Reviewerly/exHarmony huggingface dataset:
Multi Objective Salp Swarm based Energy Efficient Routing Protocol for Heterogeneous Wireless Networks

Routing is a persistent concern in wireless sensor networks (WSNs), as getting data from sources to destinations can be a tricky task. Challenges include safeguarding the data being transferred, ensuring network longevity, and preserving energy in harsh environmental conditions. Consequently, this study delves into the suitability of using multi-objective swarm optimization to route heterogeneous WSNs in the hope of mitigating these issues while boosting the speed and accuracy of data transmission. In order to achieve better performance in terms of load balancing and reducing energy expenditure, the MOSSA-BA algorithm was developed. This algorithm combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the exploiting strategy of the artificial bee colony (BA) in the neighbourhood of Salps. Inspired by the SEP and EDEEC protocols, the integrated solutions of MOSSA-BA were used to route two and three levels of heterogeneous networks. The embedded solutions provided outstanding performance in regards to FND, HND, LND, percentage of remaining energy, and the number of packages delivered to the base station. Compared to SEP, EDEEC, and other competitors based on MOSSA and a modified multi-objective particle swarm optimization (MOPSO), the MOSSA-BA-based protocols demonstrated energy-saving percentages of more than 34% in medium-sized areas of interest and over 22% in large-sized areas of detection.
A Logical Interpretation of Asynchronous Multiparty Compatibility

Session types specify the protocols that communicating processes must follow in a concurrent system. When composing two or more processes, a session typing system must check whether such processes are compatible, i.e., that all sent messages are eventually received and no deadlock ever occurs. After the propositions-as-types paradigm, relating session types to linear logic, previous work has shown that duality, in the binary case, and more generally coherence, in the multiparty case, are sufficient syntactic conditions to guarantee compatibility for two or more processes, yet do not characterise all compatible set of processes. In this work, we generalise duality/coherence to a notion of forwarder compatibility. Forwarders are specified as a restricted family of proofs in linear logic, therefore defining a specific set of processes that can act as middleware by transfering messages without using them. As such, they can guide a network of processes to execute asynchronously. Our main result establishes forwarder compatibility as a sufficient and necessary condition to fully capture all well-typed multiparty compatible processes.
Islamic Accounting Standard: An Intertemporal Sentiment Analysis

This study aims to look at related sentiments “Islamic Accounting Standard” around the world published by journals with that theme. This study uses a qualitative method with a sentiment analysis approach. The data used is secondary data with the theme “Islamic Accounting Standard” obtained from the Dimensions database of 100 scientific publications. Data is processed using software SentiStrength for extracting and classifying sentiments. The results of the study found that neutral sentiment was the highest with a percentage of 39%, followed by negative sentiment with 32% and positive sentiment with a percentage of 29%. Related research Islamic Accounting Standard experienced fluctuations in the number of sentiments throughout the period 1990 to 2023. The highest number of sentiments occurred in 2018 with a total of 13 scientific publications, which were dominated by neutral sentiment of 9 scientific publications. There are several positive and negative issues related to the topic of sharia accounting standards which can be further elaborated.
Transforming Big-Step to Small-Step Semantics Using Interpreter Specialisation

Natural semantics (big-step) and structural operational semantics (small-step) each have advantages, so it can be useful to produce both semantic forms for a language. Previous work has shown that big-step semantics can be transformed to small-step semantics. This is also the goal of our work, but our main contribution is to show that this can be done by specialisation of an interpreter that imposes a small-step execution on big-step transition rules. This is arguably more direct, transparent and flexible than previous methods. The paper contains two examples and further examples are available in an online repository.
Modeling of blockchain with encryption based secure education record management system

Blockchain technology can be employed in the education sector by building a decentralized system to store and share student records. The records can be encrypted to guarantee their confidentiality and security. With a blockchain-based system, student records can be saved in blocks that are linked and secured via cryptography. The records are decentralized and not controlled by any single entity, making them less susceptible to hacking or tampering. By using blockchain technology, educational institutions can create a more secure and efficient system for storing and sharing student records. This can streamline the process of transferring records between schools, and provide a secure and transparent way for students to access their own records. In this study, we provide a novel Merkle tree-based strategy for preserving the accuracy of student records and outline how to put it into practice. The software architecture resembled blockchain technology and was developed for private network deployment. The key components of our strategy are replacing conventional audit trails with their cryptographically secure equivalent and simplifying the Blockchain framework by avoiding mining. The cryptography system's framework is presented, and the new five dimensions of chaotic map academic records are proposed. Our study utilizes deoxyribonucleic acid (DNA) sequences and operations and the chaotic system to strengthen the cryptosystem in the blockchain authentication and authorization process. The significant advantage of this method is enhancing the generation of the hash function, which is the most critical challenge in the blockchain concept. The experimental outcomes and security analysis demonstrated that the proposed method works well in terms of different aspects. The suggested hash function's hash value distribution, sensitivity to tiny message modifications, confusion and diffusion qualities, resilience against birthday attacks, keyspace analysis, collision resistance, efficiency, and flexibility were all considered throughout the study.
Emotional analysis and prediction based on online book user comments

With the continuous development of information technology, more and more scholars use machine learning and integrated learning algorithms to analyze and predict the text emotion of online text reviews, explore the emotional tendencies of online users, which has far-reaching theoretical guiding significance and practical value for the promotion of online book sales decisions in the later stage. In this paper, user emotion is mined and analyzed based on online book reviews. The user emotion tendency is obtained through the emotion analysis method, which can help the e-commerce understand the user preferences and the quality of books on time, recommend books according to the user preferences, and play an auxiliary role in decision-making for consumers to purchase. Because of the current single method model and the inability to compare the accuracy of emotional prediction, this paper uses the public dataset Book_review on the official website of Kaggle, using NLTK (Nature Language TooKit), a natural language processing tool, to clean text data, build machine learning models and integrated learning models based on n-gram text features, obtain the results of emotional prediction through experimental analysis, and compare the machine learning model with the integrated learning model method, selecting the Logistic Regression model with the highest accuracy for emotional analysis of online book reviews, identify users’ emotional tendencies. Finally, we found that the accuracy of online comment emotion prediction based on the n-gram text feature Logistic Regression model is about 5% higher than the existing research methods.
Knowledge Graph-Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

In the realm of online tutoring intelligent systems, e-learners are exposed to a substantial volume of learning content. The extraction and organization of exercises and skills hold significant importance in establishing clear learning objectives and providing appropriate exercise recommendations. Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely, the Knowledge Graph Importance-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive Cognitive Diagnosis model to recommend the proper exercises. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each exercise and identifies the candidate exercise set <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>C</mi> </mrow> </msub> </mrow> </mfenced> </math> that exhibits the highest exercise informativeness. Moreover, the exercise representation component utilizes a graph neural network to process student records. The output of the graph neural network serves as the input for exercise-level attention and skill-level attention, ultimately generating exercise embeddings and skill embeddings. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multidimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select exercises from the candidate exercise set for the tested exercise set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two types of publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.
The Effects of Mobile-Assisted Collaborative Language Learning on EFL Students' Interpreting Competence and Motivation

As globalization continues to grow, there is an increasing need for skilled interpreters. However, the interpreting skills of many students are still deficient: firstly, they are incapable of understanding the source language clearly; secondly, they are incapable of expressing standard English accurately. Therefore, the purpose of this study is to investigate how mobile-assisted collaborative language learning (MACLL) can help English as a foreign language (EFL) students improve their interpreting competence and motivation. The study used a quantitative methodology and involved a sample of 60 students from one university. Their performance was monitored over a semester. To facilitate MACLL, mobile technologies such as Chaoxing App, iFlytek Input, and Praat were used as teaching tools. The results showed that MACLL had a positive effect on the improvement of students' interpreting competence, as evidenced by their improvement in academic performance and acquisition of language knowledge. The research also showed that students' learning motivation was enhanced in the MACLL environment.
Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets

Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances. However, the present manual examination of these images is tedious and time-consuming for pathologists. Therefore, reliable automated techniques are needed to effectively classify normal and malignant cancer images. This paper applied a deep learning approach, namely, EfficientNet and its variants from B0 to B7. We used different image resolutions for each model, from 224 × 224 pixels to 600 × 600 pixels. We also applied transfer learning and parameter tuning techniques to improve the results and overcome the overfitting problem. We collected the dataset from the Lung and Colon Cancer Histopathological Image LC25000 image dataset. The dataset acquisition consists of 25,000 histopathology images of five classes (lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and colon benign tissue). Then, we performed preprocessing on the dataset to remove the noisy images and bring them into a standard format. The model's performance was evaluated in terms of classification accuracy and loss. We have achieved good accuracy results for all variants; however, the results of EfficientNetB2 stand excellent, with an accuracy of 97% for 260 × 260 pixels resolution images.
Mutually Iso-Recursive Subtyping

Iso-recursive types are often taken as a type-theoretic model for type recursion as present in many programming languages, e.g., classes in object-oriented languages or algebraic datatypes in functional languages. Their main advantage over an equi-recursive semantics is that they are simpler and algorithmically less expensive, which is an important consideration when the cost of type checking matters, such as for intermediate or low-level code representations, virtual machines, or runtime casts. However, a closer look reveals that iso-recursion cannot, in its standard form, efficiently express essential type system features like mutual recursion or non-uniform recursion. While it has been folklore that mutual recursion and non-uniform type parameterisation can nicely be handled by generalising to higher kinds, this encoding breaks down when combined with subtyping: the classic “Amber” rule for subtyping iso-recursive types is too weak to express mutual recursion without falling back to encodings of quadratic size. We present a foundational core calculus of iso-recursive types with declared subtyping that can express both inter- and intra-recursion subtyping without such blowup, including subtyping between constructors of higher or mixed kind. In a second step, we identify a syntactic fragment of this general calculus that allows for more efficient type checking without “deep” substitutions, by observing that higher-kinded iso-recursive types can be inserted to “guard” against unwanted β-reductions. This fragment closely resembles the structure of typical nominal subtype systems, but without requiring nominal semantics. It has been used as the basis for a proposed extension of WebAssembly with recursive types.
Greedy Implicit Bounded Quantification

Mainstream object-oriented programming languages such as Java, Scala, C#, or TypeScript have polymorphic type systems with subtyping and bounded quantification. Bounded quantification, despite being a pervasive and widely used feature, has attracted little research work on type-inference algorithms to support it. A notable exception is local type inference, which is the basis of most current implementations of type inference for mainstream languages. However, support for bounded quantification in local type inference has important restrictions, and its non-algorithmic specification is complex. In this paper, we present a variant of kernel F ≤ , which is the canonical calculus with bounded quantification, with implicit polymorphism. Our variant, called F ≤ b , comes with a declarative and an algorithmic formulation of the type system. The declarative type system is based on previous work on bidirectional typing for predicative higher-rank polymorphism and a greedy approach to implicit instantiation. This allows for a clear declarative specification where programs require few type annotations and enables implicit polymorphism where applications omit type parameters. Just as local type inference, explicit type applications are also available in F ≤ b if desired. This is useful to deal with impredicative instantiations, which would not be allowed otherwise in F ≤ b . Due to the support for impredicative instantiations, we can obtain a completeness result with respect to kernel F ≤ , showing that all the well-typed kernel F ≤ programs can type-check in F ≤ b . The corresponding algorithmic version of the type system is shown to be sound, complete, and decidable. All the results have been mechanically formalized in the Abella theorem prover.
Perception Contracts for Safety of ML-Enabled Systems

We introduce a novel notion of perception contracts to reason about the safety of controllers that interact with an environment using neural perception. Perception contracts capture errors in ground-truth estimations that preserve invariants when systems act upon them. We develop a theory of perception contracts and design symbolic learning algorithms for synthesizing them from a finite set of images. We implement our algorithms and evaluate synthesized perception contracts for two realistic vision-based control systems, a lane tracking system for an electric vehicle and an agricultural robot that follows crop rows. Our evaluation shows that our approach is effective in synthesizing perception contracts and generalizes well when evaluated over test images obtained during runtime monitoring of the systems.
Getting into the Flow: Towards Better Type Error Messages for Constraint-Based Type Inference

Creating good type error messages for constraint-based type inference systems is difficult. Typical type error messages reflect implementation details of the underlying constraint-solving algorithms rather than the specific factors leading to type mismatches. We propose using subtyping constraints that capture data flow to classify and explain type errors. Our algorithm explains type errors as faulty data flows, which programmers are already used to reasoning about, and illustrates these data flows as sequences of relevant program locations. We show that our ideas and algorithm are not limited to languages with subtyping, as they can be readily integrated with Hindley-Milner type inference. In addition to these core contributions, we present the results of a user study to evaluate the quality of our messages compared to other implementations. While the quantitative evaluation does not show that flow-based messages improve the localization or understanding of the causes of type errors, the qualitative evaluation suggests a real need and demand for flow-based messages.
Data Extraction via Semantic Regular Expression Synthesis

Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic pattern matching, they fall short for data extraction tasks that involve both a syntactic and semantic component. To address this issue, we introduce semantic regexes, a generalization of regular expressions that facilitates combined syntactic and semantic reasoning about textual data. We also propose a novel learning algorithm that can synthesize semantic regexes from a small number of positive and negative examples. Our proposed learning algorithm uses a combination of neural sketch generation and compositional type-directed synthesis for fast and effective generalization from a small number of examples. We have implemented these ideas in a new tool called Smore and evaluated it on representative data extraction tasks involving several textual datasets. Our evaluation shows that semantic regexes can better support complex data extraction tasks than standard regular expressions and that our learning algorithm significantly outperforms existing tools, including state-of-the-art neural networks and program synthesis tools.
PIILO: an open-source system for personally identifiable information labeling and obfuscation

Purpose This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text. Design/methodology/approach The authors evaluate the performance of their deidentification system on two data sets of student-generated text. Each data set was human-annotated for PII. The authors evaluate using two approaches: per-token PII classification accuracy and a simulated reidentification attack design. In the reidentification attack, two reviewers attempted to recover student identities from the data after PII was obfuscated by the authors’ system. In both cases, results are reported in terms of recall and precision. Findings The authors’ deidentification system recalled 84% of student name tokens in their first data set (96% of full names). On the second data set, it achieved a recall of 74% for student name tokens (91% of full names) and 75% for all direct identifiers. After the second data set was obfuscated by the authors’ system, two reviewers attempted to recover the identities of students from the obfuscated data. They performed below chance, indicating that the obfuscated data presents a low identity disclosure risk. Research limitations/implications The two data sets used in this study are not representative of all forms of student-generated text, so further work is needed to evaluate performance on more data. Practical implications This paper presents an open-source and automatic deidentification system appropriate for student-generated text with technical explanations and evaluations of performance. Originality/value Previous study on text deidentification has shown success in the medical domain. This paper develops on these approaches and applies them to text in the educational domain.