Reviewerly/exHarmony huggingface dataset:
Evidence Profiles for Validity Threats in Program Comprehension Experiments

Searching for clues, gathering evidence, and reviewing case files are all techniques used by criminal investigators to draw sound conclusions and avoid wrongful convictions. Medicine, too, has a long tradition of evidence-based practice, in which administering a treatment without evidence of its efficacy is considered malpractice. Similarly, in software engineering (SE) research, we can develop sound methodologies and mitigate threats to validity by basing study design decisions on evidence. Echoing a recent call for the empirical evaluation of design decisions in program comprehension experiments, we conducted a 2-phases study consisting of systematic literature searches, snowballing, and thematic synthesis. We found out (1) which validity threat categories are most often discussed in primary studies of code comprehension, and we collected evidence to build (2) the evidence profiles for the three most commonly reported threats to validity. We discovered that few mentions of validity threats in primary studies (31 of 409) included a reference to supporting evidence. For the three most commonly mentioned threats, namely the influence of programming experience, program length, and the selected comprehension measures, almost all cited studies (17 of 18) did not meet our criteria for evidence. We show that for many threats to validity that are currently assumed to be influential across all studies, their actual impact may depend on the design and context of each specific study. Researchers should discuss threats to validity within the context of their particular study and support their discussions with evidence. The present paper can be one resource for evidence, and we call for more meta-studies of this type to be conducted, which will then inform design decisions in primary studies. Further, although we have applied our methodology in the context of program comprehension, our approach can also be used in other SE research areas to enable evidence-based experiment design decisions and meaningful discussions of threats to validity.
DOLOMITIZATION AND POROSITY EVALUATION OF HURMALA FORMATION, GARA ANTICLINE, DOHUK AREA, KURDISTAN REGION, IRAQ

The Khurmala Formation (Late Paleocene – Early Eocene) is well exposed at the southern limb of Gara mountain, Dohuk area, Kurdistan Region of north Iraq. The thickness of the Formation is 91 m and generally consists of buff to grey, medium to thick-bedded stromatolitic dolostone alternating with thin beds of buff to dark gray shale or marlstone. Shale interlayers increased in thickness upwards. The studied section has been divided into three units based on shale ratio. The focus of this study is to investigate the type and origin of the dolomite and its impact on the porosity using field observation, petrologic and petrographic investigations with SEM microscopy. The common dolomite types include (1) Unimodal, cryptocrystalline to fine crystalline dolomite of nonplanar mosaic. This type is associated with the relics of the stromatolites and commonly recognized in the upper part. (2) Unimodal fine to medium crystalline, planar-e to Planar-s dolomite mosaic, which is associated with dolomite horizons showing ghosts or relics of benthic foraminifera, algae, ostracods and other bioclasts. The other types are less common and include (3) Porphorotopic dolomite and (4) Bimodal medium to fine, planer-e to planar-s dolomite mosaic. The latter is associated with dolostone horizons display important moldic and vug porosity. Molds, vugs and intercrystalline porosity are noticed in all types but with variable percentages. Fenestral porosity is common in the stromatolitic dolomite of type one. The vertical distribution of the different type of dolomite and its porosities indicate cyclic alternation of the studied Khurmala section which implies effective eustatic control on deposition and dolomite diagenesis. The dolomitization resulted from a possible combined effect of seawater refluxion and sabkha evaporation mechanisms.
Path planning based on joint distribution of distribution vehicles and UAVs

In the case of epidemic situation, disaster and other urgent materials, traditional logistics distribution has problems such as low distribution efficiency and long time consumption. Based on this, this paper constructs the overall distribution model of "UAV+distribution vehicle", gives the corresponding optimal distribution scheme under different situations, calculates the shortest path distance between any two points by using Floyd algorithm, and divides 14 locations into two categories by K-means algorithm. The genetic algorithm is used to calculate two types of shortest path distribution schemes through MATLAB, to improve the efficiency of material distribution.
Hybrid Portfolio Decision: Forecasting Sharpe Ratio with ARI-LSTM Method

This study only makes price predictions for a period of time based on the price of gold and Bitcoin on the deadline and gives the best trading strategy for the day. We made price predictions and investment choices. In terms of price prediction, when predicting short-term prices, we improved the Markov chain and completed a one-year short-term price forecast; in predicting long-term prices, we mixed ARIMA and LSTM, ideally predicting the price movement of gold and Bitcoin in the last four years. In terms of investment choices, taking into account the cash available in hand, based on the Sharpe ratio and the Sortino ratio, we propose a new ratio that considers the impact of not investing in cash in hand, builds a decision model, and gives the best trading strategy.
BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER

Biomedical Named Entity Recognition (BioNER) is the fundamental task of identifying named entities from biomedical text. However, BioNER suffers from severe data scarcity and lacks high-quality labeled data due to the highly specialized and expert knowledge required for annotation. Though data augmentation has shown to be highly effective for low-resource NER in general, existing data augmentation techniques fail to produce factual and diverse augmentations for BioNER. In this paper, we present BioAug, a novel data augmentation framework for low-resource BioNER. BioAug, built on BART, is trained to solve a novel text reconstruction task based on selective masking and knowledge augmentation. Post training, we perform conditional generation and generate diverse augmentations conditioning BioAug on selectively corrupted text similar to the training stage. We demonstrate the effectiveness of BioAug on 5 benchmark BioNER datasets and show that BioAug outperforms all our baselines by a significant margin (1.5%-21.5% absolute improvement) and is able to generate augmentations that are both more factual and diverse. Code: https://github.com/Sreyan88/BioAug.
Bit-Stealing Made Legal: Compilation for Custom Memory Representations of Algebraic Data Types

Initially present only in functional languages such as OCaml and Haskell, Algebraic Data Types (ADTs) have now become pervasive in mainstream languages, providing nice data abstractions and an elegant way to express functions through pattern matching. Unfortunately, ADTs remain seldom used in low-level programming. One reason is that their increased convenience comes at the cost of abstracting away the exact memory layout of values. Even Rust, which tries to optimize data layout, severely limits control over memory representation. In this article, we present a new approach to specify the data layout of rich data types based on a dual view: a source type, providing a high-level description available in the rest of the code, along with a memory type, providing full control over the memory layout. This dual view allows for better reasoning about memory layout, both for correctness, with dedicated validity criteria linking the two views, and for optimizations that manipulate the memory view. We then provide algorithms to compile constructors and destructors, including pattern matching, to their low-level memory representation. We prove our compilation algorithms correct, implement them in a tool called ribbit that compiles to LLVM IR, and show some early experimental results.
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

Demographic biases exist in current models used for facial recognition (FR). Our Balanced Faces in the Wild (BFW) dataset is a proxy to measure bias across ethnicity and gender subgroups, allowing one to characterize FR performances per subgroup. We show that results are non-optimal when a single score threshold determines whether sample pairs are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">genuine</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">imposters</i> . Furthermore, within subgroups, performance often varies significantly from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global</i> average. Thus, specific error rates only hold for populations matching the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial features extracted from state-of-the-art neural networks, boosting the average performance. The proposed method also preserves identity information while removing demographic knowledge. The removal of demographic knowledge prevents potential biases from being injected into decision-making and protects privacy since demographic information is no longer available. We explore the proposed method and show that subgroup classifiers can no longer learn from the features projected using our domain adaptation scheme. For source code and data, see https://github.com/visionjo/facerec-bias-bfw.
Determine of Potential Evacuation Locations as Disaster Mitigation Against Landslides

Abstract Bandar Lampung City has a lowland topography, which is quite diverse in valleys to ridge hills. The city’s development made the face of the city of Bandar Lampung turn into a dense residential area between the hills. Landslide is one of the disasters that often occur in Indonesia. About 15 sub-districts that are prone to landslides in Bandar Lampung, including West Telukbetung, East Telukbetung, South Telukbetung, Bumiwaras, Panjang, Kedamaian, North Telukbetung, Tanjung Karang Pusat, Enggal, Tanjung Karang Barat, Kemiling, Langkapura, Kedaton, Rajabasa and Sukabumi (BPBD, 2018). Research about landslide-prone areas with a geospatial approach aimed at disaster mitigation efforts. The study results will be necessary information about disaster mitigation to minimize loss of life and material. The geospatial approach integrates landslide threat maps with road networks and public facilities. Also, the landslide threat area’s accessibility and affordability functions can be arranged to secure the city’s priority location. The preparation of a landslide threat map begins by compiling an entire dataset in spatial data information from various sources. Whole datasets are arranged based on parameters that affect landslides. Each parameter has a different weight and is summarized in the Overlay method. It is hoped that this research can be used as a reference in disaster management and spatial planning in Bandar Lampung in the future. The determination of public facility services using the Analytical Hierarchy Process (AHP) method.
Neuro-Symbolic Representations for Information Retrieval

This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective.
Parallel Binary QUasi-Affine TRansformation Evolution (QUATRE) Algorithm

QUasi-Affine TRansformation Evolutionary (QUATRE) generalized differential evolution algorithm to matrix form, which was designed for solving continuous problems. Binary QUATRE (BQUATRE) algorithm convert the original BQUATRE to the binary form to deal with hyperspectral dimensionality reduction. In this paper, in order to further improve the performance of the BQUATRE, the parallel mechanism is introduced to BQUATRE, and proposed parallel binary BQUATRE algorithm (PBQUATRE). The global best solution and the group best solution both guide the position update of other solutions with a probability of 0.5 in communication phase. The mutation matrix B is also affected not only by the global best solution but also by the group best solution. The proposed algorithm is applied for feature selection and hyperspectral dimensionality reduction, and experimental results show that the proposed PBQUATRE is superior to the classics and state-of-the-art algorithms.
CNN sensor based motion capture system application in basketball training and injury prevention

Basketball is a high-intensity sport, and sports injuries often occur. Therefore, how to monitor the sports status of basketball players in real time, discover and prevent the occurrence of sports injuries in time, has become an urgent problem for athletes and coaches. In this paper, a motion capture system based on CNN sensor is proposed. Through the application of sensor, real-time monitoring of athletes' motion state, the system can collect athletes' movement track, speed, acceleration, stride frequency, heart rate, energy consumption and other parameters in real time. By analyzing the movement data, it can timely warn and deal with the occurrence of sports injuries. In this paper, convolutional neural network (CNN) is used to process and analyze motion data, so that the motion capture system has higher precision and accuracy. Through the training and optimization of CNN, the system can identify and analyze motion data more accurately, and improve the performance and effect of the motion capture system. The motion capture system based on CNN sensor can realize real-time monitoring and prevention of sports injuries for basketball players, provide more comprehensive, scientific and real-time sports data for athletes and coaches, help them better conduct training and adjust tactics, and improve the competitive level and safety of basketball games.
Desktop vs. Headset: A Comparative Study of User Experience and Engagement for Flexibility Exercise in Virtual Reality

This study aimed to investigate the effectiveness of Virtual Reality (VR) technology for flexibility exercise and compare the physical outcomes, user experience, and engagement of VR desktops and VR headsets. The VR exercise application was designed using motion capture technology and exported to different VR devices. Each of the devices was used by 30 participants to perform a flexibility exercise in VR. Physical outcomes were measured using the sit-and-reach test, and user experience and engagement were evaluated using questionnaires and group discussions. The results showed that VR desktop participants had higher sit-and-reach scores. However, VR headset participants reported a more immersive experience (reality judgment) and motivation (value and usefulness). They also had higher engagement (focused attention and reward) levels than VR desktop participants. There were no significant differences between the two approaches in terms of enjoyment, effort, pressure, choice, correspondence, absorption, perceived usability, and aesthetic appeal. The study highlights the importance of considering physical outcomes, user experience, and engagement by comparing two different VR approaches for flexibility exercise. Further research is needed to explore the limitations and potential benefits of VR technology for physical activity. Doi: 10.28991/ESJ-2023-07-04-03 Full Text: PDF
Augmentative Topology Agents For Open-Ended Learning

We tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents while also evolving increasingly challenging environments. Unlike previous open-ended approaches that optimize agents using a fixed neural network topology, we hypothesize that generalization can be improved by allowing agents' controllers to become more complex as they encounter more difficult environments. Our method, Augmentative Topology EPOET (ATEP), extends the Enhanced Paired Open-Ended Trailblazer (EPOET) algorithm by allowing agents to evolve their own neural network structures over time, adding complexity and capacity as necessary. Our empirical results demonstrate that ATEP produces general agents capable of solving more environments than fixed-topology baselines. We also investigate mechanisms for transferring agents between environments and find that a species-based approach further improves the performance and generalization of agents.
Hybrid Deep Learning Algorithms for Dog Breed Identification—A Comparative Analysis

Deep learning and computer vision algorithms will be applied to find the breed of the dog from an image. The goal is to have the user submit an image of a dog, and the model will choose one of the 120 breeds stated in the dataset to determine the dog’s breed. The proposed work uses various deep learning algorithms like Xception, VGG19, NASNetMobile, EfficientNetV2M, ResNet152V2, Hybrid of Inception &Xception, and Hybrid of EfficientNetV2M, NASNetMobile, Inception & Exception to predict dog breeds. ResNet101, ResNet50, InceptionResNetV2, and Inception-v3 on the Stanford Dogs Standard Datasetswere used in the existing system. They used a transfer learning algorithm with data augmentation to increase their accuracy. They achieved a validation accuracy score of 71.63% for ResNet101, 63.78% for ResNet50, 40.72% for InceptionResNetV2, and 34.84% for InceptionV3. This paper compares the proposed algorithms with existing ones like ResNet101, ResNet50, InceptionResNetV2, and InceptionV3. In the existing system, ResNet101 gave the highest accuracy of 71.63%. The proposed algorithms give a validation accuracy score of 91.9% for Xception, 55% for VGG19, 83.47% for NASNetMobile, 89.05% for EfficientNetV2M, 87.38% for ResNet152V2, 92.4% for Hybrid of Inception-v3 &Xception, and 89.00% for Hybrid of EfficientNetV2M, NASNetMobile, Inception & Exception. Among these algorithms, the Hybrid of Inception-v3 &Xception gives the highest accuracy of 92.4%.
On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers

Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions about it. With the advent of bi-directional deep learning algorithms and large-scale datasets, MRC achieved improved results. However, these models are still suffering from two research issues: textual ambiguities and semantic vagueness to comprehend the long passages and generate answers for abstractive MRC systems. To address these issues, this paper proposes a novel Extended Generative Pretrained Transformers-based Question Answering (ExtGPT-QA) model to generate precise and relevant answers to questions about a given context. The proposed architecture comprises two modified forms of encoder and decoder as compared to GPT. The encoder uses a positional encoder to assign a unique representation with each word in the sentence for reference to address the textual ambiguities. Subsequently, the decoder module involves a multi-head attention mechanism along with affine and aggregation layers to mitigate semantic vagueness with MRC systems. Additionally, we applied syntax and semantic feature engineering techniques to enhance the effectiveness of the proposed model. To validate the proposed model’s effectiveness, a comprehensive empirical analysis is carried out using three benchmark datasets including SQuAD, Wiki-QA, and News-QA. The results of the proposed ExtGPT-QA outperformed state of art MRC techniques and obtained 93.25% and 90.52% F1-score and exact match, respectively. The results confirm the effectiveness of the ExtGPT-QA model to address textual ambiguities and semantic vagueness issues in MRC systems.
QNEAT: Natural Evolution of Variational Quantum Circuit Architecture

Quantum Machine Learning (QML) is a recent and rapidly evolving field where the theoretical framework and logic of quantum mechanics are employed to solve machine learning tasks. Various techniques with different levels of quantum-classical hybridization have been proposed. Here we focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks in the noisy intermediate-scale quantum (NISQ) era. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. This paper focuses on this last problem for finding optimal architectures of variational quantum circuits for various tasks. To address it, we propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC. In particular, we present a version of the well-known neuroevolution of augmenting topologies (NEAT) algorithm and adapt it to the case of variational quantum circuits. We refer to the proposed architecture search algorithm for VQC as QNEAT. We test the algorithm with different benchmark problems of classical fields of machine learning i.e. reinforcement learning and combinatorial optimization.
12 Review and significance of cryptography and machine learning in quantum computing

Quantum machine learning, which combines quantum computers with regular machine learning, is a much contested field of research. The goal of quantum ML is to provide light on the possible applications of quantum mechanical findings to ML challenges. The exponential growth of data that must be taught to a typical computer paradigm implies that the present cutting-edge computing equipment cannot keep up with the pace. Quantum computing might be useful in this context for continuous training with enormous datasets. Scientists have resorted to quantum machine learning to reduce the development time for new learning algorithms. Traditional machine learning seeks patterns in data and uses those patterns to forecast the future. This is machine learning’s ultimate objective. However, the new patterns created by quantum techniques imply that quantum computers may be more successful in machine learning than traditional computers. One of the most significant uses of cryptography is assuring user anonymity and data security over unsecure networks. In this section, we examine the history of quantum machine learning and summarize the most current results in the field.
17 Application of quantum computing for digital forensic investigation

In the current technological climate, there is an increase in the number of instances of cybercrime. In the real world, it is never simple to track down and apprehend a cybercriminal. Even if the perpetrator is captured, it will be challenging to secure his conviction since there are not enough standardized digital investigation models. As a consequence of a badly handled forensic investigation, a person who committed a cyber crime may go free, while an innocent person may be subject to adverse ramifications as a result of the inquiry. Forensic investigators are required to adhere to consistent and well-defined forensic methods in order to successfully capture and prosecute cyber criminals. In recent years, a number of new investigating models for digital forensics have been developed; nonetheless, the percentage of cases that result in a conviction remains low since the same investigation model is not relevant in all nations. This chapter introduces a one-of-a-kind Digital Forensic Investigation Model that is built on the architecture and cyber regulations of the Indian subcontinent.