compromise a large percentage of the users. The list is dynamic and can be changed as new users are added or when current users change their passwords. We apply maliciously secure two-party computation and differential privacy to protect the users' password privacy. Our solution does not require extra hardware or cost, and is transparent to the user. Our private heavy hitters construction is secure even against a malicious coalition of devices which tries to manipulate the protocol to hide the popularity of some password that the attacker is exploiting. It also ensures differential privacy under continual observation of the blacklist as it changes over time. As a reality check we conducted three tests: computed the guarantees that the system provides wrt a few publicly available databases, ran full simulations on those databases, and implemented and analyzed a proof-of-concept on an IoT device. Our construction can also be used in other settings to privately learn heavy hitters in the presence of an active malicious adversary. E.g., learning the most popular sites accessed by the Tor network.
Target tracking from infrared imagery via an improved appearance model

Moving target tracking from the infrared images is a challenging task due to the targets’ unstable appearance, complex background clutters and a limited number of pixels for each target. To address this challenge, we propose an infrared target tracking method that combines the Sparsity-based Generative Model (SGM) and Guided Filter-based Discriminative Classifier (GFDC) with the particle filter. The SGM model extracts an effective template histogram, which considers the spatial information of each image patch. In the GFDC model, the guided filter uses the structural information of the guidance image in order to smooth the input image, while preserving the edges. This enhances the information of templates while suppressing the noise in the image data. The Bayesian classification is then applied to take the background into consideration when the model is fused. The online update scheme not only considers appearance variations but also relieves the tracking deviation problem. At last, we utilize the joint confidence values to measure the candidate templates for the target position estimation. Experiments based on infrared video sequences have validated the superior tracking performance of the proposed method.
Batholith-stock scale exploration targeting based on multi-source geological and geophysical datasets in the Luanchuan Mo polymetallic district, China

The Luanchuan Mo polymetallic district, which hosts world-class porphyry–skarn-type Mo (W) deposits, is located in the Qinling orogenic belt of the North China Craton. The mining depth of the porphyry–skarn-type Mo (W) deposit in the Luanchuan district is close to 550 m, and most exploration drillholes extend to more than 1000 m. The upper crustal architecture is important for understanding the geological setting and metallogenic model for mineral potential targeting on a district scale. In this study, subsurface intrusions and structures were defined using multi-level gravity data processing (i.e., bi-dimensional empirical mode decomposition) and geophysical inversion (including gravity, magnetotellurics, and controlled-source audio-frequency magnetotellurics). The results of our integrated geological and geophysical studies suggest that the Late Jurassic granite stocks are controlled by NE–NEE- and NW–NWW-striking faults. The porphyry–skarn-type Mo (W) deposits are distributed around the ore-controlling Late Jurassic granite stocks, which are connected to the granite batholith at depth. The volume of this batholith is about 96.96 km3. Based on geophysical interpretations and geochemical signatures of biotites from the granite stocks, the metallogenic potential of the granite stocks at Nannihu–Shangfanggou and Huangbeiling is considered to be greater than that of the granite stocks at Shibaogou–Yuku.
Make Some ROOM for the Zeros

Exploiting data sparsity is crucial for the scalability of many data analysis tasks. However, while there is an increasing interest in efficient secure computation protocols for distributed machine learning, data sparsity has so far not been considered in a principled way in that setting.