Reviewerly/exHarmony huggingface dataset:
A Learning Approach for Ill-Posed Optimisation Problems

Supervised learning can be thought of as finding a mapping between spaces of input and output vectors. In the case that the function to be learned is multi-valued (so that there are several correct output values for a given input) the problem becomes ill-posed, and many standard methods fail to find good solutions. However, optimisation problems based on multi-valued functions are relatively common. They include reverse robot kinematics, and the research field of AutoML – which is becoming increasingly popular – where one seeks to establish optimal hyperparameters for a learning algorithm for a particular problem based on loss function values for trained networks, or to reuse training from previous networks. We present an analysis of this problem, together with an approach based on k-nearest neighbours, which we demonstrate on a set of simple examples, including two application areas of interest.
Almawave-SLU: A New Dataset for SLU in Italian.

The widespread use of conversational and question answering systems made it necessary to improve the performances of speaker intent detection and understanding of related semantic slots, i.e., Spoken Language Understanding (SLU). Often, these tasks are approached with supervised learning methods, which needs considerable labeled datasets. This paper presents the first Italian dataset for SLU. It is derived through a semi-automatic procedure and is used as a benchmark of various open source and commercial systems.
Semantic Segmentation of Street Scenes Using Disparity Information

In this work, we address the task of semantic segmentation in street scenes. Recent approaches based on convolutional neural networks have shown excellent results on several semantic segmentation benchmarks. Most of them, however, only exploit RGB information. Due to the development of stereo matching algorithms, disparity maps can be more easily acquired. Structural information encoded in disparity can be treated as supplementary information of RGB images, which is expected to boost performance. Therefore, in this work we propose to fuse disparity information in street scene understanding task. And we design four methods to incorporate disparity information into semantic segmentation framework. They are summation, multiplication, concatenation and channel concatenation. Besides, disparity map can be utilized as ground truth of a regression task, guiding the learning of semantic segmentation as a loss term. Comprehensive experiments on KITTI and Cityscapes datasets show that each method can achieve performance improvement. The experimental results validate the effectiveness of disparity information to street scene semantic segmentation tasks.
A Hybrid Statistics-based Channel Pruning Method for Deep CNNs*

In recent years, depth and width Convolution Neural Networks (CNNs) have achieved significant results in various fields of machine vision. To be honest, deploying the depth models to devices directly is Incredible. Reducing the width of the framework is an effective strategy to make the model more slender. In this paper, we propose a channel pruning method to simultaneously accelerate and compress deep CNNs while maintaining their accuracy. First, a pre-trained CNN model is evaluated layer-by-layer according to the hybrid statistics-based criterion. The negative-score channel and the corresponding filters are discarded. For the performance damaged by channel pruning, knowledge distillation is adopted to fine-tune to achieve stronger generalization ability. Experiments on the ILSVRC-12 benchmark demonstrate the effectiveness of our method when applying to the popular CNN framework. We achieve $3.79\times$ speed-up and $19.1\times$ compression baselines on VGG-16, $3.6\times$ acceleration and $5.5\times$ compression on Darknet (fully convolutional network), both with a little accuracy decrease. Even for modern networks like ResNet, we achieve $2.1\times$ speed-up and $2.2\times$ compression, suffering only 0.4% accuracy loss.
Research on Executive Control Strategy of Mimic Web Defense Gateway

Resource Scheduling Strategy on heterogeneous executives, which is the “real” service provider, is studied in this paper based on Mimic Defense Theory. Since the redundancy and heterogeneity of executives, their response times differ. In order to mitigate the Bucket Effect on the response time of heterogeneous executives in the existing mimic defense methods, this paper proposes a method based on feedback control model that executive source allocation is adjusted dynamically to improve the response time of the executive set. The comparative experiment shows that this scheduling strategy has a great improvement in response process time, comparing with the existing scheduling strategy.
Picking Particles in Cryo-EM Micrographs without Knowing the Particle Size

Picking particles in cryo-electron microscopy images, or micrographs, is a crucial first step in reconstruction of high resolution 3D structures. In this paper, motivated by a state-of-the-art object detection model, we proposed a deep learning based automatic particle pick model. Existing models usually fail to pick particles on a dataset (target domain) if they were trained on another dataset (source domain) when the particle sizes are significant different between source and target domains. We proposed diverse size data augmentation to solve this problem. Furthermore, we use the prior knowledge that the particle sizes should be similar within one micrograph as an additional loss. Once trained, the proposed model can pick particles without knowledge of the particle size. Compared with two state-of-the-art deep learning based particle picking models, our proposed model significantly outperformed on cross domain settings, while comparable on single domain settings. Furthermore, the proposed model is much faster than comparison models.
Bias and Precision in Instructor Grading of Concept Inventories in Geotechnical Engineering Courses

Abstract An assessment of bias and precision in instructor grading was undertaken at several private and public institutions with civil engineering programs. At five institutions, undergraduate civil engineering majors completed a concept inventory at the conclusion of their first course in geotechnical engineering. The ten-question instrument focused on fundamental concepts in geotechnical engineering to assess students’ knowledge gained throughout the course. A random sample of ten surveys was collected from each of the five institutions, leading to a dataset of n = 50 concept inventories encompassing a breadth of student populations. A team of geotechnical engineering professors from nine institutions (including four institutions not included in the dataset) independently graded the 50 concept inventories, using an established solution to the instrument. The end result was a distribution of nine instructor scores for each question within the dataset of student responses. The objectives of this study were (1) to quantify instructor bias in grading concept inventories by examining whether there are differences between instructors' grading of their own students and instructors' grading of anonymous surveys, and (2) to quantify instructor precision (instructor-to-instructor variability) in grading. Statistical analyses were performed on the scores of the concept inventories to quantify the distributions of instructor grading within the distributions of student scores. In the context of an undergraduate geotechnical engineering course, this paper discusses the concept inventory, grading criteria, institutional contexts, statistical analyses, and suggestions for future research. Recommendations are provided for reducing bias and increasing precision in instructor grading in undergraduate civil engineering courses.
The Power of Deep Learning: Current Research and Future Trends

Deep learning, in general, is about multi layered neural networks copying the structure and intellectual procedure of the human mind. Rather than handcrafted features, it permits the procurement of knowledge straightforwardly from information. They relapse mind boggling target works in a nested system, where more complex forms with bigger receptive fields are estimated using less abstract ones. Deep learning additionally makes it conceivable to consider formal domain knowledge and supplant an extensive collection of traditional algorithmic methods with flexible differentiable modules. These all strengthen and empower deep learning and make it adaptable while establishing the connection between the input information and target yield. Research frontiers are presently moving toward the rest of the difficulties. This paper presents a complete overview about deep learning. It illustrates where did deep learning initiate from, what had been accomplished using deep learning, What research areas are currently being investigated via deep learning, and most importantly What are the challenges and open problems of deep learning - as those are the issues, once handled, will lead to achieve the general conscious Artificial Intelligence (AI). The purpose is to empower graduates, practitioners, researchers and fans toward a powerful cooperation in the field of deep learning.
Enhance Feature Representation of Dual Networks for Attribute Prediction

Traditional single branch CNN could not extract all the details of the input, which may lose some vital information, resulting in a decrease in recognition accuracy. In this paper, we propose a novel dual branch adversarial neural network named D-BANN. Inspired by adversarial learning, we drive parallel networks to extract complementary features and adopt a novel loss function to extend the application domain of the model. Moreover, we divide the network training procedure into multi-steps to alternatively optimize the loss functions. In order to evaluate the proposed method, we carry out comprehensive experiments on three attribute datasets. The results on facial attributes demonstrate that the proposed method can outperform other single task networks in face attribute recognition. Also, D-BANN achieves competitive results in two pedestrian datasets compared to the state-of-the-art multi-task methods. We visualize the D-BANN using Grad-CAM to verify the effectiveness of feature annotation.
Evaluation of the probability of correct positioning of the beacon and its motion parameters in passive search and rescue systems

Abstract In this article, the approach to evaluate the probability of estimating the range of the beacon with a given accuracy is proposed. To determine the range to the beacon, a set of Kalman filters is applied to the beacon bearing measurements according to the given hypotheses about its motion parameters. The calculation of the probability of the correct choice of a hypothesis is presented. The hypothesis with the minimum sum of squared residuals is considered without prediction errors, its expectation and standard deviation, respectively, are. The time, required to determine the range with the probability 0.9..0.95 is calculated according to the proposed method agrees with the results obtained by statistical analysis, performed over multiple data sets. The results of the simulation of the “frequency” graph are shown.
Fully Anonymous Blockchain Constructed Based on Aggregate Signature and Ring Signature

Aiming at the problem that transaction amount and the identity of the two parties can be analyzed and predicted to reveal the privacy of the user from the open and transparent transaction information in the traditional Bitcoin system, a fully anonymous blockchain system is constructed by combining the aggregate signature and the ring signature in this paper. The scheme combines the advantages of aggregate signature and ring signature, uses aggregate signature to protect the transaction amount privacy, uses ring signature to protect the address privacy of both parties, and at the same time compresses the transaction information size while protecting the privacy, saving a certain space overhead. Through comparing with the evaluation and analysis of various blockchain privacy protection schemes, it is concluded that the scheme takes both security and performance into account, that is, the privacy protection of transaction information is greatly improved when the reasonable overhead is increased.
Application of Artificial Intelligence Technology in Decision Support Software

Aiming at overcoming the limitations of artificial intelligence (AI) and decision support system (DSS), not expressing complex decision-making process, a scheme of decision support system based on data mining is proposed after introducing the definition. The method is based on the characteristics of decision support system of artificial intelligence technology, realizes the system platform with MATLAB programming tool in which the association rule algorithm based on decision tree is implemented, and gives the concrete structure. The experimental results show that the proposed scheme and implementation platform have good passability and can provide a good platform for further research on new data mining algorithms.The validity and practicability of the improved algorithm and platform are further verified multi-attribute decision making based on calyx confirmation population problem.
A Multiview Clustering Approach for Mining Authorial Affinities in Literary Texts

In this work, we investigate the use of multiview learning for the task of authorship attribution. The main goal of this task is to assign authors to texts whose authorship is unknown or disputed. It has gained substantial attention recently because of other applications such as plagiarism detection and forensic investigation. Although the problem is traditionally seen as a supervised learning task, recent works have advocated the use of unsupervised methods as an alternative. The main argument for such an approach is that, by placing a text with a disputed or unknown author in a cluster of works from another author or group of authors, the method is revealing authorial affinities due to stylistic similarities that may be better used by domain experts. Nonetheless, there is no consensus in the literature on what set of features should be used to determine these stylistic similarities. Since the nature of the features may vary drastically, e.g. word frequencies (lexical) versus part-of-speech tags (syntactic), we adopt an agnostic view on which is the best, and, instead, believe that each set of features provides relevant, if not complementary, perspectives on the writing styles of the authors. In this sense, we investigate the use of multiview unsupervised learning for the task of authorship attribution. We use a real-world traditional corpus in authorship attribution research to assess the performance of our approach. Our experiments with the corpus containing plays from different authors from the Shakespeare Era indicate significant improvement compared to the ordinary single-view clustering approach.
A Simulation Study on Performance of Decoders in Chronic Recording

In a chronic recording, the non-stationarity of neural signals is an inevitable problem which is one of the critical problems in neural decoding. In order to explore the effects of three main recording factors including signal noise ratio (SNR) decrease, recording channel loss and variation in neural preferred direction (PD) on the decoding accuracy separately and the adaptation ability of different decoders, a simulation study was applied to generate the neural signals under these conditions. Kalman Filter (KF), Unscented Kalman Filter (UKF) and Recurrent Neural Network (RNN) are used to decode the simulated signals. We found that RNN has the best performance in all conditions and that KF and UKF have no significant difference. Besides, as the changes of conditions accumulate, the performance of all three decoders will gradually decrease.
Using syntactical and logical forms to evaluate textual inference competence

At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators. We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators. Since the syntactic tasks can be implemented in different languages, we show a successful case of cross-lingual transfer learning between English and Portuguese.
Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation.

There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific scenario, and may underperform for scenarios they are not tailored to. To this end, this paper studies Versatile Domain Adaptation (VDA), where one method can handle several different DA scenarios without any modification. Towards this goal, a more general inductive bias other than the domain alignment should be explored. We delve into a missing piece of existing methods: class confusion, the tendency that a classifier confuses the predictions between the correct and ambiguous classes for target examples, which is common in different DA scenarios. We uncover that reducing such pairwise class confusion leads to significant transfer gains. With this insight, we propose a general loss function: Minimum Class Confusion (MCC). It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set, Partial-Set, Multi-Source, and Multi-Target DA, outperforming the state-of-the-art methods in these scenarios, especially on one of the largest and hardest datasets to date (7.3% on DomainNet). Its versatility is further justified by two scenarios proposed in this paper: Multi-Source Partial DA and Multi-Target Partial DA. In addition, it can also be used as a general regularizer that is orthogonal and complementary to a variety of existing DA methods, accelerating convergence and pushing these readily competitive methods to stronger ones. Code is available at this https URL.
Intelligent Recognition Method of Fuzzy Features of Surveillance Video Image Based on Big Data

In order to solve the problem of high error rate of fuzzy feature extraction in monitoring video image based on traditional fuzzy BP fuzzy feature extraction method, a fuzzy feature extraction algorithm for monitoring video image based on big data is proposed. The matched filtering method is used to reduce the noise of the monitored video image, the adaptive association rule learning algorithm is used to construct the fuzzy feature large data set, and the edge profile feature detection algorithm is used for feature decomposition. The key action point fusion method is used to enhance the fuzzy information of the monitored video image, and the region equivalent histogram of the monitored video image is extracted. The extracted gray histogram is input into the BP neural network fuzzy feature extractor, and the adaptive association rule learning algorithm is used to optimize the monitoring video image feature clustering in the hidden layer of the fuzzy feature extractor. The fuzzy feature extraction and optimization of batch monitoring video image is realized. The simulation results show that the algorithm has good accuracy, strong ability to resist inter-class attribute disturbance, and high recall rate of monitoring video image output. The time cost of fuzzy feature extraction for monitoring video image is small.
Music in the Air with Leap Motion Controller

Not many people know about the first electronic musical instrument - the theremin - and can play it. The idea of this instrument is very groundbreaking: it is played without physical contact with it and in the same way as we sing but by using hands in place of our vocal cords. In this paper we consider how to implement the theremin with a computer using very different physical principles of optical hand tracking and by adding advantages of visual interfaces. The goal of this research is to eventually fulfill the dream of the inventor to make the theremin a musical instrument for everyone and to prove that everyone can play music.
Development of MatLab Coding for Early Detection of Leukemia through Automated Analysis of RBCs

Background: Bone marrow biopsy has become an integral part of leukemia diagnosis and its treatment. Several advancements are being made towards the analysis of digital images of biopsy samples. Recently, the FDA approved the procedures of digital health. In tune with that, digital image analysis has become propelled. With the advent of high-throughput technologies, the scientific community focuses on the red blood cells (RBCs) for early detection of cancer, including leukemia. The reasons are due to their abundance in peripheral blood and hence, easily accessible compared to the bone marrow biopsy procedure. High magnification and high-resolution electron microscopy-based ultra-structural analysis of RBCs already proved the utility of the hypothesis about a decade ago. However, in clinical set-up, electron microscopy-based procedures are the major bottleneck in the implementation of early detection of leukemia. Algorithm-based computer vision may be suitable to overcome this limitation. Methods: An intensive search with PubMed and Google for early diagnosis of leukemia through RBC light microscopic images was made. For this search, the image processing algorithm for RBC was also made in PubMed, IEEE Xplorer and Google; and the latest developments are noted. To fill the existing gap, a user-friendly MatLab coding is developed for automated analysis of RBC images. Results: RBC images from both normal and leukemia were analyzed with the developed code. Each RBC cells were analyzed individually for each sample of normal and leukemia. Therefore, in the output cellular characteristics namely, radius, perimeter, area, convexity and solidity were represented in a quantitative manner. Comparison of mean values between normal and leukemia groups for corresponding variables showed statistical significance. In the test run, data of 82 RBC cells from single leukemia sample when compared with the data of pooled data of normal samples also showed significant difference (P&lt;0.05). Conclusion: Thus, the developed code successfully distinguishes between RBC cells of leukemia and normal. We hope that this RBC based developed code would be useful in identifying earlystage of leukemia in an individual patient; however, for this, ~100 RBC cells are needed to be analyzed. As the developed code is based on RBC based diagnosis therefore, may be applied to other cancers and if needed, further modification is possible due to availability of code. Thus, it is expected that the developed MatLab code may play a role in preventive oncology.