Reviewerly/exHarmony huggingface dataset:
TGV Upsampling: A Making-Up Operation for Semantic Segmentation

With the widespread use of deep learning methods, semantic segmentation has achieved great improvements in recent years. However, many researchers have pointed out that with multiple uses of convolution and pooling operations, great information loss would occur in the extraction processes. To solve this problem, various operations or network architectures have been suggested to make up for the loss of information. We observed a trend in many studies to design a network as a symmetric type, with both parts representing the "encoding" and "decoding" stages. By "upsampling" operations in the "decoding" stage, feature maps are constructed in a certain way that would more or less make up for the losses in previous layers. In this paper, we focus on upsampling operations, make a detailed analysis, and compare current methods used in several famous neural networks. We also combine the knowledge on image restoration and design a new upsampled layer (or operation) named the TGV upsampling algorithm. We successfully replaced upsampling layers in the previous research with our new method. We found that our model can better preserve detailed textures and edges of feature maps and can, on average, achieve 1.4-2.3% improved accuracy compared to the original models.
A Ranking Based Attention Approach for Visual Tracking

Correlation filters (CF) combined with pre-trained convolutional neural network (CNN) feature extractors have shown an admirable accuracy and speed in visual object tracking. However, existing CNN-CF based methods still suffer from the background interference and boundary effects, even when a cosine window is introduced. This paper proposes a ranking based or guided attention approach which can reduce background interference with only forward propagation. This ranking stores several convolution kernels and scores them. Subsequently, a convolutional Long Short Time Memory network (ConvLSTM) is used to update this ranking, which makes it more robust to the variation and occlusion. Moreover, a part-based multi-channel convolutional tracker is proposed to obtain the final response map. Our extensive experiments on established benchmark datasets show comparable performance against contemporary tracking approaches.
Semantic Matching Evaluation of User Responses to Electronics Questions in AutoTutor

Relatedness between user input and an ideal response is a salient feature required for proper functioning of an Intelligent Tutoring System (ITS) using natural language processing. Improper assessment of text input causes maladaptation in ITSs. Meta-assessment of user responses in ITSs can improve instruction efficacy and user satisfaction. Therefore, this paper evaluates the quality of semantic matching between user input and the expected response in AutoTutor, an ITS which holds a conversation with the user in natural language. AutoTutor's dialogue is driven by the AutoTutor Conversation Engine (ACE), which uses a combination of Latent Semantic Analysis (LSA) and Regular Expressions (RegEx) to assess user input. We assessed ACE via responses from 219 Amazon Mechanical Turk users, who answered 118 electronics questions broken into 5202 response pairings (n = 5202). These analyses explore the relationship between RegEx and LSA, agreement between the two judges, and agreement between human judges and ACE. Additionally, we calculated precision and recall. As expected, regular expressions and LSA had a moderate, positive relationship, and the agreement between ACE and human was fair, but slightly lower than agreement between human.
Quasilinear Time List-Decodable Codes for Space Bounded Channels

We consider codes for space bounded channels. This is a model for communication under noise that was studied by Guruswami and Smith (J. ACM 2016) and lies between the Shannon (random) and Hamming (adversarial) models. In this model, a channel is a space bounded procedure that reads the codeword in one pass, and modifies at most a p fraction of the bits of the codeword. Guruswami and Smith, and later work by Shaltiel and Silbak (RANDOM 2016), gave constructions of listdecodable codes with rate approaching 1 - H(p) against channels with space s = clog n, with encoding/decoding time poly(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> ) = poly(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sup> ). In this paper we show that for every constant 0 <; p <; and every sufficientl small constant ε > 0, there are codes with rate R ≥ 1 - H(p) - ε, list size poly(1/ε), and furthermore: . Our codes can handle channels with space s = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω(1)</sup> , which is much larger than O(log n) achieved by previous work. . We give encoding and decoding algorithms that run in time n · polylog(n). Previous work achieved large and unspecified poly(n) time (even for space s = 1 · log n channels). . We can handle space bounded channels that read the codeword in any order, whereas previous work considered channels that read the codeword in the standard order. Our construction builds on the machinery of Guruswami and Smith (with some key modifications) replacing some nonconstructive codes and pseudorandom objects (that are found in exponential time by brute force) with efficient explicit constructions. For this purpose we exploit recent results of Haramaty, Lee and Viola (SICOMP 2018) on pseudorandom properties of “t-wise independence + low weight noise” which we quantitatively improve using techniques by Forbes and Kelly (FOCS 2018). To make use of such distributions, we give new explicit constructions of binary linear codes that have dual distance of n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω(1)</sup> , and are also polynomial time list-decodable from relative distance á1/2-ε, with list size poly(1/ε). To the best of our knowledge, no such construction was previously known. Somewhat surprisingly, we show that Reed-Solomon codes with dimension k <; √n, have this property if interpreted as binary codes (in some specific interpretation)which we term: “Raw Reed-Solomon Codes”. A key idea is viewing Reed-Solomon codes as “bundles” of certain dualBCH codewords.
Achieving Efficient Similar Document Search over Encrypted Data on the Cloud

Cloud computing, which provides scalable computing resources at an economical rate, is more important than ever. More and more data owners are storing their data on the cloud. To protect private and sensitive data, user data should be encrypted by the data owner before it is outsourced to the cloud. However, this defeats the merits of cloud computing; the data needs to be decrypted and consumed on the client side. In this paper, we introduce a practical searchable encryption scheme which supports keyword search and similar document search, based on the Vector Space Model (VSM), by harnessing the power of homomorphic encryption (HE). HE is an encryption scheme where arithmetic calculations can be performed without decryption. We first build a term index tree to filter out irrelevant documents. Subsequently, we perform cosine similarity calculation upon search requests. Additionally, we propose a parallel version of this scheme, which can effectively utilize the power of the cloud. Experiments on real-world datasets indicate that our scheme can effectively provide practical keyword search based on VSMs in a cloud environment.
Water Evaporation Optimization Algorithm Based on Classification and Unbiased Search

The existing water evaporation optimization algorithm has some disadvantages, such as slow convergence speed, poor accuracy and so on. In order to accelerate WEO’s convergence and improve its accuracy, in this paper, we propose a water evaporation optimization algorithm based on classification and unbiased search (CUS-WEO). In this algorithm, different learning objects are selected for better and poorer individuals in monolayer evaporation phase, which can balance the algorithm’s exploration ability and exploitation ability. Meanwhile, the unbiased search information is taken as the base vector, and the number of disturbance terms and the search direction are increased in droplet evaporation phase, which can improve the convergence accuracy. To verify the performance of this algorithm, a series of experiments are carried out on 15 benchmark functions and compared with WEO algorithm. The results show that the proposed algorithm can obtain global optimal solutions with higher accuracy and speed up the convergence.
KOSI- Key Object Detection for Sentiment Insights

This paper explores an original approach of using computer vision, data mining and an expert system to facilitate marketers and other interested parties to take automated data-driven decisions with the use of actionable insights. The system uses a state-of-the-art algorithm to retrieves all the images of a desired Instagram user profile. Then, the data is passed through a combination of different convolutional neural networks for object detection and a rule-based translation system to determine the interests of this profile user. Further, using a separately trained convolutional neural network with an original dataset developed as part of this study, personality insights are derived. The results from the conducted experiments yield a satisfactory prediction of interests and not very promising results for the personality prediction.
The Design and Implementation of Automatic Electronic Water Pump Test System

According to the principle of electronic water pump operation, a high-performance sensor combined with digital measurement circuit and efficient data acquisition system is designed to design an automatic electronic water pump test system. The system uses STM32 as the main chip to build the hardware circuit, and based on Visual Studio 2010, the corresponding PC software is developed. By capturing the various indicators of the pump measured by the sensor and comparing the calculated theoretical values, the performance of the pump under test is judged. The test system has strong human-computer interaction, high degree of automation, fast test speed, high test accuracy, and good performance.
A Real-time 2-Dimentional Recovery Algorithm for Blurred Video Image Filmed by Charge-Coupled Devices in Environment Sensing for Driving Safety

Enhancing driving safety is always a thorny issue for transportation management department in any cities, for road traffic environment in most cases can be complex and full of uncertainties. As traffic accidents are always caused by moving disturbing objects (including people) which may not be perceived by vehicle drivers, those disturbing objects can usually be filmed as blurred video images by certain equipments such as driving recorders. Only if those images be processed on time properly by image processing techniques, can the real-time driving environment be sensed and can the drivers be helped. Till now, most related works have been reported to solve blurred video image recovery problem via processing it offline, and the only online approaches are somehow complex or not comprehensive in computational process for moving objects, and are not proposed for the very purpose of enhancing driving safety. In this paper, a real-time, 2-dimensional recovery algorithm for blurred video image filmed by charge-coupled devices (CCD) in driving environment sensing with high computational efficiency has been proposed, the algorithm has been described in detail via mathematical derivation, and a case study has been done to verify the effectiveness of it.
Corpus In Flux: The Sonic Persona, Its Affordances &amp; The Layers of an Institution

Abstract What happens, when a sonic persona enters the museum? This contribution takes its start with the “Sonic Affordances” that predetermine any sonic experiences of a visitor in a museum, including three apparatuses that serve as major constraints: a) the historical usage, b) the contemporary technical installments and c) the security apparatuses. The second section, “The Disassembling of the Lectern,” explicates the ongoing cultural and technological transformations regarding a structural democraticization and dehierachisation taking place and the consequences regarding the forementioned constraints of the museum. In the final section, “In A Corporeal Flux,” this contribution outlines a possible utopia for curators and museums: how could exhibitions and museum presentations be organized and staged around and through sound and performativity in the near future?
Fast low-dose Computed Tomography image Super-Resolution Reconstruction via Sparse coding and Random Forests

X-ray radiation is harmful to human health. Therefore, how to obtain a better quality reconstructed image with low dose scan is a major challenge in the field of computed tomography (CT). This paper proposes a fast low-dose CT super-resolution method based on sparse coding and random forests. By using high-resolution training images and low-resolution training images to obtain high-resolution dictionaries and using back-projection to ensure global consistency, and finally using sparse coding to extract and fuse useful information in low-dose CT images, random forests complete classification. The experimental results show that compared with the dictionary learning method and the traditional interpolation method, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of CT images obtained by this method are the highest, and the reconstructed images are the most robust and have a fast running speed and training speed.
Hierarchical Semantic Loss and Confidence Estimator for Visual-Semantic Embedding-Based Zero-Shot Learning

Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize unseen-classes that are not used in training, is gaining research interest. One approach to zero-shot learning is to embed visual data such as images and rich semantic data related to text labels of visual data into a common vector space to perform zero-shot cross-modal retrieval on newly input unseen-class data. This paper proposes a hierarchical semantic loss and confidence estimator to more efficiently perform zero-shot learning on visual data. Hierarchical semantic loss improves learning efficiency by using hierarchical knowledge in selecting a negative sample of triplet loss, and the confidence estimator estimates the confidence score to determine whether it is seen-class or unseen-class. These methodologies improve the performance of zero-shot learning by adjusting distances from a semantic vector to visual vector when performing zero-shot cross-modal retrieval. Experimental results show that the proposed method can improve the performance of zero-shot learning in terms of hit@k accuracy.
Improving the Designs of ESOP-Based Reversible Circuits

Finding ways to transform traditional circuits to energy efficient designs have found immense interest in present day's design industry and the quest of attaining low power consuming design techniques breeds the concept of reversible circuit design. One such computing paradigm that enforces reversibility in design architecture is quantum computation. Since a couple of years, several researches are going to make the reversible designs improved further and one such way of making reversible circuit efficient is to reduce the cost of the design. Aiming to build cost-efficient architectures, here in the work, we develop an improved synthesis approach for reversible circuit. The synthesis process has two phases. In the first phase, we present a best neighbour based circuit design scheme, where the functional outputs share the common data with their immediate neighbors and form shared designs. In the next phase, a circuit optimization algorithm runs and the circuits generated in previous phase pass through it for possible improvements in the design. The developed technique has been tested over different benchmark functions and the experiments show that the synthesis process followed by the optimization scheme substantially improves the design cost. To this extent, a comparative study with related existing techniques is undertaken and a brief analysis over the design approach is summarized at the end of the work.
Correlation tracking via robust region proposals

Recently, correlation filter-based trackers have received extensive researching interest because of their simplicity and superior running speed. However, such trackers perform poorly when the target undergoes occlusion, viewpoint change, or other challenging attributes due to pre-defined sampling strategy. To tackle these issues, the authors propose an adaptive region proposal strategy to facilitate object tracking. To be more specific, a novel tracking monitoring indicator is advocated to forecast tracking failure. Afterwards, detection and scale proposals were incorporated, respectively, to recover from model drift as well as handle aspect ratio variation. The algorithm was tested on several challenging video sequences, which demonstrates that the proposed algorithm outperforms other state-of-the-art trackers.
SPL: Exploiting Unlabeled Data for Multi-label Image Classification

The utilization of the unlabeled data provides a beneficial attempt for improving the generalization ability of the convolutional neural network (CNN) model, just as what is applied in person re-identification task. Different from that, multi-label image classification aims to predict multiple labels for each given image. The unlabeled data should be properly assigned multiple labels for regularizing the training process of CNN model. To make full use of the unlabeled data, this paper proposes a soft pseudo labeling (SPL) method for multi-label image classification. Specifically, the unlabeled samples are first generated by DCGAN and WGAN-GP. Then, the virtual multiple labels of the generated unlabeled samples are assigned based on an initial confidence value by SoftMax function. Finally, both the generated samples and original training samples are fed into the network as input, in order to learn a CNN model with stronger generalization ability. On three public multi-label image classification datasets (i.e., WIDER-Attribute, NUS-WIDE and MS-COCO), SPL provides a stable improvement over the baseline and produces a competitive performance compared with some existing multi-label image classification methods.
Towards a Precise Calculation of the Total Intended Duration of Luciano Berio’s <i>Sequenza VII</i> for Solo Oboe

The score of Luciano Berio’s Sequenza VII for solo oboe exhibits a strict and definite temporal space, yet most performers do not manage to perform the work within the prescribed time. The score is presented as a matrix with thirteen lines and columns and each line undergoes a temporal compression. The impetus of this practitioner-based study was driven by a need to find a method of performing the unusual time increments and to be as close as possible to the composer’s intent regarding the temporal grid. The aim of this study was to investigate and demonstrate a method of calculating and performing the intended duration of Sequenza VII so that the performance realises the absolute duration intended by Berio. Two methods were used to calculate the total duration of the composition. The result of the precise calculation of the predetermined total duration of Sequenza VII is six minutes and thirty seconds, and therefore refines the results of previous explorations of this subject by other scholars.
Shuffle Single Shot Detector

Real-time object detection is of great significance to embedded mobile platforms. We propose a lightweight object detection network for embedded devices, which we call ShuffleSSD. The ShuffleNet V2 network is used as the backbone, which can effectively reduce the size of the model. In the proposed lightweight detection model, a single shot multi-box detector is adopted and a receptive field block is integrated to obtain high-quality detection results. The evaluation is performed on the public object detection data set (PASCAL VOC) and compared to the most advanced real-time object detection network. The experimental results show that the proposed network has higher detection accuracy than MobileNet-SSD, with smaller network parameters. It is more suitable for real-time object detection on embedded devices.
Monitoring and Tracking ISIS on the Dark Web

Tracking Islamic State of Iraq and Syria (ISIS) on the Deep Web requires an understanding of Internet protocols and a close look at the Deep Web’s implementation of those technologies. ISIS uses the Surface Web, leveraging all the common platforms such as Twitter, FaceBook, and Instagram. The US government response to the ISIS threat involves numerous departments and agencies. Commonly referred to as the Internet or World Wide Web, the Surface Web is Internet content visible by a normal web browser, without any use of credentials, passwords, or specialized access. The Deep Web and Dark Web are two different things. The Dark Web, also called the Dark Net, is a subset of the Deep Web that uses a different set of protocols and software to access content. Encryption is a method of protecting data at rest or in transit.
A Novel ANN-Fuzzy Formulation Towards Evolution of Efficient Clustering Algorithm

Unsupervised learning based clustering methods are gaining importance in the field of data analytics, owing to the features they possess, such as high accuracy, simple implementation and fast computation, when compared with conventional supervised learning methods. Among several types of clustering techniques, those implying optimization routines are found to be more efficient. However, explosion in number of decision variables is making these algorithms computationally intensive. The authors present an efficient two-stage optimization based fuzzy clustering formulation, which works through variable reduction approach. The membership values associated with each data point, forming the majority of decision variables, are estimated under an artificial neural networks framework. The reduction in decision variables allows the implementation of evolutionary optimization solvers to solve the single objective constrained optimization problem of fuzzy clustering increasing the chance of finding global optima. Additionally, this formulation estimates the optimal network topology and optimal number of clusters, which is not estimated rather assumed by other formulations. The proposed algorithm has been implemented on three different test data sets and the efficacy of the novel approach has been demonstrated by comparing the obtained clustering results with that of conventional fuzzy clustering approach.