Reviewerly/exHarmony huggingface dataset:
Image Super-Resolution via Deep Feature Recalibration Network

Recent years have witnessed remarkable progress in convolutional neural network (CNN) based image super-solution (SR) methods. Existing methods tend to deepen the network by means of residual skip connections to achieve better performance. However, these methods are still hard to be applied in real-world applications due to the requirement of its heavy computation. In this paper, we propose a Deep Feature Recalibration Network (DFRN), which strives for efficiency yet effective networks. We divide the process of network nonlinear mapping into two steps: information integration and feature enhancement, and proposed two types of block models: Multi-Scale Information Integration Block (MSIIB) and Feature Recalibration Block (FRB). MSIIB integrates the representation of the input data in the network with different size of receptive fields. FRB enhances the information via obtaining the attention along two different dimensions (channel and plane space of feature maps) respectively. By combining MSIIB and FRB, we provide a more efficient and time-saving method for SISR. Experiments show that the proposed DFRN method outperforms state-of-the-art methods in terms of both objective evaluation metrics (PSNR, SSIM, and running speed) and subjective perception on the generated images.
An Interpreter for the Differently Abled using Haptic Feedback and Machine Learning

One of the most valuable blessings to humanity is the ability to see, hear, talk and respond according to the situation. However, some people are deprived of this, such as the differently-abled community who find it hard to communicate and hence they are permanently dependent on someone. To overcome this dependency and to bridge the communication barrier of such deprived, an interpreter can be used. This work proposes an Interpreter system which enables two-way communication between the common people and the differently-abled regardless of the kind of sensory disability the user has. The system implemented has two modules - an American Sign Language recognition module which uses a machine-learning algorithm to cater the people with vocal disability and a speech to haptic feedback conversion module which can be used for communicating to any kind of differently-abled. Both these modules can be integrated together as a single compact device to address the communication barrier between different combinations of disabled people.
Guided Reading to Develop the Grade IV Learners Reading Ability

The researchers noted reading difficulties of learners in Lagan Elementary School. This prompted the researchers to do an assessment of their reading ability based on word recognition, reading comprehension ability and reading speed. Only four out of the 24 pupils can read well. Eighty three percent (83%) of the pupils cannot read words and sentences well. Their speed in reading is slow and their reading comprehension is also poor. The study aimed to find out the effectiveness of guided reading in developing learners’ reading ability. Specifically, it determined the level of reading capability of the pupils before the implementation of the guided reading in terms of: a) word recognition b) comprehension, and speed. One-group pseudo-experimental method of research with pretest and posttest was used. Pretest was used to assess the reading ability of students. There were four series of reading activities used in the intervention. The tool used in the pretest and posttest was the Philippine Informal Reading Inventory (PHIL-IRI) of the Department of Education. The paired t-test was used to find the difference in the reading ability of learners. It was noted that there was significant improvement of the learner’s ability to recognize words, comprehend and improvement in their speed from slow to average and even fast. Therefore, it is recommended that continuous implementation of guided reading in the classroom will be done to improve reading abilities of pupils.
A Methodology of Determining Weight Ratios of Different Question Types Based on Back Propagation Neural Network

This paper introduces an efficient method of determining weight ratios of different question types in the processing of test paper generation based on Back Propagation Neural Network, which will be used in the theory part of vocational skills online tests, which are prepared for staff in power substations. There are 3 types of test questions in the test question bank. The processing of choosing proper ratio of 3 types of test questions for different staff of different skill levels is presented in this paper, which is the key point of generating test paper automatically and reasonably. The ratios gained from this methodology are based on the test results of previous candidates.
Load Manipulation by a Triangular Formation of Quadrotors

This work discusses a trajectory tracking task accomplished by a group of three Bebop 2 quadrotors working cooperatively to transport a load. Such unmanned aerial vehicles (UAVs) are considered as a triangular formation, and the load, a triangle-shaped structure, is attached to them through massless flexible cables. The reason to use three quadrotors is the gain in terms of the payload capability and the stability of the load, since it is attached to three vehicles, staying vertically aligned with the center of mass of the triangle correspondent to the formation. A formation controller, actually a kinematic controller applied to a virtual massless robot correspondent to the center of mass of the triangle, is adopted, which generates reference velocities for the three UAVs, whose movement generates the movement and reshaping of the triangular formation. Then, individual dynamic compensators are adopted for each UAV, whose input is the reference velocity delivered by the formation controller. Finally, experimental results are discussed, which validate the proposal.
Incentive Mechanism for Social Network Data Pricing under Privacy Preservation

Online social networks have become very important sources of personal data that contain preferences, tastes, interests and friendships of their users. The potential that these data may be exploited poses a rising concern over how privacy is protected by these social network platforms. As a consequence, users are starting to demand privacy protection and privacy compensation when their data are used. This situation begs the question: How to properly compensate social network users for the disclosure of their data hosted in the network while preserving their privacy? In this paper, we consider data trade between a large amount of privacy-aware social network users, and a data broker with a budget who wants to find out aggregate friendship information of this social network. We propose an incentive mechanism for pricing social network data and privacy preservation. We prove that the proposed mechanism satisfies many desirable properties, including incentive compatibility, individual rationality, budget balance, and node differential privacy. Further, the accuracy of the proposed mechanism is theoretically analysed and its effectiveness is validated by experiments on real-world datasets.
VH3D-LSFM: Video-Based Human 3D Pose Estimation with Long-Term and Short-Term Pose Fusion Mechanism

Following the success of 2D human pose estimation from a single image, a lot of work focus on video-based 3D human pose estimation by exploiting temporal information. In this scenario, several recent works have achieved significant advances via Temporal Convolution Network (TCN). However, the current TCN fashion suffers from lacking local coherence caused by excessive dependence on local frames and limited local dynamic range, failing to estimate poses correctly in real scenes, especially that with high-speed motions. To tackle this problem, we design a Long-term Bank to select and collect candidate key poses, and further provide a LSFM (Long-term and Short-term pose Fusion Mechanism) to integrate long-term pose information into the short-term convolution window, thus to enhance the temporal coherence of local neighbor frames. Experimental results and ablation studies demonstrate that the proposed approach significantly promotes the accuracy and robustness of the state-of-the-art method.
Cross-Boundary Cyber Education Design

This paper provides a cross-boundary process to guide colleges and universities creating undergraduate cyber curriculum or reviewing established programs. There is growing demand for academic institutions to help close the skills gap by developing cyber curriculum preparing students for careers in cybersecurity. The cross-boundary process in this paper builds on a multi-level, multi-discipline approach previously used to launch a new undergraduate cyber program. This expanded approach recommends evaluating advisory consortium feedback, master's degree programs, certifications, and internal considerations (e.g., faculty expertise). This paper is prepared by cyber faculty at a university offering both residential and online general education courses as well as undergraduate and master's degrees in cybersecurity. This paper further advances the call for discussion on the topic of cyber education.
Position and Orientation Detection of Insulators in Arbitrary Direction Based on YOLOv3

Although great progress has been made in using the method based on deep learning to detect insulators, the detection effect is still unsatisfactory due to the large aspect ratio and inclination of insulators. Therefore, we propose a method capable of detecting large aspect ratio and inclination of insulators, which uses YOLOv3 framework. Firstly, we use manual generation of the anchors to generate angular anchors and fit the label box as much as possible; then we modify the backbone to increase the attention of the detected objects with SE-block; finally, we add angular parameters in rectangular box regression to achieve inclined insulator detection. Compared with the traditional methods, our proposed method significantly improves the performance, and compared with the existing deep learning-based methods, the proposed can achieve real-time detection without degrading performance, and can achieve 82.9% accuracy on our dataset.
Sentiment Analysis using Cuckoo Search and Computational Intelligence

Sentiment analysis (SA) is a computational study focusing on analyzing an individual's attitude and beliefs based on textual information. Presently, it is considered to be the most popular field in business analytics reflecting customer's opinions by reviewing feedback on their products This unstructured data is collected and pre-processed to transform it into a usable form which involves removal of normalization, punctuation removal, and stop-word removal. It is followed by the tokenization of the text reflecting the feature extraction stage in the proposed methodology. Nature-inspired Cuckoo Search (CS) is implemented to optimize the feature data that is fed to Feed Forward Back Propagation Neural Network (FFBPNN) for classification of sentiment data to reflect positive, negative, and neutral reviews The performance of the proposed SA work is evaluated in terms of precision, recall, f-measure, and classification accuracy.
Augmented Reality Board Game to Favor Water Source's Preservation

We present an architecture to integrate augmented reality (AR) elements to a board game. Our serious board game is aimed at creating consciousness about water source's preservation in a rural community.
A Novel Association Approach to Generate Patterns for Multi-valued Data in Efficient Data Classification

The real-world applications increased demand for heterogeneous data classification for text, pictures, music, movies and medical data sets. The complexity of the learning class for an object that is associated with the set of values is a key issue for multi-value data sets. The present learning approaches are based on the features characteristics favouritism observed for similar sets of class values, but favouritism characteristics measured an object deviation value instead of association class. Such methods may be unfavourable for classification as each value is made up of specific features of characterization. However, few studies have tried to solved the problem through associating values learning over multi-value data sets. This paper presents a Multi-Value Association (MVA) Approach for efficient data classification based on associated pattern generation using binary multi-value association among the data. The objective of the proposal is to identify a Single-Class-Value (SCV) which can be most suitable along with the additional features patterns which can describe it most. To evaluate the efficiency we compare with some existing proposals using multi-value datasets which shows an improvisation.
Research and Application of Three-phase Power Flow Calculation in Distribution Network

Base on the description of standard distribution network expansion package of DCIM (Distribution Common Information Model) in IEC61968, the power flow calculation related equipment model of distribution network is established. The basic principle of power flow calculation based on Newton Raphson method is adopted, and the large-scale distributed power access to distribution network is taken into account, so as to realize the design of tide flow calculation algorithm. Based on the platform of distribution automation management system, the online three-phase power flow calculation program is developed, which is reliable and easy to operate, and fully meets the needs of the project, so as to solve the practical power flow analysis and calculation problems in the actual project.
Rotation-Invariant Face Detection with Multi-task Progressive Calibration Networks

Rotation-invariant face detection (RIFD) aims to detect faces with arbitrary rotation-in-plane (RIP) on both images and video sequences, which is challenging because of large appearance variations of rotated faces under unconstrained scenarios. The problem becomes more formidable when the speed and memory efficiency of the detectors are taken into account. To solve this problem, we propose a Multi-Task Progressive Calibration Networks (MTPCN), which not only enjoy the natural advantage of explicit geometric structure representation, but also retain important cue to guide the feature learning for precise calibration. More concretely, our framework leverages a cascaded architecture with three stages of carefully designed convolutional neural networks to predict face and landmark location with gradually decreasing RIP ranges. In addition, we propose a novel loss by further integrating geometric information into penalization, which is much more reasonable than simply measuring the differences of training samples equally. MTPCN achieves significant performance improvement on the multi-oriented FDDB dataset and the rotation WIDER face dataset. Extensive experiments are performed, and demonstrate the effectiveness of our method for each of three tasks.
Autonomous Parameter Adjustment Method for Lossless Data Compression on Adaptive Stream-Based Entropy Coding

As speed of communication data path is drastically improved in this decade due to the high data rate, evolutional technology is demanded to address the fast communication implementation. In this paper, we focus on data compression technology to speed up the communication data path. We have proposed a stream-based data compression called ASE coding. It compresses data stream based on the instantaneous data entropy without buffering and stalling for the compression processes. It is also suitable for hardware implementation. However, the stream-based data compression works heuristically with sensitive parameters that affect to the data compression ratio. If the parameters are statically configured, it does not follow the dynamic data entropy, and thus, the data compression performance becomes unstable. In this paper, we will disseminate the parameters, discuss the behaviors of those parameters and propose its autonomous adjustment methods. We will also propose adjustment algorithms for those parameters that follow the data entropy of the input data stream autonomously. Through experimental evaluations applying the algorithms, we will confirm the parameters are adjusted with depending on the data entropy in the data stream. And then, the compression ratio becomes stable as the compressor exploits the minimal entropy adaptively.
F2PNet: font‐to‐painting translation by adversarial learning

For Chinese font images, when all their strokes are replaced by pattern elements such as flowers and birds, they become flower–bird character paintings, which are traditional Chinese art treasures. The generation of flower–bird painting requires professional painters’ great efforts. How to automatically generate these paintings from font images? There is a huge gap between the font domain and the painting domain. Although many image-to-image translation frameworks have been proposed, they are unable to handle this situation effectively. In this study, a novel method called font-to-painting network (F2PNet) is proposed for font-to-painting translation. Specifically, an encoder equipped with dilated convolutions extracts features of the font image, and then the features are fed into the domain translation module for mapping the font feature space to the painting feature space. The acquired features are further adjusted by the refinement module and utilised by the decoder to obtain the target painting. The authors apply adversarial loss and cycle-consistency loss to F2PNet and further propose a loss term, which is called recognisability loss and makes the generated painting have font-level recognisability. It is proved by experiments that F2PNet is effective and can be used as an unsupervised image-to-image translation framework to solve more image translation tasks.
Learning to Generate Representations for Novel Words: Mimic the OOV Situation in Training

In this work, we address the out-of-vocabulary (OOV) problem in sequence labeling using only training data of the task. A typical solution in this field is to represent an OOV word using the mean-pooled representations of its surrounding words at test time. However, such a pipeline approach often suffers from the error propagation problem, since training of the supervised model is independent of the mean-pooling operation. In this work, we propose a novel training strategy to address the error propagation problem suffered by this solution. It designs to mimic the OOV situation in the process of model training and trains the supervised model to fit the OOV word representations generated by the mean-pooling operation. Extensive experiments on different sequence labeling tasks, including part-of-speech tagging (POS), named entity recognition (NER), and chunking verified the effectiveness of our proposed method.
Research on Integrated Security Management and Control Technology of Big Data Information Platform in the Intelligent Community Based on 5G

In recent years, the construction of the safe city and smart city has been heating up. As the basic unit of urban safety, the importance of community has been growing. At present, the security systems in the community are independent of each other, and there is isolated information island. Video resource data analysis and utilization rate is small, and comprehensive governance ability is weak, and community security problems are serious. Based on 5G network, the networking of the security and fire fighting equipment is achieved, the big data information platform is established, the integration and inter-operation of multi-platform are realized, and community safety comprehensive solution is given. The Multi-Column Convolutional Neural Network algorithm is applied to analyze and process the video data of the platform, estimate the population density, improve the platform's ability of early warning of human flow, realize multi-system linkage and emergency epidemic prevention control, and build the community safety.
Agda Formalization of a Security-preserving Translation from Flow-sensitive to Flow-insensitive Security Types

The analysis of information flow is a popular technique for ensuring the confidentiality of data. It is in this context that confidentiality policies arise for giving guarantees that private data cannot be inferred by the inspection of public data. One of those policies is non-interference, a semantic condition that ensures the absence of illicit information flow during program execution by not allowing to distinguish the results of two computations when they only vary in their confidential inputs. A remarkable feature of non-interference is that it can be enforced statically by the definition of information flow type systems. In those type systems, if a program type-checks, then it means that it meets the security policy. In this paper we focus on the preservation of non-interference through program translation. Concretely, we formalize the proof of security preservation of Hunt and Sands' translation that transforms high-level While programs typable in a flow-sensitive type system into equivalent high-level programs typable in a flow-insensitive type system. Our formalization is performed in the dependently-typed language Agda. We use the expressive power of Agda's type system to encode the security type systems at the type level. A particular aspect of our formalization is that it follows a fully internalist approach where we decorate the type of the abstract syntax with security type information in order to obtain the representation of well-typed (i.e secure) programs. A benefit of this approach is that it allows us to directly express the property of security preservation in the type of the translation relation. In this manner, apart from inherently expressing the transformation of programs, the translation relation also stands for an inductive proof of security preservation.
Effective Systems Security Requirements in Product Line Engineering

ABSTRACT Requirements engineering for complex software‐intensive systems (and other systems) requires identifying, specifying, analyzing, and reviewing system requirements early in the system development process. However, many cases overlook system security requirements, treating them as an afterthought during this important initial process stage. Missing security requirements for these system types cannot guarantee system integrity. It is not cost efficient to retrofit requirements at later stages to include missing security capabilities specified earlier in‐process. Detailed analysis and understanding of security requirements enable building confidentiality and integrity into our systems. Thus, early process activities must include security requirements engineering. Product Line Engineering development must guarantee system integrity and assurance for a “family of systems” borne from a common design. Hence, detailed requirements elicitation and specification is important early in the product‐line development and must include security requirements. Further, security requirements must revisit applicability, extension, and new security requirements specified to provide for security coverage of selected features contained within the product line's instances. This paper describes an approach to security requirements engineering identification and includes introducing a security profile to facilitate developing and evolving a secure product line for software‐intensive systems.