Reviewerly/exHarmony huggingface dataset:
Once Again on Misinterpretations of Gödel’s Second Incompleteness Theorem

A response is given to the paper by A. M. Izmailova (Izmailova A. M. O kritike teoremy K. Gedelya o nepolnote A. V. Bessonovym [On A. V. Bessonov’s criticism of K. Gödel’s incompleteness theorem]. Studencheskii nauchnyi zhurnal “Grani nauki” [Student Scientific Journal "Facets of Science"], 2018, no. 1, p. 7-9. (in Russ.)) allegedly indicating a «serious error» in my analysis of K. Gödel’s second incompleteness theorem. It is shown that her criticism is based on gross logical errors, as well as on a misunderstanding of both the second incompleteness theorem and my results. Such a widespread misinterpretation is based on the inadmissible confusion of the proof of the consistency of formal arithmetic with the proof in it of a formula expressing its consistency. It is argued that Gödel's second theorem is not directly related to the proof of the consistency of formal arithmetic. It is proved that this theorem cannot be used in argumentation against feasibility of D. Hilbert’s finitistic program.
Black noise ruins: Notes after Locrian’s Rain of Ashes

In this article-cum-essay, I scrutinize the particulars surrounding Locrian’s release Rain of Ashes . The text opens with a reflection on quiddity and what I call curity (whyness) to situate the musicians aural potentialities, and move towards articulating what I define as ‘black noise’. In the context of black noise, I claim Locrian perform the ruins of black noise. I attempt to write alongside the music by replicating some of the music’s qualities and galvanize those qualities by using commentaries, reviews of the release and interviews with the band’s members while trying to analyse the push-and-pull aura of Rain of Ashes for the listener.
Introduction to Particle Swarm Optimization and Its Paradigms: A Bibliographic Survey

Particle swarm optimization (PSO) belongs to evolutionary computation algorithms that are inspired by the swarming motion of living organisms. PSO has a humble beginning where it was only able to solve the single-objective continuous optimization problems. Since then through numerous refinements and contribution voiding weakness and combining elements of other methods proposing modifications, hybridizations, velocity update rules, and population topologies, PSO has matured with immense scope of real-world applications. Though swarm stagnation and dynamic environment are still identified as significant challenges nevertheless, continued interest in PSO shows no sign of slowing. More than ten thousand contributions in the IEEE database alone after 25 years of the first appearance is a strong indicator that the mentioned challenges will come to pass. This chapter enlists and discusses in brief the major developments in PSO along with the area of successful application in a concise form.
Train unmanned driving algorithm based on reasoning and learning strategy

In this chapter, train unmanned driving algorithms based on the reasoning and learning strategy are introduced. Mainly including three aspects, namely the current status and technical progress of train unmanned controlling algorithm, the connotation and composition of train unmanned driving algorithm, and the calculation process and analysis of train unmanned driving algorithm. To comprehensively evaluate the unmanned train algorithm used, the positioning and navigation phase, the path planning phase, and the object detection phase are described. With the rapid development of artificial intelligence technology, more and more machine learning algorithms have been applied to unmanned driving applications. In the future development process, the train unmanned and automated degree will be greatly improved.
Event Classification from the Twitter Stream Using Hybrid Model

During natural disaster, user-generated social media is used for monitoring and analyzing valuable information to take appropriate actions. Tweets posted on twitter discussed everything from the tales of everyday life to the latest events and news. Detecting disaster-related tweets in the early stage can help in proper planning for rescue and relief operations and mitigate the damage. In this work, different disaster-related tweets such as earthquake, hurricane, flood, wildfire and general tweets related to car, sports and cricket are used to train a hybrid model of gated recurrent unit (GRU) and long short-term memory (LSTM) network to first classify tweets into the different classes, and then this trained system can be used to detect an event. Proposing this tweet classification from general tweets and events-related tweets from the twitter using the technique used in paper is different because no prior paper has used this task. For detecting events, if there is an unexpected rise in the volume of tweets of the particular event, that means there is a great chance of happening of the event. The proposed hybrid-based network can classify tweets with the F1-score of 0.99 which shows the effectiveness of the proposed system.
Diegetic Tool Management in a Virtual Reality Training Simulation

Researchers and developers have suggested that the use of diegetic interfaces can enhance users' sense of presence and immersion in virtual reality (VR) applications. While concepts of diegetic interfaces in VR are analogous to those seen on 2D displays, little work has considered how they might integrate with the movement-based controllers commonly used in consumer VR systems, to create higher fidelity interactions. In this paper we present a study (N = 58) in which we compare participants' experiences of diegetic and non-diegetic tool management interfaces, in a prototype VR crime scene investigation (CSI) training application. Contrary to expectations, we do not find evidence that participants' sense of presence is elevated when using the diegetic interface; however, we suggest that this may be due to reported higher levels of perceived workload, which can act to degrade user experience and engagement. We conclude by discussing the relationship between diegetic interface design and interaction fidelity, and highlighting trade-offs between fidelity, engagement, and learning outcomes in VR training applications.
Emotion Recognition for Romanian Language using Deep Learning with DL-CNN convolutional layers

In this paper, we used the SRoL (Voiced Sounds of the Romanian Language) emotional corpus for Romanian language. We used a Deep Learning Neural Network (DL-CNN) to automatically recognition four emotions: joy, sadness, fury and neutral tone. A 3-layer deep learning neural network is used (two autoencoders are associated to the first two hidden layers and the third layer is a softmax layer). The best results of the emotion recognition obtained is 84,48% with fine tunning and 74.95% without fine tunning. It was noted that the percentages of emotion recognition provided by the MFSC - mel-frequency spectral coefficients are better than those obtained by the MFCC - mel frequency cepstral coefficients.
Poster: An Accelerator for Fast Container-based Applications Deployment on the Edge

Containers are an emerging approach for application deployment on the edge, as they are modular, lightweight, and easy to use for development and maintenance. However, deploying containers in an edge computing environment brings new challenges: high latency links, limited resources, and user mobility. This work proposes a new edge deployment architecture that accelerates deployment and updates for edge applications. By overcoming the design limitations of current registries, the accelerator would reduce the deployment, start-up, and update times of container-based applications.
Automatic three‐dimensional detection and volume estimation of <scp>low‐grade</scp> gliomas

Abstract Brain tumor segmentation is routinely needed in diagnostic radiology. Automatic volume estimation has been used for diagnosis improvement and treatment process. This article presents a new approach for tumor volume estimation, using a variational level set method. The new adapted representation of intensity allows the method to be efficient in the region of interest identification, regardless the shape, size, and location of the tumor. For the estimation of an optimal bounding box, a fuzzy preference optimization model is used. The proposed approach is suitable for the zero level set initialization as well as for the reduction of the processing area, which ultimately speeds up the curve evolution. Moreover, tumor contours are determined using the hybrid level set technique, which combines the gradient and local phase information as an edge indicator term. Such an approach is robust to attenuation and intensity inhomogeneity. The proposed method is evaluated using a set of real and synthetic images. Our method achieved a performance of 96% accuracy, with an average execution time of 4.75 seconds. The proposed method is fast, accurate, and does not require training data or prior knowledge. With such experimental results, our approach outperforms 18 state‐of‐the‐art methods.
MagNet with Randomization Defense against Adversarial Examples

With the development of the neural networks, deep learning has achieved advanced performance in various tasks. In recent years, however, researchers have found that Convolutional Neural Networks (CNNs) were vulnerable to adversarial examples-adding imperceptible perturbations to the original images can cause misclassification. This paper proposes a new defense method against adversarial examples attack. Based on the defense strategy of the MagNet, we add two random layers: one is used to randomly adjust the image size and the other is used to randomly fill the image with zero to reduce the impact of adversarial attack. Our method provides the following advantages: 1)The framework does not rely on adversarial examples and its generation process. 2)The method is relatively easy and effective in practice. The experiment results show that this method not only can effectively defend the common attack but also the overall accuracy is 3% higher than MagNet.
Fingerspelling Recognition with Two-Steps Cascade Process of Spotting and Classification

In this paper, we propose a framework for fingerspelling recognition, based on the two-step cascade process of spotting and classification. This two-steps process is motivated by the human cognitive function in fingerspelling recognition. In the spotting process, an image sequence corresponding to certain fingerspelling is extracted from an input video by classifying the partial sequence into two fingerspelling categories and others. At this stage, how to deal with temporary dynamic information is a key point. The extracted fingerspelling is classified in the classification process. Here, the temporal dynamic information is not necessarily required. Rather, how to classify its static hand shape using the multi-view images is more important. In our framework, we employ temporal regularized canonical correlation analysis (TRCCA) for the spotting, considering it can effectively handle an image sequence’s temporal information. For the classification, we employ the orthogonal mutual subspace method (OMSM), since it can consider the information effectively from multi-view images to classify the hand shape fast and accurately. We demonstrate the effectiveness of our framework based on a complementary combination of TRCCA and OMSM compared to conventional methods on a private Japanese fingerspelling dataset.
Decentralized Subspace Projection for Asymmetric Sensor Networks

A large number of applications in Wireless Sensor Networks include projecting a vector of noisy observations onto a subspace dictated by prior information about the field being monitored. In general, accomplishing such a task in a centralized fashion, entails a large power consumption, congestion at certain nodes and suffers from robustness issues against possible node failures. Computing such projections in a decentralized fashion is an alternative solution that solves these issues. Recent works have shown that this task can be done via the so-called graph filters where only local inter-node communication is performed in a distributed manner using a graph shift operator. Most of the existing methods have focused on the design of graph filters for symmetric topologies to compute an exact subspace projection. However, in this paper, motivated by the asymmetric communications in Wireless Sensor Networks, we analyze the design of graph shift operators to perform decentralized subspace projection for asymmetric topologies. we first characterize the existence of solutions and then we present a convex optimization problem that considers also the efficiency of the graph filtering, with an ADMM-based solver.
Jointly Optimize Positive and Negative Saliencies for Black Box Classifiers

Neural networks are increasingly applied to high-stakes tasks, such as autonomous driving and medical applications. For these tasks, it is important to explain the contributions of data components to a model prediction. In recent times, mask-based methods have been proposed for visual explanations. They optimize masks that maximally affect the model output. In this work, we propose a novel mask-based saliency method for given black box classifiers. We jointly optimize positive and negative masks to achieve a faithful feature importance map. To optimize them effectively, we define a distance between them in terms of the selected activation maps; the corresponding kernels express the important features of an input image. Then, we impose the distance to the objective function of each mask as a regularizer. By forcing both masks to be dissimilar in terms of the influential features, they can focus on essential parts of an object while alleviating noises.
Hardness of Motion Planning with Obstacle Uncertainty in Two Dimensions

We consider the problem of motion planning in the presence of uncertain obstacles, modeled as polytopes with Gaussian-distributed faces (PGDFs). A number of practical algorithms exist for motion planning in the presence of known obstacles by constructing a graph in configuration space, then efficiently searching the graph to find a collision-free path. We show that such an exact algorithm is unlikely to be practical in the domain with uncertain obstacles. In particular, we show that safe 2D motion planning among PGDF obstacles is [Formula: see text]-hard with respect to the number of obstacles, and remains [Formula: see text]-hard after being restricted to a graph. Our reduction is based on a path encoding of MAXQHORNSAT and uses the risk of collision with an obstacle to encode variable assignments and literal satisfactions. This implies that, unlike in the known case, planning under uncertainty is hard, even when given a graph containing the solution. We further show by reduction from [Formula: see text]-SAT that both safe 3D motion planning among PGDF obstacles and the related minimum constraint removal problem remain [Formula: see text]-hard even when restricted to cases where each obstacle overlaps with at most a constant number of other obstacles.
Learning Transition Models with Time-delayed Causal Relations

This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data-efficiency and interpretability of model- based reinforcement learning (RL) techniques. The proposed algorithm initially predicts observations with the Markov assumption, and incrementally introduces new hidden variables to explain and reduce the stochasticity of the observations. The hidden variables are memory units that keep track of pertinent past events. Such events are systematically identified by their information gains. The learned transition and reward models are then used for planning. Experiments on simulated and real robotic tasks show that this method significantly improves over current RL techniques.
CC-LSTM: Cross and Conditional Long-Short Time Memory for Video Captioning

Automatically generating natural language descriptions for in-the-wild videos is a challenging task. Most recent progress in this field has been made through the combination of Convolutional Neural Networks (CNNs) and Encoder-Decoder Recurrent Neural Networks (RNNs). However, existing Encoder-Decoder RNNs framework has difficulty in capturing a large number of long-range dependencies along with the increasing of the number of LSTM units. It brings a vast information loss and leads to poor performance for our task. To explore this problem, in this paper, we propose a novel framework, namely Cross and Conditional Long Short-Term Memory (CC-LSTM). It is composed of a novel Cross Long Short-Term Memory (Cr-LSTM) for the encoding module and Conditional Long Short-Term Memory (Co-LSTM) for the decoding module. In the encoding module, the Cr-LSTM encodes the visual input into a richly informative representation by a cross-input method. In the decoding module, the Co-LSTM feeds the visual features, which is based on generated sentence and contains the global information of the visual content, into the LSTM unit as an extra visual feature. For the work of video capturing, extensive experiments are conducted on two public datasets, i.e., MSVD and MSR-VTT. Along with visualizing the results and how our model works, these experiments quantitatively demonstrate the effectiveness of the proposed CC-LSTM on translating videos to sentences with rich semantics.
Iconic-Based Retrieval of Grocery Images via Siamese Neural Network

In this paper we investigate the problem of Grocery product recognition using iconic images. Iconic images are used to advertise products and they are very different from images that are captured in-store. We investigate the use of learned features for the retrieval process. We evaluated different feature extraction strategies using Convolutional Neural Networks (CNNs) and tested the CNNs on the Grocery Store image dataset that contains 81 product categories grouped into 43 coarse-grained classes and 3 macro classes. Results show that a Siamese network with a DenseNet-169 backbone better captures relations between iconic and in-store images outperforming other architectures in the retrieval task.
Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

Abstract In a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions ( X , Y , Z ). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X -axis, Z -axis, and Y -axis, respectively. For correction of the tilt around the Z -axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X -axis (pointing to the right direction), morphological operations, and tilt correction around the Y -axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X -, Z -, and Y -directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.
Deep Fuzzy Clustering with Weighted Intra-class Variance and Extended Mutual Information Regularization

Recently, many joint deep clustering methods, which simultaneously learn latent embedding and predict clustering assignments through deep neural network, have received a lot of attention. Among these methods, KL divergence based clustering framework is one of the most popular branches. However, the clustering performances of these methods depend on an additional auxiliary target distribution. In this paper, we build a novel deep fuzzy clustering (DFC) network to learn discriminative and balanced assignment without the need of any auxiliary distribution. Specifically, we design an elaborate fuzzy clustering layer (FCL) to estimate more discriminative assignments, and utilize weighted intra-class variance (WIV) as clustering objective function to enhance the compactness of the learned embedding. Moreover, we propose extended mutual information (EMI) between input data and the corresponding clustering assignments as a regularization to achieve “fair” but “firm” assignment. Extensive experiments conducted on several datasets illustrate the superiority of the proposed approach comparing to the state-of-the-art methods.
Implement Detail Enhancement Algorithm on FPGA for Real-Time and Energy-Efficient Embedded Systems

FPGA (Field Programmable Gate Array) is a platform that allows carrying out many digital signal processing designs with numerous benefits in terms of speed, energy consumption and flexibility. The first part of this paper introduces a method enhancing the contrast of an input image while simultaneously compressing the dynamic range of that one. The second part focuses on converting that algorithm into a digital design to run successfully on Xilinx FPGA. We also estimate the outcome on Zybo Z7 board of Digilent and achieve an impressive result with 120 fps for input/output images of resolution 640×512. In addition, fixed-point numbers operations offer a high precision in comparison with floating-point numbers in MATLAB, which brings high reliability in deployment.
TransRESCAL: A Dense Feature Model for Knowledge Graph Completion

Knowledge Graph Completion (KGC) is a hot-spot in artificial intelligence research. Dozens of hundreds of models are devised to this end. The translation-based and factorization- based approaches are very popular ones for their efficiency and simplicity. TransE and RESCAL are the canonical ones in both research lines. However, the simplicity also limits the expressivity of the model to achieve better performance. To improve the results, we couple the two approaches to form a more expressive one named TransRESCAL. It is capable of distinguishing head from tail entity embedding due to TransE has the ability to be capable of doing this. Therefore, the proposed model is capable of clarifying the fact that the position of head and tail entity embeddings in score function of factorization-based models. It is beneficial for further understanding the model for KGC task. Hence, the proposed model is not only capable of capturing structural feature by TransE but also semantic ones by RESCAL. It potentially captures denser features than every single model, which is a competitive alternative to the deep neural network (DNN)-based models for KGC tasks.