Reviewerly/exHarmony huggingface dataset:
Towards verifying neural autonomous systems

In this talk I will offer a personal perspective on the increasing challenges and the correspondingly more powerful solutions being developed in the area of verification of autonomous system. I will ground the presentation on the work carried out with several colleagues over the years in the Verification of Autonomous Systems at Imperial College London [32].
A Deep Learning Scheme for Efficient Multimedia Iot Data Compression

Multimedia Internet of Things (MIoT) devices and networks will face many power and communication overhead constraints given the volume of multimedia sensed data. One classic approach to overcoming the difficulty of large-scale data is to use lossy compression. However, current lossy compression algorithms require a limited compression rate to maintain acceptable perceived image quality. This is commonly referred to as the image quality-compression ratio trade-off. Motivated by current breakthroughs in computer vision, this article proposes recovering high-quality decompressed images at the application server level using a deep learning-based super-resolution model. As a result, this paper proposes ignoring the trade-off between image quality and size and increasing the reduction size further by using a lossy compressor with downscaling to conserve energy. The experimental study demonstrates that the proposed technique effectively improves the visual quality of compressed and downscaled images. The proposed solution was evaluated on resource-constrained microcontrollers. The obtained results show that the transmission latency and energy consumption can be decreased by up to 10% compared to conventional lossy compression techniques.
Research on image mosaic technology of multi aperture array imaging system

In traditional optical design, there is a mutual restriction between large field of view and high resolution. In order to ensure large field of view and improve imaging resolution, the design of multi aperture array imaging system can be used. A set of imaging system of multi aperture array camera is introduced. By splicing the images collected by the multi aperture array system, large field of view and high resolution imaging are realized. In the process of acquiring the whole image, the mosaic of multiple sub images is one of the key technologies of multi aperture array imaging system. Aiming at the array image mosaic, SIFT matching algorithm is introduced to realize the extension of algorithm and application field. In the process of image acquisition, the traditional camera’s multiple shooting time is compressed into one shot time of array camera, which saves time to the maximum.
Designing for Positive Upcycling Experiences with People’s Well-Being in Mind

People’s usual anticipation towards material consumption is that it pleases us, yet consumption experiences are not always pleasurable nor give us long-lasting positive emotional experiences. People sometimes regret their purchase, or the pleasure from material gain may wear off quickly, which does not make people happy in the long run. We propose that upcycling as a do-it-yourself experience could be a promising pathway for individuals to gain long-lasting happiness as well as resulting outcomes of upcycling which could facilitate further positive use experiences and special meaning. This chapter introduces how researchers and practitioners in service or experience design and development could use positive emotional granularity (PEG) in order to design for positive upcycling experiences that contribute to people’s well-being.
Neural Network Calculations at the Speed of Light Using Optical Vector-Matrix Multiplication and Optoelectronic Activation

With the rapid progress of the integrated nanophotonics technology, the optical neural network architecture has been widely investigated. Since the optical neural network can complete the inference processing just by propagating the optical signal in the network, it is expected more than one order of magnitude faster than the electronics-only implementation of artificial neural networks (ANN). In this paper, we first propose an optical vector-matrix multiplication (VMM) circuit using wavelength division multiplexing, which enables inference processing at the speed of light with ultra-wideband. This paper next proposes optoelectronic circuit implementation for batch normalization and activation function, which significantly improves the accuracy of the inference processing without sacrificing the speed performance. Finally, using a virtual environment for machine learning and an optoelectronic circuit simulator, we demonstrate the ultra-fast and accurate operation of the optical-electronic ANN circuit.
Performance Analysis of Scheduling Algorithms in Apache Hadoop

Hadoop bundles the two computing resources of memory and CPU in the management resources, and then divides it into two resource models: MapSlot and ReduceSlot according to task types. MapReduce applications will have a large number of sorting operations in operation. Most of these sorts are executed iteratively, which consumes a lot of performance. Chapter 5 of this article takes this as an entry point and reorganizes the execution process of the Shuffle stage. Researched to replace quick sort with more efficient counting sorting. At the same time, the Shuffle execution is branched according to the definition of Combiner. One branch deletes the quick sort in the partition in the spill phase and the merge sort in the combine phase to reduce performance consumption. The other branch executes Combiner in advance to improve data processing efficiency. The two branches processed 21GB of log data on a 7-node PC cluster, and both achieved an efficiency improvement of about half an hour.
중소벤처기업의 흡수역량이 연구개발역량과 기술사업화역량에 미치는 영향

Purpose Establish a plan to increase technology commercialization capability based on absorption capability and R&D capabilityMethods Analyze causal relationship between factors through structural equation modelResults Absorption capability has a significant effect on both R&D capability and technology commercialization capability, but R&D capability has an effect on the productization capability of the technology commercialization capability, but not the manufacturing capability and marketing capability of the technology commercialization capability.Conclusion First, it is important to increase the absorption capacity in order to increase the R&D capacity. Second, in order to acquire external knowledge and information, various networks with the outside must be formed. Third, it is necessary to establish clear goals for R&D and practice it with manpower, equipment, and funds.
Intelligent Content-Based Hybrid Cryptography Architecture for Securing File Storage Over Cloud

With the rise of computer and mobile usage, and availability of affordable Internet and free social media, huge amount of data is generated every minute. The data consists of personal and sensitive information about users, which could be their photos, videos, documents even their passwords, and what not! But there is a question regarding the security and privacy of their data. Nowadays, cryptography and steganography techniques are more popular for data security. Using only a single algorithm does not prove to effectively provide high-level security to the data. In the proposed methodology, hybrid encryption technique for the DOCX files has been used in which the input source file is divided into different parts and encoded using two symmetric cryptography algorithms RC6 and Blowfish and then these encrypted parts are stored over the cloud.
Motion Complementary Network for Efficient Action Recognition

Both two-stream ConvNet and 3D ConvNet are widely used in action recognition. However, both methods are not efficient for deployment: calculating optical flow is very slow, while 3D convolution is computationally expensive. Our key insight is that the motion information from optical flow maps is complementary to the motion information from 3D ConvNet. Instead of simply combining these two methods, we propose two novel techniques to enhance the performance with less computational cost: fixed-motion-accumulation and balanced-motion-policy. With these two techniques, we propose a novel framework called Efficient Motion Complementary Network(EMC-Net) that enjoys both high efficiency and high performance. We conduct extensive experiments on Kinetics, UCF101, and Jester datasets. We achieve notably higher performance while consuming 4.7× less computation than I3D, 11.6× less computation than ECO, 17.8× less computation than R(2+1)D. On Kinetics dataset, we achieve 2.6% better performance than the recent proposed TSM with 1.4× fewer FLOPs and 10ms faster on K80 GPU.
A comparative study on the challenges of developing the writing skill: english language and nahuatl language

The aim of this research work is to detect challenges that English and Nahuatl learners face when developing writing skills during first level in order to suggest strategies for implementing in future studies. The importance of comparing both languages is because Nahuatl is considered as an endangered language and English has significant pedagogy for the teaching-learning process. Therefore, it is expected that some methodologies can complement each other. The following results were obtained using a qualitative case study since it is allowed to explore and identify different factors that have the same purpose. In this case, there was a comparison between English and Nahuatl learners to determine the main challenges in both situations. Hence, 2 groups were selected for being observed and interviewed: an English class and a Nahuatl class. In addition, this research was designed for being carried out online due to the Covid-19 situation that is being lived currently.
Application of Quantum Generative Adversarial Learning in Quantum Image Processing

Quantum machine learning is a hot topic in the quantum community recently. The combination of quantum machine learning and image processing is also one of the researchers' concerns. With the promotion of deep learning to process images, the field of image processing has also shown amazing potential. The combination of quantum computing and artificial intelligence can not only exert the computing power of quantum computing but also find more applications in the field of image processing. This article combines quantum generative adversarial learning with the field of image processing. A quantum generative adversarial network is designed to load and learn classical image data. Numerical simulations intuitively show that quantum algorithms can also effectively process images. In our scheme, using N qubits can load 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> classical bits of image data.
A New Design for Public Security Information System Based on Trend Search Algorithm

In the field of public security, benefiting from advanced technology, the public security and social security prevention system is also constantly enriched and improved. However, the use of information integration and technical means of illegal activities are becoming more and more diverse. At present, the relevant government departments and offices are vigorously carrying out the informatization and intelligent construction. But there are still some problems, such as the single existing technical means, the lack of technical support, and the lack of information access channels. In recent years, public security information system is also a practical action of government departments to comply with the trend of data and information age and promote the combination of industry and internet. With the help of technology and data analysis, the purpose is to realize the perception of potential risks of public security and social security, optimize the workflow, realize the transformation from empirical decision-making to data-driven, and improve the ability of control and implementation. Therefore, in order to solve the above problems, this paper puts forward a new design for public security information system based on trend search algorithm.
Local Facial Attribute Transfer through Inpainting

The term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our “Attribute Transfer Inpainting Generative Adversarial Network” (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm

Compression of genomic data has gained enormous momentum in recent years because of advances in technology, exponentially growing health concerns, and government funding for research. Such advances have driven us to personalize public health and medical care. These pose a considerable challenge for ubiquitous computing in data storage. One of the main issues faced by genomic laboratories is the 'cost of storage' due to the large data file of the human genome (ranging from 30 GB to 200 GB). Data preservation is a set of actions meant to protect data from unauthorized access or changes. There are several methods used to protect data, and encryption is one of them. Protecting genomic data is a critical concern in genomics as it includes personal data. We suggest a secure encryption and decryption technique for diverse genomic data (FASTA / FASTQ format) in this article. Since we know the sequenced data is massive in bulk, the raw sequenced file is broken into sections and compressed. The Advanced Encryption Standard (AES) algorithm is used for encryption, and the Galois / Counter Mode (GCM) algorithm, is used to decode the encrypted data. This approach reduces the amount of storage space used for the data disc while preserving the data. This condition necessitates the use of a modern data compression strategy. That not only reduces storage but also improves process efficiency by using a k-th order Markov chain. In this regard, no efforts have been made to address this problem separately, from both the hardware and software realms. In this analysis, we support the need for a tailor-made hardware and software ecosystem that will take full advantage of the current stand-alone solutions. The paper discusses sequenced DNA, which may take the form of raw data obtained from sequencing. Inappropriate use of genomic data presents unique risks because it can be used to classify any individual; thus, the study focuses on the security provisioning and compression of diverse genomic data using the Advanced Encryption Standard (AES) Algorithm.
Attributes relation pattern construction using relation weightage prediction for information neural classification

Extraction of information in enterprise applications due to complex distribution of information in large identical sources. In such a scenario, a single approach or method for extracting information text would limit information needs, which would require a high level of work and time. So, it is an open problem in data analysis application to construct a suitable classification model for unsupervised datasets which having multi level of information attributes. It is vital to analyse and discover the valuable pattern information to support the accurate classification. Selection of attributes and construction of relation model always is a challenging due the increasing attribute dimensionality. So, it is important to learn and select the best attributes patterns to enhance the information association through accurate classification. In this paper, we propose an ARP-RW method for construction of Attribute Relation Pattern (ARP) through computing attributes Relation Weightage (RW) to enhance the information classification. The ARP-RW method provides the solution to identifying the relation class of the multi-attributes documents. It initially constructs an attribute relation class matrix (ARCM) utilizing the set of data collection and the RW value of the MLA. Later, it builds an ARP based on the MLA selected to perform accurate classification. The evaluation of the proposal method using the datasets of UCI Machine Learning Repository shows improvisation in comparison the existing approaches.
Cost Volume Refinement for Depth Prediction

Light-field cameras are becoming more popular in the consumer market. Their data redundancy allows, in theory, to accurately refocus images after acquisition and to predict the depth of each point visible from the camera. Combined, these two features allow for the generation of full-focus images, which is impossible in traditional cameras. Multiple methods for depth prediction from light fields (or stereo) have been proposed over the years. A large subset of these methods relies on cost-volume estimates - 3D objects where each layer represents a heuristic of whether each point in the image is at a certain distance from the camera. Generally, this volume is used to regress a depth map, which is then refined for better results. In this paper, we argue that refining the cost volumes is superior to refining the depth maps in order to further increase the accuracy of depth predictions. We propose a set of cost-volume refinement algorithms and show their effectiveness.
IMPROVEMENT OF TECHNOLOGY CREATION OF MULTIMEDIA ELECTRONIC TEXTBOOKS ON "INFORMATICS AND INFORMATION TECHNOLOGY" FOR SCHOOLS

In work the questions of improvement of creation of multimedia electronic textbooks on Informatics and information technology for general education schools are considered.
Super-resolution Guided Pore Detection for Fingerprint Recognition

Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from low-resolution fingerprints, we adopt a joint learning-based approach that combines both super-resolution and pore detection networks. Our modified single image Super-Resolution Generative Adversarial Network (SRGAN) framework helps to reliably reconstruct high-resolution fingerprint samples from low-resolution ones assisting the pore detection network to identify pores with a high accuracy. The network jointly learns a distinctive feature representation from a real low-resolution fingerprint sample and successfully synthesizes a high-resolution sample from it. To add discriminative information and uniqueness for all the subjects, we have integrated features extracted from a deep fingerprint verifier with the SRGAN quality discriminator. We also add ridge reconstruction loss, utilizing ridge patterns to make the best use of extracted features. Our proposed method solves the recognition problem by improving the quality of fingerprint images. High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.
VR Setup to Assess Peripersonal Space Audio-Tactile 3D Boundaries

Many distinct spaces surround our bodies. Most schematically, the key division is between peripersonal space (PPS), the close space surrounding our body, and an extrapersonal space, which is the space out of one’s reach. The PPS is considered as an action space, which allows us to interact with our environment by touching and grasping. In the current scientific literature, PPS’ visual representations are appearing as mere bubbles of even dimensions wrapped around the body. Although more recent investigations of PPS’ upper body (trunk, head, and hands) and lower body (legs and foot) have provided new representations, no investigation has been made yet concerning the estimation of PPS’s overall representation in 3D. Previous findings have demonstrated how the relationship between tactile processing and the location of sound sources in space is modified along a spatial continuum. These findings suggest that similar methods can be used to localize the boundaries of the subjective individual representation of PPS. Hence, we designed a behavioral paradigm in virtual reality based on audio-tactile interactions, which has enabled us to infer a detailed individual 3D audio-tactile representation of PPS. Considering that inadequate body-related multisensory integration processes can produce incoherent spatio–temporal perception, the development of a virtual reality setup and a method to estimate the representation of the subjective PPS volumetric boundaries will be a valuable addition for the comprehension of the mismatches occurring between body physical boundaries and body schema representations in 3D.
Semantics to Space(S2S): Embedding semantics into spatial space for zero-shot verb-object query inferencing

We present a novel deep zero-shot learning (ZSL) model for inferencing human-object-interaction with verb-object (VO) query. While the previous two-stream ZSL approaches only use the semantic/textual information to be fed into the query stream, we seek to incorporate and embed the semantics into the visual representation stream as well. Our approach is powered by Semantics-to-Space (S2S) architecture where semantics derived from the residing objects are embedded into a spatial space of the visual stream. This architecture allows the co-capturing of the semantic attributes of the human and the objects along with their location/size/silhouette information. To validate, we have constructed a new dataset, Verb-Transferability 60 (VT60). VT60 provides 60 different VO pairs with overlapping verbs tailored for testing two-stream ZSL approaches with VO query. Experimental evaluations show that our approach not only outperforms the state-of-the-art, but also shows the capability of consistently improving performance regardless of which ZSL baseline architecture is used.