Reviewerly/exHarmony huggingface dataset:
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization

Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a general framework to jointly model the likelihoods of the monolingual and code-switch sub-tasks that comprise bilingual speech recognition. By defining the monolingual sub-tasks with label-to-frame synchronization, our joint modeling framework can be conditionally factorized such that the final bilingual output, which may or may not be code-switched, is obtained given only monolingual information. We show that this conditionally factorized joint framework can be modeled by an end-to-end differentiable neural network. We demonstrate the efficacy of our proposed model on bilingual Mandarin-English speech recognition across both monolingual and code-switched corpora.
FacetRules: Discovering and Describing Related Groups

Domain experts, owing to their knowledge and experience, develop an intuition for patterns in the data. They may know, for example, certain points of interest. However, they may not know exactly how to characterize what makes these data special. In our work, we start from these points of interest as seeds to derive groups of similar points automatically based on surrounding cluster structure. To aid characterization, we provide descriptive rules as an interpretable model to describe the groups. We explain this technique and present a prototype to demonstrate with a usage scenario how the technique helps the user with data exploration by discovering and describing groups in the data.
Balanced Masked and Standard Face Recognition

We present the improved network architecture, data augmentation, and training strategies for the Webface track and Insightface/Glint360K track of the masked face recognition challenge of ICCV2021. One of the key goals is to have a balanced performance of masked and standard face recognition. In order to prevent the overfitting for the masked face recognition, we control the total number of masked faces by not more than 10% of the total face recognition in the training dataset. We propose a few key changes to the face recognition network including a new stem unit, drop block, face detection and alignment using YOLO5Face, feature concatenation, a cycle cosine learning rate etc. With this strategy, we achieve good and balanced performance for both masked and standard face recognition.
Improvement of solar irradiance modelling during cloudy-sky days using measurements

Clouds have a strong influence on the amount of solar irradiance reaching the ground. However, they have large spatio-temporal variations and are difficult to model. The 1D irradiance model of code_saturne is used to estimate the global and direct solar irradiances at the ground, taking into account the impact of atmospheric gas, clouds and aerosols. Simulations are conducted and compared to measurements at the French SIRTA observatory (instrumental site for atmospheric remote sensing research), located in Palaiseau, Ile-de-France in August 2009 and in the year 2014. Although irradiance is very well modelled during clear-sky days, it is over-estimated during cloud-sky days. The estimation of irradiance during cloudy-sky days is improved by coupling the model to on-site measurements of cloud parameters from the SIRTA. RMSEs around 59 W m−2 and 50 W m−2 and MBEs around +17 W m−2 and -18 W m−2 are obtained, respectively, for global and direct irradiances during cloudy-sky days using pyranometer measurements for cloud fraction and microwave radiometric measurements for liquid water path. A sensitivity analysis on the cloud parameters that may lead to the best improvement of simulated irradiance is performed. The cloud optical depth is the most important one, followed by the cloud fraction. The different instruments used for the determination of these parameters are examined. Moreover, hourly values of solar fluxes are analysed to determine and physically understand persistent errors between model and measurements when measured cloud parameters are used.
MIxBN: library for learning Bayesian networks from mixed data

This paper describes a new library for learning Bayesian networks from data containing discrete and continuous variables (mixed data). In addition to the classical learning methods on discretized data, this library proposes its algorithm that allows structural learning and parameters learning from mixed data without discretization since data discretization leads to information loss. This algorithm based on mixed MI score function for structural learning, and also linear regression and Gaussian distribution approximation for parameters learning. The library also offers two algorithms for enumerating graph structures - the greedy Hill-Climbing algorithm and the evolutionary algorithm. Thus the key capabilities of the proposed library are as follows: (1) structural and parameters learning of a Bayesian network on discretized data, (2) structural and parameters learning of a Bayesian network on mixed data using the MI mixed score function and Gaussian approximation, (3) launching learning algorithms on one of two algorithms for enumerating graph structures - Hill-Climbing and the evolutionary algorithm. Since the need for mixed data representation comes from practical necessity, the advantages of our implementations are evaluated in the context of solving approximation and gap recovery problems on synthetic data and real datasets.
FCNNLib: A Flexible Convolution Algorithm Library for Deep Learning on FPGAs

Convolution features huge complexity and demands high computation capability.Among hardware platforms, field programmable gate array (FPGA) emerges as a promising solution for its substantial available parallelism and energy efficiency.Besides, convolution can be implemented with different algorithms, including conventional, general matrix-matrix multiplication (GEMM), Winograd, and fast Fourier transformation (FFT) algorithms, which are diverse in arithmetic complexity, resource requirement, etc. Different convolutional neural network (CNN) models have different topologies and structures, favoring different convolution algorithms.In response, software libraries such as cuDNN provide a variety of computational primitives to support these algorithms.However, supporting such libraries on FPGAs is challenging.First, multiple algorithms can share the FPGA resources spatially as well as temporally, introducing either reconfiguration overhead or resource underutilization.Second, FPGA implementation remains a significant challenge for library developers.It typically requires significant specialized hardware knowledge.In this article, we propose FCNNLib, an efficient and scalable convolution algorithm library on FPGAs.To coordinate multiple convolution algorithms on FPGAs, we develop three schedulings: 1) spatial; 2) temporal; and 3) hybrid, which exhibit different tradeoffs in latency and throughput.We explore these schedulings by balancing the reconfiguration overhead, resource utilization, and optimization objectives of the CNNs.Then, we provide efficient and tunable algorithm templates that allow performance tuning through performance and resource models.To arm the users, FCNNLib exposes a set of interfaces to support high-level application designs.We demonstrate the usability of FCNNLib with state-of-the-art CNNs.FCNNLib achieves up to 44.6× and 1.76× energy efficiency in various scenarios compared with software libraries for CPUs and GPUs, respectively.
Short of Suspension: How Suspension Warnings Can Reduce Hate Speech on Twitter

Debates around the effectiveness of high-profile Twitter account suspensions and similar bans on abusive users across social media platforms abound. Yet we know little about the effectiveness of warning a user about the possibility of suspending their account as opposed to outright suspensions in reducing hate speech. With a pre-registered experiment, we provide causal evidence that a warning message can reduce the use of hateful language on Twitter, at least in the short term. We design our messages based on the literature on deterrence, and test versions that emphasize the legitimacy of the sender, the credibility of the message, and the costliness of being suspended. We find that the act of warning a user of the potential consequences of their behavior can significantly reduce their hateful language for one week. We also find that warning messages that aim to appear legitimate in the eyes of the target user seem to be the most effective. In light of these findings, we consider the policy implications of platforms adopting a more aggressive approach to warning users that their accounts may be suspended as a tool for reducing hateful speech online.
Implementing CUDA Unified Memory in the PyTorch Framework

Popular deep learning frameworks like PyTorch utilize GPUs heavily for training, and suffer from out-of-memory (OOM) problems if memory is not managed properly. In this paper, we propose a modification that utilizes CUDA Unified Memory (UM) to expand GPU memory to the available host memory space so that practicality for the programmer can increase, and OOM memory errors will not result for any workload. We also pinpoint performance issues that result from our modifications to the framework, and outline future plans like reducing redundant memory copies, prefetching, and memory advising techniques to improve upon our design. Our implementation shows that PyTorch UM performance overheads are minimal when the data footprint is below GPU memory capacity.
Human-inspired strategies to solve complex joint tasks in multi agent systems

In this paper we propose a methodology to integrate human expertise with effective control laws to drive artificial agents in a complex joint task. We use Supervised Machine Learning to derive human-inspired strategies that succeed in task performance independently from the operating conditions of the samples provided in the training phase. Numerical simulations validate the efficiency of the proposed human-inspired strategies against simpler yet computationally expensive rule-based strategies.
Paradigms of Smart Education with IoT Approach

For the past few years, the role of education is to progressively expand its learning methods and learning environment. The incorporation of various technologies is another factor that intensifies the learning process. By using various sensing devices and Internet-connected devices, growth of students are enriched by continuous assessment and monitoring of their performance. Smart education opens many opportunities for everyone to achieve high standards and unique innovations. And IoT is the technology that pledges to upgrade the basic education system into smart education environment. The ability of IoT is to make objects interconnected and sensed data unavoidable in smart education. The application of IoT will improve the education process, so that students can learn expeditiously, effectively, and comfortably. This chapter encapsulates the basic concepts and definition of smart education with the incarnation of IoT devices.
Brexit and Political Personality: The Psychological Profiles of Boris Johnson and Jeremy Corbyn

This article focuses on the role of personality in the case of Brexit, the withdrawal of the United Kingdom from the European Union on January 31, 2020. A decisive step in Brexit were the general elections on December 12, 2019, in which Boris Johnson, Conservative Prime Minister of the UK and advocate of Brexit, defeated Jeremy Corbyn, Leader of the Labour Party and ‘neutral’ about Brexit. Political psychology considers personality as a major factor in political developments. This article presents the results of an assessment of the personalities of Johnson and Corbyn, and it argues that the personality of these key players influenced the Brexit process. The analysis is based on the theory and methods of Immelman, Immelman &amp; Steinberg, and Mastenbroek. Our findings suggest that Johnson’s charismatic personality, his dominant leadership style, and his negotiating skills contributed to his electoral victory and to the realization of Brexit. Johnson profited from Corbyn’s lack of personal charisma and from his undecisive and divisive leadership regarding Brexit.
Convolutional Neural Network (CNN) Extended Architectures for Photovoltaic Power Production Forecasting

The incorporation of PV technology in the energy mix of countries helps in bringing economic and environmental benefits. However, the operation of the power system may be fraught with many challenges owing to the randomicity of PV power output which is highly dependent on weather conditions. The development of highly accurate forecasting models for photovoltaic power generation is in great demand to ensure grid stability and high-quality electricity for end consumers. Motivated by recent development in deep learning models and their promising performance in different time series applications, two extended architectures of the convolutional neural network CNN were proposed for short-term predictions of a PV plant in this paper. The proposed models are utilized for one-day and two-days-ahead PV power forecasting. Two datasets were used a univariate one containing only power production records, and a multivariate dataset with weather variables and power output records. The performance of the proposed models is tested with a case study using an actual dataset collected from Rabat, Morocco. The values of the three error metrics, MAE, MAPE, and RMSE, show that the extended versions of the CNN model exhibit upper performance in terms of prediction accuracy and stability.
Sampling-based quasiprobability simulation for fault-tolerant quantum error correction on the surface codes under coherent noise

We propose a sampling-based simulation for fault-tolerant quantum error correction under coherent noise. A mixture of incoherent and coherent noise, possibly due to over-rotation, is decomposed into Clifford channels with a quasiprobability distribution. Then, an unbiased estimator of the logical error probability is constructed by sampling Clifford channels with an appropriate postprocessing. We characterize the sampling cost via the channel robustness and find that the proposed sampling-based method is feasible even for planar surface codes with relatively large code distances intractable for full state-vector simulations. As a demonstration, we simulate repetitive faulty syndrome measurements on the planar surface code of distance 5 with 81 qubits. We find that the coherent error increases the logical error rate. This is a practical application of the quasiprobability simulation for a meaningful task and would be useful to explore experimental quantum error correction on the near-term quantum devices.
Path planning of surface unmanned craft based on variable step-length sparse A-star algorithm

In order to further improve the speed of path planning for surface unmanned craft and the ability to find the shortest voyage, the article proposes a path planning method based on variable step sparse A* algorithm. By analyzing the existing shortcomings of the three path planning methods of Dijkstra algorithm, A* algorithm, and sparse A* algorithm, the cost function, search space, and exploration step length of the traditional A* algorithm are improved to reduce the complexity of the algorithm. Carry out the simulation experiment of ships entering the port and berth, and carry out the navigation simulation experiment on the rasterized chart of a certain sea area of Zhoushan Islands. During the path exploration, a dangerous avoidance zone of 1 nautical mile is generated around all obstacles to ensure the authenticity of the experiment. safety. The results show that the variable-step sparse A* algorithm concisely and efficiently realizes the path planning of complex waters, shortens the search time by 80% compared with the A* algorithm, and has a shorter path and better robustness.
Unmasking the imposter: Do fake hotel reviewers show their faces in profile pictures?

• Fake reviews can be identified through examining users' profile pictures. • Absence of profile picture is more likely in fake reviews. • Absence of face is more likely in profile picture of fake reviews.
Gaze-Based Biometrics From Free Observation of Moving Elements

The recent COVID-19 outbreak has highlighted the importance of contactless authentication methods, such as those based on eye or gaze features. These techniques have the advantage that they can also be used by people wearing mouth and nose masks, which would make traditional face recognition approaches difficult to apply. Moreover, they can be used in addition to traditional authentication solutions, such as those based on passwords or PINs. In this work, we propose a study on gaze-based soft biometrics exploiting simple animations as visual stimuli. Specifically, we consider four animations in which small squares move according to different patterns and trajectories. No preliminary calibration of the eye tracking device is required. The collected data were analyzed using machine learning algorithms for both identification and verification tasks. The obtained results are particularly interesting in the verification case, that is the natural application of a soft biometric system, with accuracy scores always higher than 80% and Equal Error Rate (EER) values often lower than 10%.
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering

Video Question Answering (VideoQA), aiming to correctly answer a given question based on understanding multimodal video content, is challenging due to the richness of the video content. From the perspective of video understanding, a complete VideoQA framework needs to understand the video content at different semantic levels and flexibly integrate diverse video content to distill question-related content. To this end, we propose a Lightweight Visual-Linguistic Reasoning framework named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{LiVLR}$</tex-math></inline-formula> . Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{LiVLR}$</tex-math></inline-formula> first utilizes graph-based visual and linguistic encoders to obtain multi-grained visual and linguistic representations, respectively. Subsequently, the obtained representations are integrated with the devised Diversity-aware Visual-Linguistic Reasoning module ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{DaVL}$</tex-math></inline-formula> ). <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{DaVL}$</tex-math></inline-formula> distinguishes different types of representations with the learnable index embedding in graph embedding. Therefore, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{DaVL}$</tex-math></inline-formula> can flexibly adjust the importance of different representations when generating the question-related joint representation. The proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{LiVLR}$</tex-math></inline-formula> is lightweight and shows its performance advantage on three VideoQA benchmarks, MRSVTT-QA, KnowIT VQA, and TVQA. Extensive ablation studies demonstrate the effectiveness of the key components of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{LiVLR}$</tex-math></inline-formula> .
Blockchain Use Cases in Healthcare

The amount of health data created in the world is increasing exponentially every day. Existing technologies such as data storage, fast processing of stored data, and data security are still sufficient, but it is predicted that some problems will most likely occur. The positive effects of blockchain technology have attracted attention on issues such as ensuring that people avoid direct contact in the pandemic, saving time, facilitating international cooperation, and reducing paperwork. Blockchain technology allows for the elimination of an authority or a central server. This feature makes data processing processes incredibly fast. It allows patient health data to be stored regularly, transferred quickly, and easily anonymized when necessary. In this way, the needs of patients who continue their treatment at home or in the hospital can be determined in advance. The drugs to be used, the necessary medical devices, and the treatment to be applied can be determined quickly, and, if necessary, the product supply can be automatically procured in advance and made ready.
Self-Directed Learning using Eye-Tracking: A Comparison between Wearable Head-worn and Webcam-based Technologies

The COVID-19 pandemic has accelerated our transition to an online and self-directed learning environment.In an effort to design better e-learning materials, we investigated the effectiveness of collecting psychophysiological eye-tracking data from participants in response to visual stimuli.In particular, we focused on collecting fixation data since this is closely related to human attention.Current wearable devices allow the measurement of visual data unobtrusively and in real-time, leading to new applications in wearable technology.Despite their accuracy, head-mounted eye trackers are too expensive for deployment on large-scale deployment.Therefore, we developed a low-cost, webcam-based eye tracking solution and compared its performance with a commercial head-mounted eye tracker.Four-minute lecture slides on the 3 rd year electronic engineering course were presented as stimuli to eight learners for data collection.Their eye movement was collected within the pre-defined area of interest (AOI).Our results demonstrate that a low-cost webcambased eye-tracking solution, combined with machine learning algorithms, can achieve similar accuracy to the head-worn tracker.Based on these results, learners can use the eye tracker for attention guidance.Our work also demonstrates that these webcam-based eye trackers can be scaled up and used in large classrooms to provide real-time information to instructors regarding student attention and behaviour.