Reviewerly/exHarmony huggingface dataset:
Semi-Structured Distributional Regression

AbstractCombining additive models and neural networks allows to broaden the scope of statistical regression and extend deep learning-based approaches by interpretable structured additive predictors at the same time. Existing attempts uniting the two modeling approaches are, however, limited to very specific combinations and, more importantly, involve an identifiability issue. As a consequence, interpretability and stable estimation are typically lost. We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture. To overcome the inherent identifiability issues between different model parts, we construct an orthogonalization cell that projects the deep neural network into the orthogonal complement of the statistical model predictor. This enables proper estimation of structured model parts and thereby interpretability. We demonstrate the framework’s efficacy in numerical experiments and illustrate its special merits in benchmarks and real-world applications.Keywords: Generalized additive models for locationScale and shapeIdentifiabilityMultimodal learningNeural networksOrthogonalization Supplementary MaterialsFurther Details: The Supplementary Material includes proofs, algorithmic details as well as further specifications and results of numerical experiments.Reproducibility: All codes used for this work are available at https://github.com/davidruegamer/semi-structured/_distributional/_regressionAcknowledgmentsWe thank Almond Stöcker and Dominik Thalmeier for their comments and helpful discussions.Additional informationFundingDavid Rügamer has been partly funded by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS18036A. Nadja Klein acknowledges support by the Deutsche Forschungsgemeinschaft (DFG; German research foundation) through the Emmy Noether grant KL 3037/1-1.
The Fifteen Puzzle—A New Approach through Hybridizing Three Heuristics Methods

The Fifteen Puzzle problem is one of the most classical problems that has captivated mathematics enthusiasts for centuries. This is mainly because of the huge size of the state space with approximately 1013 states that have to be explored, and several algorithms have been applied to solve the Fifteen Puzzle instances. In this paper, to manage this large state space, the bidirectional A* (BA*) search algorithm with three heuristics, such as Manhattan distance (MD), linear conflict (LC), and walking distance (WD), has been used to solve the Fifteen Puzzle problem. The three mentioned heuristics will be hybridized in a way that can dramatically reduce the number of states generated by the algorithm. Moreover, all these heuristics require only 25 KB of storage, but help the algorithm effectively reduce the number of generated states and expand fewer nodes. Our implementation of the BA* search can significantly reduce the space complexity, and guarantee either optimal or near-optimal solutions.
Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based Emotion Classification Convolution Neural Network (IBEC-CNN) model and Non-negative Matrix Factorization (NMF) for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN, based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface (Twitter API), huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are analyzed for emotions fear, joy, sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network (IBEC-CNN) model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3, with 1 being low intensity for the emotion, 2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics, Non-negative Matrix Factorization (NMF) has been employed.Analysis of emotions of COVID-19 tweets has identified, that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also, more than 75% negative tweets expressed sadness, fear are of low intensity.A qualitative analysis has also been conducted and the topics detected are grouped into themes such as economic impacts, case reports, treatments, entertainment and vaccination.The results of analysis show that the issues related to the pandemic are expressed different emotions in twitter which helps in interpreting the public insights during the pandemic and these results are beneficial for planning the dissemination of factual health statistics to build the trust of the people.The performance comparison
Comparison of Different Models to Estimate Global Solar Irradiation in the Sudanese Zone of Chad

Sustainable future development relies on solar radiation, which is the main source of renewable energy. Thus, in this article, the monthly average global solar irradiation of four sites in the Sudanian zone region of Chad is estimated using different empirical models. The data used in this study were collected at the General Directorate of Meteorology of Chad. The reliability and accuracy of six models estimating global solar radiation were validated and compared by statistical indicators identifying the most accurate model. The results obtained show that the Allen model has the best performance for the Moundou site (5.760 kWh/m²/d, R2=0.843), the Angstrom Prescott model for the Sarh sites (5.658 kWh/m²/d, R2=0.805) and Pala (5.793 kWh/m²/d, R²=0.889), the Sabbagh model for the Bongor site (5.657 kWh/m²/d, R²=0.888). These models are validated against NASA data. The results show that the Sudanian zone of Chad has good solar potential and is therefore suitable for possible exploitation.
Apology Speech Act in Indonesian and Japanese Language: A Comparative Method

The speech act of apologizing is an expressive speech act that functions to maintain harmony between the speaker and the addressee. The speech act of apologizing is closely related to the politeness aspect, so it is necessary to pay attention to the strategy used. The strategy chosen must also reflect the true feelings for the effect of the apology to be conveyed. The pattern of speech act tends to be different in every language and this will give different impact on the readers. This research will discuss the apology speech act in two languages, Indonesian and Japanese, with their own uniqueness in delivering the messages. By referring to the approach of Cooperative and Politeness Principles from Leech (1993), the apologies that violate and obey the maxims will be found. This research is based on the data taken from novels and various “billboard” in Indonesia and Japan. The result of the research proves that the strategy of apologizing in Indonesian tends to be given directly by obeying the Cooperative Principles using the irony principle. On the other hand, Japanese language obeys more on the Politeness Principles because it has the element of hairyo hyougen or expression of consideration.
An efficient sharding consensus algorithm for consortium chains

The consensus algorithm is very critical in any blockchain system, because it directly affects the performance and security of the blockchain system. At present, the classic Practical Byzantine Fault Tolerance Algorithm (PBFT), which is mainly used in the consortium chain, will lead to system communication congestion and reduced throughput when the number of nodes increases, so the PBFT algorithm is not suitable for large-scale consortium chains. In response to the above problems, this paper proposes a new clustering-based sharding consensus algorithm (KBFT), which aims to ensure that the consortium chain takes into account decentralization, security and scalability. The KBFT algorithm first uses the K-prototype clustering algorithm to shard the nodes in the network according to mixed attributes, and second, disjoint transactions are used to reach consensus in parallel in different shards. Concurrently, the KBFT algorithm introduces a supervision mechanism and a node credit mechanism, which is used to supervise and score the behavior of the nodes and select the proxy nodes, which improves security. We discuss the choice of shard size with the help of the binomial probability distribution and analyze the probability that the system can successfully form a global block under different node failure probabilities. Finally, the proposed algorithm is evaluated through theoretical analysis and simulation experiments. Results show that the proposed algorithm achieves a marked improvement in scalability and throughput along with a marked reduction in communication complexity compared with the classic baseline algorithm PBFT in this field of study, which improves the operating efficiency of the system and simultaneously guarantees the security and robustness of the system.
A new dataset for mongolian online handwritten recognition

This paper introduces a new traditional Mongolian word-level online handwriting dataset, MOLHW. The dataset consists of handwritten Mongolian words, including 164,631 samples written by 200 writers and covering 40,605 Mongolian common words. These words were selected from a large Mongolian corpus. The coordinate points of words were collected by volunteers, who wrote the corresponding words on the dedicated application for their mobile phones. Latin transliteration of Mongolian was used to annotate the coordinates of each word. At the same time, the writer's identification number and mobile phone screen information were recorded in the dataset. Using this dataset, we propose an encoder-decoder Mongolian online handwriting recognition model with a deep bidirectional gated recurrent unit and attention mechanism as the baseline evaluation model. Under this model, the optimal performance of the word error rate (WER) on the test set was 24.281%. Furthermore, we present the experimental results of different Mongolian online handwriting recognition models. The experimental results show that compared with other models, the model based on Transformer could learn the corresponding character sequences from the coordinate data of the dataset more effectively, with a 16.969% WER on the test set. The dataset is now freely available to researchers worldwide. The dataset can be applied to handwritten text recognition as well as handwritten text generation, handwriting identification, and signature recognition.
Non-interferometric key recording applied to a joint transform cryptosystem

In this Letter, we propose an opto-digital cryptosystem based on the joint transform correlator architecture without the need for a reference beam, phase-shifting techniques, or an additional window in the input plane. In this system, only two intensity recordings are necessary: the intensity of the key Fourier transform, and the joint power spectrum between the key and an arbitrary object in contact with a random phase mask. Combining them with the knowledge of their respective input modules, we implement a modified Gerchberg-Saxton algorithm to recover the phase associated with the encryption key. The validity of our approach is demonstrated by computer simulations and experimental results.
Binary Control Pulse Optimization for Quantum Systems

Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important to various quantum applications, including energy minimization and circuit compilation. In this paper we focus on discrete binary quantum control problems and apply different optimization algorithms and techniques to improve computational efficiency and solution quality. Specifically, we develop a generic model and extend it in several ways. We introduce a squared <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>-penalty function to handle additional side constraints, to model requirements such as allowing at most one control to be active. We introduce a total variation (TV) regularizer to reduce the number of switches in the control. We modify the popular gradient ascent pulse engineering (GRAPE) algorithm, develop a new alternating direction method of multipliers (ADMM) algorithm to solve the continuous relaxation of the penalized model, and then apply rounding techniques to obtain binary control solutions. We propose a modified trust-region method to further improve the solutions. Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.
Deep unfolding multi-scale regularizer network for image denoising

Abstract Existing deep unfolding methods unroll an optimization algorithm with a fixed number of steps, and utilize convolutional neural networks (CNNs) to learn data-driven priors. However, their performance is limited for two main reasons. Firstly, priors learned in deep feature space need to be converted to the image space at each iteration step, which limits the depth of CNNs and prevents CNNs from exploiting contextual information. Secondly, existing methods only learn deep priors at the single full-resolution scale, so ignore the benefits of multi-scale context in dealing with high level noise. To address these issues, we explicitly consider the image denoising process in the deep feature space and propose the deep unfolding multi-scale regularizer network (DUMRN) for image denoising. The core of DUMRN is the feature-based denoising module (FDM) that directly removes noise in the deep feature space. In each FDM, we construct a multi-scale regularizer block to learn deep prior information from multi-resolution features. We build the DUMRN by stacking a sequence of FDMs and train it in an end-to-end manner. Experimental results on synthetic and real-world benchmarks demonstrate that DUMRN performs favorably compared to state-of-the-art methods.
Deep Learning Mask Face Recognition with Annealing Mechanism

Face recognition (FR) has matured with deep learning, but due to the COVID-19 epidemic, people need to wear masks outside to reduce the risk of infection, making FR a challenge. This study uses the FaceNet approach combined with transfer learning using three different sizes of validated CNN architectures: InceptionResNetV2, InceptionV3, and MobileNetV2. With the addition of the cosine annealing (CA) mechanism, the optimizer can automatically adjust the learning rate (LR) during the model training process to improve the efficiency of the model in finding the best solution in the global domain. The mask face recognition (MFR) method is accomplished without increasing the computational complexity using existing methods. Experimentally, the three models of different sizes using the CA mechanism have a better performance than the fixed LR, step and exponential methods. The accuracy of the three models of different sizes using the CA mechanism can reach a practical level at about 93%.
A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial intelligence technology into the control system. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm, we generated a dataset and trained a deep neural network (DLNet) to improve the detection efficiency. By combining DLNet and the GJK algorithm, we propose a two-level self-collision detection algorithm (DLGJK algorithm) to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. First, the proposed algorithm uses DLNet to determine whether the current working state of the system has a risk of self-collision; since most of the working states in a system workspace do not have a self-collision risk, DLNet can effectively reduce the number of unnecessary detections and improve the detection efficiency. Then, for the working states with a risk of self-collision, we modeled precise colliders and applied the GJK algorithm for fine self-collision detection, which achieved detection accuracy. The experimental results showed that compared to that with the global use of the GJK algorithm for self-collision detection, the DLGJK algorithm can reduce the time expectation of a single detection in a system workspace by 97.7%. In the path planning of the manipulators, it could effectively reduce the number of unnecessary detections, improve the detection efficiency, and reduce system overhead. The proposed algorithm also has good scalability for a multi-manipulator system that can be split into dual-manipulator systems.
Tackling imbalanced data in cybersecurity with transfer learning: a case with ROP payload detection

In recent years, deep learning gained proliferating popularity in the cybersecurity application domain, since when being compared to traditional machine learning methods, it usually involves less human efforts, produces better results, and provides better generalizability. However, the imbalanced data issue is very common in cybersecurity, which can substantially deteriorate the performance of the deep learning models. This paper introduces a transfer learning based method to tackle the imbalanced data issue in cybersecurity using return-oriented programming payload detection as a case study. We achieved 0.0290 average false positive rate, 0.9705 average F1 score and 0.9521 average detection rate on 3 different target domain programs using 2 different source domain programs, with 0 benign training data sample in the target domain. The performance improvement compared to the baseline is a trade-off between false positive rate and detection rate. Using our approach, the total number of false positives is reduced by 23.16%, and as a trade-off, the number of detected malicious samples decreases by 0.68%.
AdaLip: An Adaptive Learning Rate Method per Layer for Stochastic Optimization

Abstract Various works have been published around the optimization of Neural Networks that emphasize the significance of the learning rate. In this study we analyze the need for a different treatment for each layer and how this affects training. We propose a novel optimization technique, called AdaLip, that utilizes an estimation of the Lipschitz constant of the gradients in order to construct an adaptive learning rate per layer that can work on top of already existing optimizers, like SGD or Adam. A detailed experimental framework was used to prove the usefulness of the optimizer on three benchmark datasets. It showed that AdaLip improves the training performance and the convergence speed, but also made the training process more robust to the selection of the initial global learning rate.
Analysis of Cybersecurity Standard and Framework Components

Satisfactory cybersecurity protection, encompassing all data security solutions, can only be achieved by adopting a cybersecurity framework that provides a structure and methodology for protecting critical digital assets. In addition, security experts recommend using cybersecurity standards which consist of a collection of best practices to protect organizations from cyber threats. However, many organizations, companies and governments lack experienced personnel in the cybersecurity domain, so they have difficulty adopting a standard approach or cybersecurity framework. Protecting organizations from cyber threats while demonstrating compliance with laws and standards is seen as extremely complex due to the difficulty on choosing the appropriate standard to be used. Moreover, lack of knowledge on the elements needed that offered by the standard is lead to the problem on identifying the started point where the protection will be began. Therefore, in this paper, a literature and the analysis is presented in identifying the elements of cybersecurity standard and framework that can be facilitate the organization or government on choosing the appropriate standard and framework to be used and utilized. The literature review was carried out to understand the various types of cybersecurity standards and frameworks and the analysis is conducted to identify the elements in each of them. In this paper, eight steps are presented and include the types of international standards, which are general, local regulation, as well as specific standards used in the industrial sector, to conclude the findings of the analysis. Furthermore, a relation map is presented using Writing a Literature Review release 2.0 approach to show the relationship between the literature review and future research.
An Integrated Optical Circuit Architecture for Inverse-Designed Silicon Photonic Components

In this work, we demonstrate a compact toolkit of inverse-designed, topologically optimized silicon photonic devices that are arranged in a "plug-and-play" fashion to realize many different photonic integrated circuits, both passive and active, each with a small footprint. The silicon-on-insulator 1550-nm toolkit contains a 2 × 2 3-dB splitter/combiner, a 2 × 2 waveguide crossover, and a 2 × 2 all-forward add-drop resonator. The resonator can become a 2 × 2 electro-optical crossbar switch by means of the thermo-optical effect, phase-change cladding, or free-carrier injection. For each of the ten circuits demonstrated in this work, the toolkit of photonic devices enables the compact circuit to achieve low insertion loss and low crosstalk. By adopting the sophisticated inverse-design approach, the design structure, shape, and sizing of each individual device can be made more flexible to better suit the architecture of the greater circuit. For a compact architecture, we present a unified, parallel waveguide circuit framework into which the devices are designed to fit seamlessly, thus enabling low-complexity circuit design.