Reviewerly/exHarmony huggingface dataset:
Optimized Tokenization Process for Open-Vocabulary Code Completion: An Empirical Study

Studies have substantiated the efficacy of deep learning-based models in various source code modeling tasks. These models are usually trained on large datasets that are divided into smaller units, known as tokens, utilizing either an open or closed vocabulary system. The selection of a tokenization method can have a profound impact on the number of tokens generated, which in turn can significantly influence the performance of the model. This study investigates the effect of different tokenization methods on source code modeling and proposes an optimized tokenizer to enhance the tokenization performance. The proposed tokenizer employs a hybrid approach that initializes with a global vocabulary based on the most frequent unigrams and incrementally builds an open-vocabulary system. The proposed tokenizer is evaluated against popular tokenization methods such as Closed, Unigram, WordPiece, and BPE tokenizers, as well as tokenizers provided by large pre-trained models such as PolyCoder and CodeGen. The results indicate that the choice of tokenization method can significantly impact the number of sub-tokens generated, which can ultimately influence the modeling performance of a model. Furthermore, our empirical evaluation demonstrates that the proposed tokenizer outperforms other baselines, achieving improved tokenization performance both in terms of a reduced number of sub-tokens and time cost. In conclusion, this study highlights the significance of the choice of tokenization method in source code modeling and the potential for improvement through optimized tokenization techniques.
Training Bachelor Students to Design Better Quality Web Apps: Preliminary Results from a Prospective Empirical Investigation

Background: There are a number of academic courses in the Bachelor Program in Computer Science (CS) on the design of Web apps. Often the internal and external quality of the developed Web apps is not adequately taken into account. Aim: We aimed to (i) estimate the quality of Web apps developed by bachelor CS students in a Software Technologies for the Web (STW) course (a.y. 2021-22) and (ii) define a training plan (on the base of the results of the first step) for the students enrolled to this course for the a.y. 2022-23 to let them design and implement better Web apps, and (iii) experimenting the training plan by comparing the quality of Web apps developed in a.y. 2021-22 and a.y. 2022-23. Method: We designed a prospective empirical investigation to study STW with respect to the training of bachelor students with respect to the quality (internal and external) of the developed Web apps. Results: We observed that quality concerns are widespread in the code of the Web apps the STW students developed in the a.y. 2021-22. Therefore, we plan to ask the students of the a.y. 2022-23 to use in their development pipeline a Static Analysis Tool (SAT) to detect quality concerns in the developed Web apps and deal with them. This second step represents an ongoing stage of our research. Conclusions: Our preliminary outcomes suggest that students must be aware that quality is of primary relevance for the development of Web apps and prepared to use SAT in the development pipeline.
Code Privacy in Detection of Web Vulnerabilities

We propose a solution combining source code static analysis with searchable symmetric encryption to detect input validation vulnerabilities of web applications in encrypted PHP code, allowing developers to protect their codebase from malicious third parties while simultaneously discovering vulnerabilities in it. Results show that our solution is capable of identifying vulnerabilities with precision similar to traditional static code, non-privacy-preserving analysers and exhibits a maximum overhead increase of around 16,55%.
Investigating Factors Influencing Students’ Assessment of Conceptual Models

This paper discusses the challenges in evaluating the quality of conceptual models in educational settings. While automated grading techniques may work for simplistic modeling tasks, realistic modeling tasks that allow for a wide variety of solutions cannot be evaluated using automated techniques. However, the traditional approach of having instructors grade the exercises may not be feasible in larger courses. To address this issue, alternative approaches, such as educating students to assess the quality of their own solutions or using calibrated peer reviews, can be used. Therefore, it is crucial to identify the quality of feedback a student can deliver on their own. As a first step, this paper reports on the results of controlled experiments with 368 participants to investigate factors that influence students’ model comprehension and to identify ways to distinguish good student assessments from bad ones.
Using InnerSource for Improving Internal Reuse: An Industrial Case Study

Background: InnerSource consists of the use of open source development techniques within the corporation. It helps improve software reuse through increased transparency and inter-team collaboration. Companies need to understand their context and specific needs before deciding to adopt any specific InnerSource practices since they cannot apply all InnerSource practices at once. Aim: This study aims to support the case company in assessing its readiness for adopting InnerSource practices to improve its internal reuse, identify and prioritize the improvement areas, and identify suitable solutions. Method: We performed a case study using a questionnaire and a workshop to check the current and desired status of adopting InnerSource practices and collect potential solutions. Results: The study participants identified that the company needs to prioritize the improvements related to the discoverability, communication channels, and ownership of the reusable assets. In addition, they identified certain InnerSource practices as solutions for the prioritized improvement areas, such as better structured repositories for storing and searching the reusable assets and standardized documentation of the reusable assets. Conclusion: The questionnaire instrument aids the case company in identifying the improvement areas related to InnerSource and reuse practices. InnerSource practices could improve the development and maintenance of reusable assets. Keywords: InnerSource, software reuse, readiness
A Study on Out-of-Distribution Detection based on Generative Models Trained for Each Discriminant Class

Since discriminative models are usually constructed by learning a set of given training data, it is impossible to guarantee the predictive performance of data that are not generated from the same distribution as the training data. Such data are known as out-of-distribution (OOD) data, while data that follow the same distribution as the training data are referred to as in-distribution data. For practical applications, it is important to detect the OOD data before they are input into highly qualified classifiers. Recently, a likelihood ratio-based method of OOD detection has been proposed. In this method, the likelihood ratio calculated using two generative models with different noise conditions functions as a detection index to evaluate semantic information only, ignoring the background information associated with all classes of data. Here, the generative model used for OOD detection should estimate the true distribution of in-distribution data accurately; all classes are estimated together, using the conventional method. However, in-distribution data may follow a simpler distribution if each class is estimated separately; these simple distribution structures are easier to learn. For this reason, estimation accuracy can be improved by models that estimate the distribution structure of each class independently. In this study, we propose an OOD detection method that uses generative models trained independently for each class. We also conduct evaluation experiments, using image datasets to demonstrate the effectiveness of the proposed method.
Store Analysis Using Latent Representation of Robust Variational Autoencoder Based on Sales History Data

This study proposes an analytical model based on the robust variational autoencoder (RVAE) to analyze store characteristics and further formulate item strategies at the store level for future reduction of the loss of unsold items. The data of the display item vector of each store characterizes the sales strategy of the store. However, it is sparse because the number of items displayed in each store accounts for only approximately 10% of the total number of item brands sold in all the stores. To manage this sparsity, a simple autoencoder model is inappropriate because of the large variation in input data, which is as a result of the store characteristics of selling unique products. Therefore, this study employs the RVAE to analyze the store characteristics of the listed items. Here, the latent representation of an RVAE is usually an output from an estimated probability distribution in the middle layer of the trained network. Generally, the similarity of the latent representations of two input data is evaluated using the sample data of the probability distributions. This study proposes a model to calculate the distance between probability distributions without sampling, for valid estimations of the similarities between latent representations. It proposes a method to use the reconstruction error obtained through the RVAE that enables the detection of stores whose trends differ significantly from those of other stores. Additionally, the proposed method can detect groups of stores with similar trends. In this study, the proposed model was applied to an actual dataset, after which its effectiveness was verified.
New Anomaly Detection Method based on the Multivariate Generalized Pareto Distributions

The extreme events of floods, rainfalls, waves, and wind often cause serious damage. The observed data can be used to detect the sign of abnormal phenomena and can prevent the crucial damage. Anomaly detection techniques, the techniques of distinguishing between normal and abnormal cases from the data, have been one of the most used methods for detecting suspicious events from the collected data. Although many methods of the anomaly detection have appeared in the literature, almost all of them are based on multivariate statistical process control techniques and machine learning techniques. Recently, under the idea that information for suspicious and critical events is more likely to be involved in the largest or smallest values than values around the mean or median, the anomaly detection methods are based on the “Extreme Value Theory” (EVT), which is the statistical theory dealing with the largest or smallest data, attract the variety of areas. However, they can be applied to only univariate data since they have been based on the EVT for univariate data, and hence there has been little work for the multivariate anomaly detection based on the EVT. The multivariate anomaly detection method is important in applications because almost all data for anomaly detection are multivariate and can be misleading by applying univariate methods to multivariate data independently. In this paper, we propose the new anomaly detection method based on the multivariate EVT. The performance of the proposed methods is evaluated by the Monte Carlo simulation. To illustrate all the proposed methods of anomaly detection for multivariate data, we apply them to analyze the new real data of precipitation events in 2021. The numerical results show that the proposed method outperforms the existing methods.
Densely sampled light field reconstruction with transformers

Densely sampled light fields (LFs) are critical for their further applications, such as digital refocus and depth estimation. However, it is costly and time-consuming to capture them. LF reconstruction, which aims at reconstructing a densely sampled LF from a sparsely sampled one, has attracted extensive attention of researchers. Although existing methods have achieved significant progress, these methods synthesize novel views, either through depth estimation and image warping, which depend heavily on the accuracy of the depth maps and are prone to cause artifacts at occluded regions, or by stacking multi-layer convolutions to learn the inherent structure of the LF, which will result in blurring results due to limited receptive fields when processing scenes with large disparities. We propose a transformer-based neural network for LF reconstruction (termed as LFRTR). Specifically, two novel transformers are introduced, namely angular transformer and spatial transformer. The former can fully explore angular information and correlations among different views, whereas the latter can capture local and non-local spatial texture information within each view. Moreover, dense skip connections are employed to enhance information flow between different layers. Thanks to the inherent global modeling ability of self-attention, the proposed LFRTR can reconstruct high-quality densely sampled LF in complex scenarios, such as large disparity, occlusion, and reflection. Experimental results on both synthetic and real-world LF datasets show that the proposed LFRTR outperforms other state-of-the-art methods in terms of both visual and numerical evaluations.
Uncertainty-aware Automatic Evaluation Method for Open-domain Dialogue Systems

Because open-domain dialogues allow diverse responses, common reference-based metrics for text generation, such as bleu, do not correlate well with human judgments unless we prepare an extensive reference set of high-quality responses for input utterances. In this study, we propose a fully automatic, uncertainty-aware evaluation method for open-domain dialogue systems, υbleu. Our method first collects diverse reference responses from massive dialogue data, annotates their quality judgments by using a neural network trained on automatically collected training data, and then computes weighted bleu using the automatically-retrieved and -rated reference responses. We also employ this method with an embedding-based metric, bertscore, instead of the word-overlap-based metric, bleu, to absorb surface variations of the reference responses. The experimental results on the meta-evaluation of our evaluation method for dialogue systems based on massive Twitter data confirmed that our method substantially improves correlations between bleu (or bertscore) and human judgments. We also confirmed that our method is effective when it is combined with a reference-free metric.
A Study of Using Plumtree Algorithm in Blockchain Networks

Blockchain is gaining attention as a technology to support cryptocurrency. However, one of the problems in using blockchain is the consumption of communication resources. It is caused by that the information received by a node can be delivered to the same node more than once through different neighbors. In this paper, we propose a method to reduce duplicate messages in a blockchain network by applying Plumtree algorithm. Through simulation experiments, we confirmed that the number of messages can be reduced by more than 70% when block propagation is simulated up to 50 blocks.
Adaptive Mode-switching From Autonomous Driving Mode to Manual Operation Mode of Mobile Robot Based on Body Sway

Transportation flexibility in manufacturing factories can be improved by introducing a function that allows the operator to physically interact with autonomous mobile robots (AMRs.) In our previous study, a manual operation mode was implemented with a sensorless power-assist scheme according to impedance control based on the touching force of the operator on a stationary AMR. In this paper, we propose a mode-switching algorithm for an AMR that switches from autonomous navigation to manual operation. Control-mode switching is achieved by detecting the body sway caused by the touching force of the operator. The experimental results demonstrate that the control mode can be successfully switched adaptively based on the intention of the operator, without any additional sensors.
Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction

Zero-shot relation extraction aims to recognize (new) unseen relations that cannot be observed during training. Due to this point, recognizing unseen relations with no corresponding labeled training instances is a challenging task. Recognizing an unseen relation between two entities in an input instance at the testing time, a model needs to grasp the semantic relationship between the instance and all unseen relations to make a prediction. This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task. A new model entirely devoted to this goal through three main aspects was proposed: learning effective relation representation, designing purposeful mini-batches, and binding two-way semantic consistency. Experimental results on two benchmark datasets demonstrate that our approach significantly improves task performance and achieves state-of-the-art results. Our source code and data are publicly available.
Comparing Object Recognition Models and Studying Hyperparameter Selection for the Detection of Bolts

The commonly-used method of bolting, used to secure parts of apparatus together, relies on the bolts having a sufficient preload force in order to the ensure mechanical strength. Failing to secure bolted connections to a suitable torque rating can have dangerous consequences. As part of a wider system that might monitor the integrity of bolted connections using artificial intelligence techniques such as machine learning, it is necessary to first identify and isolate the location of the bolt. In this study, we make use of several contemporary machine learning-based object detection algorithms to address the problem of bolt recognition. We use the latest version of You Only Look Once (YOLO) and compare it with algorithms RetinaNet and Faster R-CNN. In doing so, we determine the optimum learning rate for use with a given dataset and make a comparison showing how this particular hyperparameter has a considerable effect on the accuracy of the trained model. We also observe the accuracy levels achievable using training data that has been lowered in resolution and had augmentation applied to simulate camera blurring and variable lighting conditions. We find that YOLO can achieve a test mean average precision of 71% on this data.
Morphosyntactic Evaluation for Text Summarization in Morphologically Rich Languages: A Case Study for Turkish

The evaluation strategy used in text summarization is critical in assessing the relevancy between system summaries and reference summaries. Most of the current evaluation metrics such as ROUGE and METEOR are based on n-gram exact matching strategy. However, this strategy cannot capture the orthographical variations in abstractive summaries and is highly restrictive especially for languages with rich morphology that make use of affixation extensively. In this paper, we propose several variants of the evaluation metrics that take into account morphosyntactic properties of the words. We make a correlation analysis between each of the proposed approaches and the human judgments on a manually annotated dataset that we introduce in this study. The results show that using morphosyntactic tokenization in evaluation metrics outperforms the commonly used evaluation strategy in text summarization.
An Empirical Analysis on Lossless Compression Techniques

A method of presenting source data into it’s compact form is known as data compression. In this process, data size is minimized, redundancy is eliminated, and excess information is gotten rid of. A reduction in actual data is usually advantageous because it uses less resources overall, including bandwidth, processing, space, time, and many others. There are numerous compression algorithms for reducing the size of data of different formats. Even for compressing a particular data type, many approaches are being used. The proposed research has explored three of the lossless compression techniques which are: Run Length Encoding, Lempel Ziv Welch, and Huffman Encoding algorithms. We found out that based on compression size, compression ratio, and space saving percentage, Lempel Ziv Welch outperformed the other two. In contrast, Huffman Encoding performed better than the other two based on compression time. In the best case, LZW got a compression size of 250992 bytes, a compression ratio of 5.0106, and a space saving percentage of 80.04% while Huffman encoding got a compression time of 32.28 ms.
Cross-Domain and Cross-Language Irony Detection: The Impact of Bias on Models’ Generalization

Irony is a complex linguistic phenomenon that has been extensively studied in computational linguistics across many languages. Existing research has relied heavily on annotated corpora, which are inherently biased due to their creation process. This study focuses on the problem of bias in cross-domain and cross-language irony detection and aims to identify the extent of topic bias in benchmark corpora and how it affects the generalization of models across domains and languages (English, Spanish, and Italian). Our findings offer a first insight into this issue and showed that mitigating the topic bias in these corpora improves the generalization of models beyond their training data. These results have important implications for the development of robust models in the analysis of ironic language.
Deep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers

Current available question answering (QA) datasets fall short in two aspects - providing comprehensive answers that span over a few sentences and questions being deep or analytical in nature. Though individually these issues are addressed, a dataset that addresses both these issues is still not available. To address this gap, we introduce Deep QA(DQA), i.e., a dataset consisting of 12816 questions broadly classified into 4 types of questions. The generated dataset has been analyzed and compared with a standard QA dataset to prove that it demands higher cognitive skills. To prove the point further, state of art models trained on remembering type factive QA dataset have been pre-trained on the proposed dataset and are shown to perform poorly on the question types generated. Finally, some preliminary investigation using a graph neural model has been done to probe the possibility of an alternative answer generation technique on such a dataset of deeper questions.