Reviewerly/exHarmony huggingface dataset:
Fault Triggers in the TensorFlow Framework: An Experience Report

TensorFlow is one of the most popular machine learning frameworks for developing machine learning algorithms. Because of the popularity and large-scale use of TensorFlow, even a single bug may lead to severe consequences and impact a large number of users. With a growing number of safety-critical systems built upon TensorFlow, its reliability is becoming increasingly important. An essential step to ensure TensorFlow's reliability is to understand the characteristics of bugs that occurred in TensorFlow. This paper presents the first comprehensive empirical study on fault triggering conditions in TensorFlow. 2,285 bug reports from TensorFlow's GitHub repository are collected. A bug classification is performed based on fault triggering conditions, followed by the frequency distribution of different types of bugs and the evolution features of varying bug types over time. Then the relationships between bug types and fixing time are also investigated. In addition, the root causes of Bohrbugs and Mandelbugs are studied. Five root causes are discovered. Furthermore, the analysis of regression bugs in TensorFlow is conducted. We have revealed 10 important findings based on our empirical results. There are 8 implications based on these findings are provided for developers and users.
Discriminative Residual Analysis for Image Set Classification With Posture and Age Variations

Image set recognition has been widely applied in many practical problems like real-time video retrieval and image caption tasks.Due to its superior performance, it has grown into a significant topic in recent years.However, images with complicated variations, e.g., postures and human ages, are difficult to address, as these variations are continuous and gradual with respect to image appearance.Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations.In this work, a Discriminant Residual Analysis (DRA) method is proposed to improve the classification performance by discovering discriminant features in related and unrelated groups.Specifically, DRA attempts to obtain a powerful projection which casts the residual representations into a discriminant subspace.Such a projection subspace is expected to magnify the useful information of the input space as much as possible, then the relation between the training set and the test set described by the given metric or distance will be more precise in the discriminant subspace.We also propose a nonfeasance strategy by defining another approach to construct the unrelated groups, which help to reduce furthermore the cost of sampling errors.Two regularization approaches are used to deal with the probable small sample size problem.Extensive experiments are conducted on benchmark databases, and the results show superiority and efficiency of the new methods.
Consider ethical and social challenges in smart grid research

Artificial intelligence and machine learning are increasingly seen as key technologies for building more decentralized and resilient energy grids. However, researchers must consider the ethical and social implications of these developments.
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model

Can pretrained language models (PLMs) generate derivationally complex words?We present the first study investigating this question, taking BERT as the example PLM.We examine BERT's derivational capabilities in different settings, ranging from using the unmodified pretrained model to full finetuning.Our best model, DagoBERT (Derivationally and generatively optimized BERT), clearly outperforms the previous state of the art in derivation generation (DG).Furthermore, our experiments show that the input segmentation crucially impacts BERT's derivational knowledge, suggesting that the performance of PLMs could be further improved if a morphologically informed vocabulary of units were used.
A Photonic Recurrent Neuron for Time-Series Classification

Neuromorphic photonics has turned into a key research area for enabling neuromorphic computing at much higher data-rates compared to their electronic counterparts, improving significantly the (multiply-and-accumulate) MAC/sec. At the same time, time-series classification problems comprise a large class of artificial intelligence (AI) applications where speed and latency can have a decisive role in their hardware deployment roadmap, highlighting the need for ultra-fast hardware implementations of simplified recurrent neural networks (RNN) that can be extended in more advanced long-short-term-memory (LSTM) and gated recurrent unit (GRU) machines. Herein, we experimentally demonstrate a novel photonic recurrent neuron (PRN) to classify successfully a time-series vector with 100-psec optical pulses and up to 10 Gb/s data speeds, reporting on the fastest all-optical real-time classifier. Experimental classification of 3-bit optical binary data streams is presented, revealing an average accuracy of >91% and confirming the potential of PRNs to boost speed and latency performance in time-series AI applications.
Image splicing localization using residual image and residual-based fully convolutional network

Fully convolutional networks (FCNs) have been efficiently applied in splicing localization. However, the existing FCN-based methods still have three drawbacks: (a) their performance in detecting image details is unsatisfactory; (b) deep FCNs are difficult to train; (c) results of multiple FCNs are merged using fixed parameters to weigh their contributions. So, an improved method is proposed. Firstly, both the original spliced image and its corresponding residual image are regarded as the inputs of the network. Secondly, the residual block is introduced into FCN as residual-based FCN (RFCN) to make the network easier to optimize. Thirdly, three different RFCNs are merged to enhance locating maps with two learnable weight parameters. Besides, condition random field is introduced into the whole network to improve the results further. Experimental results on five datasets show that the proposed method performs better than some existing methods in localization ability, generalization ability, and robustness against additional operations.
Resource allocation mechanisms in cloud computing: a systematic literature review

Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random-access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non-fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.
Denial of Chain: Evaluation and prediction of a novel cyberattack in Blockchain-supported systems

Blockchain has arisen as a revolutionary technology to provide trust in scenarios where several independent agents must agree about certain information. This characteristic makes Blockchain the perfect technology to operate with future next-generation internet applications, especially when involving critical data. However, assumptions and conditions that make Blockchain a secure technology are not usually considered, so many popular and recent Blockchain-supported proposals (such as only-one-miner solutions) are unsecure in practice due to Blockchain intrinsic vulnerabilities and other technology limitations. In particular, most of these solutions are vulnerable to a novel and unstudied cyberattack: Denial of Chain (DoC). Therefore, in this paper this new cyberattack is defined and studied from a theoretical and experimental point of view. Besides, a framework to predict the occurrence of this new attack is also described. A mathematical model to represent blocks and chains in Blockchain solutions is proposed, and it is employed to create a statistical framework to analyze the novel DoC attack. Simulation instruments (such as Multichain simulators) and real hardware nodes (based on Raspberry Pi computers) are employed to validate the proposed model and framework, and the proposed solutions.
Features of digitally captured signatures vs. pen and paper signatures: Similar or completely different?

The question of whether digitally captured signatures and conventional signatures executed with a pen on paper differ in their characteristics is of practical relevance for forensic handwriting examiners. Due to gaps in the current literature, the present research is dedicated to this issue. Eighty persons signed in three conditions: a) with a stylus on a pad, b) with an inking pen on a sticky note attached to a signature pad allowing to obtain a digital and an analogue version on paper of one and the same writing simultaneously, and c) with a pen on paper. The first step was to investigate to what extent the character shape and number of pen lifts differ between the digital and analogue representation of one and the same signature. This revealed minor differences which are due to technical characteristics of the devices used. The observed distortions are of minor practical relevance according to ratings by eight participating forensic handwriting examiners. Subsequently, signature characteristics were compared between the three different writing conditions in a casework-oriented way. Statistical multi-level models indicate significant differences between the three signature types, but minor effect sizes in most of the examined characteristics. From the point of view of the participating handwriting examiners, these factors do not fundamentally restrict the comparability between digitally captured and conventional signatures in practice. However, caution should be exercised when generalising the results, as several factors, such as the usage of different signature pads as well as signatures made with the finger instead of a stylus, could result in more important differences compared to pen and paper signatures.
Quantum Neural Networks for Dynamic Route Identification to avoid traffic

Computation is the primary task performed for the evaluation of the solution for a specific problem, and in realtime, having better challenges to implementing the solution path with the better computational mechanisms. The concept of quantum computation mechanism using the neural networks is having the highest amount of the success rate in prediction models design and implementation. The idea of a dynamic routing mechanism using quantum computing and neural networks are the main essence. A better prediction model is performed for this specific kind of problem, which needs a particular focus on the latest problem-solving mechanisms. The problem-solving tools like neural networks will dynamically perform with real-time data, but a new add-on is needed to add like big data to implement the live data. The live data can help implement and understand the importance of solving the problem like dynamic routing mechanism. There is a chance of random growth in such a field of computer science. This computational mechanism using quantum computing and the neural network will track the live operations and form the dynamic route changes in the real-time scenario. This real-time scenario worked with a 95% accuracy rate. The accuracy will differ based on the number of connecting nodes are being considered to evaluate the hidden layers of the problem-solving mechanism.
A proposed Development of Clock Control Stream Cipher based on Suitable Attack

The feedback shift registers (FSR) is known to allow produce sequences with large period and fast realization. It is well known that building block used for constructing a key-stream generator, consists of a control register CR and a clock-controlled generating register (GR). The estimation of the sequence period generated by a clock-controlled (LFSR) with an irreducible feedback polynomial and an arbitrary structure of the control sequence is an important aspect in the stream cipher design. This paper offers attacks on stream cipher and concentrate on the algebraic attack because it represents the most important one among the other types of attacks. Also, the paper presents developed clock control sequence generator based on combiner with an increase in the complexity of the structure.
On deriving conceptual models from user requirements: An empirical study

Context: There are numerous textual notations and techniques that can be used in requirements engineering. Currently, practitioners make a choice without having scientific evidence regarding their suitability for given tasks. This uninformed choice may affect task performance. Objective: In this research, we investigate the adequacy of two well-known notations: use cases and user stories, as a starting point for the manual derivation of a structural conceptual model that represents the domain of the system. We also examine other factors that may affect the performance of this task. Methods: This work relies on two experiments. The first is a controlled classroom experiment. The second one is a quasi-experiment, conducted over multiple weeks, that aims at evaluating the quality of the derived conceptual model in light of the notation used, the adopted derivation process, and the complexity of the system to be. We measure quality in terms of validity and completeness of the conceptual model. Results: The results of the controlled experiment indicate that, for deriving conceptual models, user stories fit better than use cases. Yet, the second experiment indicates that the quality of the derived conceptual models is affected mainly by the derivation process and by the complexity of the case rather than the notation used. Contribution: We present evidence that the task of deriving a conceptual model is affected significantly by additional factors other than requirements notations. Furthermore, we propose implications and hypotheses that pave the way for further studies that compare alternative notations for the same task as well as for other tasks. Practitioners may use our findings to analyze the factors that affect the quality of the conceptual model when choosing a requirements notation and an elicitation technique that best fit their needs.
Frames and Numerical Approximation II: Generalized Sampling

In a previous paper (Adcock and Huybrechs in SIAM Rev 61(3):443–473, 2019) we described the numerical approximation of functions using redundant sets and frames. Redundancy in the function representation offers enormous flexibility compared to using a basis, but ill-conditioning often prevents the numerical computation of best approximations. We showed that, in spite of said ill-conditioning, approximations with regularization may still provide accuracy up to order $$\sqrt{\epsilon }$$ , where $$\epsilon $$ is a small truncation threshold. When using frames, i.e. complete systems that are generally redundant but which provide infinite representations with coefficients of bounded norm, this accuracy can actually be achieved for all functions in a space. Here, we generalize that setting in two ways. We assume information or samples from f from a wide class of linear operators acting on f, rather than inner products associated with the best approximation projection. This enables the analysis of fully discrete approximations based, for instance, on function values only. Next, we allow oversampling, leading to least-squares approximations. We show that this leads to much improved accuracy on the order of $$\epsilon $$ rather than $$\sqrt{\epsilon }$$ . Overall, we demonstrate that numerical function approximation using redundant representations may lead to highly accurate approximations in spite of having to solve ill-conditioned systems of equations.
ℓ₂–ℓ∞ Control of Discrete-Time State-Delay Interval Type-2 Fuzzy Systems via Dynamic Output Feedback

This article investigates the design of the l2-l∞ dynamic output-feedback (DOF) controller for interval type-2 (IT2) T-S fuzzy systems with state delay. For nonlinear systems, the IT2 fuzzy model is an efficient modeling method which can better express uncertainties than the (type-1) fuzzy model. In addition, state delay is also a general factor that affects system performance. After analyzing the stability of the system, based on convex linearization and the projection theorem, this article proposes a delay-dependent output-feedback controller design method. The IT2 membership functions (MFs) of the fuzzy controller are chosen to be different from those of the model so as to increase the freedom of controller selection. A membership-function-dependent (MFD) method based on the staircase MFs is applied to relax the stability analysis results. Finally, a numerical simulation example is given to illustrate the effectiveness of the results.
Intrinsic layer based automatic specular reflection detection in endoscopic images

Endoscopic images are used to observe the internal structure of the human body. Specular reflection (SR) images are mostly a consequence of the strong light and bright regions appearing on endoscopic images, which affects the performance of minimally invasive surgery. In this study, we propose a novel method for automatic SR detection based on intrinsic image layer separation (IILS). The proposed method consists of three steps. Initially, it involves the normalization of the image followed by the extraction of high gradient area, and the separation of SR is done on the basis of the color model. The image melding technique is utilized to reconstruct the reflected pixels. The experiments were conducted on 912 endoscopic images from CVC-EndoSceneStill. The results of accuracy, sensitivity, specificity, precision, Jaccard index, Dice coefficient, standard deviation, and pixel count difference show that the detection performance of the proposed method outperforms that of other state-of-the-art methods. The evaluation of the proposed IILS-based SR detection demonstrates that our method obtains better qualitative and quantitative assessments compared with other methods, which can be used as a promising preprocessing step for further analysis of endoscopic images.
Neural Topic Modeling with Cycle-Consistent Adversarial Training

Advances on deep generative models have attracted significant research interest in neural topic modeling.The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics.It is effective in discovering coherent topics but unable to infer topic distributions for given documents or utilize available document labels.To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT.ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics.Adversarial training and cycle-consistent constraints are used to encourage the generator and the encoder to produce realistic samples that coordinate with each other.sToMCAT extends ToM-CAT by incorporating document labels into the topic modeling process to help discover more coherent topics.The effectiveness of the proposed models is evaluated on unsupervised/supervised topic modeling and text classification.The experimental results show that our models can produce both coherent and informative topics, outperforming a number of competitive baselines.
Towards Enhancing Faithfulness for Neural Machine Translation

Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named \textsc{FEnmt}). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our \textsc{FEnmt} could improve translation quality by effectively reducing unfaithful translations.
Photonic realization of a quantum finite automaton

We describe a physical implementation of a quantum finite automaton recognizing a well known family of periodic languages. The realization exploits the polarization degree of freedom of single photons and their manipulation through linear optical elements. We use techniques of confidence amplification to reduce the acceptance error probability of the automaton. It is worth remarking that the quantum finite automaton we physically realize is not only interesting per se, but it turns out to be a crucial building block in many quantum finite automaton design frameworks theoretically settled in the literature.
Emotion Detection of Contextual Text using Deep learning

In recent years, the data in the form of text is created in very huge amount in day to day conversation on social media. We need a naive approach to analyzed and summarized data to extract meaningful information. Textual dialogue is given in contextual emotion detection. We have to recognize user emotions either it is happy, sad, angry or others. This paper describes Aimens system which detect emotions from textual dialogues. This system used the Long short term memory (LSTM) model based on deep learning to detect the emotions like happy, sad and angry in contextual conversation. The main input to the system is a combination of word2vec and doc2vec embeddings. The output results are shown substantial changes in f-scores over the model baseline where Aimens system score is 0.7185.