Reviewerly/exHarmony huggingface dataset:
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper we introduce new AutoML approaches motivated by our winning submission to the second ChaLearn AutoML challenge. We develop PoSH Auto-sklearn, which enables AutoML systems to work well on large datasets under rigid time limits using a new, simple and meta-feature-free meta-learning technique and employs a successful bandit strategy for budget allocation. However, PoSH Auto-sklearn introduces even more ways of running AutoML and might make it harder for users to set it up correctly. Therefore, we also go one step further and study the design space of AutoML itself, proposing a solution towards truly hands-free AutoML. Together, these changes give rise to the next generation of our AutoML system, Auto-sklearn 2.0 . We verify the improvements by these additions in a large experimental study on 39 AutoML benchmark datasets and conclude the paper by comparing to other popular AutoML frameworks and Auto-sklearn 1.0 , reducing the relative error by up to a factor of 4.5, and yielding a performance in 10 minutes that is substantially better than what Auto-sklearn 1.0 achieves within an hour.
Determining Relationship between Efficiency Criteria of Foreign Language Training of Students at University and Their Emotional Intelligence

The purpose of the study is to determine relationship between academic achievement (AA) as an important criterion of foreign language (FL) training efficiency and the main characteristics of emotional intelligence (EI) in higher education. The article considers relevance of determining this relationship, contingent upon emotional intelligence development in a modern student as a skill of the XXI century, which is in demand in the future and an integral part of efficient FL training at the university. Scientific novelty of the work lies in identifying relationship between the main characteristics of EI and students’ AA as the main criterion of FL training process efficiency, based on a review of the Russian and foreign literature. As a result, an attempt is made to analyse relationship between students’ AA as a decisive criterion of FL training efficiency and the main characteristics of EI.
Bias in Knowledge Graphs - An Empirical Study with Movie Recommendation and Different Language Editions of DBpedia.

Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.
Smartbuf: An Agile Memory Management for Shared-Memory Switches in Datacenters

Important datacenter applications generate extremely bursty traffic patterns and demand low latency tails as well as high throughput. Datacenter networks employ shallow-buffered, shared-memory switches to cut cost and to cope up with ever-increasing link speeds. End-to-end congestion control cannot react in time to handle bursty, short flows that dominate datacenter traffic and they incur buffer overflows, which cause long latency tails and degrade throughput. Therefore, there is a need for agile, switch-local mechanisms that quickly sense congestion and provision enough buffer space dynamically to avoid costly buffer overflows. We propose Smartbuf, an online learning algorithm that accurately predicts buffer requirement of each switch port before the onset of congestion. Our key novelty lies in fingerprinting bursts based on the gradient of queue length and using this information to provision just enough buffer space. Our preliminary evaluations show that our algorithm can predict buffer demands accurately within an average error margin of 6% and achieve an improvement in the 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile latency by a factor of 8x at high loads, while providing good fairness among ports.
Surprise: challenging design perceptions in immersive virtual reality environments? The case of designing a hospital project using a CAVE (Cave Automatic Virtual Environment)

Purpose The purpose of this paper is to examine how the use of immersive virtual reality (IVR) impacts on the surprise aspects of designing. Design/methodology/approach The empirical case is a new hospital in the UK wherein a CAVE (Cave Automatic Virtual Environment) type of an IVR environment was used performing six design review sessions during the bid preparation stage. Drawing from a former video-based study, the authors conducted follow-up discussions with the participants to access their perspectives on design surprises emerging from their engagement with the IVR. The study developed a reflective methodology, interviewing participants about their experiences of doing design in the immersive environment. Retrospective discussions were conducted in a data review format, through playing back video clips of the IVR design sessions and asking the participants to reflect on their IVR design experience and on design surprises emerging from their engagement with the IVR. Findings The findings indicate that IVRs, such as the CAVE, are not only enhancing existing understandings of design but also challenging the participants' understanding of the design as they experience the immersive version of it, provoking ruptures in current procedures and driving unanticipated changes to the design. Originality/value This qualitative study of surprise in design work using IVRs (for a real-life design project) brings new insights into emerging practices of designing using immersive technology, such as the CAVE.
Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks

When a computational system continuously learns from an ever-changing environment, it rapidly forgets its past experiences. This phenomenon is called catastrophic forgetting. While a line of studies has been proposed with respect to avoiding catastrophic forgetting, most of the methods are based on intuitive insights into the phenomenon, and their performances have been evaluated by numerical experiments using benchmark datasets. Therefore, in this study, we provide the theoretical framework for analyzing catastrophic forgetting by using teacher-student learning. Teacher-student learning is a framework in which we introduce two neural networks: one neural network is a target function in supervised learning, and the other is a learning neural network. To analyze continual learning in the teacher-student framework, we introduce the similarity of the input distribution and the input-output relationship of the target functions as the similarity of tasks. In this theoretical framework, we also provide a qualitative understanding of how a single-layer linear learning neural network forgets tasks. Based on the analysis, we find that the network can avoid catastrophic forgetting when the similarity among input distributions is small and that of the input-output relationship of the target functions is large. The analysis also suggests that a system often exhibits a characteristic phenomenon called overshoot, which means that even if the learning network has once undergone catastrophic forgetting, it is possible that the network may perform reasonably well after further learning of the current task.
A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

The most common type of cancer in women worldwide is the Breast Cancer. Breast cancer may be detected early using Mammograms, probably before it's spread. Recurrent breast cancer could occur months or years after initial treatment. The cancer could return within the same place because the original cancer (local recurrence), or it may spread to different areas of your body (distant recurrence). Early stage treatment is done not only to cure breast cancer however additionally facilitate in preventing its repetition/recurrence. Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback. The projected analysis can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms. Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.
Speaker Embeddings by Modeling Channel-Wise Correlations

Speaker embeddings extracted with deep 2D convolutional neural networks are typically modeled as projections of first and second order statistics of channel-frequency pairs onto a linear layer, using either average or attentive pooling along the time axis. In this paper we examine an alternative pooling method, where pairwise correlations between channels for given frequencies are used as statistics. The method is inspired by style-transfer methods in computer vision, where the style of an image, modeled by the matrix of channel-wise correlations, is transferred to another image, in order to produce a new image having the style of the first and the content of the second. By drawing analogies between image style and speaker characteristics, and between image content and phonetic sequence, we explore the use of such channel-wise correlations features to train a ResNet architecture in an end-to-end fashion. Our experiments on VoxCeleb demonstrate the effectiveness of the proposed pooling method in speaker recognition.
Positron Image Super-Resolution Using Generative Adversarial Networks

Positron images generated by positron non-destructive testing technology under rapid detection scenes such as low concentration dose, low exposure time and short imaging time, which have some problems like low-resolution and poor definition. These issues cannot be solved for the being time. Therefore, this paper pins hope on carrying out super-resolution processing on the low-resolution positron image, in order to generate images with high-resolution and clear details. The existing super-resolution algorithms which built on real world images, on the base of unfitted for the scene of super-resolution of positron images, it is easy to lose the details of the images and introduce noise textures. To make the generated super-resolution images more capable of restoring the features of low- resolution images, this research proposed a positron image super-resolution reconstruction method based on generative adversarial networks. In order to improve the input information utilization rate, long skip connections were added into the generator. In addition, the discriminant model, where composed of an image discriminator and a feature discriminator, can stimulate the generator to generate clearer super-resolution images which contain more details. In attempting to solve the problem of dataset matching, a special positron image super-resolution dataset is constructed for network application scenarios. In the adversarial training stage, perceptual similarity loss and adversarial loss are used to replace the traditional mean squared error loss to improve the images perception quality. Experimental results show that the proposed model can reconstruct low-resolution images by four times super-resolution in 0.16 seconds. The super-resolution images obtained are superior to other algorithms in visual effect, which have clearer detail structure and higher objective performance values. Hence this model can meet the requirements of rapid non-destructive testing of industrial parts.
A Study on Educational Transformation through Evolution of Online Learning in the times of COVID-19 in India

Educational institutions (schools, colleges, and universities) in India have always followed traditional modes of teaching and learning wherein the teachers impart the lectures and the students undertake the instructions and guidance in a face-to-face medium. But, with the advent of covid-19, educational institutions in India were left with no choice but to switch to online mediums for imparting instructions. The sudden outbreak of Covid-19 impacted the entire world in an unparalleled manner and had put us all to witness a never seen before deadly virus Corona Virus (SARS-CoV-2) that shook us all. World Health Organization recognized the devastating effects of Covid and declared it as a global pandemic. One of the major challenges faced by the institutions around the world was in continuing learning in traditional set-up; thus the educators around the world shifted to online modes of teaching and learning. This article incorporates the learning and significance of e-learning and Strengths, Weaknesses, and Opportunities of e-learning modes in the wake of a crisis situation. This article also additionally puts some light on the challenges faced by the institution with reference to imparting education through online learning modes.
Three-Qubit Implementation of Quantum Fourier Transform for Shor’s Algorithm

Quantum computers are capable of very fast computation as compared to the classical counterpart. Problems impossible for the classical computer are efficiently solved on a quantum computer. Shor's factoring algorithm (SFA) calculates the prime factors of a given number exponentially quicker than the available classical algorithm. The paper deals with a vivid explanation of the methodology and various other future possibilities related to the development of the SFA. The paper also emphasizes the three-qubit realization of the quantum Fourier transform on the IBM Q experience.
Proof-Carrying Data without Succinct Arguments.

Proof-carrying data (PCD) is a powerful cryptographic primitive that enables mutually distrustful parties to perform distributed computations that run indefinitely. Known approaches to construct PCD are based on succinct non-interactive arguments of knowledge (SNARKs) that have a succinct verifier or a succinct accumulation scheme.
SoK: Computer-Aided Cryptography.

Computer-aided cryptography is an active area of research that develops and applies formal, machine-checkable approaches to the design, analysis, and implementation of cryptography. We present a cross-cutting systematization of the computer-aided cryptography literature, focusing on three main areas: (i) design-level security (both symbolic security and computational security), (ii) functional correctness and efficiency, and (iii) implementation-level security (with a focus on digital side-channel resistance). In each area, we first clarify the role of computer-aided cryptography—how it can help and what the caveats are—in addressing current challenges. We next present a taxonomy of state-of-the-art tools, comparing their accuracy, scope, trustworthiness, and usability. Then, we highlight their main achievements, trade-offs, and research challenges. After covering the three main areas, we present two case studies. First, we study efforts in combining tools focused on different areas to consolidate the guarantees they can provide. Second, we distill the lessons learned from the computer-aided cryptography community’s involvement in the TLS 1.3 standardization effort. Finally, we conclude with recommendations to paper authors, tool developers, and standardization bodies moving forward.
Deep learning human action intention classification from natural eye movement patterns

As human life integrates further with machines, there is a greater need for intuitive human-machine interfaces. Gaze has for long been studied as a window into the human mind, with gaze control interfaces serving to manipulate a variety of systems from computers to drones. Present approaches do not rely on natural gaze cues, however, and use instead concepts such as dwell time or gaze cursors to capture the human command whilst avoiding the problem of Midas Touch. We present a deep learning approach to human object manipulation intention decoding solely based on natural gaze cues. We run data collection experiments with healthy right-handed adults (n=15, 11 males, 4 females), interacting with 6 different objects on a table, when cued with tasks requiring inspection and manipulation. All of them were involved in three-hour sessions with breaks where they were asked to complete visuomotor tasks. In total, around 14,000 individual trials were completed and recorded. This lead to a dataset of human motor and non-motor intentions coupled with high-frequency eye movement data, in the context of a dining table object manipulation scenario. We modelled the task as a time series classification problem and took inspiration from Natural Language Processing sentiment analysis models to design an architecture based on bidirectional LSTMs. Our model was trained and evaluated on our dataset using 5-fold cross-validation. Results show that we can decode human intention of manipulation as opposed to inspection, solely from natural gaze data, with 78.5% average accuracy (1.64% standard deviation). This shows the feasibility of natural gaze interfaces for human-machine interaction, particularly in the context of robotic systems seamlessly supporting their users with object manipulation in different settings, be that assistive for patients with movement impairments, or collaborative, in industrial or service robots.
Hash and Prediction-Error-Based Reversible Watermarking for Medical Images

In reversible watermarking (RW), the original cover image and the watermarked information are restored without any distortion. A secure RW technique using hybrid prediction and difference pair mapping along with the hashing algorithm is proposed in this paper. The four-stage imperceptible watermarking employed in this technique enlarges the payload capacity with low distortion in stego-image. The data authentication is used for protection of message integrity and validation of originator identification. Mean square error and peak signal-to-noise ratio are used for analyzing the robustness of the watermarked image. The simulation result shows that this method performs better than the other existing state-of-the-art methods.
A Recurrence Population-based Great Deluge Algorithm with Independent Quality Estimation for Feature Selection from Academician Data

Great deluge (GD) algorithm same as other metaheuristics can solve feature selection problem. The GD imitates that in a great deluge someone climbing a hill and attempt to progress in any direction that does not get his/her feet wet in the expectation of discovering a way up when the water Level rises. The drawbacks of GD are: 1) a local search, which may lead the algorithm toward a local optima and 2) a challenging estimation of quality of the final solution in solving most of the problems. In this paper, for the first issue, a population-based great deluge (popGD) algorithm with additional recurrence operation is proposed. This operation is an imitation of no progress of hill climber after a long time; the climber tries to move small steps even downward in hope of finding better way to climb. For the second problem, a technique with an automate alteration of the Level is proposed. The statistical analysis of the results from 25 test functions and 18 benchmark feature selection problems supports the ability of the method. Finally a real-world academician data are employed to perform feature selection and execute classification result with selected features.
Vehicle Counting Accuracy Improvement By Identity Sequences Detection Based on Yolov4 Deep Neural Networks

Models for vehicle detection, classification, and counting based on computer vision and artificial intelligence are constantly evolving. In this study, we present the Yolov4-based vehicle detection, classification, and counting model approach. The number of vehicles was calculated by generating the serial number of the identity of each vehicle. The object is detected and classified, marked by the display of bounding boxes, classes, and confidence scores. The system input is a video dataset that considers the camera position, light intensity, and vehicle traffic density. The method has counted the number of vehicles: cars, motorcycles, buses, and trucks. Evaluation of model performance is based on accuracy, precision, and total recall of the confusion matrix. The results of the dataset test and the calculation of the model performance parameters had obtained the best accuracy, precision. Total recall values when the model testing was carried out during the day where the camera position was at the height of 6 m and the loss of 500 was 83%, 93%, and 94%. Meanwhile, the lowest total accuracy, precision, and recall were obtained when the model was tested at night. The camera position was at the height of 1.5 m, and 900 losses were 68%, 77%, and 78%.
Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts

This work explores constituency parsing on automatically recognized transcripts of conversational speech. The neural parser is based on a sentence encoder that leverages word vectors contextualized with prosodic features, jointly learning prosodic feature extraction with parsing. We assess the utility of the prosody in parsing on imperfect transcripts, i.e. transcripts with automatic speech recognition (ASR) errors, by applying the parser in an N-best reranking framework. In experiments on Switchboard, we obtain 13-15% of the oracle N-best gain relative to parsing the 1-best ASR output, with insignificant impact on word recognition error rate. Prosody provides a significant part of the gain, and analyses suggest that it leads to more grammatical utterances via recovering function words.
A Framework of Cooperative UAV-UGV System for Target Tracking

This paper proposed a framework of cooperative UAV-UGV system. This system consists of three modules: UAV, UGV, and ground station console. It has the characteristics of high cohesion, low coupling, cross platform, and cross language. Compared with the prior methods which UAV-UGV cooperation system that only uses UAV to track UGV, our method is more effective and can give full play to the advantages of UAV and UGV. We use UAV visual tracking algorithm and coordinate transformation to get the target location, and use UGV to navigate to the target location to keep up with the target. The high mobility of UAV makes the target tracking range of the whole system larger, the robustness of UGV is good, and the tasks can be performed more precisely.