Reviewerly/exHarmony huggingface dataset:
Nonmedical prescription psychiatric drug use and the darknet: A cryptomarket analysis

Nonmedical prescription psychiatric drug use (NMPDU) is an increasing global health problem, with recent concern focusing on darknet cryptomarkets as sources of procurement. There is a shortage of evidence regarding comparative worldwide NMPDU trends, due in part to data collection difficulties. This problem is particularly marked for non-opioid drugs, particularly those psychiatric drugs which act on the central nervous system (CNS) and have high misuse potential and are associated with high levels of dependency and fatal overdose. This paper therefore has two goals: 1) to report on the kinds of psychiatric prescription drugs available on cryptomarkets, and 2) to use this data to uncover temporal and geographical trends in sales of these products, potentially informing policy regarding NMPDU more generally. Digital trace data collected from 31 cryptomarkets in operation between September 2013 and July 2016 was analysed by country of origin descriptively and for trends in the sales for 7 psychiatric drug groupings, based on their main indication or intended use in psychiatric practice. Sedatives (such as diazepam and alprazolam) and CNS stimulants (mainly Adderall, modafinil and methylphenidate) had the greatest share of sales, but usage and trends varied by location. The UK has high and rising levels of sedative sales, whilst the USA has the greatest stimulant sales and increasing sedative rates. Sales of drugs used in the treatment of opioid dependency are also substantial in the USA. The picture is less clear in mainland Europe with high sales levels reported in unexpected Central and Northern European countries. There is evidence of a move towards the more potent sedative alprazolam – already implicated as a source of problematic NMPDU in the USA – in Australia and the UK. Sales of drugs such as antidepressants, antipsychotics, mood stabilisers and antidementia drugs – all drugs with limited abuse potential – were negligible, indicating minimal levels of online cryptomarket procurement for self-medicating mental health problems. Predominantly, psychiatric drugs with potent sedative, stimulant or euphoriant effects are sold on cryptomarkets and this varies by country. With some caveats regarding the limitations of cryptomarket digital trace data taken into account, the study of trends of these products sold online over time may offer a novel and increasingly important window onto wider drug purchasing habits.
Equivalence between dropout and data augmentation: A mathematical check

The great achievements of deep learning can be attributed to its tremendous power of feature representation, where the representation ability comes from the nonlinear activation function and the large number of network nodes. However, deep neural networks suffer from serious issues such as slow convergence, and dropout is an outstanding method to improve the network’s generalization ability and test performance. Many explanations have been given for why dropout works so well, among which the equivalence between dropout and data augmentation is a newly proposed and stimulating explanation. In this article, we discuss the exact conditions for this equivalence to hold. Our main result guarantees that the equivalence relation almost surely holds if the dimension of the input space is equal to or higher than that of the output space. Furthermore, if the commonly used rectified linear unit activation function is replaced by some newly proposed activation function whose value lies in R, then our results can be extended to multilayer neural networks. For comparison, some counterexamples are given for the inequivalent case. Finally, a series of experiments on the MNIST dataset are conducted to illustrate and help understand the theoretical results.
Updatable Anonymous Credentials and Applications to Incentive Systems

We introduce updatable anonymous credential systems (UACS) and use them to construct a new privacy-preserving incentive system. In a UACS, a user holding a credential certifying some attributes can interact with the corresponding issuer to update his attributes. During this, the issuer knows which update function is run, but does not learn the user's previous attributes. Hence the update process preserves anonymity of the user. One example for a class of update functions are additive updates of integer attributes, where the issuer increments an unknown integer attribute value v by some known value k. This kind of update is motivated by an application of UACS to incentive systems. Users in an incentive system can anonymously accumulate points, e.g. in a shop at checkout, and spend them later, e.g. for a discount. In this paper, we (1) formally define UACS and their security, (2) give a generic construction for UACS supporting arbitrary update functions, and (3) construct a new incentive system using UACS that is efficient while offering offline double-spending protection and partial spending.
Social Collaborative Filtering Approach for Recommending Courses in an E-learning Platform

In recent years, learning online using the e-learning platforms becomes indispensable in the teaching process. Companies and scientific researchers try to find new optimal methods and approaches that can improve education online. In this paper, we propose a new recommendation approach for recommending relevant courses to learners. Our method is based on social filtering and collaborative filtering for defining the best way in which the learner must learn, and recommend courses which better much the learner’s profile and social content.
Research on Network Traffic Identification based on Machine Learning and Deep Packet Inspection

Accurate network traffic identification is an important basis for network traffic monitoring and data analysis, and is the key to improve the quality of user service. In this paper, through the analysis of two network traffic identification methods based on machine learning and deep packet inspection, a network traffic identification method based on machine learning and deep packet inspection is proposed. This method uses deep packet inspection technology to identify most network traffic, reduces the workload that needs to be identified by machine learning method, and deep packet inspection can identify specific application traffic, and improves the accuracy of identification. Machine learning method is used to assist in identifying network traffic with encryption and unknown features, which makes up for the disadvantage of deep packet inspection that can not identify new applications and encrypted traffic. Experiments show that this method can improve the identification rate of network traffic.
Augmented Reality in Heritage Apps:Current Trends in Europe

Although augmented reality (AR) has come to play an increasingly important role in a wide range of areas, its use remains rather limited in the realm of heritage education. This paper sets out to analyze which heritage-related apps can be found in Europe that partly or wholly use AR as a tool to help users learn about different types of heritage. Our study only identified a limited number of such apps and we used this sample both to paint a portrait of the current state of the question and also to highlight certain observable trends. The results showed that most such apps used AR to reconstruct spaces and buildings, and to a lesser extent, objects. Many of these apps used an academic mode of communication to provide a temporal perspective of monumental and (mainly) historical heritage. The paper also outlines future lines of research dedicated to finding more apps that could be used to increase the current sample size. This would allow for a more comprehensive assessment of such apps from an educational point of view. Several case studies are proffered in order to highlight the keys to successful use of AR in heritage apps.
Peer-to-Peer EnergyTrade: A Distributed Private Energy Trading Platform

Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids.However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP) and processing overheads.In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges.In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPB's applicability for energy trading.We benchmark SPB's performance against the relevant state-ofthe-art.The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions.
Transparency, Fairness, Data Protection, Neutrality

The data revolution continues to transform every sector of science, industry, and government. Due to the incredible impact of data-driven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly—in accordance with laws and ethical norms. In this article, we discuss three recent regulatory frameworks: the European Union’s General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, which aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. Our goal in this article is to bring these regulatory frameworks to the attention of the data management community and to underscore the technical challenges they raise and that we, as a community, are well-equipped to address. The main takeaway of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.
Lazy CBS: Implicit Conflict-Based Search Using Lazy Clause Generation

Conflict-based Search (CBS) is a effective approach to optimal multi-agent path finding. However, performance of CBS approaches degrade rapidly in highly-contended graphs with many agents. One of the reasons this occurs is that CBS does not detect independent subproblems; i.e. it can re-solve the same conflicts between the same pairs of agents up to exponentially many times, each time along a different branch. Constraint programming approaches with nogood learning avoid this kind of duplication of effort by storing nogoods that record the reasons for conflicts. This can exponentially reduce search in constraint programming. In this work, we present Lazy CBS, a new approach to multi-agent pathfinding which replaces the high-level solver of CBS with a lazily constructed constraint programming model with nogoods. We use core-guided depth-first search to explore the space of conflicts and we detect along each branch reusable nogoods which help to quickly identify feasible solutions. Our experiments show that Lazy CBS can significantly improve on the state-of-the-art for optimal MAPF problems under the sumof-costs metric, especially in cases where there exists significant contention.
Boosted Transformer for Image Captioning

Image captioning attempts to generate a description given an image, usually taking Convolutional Neural Network as the encoder to extract the visual features and a sequence model, among which the self-attention mechanism has achieved advanced progress recently, as the decoder to generate descriptions. However, this predominant encoder-decoder architecture has some problems to be solved. On the encoder side, without the semantic concepts, the extracted visual features do not make full use of the image information. On the decoder side, the sequence self-attention only relies on word representations, lacking the guidance of visual information and easily influenced by the language prior. In this paper, we propose a novel boosted transformer model with two attention modules for the above-mentioned problems, i.e., “Concept-Guided Attention” (CGA) and “Vision-Guided Attention” (VGA). Our model utilizes CGA in the encoder, to obtain the boosted visual features by integrating the instance-level concepts into the visual features. In the decoder, we stack VGA, which uses the visual information as a bridge to model internal relationships among the sequences and can be an auxiliary module of sequence self-attention. Quantitative and qualitative results on the Microsoft COCO dataset demonstrate the better performance of our model than the state-of-the-art approaches.
Real-time motion-induced-error compensation in 3D surface-shape measurement

Object motion can introduce unknown phase shift and thus measurement error in multi-image phase-shifting methods of fringe projection profilometry. This paper presents a new method to estimate the unknown phase shifts and reduce the motion-induced error by using three phase maps computed over a multiple measurement sequence and calculating the difference between phase maps. The pixel-wise estimation of the motion-induced phase shifts permits phase-error compensation for non-homogeneous surface motion. Experiments demonstrated the ability of the method to reduce motion-induced error in real-time, for shape measurement of surfaces with high depth variation, and moving and deforming surfaces.
Trivializations for Gradient-Based Optimization on Manifolds

We introduce a framework to study the transformation of problems with manifold constraints into unconstrained problems through parametrizations in terms of a Euclidean space. We call these parametrizations trivializations. We prove conditions under which a trivialization is sound in the context of gradient-based optimization and we show how two large families of trivializations have overall favorable properties, but also suffer from a performance issue. We then introduce dynamic trivializations, which solve this problem, and we show how these form a family of optimization methods that lie between trivializations and Riemannian gradient descent, and combine the benefits of both of them. We then show how to implement these two families of trivializations in practice for different matrix manifolds. To this end, we prove a formula for the gradient of the exponential of matrices, which can be of practical interest on its own. Finally, we show how dynamic trivializations improve the performance of existing methods on standard tasks designed to test long-term memory within neural networks.
A Discrete CVAE for Response Generation on Short-Text Conversation

Neural conversation models such as encoderdecoder models are easy to generate bland and generic responses.Some researchers propose to use the conditional variational autoencoder (CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable.With different sampled latent variables, the model is expected to generate diverse responses.Although the CVAEbased models have shown tremendous potential, their improvement of generating highquality responses is still unsatisfactory.In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation.A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables.Accordingly, we propose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task.Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and informative responses.
Compacting, Picking and Growing for Unforgetting Continual Learning

Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. By enforcing their integration in an iterative manner, we introduce an incremental learning method that is scalable to the number of sequential tasks in a continual learning process. Our approach is easy to implement and owns several favorable characteristics. First, it can avoid forgetting (i.e., learn new tasks while remembering all previous tasks). Second, it allows model expansion but can maintain the model compactness when handling sequential tasks. Besides, through our compaction and selection/expansion mechanism, we show that the knowledge accumulated through learning previous tasks is helpful to build a better model for the new tasks compared to training the models independently with tasks. Experimental results show that our approach can incrementally learn a deep model tackling multiple tasks without forgetting, while the model compactness is maintained with the performance more satisfiable than individual task training.
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition

Integrating an external language model into a sequence-to-sequence speech recognition system is non-trivial. Previous works utilize linear interpolation or a fusion network to integrate external language models. However, these approaches introduce external components, and increase decoding computation. In this paper, we instead propose a knowledge distillation based training approach to integrating external language models into a sequence-to-sequence model. A recurrent neural network language model, which is trained on large scale external text, generates soft labels to guide the sequence-to-sequence model training. Thus, the language model plays the role of the teacher. This approach does not add any external component to the sequence-to-sequence model during testing. And this approach is flexible to be combined with shallow fusion technique together for decoding. The experiments are conducted on public Chinese datasets AISHELL-1 and CLMAD. Our approach achieves a character error rate of 9.3%, which is relatively reduced by 18.42% compared with the vanilla sequence-to-sequence model.
VAA: Visual Aligning Attention Model for Remote Sensing Image Captioning

Owing to the effectiveness in selectively focusing on regions of interest of images, the attention mechanism has been widely used in image caption task, which can provide more accurate image information for training deep sequential models.Existing attention-based models typically rely on top-down attention mechanism.While somewhat effective, attention masks in these attention-based models are queried from image features by hidden states of LSTM, rather than optimized by the objective functions.This indirectly supervised training approach cannot ensure that attention layers accurately focus on regions of interest.To address the above issue, in this paper, a novel attention model, Visual Aligning Attention model (VAA), is proposed.In this model, the attention layer is optimized by a well-designed visual aligning loss during the training phase.The visual aligning loss is obtained by explicitly calculating the feature similarity of attended image features and corresponding word embedding vectors.Besides, in order to eliminate the influence of non-visual words in training the attention layer, a visual vocab used for filtering out non-visual words in sentences is proposed, which can neglect the non-visual words when calculating the visual aligning loss.Experiments on UCM-Captions and Sydney-Captions prove that the proposed method is more effective in remote sensing image caption task.
Modelling the influence of data structure on learning in neural networks

Understanding the reasons for the success of deep neural networks trained using stochastic gradient-based methods is a key open problem for the nascent theory of deep learning. The types of data where these networks are most successful, such as images or sequences of speech, are characterised by intricate correlations. Yet, most theoretical work on neural networks does not explicitly model training data, or assumes that elements of each data sample are drawn independently from some factorised probability distribution. These approaches are thus by construction blind to the correlation structure of real-world data sets and their impact on learning in neural networks. Here, we introduce a generative model for structured data sets that we call the hidden manifold model (HMM). The idea is to construct high-dimensional inputs that lie on a lower-dimensional manifold, with labels that depend only on their position within this manifold, akin to a single layer decoder or generator in a generative adversarial network. We demonstrate that learning of the hidden manifold model is amenable to an analytical treatment by proving a Gaussian Equivalence Property (GEP), and we use the GEP to show how the dynamics of two-layer neural networks trained using one-pass stochastic gradient descent is captured by a set of integro-differential equations that track the performance of the network at all times. This permits us to analyse in detail how a neural network learns functions of increasing complexity during training, how its performance depends on its size and how it is impacted by parameters such as the learning rate or the dimension of the hidden manifold.
Regularized correntropy criterion based semi-supervised ELM

Along with the explosive growing of data, semi-supervised learning attracts increasing attention in the past years due to its powerful capability in labeling unlabeled data and knowledge mining. As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown superiorities in learning efficiency and accuracy. However, the optimization of SSELM as well as most of the other ELMs is generally based on the mean square error (MSE) criterion, which has been shown less effective in dealing with non-Gaussian noises. In this paper, a robust regularized correntropy criterion based SSELM (RC-SSELM) is developed. The optimization of the output weight matrix of RC-SSELM is derived by the fixed-point iteration based approach. A convergent analysis of the proposed RC-SSELM is presented based on the half-quadratic optimization technique. Experimental results on 4 synthetic datasets and 13 benchmark UCI datasets are provided to show the superiorities of the proposed RC-SSELM over SSELM and other state-of-the-art methods.