Reviewerly/exHarmony huggingface dataset:
Families of Butterfly Counting Algorithms for Bipartite Graphs

Butterflies are an important motif found in bipartite graphs that provide a structural way for finding dense regions within the graph. Beyond counting butterflies and enumerating them, other metrics and peeling for bipartite graphs are designed around counting butterfly motifs. The importance of counting butterflies has led to many works on efficient implementations for butterfly counting, given certain situational or hardware constraints. However, most algorithms are based on first counting the building block of the butterfly motif, and from that calculating the total possible number of butterflies in the graph. In this paper, using a linear algebra approach, we show that many provably correct algorithms for counting butterflies can be systematically derived. Moreover, we show how this formulation facilitates butterfly peeling algorithms that find the k-tip and k-wing subgraphs within a bipartite graph.
Practical Effectiveness of Quantum Annealing for Shift Scheduling Problem

Quantum annealing (QA) is a novel computing approach for solving computationally demanding problems more rapidly than approaches using classical computers by exploiting parallelism in the variable state updates (spin updates). A number of previous studies have shown that QA performs well in benchmarking combinatorial optimization problems, but no works have evaluated QA in the shift scheduling problem. This paper formulates it for QA and evaluates the effectiveness of QA in comparison with other methods on a classical computer. We confirmed that QA is up to about 14 times faster than classical methods in obtaining high-quality solutions for several instances.
Throughput-oriented and Accuracy-aware DNN Training with BFloat16 on GPU

Deep Neural Networks (DNNs) have transformed the field of artificial intelligence and achieved extraordinary success in many areas. The training of DNNs is commonly compute and memory-intensive, which has resulted in several optimizations in the training phase. Among them, reduced precision is a typical and widely used technique to accelerate DNN training and reduce memory requirements. However, applying a widely adopted reduced precision format such as Float16 to all involved operations in DNN training is not optimal as the use of Float16 in some operations can hurt model accuracy. Meanwhile, additional optimizations including loss scaling and autocast techniques can mitigate the accuracy loss but lead to inherent overhead and inadequate use of reduced precision. In this work, we leverage another reduced precision format, BFloat16, and introduce a throughput-oriented and accuracy-aware approach to maximize the performance potential of DNN training. Since the high throughput provided by BFloat16 format is accompanied by low precision of the floating-point representation, this approach achieves high throughput by using BFloat16 on all DNN op-erations and avoids the accuracy loss through a customized accuracy-aware normalization. Results show that our approach outperforms the state-of-the-art mixed precision training by 1.21x on an NVIDIA A100 GPU.
Scalable Data Parallel Distributed Training for Graph Neural Networks

Graph neural networks (GNNs) operate on data represented as graphs, and are useful for a wide variety of tasks from chemical reaction and protein structure prediction to content recommendation systems. However, training for large graphs and improving training performance remain significant challenges. Existing distributed training systems partition a graph among all compute nodes to train for large graphs; however, this results in a communication overhead to degrade training performance. In this study, to solve these two problems, we propose a scalable data-parallel distributed GNN training system designed to partition a graph redundantly. It is implemented using remote direct memory access (RDMA) and nonblocking active messages to efficiently utilize network performance and hide communication overhead by overlapping with the training computation. Experimental results are presented to show the strong scalability of the proposed approach, which achieved parallel efficiencies of 0.93 using eight compute nodes for the ogbn-products dataset in the Open Graph Benchmark (OGB) and 0.95 based on two compute nodes using 32 compute nodes for the ogbn-papers100M dataset. The proposed system exhibited a training performance 18.9% better than the state-of-the-art DistDGL, even with only a single compute node. The results demonstrate that the proposed approach may be considered a promising method to achieve scalable training performance for large graphs.
A Searchable Re-encryption-based Scheme for Massive Data Transactions

In the era of big data, companies require to analyze user behaviors and customize personalized services through massive amounts of data. Coupled with the rapid development of data-centric deep learning algorithms, the demand for data transactions has also increased. Current data owners can entrust cloud storage to overcome the limitations of storage capacity and network performance, but such a data transaction model still has two key issues that need to be resolved. On the one hand, when consumers retrieve the encrypted data that are stored in cloud storage, cloud storage may tamper with the returned results. On the other hand, when there is no trusted third party, it is difficult to restrict malicious transactions and ensure the fairness of both parties to the transaction. In this article, we propose a searchable re-encryption scheme for data transactions to prevent cloud storage from returning incorrect results. We also designed a smart contract to limit malicious behaviors in the transaction process to ensure fair transactions. Security analysis and evaluation experiments show that the system takes into account both security and performance, and is more feasible and practical for massive data transaction scenarios.
Low Resource Neural Machine Translation

Neural machine translation (NMT) is the current state-of-the-art approach for machine translation. However, NMT models should be trained with a large amount of data, making NMT in low-resource scenarios a tricky issue. In this paper, we concluded three categories of methods for low-resource NMT. Firstly, data augmentation is the most direct solution, producing extra parallel corpus. Secondly, multilingual NMT model can improve the performance of low-resource languages. Finally, multimodal NMT is especially useful because multimodal information is easy to acquire online. We also illustrate some promising directions to further explore in the future.
Low Energy Consumption and Time Deterministic Energy-saving Workflow Task Scheduling Algorithm based on DVFS

With the development of streaming media, soft real-time system in today’s life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task.
A Communication-efficient Semi Asynchronous Federated Learning Network in Power Grid

The Electric Internet of Things (IoT) scenario generates a large amount of network edge data. The analysis of these data will significantly enhance the efficiency of IoT. However, traditional mobile edge computing (MEC) can not meet the need for data privacy protection. Currently, a new distributed learning approach, federated learning, has been proposed to solve data security problems and provide efficient model performance through the training of a shared model by multiple devices. Nevertheless, limited wireless communication resources and device heterogeneity are also key factors affecting FL performance Therefore, we propose a novel semi asynchronous federated learning (SAFL) framework, which aims to reduce calculation and transmission latency and improve the learning rate. We formulate a joint terminal device selection and resource allocation problem under the proposed SAFL framework to ensure communication efficiency. To deal with our proposed Markov Decision Process (MDP) problem, we introduce a deep deterministic policy gradient (DDPG) algorithm to find the optimal solution. Numerical results demonstrate the advantages of the proposed algorithm.
Noisy Student Teacher Training with Self Supervised Learning for Children ASR

Automatic Speech Recognition (ASR) is a fast-growing field, where reliable systems are made for high resource languages and for adult’s speech. However, performance of such ASR system is inefficient for children speech, due to numerous acoustic variability in children speech and scarcity of resources. In this paper, we propose to use the unlabeled data extensively to develop ASR system for low resourced children speech. State-of-the-art wav2vec 2.0 is the baseline ASR technique used here. The baseline’s performance is further enhanced with the intuition of Noisy Student Teacher (NST) learning. The proposed technique is not only limited to introducing the use of soft labels (i.e., word-level transcription) of unlabeled data, but also adapts the learning of teacher model or preceding student model, which results in reduction of the redundant training significantly. To that effect, a detailed analysis is reported in this paper, as there is a difference in teacher and student learning. In ASR experiments, character-level tokenization was used and hence, Connectionist Temporal Classification (CTC) loss was used for fine-tuning. Due to computational limitations, experiments are performed with approximately 12 hours of training, and 5 hours of development and test data was used from standard My Science Tutor (MyST) corpus. The baseline wav2vec 2.0 achieves 34% WER, while relatively 10% of performance was improved using the proposed approach. Further, the analysis of performance loss and effect of language model is discussed in details.
The Academic Evaluation Practice of Theoretical Courses of Physical and Vocal Music Education Major under the Concept of Quantitative Evaluation

Under the rapid progress of educational reform, the evaluation method of students’ studies in China is only partially adjusted or even remains at the same level for a long time. There will be a growing gap between it and the existing education system and social needs. Therefore, based on the concept of quantitative evaluation, the gap and unreasonable evaluation methods of academic evaluation of theoretical courses of physical and vocal music education majors caused by unfair educational resources and physical conditions of Chinese students have been studied. Student Growth Percentile (SGP) academic evaluation model based on the current education situation in China is proposed. By analyzing the establishment process of this model and comparing it with the current two academic evaluation models, the advantages and disadvantages of the SGP model are analyzed. Then, a practical application case of the SGP model in a place is used to show the model’s actual evaluation process and feedback results. Through the SGP model on the growth evaluation of group students and the teaching evaluation of teachers, the growth and problems of students in this stage and whether the teachers’ teaching is effective are analyzed to obtain the pertinent results. Finally, the students and teachers are investigated, and their satisfaction with this model is 81% and 75%, respectively, proving this model’s reliability and practicality.
Different, Really! A comparison of Highly-Configurable Systems and Single Systems

The development of systems that handle configuration options according to a specific environment is considered a hard activity. These kind of systems, Highly-Configurable Systems (HCS) are perceived by researchers and developers as complex and difficult to maintain due to the necessity of handling variation points. Although this perception is reported in the literature, no prior study investigated the differences between HCS and Single Systems (SS). This study investigated similarities and differences between HCS and SS using well known metrics from the literature according to three different perspectives: product perspective (bug-proneness, complexity, and change size); process perspective (number of contributors, number of core developers, and accidental contributors); and people perspective (contributor retention and number of paid contributors). To perform this comparison, we collected data from two surveys and from a mining study (within 15,769 releases of 124 GitHub projects written in C). In general, we identified that for the majority of the metrics, the perception of practitioners and researchers about HCS and SS is different from our mining results. The identification of similarities and differences of HCS and SS will help to initiate a discussion and further research in this direction.
Analysis of the Most Frequent Morphosyntatic Errors in the Italian of Egyptian Students

This research aims to examine from an analytical perspective the most frequent morphosyntactic errors that Egyptian students commit while learning the Italian language. The discussion of this topic is not a mere list of students’ errors, rather it focuses on the problem of error analysis and on the other related problems, such as the typology, the frequency and, finally, the causes of such errors. We started from the fact that in the learning phases of an SL, most students encounter difficulties especially in written production. The correction of the works produced by learners allows us to study the causes of certain errors and to isolate, analyse and correct them.
Pole-2p: Improved consensus algorithm based on Proof of Learning

PoW (Proof of Work) consensus algorithm has the advantages of simple algorithm, easy to use, allowing 50% of the nodes in the whole network to fail, but it still has some problems, such as low efficiency, mining waste a lot of resources. PoUW (Proof of Useful Work) based on PoW uses the wasted resources of PoW to carry out meaningful calculation, while PoLe (Proof of Learning) uses the wasted resources to train the machine Learning model. However, it has some problems, such as unreasonable reward mechanism, only one model training, failed to exert all the computing power of nodes. Therefore, the PoLe consensus algorithm is implemented and improved on the consortium blockchain in this paper. Based on the limited access characteristics of the consortium blockchain, some security is sacrificed in exchange for the increase of block speed.
5G marketing system based on multi-sensor collaborative filtering algorithm

Massive user interaction information provides more data choices for recommender systems. With the increase of user choices, the sparsity of user interaction data has become an important challenge. For traditional recommendation algorithms, how to extract effective features to solve the problem of data sparsity has always been a hot research direction in the field of recommendation systems. In view of the above-mentioned problems, this paper proposes a recommender based on enhanced collaborative filtering algorithm for 5G marketing system. Specifically, by broadening the input vector field and fusing the user's social information and personal interaction information, the problem of multisource information collaborative modeling is effectively solved. Further, in order to achieve the enhancement of vector structure, we introduce a social regularization term and an internal regularization term to alleviate the overfitting problem of most recommendation algorithms. Experiments on a large number of real-world datasets show that our proposed method effectively improves the performance of the model on sparse data.
Research and practice of browser electronic data forensics based on SVM

Firstly, this paper introduces the background and significance of browser automatic forensics, and analyzes the current situation and development trend of browser forensics at home and abroad. Cutting into the automatic analysis of browser history, bag-of-words model and TF-IDF model are established as feature extraction, and naive Bayes, support vector machine and convolutional neural network are used as classification algorithm models. A simulation experiment is carried out in the browser history file data, and through the analysis and comparison of the results, a better combination result is obtained from multiple feature extraction and classification algorithms.
Research on task scheduling based on improved particle swarm optimization in cloud computing environment

With the arrival of THE 5G era and the further growth of computing volume, how to rapidly process a large number of computing tasks is a problem that needs to be solved in the cloud computing field. In order to make the cloud computing task scheduling process with better performance, in this paper, the traditional particle swarm optimization (pso) algorithm was improved and applied to the task scheduling in cloud computing, in view of the task completion time as the optimization goal, main of population is initialized by the method of reverse learning, and the algorithm of the inertia weight and learning factor for dynamic adjustment. Through setting contrast experiments, it is confirmed that the improved algorithm has better performance and reduces the task completion time in cloud computing task scheduling.
Research on semantic segmentation of UAV images based on deep learning

UAV technology has developed rapidly in recent years, Images extracted by UAV are widely used in urban division, crop classification, land monitoring etc. However, there are problems in UAV image segmentation such as image category imbalance, object scale variation, and insufficient utilization of contextual information, etc. To address the above problems, this paper uses optimized deeplabv3+ network model, and cross-entropy loss function for balancing the dataset samples in the experimental process for image semantic segmentation research. The results show that the algorithm of this paper has a high accuracy rate for semantic segmentation of UAV images, and can recognize each category of UAV images better, and the segmentation effect is better.
High randomness hyperchaos-based parameterizable TRNG: Design, FPGA implementation and exhaustive security analysis

Recently, chaotic systems have become a thrilling discipline for information security applications, particularly in designing entropy sources. This paper presents a novel technique to tame an optimized 4D hyperchaotic Lorenz system by building a reconfigurable high randomness and low-cost hardware true random number generator (HC-HTRNG). Moreover, the proposed architecture has been modeled using the fourth-order Runge–Kutta method (RK4) and the 32 bits (10Q22) fixed-point arithmetic representation. The adopted methodology consists of two crucial points. First, we proposed using both intermediate and final solutions of the RK4 method as a raw data source for our HTRNG. The provided raw data are used to generate random numbers with parameterizable sizes. This approach reduces the design latency from four clock cycles to only one cycle while increasing the generation speed and the entropy value. Second, the designed HC-HTRNG integrates a FIPS 140-2-based built-in self-security test module (BISSTM). The BISSTM is used as an environment failure protection and testing mechanism (EFPTM) to guarantee online and continuous control of the proposed HC-HTRNG’s reliability and availability. Furthermore, the proposed HC-HTRNG has been implemented on the Xilinx ML605 FPGA platform using an RTL design based on the VHDL description of the RK4 method. A panoply of online/offline investigations and experiments were carried out intensely, deeply, and thoroughly to analyze, evaluate and validate the robustness and security aspects of the proposed HTRNG regarding all the aspects related to embedded system security. Notably, the evaluations were conducted regarding of chaos validation, security analysis, statistical tests, design performances, and comparisons. The investigations and implementation findings validate that the proposed architecture can attain high performance in terms of maximum post place and route operating frequency (MPRF) and throughput while occupying low FPGA space. Furthermore, timing and power efficiencies result presents an excellent trade-off between design efficiency and high-performance achievement. To the best of our knowledge, the proposed HC-HTRNG is the only one in the literature whose investigation has been subjected to 25 analyses, including 17 related to security aspects. Moreover, among all TRNGs, our generator is the only one that successfully passed seven statistical test suites, as well as the hardest and the most complex.
Human action recognition based on attention mechanism and two-stream non-local graph convolution

Action recognition methods based on human skeleton can explicitly represent human actions, and have gradually become one of the important research directions in the field of computer vision. To address the problems that the skeleton graph in graph convolutional networks is fixed to represent only the physical structure of the human body and the lack of adaptive ability to the skeleton topology graph structure, this paper proposes a dual-stream non-local graph convolutional network based on the attention mechanism. First, the temporal convolution layer is extended to a parallel structure with multiple kernels, and different temporal convolution kernel modules are adaptively selected to collect features by channel weights; second, an attention model consisting of an attention pooling layer is proposed to capture the correlation and temporal continuity among joints; finally, the nonlocal graph convolution network is used as the basic framework with joint information, skeletal information and respective motion information A dual-stream fusion model is constructed. The proposed method is compared with the mainstream methods in recent years on the action recognition dataset NTU RGB+D, and the experimental results show that the proposed method achieves high accuracy in action recognition.