Reviewerly/exHarmony huggingface dataset:
Knowledge Distillation for Efficient Sequences of Training Runs

In many practical scenarios -- like hyperparameter search or continual retraining with new data -- related training runs are performed many times in sequence. Current practice is to train each of these models independently from scratch. We study the problem of exploiting the computation invested in previous runs to reduce the cost of future runs using knowledge distillation (KD). We find that augmenting future runs with KD from previous runs dramatically reduces the time necessary to train these models, even taking into account the overhead of KD. We improve on these results with two strategies that reduce the overhead of KD by 80-90% with minimal effect on accuracy and vast pareto-improvements in overall cost. We conclude that KD is a promising avenue for reducing the cost of the expensive preparatory work that precedes training final models in practice.
Integrity of Discourse in the Radar Sulawesi Barat Daily Newspaper : The Use of Cohesion and Coherence in Learning Indonesian to Improve Students' Literacy and Knowledge Skills

This study aims to analyze cohesion and coherence in forming the wholeness of the discourse contained in the Radar Sulawesi Barat daily. This research was qualitative. The data was collected using listening and note-taking techniques with several stages: reading, marking, and classifying. The data obtained in this study were written as elements that made up the wholeness of the discourse. The method and technique of data analysis used was the intralingual equivalent method with the comparison technique of equating the main points. Based on the study's results, cohesion and coherence shape the wholeness of discourse in the Radar Sulawesi Barat daily. Discourse cohesion found in this study was a marker of grammatical and lexical cohesion. Grammatical cohesion markers include (1) references, (2) substitutions, and (3) conjunctions. Lexical cohesion markers are repetition; the repetition obtained consists of (1) complete repetition, (2) repetition with changes in form, (3) repetition with changes, (4) repetition with synonym changes, and (5) repetition with hyponyms. In addition to repetition, a marker of lexical cohesion was also found, namely collocation. Furthermore, the element that forms the wholeness of discourse was coherence. This study had a meaning or semantic relationship between elements or sentences in the discourse, which can be used as a strategy for learning Indonesian.
The Individual Differences of Indonesian High School Students’ Academic Interest in English as a Foreign Language

This study aims to examine individual differences, using gender as the exploratory variable and age as the covariate on academic interest among Indonesian high school students learning English. The research used a quantitative method. Data were collected virtually using an online questionnaire from 324 learners in four private urban high schools. The instrument used was the Academic Interest Scale for Adolescents (AISA). The process of data analysis consisted of confirmatory factor analysis, calculating Cronbach’s alpha, descriptive statistics, MANOVA, and MANCOVA. Four constructs were found valid and reliable: emotion, value, knowledge, and engagement. After validating the interest scale, data analysis disclosed the following results. Firstly, among the four constructs of academic interest, the value dimension was the highest and the knowledge factor the lowest, whereas emotion and engagement were in the middle position. Secondly, student gender differentiated significantly only in engagement. Thirdly, only the correlation between age and engagement was significant and positive in academic interest and student age. Lastly, considering student age, all the constructs except knowledge were differentiated by gender, where girls outperformed boys. These findings are valuable inputs for teachers, policymakers, and educational researchers in dealing with student language learning in Indonesian high schools.
New Record-Breaking Condorcet Domains on 10 and 11 Alternatives

The study of large Condorcet domains (CD) has been a significant area of interest in voting theory. In this paper, our goal was to search for large CDs. With a straightforward combinatorial definition, searching for large CDs is naturally suited for algorithmic techniques and optimisations. For each value of $n>2$, one can ask for the largest CD, and we suggest that finding new record-sized CDs provides an important benchmark for heuristic-based combinatorial optimisation algorithms. Despite extensive research over the past three decades, the CD sizes identified in 1996 remain the best known for many values of n. When $n>8$, conducting an exhaustive search becomes computationally unfeasible, thereby prompting the use of heuristic methods. To address this, we introduce a novel heuristic search algorithm in which a specially designed heuristic function, backed by a lookup database, directs the search towards beneficial branches in the search tree. Our algorithm found new large CDs of size 1082 (surpassing the previous record of 1069) for $n$=10, and 2349 (improving the previous 2324) for $n$=11. Notably, these newly discovered CDs exhibit characteristics distinct from those of known CDs. Our results underscore the potential of artificial intelligence-driven and inspired techniques in pushing the boundaries of mathematical research and tackling challenging complex combinatorial optimisation tasks.
Perceptual Pat: A Virtual Human System for Iterative Visualization Design

Designing a visualization is often a process of iterative refinement where the designer improves a chart over time by adding features, improving encodings, and fixing mistakes. However, effective design requires external critique and evaluation. Unfortunately, such critique is not always available on short notice and evaluation can be costly. To address this need, we present Perceptual Pat, an extensible suite of AI and computer vision techniques that forms a virtual human visual system for supporting iterative visualization design. The system analyzes snapshots of a visualization using an extensible set of filters - including gaze maps, text recognition, color analysis, etc - and generates a report summarizing the findings. The web-based Pat Design Lab provides a version tracking system that enables the designer to track improvements over time. We validate Perceptual Pat using a longitudinal qualitative study involving 4 professional visualization designers that used the tool over a few days to design a new visualization.
Course Recommendation System based on Natural Language Processing

Abstract—Computers are actually capable of understand hu- man languages thanks to the era referred to as NLP (Natural Language Processing). Deep grammatical and semantic analysis frequently makes use of words as the primary unit. The principle goal of NLP is regularly word segmentation. This undertaking can be used to deal with the sensible problem of widespread structural variations between conventional and multi-modal envi- ronments and diverse statistics modalities. The challenge presents the basics of the multi-modal function extraction approach that uses deep gaining knowledge of. A advice device primarily based on content material is also contributed by the undertaking. Based on the person’s previous behaviour or express feedback, it recommends additional items which might be just like what they already like the usage of object functions. A course recommenda-tion gadget, in a nutshell, is a device that suggests the subsequent piece of content based on what got already seen and loved. Advice structures are utilized by services like Spotify, Netflix, and Youtube to signify the subsequent movie or track you need to watch primarily based on what you’ve got already watched or heard. Primarily based at the filtering records, the advice gadget foresees gadgets which can realise and provide excessive- potential content that has been chosen by the consumer. Primarily based at the customers’ various searches, a recommendation machine become evolved to indicate courses to them. Users now have an easier time finding the proper guides based on their searches thanks to this machine. To determine availability, the system employs the TF-IDF algorithm and content material-based totally filtering. Suggestions for customers who use the collaborative filtering technique had been researched in earlier studies. Index Terms—Recommender System, Feature Extraction, Nat-ural Language Processing, Collaborative Filtering
Improving the Intent Classification accuracy in Noisy Environment

Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models. In this way, avoiding using intermediate steps, i.e. automatic speech recognition, is possible, thus the propagation of errors due to background noise, spontaneous speech, speaking styles of users, etc. Towards the development of solutions applicable in real scenarios, it is interesting to investigate how environmental noise and related noise reduction techniques to address the intent classification task with end-to-end neural models. In this paper, we experiment with a noisy version of the fluent speech command data set, combining the intent classifier with a time-domain speech enhancement solution based on Wave-U-Net and considering different training strategies. Experimental results reveal that, for this task, the use of speech enhancement greatly improves the classification accuracy in noisy conditions, in particular when the classification model is trained on enhanced signals.
New Space-Efficient Quantum Algorithm for Binary Elliptic Curves using the Optimized Division Algorithm

In previous research, quantum resources were concretely estimated for solving Elliptic Curve Discrete Logarithm Problem(ECDLP). In [1], the quantum algorithm was optimized for the binary elliptic curves and the main optimization target was the number of the logical qubits. The division algorithm was mainly optimized in [1] since every ancillary qubit is used in the division algorithm. In this paper, we suggest a new quantum division algorithm on the binary field which uses a smaller number of qubits. For elements in a field of $2^n$, we can save $\lceil n/2 \rceil - 1$ qubits instead of using $8n^2+4n-12+(16n-8)\lfloor\log(n)\rfloor$ more Toffoli gates, which leads to a more space-efficient quantum algorithm for binary elliptic curves.
Parrondo's game of quantum search based on quantum walk

The Parrondo game, devised by Parrondo, means that winning strategy is constructed a combination of losing strategy. This situation is called the Parrondo paradox. The Parrondo game based on quantum walk and the search algorithm via quantum walk have been widely studied, respectively. This paper newly presents a Parrondo game of quantum search based on quantum walk by combining both models. Moreover we confirm that Parrondo's paradox exists for our model on the one- and two-dimensional torus by numerical simulations. Afterwards we show the range in which the paradox occurs is symmetric about the origin on the $d$-dimensional torus $(d \geq 1)$ with even vertices and one marked vertex.
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search

Precisely understanding users' contextual search intent has been an important challenge for conversational search. As conversational search sessions are much more diverse and long-tailed, existing methods trained on limited data still show unsatisfactory effectiveness and robustness to handle real conversational search scenarios. Recently, large language models (LLMs) have demonstrated amazing capabilities for text generation and conversation understanding. In this work, we present a simple yet effective prompting framework, called LLM4CS, to leverage LLMs as a text-based search intent interpreter to help conversational search. Under this framework, we explore three prompting methods to generate multiple query rewrites and hypothetical responses, and propose to aggregate them into an integrated representation that can robustly represent the user's real contextual search intent. Extensive automatic evaluations and human evaluations on three widely used conversational search benchmarks, including CAsT-19, CAsT-20, and CAsT-21, demonstrate the remarkable performance of our simple LLM4CS framework compared with existing methods and even using human rewrites. Our findings provide important evidence to better understand and leverage LLMs for conversational search.
Reconstructing Graphs from Connected Triples

We introduce a new model of indeterminacy in graphs: instead of specifying all the edges of the graph, the input contains all triples of vertices that form a connected subgraph. In general, different (labelled) graphs may have the same set of connected triples, making unique reconstruction of the original graph from the triples impossible. We identify some families of graphs (including triangle-free graphs) for which all graphs have a different set of connected triples. We also give algorithms that reconstruct a graph from a set of triples, and for testing if this reconstruction is unique. Finally, we study a possible extension of the model in which the subsets of size $k$ that induce a connected graph are given for larger (fixed) values of $k$.
PCB-ready breakout boards: Bridging the gap between electronics prototyping and production

Electronics prototyping using breakout boards allows designers with and without an engineering background to rapidly create interactive prototypes. However, when it comes to transition to a production-ready PCB design, stagnation exists due to the high skill floor required for PCB design. While PCB design automation has been used successfully in recent research tools to reduce the required expertise, little has been done to integrate these tools directly into the electronics prototyping cycle. This position paper aims to bring attention to the possibility of integrating recent PCB design automation paradigms into the electronics prototyping cycle for the creation of PCB-ready breakout boards: breakout boards whose designs would have the ability to be pipelined directly into new user interfaces that leverage the use of automation for the rapid creation of production-ready PCB designs.
Evaluation of Different Ansatze Designs for Quantum Neural Network Binary Classifiers

Abstract The full text of this preprint has been withdrawn by the authors as it was submitted and made public without the full consent of all the authors. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
From Compass and Ruler to Convolution and Nonlinearity: On the Surprising Difficulty of Understanding a Simple CNN Solving a Simple Geometric Estimation Task

Neural networks are omnipresent, but remain poorly understood. Their increasing complexity and use in critical systems raises the important challenge to full interpretability. We propose to address a simple well-posed learning problem: estimating the radius of a centred pulse in a one-dimensional signal or of a centred disk in two-dimensional images using a simple convolutional neural network. Surprisingly, understanding what trained networks have learned is difficult and, to some extent, counter-intuitive. However, an in-depth theoretical analysis in the one-dimensional case allows us to comprehend constraints due to the chosen architecture, the role of each filter and of the nonlinear activation function, and every single value taken by the weights of the model. Two fundamental concepts of neural networks arise: the importance of invariance and of the shape of the nonlinear activation functions.
Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method

We propose a real-world dataset of stereoscopic videos for color-mismatch correction. It includes real-world distortions achieved using a beam splitter. Our dataset is larger than any other for this task. We compared eight color-mismatch-correction methods on artificial and real-world datasets and showed that local methods are best suited to artificial distortions and that global methods are best suited to real-world distortions. Our efforts improved on the latest local neural-network method for color-mismatch correction in stereoscopic images, making it work faster and better on both artificial and real-world distortions.
Increasing the usefulness of already existing annotations through WSI registration

Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the analysis of whole-slide-images (WSIs) of immunohistochemically (IHC) stained tissue sections could benefit from computational pathology methods. However, scoring biomarkers such as KI67 in IHC WSIs often necessitates the detection of areas of invasive cancer. Training cancer detection models often requires annotations, which is time-consuming and therefore costly. Currently, cancer regions are typically annotated in WSIs of haematoxylin and eosin (H&E) stained tissue sections. In this study, we investigate the possibility to register annotations that were made in H&E WSIs to their IHC counterparts. Two pathologists annotated regions of invasive cancer in WSIs of 272 breast cancer cases. For each case, a matched H&E and KI67 WSI are available, resulting in 544 WSIs with invasive cancer annotations. We find that cancer detection CNNs that were trained with annotations registered from the H&E to the KI67 WSIs only differ slightly in calibration but not in performance compared to cancer detection models trained on annotations made directly in the KI67 WSIs in a test set consisting of 54 cases. The mean slide-level AUROC is 0.974 [0.964, 0.982] for models trained with the KI67 annotations and 0.974 [0.965, 0.982] for models trained using registered annotations. This indicates that WSI registration has the potential to reduce the need for IHC-specific annotations. This could significantly increase the usefulness of already existing annotations.
REVIEW OF FEATURE SELECTION METHODS USING OPTIMIZATION ALGORITHM

Many works have been done to reduce complexity in terms of time and memory space. The feature selection process is one of the strategies to reduce system complexity and can be defined as a process of selecting the most important feature among feature space. Therefore, the most useful features will be kept, and the less useful features will be eliminated. In the fault classification and diagnosis field, feature selection plays an important role in reducing dimensionality and sometimes might lead to having a high classification rate. In this paper, a comprehensive review is presented about feature selection processing and how it can be done. The primary goal of this research is to examine all of the strategies that have been used to highlight the (selection) selected process, including filter, wrapper, Meta-heuristic algorithm, and embedded. Review of Nature-inspired algorithms that have been used for features selection is more focused such as particle swarm, Grey Wolf, Bat, Genetic, wale, and ant colony algorithm. The overall results confirmed that the feature selection approach is important in reducing the complexity of any model-based machine learning algorithm and may sometimes result in improved performance of the simulated model.
The Effect of Synchronous and Asynchronous Enrichment Activities on the Achievement of Arabic Language Subject of Fifth Grade Student in Jordan

The purpose of this study is to uncover the effect of synchronous and asynchronous interactive enrichment activities on fifth-grade students' achievement in the Arabic Language subject in Jordan. The study included 72 students who equally were distributed in two empirical groups. One of the groups included boys while the other group included girls. To achieve the objectives of the study, the researcher has prepared an achievement. The validity and reliability of the test were verified. To answer the two questions of the study, it used arithmetic means, standard deviations, and the One and Two-Way Analysis of Variance (ANCOVA). The results of the study indicated an improvement in students’ achievement in the Arabic Language subject, but the analysis of variance showed that there were no statistically significant differences at the significance level (α = 0.05) between the two study groups in the achievement test due to the synchronous or asynchronous interactive activities or interaction with sex. In light of the results of the study, the researcher figured out several recommendations, such as diversification of implementing interactive enrichment activities synchronously and asynchronously and conducting studies related to synchronous and asynchronous learning.
Jordanian EFL Learners’ Production of Synonyms

This paper reports on the findings of a study that explored Jordanian EFL learners’ production of synonyms. The data were elicited through a translation task which consisted of 20 Arabic sentences to measure Jordanian EFL learners’ ability to produce the correct synonym in contextualized English sentences. The subjects were asked to translate whole sentences from Arabic into English. Each sentence contains a certain Arabic word that has a specific appropriate English equivalent out of a number of semantically interrelated words that belong to the same category. These words were selected from Sheeler and Markley (1982). The findings indicate that Jordanian EFL learners face serious difficulty in dealing with synonyms on the production level. This can be attributed to some reasons, namely the transfer of training, the complexity of the foreign language, the lack of some English lexical items, the confusion of words on the ground of formal similarity, and the EFL learners' limited knowledge of English in general and of collocations in particular. Furthermore, the subjects employed a number of strategies to compensate for their lack of knowledge, such as overgeneralization, semantic approximation, literal translation, and message abandonment when they encountered a problem in rendering a target word.
Evaluation of Threat Models

Abstract: Information system security is the integrity and safety of its resources and activities. In the cyber world, it can be almost impossible to trace sophisticated attacks to their true source. The anonymity enjoyed by the malicious user or cyber attackers pose a grave threat to the global information society. Cyber threat modelling is an analytical process that is used to identify the potential threats against a system or an organization. It is a core activity and a fundamental practice in the process of building trusted technology. Threat modelling has been identified as one of the best "return on investment" activities in order to identify and address design flaws. Some threat model methods focus on identifying threats and security issues while some methods also perform assessment of the resulting risk.