Reviewerly/exHarmony huggingface dataset:
ADNet: Adaptively Dense Convolutional Neural Networks

Convolutional neural networks (CNNs) have demonstrated great success in vision tasks. However, most existing architectures still suffer from low feature reuse efficiency. In this paper, we present a layer attention based Adaptively Dense Network (ADNet) by adaptively determining the reuse status of hierarchical preceding features. Specifically, a dense residual aggregation strategy is developed to fuse multi-level internal representations in an effective manner. Furthermore, a novel layer attention mechanism is proposed to explicitly model the interrelationship among layers to automatically adjust the density of the network. It is worth noting that existing ResNets and DenseNets are both special cases of our ADNet. Extensive experiments demonstrate that the proposed architecture consistently and indubitably achieves competitive results in accuracy on benchmark datasets (CIFAR10, CIFAR100, and SVHN), while at the same time remarkably reduces computational costs and memory space. Visualization and analysis on layer-wise attention further provide better understanding on the density of feature reuse in Deep Networks.
Extraction of Emotional Words and Construction of Emotional Dictionary Based on Syntactic Dependency and Point Mutual Information

This paper designs a method based on the dependency syntax and point mutual information to extract the field emotional words. Taking the topic of the ASEAN field as an example, based on this method, the emotional dictionary of the field is automatically constructed. Since the “One Belt, One Road” initiative was put forward, ASEAN has become more and more close to China and has penetrated into all aspects of life. People are paying more and more attention to some policy measures related to China-ASEAN, including Chinese group. However, the existing open source sentiment dictionary lacks automatic expansion function, and due to the overseas Chinese language habits associated with accept bilingual education-the use of Chinese traditional language form, and use English grammar structure-lead to emotional dictionary construction is flawed, and the accuracy of emotional unit extraction is low. This paper uses the crawler system to crawl the comments on the ASEAN topic as a corpus, based on the syntax dependency, define the word pair recognition rules, extract the candidate sentiment word units, and calculate the PMI values of the candidate sentiment words and the known sentiment lexicon, and set the valve. Set the threshold and extract word pairs larger than the threshold, to determine the polarity of the emotional words, and build an ASEAN topic domain sentiment dictionary. The simulation results show that the accuracy, recall and F values of the sentiment words in this paper are higher than those of PMIIR and word2Vec. The Ngram-PMI algorithm is an effective method for extracting sentiment words, which can provide support for extracting specific sentiment words in multiple fields.
A Comparison Between Robust Image Encryption and Watermarking Methods for Digital Image Protection

Sharing digital image across networks is a common means of multimedia communication. However, these images are subject to all sorts of different attacks, like chosen plaintext attack, noise attack and, geometrical attacks. In order to resist these attacks, encryption and watermarking schemes are designed. This paper compares and analyzes the different encryption and watermarking methods.
Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios

State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances and semantic categories. Orthogonal to this, many real-life object detection applications, for example in remote sensing, instead require dealing with large images that contain only a few small objects of a single class, scattered heterogeneously across the space. In addition, they are often subject to strict computational constraints, such as limited battery capacity and computing power.To tackle these more practical scenarios, we propose a novel flexible detection scheme that efficiently adapts to variable object sizes and densities: We rely on a sequence of detection stages, each of which has the ability to predict groups of objects as well as individuals. Similar to a detection cascade, this multi-stage architecture spares computational effort by discarding large irrelevant regions of the image early during the detection process. The ability to group objects provides further computational and memory savings, as it allows working with lower image resolutions in early stages, where groups are more easily detected than individuals, as they are more salient. We report experimental results on two aerial image datasets, and show that the proposed method is as accurate yet computationally more efficient than standard single-shot detectors, consistently across three different backbone architectures.
Online For-Profit Colleges and Universities and the Evolving Role of Academic Librarianship

This presentation reports on a survey of academic librarians and their attitudes on several interrelated professional issues in the online for-profit college and university environment. Librarians from this sector offer a unique perspective on librarianship best practices for online educational delivery. They also present a clearheaded understanding on the impact of profit-based decision making on academic librarian values, responsibilities, and sense of professional duty. In addition to the survey results, this presentation discusses how academic librarianship has responded to the growing prevalence of virtual education delivery, and what roles the for-profit model might proffer for academic librarians going forward.
Analysis of a Virtual Optical Encryption Holographic System: Decrypted code using the multiple-bit virtual optical encryption holographic system based on the COMSOL Multiphysics

This paper presents a new method to encrypt holographic information utilize a virtual optical device where the laser wavelength λ and focal length of the lens f are main component parts of designing encryption keys. All bits that are obtained from light diffraction of a holographic material were simulated using COMSOL Multiphysics to evaluate the accuracy of the decrypted codes.
Open Information Extraction as Additional Source for Kazakh Ontology Generation

Nowadays, structured information that obtains from unstructured texts and Web context can be applied as an additional source of knowledge to create ontologies. In order to extract information from a text and represent it in the RDF-triplets format, we suggest using the Open Information Extraction model. Then we consider the adaptation of the model to fact extraction from unstructured texts in the Kazakh language. In our approach, we identify lexical units that name the participants of the action (the Subject and Object) and semantic relations between them based on words characteristics in a sentence. The model provides semantic functions of the action participants via logical-linguistic equations that express the relations of the grammatical and semantic characteristics of the words in a Kazakh sentence. Using the tag names and some syntactic characteristics of words in the Kazakh sentences as the values of the predicate variables in corresponding equations allows us to extract Subjects, Objects and Predicates of facts from texts of Web content. The experimental research dataset includes texts extracted from Kazakh bilingual news websites. The experiment shows that we can achieve the precision of facts extraction over 71% for Kazakh corpus.
Multi-Target Tracking and Segmentation Method for Missile-Borne Image Based on IoU Association

In order to improve the speed and accuracy of target tracking for missile-borne image, a method for detection, tracking and segmentation based on IoU correlation is proposed. By designing the anchor boxes on the multi-scale prediction branch, an enhanced YOLOv3 detection method is proposed, which improved the target detection capability. Combined with the mask branch, the tracking method based on Siammask achieved multi-target tracking and segmentation. The experimental results show that compared with SiameseNet and KCF, the tracking accuracy of the algorithm is improved by 19.5% and 24.5% respectively, and the running speed is up to 40fps, which meets the accuracy and real-time requirements of target tracking.
A Multi-Robot Path Planning Approach Based on Probabilistic Foam

Global path planning is one of the most important issues for multi-robot systems because it ensures the computation of feasible paths for the robots to perform safe movements inside an environment with obstacles. This approach typically uses the space topology to find paths for the robots in static environments. In this paper we present an off-line approach for global path planning based on Probabilistic Foam method (PFM) for a multi-robot system. PFM is a sampling-based path planner able to calculate obstacle-free paths with high clearance, using structures called bubble that tries to cover the free space. We propose some modifications in the original method in order to improve the area coverage strategy to guarantee that paths be found from different points in the space. Besides, an approach to compute bubbles using workspace information was implemented. Some experiments were performed using three wheeled-robots and a field of robot soccer as a test environment. Finally, we demonstrate that our approach was able to plan safe paths for all robots.
A Novel Two-Layer Decentralized Ledger Architecture for Internet of Things

A novel decentralized ledger architecture designed and optimized for running on Internet of Things (IoT) environment is presented, which is based on a 2-layered chain architecture, i.e. the upper layer called G-chain, which is a unified global chain and the lower layer called L-chain, which is separately running on each wireless local area network. The upper layer G-chain is responsible for running a permanent global ledger, more like the current mainstream blockchains, whereas the lower layer L-chain is operating a local, short term, adaptive light ledger which is optimized for running on the edge nodes in wireless environment. G-chain and L-chain form an integral chain together: L-chain records all the micro, short term transactions among edge nodes within a WLAN, periodically summarizes them into a macro transaction and records into G- chain. This is the first natively designed decentralized ledger architecture to our knowledge to solve the unique challenges of IoT environment. Simulation shows we can achieve million level TPS on micro transactions over L- chain.
Random Line Segment Distance Measurement Using Image Processing

Image processing has wide range of applications in Instrumentation and Measurement, robotics and automation. Random line segment detection and its distance measurement using image processing finds application in land area measurement. The proposed work focuses on random line detection and its distance measurement using image processing techniques. The proposed work has four parts viz; edge detection, line identification, smoothening of the identified line image and distance measurement of the identified lines. The results shows that all random line are identified using image processing techniques and further distance measurement has errors less than 4.5%. The proposed work forms the basis of application like land area measurement using image processing techniques.
Weakly Supervised Graph Convolutional Neural Network for Human Action Localization

Skeleton-based human action recognition from video sequences is currently an active topic of research. Conventionally, human action recognition is performed after conducting feature extraction on a given spatial-temporal representation of a human pose by using statistical methods or deep learning methods. The spatial and temporal features are globally evaluated by a classifier and used to determine which action is closest. However, the conventional methodology does not identify the temporal location of the action that determines the classification. To address this problem, we propose a skeleton-based human action recognition and localization method using weakly supervised graph convolutional neural networks, which are both spatially and temporally connected. In this method, human action localization is accomplished using time series data of human joint positions as input and then applying regression to find an expected value for each action at each time frame. Our weakly supervised training is based on multiple-instance learning inspired by deep ranking, and we devise a loss function so that high scores can be spontaneously learned for temporally important time frames. In this paper, we first explain the network architecture and then present a multiple-instance learning method for its optimization. In the experiment, we performed localization and classification of human actions by using this method and confirmed the temporal localization efficacy of the method.
Privacy Protection Algorithm for Frequent Itemset Mining

A differentially private frequent itemset mining algorithm DP-FMA is proposed for privacy protection of frequent itemset mining. The existing algorithms have great damage to the data. In order to solve this problem, DP-FMA is based on the real frequent itemset to mine the frequent itemset with noise, so that the support of frequent itemset will not decrease, and the availability of mining results will be improved. Aiming at the problem of inconsistency between the support with noise and the real support for mining in existing algorithms, a consistent constraint strategy is proposed, which makes the mining support with noise in descending order of integer and ultimately improves the accuracy of mining results. Finally, theoretical analysis is used to prove the differential privacy and availability of the algorithm. The experimental comparison proves the high availability and high accuracy of the algorithm.
Design of General Entropy-Constrained Successively Refinable Unrestricted Polar Quantizer

This paper presents an algorithm for the optimal design of general entropy-constrained successively refinable unrestricted polar quantizer, i.e., with arbitrary number L of refinement levels, for bivariate circularly symmetric sources. The optimization problem is formulated as the minimization of a weighted sum of distortions and entropies for the scenario where the magnitude quantizers' thresholds are confined to a predefined finite set. The proposed solution algorithm is globally optimal. It involves L stages, where each stage corresponds to an unrestricted polar quantizer (UPQ) level, and includes solving the minimum-weight path problem for multiple node pairs in a series of weighted directed acyclic graphs. Additionally, we derive an upper bound P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(l)</sup> , l ∈ [1 : L], on the possible number of phase levels in any phase quantizer of the l-th level UPQ, which grows linearly with l. The time complexity of the proposed approach is O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(l)</sup> ), where K is the cardinality of the predefined set of possible magnitude thresholds. Finally, the experimental results for L = 3 demonstrate the effectiveness in practice of the proposed scheme.
Janus - A Software-Defined Networking MPEG-DASH Video Streaming Load Balancer

Recently, with popularisation of video streaming service, new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. Even so, with the limitation of the TCP/IP network structure, the end user Quality of Experience (QoE) may be affected. One issue that can affect user QoE is the workload of content distribution servers. Thus, the unbalancing of server's workload comprising user's attendance can lead to a content server provider non optimised choice. This work presents two load-balancing solutions between MPEG-DASH video servers based on Software-Defined Networks, using as a balancing workload metric the throughput of the content server as well as the CPU load.
Data Visualization and Analysis of Agricultural Land Use System Employing GI

Using technology techniques like data visualization and GIS analysis, the design and construction of a model-driven decision support system assist local leaders to analyze the utilization of land in the determination of possible intervention to arbitrate problems faced by the community concerning agricultural land use. The study assesses the status of agricultural land in the province through visualization and analyses in a spatial geographic survey that determines the geographic physical information status of lands in the province of Catanduanes. The analyses of agriculture lands in the province shows that they are resistant to climate change, drought, and lack of irrigation facilities wanting in both quality and quantity necessary to alleviate their living conditions and the growing urbanization that converts lands into commercial land. The result of the study helps local leaders prioritize agriculture, revisit and make necessary policies and plan mechanism to safeguard agricultural land in the process of land conversion, utilization and maximization.
Quantum Random Number Generator based on Phase Diffusion in Lasers using an On-chip Tunable SOI Unbalanced Mach-Zehnder Interferometer (uMZI)

A 12.5Gb/s QRNG based on phase diffusion in gain switched lasers is demonstrated using a packaged on-chip SOI tunable unbalanced MZI achieving minimum entropy/bit of 5.04 for 8 bit sample passing all NIST randomness tests.
Computational Designing of Sonic Morphologies

Much electroacoustic music composition and sound art, and the commentary that surrounds them, is locked into a materialist sound-object mindset in which the hierarchical organisation of sonic events, especially those developed through abstraction, are considered antithetical to sounds ‘being themselves’. This article argues that musical sounds are not just material objects, and that musical notations, on paper or in computer code, are not just symbolic abstractions, but instructions for embodied actions. When notation is employed computationally to control resonance and gestural actuators at multiple acoustic, psychoacoustic and conceptual levels of music form, vibrant sonic morphologies may emerge from the quantum-like boundaries between them. In order to achieve that result, it is necessary to replace our primary focus of compositional attention from the Digital Audio Workstation sound transformation tools currently in vogue, with those that support algorithmic thinking at all levels of compositional design.
FPGA Implementation of Handwritten Number Recognition using Artificial Neural Network

Implementation of Deep Learning and Machine Learning Algorithms is always a challenge as they consume a lot of resources and power. In this paper, we have presented a very simple yet efficient way for creating an IP (intellectual property) core for Handwritten Number Recognition for FPGAs. The proposed ANN was verified and compared with several ANN networks on MATLAB, which gave the accuracy of about 99.38%. This network was implemented on Xilinx Zybo board XC7Z010CLG400-1. The total area covered by the IP block is 27.9%. The IP created is efficient and uses fewer resources thus suitable for other embedded applications.
Optimization of Acoustic Countermeasure Strategy for Convoy Ship Group

Torpedo defense is of great importance in naval operations. Acoustic countermeasure is an effective way to reduce torpedo threat for a ship group. The strategy optimization problem for the ship group defending against multiple batches of torpedoes are studied. After analyzing combat scenario, target characteristic model, ocean environment model, torpedo homing model and countermeasure model of convoy ship group are established. Then, genetic algorithm is used to optimize the strategy of acoustic countermeasure. Simulation result shows that from the perspective of the whole ship group, the countermeasures effectiveness of is obviously improved. The research achievement will be helpful for the acoustic countermeasure under ship group environment.
Satellite Image Enhancement and Restoration

Image received from remote sensors like satellites are of low in value of contrast which hides main records & quality offers carried by way of the picture. Hence, photo restoration is very important and must in the photo processing area to surface up all of the details as it should be in the natural and original images. The low in quality contrast satellite image recovery is essentially based on adaptive histogram equalization.
Ensembles of Cluster Validation Indices for Label Noise Filtering

Cluster validation measures are designed to find the partitioning that best fits the underlying data. In this study, we show that these measures can be used for identifying mislabeled instances or class outliers prior to training in supervised learning problems. We introduce an ensemble technique, entitled CVI-based Outlier Filtering, which identifies and eliminates mislabeled instances from the training set, and then builds a classification hypothesis from the set of remaining instances. Our approach assigns to each instance in the training set several cluster validation scores representing its potential of being a class outlier with respect to the clustering properties the used validation measures assess. In this respect, the proposed approach may be referred to a multi-criteria outlier filtering measure. In this work, we specifically study and evaluate valued-based ensembles of cluster validation indices. The added value of this approach in comparison to the logical and rank-based ensemble solutions are discussed and further demonstrated.
An Improved Stagewise Orthogonal Matching Pursuit-Recursive Algorithm for Power Quality Signal Reconstruction

An improved stagewise orthogonal matching pursuit (iStOMP) algorithm is proposed and used for power quality signal reconstruction in this paper. Firstly, the stagewise orthogonal matching pursuit (StOMP) algorithm is employed to selected atoms to form initial support set. Then the least square method is used to refine the atoms to form a new support set, and updates the residuals accordingly. The newly proposed iStOMP algorithm overcomes the shortcoming of insufficient reconstruction accuracy inherent in the StOMP algorithm and reduces the time consumption. It is because in the second selection the iStOMP algorithm eliminates some suboptimal solutions brought by the first selection and some redundant items in the last iteration. In addition, the iStOMP algorithm retains the characteristics of threshold selection of StOMP algorithm and thus has a strong ability of noise-resilient. Simulation results show that the proposed iStOMP algorithm has higher reconstruction accuracy, higher noise-resilient ability and less time consumption compared with both of the traditional Orthogonal Matching Pursuit-recursive (OMP) and StOMP algorithms. It is thus more suitable for complex power quality signal reconstruction.