Reviewerly/exHarmony huggingface dataset:
Dazed and Confused: Studying the Prevalence of Atoms of Confusion in Long-Lived Java Libraries

Program comprehension is a fundamental activity in software maintenance and evolution, impacting several tasks such as bug fixing, code reuse, and implementation of new features. The Atom of Confusion (AC) is considered the smallest piece of code that can confuse programmers, difficulting the correct understanding of the source code under consideration. Previous studies have shown that these atoms can significantly impact the presence of bugs in C++ projects and increase the time and effort to code understanding in C++ and Java programs. To gather more evidence about the diffusion of ACs in the Java ecosystem, we conduct a study to analyze the prevalence, co-occurrences (at the class level), and evolution of ACs in 27 long-lived Java libraries. To support our investigation, we developed an ACs automatic search tool, which found 11.404 occurrences in the studied libraries. The Conditional Operator and Logic as Control Flow ACs were the most prevalent among the 10 types of ACs assessed. Our findings show that Conditional Operator and Logic as Control Flow were more likely to co-occur in the same class. Finally, we observed that the prevalence of ACs did not decrease over time. On the contrary, in 13 libraries, the presence grew proportionally more than the size of the library in lines of code. Furthermore, in 15 libraries, the fraction of Java classes containing at least one AC also increases over time.
Cloud-Native Server Consolidation for Energy-Efficient FaaS Deployment

The lack of function-oriented power management scheme has seriously hindered the serverless platform’s cost efficiency. In this paper, we analyze the invocation pattern of serverless functions and investigate its implications on server energy efficiency. Rather than using a one-size-fits-all strategy, we propose DAC, a software-hardware co-design solution to offer differentiated cloud-native server consolidation. We build a proof-of-concept framework and show that DAC can improve the energy efficiency of tail function deployment by up to 23%.
Nuclear Segmentation and Classification: On Color and Compression Generalization

Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data. Existing work to address this in both pathology and natural images has focused almost exclusively on classification tasks. We explore and evaluate the robustness of the 7 best performing nuclear segmentation and classification models from the largest computational pathology challenge for this problem to date, the CoNIC challenge. We demonstrate that existing state-of-the-art (SoTA) models are robust towards compression artifacts but suffer substantial performance reduction when subjected to shifts in the color domain. We find that using stain normalization to address the domain shift problem can be detrimental to the model performance. On the other hand, neural style transfer is more consistent in improving test performance when presented with large color variations in the wild.
On Combining Robustness and Regularization in Training Multilayer Perceptrons over Small Data

Multilayer perceptrons (MLPs) continue to be commonly used for nonlinear regression modeling in numerous applications. Available robust approaches to training MLPs, which allow to yield reliable results also for data contaminated by outliers, have not much penetrated to real applications so far. Besides, there remains a lack of systematic comparisons of the performance of robust MLPs, if their training uses one of regularization techniques, which are available for standard MLPs to prevent overfitting. This paper is interested in comparing the performance of MLPs trained with various combinations of robust loss functions and regularization types on small datasets. The experiments start with MLPs trained on individual datasets, which allow graphical visualizations, and proceed to a study on a set of 163251 MLPs trained on well known benchmarks using various combinations of robustness and regularization types. Huber loss combined with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> - regularization turns out to outperform other choices; this combination is recommendable whenever the data do not contain a large proportion of outliers.
Extracting Relations from Italian Wikipedia Using Self-Training

In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particular, we exploit a self-training strategy for enriching a small number of manually labeled triples with new self-labeled examples. We integrate the supervised stage in WikiOIE, an existing framework for unsupervised extraction of relations from Wikipedia. We rely on WikiOIE and its unsupervised pipeline for extracting the initial set of unlabelled triples. An evaluation involving different algorithms and parameters proves that self-training helps to improve performance. Finally, we provide a dataset of about three million triples extracted from the Italian version of Wikipedia and perform a preliminary evaluation conducted on a sample dataset, obtaining promising results.
ProbaSAS: Modeling and Decision-Making Approach for Self-Adaptive Software Systems under Uncertainty

Self-adaptive software (SAS) systems are gaining increasing popularity in recent years. However, the changing and dynamic running environment and the diverse user requirements have introduced uncertainty, especially the random uncertainty into behaviors of the SAS systems. And the above uncertainty has posed huge challenges in software modeling and decision-making of the SAS systems. For this end, this paper presents ProbaSAS: an MDP (Markov Decision Process) based approach for SAS modeling and decision making under uncertainty. Firstly, the uncertainty within the SAS systems has been systematically analyzed, and the modeling and decision-making framework is proposed. Then, the modeling approach for three kinds of uncertainty (i.e., the probabilistic behaviors, the non-deterministic processes, and the non-functional characteristics) is created based on the MDP model. Finally, the self-adaptation reasoning and decision-making approach for two kinds of self-adaptation (i.e., the structure self-adaptation and the behavior self-adaptation) is proposed based on the probabilistic model checking technique. Taking the Ship-Supplying Information System as an example, we have evaluated the effectiveness of the ProbaSAS approach in uncertainty modeling and decision-making of the SAS systems.
Sequential Recommendation Based on Pairs of Interest and Graph Structure

The recommendation system can recommend information to users efficaciously, which helps many users to obtain information in different fields. The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The difficulty of modeling user interests has always been a challenge. The recommendation is a research topic to provide users with personalized items of interest. However, most existing approaches equally treat simple textual information as the input to learn the representation of an item, ignoring the user’s interest and structure information of the network. In the recommendation system, users and items and the interaction of their information have a crucial impact on the efficiency and accuracy of the recommendations. However, most recommendation systems are usually designed based only on users. with little consideration given to obtaining other factors that contribute to recommendation behavior. Therefore, we propose a method based on the user’s periodic pairs of interest and graph structure to obtain as much effective information as possible to recommend items. Extensive offline experiments on large-scale real data show that our method outperforms the representative baselines.
Prediction of spatiotemporal sequence based on IM-LSTM

With the development of deep learning, spatiotemporal prediction learning has many applications, such as weather prediction and human posture prediction. The continuous data question is described as a spatiotemporal sequence prediction question, in which both the input and the prediction target are spatiotemporal sequences. But the prediction of continuous frames increases the ambiguity of the image. A number of factors are known to affect the sharpness of image. Interaction between states in the network has a significant impact on sharpness of image. Based on the above discussion, the SIM (Short time interactive memory) and the LIM (Long term interactive memory) are designed. The current input and hidden states are updated by the SIM block. Memory cells and hidden states are updated by the LIM block. We built the I-LSTM unit by adding SIM and LIM to the LSTM. By stacking I-LSTM, we propose IM-LSTM (Interact memory LSTM). Experiments show the effectiveness and flexibility of our proposed method. In our experiments, we demonstrate that our proposed model accurately predicts future frames as well as other representations. It is superior to other advanced methods in index LPIPS.
A Behavioral Chatbot Using Encoder-Decoder Architecture : Humanizing conversations

Though there are so many ways of building conversational chatbots, they lack the human touch and sound very robotic. We don’t have any chatbots that aim to imitate even a speck of a personality or human-like traits, while we very well possess everything that we need in terms of data and computation. This project aims to make both efficient and human-like chatbot using the modern encoder-decoder architecture. There are many frameworks and libraries available to develop AI-based chatbots including program-based, rule-based and interface-based. But they lack the flexibility in developing real dialogues and understanding humans. The popular chatbot models don’t aim to hold conversations that imitate real human-like interactions. Current chatbots employ a rule-based approach, basic machine learning algorithms, or a retrieval-based strategy that does not provide humanized outputs, i.e., these chatbots are incapable of producing engaging dialogues. In this paper, we tried to develop a Behavioural chatbot, using modern deep learning techniques like Seq2Seq aiming to develop chatbots to understand humans and make some situation agnostic conversations that remind us of our desirable personalities. This chatbot model was trained on real human conversations extracted out of Hollywood movies, hence the dataset possesses around 2.3 lakh truly organic dialogues. The model was trained with ~4.2 million parameters, 250 epochs and attained an accuracy of 95%. This chatbot is capable of displaying subtle sarcasm and also tries to be funny at times, thanks to the dramatic Hollywood dialogue writers.
ВИКОРИСТАННЯ МЕНТАЛЬНИХ КАРТ НА НАВЧАЛЬНИХ ЗАНЯТТЯХ З ХІМІЇ

The article updates the problem of using mental maps in the educational process of a general secondary education institution, in particular, in chemistry classes.&#x0D; The purpose of the article is to substantiate the expediency of using mental maps in chemistry classes in a general secondary education institution. In the course of writing the article, such theoretical research methods as analysis, synthesis, modeling, as well as empirical methods - a pedagogical experiment were used to test the effectiveness of using mental maps in the educationalprocess of chemistry.&#x0D; The article characterizes the possibilities of using mental maps in the process of planning the activity of a chemistry teacher, in the process of forming integrated knowledge of students in chemistry lessons, as well as their application in extracurricular work of the student, in particular, in the process of creating individual and group projects. Intelligence maps help to organize, systematizescientific information, group scientific data, opinions, find new ideas, plan work, evaluate its scope, summarize scientific achievements in this field of research.&#x0D; The results of the empirical study proved that mental maps have a positive effect on the formation of the subject competence of high school students in all the main components. The need to improve the possibilities of using mental maps in relation to such important components of training as the identification of correct judgments and conclusions, as well as the formation of a reflective component of training, was established.&#x0D; It was concluded that mental maps in education are a modern and compact way of processing educational material, which makes the chemistry lesson interesting and informative, and also allows students to learn the material better. The use of mind mapping technology also opens up a range of opportunities for the development of the teacher's creative potential.
Decomposition and Merging Co-operative Particle Swarm Optimization with Random Grouping

Particle swarm optimization (PSO) does not scale well to large-scale optimization problems (LSOPs). A divide-and-conquer approach towards solving LSOPs has been shown to be very effective in scaling PSO, resulting in a family of co-operative PSO (CPSO) algorithms. Recently, two adaptive co-operative PSO approaches have been developed to improve performance on non-separable problems, namely decomposition CPSO (DCPSO) and merging CPSO (MCPSO). Though DCPSO and MCPSO were shown to perform competitively, they are limited in their ability to explore variable groupings. This paper proposes incorporating random grouping of decision variables into DCPSO (RG-DCPSO) and MCPSO (RG-MCPSO) to better cope with complex variable dependencies. These algorithms were compared to results from five other decomposition-based approaches in order to determine if applying random grouping to DCPSO and MCPSO leads to an improvement in performance. The empirical results show that when applied to function optimization problems, RG-DCPSO was able to achieve the best overall final objective function values in environments with up to 1000 dimensions. The results also show that RG-MCPSO performs well for non-separable objective functions in large-dimensional spaces with 500 and 1000 dimensions.
The Uncertainty Aware Salted Kalman Filter: State Estimation for Hybrid Systems with Uncertain Guards

In this paper, we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimation. Examining how guard surface uncertainty affects the time spent in each mode, we derive a novel guard saltation matrix- which maps perturbations prior to hybrid events to perturbations after - accounting for additional variation in the resulting state. Additionally, we propose the use of parame-terized reset functions - capturing how unknown parameters change how states are mapped from one mode to the next - the Jacobian of which accounts for additional uncertainty in the resulting state. The accuracy of these mappings is shown by simulating sampled distributions through uncertain transition events and comparing the resulting covariances. Finally, we integrate these additional terms into the “uncertainty aware Salted Kalman Filter”, uaSKF, and show a peak reduction in average estimation error by 24–60% on a variety of test conditions and systems.
A Link Prediction Model of Dynamic Heterogeneous Network Based on Transformer

It has always been a challenge to research inductive learning, which can embed newly unseen nodes. Inductive learning is a frequently encountered problem in practical applications of graph networks, but there is little research on dynamic heterogeneous network link prediction. Therefore, we propose a Heterogeneous and Temporal Model Based on Transformer (HT-Trans) for dynamic heterogeneous network, which core idea is to introduce transformer to integrate better neighbor information to capture network structure. The goal of HT-Trans is to infer proper embedding for existing nodes and unseen nodes. Experimental results show that the algorithm proposed in this paper is significantly competitive compared with baselines for link prediction tasks on three real datasets.
Distributed Processor for FPGA-based Superconducting Qubit Control

Quantum circuits utilizing real time feedback techniques (such as active reset and mid-circuit measurement) are a powerful tool for NISQ-era quantum computing. Such techniques are crucial for implementing error correction protocols, and can reduce the resource requirements of certain quantum algorithms. Realizing these capabilities requires flexible, low-latency classical control. We present the development of an open-source FPGA (field-programmable gate array) based processor for pulse-level qubit control. Our processor is designed primarily as an upgrade to QubiC, an open source qubit control platform, but can also be adapted to other systems. The processor is distributed in nature, with one core per arbitrary waveform generator (AWG); simplifying the design and allowing for straightforward scaling. Each processor core implements an instruction set consisting of timed pulse commands as well as simple arithmetic and branching instructions. Our design also includes interfaces for synchronizing the different cores and requesting/receiving (optionally processed) measurement results. Together, these features will allow a user to program arbitrary control flow within a quantum circuit based on previous measurement results. In this poster, we detail our design along with the expected resource utilization on the Xilinx Virtex 7 FPGA, and present pulse-level simulations of our system in the context of a simple feedback experiment.
Learning Mutual Modulation for Self-supervised Cross-Modal Super-Resolution

Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available. Existing methods utilize pseudo or weak supervision in LR space and thus deliver results that are blurry or not faithful to the source modality. To address this issue, we present a mutual modulation SR (MMSR) model, which tackles the task by a mutual modulation strategy, including a source-to-guide modulation and a guide-to-source modulation. In these modulations, we develop cross-domain adaptive filters to fully exploit cross-modal spatial dependency and help induce the source to emulate the resolution of the guide and induce the guide to mimic the modality characteristics of the source. Moreover, we adopt a cycle consistency constraint to train MMSR in a fully self-supervised manner. Experiments on various tasks demonstrate the state-of-the-art performance of our MMSR.
Decision Trees as a Method for Forecasting Seizure Precipitants and Identifying Their Influences on Seizure Outcome

Epilepsy is a complex disease that causes unpredictable seizures, which can lead to severe neurological impairments.Not knowing when a seizure will occur, many people with epilepsy often experience feelings such as anxiety, fear, and stress.In an effort to predict when seizures might occur, investigators have used data from patients' electronic seizure diaries, as well as machine-learning methods, like decision trees.The objective of this work is to create patientspecific decision trees to 1) forecast seizure occurrence and identify seizure precipitants that influence seizure occurrences, and 2) determine seizure precipitants' level of influence on seizure occurrences.Patients' (n=64) seizure diaries were examined individually.Diaries contained data on how patients rated mood, predictive symptoms, stress, seizure occurrences, and seizure likelihood using a 5-point Likert scale.Diaries were recorded in the morning and in the evening, thereby evaluating seizures by half days.R Programming software was used for data analysis and decision tree development, and a confusion matrix was used for predictive accuracy.Results showed that precipitants' influence on patient's seizure outcome was greater in the morning than in the evening.Patients were also categorized in groups based on shared seizure precipitants.This work introduced non-invasive, personalized healthcare regimen for people with epilepsy.
A new accelerated positive-indefinite proximal ADMM for constrained separable convex optimization problems

The alternating direction method of multipliers (ADMM) is a powerful method to solve constrained convex optimization problems with the separable structure.The ADMM with the positiveindefinite proximal terms, which has ergodic convergent rate O( 1 K ) with the number of iterations K, is more general than the ADMM with positive-definite proximal terms.In this paper, we propose a new accelerated positive-indefinite proximal linearized ADMM algorithm with positive-indefinite proximal matrix by the techniques of extrapolation.We obtain the nonergodic convergence rate O( 1 K ) in the sense of objective values and the nonergodic convergence rate O( 1 √ K ) in the sense of iterative sequence of the proposed method as well as the upper bound of the violation of constraints.Numerical results are reported to show the efficiency of the proposed method.
CMMI Software Evolution and Its Role in Pakistan

Software evolution is a process that is a current need of the software industry and software users, either it is an organization or an individual. In this paper, an analysis of different things related to software evolution will be seen. It is a study towards some mature software evolution process which Capability Maturity Model Integration (CMMI). We will see an aspect of software evolution in Pakistan. CMMI history will be under the discussion of how it evolves and the benefits of using it. We will make a comparison of different evolution process models with CMMI to have a look at its competencies. We will have a view of Lehman Law and finally CMMI involvement in the Pakistan software industry. Section 1 will be the Introduction and the importance of software evolution in Pakistan. Section 2 CMMI History will be discussed, and Sect. 3, depicts CMMI Comparison. Section 4.1 will introduce Lehman Law, and finally, Sect. 5 is Analysis.
Sexual abuse in schools

An Ofsted review found worrying levels of sexual abuse in schools and colleges. It recommends changes to staff training and teaching practices to improve awareness, reports Jo Sephenson
Methodical procedure for creating content for interactive augmented reality

The article describes a methodological procedure for the development of interactive applications in augmented reality (ARIT) by demonstrating it on a life-size 3D hologram of engineering equipment. In this article, we consider the creation of a general application model using the Unity 3D game engine for development for the Head mounted display (HMD). We used the Hololens device to display a demonstration of the model we created and for analysis problems faced during the initial development of interactive content in mixed reality.
Anchoring code understandability evaluations through task descriptions

In code comprehension experiments, participants are usually told at the beginning what kind of code comprehension task to expect. Describing experiment scenarios and experimental tasks will influence participants in ways that are sometimes hard to predict and control. In particular, describing or even mentioning the difficulty of a code comprehension task might anchor participants and their perception of the task itself.