Reviewerly/exHarmony huggingface dataset:
Stretching the Glasgow Haskell Compiler: Nourishing GHC with Domain-Driven Design

Over the last decade Haskell has been productized; transitioning from a research language to an industrial strength language ready for large-scale systems. However, the literature on architecting such systems with a pure functional language is scarce. In this paper we contribute to that discourse, by using a large-scale system: the Glasgow Haskell Compiler (GHC), as a guide to more maintainable, flexible and effective, pure functional architectures. We describe, from experience, how GHC as a system, violates the desirable properties that make pure functional programming attractive: immutability, modularity, and composability. With these violations identified, we provide guidance for other functional system architectures; drawing heavily on Domain-Driven Design. We write from an engineering perspective, with the hope that our experience may provide insight into best practices for other pure functional software architects.
Weighted Refinement Types for Counterpoint Composition

Refinement types are useful for describing specifications of programs. When applied to music theory, however, refinement types are too restrictive in that they do not allow breaking of rules. To relax this restriction, we propose weighted refinement types, a variation of refinement types where each refinement predicate carries a weight representing the importance of that predicate. In this paper, we present a weighted refinement type system that has core features required for composing species counterpoint. We also discuss potential applications of weighted refinement types in non-musical domains.
Types that Change: The Extensible Type Design Pattern

Compilers are often structured as chains of transformations, from source code to object code, through multiple intermediate representations. The existence of different representations of the same program presents challenges both for code maintenance and in terms of architecture. The types used to capture programs at multiple stages may be similar but not interchangeable, leading to code duplication. Methods to alleviate such duplication often lead to violations of software engineering principles of abstraction and encapsulation. This pearl discusses a design pattern where an algebraic data type (ADT) is extended with an argument type function that is applied to every component of the ADT. The resulting parametric type can be instantiated with multiple type functions, each providing a different feature. We demonstrate the versatility of this pattern by capturing notions of traceability and error recovery, and demonstrate that it can also be used to selectively modify existing types, as well as to extend them. Our proposal has been validated by applying it to a real-world use case with very good results.
Phases in Software Architecture

The large-scale structure of executing a computation can often be thought of as being separated into distinct phases. But the most natural form in which to specify that computation may well have a different and conflicting structure. For example, the computation might consist of gathering data from some locations, processing it, then distributing the results back to the same locations; it may be executed in three phases—gather, process, distribute—but mostly conveniently specified orthogonally—by location. We have recently shown that this multi-phase structure can be expressed as a novel applicative functor (also known as an idiom, or lax monoidal functor). Here we summarize the idea from the perspective of software architecture. At the end, we speculate about applications to choreography and multi-tier architecture.
An Attack for a 116bit ECDLP for a Barreto-Naehrig Curve

This is a success report of an experiment of an attack for an ECDLP of a Pollard’s parallel rho method with skew Frobenius mapping for a 116bit prime order Barreto-Naehrig curve.
A Knowledge Graph Recommendation Approach Incorporating Contrastive and Relationship Learning

Knowledge graphs, encompassing abundant structured and semantic information, exhibit remarkable potential within personalized recommendation fields. However, most current recommendation approaches reliant on knowledge graphs are susceptible to the disturbances of noise and data sparsity present in low-quality knowledge graphs, thereby undermining the efficacy of these recommendations. We propose a recommendation model grounded on contrastive learning and item relation learning to address these challenges. Specifically, to alleviate the issue of noise interference in knowledge graph feature learning and enhance model robustness, we introduce a hierarchical contrastive learning method. This approach leverages contrastive learning within knowledge graphs to extract unique features from the same propagation layer, thereby mitigating the impact of noise. In the face of the data sparsity endemic to low-quality knowledge graphs, we propose an item relation learning strategy that uses data augmentation to generate unknown items and their interrelations, hence enriching the content of the knowledge graph. We evaluate our model utilizing two public datasets, and the results, in comparison to other state-of-the-art methodologies, indicate a superior recommendation performance under conditions of a long-tail issue and data scarcit.
A Calculus of Inductive Linear Constructions

In this paper, we present a novel calculus of inductive linear constructions (CILC), combining linear types and dependent types. Our type theory addresses a looming issue in the research on linear dependent types: the lack of a general mechanism for defining sound linear inductive types. CILC allows one to encode in a straightforward manner the linear connectives that must be baked into the core of other systems. This greatly lowers the difficulty of encoding various data structures and makes writing non-trivial programs humanly feasible. We study the standard meta theory of our calculus, showing that it is sound in the usual sense. Through a heap-based operational semantics, we show that CILC safely manipulates resources and runs memory clean. We have formalized and proven correct most of the reported results in the Coq Proof Assistant.
A Few-Shot Automatic Modulation Classification Method Based on Temporal Singular Spectrum Graph and Meta-Learning

Deep learning-based Automatic Modulation Classification (AMC) has achieved excellent classification accuracy. However, most deep learning-based AMC methods have an inherent drawback. They exhibit a strong dependency on massive labeled samples, which is precisely difficult to obtain in real-world scenarios. This paper presents a few-shot AMC approach that integrates signal transformation and meta-learning. The former enhances class separability, while the latter addresses challenges posed by limited sample sizes. The results of simulation experiments conducted on the RadioML.2018.01a dataset demonstrate that the proposed technique achieves a classification accuracy of 74.21% when using one sample per class and increases to 82.27% when using five samples. The few-shot AMC efficacy of this proposed approach exhibits an outperformance over the classical deep learning methods.
Integrating Text Classification into Topic Discovery Using Semantic Embedding Models

Topic discovery involves identifying the main ideas within large volumes of textual data. It indicates recurring topics in documents, providing an overview of the text. Current topic discovery models receive the text, with or without pre-processing, including stop word removal, text cleaning, and normalization (lowercase conversion). A topic discovery process that receives general domain text with or without processing generates general topics. General topics do not offer detailed overviews of the input text, and manual text categorization is tedious and time-consuming. Extracting topics from text with an automatic classification task is necessary to generate specific topics enriched with top words that maintain semantic relationships among them. Therefore, this paper presents an approach that integrates text classification for topic discovery from large amounts of English textual data, such as 20-Newsgroups and Reuters Corpora. We rely on integrating automatic text classification before the topic discovery process to obtain specific topics for each class with relevant semantic relationships between top words. Text classification performs a word analysis that makes up a document to decide what class or category to identify; then, the proposed integration provides latent and specific topics depicted by top words with high coherence from each obtained class. Text classification accomplishes this with a convolutional neural network (CNN), incorporating an embedding model based on semantic relationships. Topic discovery over categorized text is realized with latent Dirichlet analysis (LDA), probabilistic latent semantic analysis (PLSA), and latent semantic analysis (LSA) algorithms. An evaluation process for topic discovery over categorized text was performed based on the normalized topic coherence metric. The 20-Newsgroups corpus was classified, and twenty topics with the ten top words were identified for each class. The normalized topic coherence obtained was 0.1723 with LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters Corpus was also classified, and twenty and fifty topics were identified. A normalized topic coherence of 0.1441 was achieved when applying the LDA algorithm, obtaining 20 topics for each class; with LSA, the coherence was 0.1360, and with PLSA, it was 0.1436.
Demographical Based Sentiment Analysis for Detection of Hate Speech Tweets for Low Resource Language

Advancement in IT and communication technology provides the opportunity for social media users to communicate their ideas and thoughts across the globe within no time as well big data promulgated in a result of the communication process itself has immense challenges. Recently, the provision of freedom of speech has witnessed immense promulgation of offensive and hate speech content on the internet aimed the basic human rights violation. The detection of abusive content on social media for rich resource language has become a hot area for researchers in the recent past. However, low-resource languages are underprivileged due to the non-availability of large corpus and its complexity to understand. The proposed methodology mainly has two parts. One is to detect abusive content and the other is to have a demographical analysis of the Indigenously developed dataset. The process starts with the development of a unique unlabeled Urdu dataset of 0.2 M from Twitter through a web scrapper tool named snscraper. The dataset is collected against the 36 districts of Punjab from Pakistan and from the duration 2018- Apr 2022. The dataset is labeled into three target classes Neutral, Offensive, and Hate Speech. After data cleaning, the feature extraction process is achieved with the help of traditional techniques such as Bow and tf-idf with the combination of word and char n-gram and word embedding word2Vec. The dataset is trained on both machine learning algorithms SVM and Logistic regression and deep learning techniques Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). The best F score achieved through LSTM on this dataset is 64 and accuracy is 93 through CNN algorithms. A Choropleth map is used for visualization of the dataset distributed among 36 districts of Punjab and a time series plot for time analysis covers five years duration from 2018-Apr to 22.
Improving the design of public health infographics using a motion graphic educational resource to enhance design principle application

Abstract Infographics are commonly used in public health to disseminate key messages to wide audiences. Although health organisations are making increasing use of infographics, their designs are of variable quality. The research reported here aimed to develop an educational tool that could improve public health infographic design, using motion graphics to teach users with limited design experience how to apply research-based design principles. Results were positive, with significant improvements in performance (including information location time, memorability, and user perception) observed for the infographics designed after the resource was used, compared to the infographics created before.
Automatic Resource Augmentation for Machine Translation in Low Resource Language: <tt>EnIndic Corpus</tt>

Parallel corpus is the primary ingredient of machine translation. It is required to train the statistical machine translation (SMT) and neural machine translation (NMT) systems. There is a lack of good quality parallel corpus for Hindi to English. Comparable corpora for a given language pair are comparatively easy to find, but this cannot be used directly in SMT or NMT systems. As a result, we generate a parallel corpus from the comparable corpus. For this purpose, the sentences (which are translations of each other) are mined from the comparable corpus to prepare the parallel corpus. The proposed algorithm uses the length of the sentence and word translation model to align sentence pairs that are translations of each other. Then, the sentence pairs that are poor translations of each other (measured by a similarity score based on IBM model 1 translation probability) are filtered out. We apply this algorithm to comparable corpora, which are crawled from speeches of the President and Vice-President of India, and mined parallel corpora out of them. The prepared parallel corpus contains good quality aligned sentences (with 96.338% f-score). Subsequently, incorrect sentence pairs are filtered out manually to make the corpus in qualitative practical use. Finally, we gather various sentences from different sources to prepare the EnIndic corpus, which comprises 1,656,207 English-Hindi sentence pairs (miscellaneous domain). We have deployed this prepared largest English-Hindi parallel corpus at https://github.com/debajyoty/EnIndic.git and the source code at https://github.com/debajyoty/EnIndicSourceCode.git.
Big data decision tree for continuous-valued attributes based on unbalanced cut points

Abstract The decision tree is a widely used decision support model, which can quickly mine effective decision rules based on the dataset. The decision tree induction algorithm for continuous-valued attributes, based on unbalanced cut points, is efficient for mining decision rules; however, extending it to big data remains an unresolved. In this paper, two solutions are proposed to solve this problem: the first one is based on partitioning instance subsets, whereas the second one uses partitioning attribute subsets. The crucial of these two solutions is how to find the global optimal cut point from the set of local optimal cut points. For the first solution, the calculation of the Gini index of the cut points between computing nodes and the selection of the global optimal cut point by communication between these computing nodes is proposed. However, in the second solution, the division of the big data into subsets using attribute subsets in a way that all cut points of an attribute are on the same map node is proposed, the local optimal cut points can be found in this map node, then the global optimal cut point can be obtained by summarizing all local optimal cut points in the reduce node. Finally, the proposed solutions are implemented with two big data platforms, Hadoop and Spark, and compared with three related algorithms on four datasets. Experimental results show that the proposed algorithms can not only effectively solve the scalability problem, but also have lowest running time, the fastest speed and the highest efficiency under the premise of preserving the classification performance.
On-chip wavefront shaping in spacing-varied waveguide arrays

Abstract The ability to manipulate light propagation sets the foundations for optical communication and information processing systems. With the ever-growing data capacity and data rate, photonic integrated circuits have attracted increasing attentions of researchers owing to their large-volume integration capacity and fast operation speed. In this work, we proposed and experimentally demonstrated a new wavefront shaping method using waveguide arrays with hyperbolic secant refractive index profiles. Through theoretically analyzing the diffraction and coherence properties, we found that a single waveguide array can perform both imaging and phase transformation, which are the two primary functions of optical lenses. We further expanded this function and fabricated the corresponding devices on a silicon nitride waveguide platform. Deterministic beam shaping, such as focusing, expansion, collimation, and steering, is successfully realized. This wavefront control method exhibits the potential for on-chip optical routing, ranging, sensing, etc., with high integration density and scalability.
Effects on Long-Range Dependence and Multifractality in Temporal Resolution Recovery of High Frame Rate HEVC Compressed Content

In recent years, video research has dealt with high-frame-rate (HFR) content. Even though low or standard frame rates (SFR) that correspond to values less than 60 frames per second (fps) are still covered. Temporal conversions are applied accompanied with video compression and, thus, it is of importance to observe and detect possible effects of typical compressed video manipulations over HFR (60 fps+) content. This paper addresses ultra-high-definition HFR content via Hurst index as a measure of long-range dependency (LRD), as well as using Legendre multifractal spectrum, having in mind standard high-efficiency video coding (HEVC) format and temporal resolution recovery (TRR), meaning frame upconversion after temporal filtering of compressed content. LRD and multifractals-based studies using video traces have been performed for characterization of compressed video, and they are mostly presented for advanced video coding (AVC). Moreover, recent studies have shown that it is possible to perform TRR detection for SFR data compressed with standards developed before HEVC. In order to address HEVC HFR data, video traces are analyzed using LRD and multifractals, and a novel TRR detection model is proposed based on a weighted k-nearest neighbors (WkNN) classifier and multifractals. Firstly, HFR video traces are gathered using six constant rate factors (crfs), where Hurst indices and multifractal spectra are calculated. According to TRR and original spectra comparison, a novel detection model is proposed based on new multifractal features. Also, five-fold cross-validation using the proposed TRR detection model gave high-accuracy results of around 98%. The obtained results show the effects on LRD and multifractality and their significance in understanding changes in typical video manipulation. The proposed model can be valuable in video credibility and quality assessments of HFR HEVC compressed content.
User elicited gesture-based locomotion techniques for immersive VEs in a seated position: a comparative evaluation

Locomotion is a fundamental task for exploring and interacting in virtual environments (VEs), and numerous locomotion techniques have been developed to improve the perceived realism and efficiency of movement in VEs. Gesture-based locomotion techniques have emerged as a more natural and intuitive mode of interaction than controller-based methods of travel in VEs. In this paper, we investigate the intuitiveness, comfort, ease of use, performance, presence, simulation sickness, and user preference of three user-elicited body-based gestures: the Calling gesture, Deictic Pointing gesture, and Mirror Leaning gesture. These gestures are intended to be used in three different seated multitasking scenarios involving virtual travel and various levels of hand engagement in selection. In the first study, participants compared the Calling gesture with the Tapping and Teleportation gestures for Scenario 1, which involved virtual travel only. The Calling gesture was found to be the most intuitive, with increased presence, while the Teleportation gesture was the preferred travel technique. The second study involved participants comparing the Deictic Pointing gesture with the Tapping and Teleportation gestures for Scenario 2, which involved virtual travel and one hand engaged in selection. The Deictic Pointing gesture was found to be more intuitive than the other gestures in terms of performance, comfort, ease of use, and presence. The third study introduced a new group of participants who compared the Mirror Leaning gesture with the Tapping and Teleportation gestures for Scenario 3, which involved virtual travel and both hands engaged in selection. The Mirror Leaning gesture was found to be the most intuitive, with increased presence and performance compared to the other gestures. We compared the gestures of the scenarios in three complementary search tasks: traveling in a straight-line path, moving in a directed path, and moving in an undirected path. We believe that the qualitative and quantitative measures obtained from our studies will help researchers and interaction design experts to design efficient and effective gesture-based locomotion techniques for traveling in a seated position in multitasking VEs.
Smart Farming: Monitoring of Field Status and Control of Irrigation Using Sensors and Esp8266 Nodemcu Module

Abstract The concept of smart work is the trend today, which has assimilated into every walk of life. While agriculture sector is growing but the speed of growth is far less compared to othersectors because of not using new technologies and gadgets. This has led to people migrating from agriculture sector to other sectors in search of better options. This has to be corrected and people have to be made aware about new technologies that are very helpful in assisting them in agricultural works. This project is for farmers whose fields are not close to their home, which is case with majority of farmers, so that they can easily monitor field status from remote place and don’t have to take multiple tris for issues concerning irrigation etc. This project uses NodeMCU to facilitate the communication of data from sensors to servers, from where it is sent to mobile application through Blynk platform for user’s convenience.
Novel Spatial Interaction Method for Viewing Ancient Chinese Scroll Paintings

The pictorial space described in Chinese scroll paintings is based on parallel projection and thus can be extended horizontally. As a result, when displayed in secure boxes in museums or galleries, one cannot view with the intended collaboration of the hand and the eye, and the perceptual experience is lacking. This study presents an interactive device that restores the authentic viewing experience with a tangible handscroll and interactive digital content, allowing viewers to enter the façade and visually experience the space in perspective.
Modeling System Configurations Over Time

Abstract One of the most troublesome and challenging parts of systems engineering is that systems keep changing. Some changes are planned as part of a phased system rollout. Others can occur because of changes in the environment, technology, customer need, unexpected emergent properties, competition, discovered system vulnerabilities, etc. Regardless of why, the systems engineer needs to document these configurations as part of a digital thread leading to physical systems that will be manufactured and installed. When using SysML as part of MBSE, there are advantages and disadvantages regarding the modeling variability using inheritance and other structural modeling techniques. Elements in a so‐called superclass will be inherited by all members. This is advantageous for additive systems but can cause issues when configurations require removal of systems. This paper will examine system configuration over time showing how to model configurations and changes. It will also look at other techniques such as Product Line Engineering (PLE) with OVM and Feature Based Modeling.