Reviewerly/exHarmony huggingface dataset:
Quantum Software Models: Software Density Matrix is a Perfect Direct Sum of Module Matrices

Quantum Software Models is a theoretical framework to systematically design and analyze any software system -be it quantum, classical or hybrid -representing it by a design Density Matrix.Recently, we have demonstrated a top-down approach, to decompose a whole software system Density Matrix into modules, using basis vector projectors of the Matrix.However, it would be even more natural to have a systematic bottom-up procedure, to compose a whole software system Density Matrix, given a set of well-designed software module matrices, taken as sub-systems.This is exactly the paper's purpose.The result obtained: the whole software system Density Matrix is a perfect Direct Sum of module density matrices.This result yields clear software design benefits: it is bidirectional, one can traverse the software system hierarchy top-down or bottom-up, in particular, gradually building up the whole system from verified correct modules, assured by spectral decoupling techniques.The claim is formally validated and is illustrated by software system studies.
Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them.Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed.However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust.Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code.This leads to a large number of similar contracts with potential security risks.Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum.In this paper, we design a learning-effective and costefficient model, called SmartSD, for Ethereum smart contract similarity detection.Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks.The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers.
DDMin versus QuickXplain - An Experimental Comparison of two Algorithms for Minimizing Collections

About two decades ago, two algorithms, i.e., DDMin and QuickXPlain, for minimizing collections, were independently proposed and gained attention in the two research areas of Software Engineering and Artificial Intelligence, respectively.Whereas DDMin was developed for reducing a given test case, QuickXPlain was intended to be used for obtaining minimal conflicts efficiently.In this paper, we compare the performance of both algorithms with respect to their capabilities of minimizing collections.We found out that one algorithm outperforms the other under given prerequisites and vice versa.These findings help to select the suitable algorithm for a given task.
Towards Accurate Knowledge Transfer between Transformer-based Models for Code Summarization

Automatic code summarization generates high-level natural language descriptions of code snippets, which can benefit software maintenance and code comprehension.Recently, Transformer-based models achieved state-of-the-art performance on code summarization tasks.However, there are data gaps in neural model training for some programming languages.To fill this gap, we propose a novel transfer learning approach to accurately transfer knowledge between Transformer-based models.We train a discriminator to identify which heads of the multi-head attention module should be transferred.On this basis, we define a transfer strategy of parameter matrices.We evaluated the proposed transfer learning approach on four state-of-the-art Transformer-based code summarization models.Experimental results show that models with transferred knowledge outperform original models up to 10.70% in BLEU, 5.36% in ROUGE-L, and 4.34% in METEOR.
Self-Adaptive Task Allocation for Decentralized Deep Learning in Heterogeneous Environments

The demand for large-scale deep learning is increasing, and distributed training is the current mainstream solution.Decentralized algorithms are widely used in distributed training.However, in a heterogeneous environment, each worker computes the same amount of training data resulting in a lot of wasted time for waiting the straggler.In this paper, we proposed a selfadaptive task allocation algorithm (SATA) which allows that each worker acquires the amount of training data adaptively based on the performance of workers in the heterogeneous environment.In order to show the applicability of SATA in heterogeneous clusters better, we set up the heterogeneous cluster composed of two or three different types of GPUs.Besides, we conduct a series of experiments to show the performance of SATA.The experimental results illustrate several advantages of SATA.SATA can accelerate distributed training about 3.3X that of All-Reduce as well as about 1.9X-3.8Xthat of AD-PSGD algorithm.And the total training time of SATA is reduced from 20 to 40 percentage compared to All-Reduce.
Data Driven Testing for Context Aware Apps

Context driven environments are growing in popularity.Mobile applications, Internet of Things devices, autonomous vehicles, and future technologies respond to context events in their environments.This work uses a set of context events from real users to guide the generation of context driven test cases.Context event sequences are obtained by applying Conditional Random Fields (CRF).Test suites are then constructed by interleaving the context event sequences with GUI events.The choice of context event is made based on transitions obtained from the CRF.Results of the empirical studies show that techniques that incorporate context events provide better code coverage than NoContext for the subject applications.A heuristic technique introduced in this work, ISFreqOne, yields 4x better coverage than NoContext, 0.06x better coverage than Random Start Context, 0.05x better coverage than Iterative Start Context, which are control context generation techniques, and 0.04x better coverage than ISFreqTwo, another heuristic introduced in this work.
Postoperative MPA-AUC0-12h Prediction for Kidney Transplant Recipients based on Few-shot Learning

Mycophenolic acid (MPA) is a commonly used immunosuppressive drug.The anti-immune rejection effect of mycophenolic acid is closely related to its exposure level in the body.In clinical practice, mycophenolate acid drug exposure level is usually reflected by monitoring the area under the drug-time curve MPA-AUC0-12h.Calculating the MPA-AUC0-12h requires numerous blood sampling time points.Not only does the medical staff have more work, but patients suffer more as well.Limited sampling strategies (LSS) are generally used to reduce the number of time points.Nevertheless, this method involves complicated calculations and the predictive accuracy is very low for small sample data.A new method of predicting the MPA-AUC0-12h value is proposed based on the selection of SHAP features with an improved neural network for small sample data.The experimental results show that the average prediction errors of the MPA-AUC0-12h values on different data sets by our method are better than that of the baseline models.
Correlation Feature Mining Model Based on Dual Attention for Feature Envy Detection

Feature Envy is a code smell due to the abnormal calling relationships between methods and classes, which adversely affects software scalability and maintainability.Existing methods mainly use various technologies to model abnormal relationships to detect feature envy.However, these methods only rely on local features such as entity names, which is not robust enough.Moreover, the mining depth of correlation features between entities involved in feature envy is limited.In this paper, we propose a correlation feature mining model based on dual attention to detect feature envy.Firstly, we propose a multi-view-based entity representation strategy, which enhanced the robustness of the model while improving the suitability of the correlation feature and model.Secondly, we add attention mechanism to the channel dimension and spatial dimension of CNN to control the flow of information and capture the correlation features between entities more accurately.Finally, the evaluation results on projects both with and without feature envy injected show that our proposed approach outperforms the state-of-the-art methods.
Formal Verification of COCO Database Framework Using CSP

Nowadays, many applications are built on distributed databases for scalability and high availability.Therefore, the architecture design of distributed databases needs to satisfy some functional properties to ensure that the database can perform transactions reliably and efficiently.COCO is a distributed OLTP database that supports epoch-based commit and replication and two variants of optimistic concurrency control which use physical time or logical time.In this paper, we first use process algebra CSP to model COCO's architecture.Then we use model checker PAT to verify seven properties, including deadlockfree, consistency, availability, partition tolerance (CAP), and basically availability, soft state, eventual consistency (BASE).The results show COCO's commit and replication protocol satisfy the CAP theorem, and two optimistic concurrency control variants satisfy the BASE theorem.
A Distributed Graph Inference Computation Framework Based on Graph Neural Network Model

A graph is a structure that can effectively represent objects and the relationships between them.Graph Neural Networks (GNNs) enable deep learning to be applied in the graph domain.However, most GNN models are trained offline and cannot be directly used in real-time monitoring scenarios.In addition, due to the very large data scale of the graph, a single machine cannot meet the demand, and there is a performance bottleneck.Therefore, we propose a distributed graph neural network inference computing framework, which can be applied to GNN models in the form of Encoder-Decoder.We propose the idea of "single-point inference, message passing, distributed computing", which enables the system to use offline-trained GNNs for real-time inference computations on graph data.To maintain the model effect, we add the second-degree subgraph and mailbox mechanism to the continuous iterative calculation.Finally, our results on public datasets show that this method greatly improves the upper limit of inference computation and has better timeliness.And it maintains a good model effect on three types of classical tasks.The source code is published in a Github repository.
Managing Risks in Agile Methods: a Systematic Literature Mapping

Agile software development methods have been around since at least 2001.They accommodate changing requirements with the flexibility to deal with cost and scope and have increasingly been used.However, explicit risk management is often ignored as agile methods deal with risk intrinsically and focus on rapid value delivery.In certain contexts, explicit risk management practices are needed to complement agile methods.Thus, this paper presents a systematic literature mapping aiming to discover how do software organizations integrate explicit risk management practices into agile methods.As a result we found 23 primary studies that, in majority, applied case studies in the industry, using agile methods such as Scrum, and adapting agile practices such as Daily Meeting and Iteration Planning Meeting to manage risks related to schedule and communication, for example.The selected primary studies raise evidence that the introduction of explicit risk management practices bring benefits to agile methods.
A Modern Tool of Conversation: Chatbot

A chatbot is a work of artificial intelligence technology that simulates a conversation (or chat) in natural language with a user via messaging applications, internet sites, smartphone apps, or the telephone. Chatbots are used in a range of conversation systems for a variety of purposes, including customer assistance, request processing, and information acquisition. Chatbots have been around for quite some time, but it has only been in the recent past few years that they have seen a significant uptick in popularity among consumers and companies. This change in the perspective of chatbots and conversational interfaces was heavily impacted by the advancements in artificial intelligence and machine learning, as well as by the expanding usage of messaging app technologies. This study offers a comprehensive analysis of the conversational tool known as chatbots, which emerged in the contemporary era. This paper also discusses how this tool is expanding its root in the life of human beings as well as the pros-cons that will be generated by the chatbots.
Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark

This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task.
A Case Study on the Development and Utilization of the Shared Index for the Optimization of Liberal Arts Curriculum at College

The purpose of this study is to clarify a concept of the shared index developed by University A to optimize the liberal arts curriculum in colleges. The development of the ‘shared index’ is originated from the recognition of the issue of how to apply basic liberal arts subjects of a major. A researcher embodied the concept of the shared index by referring to previous studies dealing with the problem of curriculum optimization, and then calculated the shared index of subjects organized as liberal arts curriculum in colleges. The shared index is calculated as a combination of ‘shared level’, which shows how commonly certain subjects are organized in the curriculum by various departments and divisions, and the ‘probability of students’ selection’, which indicates the probability of students taking certain courses. As a result of calculating the change in the shared index before and after reorganization of the liberal arts curriculum, the shared index of liberal arts subjects on the basic nature of a major rose by 0.07. This study presents quantitative indicators that can be taken in account, in addition to the content characteristics of courses in the liberal arts area regarding the basic nature of a major.
Design of a Higher Education Question and Answer System Based on Multimodal Adversarial Networks

Higher education question and answer system is a current research hotspot in the field of natural language processing and artificial intelligence, which can return accurate answers directly and also allow users to input in natural language, avoiding the need to return large collections of valid and invalid data links for users to filter twice. In recent years, deep learning techniques have made great progress in the field of natural language processing, thus making it possible to apply it in the field of teaching and learning in junior high school. Most of the traditional quiz systems based on traditional retrieval techniques have problems such as insufficient semantic portrayal of text, inability to extract semantic features in context, and poor processing of complex utterances. In order to fill the gap and deficiencies of question and answer systems in junior high school teaching, this paper applies multimodal and adversarial network-related technologies to build a multimodal adversarial network-based question and answer system for higher education. The experimental results show that the principles of the traditional application system and the multimodal adversarial system designed in this paper are similar, but the current application system is relatively more effective.
Blockchain Technologies and Media Transformation

In this paper we start from the assumption that social dynamics and cultural-civilizational progress cannot be viewed separately from the social history of the media as complex technological-communication structures. In this paper we deal with the influence of new disruptive technologies DLT (Distributed Ledger Technologies) better known as blockchain technologies which, thanks to the latest technological solutions, have great potential for transforming the media and journalism as a profession, thus we have explained the technological concept on which they are based. We will discuss the concept of personalized blockchain journalism and propose a user economy model that guarantees the financial sustainability of the platform. Observing information as a user experience, we have discussed the possibility of adequate cognitive response of users in conditions of information saturation, and raised the issue of structuring information in designing a journalistic platform on the blockchain, as an ecosystem that will distance itself from existing polluted media space. Finally, we have proposed a topic for further reflection and scientific research, which is the latest technological transition based on quantum computing, artificial intelligence and the Metaverse, which will provide a completely new user experience in 3D virtual space, where user avatars will rely less and less on traditional media and journalism as we know it today.
The Artificial Intelligence and Competition Law - Challenges for Bosnia and Herzegovina

The digital economy is characterized by new business models based on the use of electronic communications and digital technology for the production and distribution of goods and services. Big data is becoming an invaluable resource in the functioning of the digital economy. In order to survive in the market and be more competitive, firms are changing the way they make their strategic decisions and are increasingly resorting to the use of big data and technologically advanced tools, such as pricing algorithms. Companies increasingly use computer algorithms to improve their pricing models, adjust services and predict market trends, which is recognized as a model to increase efficiency. The use of algorithms in everyday business brings benefits to businesses, consumers and, ultimately, society as a whole, but it also raises a number of legal issues. Many questions also arise in competition law, which aims to protect the competition process in the market by banning certain business practices. Use of algorithms can be new sophisticated method, which will make easier for companies to reach and maintain collusion without any formal agreement or human interaction. The use of algorithms contrary to the provisions of competition law, raises the question of whether competition authorities need to improve existing tools for detecting prohibited agreements as well as a number of other issues. Bosnia and Herzegovina is a country in transition with a small open economy, which has the status of a potential candidate for EU membership. Modern competition law in Bosnia and Herzegovina is the result of foreign pressure and meeting the conditions for membership in the European Union. Lawmakers in Bosnia and Herzegovina face the challenge of harmonizing competition law with the acquis and improving methods and tools that would enable the Council of Competition, as a body to protect competition in BiH, to respond to the use of algorithms for illicit business practices. The paper aims to investigate whether and to what extent business processes in Bosnia and Herzegovina have been digitized, i.e. whether business entities use algorithms and to what extent, and whether and to what extent the BiH Council of Competition is ready for challenges of competition law in digital economy. Key words: Competition law, algorithmic cartels, digitalisation, Council of Competition
Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells

Multiple blood images of stressed and sheared cells, taken by a Lorrca Ektacytometery microscope, needed a classification for biomedical researchers to assess several treatment options for blood-related diseases. The study proposes the design of a model capable of classifying these images, with high accuracy, into healthy Red Blood Cells (RBCs) or Sickle Cells (SCs) images. The performances of five Deep Learning (DL) models with two different optimizers, namely Adam and Stochastic Gradient Descent (SGD), were compared. The first three models consisted of 1, 2 and 3 blocks of CNN, respectively, and the last two models used a transfer learning approach to extract features. The dataset was first augmented, scaled, and then trained to develop models. The performance of the models was evaluated by testing on new images and was illustrated by confusion matrices, performance metrics (accuracy, recall, precision and f1 score), a receiver operating characteristic (ROC) curve and the area under the curve (AUC) value. The first, second and third models with the Adam optimizer could not achieve training, validation or testing accuracy above 50%. However, the second and third models with SGD optimizers showed good loss and accuracy scores during training and validation, but the testing accuracy did not exceed 51%. The fourth and fifth models used VGG16 and Resnet50 pre-trained models for feature extraction, respectively. VGG16 performed better than Resnet50, scoring 98% accuracy and an AUC of 0.98 with both optimizers. The study suggests that transfer learning with the VGG16 model helped to extract features from images for the classification of healthy RBCs and SCs, thus making a significant difference in performance comparing the first, second, third and fifth models.
Pengendalian Persediaan Darah untuk Pasien dengan Hemoglobin Rendah Menggunakan Metode Backpropagation

The Blood Transfusion Unit (UTD) of the Rokan Hulu Regional General Hospital (RSUD) has an important role to fulfill the demand for blood from patients. Patients who need blood donation are patients with low hemoglobin levels. The problem faced by the UTD-RS is that they have not been able to meet the needs of each patient's blood request optimally. The reason is because it is not able to predict the need for blood that will come. To see the pattern of blood demand and then determine the appropriate inventory control method to assist the planning process for the fulfillment of blood supply at UTD in the next period. Materials (data) and Methods: The data processed in this study were patient data and blood demand data from January 2020 to January 2021. The data were sourced from the Laboratory Installation and UTD at the Rokan Hulu Hospital. The data is divided into training data and testing data. Then the blood demand data is processed by normalizing it first and then the prediction process is carried out using the Backpropagation method. Then analyzed and tested with the help of Matlab software. This study uses the best network architecture pattern produced is 5-5-1 with an accuracy rate of 68% and a Mean Squared Error value of 0.198. The backpropagation method used is able to help UTD Rokan Hulu Hospital to find out the blood needs that must be met so that the blood supply can be controlled. So that every blood request from patients with low hemoglobin can be met quickly.