Reviewerly/exHarmony huggingface dataset:
FKTAN: Fusion Keystroke Time-Textual Attention Networks for Continuous Authentication

With the rapid development of computer technology, the traditional Internet data security and information privacy issues are gradually expanding to all aspects of society as a whole. As the first line of defense for information security, identity authentication technology becomes crucial. Among the many authentication technologies, continuous authentication technology has gained increasing attention. In this paper, we design fusion keystroke time-textual attention networks for continuous authentication based on the keystroke data (keystroke time series, keystroke text) when users enter free-text. Specifically, the corresponding keystroke time series and the corresponding keystroke text are first obtained based on the original keystroke data, and then the keystroke time series and the keystroke text are input into the BiLSTM model and the pre-training model, respectively; the BiLSTM can better capture the temporal features, and the pre-training model can better capture the textual features when authenticating the user. Finally, the two information are fed into the cross attention model to better integrate the two information. Experiments show that the FKTAN model achieves promising results on two datasets, Clarkson II keystroke dataset and Buffalo dataset, outperforming all baseline models.
Towards efficient real-time traffic classifier: A confidence measure with ensemble Deep Learning

Traffic classification is a key network function for managing both Quality of Service (QoS) and security. While some traffic classification applications (e.g. QoS based path allocation) can tolerate delays, other applications (e.g. attack detection) are time critical. In this context, early traffic classification has been proposed based on the first few packets of flows. However, the choice of the number of packets to inspect is method dependent and based on empirical assessment without considering the information carried by these packets (features). In this paper, we aim at identifying the sufficient number of packets, N, that guarantees high classification accuracy while optimizing the response time, based on both empirical classification results and information theory. We propose a confidence measure based on the variations in the model training accuracy and the average mutual information among the packets’ features and the label vector. This measure is then used to define the value of N, which optimizes the trade-off between the time overhead and the classification accuracy. In addition, we propose an ensemble Deep Learning (DL)-based classifier model to enhance the classification accuracy by training successive DL models based on the traffic stream. The proposed ensemble method output is based on the average of the individual classifiers predictions. The experimental results show that when using the proposed confidence measure, we can achieve good classification accuracy at early phase of the flow. In addition, using the proposed ensemble method presents enhancement in the early classification accuracy. Consequently, combining the ensemble method with the confidence measure criteria allows for striking a good balance between high accuracy and fast response time.
Big Data Performance in Private Clouds. Some Initial Findings on Apache Spark Clusters Deployed in OpenStack

In recent years Apache Spark has become one the most important Big Data platform. In-memory processing performance and the ability to connect with any major data server/source/format have been two of the main drivers of Spark’s popularity. But finding the most suitable setup for a given data processing task is challenging, depending not only on the data structure and the nature/complexity of the task, but also because of the myriad of setup parameters to be tweaked. In this paper we propose a model for assessing the processing performance of a Spark-and-Hadoop cluster, deployed on a university cloud managed with OpenStack. Randomly generated SparkSQL queries on the TPC-H benchmark schema were executed for data sets of 5GB, 10GB and 50GB, varying four data source formats and two memory settings. Predictive models built with three Machine Learning techniques (Multivariate Adaptive Regression Splines, Random Forest, and Extreme Gradient Boosting) provided encouraging results. For the given data sets, the most important predictors seem to be related with the volume of processed data and the query complexity whereas the data formats and memory settings seem less important.
Prediction Based on CA-GEP algorithm predicted rainfall

The traditional Gene Expression Programming (GEP) algorithm has the disadvantages of weak global search ability and difficult to jump out of local optimization. In order to solve these shortcomings, this paper proposes Chromosome Adaptive-Gene Expression Programming (CA-GEP) algorithm, which appropriately increases the mutation rate of the algorithm in the iterative process to enhance the population diversity, makes use of the characteristics of population diversity to jump out of the local optimization and improve the global search ability. While ensuring better population diversity, it also increases the crossover rate and reduces the mutation rate to speed up the convergence of the algorithm, so as to improve the convergence efficiency of the algorithm. According to the concentration and dispersion of individual fitness values in the population, CA-GEP algorithm adaptively adjusts the crossover rate and mutation rate of each iteration through fuzzy control, so as to make the iterative process of the algorithm more efficient. The experimental results of rainfall prediction show that CA-GEP algorithm has significantly improved the stability, global convergence ability and optimization speed, compared with BP algorithm and SVM algorithm.
Scene Text Recognition with Multi-decoders

In this article, we focus on the scene text recognition problem, which is one of the challenging sub-files of computer vision because of the random existence of scene text. Recently, scene text recognition has achieved state-of-art performance because of the improvement of deep learning. At present, encoder-decoder architecture was widely used for scene recognition tasks, which consist of feature extractor, sequence module. Specifically, at the decoder part, connectionist temporal classification(CTC), attention mechanism, and transformer(self-attention) are three main approaches used in recent research. CTC decoder is flexible and can handle sequences with large changes in length for its align sequences features with labels in a frame-wise manner. Attention decoder can learn better and deeper feature expression and get the better position information of each character. Attention decoder can get more robust and accurate performance for both regular and irregular scene text. Moreover, a novel decoder mechanism is introduced in our study. The proposed architecture has several advantages: the model can be trained using the end-to-end manner under the condition of multi decoders, and can deal with the sequences of arbitrary length and the images of arbitrary shape. Extensive experiments on standard benchmarks demonstrate that our model's performance is improved for regular and irregular text recognition.
Evaluation on Operation Performance Improvement of RCC Dam using Cloud Model

The objective function of economic benefit evaluation of engineering reinforcement is established for the improvement of operation performance of roller compacted concrete (RCC) dam, through maximizing the comprehensive benefit, minimizing the repair cost and the risk degree. Based on the cloud model theory, an innovative optimal decision-making model is proposed. These methods were applied in a RCC dam under operation for nearly 20 years, the variation laws of deformation, stress and seepage under different reinforcement schemes were explored through numerical simulation, and the evaluation indexes were scored by experts. The calculation results of the cloud model comprehensive evaluation set for different schemes are obtained, and the implementation effect of each scheme is evaluated accordingly.
Performance Analysis of Immune Plasma Algorithm with Different Donor-Receiver Configurations

Immune Plasma Algorithm (IPA) is a novel meta-heuristic algorithm inspired by immune plasma transfer treatment.Many metaheuristic algorithms are used for solving complex optimization problems, but their performance is mostly inspected on problems with 30 dimensions.Nowadays we are dealing with far more complex systems that require solving high-dimensional optimization problems with over 50 dimensions whereas performance of meta-heuristic algorithms for high-dimensional problems is mostly unexamined.So to overcome this problem, in this study, performance of IPA on solving high-dimensional problems is investigated.In this case, it is used to solve five well-known benchmark optimization problems with 100 dimensions.In this work, Immune Plasma Algorithm (IPA) is used for solving Sphere, Quartic, Rastrigin, Ackley and Griewank functions.It is compared with some other stateof-the-art meta-heuristic algorithms.Experimental results demonstrate that IPA outperforms these algorithms in finding best objective values, and has best standard deviation, and best mean value for most of the tested optimization problems.
VIRKOM as Augmented Reality Application for Visualization of Computer Maintenance Learning Material

Practical learning on computer maintenance for vocational students during the COVID-19 pandemic cannot be implemented optimally. Hardware access problems that are difficult to reach by students cause the learning barriers in this subject to be even greater. Therefore, through this research, a mobile-based learning media was developed to visualize computer component devices with AR technology and 3D animation. The research method used is the ADDIE model. The research begins by diagnosing the problem, describing needs, and finding appropriate solutions for computer maintenance learning. Next, the product design process and user journey are carried out and then develop applications with 3D animation assets and learning materials. Implementation activities go through an evaluation process to media and content experts to determine the validity of the application. The media and content validation instrument consists of 4 aspects with 46 items. This media is equipped with 3D objects that can be used to help students observe computer hardware. Media validation got a value of 79.49% and was included in the valid criteria so that it could be used in learning. Content validation is in the valid category with a value of 80.2%. Several improvements were made to increase the usability and attractiveness of the media so that students' interest in using the media increased. In the future this media can be applied in learning so that it can be seen the impact, both on learning outcomes, student interest and critical thinking on computer troubleshooting.
Development of simple science kits as dynamic electricity learning media for junior high school

Abstract The aim of this study is to produce Simple IPA KIT as science learning media for dynamic electricity material. This Simple IPA KIT contains several puzzle blocks that could be set into several electrical circuits. The type of the study was Research and Development, which refers to the model of Puslitjaknov (a team in the Center for Policy and Inovation Studies). The five-scales Likert questionnaire were used to obtain the data. The product was assessed by the media and material experts, physics teachers and high school students. The result of media experts showed a percentage value of 85%, in “Very Good” category, while it was 80% in “Good” category for material expert assessment. Meanwhile, the responses of teachers and students showed 95.6% and 84%, respectively, with “Very Good” category. It indicates that the Simple KIT media is suitable for learning media.
A High-performance Post-deduplication Delta Compression Scheme for Packed Datasets

Data deduplication has become a standard feature in most storage backup systems to reduce storage costs. In real-world deduplication-based backup products, small files are grouped into larger packed files prior to deduplication. For each file, the grouping entails a backup product inserting a metadata block immediately before the file contents. Since the contents of these metadata blocks vary with every backup, different backup streams of the packed files from the same or highly similar small files will contain chunks that are considered mostly unique by conventional deduplication. That is, most of the contents among these unique chunks in different backups are identical, except for metadata blocks. Delta compression is able to remove those redundancy but cannot be applied to backup storage because the extra I/Os required to retrieve the base chunks significantly decrease backup throughput. If there are many grouped small files in the backup datasets, some duplicate chunks, called persistent fragmented chunks (PFCs), may be rewritten repeatedly. We observe that PFCs are often surrounded by substantial unique chunks containing metadata blocks. In this paper, we propose a PFC-inspired delta compression scheme to efficiently perform delta compression for unique chunks surrounding identical PFCs.In the process of deduplication, containers holding previous copies of the chunks being considered for storage will be accessed for prefetching metadata to accelerate the detection of duplicates. The main idea behind our scheme is to identify containers holding PFCs and prefetch chunks in those containers by piggybacking on the reads for prefetching metadata when they are accessed during deduplication. Base chunks for delta compression are then detected from the prefetched chunks, thus eliminating extra I/Os for retrieving the base chunks. Experimental results show that PFC-inspired delta compression attains additional data reduction by about 2x on top of data deduplications and accelerates the restore speed by 8.6%-49.3%, while moderately sacrificing the backup throughput by 0.5%-11.9%.
Neural Architecture Search for Computation Offloading of DNNs from Mobile Devices to the Edge Server

With the rapid development of modern deep learning technology, deep neural network (DNN)-based mobile applications have also been considered for various areas. However, since mobile devices are not optimized to run the DNN applications due to their limit of computational resources, several computation offloading-based approaches have been introduced to overcome the issue; for DNN models, it was reported that, their elaborate partitioning, which allows that input samples are partially executed on mobile devices and then the edge server processes the rest of the execution, can be effective in improving runtime performance. In addition, to improve communication-efficiency in the offloading scenario, there have been also studies to reduce transmitted data from a mobile device and the edge server by leveraging model compression. However, the existing approaches have the root limitation that the performance eventually depend on that of the architecture of original DNN models. To overcome this, we propose a novel neural architecture search (NAS) method to consider the computation offloading cases. On the top of the existing NAS approaches, we additionally introduce resource and channel selection mask. The resource selection mask effectively divides the operations in the target model into those for a mobile device and the edge server; the channel selection mask allows to transmit only selected channels to the edge server without the reduction of task performance (e.g., accuracy). Based on the two additional masks, for the NAS procedure we introduce a new loss function to take into account end-to-end inference time as well as the task performance which is the original goal of NAS. In the evaluation, the proposed method is compared to existing approaches; we see from the experimental results that our method outperforms both the previous NAS and pruning-based model partitioning approaches.
Reversible Information of Encrypted Image Based on Feature Difference Detection and Wavelet Transform

Aiming at the shortcomings of the existing lossless digital watermarking algorithm based on frequency domain in reversibility and embedding capacity, this study proposes a lossless digital image watermarking algorithm based on fractional wavelet transform, which is used for large-capacity reversible information hiding of images. First, the image is transformed by LeGall5/3 fractional wavelet, and then, the watermark is embedded in the high-frequency subband by the histogram shift method. In order to obtain maximum embedding capacity and reduce image distortion, the methods of selecting embedding parameters and stopping parameters are proposed, respectively. At the same time, in order to prevent overflow and reduce additional information, a new method of generating position map is proposed. The experimental results show that Lena is the result of multilayer embedding based on the algorithm in this study. In order to better observe the distortion phenomenon and enlarge the image, the Lena test image is the watermark image obtained after two and three layers of embedding, and its embedding capacity can be 2.7 bpp. It is proved that wavelet transform is suitable for encrypted images to implement covert communication.
Data Mining for Software Engineering: A Survey

In this present world of technology, software engineering is needed almost in every industry, institution etc. On the other hand, data mining processes raw data to obtain useful information. By implementing data mining in software engineering, software quality and productivity can be improved. This paper examines this fascinating and still advancing research area, so that readers can easily get an elaborate outline. We review in detail existing techniques of data mining for software engineering research and provide a comparative evaluation.
Emotion Guided Speech-Driven Facial Animation

The modern deep neural network has allowed an applicable level of speech-driven facial animation, simulating natural and precise 3D animation from speech data. Regardless, many of the works show weakness in drastic emotional expression and flexibility of the animation. In this work, we introduce emotion guided speech-driven facial animation, simultaneously proceeding with classification and regression from the speech data to generate a controllable level of evident emotional expression on facial animation. Performance using our method shows reasonable expressiveness of facial emotion with controllable flexibility. Extensive experiments indicate that our method generates more expressive facial animation with controllable flexibility compared to previous approaches.
Using a Participatory Toolkit to Elicit Youth’s Workplace Privacy Perspectives

The rapid evolution of technology has enabled us to perform complex, interdependent, and geographically distributed work. As a result, the effective use of communication and coordination technologies is increasingly crucial to success in the workplace, raising at the same time concerns about workplace privacy. In this paper, we present a case study showing how we adapted and used a participatory toolkit to elicit the privacy perspectives of a 3D print shop’s youth employees. Participants expected their managers and co-workers, rather than other third-parties, to see their data, and yet prioritized keeping their co-workers informed rather than being overly concerned about third-parties accessing their data. We found this approach effective at creating an expressive space for the youth to reflect on and share their expectations and preferences on workplace data privacy, a practice that can enhance both their workplace participation and professional communication training. We conclude with thoughts on how using open-ended participatory mechanisms can support employees’ ongoing reflection on the privacy of communication and coordination technologies, leading to increased fluency and participation in workplace decision-making.
Decentralized Multiagent Actor-Critic Algorithm Based on Message Diffusion

The exponential explosion of joint actions and massive data collection are two main challenges in multiagent reinforcement learning algorithms with centralized training. To overcome these problems, in this paper, we propose a model-free and fully decentralized actor-critic multiagent reinforcement learning algorithm based on message diffusion. To this end, the agents are assumed to be placed in a time-varying communication network. Each agent makes limited observations regarding the global state and joint actions; therefore, it needs to obtain and share information with others over the network. In the proposed algorithm, agents hold local estimations of the global state and joint actions and update them with local observations and the messages received from neighbors. Under the hypothesis of the global value decomposition, the gradient of the global objective function to an individual agent is derived. The convergence of the proposed algorithm with linear function approximation is guaranteed according to the stochastic approximation theory. In the experiments, the proposed algorithm was applied to a passive location task multiagent environment and achieved superior performance compared to state-of-the-art algorithms.
Electricity Federated Strategies Based on Restricted Solution Space

Technology of big data can provide effective technical support for decision-making in production, sales, service of electricity-related industries. However, electricity data can reflect user privacy information and enterprises business secrets. The increasingly privacy protection restrictions make it impossible to directly use electricity data for interaction and sharing. Federated- learning technology helps power grid enterprises to do collaborative analysis and joint model establishment under the condition of ensuring data privacy. The power system has a large geographical span and wide coverage, and there are huge differences in geography, climate, and development in different regions. Differences in data distribution have a greater impact on the effect of federated analysis and modeling. Accoding to the high-dimension and non-convex characteristic of neural network, we propose comprehensive strategies including learning rate fast attenuation, strong regularization, and an additional term to limit the solution space, so as to reduce the negative impact of dataset differences of electricity data on the final model.
OverlapShard: Overlap-based Sharding Mechanism

The sharding-based protocols provide an effective solution on scaling decentralized blockchains. However, the performance of a sharding-based blockchain protocol can be greatly degraded by the existence of cross-shard transactions. This paper proposes an overlap-based sharding mechanism for sharding protocols, OverlapShard, to weaken the adverse impact of cross-shard transactions by mapping each node into multiple actual shards. The virtual shards composed of overlapping nodes can be used to process cross-shard transactions. The results of empirical analysis show that when a transaction contains 2 inputs and 2 outputs, the consensus overheads of OverlapShard account for 25% of that of the nonoverlapping mechanism, and OverlapShard's communication overheads are reduced by about 33.3 %. The evaluation results show that the average communication overhead of OverlapShard is reduced by 63.30%. And the average cross-shard transaction ratio of overlap-based sharding model with 3-layer virtual shard structure is 43.76% lower than that of the non-overlapping sharding model.