Reviewerly/exHarmony huggingface dataset:
Cash, crime, and cryptocurrencies

In The Curse of Cash, Kenneth Rogoff lists reductions in criminal activity and tax evasion among the primary benefits of eliminating cash. We maintain that, to the extent that individuals are interested in purchasing illicit goods and services or evading taxes, eliminating cash will encourage them to switch to close substitutes. Hence, governments intent on realizing the benefits cited by Rogoff would not merely need to eliminate cash. They would also need to ban alternatives. This is especially relevant given the proliferation of cryptocurrencies, which provide a fair degree of anonymity for users.
Learning-Free Unsupervised Extractive Summarization Model

Text summarization is an information condensation technique that abbreviates a source document to a few representative sentences with the intention to create a coherent summary containing relevant information of source corpora.This promising subject has been rapidly developed since the advent of deep learning.However, summarization models based on deep neural network have several critical shortcomings.First, a large amount of labeled training data is necessary.This problem is standard for lowresource languages in which publicly available labeled data do not exist.In addition, a significant amount of computational ability is required to train neural models with enormous network parameters.In this study, we propose a model called Learning Free Integer Programming Summarizer (LFIP-SUM), which is an unsupervised extractive summarization model.The advantage of our approach is that parameter training is unnecessary because the model does not require any labeled training data.To achieve this, we formulate an integer programming problem based on pre-trained sentence embedding vectors.We also use principal component analysis to automatically determine the number of sentences to be extracted and to evaluate the importance of each sentence.Experimental results demonstrate that the proposed model exhibits generally acceptable performance compared with deep learning summarization models although it does not learn any parameters during the model construction process.
Augmented Mirrors

A recurrent problem in egocentric Augmented Reality (AR) applications is the misestimation of depth. Providing alternative views from non-egocentric perspectives can convey useful information for applications that require the correct judgment of depth as it is in the case of placement and alignment of virtual and real content, but also for exploration and visualization tasks.In this paper, we introduce Augmented Mirrors. Through the integration of a real mirror, our approach is capable to reflect changes of the real and virtual content of an AR application while users benefit from the perceptual advantages of using mirrors. Our concept, simple yet effective, only requires tracking the user and mirror poses with the accuracy demanded by a specific application. To showcase the potential and flexibility of the Augmented Mirrors, we present and discuss multiple examples ranging from alignment, exploration, spatial understanding, and selective content visualization using different AR-enabled devices and tracking technologies. We envision the Augmented Mirrors as a new and valuable concept that can be used in applications that benefit from additional viewpoints and require the simultaneous visualization of real and virtual content.
The Effects of Mobile Phone Technology, Knowledge Creation and Diffusion on Inclusive Human Development in Sub-Saharan Africa

This paper examines the joint effects of mobile phone technology, knowledge creation and diffusion on inclusive human development in 49 sub-Saharan African (SSA) countries. The empirical evidence is based on Tobit regressions for the period 2000–2012. The net effects of interactions between the mobile phone, knowledge creation and diffusion variables are positive indicating that the combined effects of these variables improve inclusive human development in SSA countries. Further analysis dividing the dataset into a number of fundamental characteristics based on economic, legal, religion and political stability associated with African economies show that mobile phone penetration and associated innovation in SSA improve inclusive human development irrespective of the country's level of income, legal origins, religious orientation and the state of the nation. The pupil-teacher ratio exerts a negative influence on the outcome variable which is favourable for inclusive human development because higher ratios denote lower education quality since more pupils are accommodated by fewer teachers. The study contributes to innovation diffusion theory and economic development literature.
Re-territorialising the policing of cybercrime in the post-COVID-19 era: towards a new vision of local democratic cyber policing

Purpose The purpose of this study is to develop the theorisation of cybercrime in the context of the pandemic, and to sketch out a vision of how law enforcement might respond to a transformed landscape of online crime and offending. Design/methodology/approach This conceptual paper draws on empirical evidence from a range of sources (including official statistics) and the existing research literature, and revisits routine activities theory to illuminate the way that cybercrime patterns are being transformed by the pandemic. Findings The pandemic is reshaping the routine activities of societies en masse, leading to changes in the ecology of risk and opportunity for cybercrime. There is evidence of a large increase in the prevalence of cybercrime as a result, yet much of this has a paradoxically “local” character. Practical implications The authors identify specific practical implications for law enforcement, namely, that the role of local police in policing cybercrime should be re-envisioned, with a democratic, community-oriented approach at its heart. Originality/value The theoretical perspective outlined is a novel and critical development of a well-established framework, opening up new paths to the theorisation of cybercrime and cybercrime policing. The authors’ suggestions for practitioners have the potential for direct impact, both at the level of practice and in terms of broader imaginaries and organisation of police and policing.
Developing a Clinical Prediction Score: Comparing Prediction Accuracy of Integer Scores to Statistical Regression Models

Researchers often convert prediction tools built on statistical regression models into integer scores and risk classification systems in the name of simplicity. However, this workflow discards useful information and reduces prediction accuracy. We, therefore, investigated the impact on prediction accuracy when researchers simplify a regression model into an integer score using a simulation study and an example clinical data set. Simulated independent training and test sets (n = 1000) were randomly generated such that a logistic regression model would perform at a specified target area under the receiver operating characteristic curve (AUC) of 0.7, 0.8, or 0.9. After fitting a logistic regression with continuous covariates to each data set, continuous variables were dichotomized using data-dependent cut points. A logistic regression was refit, and the coefficients were scaled and rounded to create an integer score. A risk classification system was built by stratifying integer scores into low-, intermediate-, and high-risk tertiles. Discrimination and calibration were assessed by calculating the AUC and index of prediction accuracy (IPA) for each model. The optimism in performance between the training set and test set was calculated for both AUC and IPA. The logistic regression model using the continuous form of covariates outperformed all other models. In the simulation study, converting the logistic regression model to an integer score and subsequent risk classification system incurred an average decrease of 0.057-0.094 in AUC, and an absolute 6.2%-17.5% in IPA. The largest decrease in both AUC and IPA occurred in the dichotomization step. The dichotomization and risk stratification steps also increased the optimism of the resulting models, such that they appeared to be able to predict better than they actually would on new data. In the clinical data set, converting the logistic regression with continuous covariates to an integer score incurred a decrease in externally validated AUC of 0.06 and a decrease in externally validated IPA of 13%. Converting a regression model to an integer score decreases model performance considerably. Therefore, we recommend developing a regression model that incorporates all available information to make the most accurate predictions possible, and using the unaltered regression model when making predictions for individual patients. In all cases, researchers should be mindful that they correctly validate the specific model that is intended for clinical use.
Multi-task learning for simultaneous script identification and keyword spotting in document images

In this paper, an end-to-end multi-task deep neural network was proposed for simultaneous script identification and Keyword Spotting (KWS) in multi-lingual hand-written and printed document images. We introduced a unified approach which addresses both challenges cohesively, by designing a novel CNN-BLSTM architecture. The script identification stage involves local and global features extraction to allow the network to cover more relevant information. Contrarily to the traditional feature fusion approaches which build a linear feature concatenation, we employed a compact bi-linear pooling to capture pairwise correlations between these features. The script identification result is, then, injected in the KWS module to eliminate characters of irrelevant scripts and perform the decoding stage using a single-script mode. All the network parameters were trained in an end-to-end fashion using a multi-task learning that jointly minimizes the NLL loss for the script identification and the CTC loss for the KWS. Our approach was evaluated on a variety of public datasets of different languages and writing types.. Experiments proved the efficacy of our deep multi-task representation learning compared to the state-of-the-art systems for both of keyword spotting and script identification tasks.
Generating UML Class Diagram using NLP Techniques and Heuristic Rules

Several tools and approaches have been proposed to generate Unified Modeling Language (UML) diagrams. Researchers focus on automating the process of extracting valuable information from Natural Language (NL) text to generate UML models. The existing approaches show less accurateness because of the ambiguity of NL. In this paper, we present a method for generation class models from software specification requirements using NL practices and a set of heuristic rules to facilitate the transformation process. The NL requirements are converted into a formal and controlled representation to increase the accuracy of the generated class diagram. A set of pre-defined rules has been developed to extract OO concepts such as classes, attributes, methods, and relationships to generate a UML class diagram from the given requirements specifications. The approach has been applied and evaluated practically, where the results show that the approach is both feasible and acceptable.
Coming to Grips with Age Prediction on Imbalanced Multimodal Community Question Answering Data

For almost every online service, it is fundamental to understand patterns, differences and trends revealed by age demographic analysis—for example, take the discovery of malicious activity, including identity theft, violation of community guidelines and fake profiles. In the particular case of platforms such as Facebook, Twitter and Yahoo! Answers, user demographics have impacts on their revenues and user experience; demographics assist in ensuring that the needs of each cohort are fulfilled via personalizing and contextualizing content. Despite the fact that technology has been made more accessible, thereby becoming evermore prevalent in both personal and professional lives alike, older people continue to trail Gen Z and Millennials in its adoption. This trailing brings about an under-representation that has a harmful influence on the demographic analysis and on supervised machine learning models. To that end, this paper pioneers attempts at examining this and other major challenges facing three distinct modalities when dealing with community question answering (cQA) platforms (i.e., texts, images and metadata). As for textual inputs, we propose an age-batched greedy curriculum learning (AGCL) approach to lessen the effects of their inherent class imbalances. When built on top of FastText shallow neural networks, AGCL achieved an increase of ca. 4% in macro-F1-score with respect to baseline systems (i.e., off-the-shelf deep neural networks). With regard to metadata, our experiments show that random forest classifiers significantly improve their performance when individuals close to generational borders are excluded (up to 20% more accuracy); and by experimenting with neural network-based visual classifiers, we discovered that images are the most challenging modality for age prediction. In fact, it is hard for a visual inspection to connect profile pictures with age cohorts, and there are considerable differences in their group distributions with respect to meta-data and textual inputs. All in all, we envisage that our findings will be highly relevant as guidelines for constructing assorted multimodal supervised models for automatic age recognition across cQA platforms.
Building a Medical Chatbot using Support Vector Machine Learning Algorithm

Chatbot is used extensively to check the state of health at any time. It is the same as going to a doctor and having the medication prescribed. This paper discusses about medical chatbot using the machine learning algorithm which predicts the accuracy of the disease. There are many machine learning algorithms that can be used to predict the disease. Support Vector Machine learning technique is primarily used to achieve precise prediction and boost the efficiency of the model. The system uses Natural Language Processing to achieve the style of chatting. Using this approach people can reduce spending time in hospitals and receive low cost or cost-free services.
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos

Unsupervised landmark learning is the task of learning semantic keypoint-like representations without the use of expensive input keypoint annotations. A popular approach is to factorize an image into a pose and appearance data stream, then to reconstruct the image from the factorized components. The pose representation should capture a set of consistent and tightly localized landmarks in order to facilitate reconstruction of the input image. Ultimately, we wish for our learned landmarks to focus on the foreground object of interest. However, the reconstruction task of the entire image forces the model to allocate landmarks to model the background. Using a motion-based foreground assumption, this work explores the effects of factorizing the reconstruction task into separate foreground and background reconstructions in an unsupervised way, allowing the model to condition only the foreground reconstruction on the unsupervised landmarks. Our experiments demonstrate that the proposed factorization results in landmarks that are focused on the foreground object of interest when measured against ground-truth foreground masks. Furthermore, the rendered background quality is also improved as ill-suited landmarks are no longer forced to model this content. We demonstrate this improvement via improved image fidelity in a video-prediction task. Code is available at https://github.com/NVIDIA/UnsupervisedLandmarkLearning.
Knowing Where to Leverage: Context-Aware Graph Convolutional Network With an Adaptive Fusion Layer for Contextual Spoken Language Understanding

Spoken language understanding (SLU) systems aim to understand users’ utterance, which is a key component of task-oriented dialogue systems. In this paper, we focus on improving the contextual SLU. The contextual SLU systems mainly focus on how to effectively incorporate dialog context information (contextual information). The existing approaches all use the same contextual information to guide slot filling at all tokens, which may inject the irrelevant information and result in ambiguity. To tackle this problem, we propose a context-aware graph convolutional network (GCN) with an adaptive fusion layer for contextual SLU. The context-aware GCN is proposed to automatically aggregate the contextual information, which frees our model from the manually designed heuristic aggregation function. Meanwhile, an adaptive fusion layer is applied at each token to dynamically incorporate relevant contextual information, which achieves a fine-grained contextual information transfer to guide the token-level slot filling. Experiments on the Simulated Dialog Dataset show that our model achieves state-of-the-art performance and outperforms other previous methods by a large margin (+3.67% on Sim-R, +4.18% on Sim-M and +3.75% on Overall dataset). In addition, we explore and analyze the pre-trained model (i.e., BERT) in our framework. We show that incorporating BERT brings a large improvement in low-resource setting.
Handwritten Character Recognition of MODI Script using Convolutional Neural Network Based Feature Extraction Method and Support Vector Machine Classifier

Deep learning based algorithms are used in various pattern recognition tasks, including character recognition. Convolutional Neural Network (CNN) is effectively implemented for character recognition and is one of the best performing deep learning models. CNN can be used for character recognition directly or it can be used for extracting features in the character recognition process. Implementation of a feature extraction method using CNN autoencoder for MODI script character recognition is discussed in the paper. The extracted features are then subjected to Support Vector Machine (SVM) for the purpose of classification. The On-the-fly data augmentation method is used to add variability and generalization of the data set. MODI Script is an ancient Indian script and was used for writing Marathi until 1950. Various libraries and temples in India and abroad have a large collection of MODI documents. Character recognition related research of MODI script is still in infancy and research and development is necessary to extract the information from MODI manuscripts stored in various libraries. The performance of the proposed method, which uses CNN autoencoder as a feature extractor and an SVM based classifier gives very high accuracy and is better compared to the most accurate MODI character recognition method reported so far.
A Polymorphic Advanced Encryption Standard – A Novel Approach

To secure various forms of data, a polymorphic variant of the Advanced Encryption Standard (P-AES) has been introduced.In the P-AES, the AES parameters' values will change with every new key.The exact values will be available only to legitimate communicating parties during execution time.To achieve these objectives, the basic AES transformations, SubBytes, ShiftRows, and MixColumns, have been made key-dependent in the proposed P-AES.Hence, with every new key, these transformations will operate differently.The receiver can retrieve the operations' details from the encryption key.Consequently, polymorphism has been achieved and interoperability remains intact.P-AES has been implemented seamlessly using the existing AES modules, and the performance was more or less equal to the AES performance (71 and 70 milliseconds to encrypt 500 bytes using the P-AES and the AES respectively).From a security standpoint, the proposed P-AES fully complies with Kerckhoff's principle.This means the cipher has an open design, and the security provided by the P-AES depends only on the secrecy of the encryption key.The cipher resistance to differential and linear attacks has been proved.Moreover, the resulting proposed cipher can operate in 128 different ways, which will significantly reduce the capabilities of any sophisticated attacker.Furthermore, the proposed P-AES's scores of the key avalanche and the plaintext avalanche were 0.496 and 0.504 respectively.Finally, the Statistical Test Suite (STS) recommended by the NIST has been used to ensure the randomness of the cipher output, and the cipher has passed all the STS tests.
A Systematic Comparison of Depth Map Representations for Face Recognition

Nowadays, we are witnessing the wide diffusion of active depth sensors. However, the generalization capabilities and performance of the deep face recognition approaches that are based on depth data are hindered by the different sensor technologies and the currently available depth-based datasets, which are limited in size and acquired through the same device. In this paper, we present an analysis on the use of depth maps, as obtained by active depth sensors and deep neural architectures for the face recognition task. We compare different depth data representations (depth and normal images, voxels, point clouds), deep models (two-dimensional and three-dimensional Convolutional Neural Networks, PointNet-based networks), and pre-processing and normalization techniques in order to determine the configuration that maximizes the recognition accuracy and is capable of generalizing better on unseen data and novel acquisition settings. Extensive intra- and cross-dataset experiments, which were performed on four public databases, suggest that representations and methods that are based on normal images and point clouds perform and generalize better than other 2D and 3D alternatives. Moreover, we propose a novel challenging dataset, namely MultiSFace, in order to specifically analyze the influence of the depth map quality and the acquisition distance on the face recognition accuracy.