Reviewerly/exHarmony huggingface dataset:
DualNER: A Trigger-Based Dual Learning Framework for Low-Resource Named Entity Recognition

Most of the existing named entity recognition methods require a large amount of annotated data to obtain good performance. Once the annotated data are insufficient, the performance of these methods will degrade significantly. However, data annotation is time-consuming and laborious work. Therefore, how to achieve better performance with small amounts of annotated data in the named entity recognition task is an urgent task to be solved. In real scenarios, human beings always rely on some other words in the sentence when they recognize named entities, these words are called triggers. Motivated by this, we first verified the effectiveness of entity triggers for named entity recognition, then constructed a dual learning framework for low-resource named entity recognition based on the duality of entity triggers and corresponding entity, termed DualNER. Experiments show that the proposed methods use 20% trigger-entity annotated data and can achieve comparable results with the conventional model, which is trained by 50%–70% conventional annotated data.
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent

In this paper, we propose an energy-efficient federated meta-learning framework. The objective is to enable learning a meta-model that can be fine-tuned to a new task with a few number of samples in a distributed setting and at low computation and communication energy consumption. We assume that each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model. Assuming each task was trained offline on the agent&#x0027;s local data, we propose a lightweight algorithm that starts from the local models of all agents, and in a backward manner using projected stochastic gradient ascent (P-SGA) finds a meta-model. The proposed method avoids complex computations such as computing hessian, double looping, and matrix inversion, while achieving high performance at significantly less energy consumption compared to the state-of-the-art methods such as MAML and iMAML on conducted experiments for sinusoid regression and image classification tasks.
Early Detection of Spam Domains with Passive DNS and SPF

Spam domains are sources of unsolicited mails and one of the primary vehicles for fraud and malicious activities such as phishing campaigns or malware distribution. Spam domain detection is a race: as soon as the spam mails are sent, taking down the domain or blacklisting it is of relative use, as spammers have to register a new domain for their next campaign. To prevent malicious actors from sending mails, we need to detect them as fast as possible and, ideally, even before the campaign is launched. In this paper, using near-real-time passive DNS data from Farsight Security, we monitor the DNS traffic of newly registered domains and the contents of their TXT records, in particular, the configuration of the Sender Policy Framework, an anti-spoofing protocol for domain names and the first line of defense against devastating Business Email Compromise scams. Because spammers and benign domains have different SPF rules and different traffic profiles, we build a new method to detect spam domains using features collected from passive DNS traffic. Using the SPF configuration and the traffic to the TXT records of a domain, we accurately detect a significant proportion of spam domains with a low false positives rate demonstrating its potential in real-world deployments. Our classification scheme can detect spam domains before they send any mail, using only a single DNS query and later on, it can refine its classification by monitoring more traffic to the domain name.
Introduction to Evolutionary Algorithms

Real-world has many optimization scenarios with multiple constraints and objective functions that are discontinuous, nonlinear, non-convex, and multi-modal in nature. Also, the optimization problems are multi-dimensional with mixed types of variables like integer, real, discrete, binary, and having a different range of values which demands normalization. Hence, the search space of the problem cannot be smooth. Evolutionary algorithms have started gaining attention and have been employed for computational processes to solve complex engineering problems. Because it has become an instrument for research scientists and engineers who need to apply the supremacy of the theory of evolution to shape any optimization-based research problems and articles. In this chapter, there is a comprehensive introduction to the optimization field with the state-of-the-art in evolutionary computation. Though many books have described such areas of optimization in any form as evolution strategies, genetic programming, genetic algorithms, and evolutionary programming, evolutionary algorithms, that is, evolutionary computation is remarkable for considering it to discuss in detail as a general class.
Challenges in Scholarly Communication and Scientific Publication – Present Scenario and Opportunities through Blockchain Solutions

The internet is flooded with predatory, cloned and hijacked journals. Even the senior researchers fall pray to these dubious publications. Blockchain being transparent, decentralized and immutable in nature has great potential in scientific publication industry. We found that certain blockchain based frameworks for journal publication are being developed. In this paper we have listed the opportunities through blockchain technology so as to disrupt the scientific publication houses. We have also compared the existing blockchain based frameworks and open research problems to tackle limitations faced in traditional scholarly communication and scientific publication. Development in the area of consensus algorithm, adopting Management Principles such as Business Process Management (BPM) and Lean Six Sigma (LSS), Smart Contract, verifying the genuinity of publication, reviewer selection process are the identified problems that can help scientific publication industry to be sustainable.
On the Variability of Software Engineering Needs for Deep Learning: Stages, Trends, and Application Types

The wide use of Deep learning (DL) has not been followed by the corresponding advances in software engineering (SE) for DL. Research shows that developers writing DL software have specific development stages (i.e., SE4DL stages) and face new DL-specific problems. Despite substantial research, it is unclear how DL developers’ SE needs for DL vary over stages, application types, or if they change over time. To help focus research and development efforts on DL-development challenges, we analyze 92,830 Stack Overflow (SO) questions and 227,756 READMEs of public repositories related to DL. Latent Dirichlet Allocation (LDA) reveals 27 topics for the SO questions where 19 (70.4%) topics mainly relate to a single SE4DL stage, and eight topics span multiple stages. Most questions concern <i>Data Preparation</i> and <i>Model Setup</i> stages. The relative rates of questions for 11 topics have increased, for eight topics decreased over time. Questions for the former 11 topics had a lower percentage of accepting an answer than the remaining questions. LDA on README files reveals 16 distinct application types for the 227k repositories. We apply the LDA model fitted on READMEs to the 92,830 SO questions and find that 27% of the questions are related to the 16 DL application types. The most asked question topic varies across application types, with half primarily relating to the second and third stages. Specifically, developers ask the most questions about topics primarily relating to <i>Data Preparation</i> (2nd) stage for four mature application types such as <inline-formula><tex-math notation="LaTeX">${{\sf Image\ Segmentation}}$</tex-math></inline-formula> , and topics primarily relating to <i>Model Setup</i> (3rd) stage for four application types concerning emerging methods such as <inline-formula><tex-math notation="LaTeX">${{\sf Transfer\ Learning}}$</tex-math></inline-formula> . Based on our findings, we distill several actionable insights for SE4DL research, practice, and education, such as better support for using trained models, application-type specific tools, and teaching materials.
Coin-based Secure Computations

Abstract In the history of cryptography, many cryptographic protocols have relied on random coin tosses to prove their security. Although flipping coins is indispensable in this manner, the coins themselves have never been in the spotlight. Therefore, we would like to make physical coins rise to the level of cryptography, just as a deck of physical playing cards has been used to perform a secure multi-party computation. Such a card-based protocol is known to be helpful both to perform a secure computation without any black-box computers and to understand the principles of secure protocols. In this paper, we propose a new framework of secure multi-party computation using physical coins, named a coin-based protocol . One advantage of the use of coins is that they are more ubiquitous than cards. Whereas a face-down card can conceal the information about its face side, one side of a coin reveals the information of its other side. Hence, more careful design is required for a secure coin-based protocol than for a card-based one. We formalize a computational model of the coin-based protocol and explicitly give protocols for NOT, AND, COPY, OR, and XOR computations. We also discuss the composability of the extended protocols and how to implement them in practice.
Sentiment Analysis to Assess Educational Methodologies in a Competency-Based Educational System

This paper presents the results of rule-based sentiment analysis to assess and adjust several interventional methodologies designed to develop students' mathematical and data-analytical competencies in mathematics, statistics, and data science e-learning courses. Regarding spatial mathematical skills, Tukey's simultaneous tests for the difference of means <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p}=0.05)$</tex> revealed that the Project-Based Learning methodology using 3D augmented reality and 3D printing obtained a significantly greater mean polarity than other methodologies. The same was true of data-analytical skills development using Research-Based Learning for Digital Storytelling and the Python programming language. Furthermore, feedback sentiment probabilities (positive, negative, and neutral) obtained from the rule-based sentiment analysis naturally defined an intensity-sentiment 3-dimensional model containing the feedback sample data. Persistent homology generated topological summaries of such feedback per semester and educational methodology in persistence diagrams. Amplitude vectors were calculated to quantify the closeness of the persistence diagrams.
Summarization tool for multimedia data

Text summarization is an important Natural Language Processing problem. Manual text summarization is a laborious and time-consuming task. Owing to the advancements in the field of Natural Language Processing, this task can be effectively moved from manual to automated text summarization. This paper proposes a model named Term Frequency-Inverse Document Frequency (TF-IDF) Summarization Tool which implements a text analytics approach called TF-IDF to generate a meaningful summary. TF-IDF is used to identify the topic or context of the text statistically. As data today is mostly unstructured in nature, this paper aims to explore a combination of NLP techniques such as Speech Recognition and Optical Character Recognition to summarize multimedia data as well. The TF-IDF Summarization Tool is seen to produce summaries with Jaccard's Similarity value of 67% and Rogue-1 of 64.9%, Rogue-2 of 48.2%, and Rogue-L of 56.4% based on a self-developed dataset.
Cryptography Using Blowfish Algorithm

Security of data whether it is personal, professional or organizational from various attackers in today’s world has become a very tough task. One of the easiest ways to protect the data is to use the technique of cryptography to convert the data into some non-readable form. There is a wide variety of algorithms that are used for data encryption, this review paper explains the Blow-fish algorithm for data encryption which is a symmetric encryption algorithm and uses the matching key for encryption and decryption. The data is converted into encrypted data (ciphertext) and can be accessible to only those who have the same key to decrypt the data. Therefore, the analysis will be done for an application that implements the blow-fish algorithm for data encryption.
Hardware-Implemented Security Processing Unit for Program Execution Monitoring and Instruction Fault Self-Repairing on Embedded Systems

Embedded systems are increasingly applied in numerous security-sensitive applications, such as industrial controls, railway transports, intelligent vehicles, avionics and aerospace. However, embedded systems are compromised in the execution of untrusted programs, where the instructions could be maliciously tampered with to cause unintended behaviors or program execution failures. Particularly for remote-controlled embedded systems, program execution monitoring and instruction fault self-repair are important to avoid unintended behaviors and execution interruptions. Therefore, this paper presents a hardware-enhanced embedded system with the integration of a Security Processing Unit (SPU) in which integrity signature checking and checkpoint-rollback mechanisms are coupled to achieve real-time program execution monitoring and instruction fault self-repairing. This System-on-Chip (SoC) design was implemented and validated on the Xilinx Virtex-5 FPGA development platform. Based on the evaluation of the SPU in terms of the performance overhead, security capability, and resource consumption, the experimental results show that, while the CPU executes different benchmarks, the average performance overhead of the SPU lowers to 1.92% at typical 8-KB I/D caches, and it provides both program monitoring and fault self-repairing capabilities. Unlike conventional hardware detection technologies that require manual handling to recovery program executions, the CPU–SPU collaborative SoC is a resilient architecture equipped with instruction tampering detection and a post-detection strategy of instruction fault self-repairing. Moreover, the embedded system satisfies a good balance between high security and resource consumption.
How to Realize Efficient and Scalable Graph Embeddings via an Entropy-Driven Mechanism

Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE+, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE+ leverages hybrid-property heuristic random walk to capture node features, which considers both information content of nodes and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE+ adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE+ achieves both efficiency and performance advantages over recent popular graph embedding approaches. For three downstream graph tasks, our approach not only offers >10% average gains, but also exhibits 23×–127× speedup over existing sampling-based methods. In addition, HuGE+ significantly reduces memory footprint by an average of 68.9%, facilitating training for billion-node-scale graph embeddings.
Cloud-based data pipeline orchestration platform for COVID-19 evidence-based analytics

Identifying high-quality publications remains a critical challenge for health-care data consumers (e.g., immunologists, clinical researchers) who seek to make timely decisions related to the COVID-19 pandemic response. Currently, researchers perform a manual literature review process to compile and analyze publications from disparate medical journal databases. Such a process is cumbersome, inefficient, and increases the time to complete research tasks. In this book chapter, we describe a cloud-based, intelligent data pipeline orchestration platform, viz., “OnTimeEvidence” that provides health-care consumers with easy access to publication archives and analytics tools for rapid pandemic-related knowledge discovery tasks. This platform aims to reduce the burden and expensive time to find, sort, and analyze publications in terms of their level of evidence. We also present a case study of how OnTimeEvidence platform can be configured to help health-care data consumers to combine and analyze multiple data sources using interactive interfaces featuring workspaces equipped with analytics tools.
Deeper Insights into Neural Nets with Random Weights

In this work, the “effective dimension” of the output of the hidden layer of a one-hidden-layer neural network with random inner weights of its computational units is investigated. To do this, a polynomial approximation of the sigmoidal activation function of each computational unit is used, whose degree is chosen based both on a desired upper bound on the approximation error and on an estimate of the range of the input to that computational unit. This estimate of the range is parameterized by the number of inputs to the network and by an upper bound both on the size of the random inner weights of the network and on the size of its inputs. The results show that the Root Mean Square Error (RMSE) on the training set is influenced by the effective dimension and by the quality of the features associated with the output of the hidden layer.
Efficient quantum secure direct communication scheme based on single photons

In this work, we first introduce the specific steps of a quantum-secure direct communication scheme that sends a single photon at a time. Based on the basic steps of the scheme, it is gradually extended to a quantum secure direct communication scheme that transmits single-photon sequences twice and four times, with emphasis on the coding rules corresponding to each scheme. The purpose is that through the above scheme, it can be intuitively seen in the subsequent efficiency analysis that with the increase of the number of transmissions, the classification of single photons can be increased, and the encoding capacity of each single photon and the transmission efficiency of quantum states in the entire communication can be greatly improved. Finally, a universal scheme and coding rules for quantum secure direct communication by sending single photons in an integer power of 2 are proposed, and after security analysis the scheme proves to be safe and feasible. Through the efficiency analysis, the communication efficiency of this scheme is higher than that of the existing scheme, and the implementation of this scheme only uses a single photon, does not involve with quantum entanglement, and this scheme has more application values.
Study of Keypoints Detectors and Descriptors Performance on X-Ray Images Compared to the Visible Light Spectrum Images

In this work, we study the performance of wide-used keypoints detection and description algorithms: Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Invariant Scalable Keypoints(BRISK), Accelerated KAZE(AKAZE), which were originally developed for images taken in visible light but widely applied in the fields where images are taken in a different spectrum. We compare the quality of algorithms and their robustness to various image transformations. The algorithms&#x2019; performance is tested on two image sets in the different spectra: digital X-Ray images and images taken in the visible spectrum. Each dataset captures complex scenes with many objects and partial occlusions. Geometrical transformations (rotation, shearing, scaling), linear color transformations, Gaussian blur are applied to the images. Then the detection and description algorithms are tested on the original and transformed images. The repeatability and number of corresponding points are calculated to assess detection algorithms. The ratio of correctly matched descriptors together with the ratio of the distances between the query descriptor, the nearest descriptor, and the second matched descriptor is computed to evaluate descriptors&#x2019; quality. The algorithms showed different behavior on different spectra. SURF demonstrated to be the best X-ray keypoint detector and for the visible spectrum, it shares first place with AKAZE detector. SIFT is the best descriptor in both spectra. The strong and weak points of each algorithm are discussed in the paper.
Crafting ASR and Conversational Models for an Agriculture Chatbot

In recent years, artificial intelligence chatbots have attracted more and more attention. The stability and accuracy of automatic speech recognition (ASR) have been improved, making voice more critical in the transaction process and voice consultation of e-commerce purchases. ASR matches the learning model based on contextual cues. Eliminating unnecessary text plays an important role. We use the LSTM model and change it to contextualized custom text. In addition, to use our robot for testing, we propose a multi-task model that can jointly perform content re-scoring and has excellent responsiveness in the text of the input entity. Therefore, this article recommends using ASR technology to interpret and predict the answers to the LSTM model, allowing users to obtain expected results from actual measurements and understand which aspects are suitable for predicting specific emotions tested on this group. This article discusses chatbots and the design techniques used in platform translation, early and modern chatbots combined with ASR and artificial intelligence technology.
Japanese fingerspelling identification by using MediaPipe

In order to realize a barrier free society, it is important to realize sign language recognition on smartphones. Several methods have been proposed for fingersplling recognition, which is the basis of sign language recognition. In this paper, we propose a simple still fingerspelling recognition system. The system apply the two dimensional hand point information obtained by MediaPipe. The obtained information is identified by a linear SVM. The proposed system requires only a smaller amount of input data than the previous ones, and the processing is realized by SVM which requires a small amount of computation. Although the proposed system requires only a small amount of computation, it achieves extremely recognition accuracy for still fingerspellings.