Reviewerly/exHarmony huggingface dataset:
A Survey of Abstractive Text Summarization Utilising Pretrained Language Models

We live in a digital era - an era of technology, artificial intelligence, big data, and information. The data and information on which we depend to fulfil several daily tasks and decision-making can become overwhelming to deal with and requires effective processing. This can be achieved by designing improved and robust automatic text summarization systems. These systems reduce the size of text document while retaining the salient information. The resurgence of deep learning and its progress from the Recurrent Neural Networks to deep transformer based Pretrained Language Models (PLM) with huge parameters and ample world and common-sense knowledge have opened the doors for huge success and improvement of the Natural Language Processing tasks including Abstractive Text Summarization (ATS). This work surveys the scientific literature to explore and analyze recent research on pre-trained language models and abstractive text summarization utilizing these models. The pretrained language models on abstractive summarization tasks have been analyzed quantitatively based on ROUGE scores on four standard datasets while the analysis of state-of-the-art ATS models has been conducted qualitatively to identify some issues and challenges encountered on finetuning large PLMs on downstream datasets for abstractive summarization. The survey further highlights some techniques that can help boost the performance of these systems. The findings in terms of performance improvement reveal that the models with better performance use either one or a combination of these strategies: (1) Domain Adaptation, (2) Model Augmentation, (3) Stable finetuning, and (4) Data Augmentation.
Sustained Space and Cumulative Complexity Trade-Offs for Data-Dependent Memory-Hard Functions

Memory-hard functions (MHFs) are a useful cryptographic primitive which can be used to design egalitarian proof of work puzzles and to protect low entropy secrets like passwords against brute-force attackers. Intuitively, a memory-hard function is a function whose evaluation costs are dominated by memory costs even if the attacker uses specialized hardware (FPGAs/ASICs), and several cost metrics have been proposed to quantify this intuition. For example, space-time cost looks at the product of running time and the maximum space usage over the entire execution of an algorithm. Alwen and Serbinenko (STOC 2015) observed that the space-time cost of evaluating a function multiple times may not scale linearly in the number of instances being evaluated and introduced the stricter requirement that a memory-hard function has high cumulative memory complexity (CMC) to ensure that an attacker’s amortized space-time costs remain large even if the attacker evaluates the function on multiple different inputs in parallel. Alwen et al. (EUROCRYPT 2018) observed that the notion of CMC still gives the attacker undesirable flexibility in selecting space-time tradeoffs e.g., while the MHF $$\texttt{Scrypt}$$ has maximal CMC $$\varOmega (N^2)$$ , an attacker could evaluate the function with constant O(1) memory in time $$O(N^2)$$ . Alwen et al. introduced an even stricter notion of Sustained Space complexity and designed an MHF which has $$s=\varOmega (N/\log N)$$ sustained complexity $$t=\varOmega (N)$$ i.e., any algorithm evaluating the function in the parallel random oracle model must have at least $$t=\varOmega (N)$$ steps where the memory usage is at least $$\varOmega (N/\log N)$$ . In this work, we use dynamic pebbling games and dynamic graphs to explore tradeoffs between sustained space complexity and cumulative memory complexity for data-dependent memory-hard functions such as Argon2id and $$\texttt{Scrypt}$$ . We design our own dynamic graph (dMHF) with the property that any dynamic pebbling strategy either (1) has $$\varOmega (N)$$ rounds with $$\varOmega (N)$$ space, or (2) has CMC $$\varOmega (N^{3-\epsilon })$$ —substantially larger than $$N^2$$ . For Argon2id we show that any dynamic pebbling strategy either(1) has $$\varOmega (N)$$ rounds with $$\varOmega (N^{1-\epsilon })$$ space, or (2) has CMC $$\omega (N^2)$$ . We also present a dynamic version of DRSample (Alwen et al. 2017) for which any dynamic pebbling strategy either (1) has $$\varOmega (N)$$ rounds with $$\varOmega (N/\log N)$$ space, or (2) has CMC $$\varOmega (N^3/\log N)$$ .
Short Paper: What Peer Announcements Tell Us About the Size of the Bitcoin P2P Network

Bitcoin is based on a P2P network of which only a few quantities are publicly known. While the number of peers that disseminate transactions and blocks is relevant for the robustness of the network, only the number of reachable peers is so far being measured. However, there exists an unknown number of unreachable peers in the network, that is, peers that do not accept incoming connections but typically also disseminate transactions and blocks. We propose the Passive Announcement Listening (PAL) method that gives an estimate of the number of unreachable peers by observing peer announcements in addr messages. We use the PAL method to analyze data from a long-term measurement of the Bitcoin P2P network from 2015 to 2022. The PAL estimate shows that since 2018 the number of unreachable peers is at least three times higher than the number of reachable peers. An empirical validation indicates that about 76% of all unreachable peers announce their address and the PAL approach finds about 94% of these unreachable peers. Thus, we estimate the total number of unreachable peers in May 2022 to be around 34,000. We also report on a spam wave of addr messages that shows that peer announcements ‘leak’ even more information than the size of the network.
Spam Email Categorization with NLP and Using Federated Deep Learning

Emails are the most popular and efficient communication method that makes them vulnerable to misuse. Federated learning (FL) provides a decentralized machine learning (ML) model, where a central server coordinates clients that collaboratively train a shared ML model. This paper proposes Federated Phishing Filtering (FPF) technique based on federated learning, natural language processing, and deep learning. FL for intelligent algorithms fuses trained models of ML algorithms from multiple sites for collective learning. This approach improves ML performance by utilizing large collective training data sets across the corporate client base, resulting in higher phishing email detection accuracy. FPF techniques preserve email privacy using local feature extraction on client email servers. Thus, the contents of emails do not need to be transmitted across the network or stored on third-party servers. We have applied FL and Natural Language Processing (NLP) for email phishing detection. This technique provides four training modes that perform FL without sharing email content. Our research categorizes emails as benign, spam, and phishing. Empirical evaluations with publicly available datasets show that accuracy is improved by the use of our Federated Deep Learning model.
Near real-time seismic data processing helps scientist understand aftershocks

A new method of automatically detecting and locating earthquakes utilizes artificial intelligence. The system successfully located recent Taiwan earthquakes and could help scientists monitor ongoing events.
Video Question Answering with Iterative Video-Text Co-tokenization

Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering that uses multiple video inputs and a new video-text iterative co-tokenization approach to answer a variety of questions related to videos. We experimentally evaluate the model on several datasets, such as MSRVTT-QA, MSVD-QA, IVQA, outperforming the previous state-of-the-art by large margins. Simultaneously, our model reduces the required GFLOPs from 150–360 to only 67, producing a highly efficient video question answering model (Code: https://sites.google.com/view/videoqa-cotokenization ).
The effect of corporate governance, dividend policy and informativeness of risk disclosure on the firm value: Egyptian evidence

This paper aims at investigating the effect of corporate governance mechanisms, dividend policy and the risk disclosure level on firm value in Egypt using a sample of annual reports of listed companies (EGX30) of the years 2013-2019. The findings suggest that: 1) the CEO duality shows a significant positive effect on the firm value, while the board independence and board size have an insignificant effect on the firm value; 2) risk disclosure level allows investors to better predict future earnings growth. Furthermore, dividend policy and risk disclosure informativeness are significantly affecting the firm's ability to raise money and its value in a positive direction. This paper sheds lights on the main obstacles and benefits that can face the Egyptian listed companies in complying with the Egyptian code of the corporate governance and the disclosure requirements raised by the Egyptian Financial Regulatory Authority, and its magnitude on the firm value.
Eye-tracking analysis of source code reading on a line-by-line basis

Code reading differs from natural language reading in that it is often not a linear reading process. As such, novices may not have already acquired all the skills necessary to read code in an optimal and efficient way. This study illustrates that novices read method signatures with less processing than code lines in the method body. Furthermore, they transition between all lines of code, rarely recognizing that some lines are irrelevant to the current line being read. An informal comparison with experts shows that experts are skilled at disregarding irrelevant lines to the current line being read. Furthermore, when reading a loop, novices will continuously read each line contained in the loop while experts will avoid re-reading simpler lines in successive iterations of the loop, rather concentrating on lines that contain higher computational complexity. This paper therefore shows additional differences between novices and experts and identifies some novel idiosyncrasies of novice code readers that should be taken into account during teaching.
Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization

While hematoxylin and eosin (H &E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KIN), which preserves local information and successfully achieves seamless stain transformation with constant GPU memory usage. Given a patch, corresponding position, and a kernel, KIN computes local statistics using convolution operation. In addition, KIN can be easily plugged into most currently developed frameworks without re-training. We demonstrate that KIN achieves state-of-the-art stain transformation by replacing instance normalization (IN) layers with KIN layers in three popular frameworks and testing on two histopathological datasets. Furthermore, we manifest the generalizability of KIN with high-resolution natural images. Finally, human evaluation and several objective metrics are used to compare the performance of different approaches. Overall, this is the first successful study for the ultra-high-resolution unpaired image-to-image translation with constant space complexity. Code is available at: https://github.com/Kaminyou/URUST .
Optimizing MobileNetV2 Architecture Using Split Input and Layer Replications for 3D Face Recognition Task

Facial recognition is one of the problems that has been focused for a long time. In this paper, we consider the 3D face data set, and explore its facial recognition task considering automatic architecture finding. We present the approach to customize MobileNet architecture and automatically find a good architecture variant for the 3D face recognition task. The main concept is based on the split input and lengthening the network by the layer replication. The evaluation is done by using the dataset generated by the GAN model with style transfer to augment the makeup faces. The results show that the found modified model from our automatic finding approach yields the more cost-effective model, i.e., with a 0.005% increase in size compared to baseline 3D Mobilenet and 0.01% compared to a simple Mobilenet while the found model has 12% more accuracy compared to the 3D MobileNetV2 and 11% compared to the traditional MobileNetV2.
Unravelling an optical extreme learning machine

Extreme learning machines (ELMs) are a versatile machine learning technique that can be seamlessly implemented with optical systems. In short, they can be described as a network of hidden neurons with random fixed weights and biases, that generate a complex behaviour in response to an input. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding about their optical implementations. This work makes use of an optical complex media to implement an ELM and introduce an ab-initio theoretical framework to support the experimental implementation. We validate the proposed framework, in particular, by exploring the correlation between the rank of the outputs, H , and its generalization capability, thus shedding new light into the inner workings of optical ELMs and opening paths towards future technological implementations of similar principles.
Efficient One Pass Self-distillation with Zipf’s Label Smoothing

Self-distillation exploits non-uniform soft supervision from itself during training and improves performance without any runtime cost. However, the overhead during training is often overlooked, and yet reducing time and memory overhead during training is increasingly important in the giant models’ era. This paper proposes an efficient self-distillation method named Zipf’s Label Smoothing (Zipf’s LS), which uses the on-the-fly prediction of a network to generate soft supervision that conforms to Zipf distribution without using any contrastive samples or auxiliary parameters. Our idea comes from an empirical observation that when the network is duly trained the output values of a network’s final softmax layer, after sorting by the magnitude and averaged across samples, should follow a distribution reminiscent to Zipf’s Law in the word frequency statistics of natural languages. By enforcing this property on the sample level and throughout the whole training period, we find that the prediction accuracy can be greatly improved. Using ResNet50 on the INAT21 fine-grained classification dataset, our technique achieves +3.61% accuracy gain compared to the vanilla baseline, and 0.88% more gain against the previous label smoothing or self-distillation strategies. The implementation is publicly available at https://github.com/megvii-research/zipfls .
Evaluation Metrics in Explainable Artificial Intelligence (XAI)

Although AI is spread across all domains and many authors stated that providing explanations is crucial, another question comes into play: How accurate are those explanations? This paper aims to summarize a state-of-the-art review in XAI evaluation metrics, to present a categorization of evaluation methods and show a mapping between existing tools and theoretically defined metrics by underlining the challenges and future development. The contribution of this paper is to help researchers to identify and apply evaluation metrics when developing an XAI system and also to identify opportunities for proposing other evaluation metrics for XAI.
Health Consultant Bot: Primary Health Care Monitoring Chatbot for Disease Prediction

This research paper presents a disease prediction chatbot that is intelligent enough to communicate with patients to predict their disease by detecting their symptoms through natural language processing. This system allows the user to describe their medical health condition in natural language, and by processing their natural language-based statement, our system detects the symptoms, predicts the disease, and provides basic precautions as well as a brief introduction about the disease. We have used IBM Watson Assistant to build this system. Watson assistant provides several machine learning algorithms to process user statements and symptoms extraction. In our system, symptoms were mapped by considering the community data which resulted in a predicted disease. Our system provides the relevant information about the predicted disease from the system's database. In an experimental evaluation, we carried out a study having 156 subjects, who interact with the system in a daily use scenario. Results show the effectiveness and accuracy of our system to support the patient in taking good care of their health.
Building the “Complete Game”: An Overview Study of a Development Strategy for Geo AR Mobile Games

This paper presents a bibliometric analysis and literature review on the Geo AR mobile game, a new form of video game enabled by geolocation and augmented reality technology. The definition of the Geo AR mobile game, its development history and business status are identified and with the current research topics around it. It’s noted that there is a prevailing problem of sustainable success among Geo AR mobile games, and it is little addressed in current academic conversations. Therefore, this paper proposes that Geo AR mobile games should be built as the “complete game” for sustainable success.
On cyclic and negacyclic codes with one-dimensional hulls and their applications

Linear codes over finite fields with small dimensional hulls have received much attention due to their applications in cryptology and quantum computing.In this paper, we study cyclic and negacyclic codes with onedimensional hulls.We determine precisely when cyclic and negacyclic codes over finite fields with one-dimensional hulls exist.We also introduce onedimensional linear complementary pairs of cyclic and negacyclic codes.As an application, we obtain numerous optimal or near optimal cyclic codes with onedimensional hulls over different fields and, by using these codes, we present new entanglement-assisted quantum error-correcting codes (EAQECCs).In particular, some of these EAQEC codes are maximal distance separable (MDS).We also obtain one-dimensional linear complementary pairs of cyclic codes, which are either optimal or near optimal.1. Introduction.The notion of the hull of a code was first defined by Assmus and Key in 1990 in order to study the codes generated by the characteristic functions of the lines of a finite projective plane.The hull of a linear code C, denoted by Hull(C), is its intersection with its dual code, that is Hull(C) = C ∩ C ⊥ , where C ⊥ is the Euclidean dual of the code C.This notion is an important tool for determining the automorphism group of a linear code and testing the complexity of some algorithms for checking permutation equivalence of two linear codes [24,25,38,39].More precisely, the algorithms that check the permutation equivalence work well if the size of the hull is small.Many papers have been devoted to linear codes with small hulls such as LCD codes, and linear codes with one-dimensional hulls due to their importance in applications [12,17,26,32,33,37,34].Another motivation for studying the hulls of linear codes comes from entanglement-assisted quantum error-correcting codes (EAQECCs).In [5], Brun et al. introduced entanglement-assisted quantum error-correcting codes (EAQECCs), which allow the use of classical error-correcting codes without self-orthogonality.They proved that if shared entanglement is available between the sender and receiver in advance, non-self-orthogonal classical codes can be used to construct EAQECCs.Many researchers presented constructions of good EAQECCs, see for example [3,18,20,21,29,31,36,41,42].An [[n, k, d; c]] q EAQECC encodes k
Adversarial Logic

We introduce Adversarial Logic, an extension of Incorrectness Logic [1] with an explicit Dolev-Yao [2] adversary to statically analyze the severity of security vulnerabilities in the under-approximate setting. Adversarial logic is built on the ability to separate logical facts known to the adversary from facts solely known to the program under analysis. This flavor of program incorrectness can be used to analyze software in which error behavior occurs at deeper levels of interaction between the program and its environment, such as subtle cases of information disclosure requiring multiple program executions to be uncovered. We introduce the Oscillating Bit Protocol, an example algorithm where such a vulnerability can be detected using adversarial logic while remaining elusive to other frameworks. We define a flavor of symbolic execution in which the adversary guides the introduction of symbolic variables and the checking of attack assertions. Additionally, we introduce equivalence testing, an under-approximate version of program equivalence only proven on specific program paths and used to extract differences between comparable implementations. We provide a denotational semantics for adversarial logic and prove its soundness, thereby guaranteeing that extracted attack paths are true positives.
Cryptocurrency Price Prediction using Machine Learning Algorithm

In today's world we can see the trend of cryptocurrency is constantly increasing every day. In the financial sector, cryptocurrency has become a huge topic and the right prediction has become more important to gain profits. For determining the right prediction with good accuracy, we performed deep analysis on dataset to understand the market behavior by using different machine learning algorithms like Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost to predict the daily price behavior of top 4 cryptocurrencies like Bitcoin, XRP, Ethereum, and Stellar using these machine learning algorithms. Our experimental result reaches to 95–97 percent validation accuracy.
Assessing Anonymous and Selfish Free-rider Attacks in Federated Learning

Federated Learning (FL) is a distributed learning framework and gains interest due to protecting the privacy of participants. Thus, if some participants are free-riders who are attackers without contributing any computation resources and privacy data, the model faces privacy leakage and inferior performance. In this paper, we explore and define two free-rider attack scenarios, anonymous and selfish free-rider attacks. Then we propose two methods, namely novel and advanced methods, to construct these two attacks. Extensive experiment results reveal the effectiveness in terms of the less deviation with conventional FL using the novel method, and high false positive rate to puzzle defense model using the advanced method.
Applying blockchain-based smart contracts to eliminate unfair trading practices in the food supply chain

In this paper we critically evaluate the applicability of blockchain-based smart contracts to reduce unfair trading practices (UTPs) in the food supply chain. Face-to-face interviews and a quantitative survey were used to collect and analyse the opinions of 20 experts in blockchain and food supply management. The results show that contractual parties with asymmetric bargaining power along the food supply chain can implement blockchain-based smart contracts to reduce the negative impacts of UTPs. The creation of templates for such types of contracts and promotion by regulative authorities could help their dissemination. This is the first study to explore the applicability of blockchain-based smart contracts in the food supply chain and its impact on UTPs. We especially focus on the needs of small and medium-sized food producers and farmers who experience negative impacts on their businesses due to existing power asymmetries.