Reviewerly/exHarmony huggingface dataset:
Rookies connected: Interpersonal relationships among newcomers, newcomer adjustment processes, and socialization outcomes.

How do relationships among newcomers in the same cohort impact how quickly they develop clear understandings of their new roles and, ultimately, key socialization outcomes? We study newcomers' relationships with cohort members in the same unit (i.e., intra-unit relationships) and those in different units (i.e., inter-unit relationships). While organizations invest substantial time and resources in promoting broad networking among newcomers, we offer a theoretical and empirical account of how too many connections among fellow newcomers early in the socialization process can slow their adjustment-namely, their growth in role clarity. In Study 1, we surveyed 189 newcomers in an international conglomerate from their orientations through their first 4 months and linked responses to 3 years of their job performance and turnover records. After controlling for the initial level of role clarity, we found an inverted U-shaped relationship between newcomers' intra-unit peer connections and growth in role clarity, whereas their inter-unit peer connections did not significantly relate to growth in role clarity. Growth in role clarity positively related to subsequent newcomers' job satisfaction and job performance, which were then negatively related to turnover. Study 2's results indicated that sensemaking with fellow newcomers operated as a key mechanism linking the nonlinear relationships between intra-unit newcomer-newcomer relationships and growth in role clarity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Initial coin offerings: an emergent research area

Purpose The purpose of this paper is to identify the main insights current literature offers regarding initial coin offerings (ICOs) and the avenues for future research. Design/methodology/approach The approach consists of a systematic literature review of 130 papers from the SCOPUS database published in English between January 2018 and December 2020, with supplemental semantic analysis of the abstracts to obtain key themes and concepts. Findings Regulation and the determinants of ICO success are the main themes for current research and represent fruitful areas of continued scholarship. The research agenda in ICOs is just beginning and several topics and questions merit future inquiry: the behaviour of issuers and investors, the importance of human capital, the role of intermediaries and infomediaries and the use of signalling. Originality/value To the knowledge, this is one of the first systematic studies of current literature in ICOs. It provides a roadmap for future work on a phenomenon that will only grow in significance.
S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computing system design. Through optimizing parallel executions and data reuse in convolution, systolic architecture demonstrates great advantages in accelerating CNN computations. However, regular internal data transmission path in traditional systolic architecture prevents the systolic architecture from completely leveraging the benefits introduced by neural network sparsity. Deployment of fine-grained sparsity on the existing systolic architectures is greatly hindered by the incurred computational overheads. In this work, we propose S2Engine $-$ a novel systolic architecture that can fully exploit the sparsity in CNNs with maximized data reuse. S2Engine transmits compressed data internally and allows each processing element to dynamically select an aligned data from the compressed dataflow in convolution. Compared to the naive systolic array, S2Engine achieves about $3.2\times$ and about $3.0\times$ improvements on speed and energy efficiency, respectively.
A Plurality of Practices: Artistic Narratives in HCI Research

The arts and Human Computer Interaction (HCI) have a lot in common. As part of computer science HCI is ground breaking, interdisciplinary and focused on the interactions that form part of our everyday world. As part of the arts, HCI is a lens on technology, showing us spaces where there is room to interact and create new and meaningful blended experiences. It is therefore no surprise that many researchers and practitioners in our field have and maintain creative practices alongside, and as part of their research. We explore how these dual practices relate to each other, and how we might reconcile our mindful creative experiences with the formality of research. What benefits does such duality have, and can we illustrate the value of arts practice in HCI? This pictorial curates diverse artistic practice from a range of researchers, and offers reflection on the benefits and tensions in creativity and computing.
Path Planning with Q-Learning

Abstract As science and technology advance rapidly, the scope of mobile robot applications continues to expand. In mobile robotics, path planning, which has formed a relatively complete theoretical system, is one of the most momentous and challenging tasks, especially in uncertain environments. Path planning has been a popular research problem in recent years, and it has been widely utilized in various fields, mainly including robotic surgery, logistics transportation, and self-driving automobile. To solve path planning, this paper presents an approach that uses Q-learning Algorithm to find multiple feasible paths within obstacle environments. First, the algorithm of Q-learning was pre-trained to make it suitable for path planning. Then an obstacle environment map was modeled and a path planning program was compiled by applying the state action-value function. Finally, the experimental results were collected, including the path planning maps and corresponding graphs of training progress. To guarantee the effectiveness of the final paths, all the parameters in the function are set to be constant. The experimental results show that the Q-learning algorithm can succeed in solving the problem of multi-path planning.
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection

Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning based topic models, specifically aspect-based autoencoder, suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To tackle these challenges, in this paper, we first propose a self-supervised contrastive learning framework and an attention-based model equipped with a novel smooth self-attention (SSA) module for the UAD task in order to learn better representations for aspects and review segments. Secondly, we introduce a high-resolution selective mapping (HRSMap) method to efficiently assign aspects discovered by the model to the aspects of interest. We also propose using a knowledge distillation technique to further improve the aspect detection performance. Our methods outperform several recent unsupervised and weakly supervised approaches on publicly available benchmark user review datasets. Aspect interpretation results show that extracted aspects are meaningful, have a good coverage, and can be easily mapped to aspects of interest. Ablation studies and attention weight visualization also demonstrate effectiveness of SSA and the knowledge distillation method.
Fast and Compact Bilinear Pooling by Shifted Random Maclaurin

Bilinear pooling has achieved an excellent performance in many computer vision tasks such as fine-grained classification, scene recognition and texture recognition. However, the high-dimension features from bilinear pooling can sometimes be inefficient and prone to over-fitting. Random Maclaurin (RM) is a widely used GPU-friendly approximation method to reduce the dimensionality of bilinear features. However, to achieve good performance, large projection matrices are usually required in practice, making it costly in computation and memory. In this paper, we propose a Shifted Random Maclaurin (SRM) strategy for fast and compact bilinear pooling. With merely negligible extra computational cost, the proposed SRM provides an estimator with a provably smaller variance than RM, which benefits accurate kernel approximation and thus the learning performance. Using a small projection matrix, the proposed SRM achieves a comparable estimation performance as RM based on a large projection matrix, and thus boosts the efficiency. Furthermore, we upgrade the proposed SRM to SRM+ to further improve the efficiency and make the compact bilinear pooling compatible with fast matrix normalization. Fast and Compact Bilinear Network (FCBN) built upon the proposed SRM+ is devised, achieving an end-to-end training. Systematic experiments conducted on four public datasets demonstrate the effectiveness and efficiency of the proposed FCBN.
Out-of-Town Recommendation with Travel Intention Modeling

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user’s home-town preference but also the user’s travel intention. Besides, the user’s travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The proposed TRAINOR framework distinguishes itself from existing out-of-town recommenders in three aspects. First, graph neural networks are explored to represent users’ home-town check-in preference and geographical constraints in out-of-town check-in behaviors. Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM). Third, a non-linear mapping function, as well as a matrix factorization method, are employed to transfer users’ home-town preference and estimate out-of-town POI’s representation, respectively. Extensive experiments on real-world data sets validate the effectiveness of the TRAINOR framework. Moreover, the learned travel intention can deliver meaningful explanations for understanding a user’s travel purposes.
Learning multiple defaults for machine learning algorithms

Modern machine learning methods highly depend on their hyper-parameter configurations for optimal performance. A widely used approach to selecting a configuration is using default settings, often proposed along with the publication of a new algorithm. Those default values are usually chosen in an ad-hoc manner to work on a wide variety of datasets. Different automatic hyperparameter configuration algorithms which select an optimal configuration per dataset have been proposed, but despite its importance, tuning is often skipped in applications because of additional run time, complexity, and experimental design questions. Instead, the learner is often applied in its defaults. This principled approach usually improves performance but adds additional algorithmic complexity and computational costs to the training procedure. We propose and study using a set of complementary default values, learned from a large database of prior empirical results as an alternative. Selecting an appropriate configuration on a new dataset then requires only a simple, efficient, and embarrassingly parallel search over this set. To demonstrate the effectiveness and efficiency of the approach, we compare learned sets of configurations to random search and Bayesian optimization. We show that sets of defaults can improve performance while being easy to deploy in comparison to more complex methods.
Blockchain Technology in Smart-Cities

With the fast pace of population, intelligent city wants effective and sustainable smart solutions in transport, climate, energy, and government affairs. Amongst the most sensible solutions is the smart city platform which includes IoT, Big Data and the Internet of Energy. It faces many issues, including such inadequate IoT security, trouble maintaining and improving efficiency, higher operating expenses of large amounts of data center construction, good permeability to damage, difficulty building confidence in electricity internet users, quick leakage of consumer privacy and a business model which is not acceptable etc. Blockchain is one of today’s most disruptive technologies. As part of the overall efforts to shape the urban future, numerous cities around the world are launching blockchain initiatives. With a range of potential advantages, digital transformation poses many key issues like data security and confidentiality. This study proposed a security architecture which utilizes the blockchain-based with smart devices and provide a secure communication system in an intelligent city.
POI Recommendation with Federated Learning and Privacy Preserving in Cross Domain Recommendation

Point-of-Interest (POI) recommendation is one of the most popular recommendation methodologies. However, POI data is very sensitive and sparse. Users' reluctance to share their context information due to privacy concerns, along with the cold-start problem caused by data sparsity reduces recommendation efficiency. To address these issues, we propose a POI framework for cross-domain recommendation with federated learning and privacy protection features. It utilizes data in an auxiliary domain in users' interest analysis to alleviate the cold-start problem. Moreover, it applies federated learning by analyzing the users' historical data locally and encrypts latent feature distribution for knowledge migration to protect users' privacy. Experiments on real datasets have shown that our framework improves recommendation accuracy while preserving users' privacy as compared to convolutional neural network-based methods when analyzing users' comments.
Hematological Parameters Changes in Patients with Breast Cancer

BACKGROUND Breast cancer is the most common cancer diagnosis among women worldwide. It accounts for 25% of all women's cancers. This cancer is invasive with a high mortality rate. Evaluation of hematological factors is one of the reliable paraclinical methods to diagnose diseases. Hematological parameters can predict severity, mortality, and follow-up treatment in patients with breast cancer. The aim of this study was to evaluate hematological parameters as useful markers to distinguish between patients with breast cancer and healthy individuals. METHODS This study was conducted on 160 women with breast cancer as case groups and 160 healthy women as controls. Hematological parameters were analyzed using the Sysmex KX-21N™ automated hematology analyzer system. RESULTS The difference between the breast cancer patients and the control group was significant in Hb, HCT, MCV, RDW parameters, and MPV/PLT ratio (p 0.8). Also, there was a moderate difference between the two groups in MPV and MCH parameters (p < 0.05) (0.5 < effect size < 0.8). Meanwhile, two groups had a minimal difference in NLR, PLR, and MCHC parameters and WBC and RBC count (p < 0.05) (0.2 < effect size < 0.5). CONCLUSIONS The routine blood test is the most accessible and essential examination tool for disease diagnosis. Our results showed that hematological parameters, like MCV, RDW, MPV, MPV/PLT count, NLR, and PLR, can differentiate breast cancer patients from healthy individuals.
Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.
Determination of the stress and strain fields in porous structures by photoelasticity and digital image correlation techniques

Quantitative visualization of the full-field stress and strain in porous structures is of significance to reveal the mechanism of the damage and failure of engineering materials. To quantitatively characterize the full-field stresses and strains, optical measurement methods, such as photoelasticity for determining the stress fields, moiré and digital image correlation (DIC) methods for measuring the strain fields, have been developed in previous works. However, it is challenging to combine these methods to measure stress and strain fields simultaneously because the speckles and gratings for strain measurement will disturb the interference fringes in the photoelasticity. In this study, a method incorporating photoelasticity and DIC techniques was developed to determine the stress and strain fields simultaneously in different porous structures which were fabricated by three-dimensional (3D) printed technique. The tested models were printed with a highly transparent, colourless material and the speckles on the front surface of the tested models were printed with a translucent, magenta material. Under compressive loads, the stress fields in these models were obtained based on the photoelastic patterns and the strain fields were determined by DIC method. The results indicate the proposed method can simultaneously determine the full-field stresses and strains and the comparison of the distribution of the principal stress and strain difference and the distribution of the shear stress and strain verified its good accuracy. In addition, the stress and strain distribution in different pore structures were compared and found that the optimal design of the pore distribution can greatly change the concentration of the stress and strain fields.
Client-server sessions in linear logic

We introduce coexponentials, a new set of modalities for Classical Linear Logic. As duals to exponentials, the coexponentials codify a distributed form of the structural rules of weakening and contraction. This makes them a suitable logical device for encapsulating the pattern of a server receiving requests from an arbitrary number of clients on a single channel. Guided by this intuition we formulate a system of session types based on Classical Linear Logic with coexponentials, which is suited to modelling client-server interactions. We also present a session-typed functional programming language for client-server programming, which we translate to our system of coexponentials.
4-Bit Quantization of LSTM-Based Speech Recognition Models

We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition (ASR): hybrid Deep Bidirectional LSTM - Hidden Markov Models (DBLSTM-HMMs) and Recurrent Neural Network - Transducers (RNN-Ts). Using a 4-bit integer representation, a naive quantization approach applied to the LSTM portion of these models results in significant Word Error Rate (WER) degradation. On the other hand, we show that minimal accuracy loss is achievable with an appropriate choice of quantizers and initializations. In particular, we customize quantization schemes depending on the local properties of the network, improving recognition performance while limiting computational time. We demonstrate our solution on the Switchboard (SWB) and CallHome (CH) test sets of the NIST Hub5-2000 evaluation. DBLSTM-HMMs trained with 300 or 2000 hours of SWB data achieves $<$0.5% and $<$1% average WER degradation, respectively. On the more challenging RNN-T models, our quantization strategy limits degradation in 4-bit inference to 1.3%.
Adapting Speaker Embeddings for Speaker Diarisation

The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation. The quality of speaker embeddings is paramount to the performance of speaker diarisation systems. Despite this, prior works in the field have directly used embeddings designed only to be effective on the speaker verification task. In this paper, we propose three techniques that can be used to better adapt the speaker embeddings for diarisation: dimensionality reduction, attention-based embedding aggregation, and non-speech clustering. A wide range of experiments is performed on various challenging datasets. The results demonstrate that all three techniques contribute positively to the performance of the diarisation system achieving an average relative improvement of 25.07% in terms of diarisation error rate over the baseline.
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-Supervised Learning

Recently, self-supervised pre-training has gained success in automatic speech recognition (ASR). However, considering the difference between speech accents in real scenarios, how to identify accents and use accent features to improve ASR is still challenging. In this paper, we employ the self-supervised pre-training method for both accent identification and accented speech recognition tasks. For the former task, a standard deviation constraint loss (SDC-loss) based end-to-end (E2E) architecture is proposed to identify accents under the same language. As for accented speech recognition task, we design an accent-dependent ASR system, which can utilize additional accent input features. Furthermore, we propose a frame-level accent feature, which is extracted based on the proposed accent identification model and can be dynamically adjusted. We pre-train our models using 960 hours unlabeled LibriSpeech dataset and fine-tune them on AESRC2020 speech dataset. The experimental results show that our proposed accent-dependent ASR system is significantly ahead of the AESRC2020 baseline and achieves $6.5\%$ relative word error rate (WER) reduction compared with our accent-independent ASR system.