Reviewerly/exHarmony huggingface dataset:
Weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity

A recommendation system (RS) can be defined as a tool that can filter an abundant amount of information and provide suggestions based on the preferences and the behavior of a user. Two of the most popular and widely used RSs are Collaborative Filtering (CF) and Content-based Filtering (CBF). Both of these RSs have their highlights and challenges. CF makes recommendations based on the past ratings given by a user to different items. This makes the recommendations more personalized, but on the downside, if the user or the item is new, CF becomes unreliable. CBF only takes the features of an item into consideration while making recommendations since it assumes that each user is independent. This makes the recommendations common for all the users. But, on the plus side, CBF does not suffer from a new item cold-start problem. To alleviate the individual weaknesses of these systems, a hybrid model that combines the two RSs can be employed. This paper proposes a Weighted Hybrid RS that employs model-based CF using Singular Value Decomposition (SVD), and CBF using cosine similarity. To evaluate the proposed system and compare it with other systems, personalization and diversity have been adopted as the primary evaluation metrics. The experiments conducted in this paper indicate that our proposed system generates a significantly better result than the individual CF and CBF techniques, as well as a cascaded hybrid system in terms of personalization and diversity. Our system also alleviates the new item cold-start problem associated with CF.
A Method for Identification and Ranking of Requirements Sources

Context: Requirements engineering (RE) literature acknowledges the importance of early stakeholder identification. The sources of requirements are many and also constantly changing as the market and business constantly change.Identifying and consulting all stakeholders on the market is impractical; thus many companies utilize indirect data sources, e.g. documents and representatives of larger groups of stakeholders. However, companies often collect irrelevant data or develop their products based on the sub-optimal information sources that may lead to missing market opportunities.Objectives: We propose a collaborative method for identification and selection of data sources. The method consists of four steps and aims to build consensus between different perspectives in an organization.Methods: We develop the method following the design science research method. We demonstrate the use of the method with three industrial case studies. Results: Our results show that the method can support the identification and selection of data sources in three ways: (1) by providing systematic steps to identify and prioritize data sources for RE, (2) by highlighting and resolving discrepancies between different perspectives in an organization, and (3) by analyzing the underlying rationale for using certain data sources.Conclusions: We conclude that our proposed method is well suited to support systematic identification of requirements sources in industry. Further work on the method include validation and adaptation for use different contexts, and developing tool support.
Mechanism Design Approaches to Blockchain Consensus

Blockchain consensus is a state whereby each node in a network agrees on the current state of the blockchain. Existing protocols achieve consensus via a contest or voting procedure to select one node as a dictator to propose new blocks. However, this procedure can still lead to potential attacks that make consensus harder to achieve or lead to coordination issues if multiple, competing chains (i.e., forks) are created with the potential that an untruthful fork might be selected. We explore the potential for mechanisms to be used to achieve consensus that are triggered when there is a dispute impeding consensus. Using the feature that nodes stake tokens in proof of stake (POS) protocols, we construct revelation mechanisms in which the unique (subgame perfect) equilibrium involves validating nodes propose truthful blocks using only the information that exists amongst all nodes. We construct operationally and computationally simple mechanisms under both Byzantine Fault Tolerance and a Longest Chain Rule, and discuss their robustness to attacks. Our perspective is that the use of simple mechanisms is an unexplored area of blockchain consensus and has the potential to mitigate known trade-offs and enhance scalability.
Active Defense Decision Model Based on Multi-stage Attack Defense Game

In the previous research of network attack and defense confrontation, it is often set that the attacker is in the active state and the defender is in the passive state. For this research loophole, a multi-stage offensive and defensive game model in which the defender actively sends inducement signals and carries out security defense is constructed, and a decision-making algorithm dominated by the defender on the basis of the multi-stage offensive and defensive game model is proposed. Firstly, under the premise of incomplete information constraints and complete rationality of both sides of the game, a multi-stage offensive and defensive game model dominated by defenders is established. Based on attack and defense analysis, the equilibrium solution method of multi-stage network offensive and defensive game is improved, and the optimal network assault and guard strategy selection algorithm is designed. Finally, a real network attack scenario is constructed through simulation, and the proposed model is verified and analyzed. The analysis results show that the proposed assault and protect game model is more in line with the actual network security situation, and a detailed solution algorithm is given for the accurate selection of the optimal defense strategy in different stages.
Mining Minimal Non-redundant Image Association Rules Using Statistical Texture Feature Based Frequent Itemsets

A method for mining images using minimal non-redundant image association rules based on frequent itemset is presented in this paper. It is a two-phase approach. Firstly, we pre-processed the images and extract statistical texture features. In the second phase, a feature vector is created using statistical texture features which are then further optimized with the help of feature selection and discretization. Using this optimized feature vector, transaction database is prepared for mining image association rules using Apriori algorithm. We further apply HCIAR algorithm to obtain minimal non-redundant image association rules based on frequent itemsets. Outcomes of the experiment reveal that mining images is viable and gives highly correlated image association rules.
A Parallel Implementation of FastBit Radix Sort Using MPI and CUDA

The sorting operation arranges the data in an easily reproducible format, making it easy to search and store the data. With the search operation being highly optimized on sorted data, it is no surprise that sorting has become a very important computing operation. Many sorting algorithms have been developed over the years and the main aim is to reduce the time and space complexities for sorting in the worst- and average-case scenarios. Radix sort is one of the non-comparative-based sorting algorithms that performs the sorting operation in linear time. This paper proposes a parallel approach on a variation of Radix Sort namely, FastBit Radix Sort. 9 × speedup is achieved through the parallel algorithm proposed.
Automatic Story Generation: Case Study of English Children’s Story Generation Using GPT-2

Generative Pre-trained Transformer 2 (GPT-2) has shown awe-inspiring effectiveness of pre-trained language models on several tasks, especially the generation of coherent text. Automatic stories generation represents a great research area that has rarely been studied in the past and presents a unique issue for artificial intelligence. For this reason, we present in this work a novel approach to automatic children’s stories generation based on Generative Pre-trained Transformer 2 to increase reading skills. In our implementation, we identified the Simple Transformers library, which is built like a wrapper around the famous Trans-formers library by Hugging Face. This allows us to train and evaluate Transformer models very quickly.
Establishment of Deep Learning Financial Product Recommendation Process

The life cycle of commercial banks' financial products is designed based on long term credit assignment. In addition, the financial products sold by the first financial sector are subject to various regulations and institutional controls on the financial products of commercial banks. Therefore, an AI methodology that takes into account these institutional regulations and consumer protection aspects is needed, and the process to which it is applied must be structured.
Classification of Bone Marrow Cells for Medical Diagnosis of Acute Leukemia

Leukemia is the cancer that starts in the blood cells due to the excess production of immature leucocytes that replace the cells with normal blood cells.Physicians rely on their experience to determine the type and subtype of Leukemia from the blood sample.Most people are misdiagnosed when it comes to its subtypes, the error rates can be up to 40% during the classification process.That too depends on the expertise of the physician.This research represents a Convolutional Neural Network based medical image classifier.The proposed technique can classify Leukemia and its five subtypes.State of the art deep learning and transfer learning techniques have been used, all of the network architectures are fine-tuned followed by feature extraction.This research can assist the pathologist in decision making or can be used for the process automation.The proposed methodology can give better results in terms of accuracy and is more effective than the hematologist's visual classification in terms of time.
Argument Dialectic

My analysis of the Copleston-Russell debate can be described as both logical and dialectical. Our understanding of the main perspectives on argumentation derives largely from Wenzel’s three Ps principle: the rhetorical perspective understands and evaluates arguing as a natural process of persuasive communication; the dialectical perspective understands and evaluates arguing as a procedure or cooperative method for making critical decisions; and the logical perspective understands and evaluates arguments as products that people create. My analysis is logical in that it focuses on intra- and inter-argumentative relations, and at the same time it is dialectical in that it places at the core the relations of opposition between arguments.
Word Equations in the Context of String Solving

String solvers are tools for automatically reasoning about words over some finite alphabet. They are commonly used to perform analyses of string manipulating programs. A fundamental problem which string solvers need to address is solving word equations, usually in combination with additional constraints involving e.g. string lengths or regular languages. In this article, a survey of results on the topic of word equations is presented with an emphasis on recent results which are relevant to the theoretical foundations of string solvers.
Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.
A Micro-moment recommendation framework in industrial environments

Today, a large part of the labor policies in the EU aim at extending the active participation of older (i.e. 50+) employees in the workforce in order to avoid the respective pressure on the national economies and health systems as well as potential shortcomings in qualified personnel due to demographical changes in the entire population. Preventing involuntary early retirement goes hand in hand with supporting self-sufficient and healthy living. The present work considers the use and exploitation of modern technological advancements to support the achievement of the above goal. Specifically, we propose a new approach to developing complex recommendation systems, which are capable of monitoring and supporting the daily activities of employees in a personalized manner, both at work and during their broader daily activities. The proposed approach is based on the new Micro-Moments (MiMos) concept for critical event recognition, incorporating multiple streams of complementary information from a distributed sensor network that is flowing into the system based on IoT technologies. The recommendation system follows a user-centered approach for providing (personalized) suggestions that support the occupational safety of users, improve their health and enhance their productivity, in a personalized way. This paper summarizes the concept of Micro-Moments (MiMos) and how it contributes to issuing recommendations based on specific user needs. We also present the current version and implementation of the system in the field of port logistics, where it is observed that recommendations delivered at the right time to the right person can help improve the efficiency of the workforce and extend its working capacity.
Digital Single Market, First Stop to The Metaverse: Counterlife of Copyright Protection Wanted

Building upon the “fair use by design” concept of Niva Elkin-Koren, this chapter is exploring how artificial intelligence can be used to implement exceptions or limitations. Section 2 will give a brief overview of the evolution of the copyright acquis in the US and EU, in regard to platforms, discuss the implications of strict liability in an era of massive online use and infringements, which already has pushed and, a fortiori, will push platforms in the metaverse in the direction of automatic, scalable solutions, which on their turn, will increase the need for sufficient safeguards of unauthorized but legal use that falls under an exception or limitation. Section 3 introduces the implications of the metaverse in regard to intermediary liability of copyright infringement and the need for “breathing space” for users and experimenting. Section 4 explores the safeguards for legitimate use of content, which includes exceptions and limitations. Building on Elkin-Koren’s “fair use by design” concept, Section 5 provides the prerequisites of designing algorithmic exceptions or limitations, and whether automated content recognition tools should be qualified as “high risk AI” under the proposed Artificial Intelligence Act, and incentives against over-blocking. Followed by Section 6 which provides the Conclusions.
Bidirectional Gated Edge-Labeling Graph Recurrent Neural Network for Few-Shot Learning

Many existing graph-based methods for few-shot learning problem focused on either separately learning node features or edge features or simply utilizing graph convolution, failing to fully retain or exploit graph structure information. In this article, we proposed a bidirectional gated edge-labeling graph recurrent neural network (bi-GEGRN) which adopts both edge-labeling graph framework and graph convolution operation in the meta-learning scheme. We modified the gated graph neural network to adjacency matrix generator-based bidirectional formation which is able to process sequence graph data in two directions and then organically combined it with edge-labeled graph framework to cyclically upgrade features meanwhile aggregate graph structure information. In view of the excellent aggregating capability of graph convolution and good performance of the alternately cyclic update strategy, bi-GEGRN improves the information transferring between tasks in meta learning. To verify the validity and universality on both supervised and semi-supervised regimes, extensive experiments were conducted on three few-shot benchmark data sets and bi-GEGRN showed a good performance.
Efficient Medical Image Encryption Framework against Occlusion Attack

Image encryption has attracted a lot of interest as an important security application for protecting confidential image data against unauthorized access. An adversary with the power to manipulate cipher image data can crop part of the image out to prevent decryption or render the decrypted image useless. This is known as the occlusion attack. In this paper, we address a vulnerability to the occlusion attack identified in the medical image encryption framework recently proposed in []. We propose adding a pixel scrambling phase to the framework and show through simulation that the extended framework effectively mitigates the occlusion attack while maintaining the other attractive security features. The scrambling is performed using a separate chaotic map which is securely initialized using a secret key and a random nonce to deter chosen-plaintext attacks. Moreover, we show through simulation that the choice of chaotic map used for scrambling is irrelevant to the effectiveness of the scrambling algorithm against the occlusion attack.
Datalog Static Analysis in Secrecy

We present a secure static-analysis-as-a-service (SaaaS) system where a client may outsource static analysis to the cloud. To address copyright concerns associated with SaaaS, clients are allowed to encrypt the source code of a target program and upload it to the cloud. Our goal is to secure the privacy of the design and implementation of static analysis as well as the source code of the target program. Considering a family of static analyses written in Datalog, we propose a generic protocol that combines homomorphic encryption (HE) with secure two-party computation to manage the huge cost of HE operations. The server occasionally delegates sub-parts of analysis which are costly in the cipher-world to the client without exposing the design of analysis. During server-client interactions, the information of both sides (client and server) is not leaked to the opposite. We evaluated our system on two static analyses in Datalog in secrecy, which have not been feasible using the previous techniques. For example, Andersen pointer analysis is completed in an average of 45 mins for 14 C programs comprising up to 1.6 KLoC.
Adversarial Defense with Secret Key

Adaptive attacks are known to defeat most adversarial defenses including state-of-the-art ones, so conventional defenses either reduce the classification accuracy or are completely broken. To overcome this problem, an encryption-inspired adversarial defense with a secret key was proposed motivated by image encryption methods. The adversarial defense with a secret key achieves high classification performance even when adaptive attacks were carried out. The adversarial defense was also demonstrated to outperform state-of-the-art defenses. In this chapter, the adversarial defense with a secret key is demonstrated to be robust against various attacks in different datasets.
Ftan: Frame-to-Frame Temporal Alignment Network with Contrastive Learning for Few-Shot Action Recognition

Most current few-shot action recognition approaches follow the metric learning paradigm, measuring the distance of any sub-sequences (frames, any frame combinations or clips) between different actions for classification. However, this disordered distance metric between action sub-sequences ignores the long-term temporal relations of actions, which may result in significant metric deviations. What’s more, the distance metric suffers from the distinctive temporal distribution of different actions, including intra-class temporal offsets and inter-class local similarity. In this paper, a novel few-shot action recognition framework, Frame-to-frame Temporal Alignment Network (FTAN), is proposed to address the above challenges. Specifically, an attention-based temporal alignment (ATA) module is devised to calculate the distance between corresponding frames of different actions along the temporal dimension to achieve frame-to-frame temporal alignment. Meanwhile, the Temporal Context module (TCM) is proposed to increase inter-class diversity by enriching the frame-level feature representation, and the Frames Cyclic Shift Module (FCSM) performs frame-level temporal cyclic shift to reduce intra-class inconsistency. In addition, we present temporal and global contrastive objectives to assist in learning discriminative and class-agnostic visual features. Experimental results show that the proposed architecture achieves the state of the art on HMDB51, UCF101, Something-Something V2 and Kinetics-100 datasets.
Deepfake Images, Videos Generation, and Detection Techniques Using Deep Learning

Deep learning is utilized as a deepfake detection and creation method. Deepfake produces fake videos and images, which are difficult to distinguish between authentic images or videos. A free software learning platform has allowed the development of credible faceswaps in videos in recent months, which leaves only a small residue of manipulation in so-called deepfake videos. GANs create deepfakes with two computer models coming out. One is trained on the dataset, and the other aims to explain deepfakes faults. Forger produces fakes until another model does not identify forgery. Deepfakes create fake news that can contribute to financial and social fraud, videos, photographs, and terrorism. Religions, organizations, citizens and societies, environment, protection and democracy are increasingly being influenced. These realistic fake videos cause political distress, challenging someone, or fake terrorist activities are easily conceived. When deep videos and photographs expand in social media, people disregard the facts. This analysis discussed available datasets, deepfake development, deepfake issues, and fake video detection techniques, including fake video detection using GANs. Besides this, deep learning techniques for deepfake detection detailing are provided in this paper.
Temporal-Attribute Inference Using Dynamic Bayesian Networks

As social networks continue to grow in popularity, it is essential to understand what can be learned about private attributes of social-network users by mining social-network data. Previous work focused on the inference of time-invariant attributes such as personality traits. By contrast, in this paper we focus on the inference of dynamic, time-varying attributes. We present a new approach to modeling social-network users and mining time-varying attributes using dynamic bayesian networks (DBNs). We then explore the extent to which such temporal models can improve the inference results of various dynamic attributes. This work is the first to take a DBN-based approach to the task of private-attribute inference in social networks.