Reviewerly/exHarmony huggingface dataset:
Intelligent data routing model for social network using semantic web

The main focus of the work is to propose a semantic intelligent based framework for information processing. Value, Association and the Context are the three main attributes of any information while being transacted over different social networks to opt for an intelligent decision at that instance of time. The information or data as a service in a public cloud environment like social networks is to be processed in order to make it suitable for the future application development. The capacity for users to reuse their data across interoperable applications is known as data portability. It also refers to the user’s ability to control their identity, media, and other forms of personal data. Here, we present a formal technique for securely and reliably accessing social networks, as well as explain many problems encountered along the way to facilitate data portability and take advantage of Semantic Web Technologies. We also discussed about several form of heterogeneity between ontologies and how to match them using approaches like string, linguistic, and structural similarities, as well as aggregation using rough sets.
A Note on Quantum Collision Resistance of Double-Block-Length Compression Functions

In 2005, Nandi presented a class of double-block-length compression functions specified as \(h^{\pi }(x):=(h(x),h(\pi (x)))\), where h is assumed to be a random oracle producing an n-bit output and \(\pi \) is a non-cryptographic permutation. He showed that the collision resistance of \(h^{\pi }\) is optimal if \(\pi \) has no fixed point. This manuscript discusses the quantum collision resistance of \(h^{\pi }(x)\). First, it shows that the quantum collision resistance of \(h^{\pi }\) is not always optimal even if \(\pi \) has no fixed point: One can find a colliding pair of inputs for \(h^{\pi }\) with only \(O(2^{n/2})\) queries to h by using the Grover search if \(\pi \) is an involution. Second, this manuscript shows that there really exist cases that the quantum collision resistance of \(h^{\pi }\) is optimal. More precisely, a sufficient condition on \(\pi \) is presented for the optimal quantum collision resistance of \(h^{\pi }\), that is, any collision attack needs \({\varOmega }(2^{2n/3})\) queries to find a colliding pair of inputs. The proof uses the recent technique of Zhandry’s compressed oracle. Finally, this manuscript makes some remarks on double-block-length compression functions using a block cipher.
Performance Analysis of Machine Learning Algorithms for Intelligent Recommender System

In today’s Society 5.0, the recommender system analyzes the user preferences to provide the required potential targets. Graph-Based Collaborative filtering has become the new state-of-the-art to take the edge of selection parameters for the user-item matrix. The author analyzed the following machine learning algorithms: Linear Regression, Logistic Regression Naive Bayes classifier, Support vector machine, and J48. The model Prediction depends upon the characteristics of genres associated with game play. Shortcomings of data take into account for model development, testing, and benchmarking.
Constructing Digital Signature Algorithm Based on a New Key Scheme

Xuan, Van Luu Van, Hoa Doan Hong, Dung LuuE-government is the latest trend in many countries in which the government is using the online system for delivering government services to citizens, and the digital signature is used to validate contents and authorize users who are involving in the E-governance system. Although there are many digital signature schemes currently known as RSA, Elgamal, the approach of improving the safety level of digital signature schemes based on the hardness of solving simultaneous difficult assumptions has still remained underdeveloped and attracted rising attentions from researchers. In this research, with the target to improve safety level of digital signature schemes, we present a new approach for constructing digital signature scheme. Algorithms that used in this digital signature scheme are built based on a new key form. This new key format is constructed using a new difficulty problem without high-efficiency solution, and it can be applied to construct digital signature algorithms. According to this research, a high-security digital signature class can be constructed by the proposed method in some actual situation.
Depth Image-Based Rendering System by Kinect Sensor

Various technologies are being studied for realistic video conferencing. Research on stereoscopic images is active during the study of realistic video conferencing. In this paper, we propose a depth image-based rendering (DIBR) system with Kinect. Kinect is a device that can obtain 3D information with a depth camera and is used to generate 3D images. In the proposed system, we create 3D images from depth images obtained from Kinect through DIBR technology. Also, we control the three dimensionality through hand motion recognition.
Analyzing Transnational Narratives in Twitter: An Unsupervised Approach Using PARAFAC

The unrest in Hong Kong has given rise to consternation among the governments of other countries, particularly in the Asia–Pacific region, about how this unrest has the potential to spill over into areas outside Hong Kong. We propose a novel application of an existing technique, PARAFAC, which extends traditional topic analysis to enable an analyst to understand the nexus between place, time and topics discussed. The methodology is from the class of unsupervised data analytics techniques, meaning nothing about it is specific to this particular dataset. It is also language-independent. Our approach is based on PARAFAC, which is a generalization of Singular Value Decomposition (SVD) from matrices to tensors. SVD is widely used for topic extraction, but is limited to use with matrices. Therefore, its cousin PARAFAC is needed for arrays of data in more than two modes. Here, we apply PARAFAC to a term-by-time-by-country tensor of data derived from Twitter data centering on the Hong Kong protests which started in 2019. This enables us automatically to separate topics specific to particular countries and times from those crossing national boundaries, a useful first step for an analyst who wants to understand and further explore the data at a high level.
Real time visual interpretation for the blind

Blindness is a very common and unendurable disability among many disabilities. According to a report from WHO, there are 285 million visually impaired people. This paper presents the idea of developing a smart system that can assist visually impaired people. Visual impairment is one of the main problems for humankind. Those people need assistance to perform day-to-day tasks. People can live a simpler and more efficient existence thanks to technological advancements. However, not everyone shared this sentiment, as some people with impairments, such as the blind, continue to suffer in life despite the availability of technology or smart devices to assist them. Here we are coming up with a system to help those people, to experience the surroundings by real-time interpretation in which the visually impaired people don't need another person to interpret. Our idea is a mobile application and visually impaired people can use their smartphones to interpret the real world. A voice-controlled smartphone application in which, upon a specific command, a visual is captured and processed back into a meaningful sentence. This text is then passed through a text-to-speech model which converts it into the language preferred by the user. As a way of interaction through voice input, our program employs voice user interface technology such as speech recognizing and speech synthesis.
Stack Multiple Shallow Autoencoders into a Strong One: A New Reconstruction-Based Method to Detect Anomaly

Anomaly detection methods based on deep learning typically utilize reconstruction as a proxy task. These methods train a deep model to reconstruct the input from high-level features extracted from the samples. The underlying assumption of these methods is that a deep model trained on normal data would produce higher reconstruction error for abnormal input. But this underlying assumption is not always valid. Because the neural networks have a strong capacity to generalize, the deep model can also reconstruct the unseen abnormal input well sometimes, leading to a not prominent reconstruction error for abnormal input. Hence the decision-making process cannot distinguish the abnormal samples well. In this paper, we stack multiple shallow autoencoders (StackedAE) to enlarge the difference between reconstructions of normal and abnormal inputs. Our architecture feeds the output reconstruction of prior AE into the next one as input. For abnormal input, the iterative reconstruction process would gradually enlarge the reconstruction error. Our goal is to propose a general architecture that can be applied to different data types, e.g., video and image. For video data, we further introduce a weighted loss to emphasize the importance of the center frame and its near neighbors because it is unfair to treat all frames in a 3D convolution frame cuboid equally. To understand the effectiveness of our proposed method, we test on video datasets UCSD-Ped2, CUHK Avenue, and the image dataset MNIST. The results of the experiments demonstrate the effectiveness of our idea.
UED: A Unified Encoder Decoder Network for Visual Dialog

This paper addresses the problem of visual dialog, which aims to answer multi-round questions based on the dialog history and image content. This is a challenging task because a question may be answered in relations to any previous dialog and visual clues in image. Existing methods mainly focus on discriminative setting, which design various attention mechanisms to model interaction between answer candidates and multi-modal context. Despite having impressive results with attention based model for visual dialog, a universal encoder-decoder for both answer understanding and generation remains challenging. In this paper, we propose UED, a unified framework that exploits answer candidates to jointly train discriminative and generative tasks. UED is unified in that (1) it fully exploiting the interaction between different modalities to support answer ranking and generation in a single transformer based model, and (2) it uses the answers as anchors to facilitate both two settings. We evaluate the proposed UED on the VisDial dataset, where our model outperforms the state-of-the-art.
Consumer and Farmer Centric Subscription Based Organic Vegetable/Fruit Delivery System

For centuries, the economy of Sri Lanka has been backed by the agricultural sector. Even though it has contributed to the development of the nation on a large scale, the economies and living standards of the farmers have not been improved much. Most of the farmers remain poor mainly due to not knowing the market demand and the low buying prices of the middleman. Meanwhile, the consumers in Sri Lanka are buying non-organic vegetables and fruits which are highly contaminated with dangerous chemicals which in turn has contributed to the rapidly increasing non-communicable disease rates in both consumers and farmers. The proposed system is an attempt to solve these problems faced by both farmers and consumers by reducing the farmers-consumer knowledge gap and providing consumers a trustworthy and personalized subscription for organic vegetables/fruits with a donation platform.
Accurate and Real Time Assisted Cataloging in E-commerce using Dual Images

Product classification is an essential first step in e-commerce starting from cataloging. It impacts various downstream processes such as: surfacing relevant attribute lists to capture product information, recommending right categories for sellers to list their product in(seller support), deciding on what quality control steps a product has to go through and so on. We identify the challenges involved in fashion product classification and show how dual-image based approach addresses them. We demonstrate this using large scale, real-world Fashion/Lifestyle data from Flipkart - India’s largest e-commerce company. This dataset contains a mix of well known western concepts of fashion such as top, t-shirt, blazer and India specific fashion concepts such as sari, lehenga choli, kurta etc. We show that a dual-image based classifier gives 6% improvement in f1-score compared to a single-image based classifier.
Event Attendance Prediction: Learning Methods

After all the attributes are extracted, a learning method is utilized to learn how the extracted attributes affect users’ decisions on event attendance. This process is also referred to as supervised binary classification, considering that ‘attend or not’ is a binary classification. There are many supervised classifiers in the literature, and we adopt three classifiers, including logistic regression, decision tree and naïve Bayes. In this chapter, we first give an overview of the data mining process, and then present the details of these supervised classifiers.
The Individual’s Perception Space of the Digital Earth

Just as the space of the real world is represented in the human brain (in its perceptual space) not as it really is, so the space of the Digital Earth is represented in the individual's space perception in a distorted way. The mechanism of forming an idea about an object in the perception space of an individual is represented. The structure of the Digital Earth space is discussed. The concept of "virtual-shift" is introduced. It is concluded that the perceptual spaces of the real world and the Digital Earth are turning into a single complex, which can help make the management of sophisticated objects, such as the economy of a particular country or the world economy, more adequate, but can also seriously disrupt it. Assumptions are made as to how this complex is represented in the space of perception of an individual and what are the consequences for the management of the country and international systems.
Sources of knowledge in the process of scientific research of security phenomena

Sources of scientific knowledge and the process of scientific research are necessary for defining security phenomena. The levels and types of scientific knowledge and scientific sources are numerous. The aim of this paper is to define, describe and classify the levels (quality and quantity) of scientific knowledge and sources of that knowledge. In this regard, the paper discusses the evaluation of knowledge and sources of knowledge, points to various personal and material sources of knowledge, and various public and confidential - written and audio-visual scientific sources and objects. The conclusion of the author is that the evaluation of the source of scientific knowledge, as well as the scientific facts themselves, is important for the outcome of the research process and the achievement of true and objective scientific knowledge about security phenomena.
The security analysis of the BB84 protocol in the case of Calderbank–Shor–Steane code leakage

Quantum key distribution (QKD) allows authenticated parties to share secure keys. Its security comes from quantum physics rather than computational complexity. The previous work has been able to demonstrate the security of the BB84 protocol based on the uncertainty principle, entanglement purification and information theory. In the security proof method based on entanglement purification, it is assumed that the information of Calderbank–Shor–Steane (CSS) error correction code cannot be leaked, otherwise, it is insecure. However, there is no quantitative analysis of the relationship between the parameter of CSS code and the amount of information leaked. In the attack and defense strategy of the actual quantum key distribution system, especially in the application of the device that is easy to lose or out of control, it is necessary to assess the impact of the parameter leakage. In this paper, we derive the relationship between the leaked parameter of CSS code and the amount of the final key leakage based on the BB84 protocol. Based on this formula, we simulated the impact of different CSS code parameter leaks on the final key amount. Through the analysis of simulation results, the security of the BB84 protocol is inversely proportional to the value of [Formula: see text] and [Formula: see text] in the case of the CSS code leak.
Arabic Part Of Speech (POS) Tagging Analysis using Bee Colony Optimization (BCO) Algorithm on Quran Corpus

Part Of Speech (POS) tagging is an automated process for determining the appropriate grammatical label or syntactic category of a word depending on the context. POS tagging is one of the important processes in Natural Language Processing (NLP) applications such as summarization text, Speech Recognition (SR), Question Answering (QA) and Information Retrieval (IR). Automatic POS tagging is needed because manual POS tagging takes a long time and is expensive because it requires a linguist. The main problem in POS tagging automatically is words that have different properties if placed in different contexts (ambiguous) and words that are in the test corpus but not in the Out of Vocabulary (OOV) training corpus. In this study, an efficient POS tagging approach for Arabic text will be discussed using the Bee Colony Optimization (BCO) algorithm. The POS tagging problem is represented as a graph and a new weighting technique is proposed to assign a transition value to each word class label which may not be probability, then the bees look for the best solution path. The dataset used in this study comes from the transliterated Quranic corpus consisting of 150 simple perfect sentences as an easy dataset category, 50 sentences with more than one S/P/O/K as a medium dataset category, and 50 selected Quran verses as a category. difficult datasets. The proposed approach is evaluated using a cross-validation technique, namely k-fold cross validation. The results showed an average accuracy of 100% for the easy dataset category, 98.96% for the medium dataset category, and 94.96% for the difficult dataset category.
Intelligence in Knowledge Economies and Organizations

Chapter Summary This chapter introduces the concept of an intelligent organization in the context of the twenty-first-century knowledge economy. An intelligent organization is one in which individuals behave intelligently, work is grounded in intelligent methods and choices, and rich stocks of intelligence in the form of knowledge capital to support intelligence work and choices. Intelligence is defined as both a thing and attribute and behavior and way of working. The chapter also highlights examples of intelligent behaviors and or organizational pathologies. The chapter also highlights the importance of becoming aware of intelligent and unintelligent choices.
MVPose: Realtime Multi-Person Pose Estimation Using Motion Vector on Mobile Devices

We present MVPose, a novel system designed to enable real-time multi-person pose estimation (PE) on commodity mobile devices, which consists of three novel techniques. First, MVPose takes a motion-vector-based approach to fast and accurately track the human keypoints across consecutive frames, rather than running expensive human-detection model and pose-estimation model for every frame. Second, MVPose designs a mobile-friendly PE model that uses lightweight feature extractors and multi-stage network to significantly reduce the latency of pose estimation without compromising the model accuracy. Third, MVPose leverages the heterogeneous computing resources of both CPU and GPU to execute the pose estimation model for multiple persons in parallel, which further reduces the total latency. We present extensive experiments to evaluate the effectiveness of the proposed tecniques by implemented the MVPose on five off-the-shelf commercial smartphones. Evaluation results show that MVPose achieves over <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</b> frames per second PE with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</b> persons per frame, which significantly outperforms the state-of-the-art baseline, with a speedup of up to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5.7×</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3.8×</b> in latency on CPU and GPU, respectively. Compared with baseline, MVPose achieves an improvement of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10.1%</b> in multi-person PE accuracy. Furthermore, MVPose achieves up to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">74.3%</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">57.6%</b> energy-per-frame saving on average in comparison with the baseline on mobile CPU and GPU, respectively.
Fast RTL Implementation of A* Path Planning Algorithm

The conventional A* algorithm consumes a lot of time due to its large number of iterations. In every iteration, the memory is accessed for multiple data structures, functions are evaluated then sorted into queues which makes it sometimes not suitable for real-time applications. This paper proposes a fast implementation for the A* algorithm to meet requirements of real-time applications. The proposed implementation uses parallelism and caching to achieve better performance. We used Register Transfer Level (RTL) simulation and formal verification to do functional verification of the implemented design.The design is implemented on Xilinx Virtex-7 to be evaluated. Experiments prove that this implementation achieves 100 times enhancement for low obstacle maps and 50 times for high ones relative to software implementation. The design is suitable for real-time applications.
Quantum Secure Direct Communication Protocol based on Quantum Fingerprint

To make the communication channel more resistant to noise, and to make the transmission of messages in the channel both safe and efficient, this paper proposes a quantum secure direct communication protocol using quantum fingerprint and quantum teleportation in a decoherence-free subspace. Firstly, the quantum fingerprint is applied to the identity authentication of the protocol, so that the two qubits are smoothly transmitted from the sender to the receiver through the quantum channel. Then, the order of the measured messages is restored. Finally, security analysis and comparison with other protocols are carried out. The analysis result shows that the proposed protocol can transmit messages safely and efficiently, and its channel capacity is also high.
An Efficient Coding Technique for Stochastic Processes

In the framework of coding theory, under the assumption of a Markov process (Xt) on a finite alphabet A, the compressed representation of the data will be composed of a description of the model used to code the data and the encoded data. Given the model, the Huffman's algorithm is optimal for the number of bits needed to encode the data. On the other hand, modeling (Xt) through a Partition Markov Model (PMM) promotes a reduction in the number of transition probabilities needed to define the model. This paper shows how the use of Huffman code with a PMM reduces the number of bits needed in this process. We prove the estimation of a PMM allows for estimating the entropy of (Xt), providing an estimator of the minimum expected codeword length per symbol. We show the efficiency of the new methodology on a simulation study and, through a real problem of compression of DNA sequences of SARS-CoV-2, obtaining in the real data at least a reduction of 10.4%.