Reviewerly/exHarmony huggingface dataset:
Enhancing Graph Convolution Network for Novel Recommendation

Graph convolution network based recommendation methods have achieved great success. However, existing graph based methods tend to recommend popular items yet neglect tail ones, which are actually the focus of novel recommendation since they can provide more surprises for users and more profits for enterprises. Furthermore, current novelty oriented methods treat all users equally without considering their personal preference on popular or tail items. In this paper, we enhance graph convolution network with novelty-boosted masking mechanism and personalized negative sampling strategy for novel recommendation. Firstly, we alleviate the popularity bias in graph based methods by obliging the learning process to pay more attention to tail items which are assigned to a larger masking probability. Secondly, we empower the novel recommendation methods with users' personal preference by selecting true negative popular samples. Extensive experimental results on three datasets demonstrate that our method outperforms both graph based and novelty oriented baselines by a large margin in terms of the overall F-measure.
Genre Classification of Movies from a Single Poster Image Using Feature Fusion

The movie industry is one of the largest and most influential sectors of any community. Each movie in the industry consists of different elements such as actors, directors, preparation elements, posters, etc. One of the most important elements in any movie is its poster, that plays an important role in attracting the audience. Various information can be obtained from the movie poster, including the movie genre. Today, the movie genre is recognized manually. In this paper, we aim to consider the automatic detection of movie genres based on its poster. Automatic detection of movie genres can have various applications in movie archive systems, search engines, recommender systems, and more. In the proposed method of this paper, four categories of embedding features including the objects in the poster, identifying the actors, age, and gender of the actors in the poster, and their facial expressions are used. Our proposed method is compared with some outstanding previous works over the IMDB dataset poster. By incorporating an ensemble classification approach in our work, the results of our proposed method could achieve the average predicting accuracy of 92&#x0025; which could outperform the previous works.
Handwritten Note Recognition Based on Digital Music Classroom Teaching

With the rapid development of computer technology, people pay more and more attention to the application of modern information technology in education and teaching. A large number of teaching software has gradually infiltrated into the teaching of various disciplines. However, the contradiction between the low-speed input of traditional software and the high-speed processing of computer has brought inconvenience to teaching, which urgently needs to transform the traditional keyboard based input into handwritten input in line with people’s daily habits, so as to improve the efficiency of classroom teaching. Based on the metaphor of “pen and paper”, this paper studies the online recognition of handwritten notes in music teaching with the idea of user centered natural interaction, so as to provide a natural and efficient teaching environment for music education.
Manifold biomedical text sentence embedding

Pretrained distributed sentence embeddings have been proven to be useful in various biomedical text tasks. However, the current research on biomedical text sentence embeddings is mainly based on Euclidean space. The geometric structure of sentences and the relations with the representations of sentence context contribute to more accurate representations of sentence semantics and still need further investigation. To address this issue, in this study, we propose a manifold biomedical text sentence embedding model. To learn biomedical text sentence embedding in the manifold space, we develop an efficient optimization algorithm with neighbourhood preserving embedding based on manifold optimization. We conducted experiments on two tasks of biomedical text classification and clustering, and the experimental results outperformed the state-of-the-art baseline models.
Encoding hieroglyph segments to represent hieroglyphs following the bag of visual word model for retrieval

The representation of hieroglyphs in retrieval systems represents a great challenge since these systems’ results highly depend on the used representation. In the literature, the most successful works in this area compute local descriptors from a set of points of interest taken from the entire hieroglyph and use them under the BoVW model to represent hieroglyphs. Unlike these works, this paper presents a way to extract segments from hieroglyphs and, by encoding the extracted segments through local descriptors, proposes to use them under the Bag of Visual Words (BoVW) model to represent hieroglyphs. Our experiments show that the proposed representation allows us to obtain retrieval results that overcome those reported by using state of the art representations.
Operationalizing Rule-Based Socio-Ethical Behaviour in IoT Collectives: a Policy Management Approach

We are witnessing a trend of IoT devices with agency and often said to be smart (i.e., connected, sensor-equipped, and capable of autonomous action). This paper presents a policy management framework which we have named as the SANIJO policy framework. The framework consists of two types of policies, which are socio-ethical and administrative policies. Each policy further comprises three different types of rules, which are authorisation, obligation and prohibition rules that are applicable to smart internet-connected devices for managing their interactions in IoT collectives. An XML policy language which we have named as the SANIJO language is also designed for describing the desired interaction behaviours of smart devices. The policy management framework and the policy language are prototyped in the SANIJO system, which is a collection of mobile applications, multiple robot applications, smart speakers and smart camera applications. The SANIJO system is then illustrated based on an education open day scenario.
Signaling irony, displaying politeness, replacing words

Abstract This paper aims to identify and define the different linguistic functions that emoji can carry in French and English computer-mediated discourse (CMD). Emoji are graphical icons, representing attitudes or concepts, that can be used on any smartphone or computer with a variety of communicative roles inside a conversation. Using a multimodal corpus containing 1200 emoji and the messages in which they were used, we tested an initial typology of emoji functions inspired by previous frameworks. The quantitative analysis of the data, along with the detailed examination of specific examples, led to the construction of a new, more refined typology consisting of three primary functions (expressive, interpretative and referential) and five secondary functions (relational, politeness, emphatic, structural and aesthetic).
Tempo Synchronization of Physical Activities with a Mixed-Reality Human-Machine-Interface

Physical trainers aid trainees to increase their motor learning of physical activities by providing multimodal feedback as well as adapting the training to their needs and progress. However, due to the fact that trainers are a scarce resource, added to the social distancing caused by the COVID19 pandemic, not many people have access to a trainer. In order to encourage people to continue practicing physical activities, this work presents a multimodal human-machine interface that automatically coaches ballroom dance. This is achieved by providing haptic, auditory, and visual feedback of the reference position and velocity trajectories, as well as its tempo. The proposed approach was tested in an experiment with 20 subjects. It was found out that subjects were able to reduce their position, velocity, and synchronization errors during and after the training, suggesting that users can retain the learned skills. Furthermore, we observed that higher performance is obtained at faster tempos.
Multi-UAV Collaborative Path Planning Based on Improved Genetic Algorithm

With the increase of mission requirements, multi-Unmanned Aerial Vehicle cooperative work has been widely used in many fields. However, when performing tasks in a complex environment, how to quickly plan the collaborative flight path of multi-UAV is always a challenging research topic. Aiming at the problem that the traditional genetic algorithm has a single way of connecting path points in the process of path planning, it is difficult to adapt to the complex environment and the long path length, this paper proposes an improved genetic algorithm. Wherein, the Rapidly-Exploring Random Tree is used as the initial path scheme and insertion operator. Then, the proposed algorithm introduces a deletion operator to increase the path length, removes the crossover operator which has little contribution to the path optimization, and takes the trajectory of other UAVs in the cluster as the constraint condition in the planning. Finally, the improved genetic algorithm is applied to the cooperative path planning of multi-UAV. The simulation results show that compared with the traditional genetic algorithm, the path length of the improved genetic algorithm is reduced by 38%, and the time consumption of the improved genetic algorithm is reduced by 77%.
Mixup Augmentation for Deep Hashing

Deep hashing methods has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval, but exhibit undesirable behaviors such as sensitivity to adversarial examples. In the paper, inspired by the success of mixup-based data augmentation in adversarial training, we for the first time apply this technique to deep hash codes-based image retrieval, and evaluate its performance on six kinds of typical deep hashing methods. Experiments show that the mixup augmentation indeed could provide stable performance gains, ranging from 0.2% to 1.3%.
A Novel Method to Prevent Multiple Withdraw Attack on ERC20 Tokens

ERC20 is the first token standard on Ethereum and is widely used in ICOs, voting, and various asset representations. However, some methods defined in ERC20 imply potential vulnerabilities and Multiple Withdrawal Attack is one of them. Attackers can transfer more tokens than the actual allowance through this vulnerability. The current prevention methods for Multiple Withdrawal Attack include changing the transaction process, modifying the API of ERC20, and modifying the implementation of functions, etc. However, these methods have disadvantages such as poor compatibility, incomplete resolution, and high gas consumption. In this paper, we describe the process of Multiple Withdrawal Attack and analyze the shortcomings of the existing methods, and then propose a solution with lower gas consumption. In our method, a variable is added to record the allowance in the approval function to prevent tokens from being transferred repeatedly. Finally, the effectiveness and the performance of the proposed method is analyzed. The result shows that the method proposed in this paper is safe and has lower gas consumption than the existing methods.
Java Cryptography Architecture

The Java Cryptography Architecture (JCA) framework offers capabilities for working with cryptography algorithms and offers methods for a variety of cryptography primitives. The cryptography algorithms and methods are implemented and offered through the java.security package.
The Role of the Paper Packaging Industry in the Circular Economy

The circular economy movement has paved the way for the sustainable supply chains with its nature-friendly structure via recycling and repeated use of products. Hence, this study aims to determine the factors affecting the paper packaging industry in the circular economy in order to provide a conceptual model of the cause-and-effect relationships through neutrosophic cognitive mapping. Paper packaging machinery manufacturer Windmöller &amp; Hölscher's representatives have been in-depth interviewed to gather their opinions. Since there are some relations that cannot be determined (i.e., existence of indeterminacy), neutrosophy became a necessity in this chapter. Delphi technique is implemented to obtain a single neutrosophic adjacency matrix. As a result, the sustainable supply chain structure based on environmental awareness and recycling has been modeled visually, and the causal relationships were determined.
Differential Transform for Video-Based Plenoptic Point Cloud Coding

Point cloud compression has been studied in standard bodies and we are here concerned with the Moving Picture Experts Group video-based point cloud compression (V-PCC) solution. Plenoptic point clouds (PPC) is a novel volumetric data representation wherein points are associated with colors in all viewing directions to improve realism. It is sampled as a number ( Nc ) of attribute colors per point. We propose a new method for the efficient video-based compression of PPC that is backwards compatible with the existing single-color V-PCC decoder. V-PCC generates three image atlases which are encoded using an image/video encoder. We assume there may be a reference color which is to be encoded as the main payload. We generate Nc+3 atlases and we produce Nc differential images against the reference color image. Those difference images are pixel-wise transformed using an Nc -point discrete cosine transform, generating Nc transformed atlases which are encoded, forming the secondary payload. Such secondary information is the plenoptic enhancement to the point cloud. If there is no reference attribute, we skip the differences and use the lowest frequency of the transformed atlases as the main payload. Results are presented that show an unrivaled performance of the proposed method.
Generalized Negative Sampling for Implicit Feedback in Recommendation

In a typical model-based collaborative filtering with implicit feedback, negative sampling is getting more and more popular to obtain negative labeled inputs from massive unobserved data. However, this approach wrongly samples false negatives, resulting in unacceptable recommender model. In this work, we first identify a situation where false negatives are problematic and estimate their impact on a model accuracy. Then, we take our negative sampling as a classification task and demonstrate that a recommender model and a negative sampling method actually share the same goal: identification of false negatives from other unobserved data. We also estimate the actual upper bound of the accuracy improvements with a feasible negative sampling. Lastly, we propose a generalized negative sampling that can alleviate the impact of false negatives by introducing two robustness against false negatives: self-sampling and dynamic sub-sampling. In user-item interaction matrix, experimental results on publicly available datasets show that our approach outperforms some state-of-the-arts with statistical significance.
Applying Human Values Theory to Software Engineering Practice: Lessons and Implications

The study of human values in software engineering (SE) is increasingly recognised as a fundamental human-centric issue of SE decision making.However, values studies in SE still face a number of issues, including the difficulty of eliciting values in a systematic and structured way, the challenges of measuring and tracking values over time, and the lack of practice-based understanding of values among software practitioners.This paper aims to help address these issues by: 1) outlining a research framework that supports a systematic approach to values elicitation, analysis, and understanding; 2) introducing tools and techniques that help elicit and measure values during SE decision making processes in a systematic way; and 3) applying such tools to a month-long research sprint co-designed with an industry partner and conducted with 27 software practitioners.The case study builds on lessons from an earlier pilot (12 participants) and combines in-situ observations with the use of two values-informed tools: the Values Q-Sort (V-QS), and the Values-Retro.The V-QS adapts instruments from values research to the SE context, the Values-Retro adapts existing SE techniques to values theory.We distil implications for research and practice in ten lessons learned.
Improving Security and Privacy in Attribute-Based Encryption with Anonymous Credential

Attribute-based encryption [19] is developed based on fuzzy identity encryption. In the identity-giving cryptosystem, the user’s public key can be any information that identifies the users. The original ABE schemes define a Central Authority (CA) for key management, allowing users to verify the ownership of attributes and thereby get access to sensitive resources. However, this CA construction is contradictory to the distributed computing requirement and compromises the users’ privacy. Meanwhile, it does not fit the large-scaled computing environment. Thus, in our paper, we propose a new anonymous, decentralized, unlinkable access and collusion attack that prevented CP-ABE scheme with a pseudonym system by Chaum to improve user privacy and data security.
Application of Cryptography Based on Elliptic Curves

ECC (Elliptic Curve Cryptography) is based on the algebraic structure of elliptic curves over finite fields that allows the use of smaller encryption keys compared to other cryptographies (e.g., using Galois fields) while maintaining an equivalent level of security [1]. Elliptic curves are used for key exchange, digital signatures, pseudo-random generators, and other tasks. They can also be used for encryption in combination with symmetric encryption. The content of this article is the protection of data transmission in a wireless network using elliptic curves.
Efficient Analogy-based Software Effort Estimation using ANOVA Convolutional Neural Network in Software Project Management

The analogy-based software effort estimation (SEE) technique assesses the requisite effort aimed at an advanced software project centred upon the whole effort utilized in accomplishing past similar projects. Therefore, it is not often probable aimed at antedating the precise guesses in the software development effort’s estimation. Existent works have established numerous techniques aimed at SEE, however, no consensus regarding the methodology and settings apt to generate precise estimates are attained. Aimed at boosting the accuracy and decrementing the training time, this research methodology presented an ANOVA convolutional neural network (A-CNN) methodology for effort estimation (EE). Initially, the input data are pre-processed, then the features are taken out as of the pre-processed data and the vital features are chosen by employing the linear scaling-based deer hunting optimization (LS-DHO) technique. Then, the weighting value is enumerated aimed at the features selected. Utilizing the modified K-harmonic means (MKHM) algorithm, the similar values are clustered centred on those values. Consequently, the data clustered are inputted to the A-CNN algorithm. It estimates the software project developments’ effort. In an experiential evaluation, the methodology proposed is examined with the existent research methodologies centred upon the accuracy, mean magnitude of relative error (MMRE), PRED, and also execution time metrics. The methodology proposed attains excellent outcomes analogized to the existent methodologies.
Deep Ranking Exemplar-Based Dynamic Scene Deblurring

Dynamic scene deblurring is a challenging problem as it is difficult to be modeled mathematically. Benefiting from the deep convolutional neural networks, this problem has been significantly advanced by the end-to-end network architectures. However, the success of these methods is mainly due to simply stacking network layers. In addition, the methods based on the end-to-end network architectures usually estimate latent images in a regression way which does not preserve the structural details. In this paper, we propose an exemplar-based method to solve dynamic scene deblurring problem. To explore the properties of the exemplars, we propose a siamese encoder network and a shallow encoder network to respectively extract input features and exemplar features and then develop a rank module to explore useful features for better blur removing, where the rank modules are applied to the last three layers of encoder, respectively. The proposed method can be further extended to the way of multi-scale, which enables to recover more texture from the exemplar. Extensive experiments show that our method achieves significant improvements in both quantitative and qualitative evaluations.
Integrity Assurance Method of Multi-Keyword Query for Encrypted Outsourced Data

As the data scale exceeds the petabyte level, more and more users outsource their local databases to third-party service providers to save local storage costs and avoid cumbersome local data management.However, because thirdparty service providers are not fully trusted, they may leak outsourced data and bring security risks to the personal privacy of data users.The service provider may also return incorrect or incomplete query results to the data user.Therefore, in this paper, we propose a Lightweight and Verifiable Multi-keyword Query Scheme for the integrity verification of multi-keyword query of outsourced data.This scheme supports multi-keyword query integrity verification and does not require a third-party auditor, combining a hash-based accumulator that provides correctness verification and a cuckoo filter that provides integrity verification to form a provably secure composite verification structure, and at the same time, it uses controllable Paillier encryption to support data update operations.This scheme strikes a balance between the cost of verification structure generation, verification object generation, and proof verification cost to enable data users to perform secure multi-keyword query while efficiently verifying the integrity of query results.The security proof and the performance analysis show that the proposed scheme is secure and efficient for practical deployment.
An overview of the application of deep learning in face age image recognition

Face provides perceptible information such as a person's age, gender, identity, race and emotion, and age is one of the more difficult information to identify. Because age change is a random and personalized process, age recognition poses a dynamic theoretical and practical challenge to the field of computer vision and pattern recognition. This paper introduces the application, process and method of face age image recognition from two aspects of face image age recognition and cross-age recognition, and focuses on the application of deep learning in face age image recognition, which provides convenience for those who study the application of deep learning in face age recognition.