Reviewerly/exHarmony huggingface dataset:
Mathematical Attack of RSA by Extending the Sum of Squares of Primes to Factorize a Semi-Prime

The security of RSA relies on the computationally challenging factorization of RSA modulus N=p1 p2 with N being a large semi-prime consisting of two primes p1and p2, for the generation of RSA keys in commonly adopted cryptosystems. The property of p1 and p2, both congruent to 1 mod 4, is used in Euler’s factorization method to theoretically factorize them. While this caters to only a quarter of the possible combinations of primes, the rest of the combinations congruent to 3 mod 4 can be found by extending the method using Gaussian primes. However, based on Pythagorean primes that are applied in RSA, the semi-prime has only two sums of two squares in the range of possible squares N−1, N/2 . As N becomes large, the probability of finding the two sums of two squares becomes computationally intractable in the practical world. In this paper, we apply Pythagorean primes to explore how the number of sums of two squares in the search field can be increased thereby increasing the likelihood that a sum of two squares can be found. Once two such sums of squares are found, even though many may exist, we show that it is sufficient to only find two solutions to factorize the original semi-prime. We present the algorithm showing the simplicity of steps that use rudimentary arithmetic operations requiring minimal memory, with search cycle time being a factor for very large semi-primes, which can be contained. We demonstrate the correctness of our approach with practical illustrations for breaking RSA keys. Our enhanced factorization method is an improvement on our previous work with results compared to other factorization algorithms and continues to be an ongoing area of our research.
Knowledge aggregation with subjective logic in multi-agent self-adaptive cyber-physical systems

Modern software systems, such as cyber-physical systems (CPSs), operate in complex and dynamic environments. With the continuous and unanticipated change in the operational environment, these systems are subjected to a variety of uncertainties. Self-adaptive CPSs (SACPSs) can adjust their behavior or structure at run-time as a response to the changes in their perceived environment. Namely, self-adaptation is commonly realized through a MAPE-K feedback loop incorporating newly derived knowledge obtained by the sensed data from the run-time monitoring, during the operation of decentralized SACPSs. However, to build the knowledge, the need for run-time observations' aggregation and reasoning emerges, since the observations made by the decentralized systems might be conflicting. In this paper, we propose an approach for observations aggregation and knowledge modeling in SACPSs that is domain-independent and can deal with inaccurate, partial, and conflicting observations, based on the formalisms of Subjective Logic.
Image Classification Techniques

The image processing task, aimed at interpreting and classifying the contents of the images, has attracted the attention of researchers since the early days of computers. With the advancement of computing system technology, image categorization has found increasingly broader applications, covering new generation disciplines such as image analysis, object recognition, and computer vision, with applications quite general both in scientific and humanistic fields. The automatic recognition, description, and classification of the structures contained in the images are of fundamental importance in a vast set of scientific and engineering fields that require the acquisition, processing, and transmission of information in visual form. Classification tasks also include those related to the categorization of images, such as the construction of a recognition system, the representation of patterns, the selection and extraction of features, and the definition of automatic recognition methods. Image analysis is of collective interest and it is a hot topics of current research.
An Accelerator Design Using a MTCA Decomposition Algorithm for CNNs

Due to the high throughput and high computing capability of convolutional neural networks (CNNs), researchers are paying increasing attention to the design of CNNs hardware accelerator architecture. Accordingly, in this paper, we propose a block parallel computing algorithm based on the matrix transformation computing algorithm (MTCA) to realize the convolution expansion and resolve the block problem of the intermediate matrix. It enables high parallel implementation on hardware. Moreover, we also provide a specific calculation method for the optimal partition of matrix multiplication to optimize performance. In our evaluation, our proposed method saves more than 60% of hardware storage space compared with the im2col(image to column) approach. More specifically, in the case of large-scale convolutions, it saves nearly 82% of storage space. Under the accelerator architecture framework designed in this paper, we realize the performance of 26.7GFLOPS-33.4GFLOPS (depending on convolution type) on FPGA(Field Programmable Gate Array) by reducing bandwidth and improving data reusability. It is 1.2×-4.0× faster than memory-efficient convolution (MEC) and im2col, respectively, and represents an effective solution for a large-scale convolution accelerator.
MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

The rapid development of location-based social networks (LBSNs) produces the increasing number of check-in records and corresponding heterogeneous information which bring big challenges of points-of-interest (POIs) recommendation in our daily lives. The emergence of various recommender techniques bridges the gap between the numerous heterogeneous check-ins and the personalized POI recommendation. However, due to the differences between LBSNs and conventional recommendation tasks, besides the user feedback, the spatio-temporal information is also significant to precisely capture the user preferences. In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and geographical locations. The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems. The temporal check-ins not only present the preference but also show the lifestyle of an individual while the geographical locations describe the active region of users which further filters POIs far from the feasible region. The multi-task learning strategy is capable of combining the information of temporal check-ins and geographical locations to improve the performance of personalized POI recommendation. We conduct the experiments on two real-world LBSNs datasets and the experimental results show the effectiveness of our proposed approach.
Security-Driven Placement and Routing Tools for Electromagnetic Side-Channel Protection

Side-channel analysis (SCA) attacks are major threats to hardware security. Upon this security threat, various countermeasures at different design layers have been proposed against SCA attacks. These approaches often introduce significant overheads and impose high requirements of side-channel security backgrounds to integrated circuit (IC) designers. In this article, we propose an automatic computer-aided design (CAD) tool that can be utilized to enhance the circuit resistance against electromagnetic (EM) SCA attacks. This new tool will guide security-driven placement and routing processes and can be seamlessly integrated into the modern IC design flow. The protected IC design will be resilient to SCA attacks with negligible area and power overheads. In order to develop this tool, we first investigate the root-cause of EM leakage at the layout level and mathematically demonstrate the feasibility of security-driven placement and routing through the EM leakage modeling. We then identify that the correlation between the data under protection and the EM leakage can be significantly reduced through data-dependent register reallocation and wire length adjustments. Simulation results on cryptographic circuits prove the effectiveness of both the constructed EM leakage model and the EM model-based CAD tool for EM side-channel security.
A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

Abstract Background Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. Methods We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. Conclusions This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.
Hybrid Workflow Provisioning and Scheduling on Edge Cloud Computing Using a Gradient Descent Search Approach

The dramatic growth of the Internet of Things (IoT) technology in many application domains, ranging from intelligent video surveillance, smart retail to the Internet-of-Vehicles brings new computation challenges for rationalized utilization of computing resources. IoT application execution refers to hybrid processing model of stream and batch to achieve data analytics objectives. Hybrid workflow execution combines the challenges of latency-sensitive and resource-intensive processing. To resolve these challenges, we proposed a two stages hybrid workflow scheduling framework on edge cloud computing. In the first stage, we proposed a resource estimation algorithm based on a linear optimization approach, the gradient descent search (GDS) and in the second stage, we adopted a cluster-based provisioning and scheduling technique on heterogeneous edge cloud resources. This work provides a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrated the framework performance in controlling the execution of hybrid workflows by an efficient tuning for stream processing parameters, such as arrival rate and processing throughput. Under working constraints, the proposed scheduler provides significant improvement for large hybrid workflows in terms of execution time and monetary cost with an average of 8% and 35%, respectively.
Utilizing QR codes to verify the visual fidelity of image datasets for machine learning

Machine learning is becoming increasingly popular in modern technology and has been adopted in various application areas. However, researchers have demonstrated that machine learning models are vulnerable to adversarial examples in their inputs, which has given rise to a field of research known as adversarial machine learning. Potential adversarial attacks include methods of poisoning datasets by perturbing input samples to mislead machine learning models into producing undesirable results. While such perturbations are often subtle and imperceptible from the perspective of a human, they can greatly affect the performance of machine learning models. This paper presents two methods of verifying the visual fidelity of image-based datasets by using QR codes to detect perturbations in the data. In the first method, a verification string is stored for each image in a dataset. These verification strings can be used to determine whether or not an image in the dataset has been perturbed. In the second method, only a single verification string is stored and can be used to verify whether an entire dataset is intact.
Simulation Methodology-Based Context-Aware Architecture Design for Behavior Monitoring of Systems

Generally, simulation models are constructed to replicate and predict the behavior of real systems that currently exist or are expected to exist in the future. Once a simulation model is implemented, the model can be connected to a real system for which the model has been built through sensors or networks so that important activities in the real system can be monitored indirectly through the model. This article proposes a modeling formalism BM-DEVS (Behavior Monitor-DEVS) that defines simulation models capable of monitoring the desired behavior patterns within the models so that the target system’s behavior can be monitored indirectly. In BM-DEVS, an extension of classic Discrete Event System Specification (DEVS), the behavior to be monitored is expressed as a set of temporal logic (TL) production rules within a multi-component model that consists of multiple component models to be monitored. An inference engine module for reasoning with the TL rules is designed based on the abstract simulator that carries out instructions in the BM-DEVS models to perform the simulation process. The major application of BM-DEVS is in the design and implementation of the context-aware architecture needed for various intelligent systems as a core constituent. Essentially all systems where some form of behavior monitoring is required are candidate applications of BM-DEVS. This research is motivated by the view that there exists symmetry between the real-world and the cyber world, in that the problems in both environments should be expressed with the same basic constituents of time and space; this naturally leads to adopting spatiotemporal variables composed of simulation models and developing a problem solver that exploits these variables.
Evolutionary Approach in Recommendation Systems for Complex Structured Objects

Tasks as physical training planning, computer hardware configuration or fully dietary advice, are problems that exhibit multiple choices, composed in turn of simpler items (specific exercises, components or recipes). An ideal recommender system would not only recommend simple items based on the user’s tastes, but would offer a set of items that suit the user’s needs and preferences so that they form a meaningful structure that can evolve in time. Taking this idea as our main cornerstone, this Ph.D. face two objectives: being able to generate an item with a complex structure from simpler items, and integrating the user’s limitations and preferences to develop an adaptive recommender system.
Blocking Bug Prediction Based on XGBoost with Enhanced Features

With a growing number of software projects, software quality is increasingly crucial. Researchers and engineers in the software engineering field often pay much attention to bug management tasks, such as bug localization, bug triage, and duplicate bug detection. However, there are few researchers to study blocking bug prediction. Blocking bugs prevent other bugs from being fixed and usually need more time to be fixed. Thus, developers need to identify blocking bugs and reduce the impact of blocking bugs. The previous studies utilized supervised algorithms to implement this task. However, they did not consider the dependencies among individual classifiers so that they cannot get the perfect accuracy for blocking bug prediction. In this paper, we propose a new framework XGBlocker that includes two stages. In the first stage, XGBlocker collects more features from bug reports to build an enhanced dataset. In the second stage, XGBlocker exploits XGBoost technique to construct an effective model to perform the prediction task. We conduct experiments on four projects with three evaluation metrics. The experimental results show that our method XGBlocker achieves promising performance compared with baseline methods in most cases. In detail, XGBlocker achieves F1-score, ER@20%, and AUC of up to 0.808, 0.944, and 0.975, respectively. On average across the four projects, XGBlocker improves F1-score, ER@20%, and AUC over the state-of-the-art method ELBlocker by 17.27%, 12.67%, and 4.85%, respectively.
Method for selecting informative and non-informative features

Abstract Today, solving the problems of choosing informative and non-informative features is an urgent problem, since the areas of application of methods and algorithms for pattern recognition to solve modern applied problems are expanding. But when solving these problems, problems arise that interfere with the effective use of well-known methods for choosing informative and non-informative features. The choice of a combination of a method and a criterion is one of these problems that is most suitable for the task, that is, with the search for such a pair of methods and criteria in which computational costs and recognition error are minimized. This article proposes a new method for choosing informative and non-informative features.
Broad Echo State Network for Channel Prediction in MIMO-OFDM Systems

Channel prediction is a vital technique that can be used to support adaptive transmissions and mitigate the feedback delay of the channel state information (CSI) in the wireless communications, especially in the frequency division duplex (FDD) system. In this paper, we focus on the channel prediction issue in multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems. First, we introduce the general channel prediction framework based on the temporal and spatial correlations in MIMO-OFDM systems. Then, we combine the broad learning idea and recurrent computation, and further introduce the broad echo state network (BESN) for channel prediction in MIMO-OFDM systems. Third, to estimate the output weight matrix, we offer two versions of the BESN, i.e., the basic BESN (B-BESN) and the group forward variable selection (GFVS)-based BESN (GFVS-BESN). In the latter, we develop the GFVS strategy to further extract useful information from those collected features in the BESN and make the BESN more able to process the CSI samples. Fourth, inspired by deep learning, we import the conjugate gradient descent backpropagation (CGDBP) technique to fine-tune the random weights and biases in the BESN and give the related derivations. Then, we prove the echo state property in the BESN and analyze the computational complexity. In the simulation section, we comprehensively evaluate the prediction performances for the standard Extended Vehicular A model (EVA) and Extended Typical Urban model (ETU) under different signal-to-noise ratios (SNRs), different antenna configurations, different spatial correlations, different maximum Doppler shifts and different channel prediction methods. The simulation results indicate that the BESN has excellent prediction performance in MIMO-OFDM systems.
A Text Representation Model Based on Convolutional Neural Network and Variational Auto Encoder

In the era of big data, the internet produces vast amounts of data every day, among which text data occupies the main position. It is difficult for manual processing to deal with the increasing growth rate of text data. As basis of most natural language processing (NLP) tasks, text representation aims to transform text into a vector that can be processed by computer without losing the original important semantic information. It has become an important research direction in the field of NLP that effectively organize, manage and quickly use the complex text information to extract useful semantics from it. Therefore, a text feature representation model based on convolutional neural network (CNN) and variational auto encoder (VAE) is proposed to extract the text features and apply the obtained text feature representation to text classification scene. CNN is used to extract local features and VAE makes the extracted features more consistent with Gaussian distribution. The proposed method has best performance compared with w2v-avg and CNN-AE in k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) classification algorithms.
Quantum transport on generalized scale-free networks

We consider quantum transport on generalized scale-free networks (GSFNs) in the continuous-time quantum walk (CTQW) model. The efficiency of the transport is monitored through the exact and the average return probabilities. In this model these probabilities are fully determined by the eigenvalues and eigenvectors of the connectivity matrix. In the case of GSFNs we observe a nontrivial interplay between strong localization effects, due to starlike segments, and good spreading because of the linear segments. We show that the quantum transport on GSFNs can be increased by varying the minimum or the maximum allowed degrees, i.e., the limiting number of links emerging from every node. The same quantum efficiency is reached by considering various combinations of the construction parameters of the network, which normally show different topological features.
Relationship between digital development and subjective well-being in chilean school children

This study explores how access, management and use of Information and Communication Technologies in secondary schools are related to the subjective well-being of adolescents. The central hypothesis of this work is that students attending schools with high digital development have higher levels of subjective well-being. To test this hypothesis, two quantitative empirical studies carried out in Chile are used as sources of information. The first one is the 2013 National Survey of School Well-being, applied to 15-year-old students (4964 cases) from 191 schools, and the second one is the 2012 National Census of Educational Computing. Subsequently, after both sources are merged, a descriptive analysis and an explanatory model are created based on the dependent variable Personal Wellbeing Index (PWI), and the independent variables School Digital Development Index (SDDI) and attributes of establishment and subjects. Among the results, the finding of a positive and significant relationship between school digital development and higher subjective well-being scores of students stands out.
BERT-Based Named Entity Recognition in Chinese Twenty-Four Histories

Named entity recognition in classical Chinese plays a fundamental role in improving the ability of information extraction and constructing knowledge graphs from classical Chinese. However, due to the lack of annotated data and the complexity of grammatical rules, named entity recognition in classical Chinese has made little progress. In order to solve the problem of lack of labeled data, we propose an end-to-end solution that is not based on domain knowledge, which instead is based on the pre-trained BERT-Chinese model and integrates the BiLSTM-CRF model for classical Chinese named entity recognition. The BERT-Chinese model converts the input text into a character-level embedding vector, then the BiLSTM model is used for future training, and finally CRF is able to normalize the output of BiLSTM to obtain a globally optimal labeling sequence. We conducted fine-tuning training on ChineseDailyNerCorpus. By designing a optimized fine-tuning method, we have realized the named entity recognition task in the Chinese Twenty-Four Histories. We evaluated our model on the Chinese twenty-four histories data, and finally achieved an average F1 value of about 75%. The experimental results also show that the BERT model has a strong ability in transfer learning.