Reviewerly/exHarmony huggingface dataset:
HUDD: A tool to debug DNNs for safety analysis

We present HUDD, a tool that supports safety analysis practices for systems enabled by Deep Neural Networks (DNNs) by automatically identifying the root causes for DNN errors and retraining the DNN. HUDD stands for Heatmap-based Unsupervised Debugging of DNNs, it automatically clusters error-inducing images whose results are due to common subsets of DNN neurons. The intent is for the generated clusters to group error-inducing images having common characteristics, that is, having a common root cause.HUDD identifies root causes by applying a clustering algorithm to matrices (i.e., heatmaps) capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters. Our empirical evaluation with DNNs from the automotive domain have shown that HUDD automatically identifies all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches. A demo video of HUDD is available at https://youtu.be/drjVakP7jdU.
NaturalCC: An Open-Source Toolkit for Code Intelligence

We present NaturalCC, an eﬃcient and extensible open-source toolkit for machine-learning-based source code analysis (i.e., code intelligence). Using NaturalCC, researchers can conduct rapid prototyping, reproduce state-of-the-art models, and/or exercise their own algorithms. NaturalCC is built upon Fairseq and PyTorch, providing (1) a collection of code corpus with preprocessing scripts, (2) a modular and extensible framework that makes it easy to repro-duce and implement a code intelligence model, and (3) a benchmark of state-of-the-art models. Furthermore, we demonstrate the usability of our toolkit over a variety of tasks (e.g., code summarization, code retrieval, and code completion) through a graphical user interface. The website of this project is http://xcodemind.github.io, where the source code and demonstration video can be found.
EAGAN: Event‐based attention generative adversarial networks for optical flow and depth estimation

Event camera is a new vision sensor that produces independent asynchronous responses to each pixel's change of illumination intensity. The unique principle of event camera has many advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range (HDR). These advantages make event camera ideal for dealing with high speed, HDR visual tasks, especially in automatic driving scenes. In this study, we propose an image generation network named Event-based attention generative adversarial networks (EAGAN), which simultaneously deals with optical flow and depth estimation of monocular event camera data. In addition to the innovative network architecture and loss function suitable for depth estimation, we are also the first to process incomplete training data to obtain more dense and uniform prediction results. Experiments on the multi-vehicle stereo event camera dataset show that our EAGAN is competitive on the depth estimation task and achieves the state-of-the-art effect in the optical flow estimation task.
Lagrangian Regularized Twin Extreme Learning Machine for Supervised and Semi-Supervised Classification

Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine learning, the need to solve two quadratic programming problems (QPPs) for TELM has greatly limited its development. In this paper, we propose a novel TELM framework called Lagrangian regularized twin extreme learning machine (LRTELM). One significant advantage of our LRTELM over TELM is that the structural risk minimization principle is implemented by introducing the regularization term. Meanwhile, we consider the square of the l2-norm of the vector of slack variables instead of the usual l1-norm in order to make the objective functions strongly convex. Furthermore, a simple and fast iterative algorithm is designed for solving LRTELM, which only needs to iteratively solve a pair of linear equations in order to avoid solving two QPPs. Last, we extend LRTELM to semi-supervised learning by introducing manifold regularization to improve the performance of LRTELM when insufficient labeled samples are available, as well as to obtain a Lagrangian semi-supervised regularized twin extreme learning machine (Lap-LRTELM). Experimental results on most datasets show that the proposed LRTELM and Lap-LRTELM are competitive in terms of accuracy and efficiency compared to the state-of-the-art algorithms.
Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach

Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.
An RBF online learning scheme for non-stationary environments based on fuzzy means and Givens rotations

Learning on non-stationary environments is laden with many challenges, as the procedure is usually characterized by drifts and data unavailability; on the other hand, it is of great importance with regard to data stream modeling, where an effective and up-to date model is required as the data stream evolves. This work presents a new method for producing highly accurate models for learning on such environments. The method is based on artificial neural networks (ANNs) and more specifically on the efficient architecture of radial basis function (RBF) networks. A novel RBF online training scheme for real time adaption of both the network structure and parameter values is proposed based on the fuzzy means (FM) algorithm and the Givens rotations technique. Within this integrated framework, it is guaranteed that for each update of the network structure, the optimal values for the synaptic weights are calculated efficiently by maintaining low order matrix updates. The proposed approach is evaluated on 9 real-word and artificial benchmark data streams, including a challenging real-world application which involves precipitation prediction, and is compared to other well-known methodologies from the literature. The results show that the FM-Givens algorithm produces models with highly competitive online accuracy for non-stationary environments in the presence of drifts, while maintaining low model-updating times.
Sending-or-not-sending twin field quantum key distribution with imperfect vacuum sources

Abstract The sending-or-not-sending (SNS) protocol of the twin-field (TF) quantum key distribution (QKD) can tolerant large misalignment error and its key rate can exceed the linear bound of repeaterless QKD. The original SNS protocol and all variants of TF-QKD require perfect vacuum sources, but in the real world experiments there is no practical perfect vacuum source. Instead, experimenters use extremely weak sources to substitute vacuum sources, which may break the security of the protocol. Here we propose an SNS protocol with imperfect vacuum sources and give the non-asymptotic decoy-state analysis of this protocol. Our numerical simulation shows that when the imperfect vacuum sources are close to perfect vacuum sources, our protocol can obtain similar key rate as that with perfect vacuum sources. This is the first result that closes the potential security loophole due to imperfect vacuum of TF-QKD.
Foreign Capital Flows and Human Development in Developing Countries: Does Institutional Quality Matter?

Abstract The impact of foreign capital on human development has been at best ambiguous, while that of institutions is undoubtedly favorable. That said, the way foreign capital relates to human development may be affected by the quality of institutions. This paper assesses this very phenomenon in 65 developing countries over the time period 1984-2014. In this regard, this study incorporates three indicators of human development namely, per capita income (PCI), Secondary School Enrollment (SSE) and Life Expectancy (LE). Using two step system GMM estimation technique, we found that the impact of foreign capital varies with respect to the indicators of human development and the type of foreign capital being studied. Both FDI and FPI negatively affect per capita income and secondary school enrollment, while, remittances affect all the indicators of human development positively, except for life expectancy. The interaction between institutions and each type of foreign capital flow exerts a positive influence on all indicators of human development. However, this positive interaction fails to completely eliminate the adverse influence of the capital flows, which reflects inadequacy of existing institutional quality in developing countries and the need for institutional reforms.
Cyber Bullying Detection using Natural Language Processing (NLP) and Text Analytics

This final year project is a Twitter dependant application called Cyberbullying Detection using Natural Language Processing and Text Analytics which focused on handling cyberbullying by removing inappropriate tweet content and alert the parents, guardian, and counsellor about the case via email. The application is designed for the student with general IT knowledge. It was fully developed using the Python programming language. Additionally, a feature to update the white-listed username in real-time has been added to the application by the developer based on the feedback from testers. The developer believed that this application will surely help in protecting the mental development of the student by tackling the cyberbullying issues.
The current practice of telecytology for rapid on-site evaluation (ROSE): Practical considerations and limitations

Advances in digital imaging technology and development of high-speed internet has brought a change in ROSE practice from the traditional in-person to remote evaluation. The rapid expansion of image-guided procedures to obtain tissues for diagnosis and ancillary testing has put a huge demand on cytopathologists' time to perform on-site adequacy assessment. The technology of transmitting digital slide images in real-time via the internet from procedure site that can be viewed remotely and provide preliminary diagnosis, has had a huge impact on the practice of ROSE. Telecytology (TC) has increased the efficiency of cytopathologists, by cutting down on travel time to procedure sites and eliminate the long wait time between procedures/needle passes. It also provides the cytopathologist with the flexibility of covering ROSE procedures occurring at several locations simultaneously. The options and design of TC systems are driven by clinical needs, availability of resources and case volume. A buy-in from stakeholders early in the process, infrastructure planning and information technology (IT) support are critical for the successful implementation of TC. Training of staff, validation study and compliance training should be performed according to established guidelines. There are different TC platforms commercially available in the market today, these include static image sharing, real-time video streaming, robotic microscopy and whole slide imaging (WSI). Additionally, low-cost TC system can be built and designed using equipment that are available off-the-shelf. The intent of this review is to highlight the current practices of TC, the pros and cons of each system are discussed.
FPGA Implementation of Reversible LFSR with Primitive Polynomial using Verilog HDL

Linear Feedback Shift Register (LFSRs) based pseudo-random number generators (PRNGs) are used in test pattern generators (TPGs). Because FPGAs are reprogrammable, you can implement designs of LFSR on FPGA and simulate different lengths. It is important to test and validate the simulated results and synthesis results. The primitive polynomial determines the total number of pseudo-random output states of LFSRs. If the polynomial used is a primitive, the output random state is up to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> - 1 state. This paper presented the designed, developed, and implemented 4, 8, 16, and 32 bit reversible LFSRs in FPGAs by performing, analyzing, and investigating random behavior using the Verilog HDL code. The analysis is performed to find the design speed requirements in FPGA when the number of constant inputs, the number of garbage outputs, the number of reversible logic gates, and the increased number of bits. In this paper, shows a comparative study of 4, 8, 16, and 32 bit reversible LFSRs on FPGAs to understand their performance. According to the results, the proposed reversible LFSR design using the Pareek gate approach reduces power by 10% when compared to the irreversible approach. As a result, the proposed design is used to develop Built-In-Self-Test (BIST) module.
Temporal feature enhancement network with external memory for live-stream video object detection

This paper proposes a method exploiting temporal context with an attention mechanism for detecting objects in real-time in a live streaming video. Video object detection is challenging and essential in practical applications such as robotics, smartphones, and surveillance cameras. Although methods have been proposed to improve the accuracy or run-time speed by exploiting temporal information, the trade-off between them tends to be ignored. We thus focus on the trade-off between accuracy and speed, and propose a method to improve the accuracy by aggregating the past information from a lightweight feature extractor with an attention mechanism. Evaluations on the UA-DETRAC and ImageNet VID datasets demonstrate our model’s superior performance to state-of-the-art methods on live streaming real-time object detection.
Secondary Education Students’ Knowledge Gain and Scaffolding Needs in Mobile Outdoor Learning Settings

Science education enhances students’ scientific literacy in order to interact with the world responsibly and contribute to democratic and informed decision-making. The emergence of place-responsive pedagogy and mobile technology with a variety of affordances has refocused attention on students’ direct embodied experience. P-responsive pedagogy combined with mobile technologies provides numerous opportunities for investigating, across contexts, everyday socio-economical environmental problems inherent to a particular location. Forming an evidence-based decision on socio-economical environmental real-life problems requires a more in depth understanding of natural processes than just making use of everyday knowledge that is based on perceptions and direct observations. Therefore, this paper aims to explore the secondary education students’ (a) awareness and understanding about a timely socio-environmental challenge, (b) development of the scientific vocabulary, (c) scaffolding needs during the mobile outdoor collaborative inquiry-based learning event. To fulfill the aims, action research with an experimental technology-enhanced collaborative inquiry learning design was created to investigate students’ knowledge gain and scaffolding needs. Three interventions with a total of 68 secondary education students (age 14–15) were conducted. Both quantitative and qualitative data were collected and analyzed. The results demonstrate the change in students’ opinions about the complex socio-economical environmental challenge and transformation from everyday concepts to more scientific knowledge, and their need for conceptual and procedural scaffolding. This paper adds new insights on how to utilize non-gamified use of mobile technology to empower secondary students’ scientific literacy and understanding in authentic settings.
Adoption of Future Banking Using Biometric Technology in Automated Teller Machine (ATM)

The banking sector are improvising their services on regular basis but the implementation of biometrics in Automated Teller Machine has never been a solution as the information is lacking and the full potential of this mechanism has not been explored. Researchers highlighted about the biometric technology an in-depth study is done to identify the available types of biometric technology which lead and suits the idea of future banking. The major finding by reviewers leads to the high potential of biometric as security aspect in achieving an ideal future banking solution through a comparison of biometrics, where fingerprint biometrics has an average of above 50 percent on compatibility and the flexibility of the implementation and benefits compared to face image, iris recognition, voice recognition and vein pattern. Past reviews highlighted that another factor that determine that the facial and iris recognition can be a possibility or a substitute for fingerprint recognition is the complexity where attempts to steal this type of security solution is almost to impossible. Biometric technology is proven that it is a technology which able to enlighten any banks towards a desired future banking ideal. This paper will identify the adoption of biometric technology in Automated Teller Machine and its gaps in achieve the ideal future banking.
An Enhanced Deep Learning Model for Duplicate Question Detection on Quora Question pairs using Siamese LSTM

The question answering platform Quora has millions of users which increases the probability of questions asked with similar intent. One question may be structured in two different ways by two users, and answering similar questions repeatedly impacts user experience. Manual filtration of such questions is a tedious task, so Quora attempts to detect and remove these duplicate questions by using the Random Forest Model, which is not completely effective. As Quora contains question answers in the form of text data, different Natural Language Processing techniques are used to transform the text data into numerical vectors. In this research, the log loss metric acts as the primary metric to evaluate different models. The primary contribution is that the Siamese network is used to process two questions parallelly and find vectors representation of each question. The vectors computed by this method enables similarity detection which is more effective than existing models. GloVe word embedding is used to understand the semantic similarity between two questions. The random classifier is built as the base model and logistic regression, linear SVM and XGBoost model are used to reduce the log loss. Finally, a Siamese LSTM is proposed which reduces the loss dramatically.
High-Accuracy Contactless Detection of Eyes' Activities based on Short-Range Radar Sensing

In this paper, a contactless detection technique for eyes' blink and rotation is proposed by using a custom-designed 120 GHz radar sensing system. The experimental scene to test the movement of eyes is also given. Experiments have been performed and the results show that the proposed technique performs well in detecting and distinguishing the different movement situations of eyes, such as eyelids' blinking and eyeballs' rotation. The precise detection of the behavioral states of the eyes provides foundation for the application for health monitoring and other medical scenarios.
Uralic typology in the light of a new comprehensive dataset

Abstract This paper presents the Uralic Areal Typology Online (UraTyp 1.0), a typological dataset of 35 Uralic languages and a total of 360 features, mainly covering the levels of morphology, syntax, and phonology. The features belong to two different datasets: 195 features’ definitions originate from the Grambank (GB) database, developed for comparison of world language typology, whereas 165 features (UT) have been designed specifically to describe the typological variation within the Uralic language family. We present a series of analyses of the dataset demonstrating its scope and possibilities. The complete data set correctly identifies the main Uralic subgroups in a Principal Components Analysis, whereas GB data alone is insufficiently granular to detect this family-internal structure. Similar analyses limited to various typological subdomains also give variable results. A model-based admixture analysis identifies four distinct areas of historical interaction: Saami, Finnic, the Volga area and Ob-Ugric.
Automation of cleaning and ensembles for outliers detection in questionnaire data

This article is focused on the automatic detection of the corrupted or inappropriate responses in questionnaire data using unsupervised outliers detection. The questionnaire surveys are often used in psychology research to collect self-report data and their preprocessing takes a lot of manual effort. Unlike with numerical data where the distance-based outliers prevail, the records in questionnaires have to be assessed from various perspectives that do not relate so much. We identify the most frequent types of errors in questionnaires. For each of them, we suggest different outliers detection methods ranking the records with the usage of normalized scores. Considering the similarity between pairs of outlier scores (some are highly uncorrelated), we propose an ensemble based on the union of outliers detected by different methods. Our outlier detection framework consists of some well-known algorithms but we also propose novel approaches addressing the typical issues of questionnaires. The selected methods are based on distance, entropy, and probability. The experimental section describes the process of assembling the methods and selecting their parameters for the final model detecting significant outliers in the real-world HBSC dataset.