Reviewerly/exHarmony huggingface dataset:
Interval type-2 fractional fuzzy inference systems: Towards an evolution in fuzzy inference systems

Following type-1 fractional fuzzy inference systems presented recently as the new generation of fuzzy inference systems, interval type-2 fractional fuzzy inference systems (IT2FFISs) as a leap further ahead in the evolution of fuzzy inference systems (FISs) are introduced in this article. The IT2FFISs, which are outlined in this article, add to the armamentarium of FISs some particular concepts such as interval type-2 fractional membership functions, type-2 fractional translation rule, type-2 fracture index, the concept of switching, the entanglement, the degeneracy concept, and so forth. An IT2FFIS exploits not only the tolerance for the uncertainty in the interpretation of the meaning of a word, but also the relevance between the quality and quantity levels of the given information to infer an answer to an inference query. The IT2FFISs make an increase in machine intelligence quotient possible by an increase in the range of FISs order rather than their type. Moreover, the synergy of the concepts coming with various modes of IT2FFISs such as the aggressive mode opens a gate to a space of fuzzy systems outputs which used to be indiscoverable. Furthermore, it is demonstrated that as the type-2 fracture index approaches zero, the space of IT2FFISs outputs contracts and eventually it coincides the space of IT2FISs output when the fracture index is equal to zero. It is also proved that, provided a particular order of the IT2FFIS is taken into account, independent of the problem in question, a typical IT2FIS never leads to results which are more satisfactory than those obtained by the IT2FFIS corresponding to the typical IT2FIS.
Pairwise VLAD Interaction Network for Video Question Answering

Video Question Answering (VideoQA) is a challenging problem, as it requires a joint understanding of video and natural language question. Existing methods perform correlation learning between video and question have achieved great success. However, previous methods merely model relations between individual video frames (or clips) and words, which are not enough to correctly answer the question. From human's perspective, answering a video question should first summarize both visual and language information, and then explore their correlations for answer reasoning. In this paper, we propose a new method called Pairwise VLAD Interaction Network (PVI-Net) to address this problem. Specifically, we develop a learnable clustering-based VLAD encoder to respectively summarize video and question modalities into a small number of compact VLAD descriptors. For correlation learning, a pairwise VLAD interaction mechanism is proposed to better exploit complementary information for each pair of modality descriptors, avoiding modeling uninformative individual relations (e.g., frame-word and clip-word relations), and exploring both inter- and intra-modality relations simultaneously. Experimental results show that our approach achieves state-of-the-art performance on three VideoQA datasets: TGIF-QA, MSVD-QA, and MSRVTT-QA.
Towards optimal quality requirement documentation in agile software development: A multiple case study

Agile software development (ASD) promotes minimal documentation and often prioritizes functional requirements over quality requirements (QRs). The minimal documentation emphasis may be beneficial in reducing time-to-market for software. However, it can also be a concern, especially with QRs, since they are challenging to specify and document and are crucial for software success. Therefore, understanding how practitioners perceive the importance of QR documentation is valuable because it can provide insight into how they approach this task. It also helps in developing models and guidelines that support the documentation of QRs in ASD, which is a research gap. We aim to understand practitioners’ perceptions of QR documentation and factors influencing this task to derive a model that supports optimal QR documentation in ASD. We conducted a multiple case study involving 12 participants from three cases that apply ASD. Practitioners identify QR documentation as important and perceive it as contributing to ensuring quality, clarifying QRs, and facilitating decision-making. Time constraints, QR awareness, and communication gaps affect QR documentation. Missing and outdated QR documentation may lead to technical debt and a lack of common understanding regarding QRs. We introduce a model to support optimal QR documentation in ASD by focusing on the factors: time constraints, QR awareness, and communication gaps. The model provides a representation and explanation of the factors affecting QR documentation in ASD and identifies mitigation strategies to overcome issues that may occur due to these factors. The study reveals the importance of documenting QRs in ASD. It introduces a model that is based on empirical knowledge of QR documentation practices in ASD. Both practitioners and researchers can potentially benefit from the model. For instance, practitioners can analyze how time constraints or QR awareness affect documentation, see potential issues that may arise from them, and utilize strategies suggested by the model to address these issues. Researchers can learn about QR documentation in ASD and utilize the model to understand the topic. They can also use the study as a baseline to investigate the topic with other cases.
Class-Incremental Learning Network for Small Objects Enhancing of Semantic Segmentation in Aerial Imagery

Due to the differences in the feature distribution between classes, when the model learns in a continuous data stream, it will encounter catastrophic forgetting. The incremental learning methods have shown great potential to solve this problem. However, most existing methods based on task-incremental learning are difficult to adapt to characteristics of remote sensing scenes with few differences in appearance but large differences in features, which is not conducive to artificially distinguish task-identity document (ID). Thus, we propose a class-incremental learning (CIL) network for small objects enhancing semantic segmentation in aerial imagery. Specifically, considering the superior accuracy of the binary classifier, we propose a twin-auxiliary (TA) model that adds an auxiliary binary classification task. Then, for expansion and contraction at the edge and small object confusion problems, we introduce a diversity distillation loss, using the results of binary-classifier to constrain the multiclass segmentation results and strengthen the attention to the locations of the segmentation results that have changed. Finally, we design a conflict reduction mechanism for multihead classifier to achieve single-head prediction for CIL. Experiments demonstrate that our method has good performance on the Vaihingen and Potsdam datasets by the International Society for Photogrammetry and Remote Sensing (ISPRS), outperforming state-of-the-art (SOTA) incremental learning methods. The code will be available soon.
Short Text Classification Using Contextual Analysis

Micro blogging tools provide a real time service for the public to express opinions, to broadcast news and information and offer an opportunity to comment and respond to such output. Word usage in social media is continually evolving. Micro bloggers may use different sets of words to describe a specific event and they may use new words (i.e. neither exist in the training dataset nor in informal or formal dictionaries) or use words in new contexts. Dynamically capturing new words and their potential meaning from their context can help to reflect the words relationship in social media, which then can be useful for solving various problems, like the event classification task. Different approaches have been proposed in this regard, one of them is Contextual Analysis. This paper focuses on examining the potential of this approach for grouping short texts (tweets) talking about the same event into the same category. A new transparent method for text multi-class categorization is presented. It uses the Contextual Analysis approach to capture the most important words in the context of an event and to detect the usage of similar words in different contexts. In order to test the efficacy in these areas, this study evaluates the performance of the proposed method and other well known methods, such as Naïve Bayes, Support Vector Machines, K-Nearest Neighbors and Convolutional Neural Networks. On average, the experiments’ results show that the proposed multi-class classification method can effectively categorize tweets into various event groups, with a high f1-measure score f1>97.09% and f1>95.27%, in the imbalanced classes and high number of classes experiments, respectively. However, similar to the baseline methods, the performance is negatively influenced by the imbalanced dataset. The Convolutional Neural Networks method produces the best performance among the other algorithms with f1>97.74% in all experiments, which is 1.73% and 2.72% higher than the lowest performance of Naive Bayes and K-Nearest Neighbors, respectively, but does not meet the requirements of transparency of results.
Mediated semi-quantum key distribution with improved efficiency

Abstract Mediated semi-quantum key distribution involves the use of two end-users who have very restricted, almost classical, capabilities, who wish to establish a shared secret key using the help of a fully-quantum server who may be adversarial. In this paper, we introduce a new mediated semi-quantum key distribution protocol, extending prior work, which has asymptotically perfect efficiency. Though this comes at the cost of decreased noise tolerance, our protocol is backwards compatible with prior work, so users may easily switch to the old (normally less efficient) protocol if the noise level is high enough to justify it. To prove security, we show an interesting reduction from the mediated semi-quantum scenario to a fully-quantum entanglement based protocol which may be useful when proving the security of other multi-user quantum key distribution protocols.
Phishing URL Classification Analysis Using ANN Algorithm

Over the years in social engineering, phishing URLs became a significant threat for internet users as most cyber-crime or attacks redirect to individual target users by sending a malicious or crafted URL which allows the attacker to capture sensitive pieces of information of the victim users. According to this research, most of the existing phishing detection tools showed an overall accuracy level of 70% to 92.52%. This paper introduced a random forest (RF) and artificial neural network(ANN) based machine learning (ML) algorithmic classification models for detecting phishing URLs effectively with higher accuracy. The experimental results showed that the proposed RF and ANN models could classify phishing URLs' legitimacy labels with an efficiency rate of up to 99%, much more significant than traditional ways. In this paper, this comparison's algorithmic learning models demonstrate that the proposed ANN classification holds a competent accuracy label of 98.72%.
A heterogeneous guided ant colony algorithm based on space explosion and long–short memory

Ant colony optimization (ACO) has a good performance in solving discrete problems, but it inevitably has some disadvantages. Although it has good stability, it has some shortcomings in the convergence speed and solution accuracy when dealing with a large amount of data. Therefore, to solve the above problems, we proposed a heterogeneous guided ant colony algorithm based on space explosion and long–short memory. First, the difference between the current optimal path and the globally optimal path is used to search for the solution, which could accelerate the convergence of the algorithm. Second, the space explosion strategy is used to recombine the solution in different directions to avoid the algorithm falling into the local optimum. In this paper, 37 TSP data sets are selected to carry out simulation experiments. From the results and the rank-sum test, it could be concluded that the improved ant colony algorithm improved significantly in terms of convergence speed and solution accuracy.
An efficient and forward-secure lattice-based searchable encryption scheme for the Big-data era

As the amount of data generated by scaled systems in Electronic Medical Records (EMRs) and the Industrial Internet of Things (IIoT) keeps increasing, third-party servers are essential for data storage and manipulation, and with them come privacy concerns. Encrypting the uploaded data strips the server’s ability to search over it for keywords: a highly desirable requirement in some use-cases as EMRs and IIoT. Subsequent efforts at constructing efficient and secure post-quantum searchable encryption schemes have failed to prevent a curious server from launching inside offline keyword guessing attack. For every intended receiver, the data owner performs computation separately, implying the requirement of prior knowledge about recipients (which is not practical in a use-case such as EMR) and a high overhead is incurred in the big data era. We provide a detailed cryptanalysis of existing theoretically secure schemes and leverage blockchain for load balancing. We then propose a scheme secure from an honest-but-curious server. We also present a detailed comparative analysis with existing schemes as well as efficient methods to mitigate blockchain overheads. • Proposed a post-quantum forward-secure lattice based searchable encryption scheme • The scheme is secure in threat model: ‘honest-but-curious server’ • The scheme offers effective load balancing with the use of blockchain • The scheme delegates computational and storage load from server to users
Supervised Method Pursued For Overall Impact of Online Class During Lockdown in Bangladesh

The purpose behind this survey is to know what is the perspective of teachers and students on online education and to predict the future of online education in Bangladesh by collaborating with their perspective. The participants are students and teachers from the school, college and university of Bangladesh. The data for this research has been collected through a google form where there are two separate sections of the question set both for teachers and students. There are already some existing works on this topic but they only focus on the perspective of the students on online education and they only show what students feel about online learning. They don't give us the data what teachers have to say about online learning and they don't also mention the steps we should take to make online learning system suitable both for teachers and students. As we know to have a proper education environment the cooperation between both students and teachers should be satisfactory. So, by our survey we tried to cover the point of view of both students and teachers and by comparing their viewpoint we will give the decision if this online education will be fruitful and if it's not then what steps we should take to make it fruitful. This research can help both students and teachers to know each other's points of view so they can participate in online learning sessions by cooperating with each other and also can help the education sectors to know what they should do to support all the students and teachers with online education.
A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms

Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets-(1) Klim and (2) Skagen-collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds' mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.
Medical Records Management Using Distributed Ledger and Storage

In the last decade, blockchain technology has seen adoption to infinite domains, health care sector is one of the major domains where there are a greater opportunity and advantage to leverage the benefits of distributed ledgers in storing and securing patient medical records. The Government of India (GoI) is also very keen on digitization in addition to their wider adoption of blockchain technology to serve the citizens by ensuring their privacy and security of personally sensitive . In this paper, we propose a framework for the secure management of patient medical records (PMR) based on the Ethereum blockchain. The PMR is tokenized using the ERC-721 standard which is a Non-Fungible Token (NFT) that can be uniquely mapped to the individual patient. To ensure a higher level of security and non-redundancy, the medical records are stored and maintained in distributed storage like InterPlanetary File System (IPFS); to ensure privacy, the patient's sensitive information is anonymized through a privacy-preserving scheme. To provide complete control of the medical record to individual patients, we have employed a secret sharing scheme with essential share that is owned by the patient. The proposed framework aims to provide correctness and consistency in managing the PMR over its life cycle.
A Triangle Inequality for Cosine Similarity

Similarity search is a fundamental problem for many data analysis techniques. Many efficient search techniques rely on the triangle inequality of metrics, which allows pruning parts of the search space based on transitive bounds on distances. Recently, cosine similarity has become a popular alternative choice to the standard Euclidean metric, in particular in the context of textual data and neural network embeddings. Unfortunately, cosine similarity is not metric and does not satisfy the standard triangle inequality. Instead, many search techniques for cosine rely on approximation techniques such as locality sensitive hashing. In this paper, we derive a triangle inequality for cosine similarity that is suitable for efficient similarity search with many standard search structures (such as the VP-tree, Cover-tree, and M-tree); show that this bound is tight and discuss fast approximations for it. We hope that this spurs new research on accelerating exact similarity search for cosine similarity, and possible other similarity measures beyond the existing work for distance metrics.
Mixed Certificate Chains for the Transition to Post-Quantum Authentication in TLS 1.3

Large-scale quantum computers will be able to efficiently solve the underlying mathematical problems of widely deployed public key cryptosystems in the near future. This threat has sparked increased interest in the field of Post-Quantum Cryptography (PQC) and standardization bodies like NIST, IETF, and ETSI are in the process of standardizing PQC schemes as a new generation of cryptography. This raises the question of how to ensure a fast, reliable, and secure transition to upcoming PQC standards in today's highly interconnected world.
SAGA: Collaborative Storytelling with GPT-3

When friends live across different time zones, have incompatible work schedules, or have different levels of access to technology, synchronous communication becomes infeasible. To address this challenge, we developed a web application that allows friends to asynchronously collaborate creatively. In this application, multiple people can contribute to the writing of a story, told partially by a natural language AI system. By offloading some of the creative work to the AI, the human writers have the opportunity to also act as readers, being surprised by new events in the story. To gain preliminary insights into the experience of using this system, we conducted an informal pilot study over a span of 5 days. Through this process, we learned that storytelling with an AI system can encourage roleplay, it can be a cathartic experience, and it is curiosity-driven. Our recommendations for future research include (1) investigating new turn-taking strategies, and clearly communicating turns through the interface, (2) providing guidance for the prompt-writing process, perhaps through editable prompt templates, and (3) conducting a thorough evaluation of the system with friend groups of various sizes and timezones.
Comparison Between Two Novel Approaches in Automatic Breast Cancer Detection and Diagnosis and Its Contribution in Military Defense

Breast cancerPereira-Carrillo, Jackeline is a serious global healthSuntaxi-Dominguez, Diego problem to whichGuarnizo-Cabezas, Oscar we are all prone, taking into accountVillalba-Meneses, Gandhi the risk factors we areTirado-Espín, Andrés exposedAlmeida-Galárraga, Diego to daily, especially those who work abroad, such as military personnel. An incorrect diagnostic could be translated into a bad or inexistent treatment, and in the worst-case flowing into a patient‘s death. Nowadays, technological approaches allow us to create and design tools to identify and classify these pathologies using Machine learning methods. Nevertheless, the current neural networks are designed to identify and classify natural objects with different properties than medical images have, causing that the predictions made from them do not have medical validity. For those reasons, this paper presents a comparison review between two models of convolutional neural networks, based on modified architectures that pretend to adapt to the unique characteristics of medical images. This work proves the relevance of this technology, its impact into the medical field, and its repercussion and importance of these new tools for the near future of military medicine.
Towards scalable deployment of Hidden Markov models in occupancy estimation: A novel methodology applied to the study case of occupancy detection

Occupancy detection and estimation are two of the main areas of research in smart buildings. This is due to its significant effect in the deployment of energy saving buildings and various other futuristic user-centered applications. Occupancy detection refers to the determination of presence of occupants in a space or smart building, whereas occupancy estimation represents the exact determination of the number of occupants. Recently, machine learning approaches have made momentous advancements in the smart buildings field. Hidden Markov models (HMMs) have played an important role in such amelioration. Consequently, in this paper, we propose and comprehensively study a novel approach for the employment of HMMs in occupancy applications of smart buildings. In particular, the benchmark approach in the literature relies on a state-based model deployment. Hence, an implicit assumption is made that the states would automatically represent the physical phenomena of the data. This is not always the case. Our proposed framework promises a scalable stable deployment of HMMs, particularly in relation to the status quo, with model selection criteria for the determination of the number of states. Our extensive experiments show considerable improvement across the various evaluation metrics. Finally, this work also establishes multiple promising venues of future investigation for practitioners and researchers in the field, of which we discuss some of, especially at the intersection of machine learning and smart buildings.