Reviewerly/exHarmony huggingface dataset:
Modifications of Key Schedule Algorithm on RECTANGLE Block Cipher

Key schedule algorithm is one of the core elements that significantly affect the security of an encryption algorithm. While its importance is undeniable, the key schedule algorithm has not been given comprehensive attention compared to the encryption algorithm. RECTANGLE block cipher is very efficient in terms of encryption speed performance among the existing lightweight algorithms. However, its non-robust round keys generation seems to be the weakest point of the algorithm. A robust key schedule algorithm should produce round keys with random characteristics, independent, and not correlated to one another as defined in the randomization and confusion properties. Therefore, the objective of this paper is to improve the RECTANGLE key schedule algorithm to increase its randomization and confusion properties against high correlation keys as well as the speed and throughput performances. Three experiments were conducted based on the randomness, key sensitivity, and performance tests. The results show that our modified designs have produced lower correlation keys by 0.16% to 0.45% improvement, more random ciphertext with an increase of 13.34% to 20.00% passing rate, and better performance that recorded 1.30% to 7.82% faster and increased by 1.33% to 8.50% throughput than the original RECTANGLE.
A continuous variable quantum key distribution protocol based on multi-dimensiondata reconciliation with Polar code

Abstract The continuous variable quantum key distribution protocol (CV-QKD) has the advantages of easy generation of quantum signals, convenient measurement and high communication capacity. However, the multi-dimensional data reconciliation is mainly implemented by binary LDPC code as quantum error correction code, which is greatly affected by complex environments and the bit error rate is high. In order to solve this problem, acontinuous variable quantum key distribution protocol based onmulti-dimension data reconciliation with Polar code, that can be applied to long-distance transmission in power systems, is proposed. First, we prepare an EPR quantum state, and perform heterodyne detection on one of the quantum states, and obtain the values of two orthogonal components at the same time which is the coherent state of the information carrier. Then the continuous variable quantum key distribution is realized by six steps (i.e., quantum state preparation, gaussian random detection, data filtering, calculation of bit error rate, data reconciliation, privacy amplification).In data reconciliation, we use Polar code as the error correction code and use reverse coding technology to achieve key error correction, which can effectively improve the negotiation efficiency. Numerical simulation verifies the feasibility of the protocol, and the protocol is more efficient than the multi-dimensiondata reconciliation protocol based on LDPC code under the same conditions.
Modeling Daily Crime Events Prediction Using Seq2Seq Architecture

Early prediction of the crime occurrence reduces its impact. Several studies have been conducted to forecast crimes. However, these studies are not highly accurate, particularly in short-term forecasting such as over one week. To respond to this, we examine sequence to sequence (Seq2Seq) based encoder-decoder LSTM model using two real-world crime datasets of Brisbane and Chicago, extracted from the open data portal, to make one week ahead of total daily crime forecasting. We have built an ARIMA statistical model and three machine learning-based regression models that differ in their architecture, namely, simple RNN, LSTM, and Conv1D with a novel approach of walk-forward validation. Using a grid search strategy, the hyperparameters of the models are optimized. The obtained results demonstrate that the proposed Seq2Seq model is highly effective, if not superior, compared to its counterparts and other algorithms. This proposed model achieves state-of-the-art results with a relatively Root Mean Squared Error (RMSE) of 0.43 and 0.86 on both datasets, respectively.
Forward Chaining Hierarchical Partial-Order Planning

In recent years, the surge in interest in integrated Task and Motion Planning (TAMP) has caused a resurgence in Task Planning research in the robotics community. Meanwhile, in the broader AI community, combining partial-order planning with grounded forward chaining has become popular for temporal task planning, preserving the ability of partial-order planners to deal with concurrent actions while exploiting the speed of modern-day grounded forward search. In this paper, we present two new planning algorithms. The first, FCPOP, combines full utilization of the delayed action ordering commitment of partial-order planning with grounded forward search guided by a temporally informed heuristic. This results in a planner that is flexible in how it structures plans with respect to action parallelism, creates high-quality plans with low makespans, and computes plans quickly. FCPOP is shown empirically to outperform state-of-the-art temporal planners on several benchmark planning problems. The second planning algorithm FCHPOP introduces hierarchical information in the form of abstract actions. This reduces the number of nodes that need to be explored and speeds up the planning process while still generating quality plans.
Product Recommendation Algorithm Combining Network Structure and Text Attributes

Most traditional recommendation algorithms rely on common scoring items between users when making product recommendations. The data is highly sparse and the recommendation effect is not good. Therefore, an improved collaborative filtering recom-mendation algorithm is proposed. Based on user purchase rec-ords, the algorithm uses a representation learning method to construct a user product network, obtains the low-dimensional em-bedded semantic relationship between users and product no-des, and uses cosine similarity to measure the semantic similarity between products. Then, according to the hidden Dirichlet topic distribution model, the topic features of the products are obtained, and the cosine similarity is used to calculate the similarity of the topic features between the products. The linear fusion method is adopted to effectively alleviate the data sparse problem and improve the recommendation performance. Through Amazon product reviews Data set to verify the effectiveness of the recommendation algorithm.
Artificial intelligence tools for analyzing emotionally colored information from customer reviews in the service sector

Abstract On the Internet, you can find millions of service reviews. This information can be useful to support decision-making for both potential customers and for managing a service business It is especially important to consider the emotional tone of such reviews, because emotions can attract or repel potential customers. The article discusses the actual task of the automatic processing of customer reviews and identifying emotionally colored information in it. In this paper we provide the definition of emotions and describe the results of the analysis of their classification. Further on, we identify six main emotions from the customer reviews. As our methodological basis we use a well-known approach for accounting emotionally colored information in customer reviews. This approach made it possible to set the task of automatic analysis of customer reviews, which we applied in this paper.
Blockchain and the Law – Legality, Law-like Characteristics, and Legal Applications

Because of the range of potential applications of blockchain, the technology has a complex relationship with the law. As a novel and economically significant form of organization, blockchain’s intersection with the law tends to involve one of three things. First, when describing the most salient application of the technology - to develop and secure cryptocurrency networks - the question tends to deal with the legal status of a given cryptocurrency. Second, blockchains themselves have important law-like characteristics in terms of their effects in constraining and incentivizing network participants to act in the interest of network users. Third, blockchains have several potentially transformative legal applications in terms of facilitating smart contracts and more complex associational forms. In this chapter I detail each of these distinct relationships between blockchain and the law in order to clarify the legal space for blockchain scholars and protocol designers.
Inclusion and Independence: The impact of Mobile Technology on the Lives of Persons with Disabilities in Kenya and Bangladesh

Globally, mobile technology plays a significant role connecting and supporting people with disabilities. However, there has been limited research focused on understanding the impact of mobile technology in the lives of persons with disabilities in low or middle- income countries. This paper presents the findings of a participatory photovoice study looking at the role that mobile phones play in the daily lives of 16 persons with disabilities in Kenya and Bangladesh. Participants used a combination of pictures and voice recordings to capture their own stories and illustrate the impact that mobile phone use has on their lives. Through thematic analysis, we categorized the benefits of mobile phones captured by participants as 1) Improved social connection; 2) Increased independence and 3) Access to opportunities. While mobile phones are ubiquitously used for communication, for persons with disabilities they become essential assistive technologies that bridge barriers to opportunities which are not accessible otherwise. Our paper adds evidence to the need for mobile phones for persons with disabilities to enable communication and connectivity in support of development.
BRUCS: a new system for classifying and naming mappable rock units

A new scheme is introduced for classifying and naming mappable rock bodies that lack primary stratification.In recognition of their distinctive geological characteristics, these 'nonstratiform' bodies are defined and classified according to their 3D form, spatial distribution and genetic relationships, in two hierarchical ( parent-child) chains: one for intrusions and one for tectonometamorphic units.Geologically complex units, encompassing bodies of different genetic classes, are classified in a third chain reserved specifically for 'mixed-class' units.The new classification scheme is offered as an alternative to existing recommendations in the International Stratigraphic Guide and North American Stratigraphic Code, in which nonstratiform bodies are recognized and defined primarily by their lithological character.BRUCS (the BGS Rock Unit Classification System) combines the three new parent-child chains for nonstratiform units with the well-established chain for stratiform units (bed-member-formation-group-supergroup) to create a flexible, practical and effective solution for classifying and naming all mappable rock bodies.The taxonomic rigour of BRUCS means that the considerable capabilities of modern digital systems for managing and communicating mapping data can be exploited fully.
Case-based Reasoning for Resolving the Diversity-accuracy Dilemma of Recommendation System through Subdivision

Recommendation systems are widely used to help their netizens find what they want from cyber space and many various applications have been developed in different areas like news feeding, entree recommending and online shopping, etc. Many researches have found that their system performance is obsession with diversity-accuracy dilemma, and recommender has to balance recommendation sequence results. CBR-recommender is suggested for it has a comprehensive expression of human sense, logics and result explanation, and has more system flexibility to integrate many AI tools in dealing with such diversity-accuracy dilemma. Covering algorithm is also proposed to dynamically cluster similar users or items as subdomain for which all users are dynamitic divided into multiple clusters according to a certain criterion. Our experiments results indicate that users with similar hobbies are assigned to the same sub-domain as many specific sub-classes, and the refined classification results are more conducive for the problem-solving.
Research on Small Object Detection Algorithm Based on Feature Enhancement Module

Abstract Aiming at the disadvantage of the SSD algorithm’s poor detection effect on small objects in the process of detecting objects, a detection method based on feature fusion and feature enhancement is proposed. Our model combines deep and shallow features to improve the model’s ability to detect small objects. Besides, the idea of a channel attention mechanism is introduced into the model structure to increase the feature extraction ability of the network and effectively improve the detection accuracy of the network. Experimental results show that the detection accuracy (mAP) is 2.3% h igher than that of SSD. The detection accuracy (mAP) is significantly improved compared to the current series of excellent object detection algorithms. The algorithm in this paper can detect small objects that are not detected by most SSD algorithms, which improves the average detection accuracy
Information theory and player archetype choice in Hearthstone

Using three years of game data of the online collectible card game Hearthstone, we analyse the evolution of the game’s system over the period 2016–2019. By considering the frequencies that archetypes are played, and their corresponding win-rates, we are able to provide narratives of the system-wide changes that have occurred over time, and player reactions to them. Applying the archetype frequencies to analyse the system’s Shannon entropy, we characterise the salient features of the time series of player choice. Paying particular attention to how entropy is affected during periods of both small and large-scale change, we are able to demonstrate the effects of increased player experimentation before popular decks and tactics emerge. Furthermore, constructing conditional probabilities that simulate understandable player behaviour, we analyse the system’s information storage and test the explain-ability of current player choice based on previous decision-making.
Posture Recognition Based on the Improved Optical Diffractive Neural Network

Optical computing has significant advantages in power consumption efficiency and computing speed. As an optical machine learning framework, diffractive optical neural networks have achieved good results in the application of feature detection and target classification. This paper introduces an optical neural network structure-express wavenet, which has a random shift wavelet pattern and a highway structure, which can greatly reduce the parameters generated in the modulation process of light and solve the problem of gradient disappearance during network training. The classification rate of express wavenet network for the 4 types of different posture and action image data sets reached 87.96%, which is a greater improvement than the 77.8% recognition rate of the diffraction deep neural network D2NN.
Blending Technology with Post Method Pedagogy for Teaching Learning Process

Abstract Pandemic era bring new paradigm in teaching learning process. The lecturers must work hard to create new model, new strategies, and new ways in transmitting their information to the students. This study appears blending technology with post method pedagogy, where the learning process focuses on students need. There are three principles of post method pedagogy, they are particularity, practically, possibility. The lecturers blend conventional interaction with learning based technology. It blends based on students’ need and the target of the material itself. Post method pedagogy gives us challenges to create approptiate method for our class. Lecturers should help the students to success the learning process. The lecturers can used post method pedagogy in main part. In appling three parameters; particularly, possibility and practicallity based on students need.
Recognize Vietnamese Sign Language Using Deep Neural Network

World Health Organization published an article called `Deafness and hearing loss' in March 2020, it said that more than 466 million people in the world lost their hearing ability, and 34 million of them were children. Sign Language has been born and developed for a long time, but its application to communicate has met with many inadequacies and difficulties. Many methods of Computer Vision-based approach gave good results on Sign Language Alphabet Recognition but all of them require the perfect result from background removing step. However, when it comes to real life, removing a complex background is too difficult for any simple background removing algorithms. In this work, our main purpose is to build a model based on deep learning that can recognize Vietnamese Sign Language Alphabet in a complex environment. Results obtained show a robust accuracy of this model in recognizing Vietnamese Sign Language Alphabet.
Hybrid mean impact value with nonnegative garrote for input variable selection of multi-layer perceptron

In the paper, a new adaptive variable selection algorithm for nonlinear regression multi-layer perceptron (MLP) is proposed. The proposed algorithm applies the nonnegative garrote (NNG) to shrink the input weights of the trained MLP. An adaptive indicator is designed by introducing the mean impact value (MIV) algorithm, which can improve the coefficient shrinkage efficiency of the NNG algorithm. The proposed algorithm uses cross-validation and Bayesian information criterion to calculate the optimal shrinkage parameter. The performance of adaptive variable selection algorithm is demonstrated by an example of artificial dataset. Simulation results show that the algorithm has better model accuracy and simplicity than other state-of-art algorithms.
An improved Knowledge Graph Model Based on Fuzzy Theory and TransR

Thispaper relates to the field of knowledge management and information retrieval technology, and specifically provides a method for constructing a knowledge graph. The method for constructing the knowledge graph includes: acquiring triple data, converting the triple data into a normalized triple vector, using the triple vector to construct a knowledge graph based on fuzzy relations, and The minimization objective optimization function of the triple vector is acquired, and the knowledge graph is optimized. This paper reduces the complexity of the knowledge map training process and the time required for the training process.
A Role-Based Orchestration Approach for Cloud Applications

With a rapid growth of cloud service offerings, application developers face a critical challenge to adequately orchestrate their applications on diverse cloud platforms. A desirable orchestration solution should ensure that the applications are deployed, configured, and operated in an appropriate way to the given situation. This paper proposes a model-driven orchestration approach that specifies particular tasks depending on the roles of involved individuals. By keeping the concerns of applications developers and operators separated, our proposed scheme allows them to better focus on what are expected from themselves to achieve the overall orchestration goal. To show the feasibility and effectiveness of the proposal, we present a proof-of-concept system implementation and demonstrate its orchestration operations on Kubernetes platform.
Research on Filling and Sealing Robot Positioning Algorithm with Gaussian Mixture Model

Pharmaceutical manufacturing is a basic yet important industry for human health. And many pharmaceutical manufacturers using robots to ensure pharmaceutical quality and safety. This paper focuses on the Filling and Sealing Robot for Abe bottle pharmaceutical product manufacture. The inaccurate positioning of the bottle mouth will lead to robot operation error. So, we proposed an accurate positioning method for the Filling and Sealing Robot by applying the Gaussian mixture model (GMM). The bottle mouth image was radially expanded with a selected positioning coordinate and clustered by the GMM. The Euclidean metric distance between each cluster center and the mean of input data was calculated and modeled by multiple linear regression and then given the predicted position. Experimental results showed that this method accurately detects the center position of the bottle mouth, which guaranteed successful automatic filling and sealing by the Filling and Sealing Robot within the pharmaceutical assembly line.
MaskNet: Detecting Different Kinds of Face Mask for Indian Ethnicity

The COVID-19 pandemic has rendered social distancing and use of face masks as an absolute necessity today. Coming out of the epidemic, we're going to see this as the new normal and therefore most workplaces will require an identification system to permit employees based on the compliance of protocols. To ensure minimal contact and security, automatic entrance systems need to be employed in workplaces and institutions. For the implementation of such systems, we have investigated the performance of three object detection algorithms, namely SSD MobileNet V2, YOLO v3 and YOLO v4 in the context of real-time face mask detection. We conducted training and testing of these algorithms on our dataset focusing on various type of masks in the Indian community. We have exhibited in this paper that YOLOv4 transcends both YOLO v3 and SSD MobileNet V2 in sensitivity and precision and thus has a major use case in building AI identification systems.
Verification algebra for multi‐tenant applications in VaaS architecture

Summary This paper proposes an algebraic system, verification algebra (VA), for reducing the number of component combinations to be verified in multi‐tenant architecture (MTA). MTA is a design architecture used in SaaS (Software‐as‐a‐Service) where a tenant can customize its applications by integrating services already stored in the SaaS databases or newly supplied services. Similar to SaaS, VaaS (Verification‐as‐a‐Service) is a verification service in a cloud that leverages the computing power offered by a cloud environment with automated provisioning, scalability and service composition. In VaaS architecture, however, there is a challenging problem called ‘combinatorial explosion’ that it is difficult to verify a large number of compositions constructed by both quantities of components and various combination structures even with computing resources in cloud. This paper proposes rules to emerge combinations status for future verification, on the basis of the existing results. Both composition patterns and properties are considered and analysed in VA rules.
Self-Adaptive Ensemble-based Differential Evolution with Enhanced Population Sizing

Differential Evolution (DE) a branch of Evolutionary Algorithm (EA) is recently prominently sough after in teerms of global optimization purpose. The pinpointed issues for this work sheds light on its impact towards population diversity and dimension size. In retrospect of balance incorporation among exploration and exploitation capability, DE emphasizes on preference of control parameters. The algorithm scheme, known as self-adaptive ensemble-based differential evolution with enhanced population sizing (SAEDE- EP), is compared to self-adaptive ensemble-based DE (SAEDE). The SAEDE-EP algorithm is proposed to minimize user setting and exhausting trial-and-error procedure for appropriately configuring scale factor, crossover rate, mutation strategy, and population size. In SAEDE-EP, an ensemble is used to trigger the change of diversity in population when the best solutions between generations have been stagnant for a certain period. The performance is appraised based on 26 benchmark functions comprising on 8 low dimensions and 18 high dimensions. Experimental results indicate that SAEDE-EP achieves maximum success rate with better efficiency in 24 out of the 26 benchmark functions.