Reviewerly/exHarmony huggingface dataset:
Structural and Semantic Similarity Measurement of UML Use Case Diagram

Reusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses mature artifacts from previous artifacts. One of software artifacts is diagram, and in order to assist the reusing diagram is to find the level of similarity of diagrams. This paper proposes a method for measuring the similarity of the use case diagram using structural and semantic aspects. For structural similarity measurement, Graph Edit Distance is used by transforming each factor and use case into a graph, while for semantic similarity measurement, WordNet, WuPalmer,and Levenshtein were used. The experimentation was conducted on ten datasets from variousprojects. The results of the method were compared with the results of assessments from experts.The measurement of agreement between experts and method was done by using Gwet’s AC1 andPearson correlation coefficient. Measurement results with Gwet’s AC1 diagram similarity are 0,60,which were categorized as “moderate" agreement and the result of measurement with Pearsonis 0.506 which means there is a significant correlation between experts and methods. The resultshowed that the proposed method can be used to find the similarity of the diagram, so finding andreuse of the diagram as a software component can be optimized.
SPRINT: A High-Performance, Energy-Efficient, and Scalable Chiplet-Based Accelerator With Photonic Interconnects for CNN Inference

Chiplet-based convolution neural network (CNN) accelerators have emerged as a promising solution to provide substantial processing power and on-chip memory capacity for CNN inference. The performance of these accelerators is often limited by inter-chiplet metallic interconnects. Emerging technologies such as photonic interconnects can overcome the limitations of metallic interconnects due to several superior properties including high bandwidth density and distance-independent latency. However, implementing photonic interconnects in chiplet-based CNN accelerators is challenging and requires combined effort of network architectural optimization and CNN dataflow customization. In this article, we propose SPRINT, a chiplet-based CNN accelerator that consists of a global buffer and several accelerator chiplets. SPRINT introduces two novel designs: (1) a photonic inter-chiplet network that can adapt to specific communication patterns in CNN inference through wavelength allocation and waveguide reconfiguration, and (2) a CNN dataflow that can leverage the broadcasting capability of photonic interconnects while minimizing the costly electrical-to-optical and optical-to-electrical signal conversions. Simulations using multiple CNN models show that SPRINT achieves up to 76% and 68% reduction in execution time and energy consumption, respectively, as compared to other state-of-the-art chiplet-based architectures with either metallic or photonic interconnects.
Stacking regularization in analogy-based software effort estimation

Analogy-based estimation (ABE) estimates the effort of the current project based on the information of similar past projects. The solution function of ABE provides the final effort prediction of a new project. Many studies on ABE in the past have provided various solution functions, but its effectiveness can still be enhanced. The present study is an attempt to improve the effort prediction accuracy of ABE by proposing a solution function SABE: Stacking regularization in analogy-based software effort estimation. The core of SABE is stacking, which is a machine learning technique. Stacking is beneficial as it works on multiple models harnessing their capabilities and provides a better estimation accuracy as compared to a single model. The proposed method is validated on four software effort estimation datasets and compared with the already existing solution functions: closet analogy, mean, median and inverse distance weighted mean. The evaluation criteria used are mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), prediction (PRED) and standard accuracy (SA). The results suggested that the SABE showed promising performance for almost all the evaluation criteria when compared with the results of the earlier studies.
State-aligned misogynistic disinformation on Arabic Twitter: The attempted silencing of an Al Jazeera journalist

Abstract A product of the global rise of right-wing populism has been a seeming normalisation of gendered public disinformation, which portrays female public figures as unintelligent, untrustworthy, irrational, and libidinous. Social media has also allowed gendered disinformation to be used in targeted harassment campaigns that seek to intimidate and shame women, reducing their public visibility through psychological violence. Despite this, very few studies on social media involving the Arabic language have explored in detail this phenomenon in the Persian Gulf, despite numerous examples of harassment against women public figures. Since 2017, women journalists critical of regional governments have been subjected to increased attacks online, but none as intense as the attack on Al Jazeera anchor Ghada Oueiss in June 2020. Through keyword analysis, network analysis, and open-source intelligence techniques (OSINT), this paper highlights the intensity and scale of one such attack, identifying the increasing role of malinformation and disinformation in attempting to silence journalists. Such documentation can be useful in demonstrating the volume, velocity, and discursive nature of the attacks threatening women’s visibility online. This research also accounts for a potential mechanism of such attacks, which follow a playbook of: 1) leaking information through anonymous accounts, 2) co-opted or loyalist influencers amplifying the attacks, and 3) uncritical local media jumping on the attacks (breakout). From a transformative perspective, it is increasingly important that such attacks are documented, exposed, and analysed to provide evidentiary claims of such abuse. It also highlights the issues of such abuse in authoritarian regimes, who clamp down on online debate, except appear not to do so when the messaging reflects state propaganda.
Multiple Graphs and Low-Rank Embedding for Multi-Source Heterogeneous Domain Adaptation

Multi-source domain adaptation is a challenging topic in transfer learning, especially when the data of each domain are represented by different kinds of features, i.e., Multi-source Heterogeneous Domain Adaptation (MHDA). It is important to take advantage of the knowledge extracted from multiple sources as well as bridge the heterogeneous spaces for handling the MHDA paradigm. This article proposes a novel method named Multiple Graphs and Low-rank Embedding (MGLE), which models the local structure information of multiple domains using multiple graphs and learns the low-rank embedding of the target domain. Then, MGLE augments the learned embedding with the original target data. Specifically, we introduce the modules of both domain discrepancy and domain relevance into the multiple graphs and low-rank embedding learning procedure. Subsequently, we develop an iterative optimization algorithm to solve the resulting problem. We evaluate the effectiveness of the proposed method on several real-world datasets. Promising results show that the performance of MGLE is better than that of the baseline methods in terms of several metrics, such as AUC, MAE, accuracy, precision, F1 score, and MCC, demonstrating the effectiveness of the proposed method.
User pairing using laser chaos decision maker for NOMA systems

Non-Orthogonal Multiple Access is one of the most important technologies in 5G and Beyond 5G wireless communications, which improve system performance by power domain multiplexing. In realizing Non-Orthogonal Multiple Access, the pairing of multiple users is necessary where efficient principles are highly demanded in dynamically changing electromagnetic environments. In the meantime, ultrafast methods of solving multi-armed bandit problems have been developed using chaotic laser time series. In this paper, we consider the user pairing problem in Non-Orthogonal Multiple Access as a multi-armed bandit problem and propose an ultra-fast user pairing algorithm based on the laser chaos decision maker. We numerically demonstrate that the proposed scheme accomplishes higher throughputs compared with traditional user pairing algorithms, especially in cases with lower user density.
LPSO: Multi-Source Image Matching Considering the Description of Local Phase Sharpness Orientation

To solve the matching problems caused by the large intensity difference between the multi-source images and the nonlinear radiation distortion, we present a multi-source image matching approach that considers the orientation of the phase sharpness. First, the scale-space of the image pyramid was constructed, and the phase consistency of the image frequency domain was solved to obtain the maximum moment feature, and the KAZE operator is used to extract the feature points. Next, the Log-Gabor even symmetric filter was used to perform Fourier transform, and the improved local phase sharpness feature and phase orientation feature were constructed respectively to replace the gradient amplitude and gradient direction feature of the image. A descriptor of local phase sharpness orientation (LPSO) was established through the log-polar description template, and finally the Euclidean distance was used to measure the similarity to obtain the corresponding points. Multiple sets of typical multi-source images were used as data sources, and the LPSO algorithm was compared with SIFT, LGHD, RIFT and HAPCG algorithms in datasets. Evaluation of the algorithm performances indicates that the proposed method is more accurate and robust in the task of multi-source image matching.
Human Action Recognition in Video Sequence using Logistic Regression by Features Fusion Approach based on CNN Features

Human Action recognition (HAR) gains too much attention due to its wide range of real world applications, such as video surveillance, robotics and computer vision.In video surveillance systems security cameras are placed to monitor activities and motion, generate alerts in undesirable situations.Due to such importance of video surveillance in daily life, HAR becomes the primary and key factor of video surveillance systems.Many researchers worked on human action recognition but HAR still a challenging problem, due to large variation among human to human and human actions in daily life, which make human recognition very challenging and makes surveillance system difficult to outperform.In this article a novel method is proposed by features fusion of pre-trained convolution neural network (CNN) features.Initially pre-trained CNN VGG 19 weights are exploited to extract fully connected 7 th layer (FC7) of the selected dataset, subsequently pre-trained fully connected 8 th layer features (FC8) extracted by employing pre-trained weights of the same neural network.However the resultant feature fused vector further optimized by employing two statistical features selection techniques, chi-square test and mutual information to select best features among them to reduced redundancy and increase performance accuracy of human action, a threshold value used for selecting best features.Furthermore the best features are fused, then grid search with 10 fold cross validation is applied for tuning hyper parameter to select best k fold and the resulting best parameter are feed to Logistic regression (LR) classifier for recognition.The proposed technique used You Tube 11 action dataset and achieved 98.49% accuracy.Lastly the proposed method compares with the existing state of the art methods which show dominance performance.
Twist: sound reasoning for purity and entanglement in Quantum programs

Quantum programming languages enable developers to implement algorithms for quantum computers that promise computational breakthroughs in classically intractable tasks. Programming quantum computers requires awareness of entanglement, the phenomenon in which measurement outcomes of qubits are correlated. Entanglement can determine the correctness of algorithms and suitability of programming patterns. In this work, we formalize purity as a central tool for automating reasoning about entanglement in quantum programs. A pure expression is one whose evaluation is unaffected by the measurement outcomes of qubits that it does not own, implying freedom from entanglement with any other expression in the computation. We present Twist, the first language that features a type system for sound reasoning about purity. The type system enables the developer to identify pure expressions using type annotations. Twist also features purity assertion operators that state the absence of entanglement in the output of quantum gates. To soundly check these assertions, Twist uses a combination of static analysis and runtime verification. We evaluate Twist's type system and analyses on a benchmark suite of quantum programs in simulation, demonstrating that Twist can express quantum algorithms, catch programming errors in them, and support programs that several languages disallow, while incurring runtime verification overhead of less than 3.5%.
Mœbius: metaprogramming using contextual types: the stage where system f can pattern match on itself

We describe the foundation of the metaprogramming language, Mœbius, which supports the generation of polymorphic code and, more importantly, the analysis of polymorphic code via pattern matching. Mœbius has two main ingredients: 1) we exploit contextual modal types to describe open code together with the context in which it is meaningful. In Mœbius, open code can depend on type and term variables (level 0) whose values are supplied at a later stage, as well as code variables (level 1) that stand for code templates supplied at a later stage. This leads to a multi-level modal lambda-calculus that supports System-F style polymorphism and forms the basis for polymorphic code generation. 2) we extend the multi-level modal lambda-calculus to support pattern matching on code. As pattern matching on polymorphic code may refine polymorphic type variables, we extend our type-theoretic foundation to generate and track typing constraints that arise. We also give an operational semantics and prove type preservation. Our multi-level modal foundation for Mœbius provides the appropriate abstractions for both generating and pattern matching on open code without committing to a concrete representation of variable binding and contexts. Hence, our work is a step towards building a general type-theoretic foundation for multi-staged metaprogramming that, on the one hand, enforces strong type guarantees and, on the other hand, makes it easy to generate and manipulate code. This will allow us to exploit the full potential of metaprogramming without sacrificing the reliability of and trust in the code we are producing and running.
Yüksek Boyutlu Problemlerin Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanizmasıyla Runge Kutta Optimize Edicinin İyileştirilmesi

Runge Kutta (RUN) is an up-to-date and well-founded metaheuristic algorithm. The RUN algorithm aims to find the global best in solving problems by going beyond the traps of metaphors. For this purpose, enhanced solution quality mechanism is used to avoid local optimum solutions and increase the convergence speed. Although the RUN algorithm offers promising solutions, it is seen that this algorithm has shortcomings, especially in solving high dimensional multimodal problems. In this study, the solution candidates that guide the search process in the RUN algorithm are developed using the Fitness-Distance Balance (FDB) method. Thus, using the FDB-based RUN algorithm, the global optimum value of many optimization problems will be obtained in the future. CEC 2020 which has current benchmark problems was used to test the performance of the developed FDB-RUN algorithm. 10 different unconstrained benchmark problems taken from CEC 2020 were designed by arranging them in 30/50/100 dimensions. Experimental studies were carried out using the designed benchmark problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it was seen that the FDB-RUN variations showed a superior performance compared to the base algorithm (RUN) in all experimental studies. In particular, it has been shown to provide more effective results for the continuous optimization of high-dimensional problems.
Application of Blockchain Technology in Value Chain of Procurement in Manufacturing Enterprises

Blockchain technology, as a database which combines encryption algorithm, smart contract, consensus mechanism, time stamp, and other technologies, has received wide attention from all walks of life and can be used to solve such problems as asymmetric, incomplete, and untimely information in both interenterprise transactions and enterprise internal control management as a good solution owing to its characteristics of decentralization, traceability, and tamper-proofness. Consequently, it is worthy of studying how to apply blockchain technology to daily business activities of enterprises. In this paper, some problems existing in the value chain activities of the enterprise internal procurement are analyzed by designing the solutions to requisition, warehousing, and payment based on blockchain technology, in which the Hyperledger Fabric platform is selected as an implementation tool to simulate some purchasing data for testing procedures. After the successful test, the research conclusions and research prospects of this paper are thus proposed, proving the feasibility of applying the blockchain technology to the internal value chain management of the enterprise, which provides reference for the construction of the internal value chain management of the enterprise by the blockchain.
Digital Technology in Diagnostic Breast Pathology and Immunohistochemistry

Digital technology has been used in the field of diagnostic breast pathology and immunohistochemistry (IHC) for decades. Examples include automated tissue processing and staining, digital data processing, storing and management, voice recognition systems, and digital technology-based production of antibodies and other IHC reagents. However, the recent application of whole slide imaging technology and artificial intelligence (AI)-based tools has attracted a lot of attention. The use of AI tools in breast pathology is discussed briefly as it is covered in other reviews. Here, we present the main application of digital technology in IHC. This includes automation of IHC staining, using image analysis systems and computer vision technology to interpret IHC staining, and the use of AI-based tools to predict marker expression from haematoxylin and eosin-stained digitalized images.
InfoGram and admissible machine learning

We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.
Multi-Model Selective Backdoor Attack with Different Trigger Positions

Deep neural networks show good performance in image recognition, speech recognition, and pattern analysis. However, deep neural networks show weaknesses, one of which is vulnerability to backdoor attacks. A backdoor attack performs additional training of the target model on backdoor samples that contain a specific trigger so that normal data without the trigger will be correctly classified by the model, but the backdoor samples with the specific trigger will be incorrectly classified by the model. Various studies on such backdoor attacks have been conducted. However, the existing backdoor attack causes misclassification by one classifier. In certain situations, it may be necessary to carry out a selective backdoor attack on a specific model in an environment with multiple models. In this paper, we propose a multi-model selective backdoor attack method that misleads each model to misclassify samples into a different class according to the position of the trigger. The experiment for this study used MNIST and Fashion-MNIST as datasets and TensorFlow as the machine learning library. The results show that the proposed scheme has a 100% average attack success rate for each model while maintaining 97.1% and 90.9% accuracy on the original samples for MNIST and Fashion-MNIST, respectively.
Quantum Fisher information maximization in an unbalanced interferometer

In this paper we provide the answer to the following question: given an arbitrary pure input state and a general, unbalanced, Mach-Zehnder interferometer, what transmission coefficient of the first beam splitter maximizes the quantum Fisher information (QFI)? We consider this question for both single- and two-parameter QFI, or, in other words, with or without having access to an external phase reference. We give analytical results for all involved scenarios. It turns out that, for a large class of input states, the balanced (50:50) scenario yields the optimal two-parameter QFI; however, this is far from being a universal truth. When it comes to the single-parameter QFI, the balanced scenario is rarely the optimal one and an unbalanced interferometer can bring a significant advantage over the balanced case. We also state the condition imposed upon the input state so that no metrological advantage can be exploited via an external phase reference. Finally, we illustrate and discuss our assertions through a number of examples, including both Gaussian and non-Gaussian input states.
Development of a Bathing Accident Monitoring System Using a Depth Sensor

Among the domestic accidents that occur globally, a few percent are due to accidental drowning, mostly related to bathing.This letter examines countermeasures against bathing accidents and proposes a bathing accident monitoring system to prevent accidental drowning due to loss of consciousness during bathing.This system considers the user's privacy and uses only the depth information acquired by a depth sensor installed in the bathroom.This system grasps the movement of the bather with a depth sensor and informs others, such as the bather's family, when drowning might be occurring.In this letter, we mainly analyzed depth data from depth sensors and developed automatic detection of drowning conditions.Automatic detection of the drowning state was experimentally evaluated in an actual bathroom.The scenario for the experiment was the natural flow of a series of general bathing movements (e.g., normal bathing, wiping the face, and touching the shoulders) and reproduction of the drowning state.As a result, it was shown that the automatic detection rate of the drowning state was at the applicable level.