Reviewerly/exHarmony huggingface dataset:
Image-based Bengali Sign Language Alphabet Recognition for Deaf and Dumb Community

For the deaf and dumb (D&D) people, sign language is one of the primary and most used methods for communication. All over the world, every day the D&D community faces difficulties while communicating with the general mass. Most of the times, they need an interpreter to communicate with others and the interpreter may not always be available. The issue is also faced by the people using Bengali Sign Language (BdSL) due to the lack of BdSL interpreters. Recently, computer vision-based systems have been introduced for automatic recognition of sign languages to mitigate this problem. But so far the number of reliable works done for the recognition of BdSL is not adequate. In this paper, we propose a method for automatic detection of BdSL alphabets. Our system solely relies on the images of bare hands, which allows the users to interact with the system in a natural way. We have collected in total 12581 different hand signs for the 38 BdSL alphabets in collaboration with the National Federation of the Deaf. We propose a VGG19 based convolutional neural network for the recognition of 38 classes and achieve an overall test accuracy of 89.6%.
Deep Adversarial Completion for Sparse Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) contains multiple types of entities and relations. Most of existing HIN embedding methods learn the semantic information based on the heterogeneous structures between different entities, which are implicitly assumed to be complete. However, in real world, it is common that some relations are partially observed due to privacy or other reasons, resulting in a sparse network, in which the structure may be incomplete, and the ”unseen” links may also be positive due to the missing relations in data collection. To address this problem, we propose a novel and principled approach: a Multi-View Adversarial Completion Model (MV-ACM). Each relation space is characterized in a single viewpoint, enabling us to use the topological structural information in each view. Based on the multi-view architecture, an adversarial learning process is utilized to learn the reciprocity (i.e., complementary information) between different relations: In the generator, MV-ACM generates the complementary views by computing the similarity of the semantic representation of the same node in different views; while in the discriminator, MV-ACM discriminates whether the view is complementary by the topological structural similarity. Then we update the node’s semantic representation by aggregating neighborhoods information from the syncretic views. We conduct systematical experiments1 on six real-world networks from varied domains: AMiner, PPI, YouTube, Twitter, Amazon and Alibaba. Empirical results show that MV-ACM significantly outperforms the state-of-the-art approaches for both link prediction and node classification tasks.
Learning Temporal Interaction Graph Embedding via Coupled Memory Networks

Graph embedding has become the research focus in both academic and industrial communities due to its powerful capabilities. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over time, thus putting forward huge challenges in learning effective node representations. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in external matrices explicitly and dynamically, which forms deep matrix representations and could enhance the expressiveness of the node embeddings. We conduct experiments on two real-world datasets and the experimental results empirically demonstrate that TigeCMN can outperform the state-of-the-arts with different gains.
DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction

Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The core problem of patient-trial matching is to find qualified patients for a trial, where patient information is stored in electronic health records (EHR) while trial eligibility criteria (EC) are described in text documents available on the web. How to represent longitudinal patient EHR? How to extract complex logical rules from EC? Most existing works rely on manual rule-based extraction, which is time consuming and inflexible for complex inference. To address these challenges, we proposed a cross-modal inference learning model to jointly encode enrollment criteria (text) and patients records (tabular data) into a shared latent space for matching inference. pplies a pre-trained Bidirectional Encoder Representations from Transformers(BERT) model to encode clinical trial information into sentence embedding. And uses a hierarchical embedding model to represent patient longitudinal EHR. In addition, s augmented by a numerical information embedding and entailment module to reason over numerical information in both EC and EHR. These encoders are trained jointly to optimize patient-trial matching score. We evaluated n the trial-patient matching task with demonstrated on real world datasets. utperformed the best baseline by up to 12.4% in average F1.
Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: A chain-structure echo state network approach

In this paper, a chain-structure echo state network (CESN) is newly proposed to enhance the scalability, robustness and computational efficiency in spatio-temporal solar irradiance prediction. Firstly, the network structure, mathematical model, stability analysis, and training mechanism are studied for CESN. Secondly, according to the spatial correlation analysis, the number of ESN modules in CESN can be determined, such that the dynamics of different features can be fully fitted. In addition, the autocorrelation analysis is adopted in temporal information of each spatial variable to provide appropriate inputs for each ESN module. Finally, the spatio-temporal solar irradiance prediction model is established based on CESN. Simulation results illustrate that the CESN could achieve more accurate prediction, compared with backpropagation (BP) and Elman neural networks, classical ESNs.
Learning With Stochastic Guidance for Robot Navigation

Due to the sparse rewards and high degree of environmental variation, reinforcement learning approaches, such as deep deterministic policy gradient (DDPG), are plagued by issues of high variance when applied in complex real-world environments. We present a new framework for overcoming these issues by incorporating a stochastic switch, allowing an agent to choose between high- and low-variance policies. The stochastic switch can be jointly trained with the original DDPG in the same framework. In this article, we demonstrate the power of the framework in a navigation task, where the robot can dynamically choose to learn through exploration or to use the output of a heuristic controller as guidance. Instead of starting from completely random actions, the navigation capability of a robot can be quickly bootstrapped by several simple independent controllers. The experimental results show that with the aid of stochastic guidance, we are able to effectively and efficiently train DDPG navigation policies and achieve significantly better performance than state-of-the-art baseline models.
Determining the Core Primitive for Optimally Secure Ratcheting

After ratcheting attracted attention mostly due to practical real-world protocols, recently a line of work studied ratcheting as a primitive from a theoretic point of view. Literature in this line, pursuing the strongest security of ratcheting one can hope for, utilized for constructions strong, yet inefficient key-updatable primitives – based on hierarchical identity based encryption (HIBE). As none of these works formally justified utilizing these building blocks, we answer the yet open question under which conditions their use is actually necessary. We revisit these strong notions of ratcheted key exchange (RKE), and propose a more realistic (slightly stronger) security definition. In this security definition, both exposure of participants' local secrets and attacks against executions' randomness are considered. While these two attacks were partially considered in previous work, we are the first to unify them cleanly in a natural game based notion. Our definitions are based on the systematic RKE notion by Poettering and Rösler (CRYPTO 2018). Due to slight (but meaningful) changes to regard attacks against randomness, we are ultimately able to show that, in order to fulfill strong security for RKE, public key cryptography with (independently) updatable key pairs is a necessary building block. Surprisingly, this implication already holds for the simplest RKE variant. Hence, (1) we model optimally secure RKE under randomness manipulation to cover realistic attacks, (2) we (provably) extract the core primitive that is necessary to realize strongly secure RKE, and (3) our results indicate which relaxations in security allow for constructions that only rely on standard public key cryptography.
DIGITAL TRANSFORMATION OF ECONOMY AND TECHNOLOGICAL REVOLUTIONS: CHALLENGES FOR THE CURRENT PARADIGM OF MANAGEMENT AND CRISIS MANAGEMENT

The digital transformation of the economy leads to dramatic changes in business and society and requires the reconfiguration of all socio-economic institutions. Such radical changes include economic science and management, challenging the traditional economic laws and management tools that were formed in the “digital era”. The purpose of the study is to identify the key challenges of the digital transformation of the economy for economic science, management and business, as well as the main directions of development of crisis management of companies in the era of the digital economy. The study identified the main factors of digital transformation of the economy that affect the methods and conditions of doing business, and the challenges of digitalization for business. The necessity of developing a new crisis toolkit for companies of the “digital” and “non-digital” eras is substantiated, for which a comparative analysis of the characteristics of such companies and key indicators of their financial and economic status is carried out. In addition author defined several economic paradoxes that the author called the “scissor effect” (a gap in the dynamics of the capitalization of digital and non-digital companies) and the “paradox of profitability” (growth in the capitalization of digital companies against the backdrop of chronic loss-making activities). Author used various methods of scientific research: deduction and induction when conducting an empirical analysis of the activities of digital and non-digital companies, analytical methods and historical analysis. The results of the study will contribute to the development of the theory of management and crisis management in the era of the digital economy, and also will determine the future directions of theoretical and practical development of these scientific disciplines.
Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning Framework

Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.
ESeismic: Towards an Ecuadorian volcano seismic repository

Abstract In this work, we present the development, description, and performance evaluation of two volcano seismological datasets: one containing raw seismic signals (MicSigV1) and another containing a set of 84 distinctive attributes or features for bench-marking (SeisBenchV1), which was extracted from the MicSigV1 dataset, recorded at Cotopaxi volcano in Ecuador. These datasets represent the first public Ecuadorian volcano seismicity repository that will update, publish, and support the release of data to the scientific community on a continual basis. Each dataset contains samples of mostly long-period and volcano-tectonic event classes. Other types of events, such as regional, hybrid, and icequakes, were also included despite their minor presence in the datasets. These datasets will serve to provide an important resource for research in signal processing, feature calculation, feature selection, feature space reduction, seismic events detection, and classification areas. The datasets can be downloaded through a copyright agreement that supports the correct use of the released datasets.
Secure Lending: Blockchain and Prospect Theory-Based Decentralized Credit Scoring Model

Credit scoring is a rigorous statistical analysis carried out by lenders and other third parties to access an individual's creditworthiness. Lenders use credit scoring to estimate the degree of risk in lending money to an individual. However, credit score evaluation is primarily based on a transaction record, payment history, professional background, etc. sourced from different credit bureaus. So, evaluating a credit score is a laborious and tedious task involving a lot of paperwork. In this paper, we propose how blockchain can provide the solution to decentralized credit scoring evaluation and reducing the amount of dependence of paperwork. Lending money is not always objective but subjective to every lender. The decision of lending involves different levels of risk and uncertainty, depending on their perspective. This paper uses the prospect theory to model the optimal investment strategy for different risk vs. return scenarios.
Identifying smartphone users based on how they interact with their phones

Abstract The continuous advancement in the Internet of Things technology allows people to connect anywhere at any time, thus showing great potential in technology like smart devices (including smartphones and wearable devices). However, there is a possible risk of unauthorized access to these devices and technologies. Unfortunately, frequently used authentication schemes for protecting smart devices (such as passwords, PINs, and pattern locks) are vulnerable to many attacks. USB tokens and hardware keys have a risk of being lost. Biometric verification schemes are insecure as well as they are susceptible to spoofing attacks. Maturity in sensor chips and machine learning algorithms provides a better solution for authentication problems based on behavioral biometrics, which aims to identify the behavioral traits that a user possesses, such as hand movements and waving patterns. Therefore, this research study aims to provide a solution for passive and continuous authentication of smartphone users by analyzing their activity patterns when interacting with their phones. The motivation is to learn the physical interactions of a smartphone owner for distinguishing him/her from other users to avoid any unauthorized access to the device. Extensive experiments were conducted to test the performance of the proposed scheme using random forests, support vector machine, and Bayes net. The best average recognition accuracy of 74.97% is achieved with the random forests classifier, which shows the significance of recognizing smartphone users based on their interaction with the phones.
AI explainability 360

This tutorial will teach participants to use and contribute to a new open-source Python package named AI Explainability 360 (AIX360) (https://aix360.mybluemix.net), a comprehensive and extensible toolkit that supports interpretability and explainability of data and machine learning models.
Virtual reality simulation‐based learning of teaching with alternative perspectives taking

Abstract This study examined participatory simulation‐based learning of teaching in a virtual reality (VR) supported learning environment and its affordance for alternative perspectives taking. This VR‐supported, embodiment‐integrated learning environment is designed to enable collaborative and contextualized microteaching practicing by university teaching assistants. A total of 42 chemistry teaching assistants participated in a 3‐hour teaching training session, during which they were randomly assigned to a VR‐supported simulation group and a live simulation group. The study found teaching knowledge development for all participants after simulation‐based training. The live simulation participants better performed in the post‐session knowledge test than the VR simulation participants. Qualitative data indicated a multitude of challenges and opportunities in VR simulation‐based learning, including a concurrent, yet, inequivalent experience of the “diving‐in” and “stepping‐out” learning perspectives during virtual collaborative role‐playing in a hybrid learning space. Practitioner Notes What is already known about this topic Participatory simulation is implemented and found effective in diverse academic fields. The design and research of alternative perspectives for simulation‐based learning is lacking. The training of current and future instructors on effective teaching practices is a critical issue. What this paper adds Participatory simulation‐based teaching training is feasible and effective. Live simulation outperforms the virtual reality simulation in a physically collocated learning setting. Pros and cons of a hybrid learning space for participatory simulation and alterative perspectives taking are discussed. Implications for practice and/or policy We should design a consecutive and intelligible experience of alternative learning perspectives in simulation‐based learning. It is important to prepare and scaffold learners for virtual participatory simulation‐based learning.
Effekt: Capability-passing style for type- and effect-safe, extensible effect handlers in Scala

Abstract Effect handlers are a promising way to structure effectful programs in a modular way. We present the Scala library Effekt , which is centered around capability passing and implemented in terms of a monad for multi-prompt delimited continuations. Effekt is the first library implementation of effect handlers that supports effect safety and effect polymorphism without resorting to type-level programming. We describe a novel way of achieving effect safety using intersection types and path-dependent types. The effect system of our library design fits well into the programming paradigm of capability passing and is inspired by the effect system of Zhang &amp; Myers (2019, Proc. ACM Program. Lang. 3 (POPL), 5:1-5:29). Capabilities carry an abstract type member, which represents an individual effect type and reflects the use of the capability on the type level. We represent effect rows as the contravariant intersection of effect types. Handlers introduce capabilities and remove components of the intersection type. Reusing the existing type system of Scala, we get effect subtyping and effect polymorphism for free.
Evaluation of the reanalysis surface solar radiation from NCEP, ECMWF, NASA, and JMA using surface observations for Balochistan, Pakistan

Solar radiation from reanalysis datasets can be used to estimate solar energy potential in the absence of long-term surface measured data. Comprehensive validation of reanalysis data against short-term measurements of solar radiation is necessary to find uncertainties. The evaluation of the performance of seven reanalysis datasets (ERA-5, MERRA-2, ERA-Interim, JRA-55, NCEP-NCAR, NCEP-DOE, and CFSR) for Balochistan against high-quality surface measured data was performed using statistical analysis. The relative mean bias error (rMBE), relative mean absolute error (rMAE), and relative root mean square error of reanalysis solar radiation range from −4 to 7%, 8 to 14%, and 16 to 21%, respectively, using sub-daily data of six datasets except for NCEP-NCAR. Seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons. Analysis of monthly mean data shows that rMBE and rMAE are less than 3 and 5% for ERA-5, respectively. The performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 can be used for preliminary solar resource assessment and power estimation.
Mapping Magmatic and Hydrothermal Processes from Routine Exploration Geochemical Analyses

Abstract Analytical methods used by commercial assay laboratories have improved enormously in recent years. Inductively coupled plasma-atomic emission spectroscopy and inductively coupled plasma-mass spectrometry methods now report analyses for half of the periodic table with exceptional detection limits and precision. It is becoming commonplace for mining companies to use such methods routinely for the analysis of drill samples throughout mineral deposits. Improvements in software and computing power now allow rapid interrogation of upward of 100,000 assay samples. Geochemical analyses are quantitative, are independent of observer bias, and can form the basis for robust geologic and mineralogical models of mineral deposits, as well as shed light on scientific questions. In particular, consistently collected, high-quality geochemical analyses can significantly improve and systematize logging of lithological and hydrothermal alteration mineralogic changes within drill core. In addition, abundant, high-quality geochemical data provide insights into magmatic and hydrothermal processes that were previously difficult to recognize and that have obvious applications to mineral exploration and improved genetic models of ore deposits. This paper describes a workflow that mining industry geologists can apply to their multielement analysis data to extract more information about magma compositions and gangue mineralogy.