Reviewerly/exHarmony huggingface dataset:
An Experience Report About Challenges of Software Engineering as a Second Cycle Course

The dynamism of the software development market has demanded increasingly qualified professionals with practical knowledge on the implementation of paradigms and methodologies in the development of projects. In this context, there are undergraduate courses in Software Engineering (SE) offered in the format of two cycles, to try to deliver to the market professionals who meet the demands of the sector. Therefore, it is important to know the challenges that arise in the teaching scenario and the emerging topics of SE, in order to provide students with an academic-professional training closer to the real scenario of the software industry. Thus, this article aims to identify the difficulties observed in an undergraduate course in SE and its emerging topics, in the form of two cycles. For this, a survey with professors from a two-cycle SE course was carried out. Through a quantitative and qualitative analysis of the results, it was possible to identify some main challenges faced by professors. This type of analysis has proved to be useful to identify opportunities for improvement in the academic-professional training of SE students and to provide the software industry with more qualified professionals.
PACCP: A Price-Aware Congestion Control Protocol for Datacenters

To date, customers using infrastructure-as-a service (IaaS) cloud services are charged for the usage of computing/storage resources, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost. To tackle this challenge, in this paper, we propose PACCP, an end-to-end Price-Aware Congestion Control Protocol for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained Virtual machine (VM)-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. PACCP is evaluated by both large scale simulation and small testbed implementation. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.
RPCC: A Replica Placement Method to Alleviate the Replica Consistency under Dynamic Cloud

In dynamic cloud, more replicas can reduce access delay and balance the node load, but more difficulty of replica consistency and vice versa. To deal with this problem, this paper designed and implemented a dynamic replica placement method based on the node's credit risk, named RPCC, aiming to use replica placement to balance the relationship between system performance and replica consistency. To achieve its goal, RPCC uses the decentralized adaptive manner to dynamically create and place replicas. This method selects files with high popularity and low update rate to create replicas when creating new replicas. Meanwhile, it plots the credit risk map according to node load, node degree and access delay. RPCC selects optimal nodes to place replicas according to the credit map. Extensive experiments show that RPCC can obtain superior performances in consistency update probability increase around 5% on average, in access latency decrease around 10% and higher load balance than other similar methods.
DenseNet-ResNet-LSTM model for modulation recognition of communication signal

Abstract The traditional artificial neural network provides a low recognition rate in the modulation recognition of communication signals, suffering from the difficulty of feature extraction. Also, it requires a high signal-to-noise ratio (SNR). In order to solve these problems, this paper proposes the combined model: DenseNet-ResNet-LSTM. In the proposed model, DenseNet and ResNet extract different spatial features of samples, and then LSTM extracts the sequence of samples. Also, the attention mechanism is employed to improve the learning efficiency and ability to learn important features. Experimental results show that the proposed model achieves higher accuracy and better generalization ability over the CNN-LSTM network.
A Systematic Literature Review in Causal Association Rules Mining

As quoted recently, this is the age of information, and for information we need data. Data is everywhere around us and it is expanding dramatically. The aim of this research is to inspect and summarize the state-of-the-art approaches and studies of machine learning methods to causal inference techniques. This review utilizes a systematic literature research to the mostly prominent digital database libraries in the field of computer sciences in recent years. The objective is to identify and investigate three raised research questions to broadly analyze and detailly explore several points of view concerning causal association rules and their application in real-world problems.
Subjective QoE assessment method for 360° videos

This paper proposes a subjective assessment method for video quality and sense of presence for 360° videos. Since it is necessary to view 360° videos with a wide field of view, we adopted a subjective assessment method that allows repeated viewing of 10 s long videos. First, by analysis of the subject’s observation behavior (i.e., head movement), it was clarified that the appropriate number of observation repetitions was 2. The relationship between the video quality and the sense of presence was then quantified using the proposed method. Finally, we determined the number of subjects necessary to derive stable evaluation results.
ALT: Optimizing Tensor Compilation in Deep Learning Compilers with Active Learning

Deep learning compilers serve as the central role of scheduling neural network execution. State-of-the-art method of tensor compilation in deep learning compilers requires a long time tuning, which greatly hinders the model's deployment. In this paper, we propose ALT, an active learning tuning method for tensor computation compilation. ALT leverages a sampling strategy based on active learning to find more informative samples to be labeled. The sampling strategy is performed by an active learning exploration module which mainly consists of an uncertainty predictor, which predicts uncertainty of unseen samples, and a score predictor which evaluates the current performance of the whole method. We design a novel ping-pang way of iteration between the score predictor and the uncertainty predictor. Experiments on real workloads show that ALT helps to achieve 1.93 × - 2.49 × time reduction to obtain the optimal schedule compared to state-of-the-art deep learning compilers. When tuning under the same time budget, the end to end inference time of a set of neural networks can be improved by 1.04× - 1.07×.
Factors correlation mining on railway accidents using association rule learning algorithm

Although much research work for the operation safety has been taken in the railway domain, some accidents still occur because past experiences of accident analysis were not fully accumulated for safety improvement. This study aims to identify potential causal relationships among the many factors playing a role in railway accidents. A new interestingness measure, Confidence_interestingness ("C_" Inter) and corresponding improved algorithm, Positive and Negative Association Rules Algorithm based on "C_" Inter (PNARA_CI) were put forward in our study. Compared with traditional association rule mining algorithms, the PNARA_CI does not generate candidate association rules by means of frequent itemsets, but by the combination between every two accident factors, which can mine the positive and negative association rules with practical value to the maximum. And they were applied to railway accidents data to explore the association rules of the causal factors in the case study. The effectiveness of the algorithm was verified.
Vehicle Trajectory Optimization Based on Limiting Average Algorithm

Due to multipath effects and other complex environments, GPS data received by the server contains noise. This paper introduces a solution to reduce the interference of the noise. First, a limiting filtering algorithm helps to overcome the accidental noise of received GPS data. Next, the received GPS data filtered with a moving average algorithm to inhibit periodic noise. Finally, the Douglas-Peucker algorithm thins out the data while ensuring the trajectory curve remains unchanged. In this way, we can not only make the trajectory curve smoother but also save memory consumptions. The experimental results show that the proposed method effectively removes noise and redundant data, and the effect of trajectory optimization is obvious.
$$AC^0$$ Constructions of Secret Sharing Schemes – Accommodating New Parties

The possibility of implementing secret sharing in the complexity class $$AC^0$$ was shown in a recent work by Bogdanov et al. (Crypto’16) who provided constructions of ramp schemes. Cheng-Ishai-Li (TCC’17) forwarded the work by achieving robustness for such schemes. In this paper we construct secret sharing schemes which can include new parties over time keeping the entire construction implementable by $$AC^0$$ circuits. We provide $$AC^0$$ constructions of a dynamic secret sharing scheme and an evolving secret sharing scheme. The constructions are more flexible than similar existing schemes, use less resources and have several notable advantages.
Optimising AI Training Deployments using Graph Compilers and Containers

Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems like image analysis and speech recognition. Training a DNN is computationally intensive and High Performance Computing (HPC) has been a key driver in AI growth. Virtualisation and container technology have led to the convergence of cloud and HPC infrastructure. These infrastructures with diverse hardware increase the complexity of deploying and optimising AI training workloads. AI training deployments in HPC or cloud can be optimised with target-specific libraries, graph compilers, and by improving data movement or IO. Graph compilers aim to optimise the execution of a DNN graph by generating an optimised code for a target hardware/backend. As part of SODALITE (a Horizon 2020 project), MODAK tool is developed to optimise application deployment in software defined infrastructures. Using input from the data scientist and performance modelling, MODAK maps optimal application parameters to a target infrastructure and builds an optimised container. In this paper, we introduce MODAK and review container technologies and graph compilers for AI. We illustrate optimisation of AI training deployments using graph compilers and Singularity containers. Evaluation using MNIST-CNN and ResNet50 training workloads shows that custom built optimised containers outperform the official images from DockerHub. We also found that the performance of graph compilers depends on the target hardware and the complexity of the neural network.
Preference and Constraint Factor Model for Event Recommendation

Newly emerging Event-based Social Network (EBSN) concentrates on connecting both online social relationships and offline local events. For the growing amount of events published on EBSNs, personalized event recommendation becomes essential to help users choose attractive events. But most of existing event recommendation algorithms fail to distinguish constraint factors of users’ event participation behaviors from preference factors, which reflects the cost of event participation that hinders users from attending interested events. To take full advantage of influences from contextual information on users’ event participation, we differentiate preference and constraint factors which contribute to users’ decision for event participation, and extract the soft spatial and temporal constraints from event venue and start time contexts respectively. Then we propose the Preference and Constraint Factor Model (PCFM) based on factorization machine model, using attentive mechanism to weight feature interactions and incorporate latent factors of users and contextual features for personalized perference modeling and event recommendation. Moreover, learning-to-rank techniques are utilized to train PCFM as a ranking model for the implicit feedback nature of responses from users. Extensive experiments evaluate the performance of our proposed recommendation model on real-world EBSN datasets, and demonstrate the outperformance than state-of-art event recommendation methods on many metrics.
Interpretable Natural Language Segmentation Based on Link Grammar

Natural language segmentation (NLS), or text segmentation, refers to the process of dividing written text into meaningful units. Sentence segmentation, a subfield of text segmentation, is the problem of dividing a string of natural language text into its component sentences. Current methods of sentence segmentation are often either hardcoded-they require manual implementation of fixed grammar and segmentation rules-or require extensive training on labeled corpora and are not explainable-they are "black box" algorithms that cannot be understood by humans. In this paper, we present a novel explainable sentence segmentation method capable of separating bodies of text into grammatically valid sentences solely based on the grammatical relationships between individual words or tokens. The proposed NLS architecture can both automate the input query parsing and semantic query execution components of voice-activated question answering and information retrieval systems as well as enable automatic summarization, entity extraction, sentiment identification, and a variety of other natural language processing (NLP) algorithms that operate at the sentential level.
Efficient shuffle management for DAG computing frameworks based on the FRQ model

In large-scale data-parallel analytics, shuffle, namely the cross-network read and the aggregation of partitioned data between tasks with data dependencies, usually bring in large overhead. To reduce shuffle overhead, we present SCache, an open-source plug-in system that particularly focuses on shuffle optimization. SCache adopts heuristic pre-scheduling combining with shuffle size prediction to pre-fetch shuffle data and balance load on each node. Meanwhile, SCache takes full advantage of the system memory to accelerate the shuffle process. We also propose a new performance model called Framework Resources Quantification (FRQ) model to analyze DAG frameworks and evaluate the SCache shuffle optimization. The FRQ model quantifies the utilization of resources and predicts the execution time of each phase of DAG jobs. We have implemented SCache on both Spark and Hadoop MapReduce. The performance of SCache has been evaluated with both simulations and testbed experiments on a 50-node Amazon EC2 cluster. Those evaluations have demonstrated that, by incorporating SCache, the shuffle overhead of Spark can be reduced by nearly 89%, and the overall completion time of TPC-DS queries improves 40% on average. On Apache Hadoop MapReduce, SCache optimizes end-to-end Terasort completion time by 15%.
An Ensemble Learning based Hierarchical Multi-label Classification Approach to Identify Impacts of Engineering Changes

In the process of complex products development, design decisions of products are constantly changed to improve quality or functionality, reduce costs, respond to statutory constraints or implement wishes from customers, with respect to new functionality. The changes on already released design decisions are known as Engineering Changes (EC). Due to interdependency, changes on one component may cause changes on another, and this will spread along the product structure. Therefore, to completely identify the affected components of engineering changes is a major challenge. This paper presents a novel approach to use properties of components as target variables and applying the predicted properties to locate the EC affected components in product structure. We create a hierarchy of the properties and divide the label space into separate communities. A stacked multi-label classifier is trained in each community, the result is obtained by union of assigned labels from different communities. Finally, the predicted labels are adjusted by incorporating the hierarchical relation. Experiments conducted on real-world industrial EC dataset with mixed data types. Results demonstrated that, the ensemble framework in our approach is more efficient and effective than our baseline models and has achieved superior performance on real industrial engineering change data.
Research on Optimization Performance of Nonlinear Function Based on Multigroup Genetic Algorithm

In order to get over the shortcoming of the genetic algorithm of a single population that is easy to fall into local convergence, a multi-group genetic algorithm based on two and three populations is designed and implemented respectively. The immigration operators and elite retention mechanisms are used to ensure population diversity and convergence performance. Simulation analysis shows that the genetic algorithm based on multiple groups can effectively avoid the local convergence problem. By comparing with the traditional genetic algorithm, the algorithm adopted in this paper has fast convergence speed and high convergence efficiency as a feasible and effective algorithm.
Lightweight KPABE Architecture Enabled in Mesh Networked Resource-Constrained IoT Devices

Internet of Things (IoT) environments are widely employed in industrial applications including intelligent transportation systems, healthcare systems, and building energy management systems. For such environments of highly sensitive data, adapting scalable and flexible communication with efficient security is vital. Research investigated wireless Ad-hoc/mesh networking, while Attribute Based Encryption (ABE) schemes have been highly recommended for IoT. However, a combined implementation of both mesh networking and Key-Policy Attribute Based Encryption (KPABE) on resource-constrained devices has been rarely addressed. Hence, in this work, an integrated system that deploys a lightweight KPABE security built on wireless mesh networking is proposed. Implementation results show that the proposed system ensures flexibility and scalability of self-forming and cooperative mesh networking in addition to a fine-grained security access structure for IoT nodes. Moreover, the work introduces a case study of an enabled scenario at a school building for optimizing energy efficiency, in which the proposed integrated system architecture is deployed on IoT sensing and actuating devices. Therefore, the encryption attributes and access policy are well-defined, and can be adopted in relevant IoT applications.
Opinion Spam Detection based on Supervised Sentiment Analysis Approach

Reading other user's experience on different products and services becomes part of customer's behavior for purchase decision making process nowadays. For this reason, such online resources grow into a target for review spammers with the aim of either boosting their desired products or destroying the reputation of their competitors. Distinguishing between spam and true expressed sentiments is highly challenging due to the fact that this process demands linguistic and grammatical knowledge. Because of the language dependency nature of opinion analysis, natural language processing and opinion mining fields are utilized to overcome the challenges in each language. In this paper, we focus on building up a feature set to be employed with different classifiers as a trustworthy input for opinion spam detection in Persian language. Research on review spam detection in English uncovered some meaningful features so far that we utilized some of them, modify the meaning and usage of some of them to be adapted on Persian language and also add some innovative features accordingly. Our experiments reveal that first Decision Tree and then AdaBoost with the accuracy percentage of 98.67 and 98.00 are the best classifiers for Persian opinion spam detection. Also, the robustness of our extended feature set has been checked in comparison to other feature sets in the task of opinion spam detection.
Anomalous State Recognition of Lane-changing Behavior using a Hybrid Autoencoder Architecture

This paper presents a hybrid unsupervised architecture for anomalous lane-changing behavior recognition. Anomaly detection aims to identify unusual driving behavior caused by either environmental or phycological stimuli, and is of great important in road safety. First, a Recurrent Convolutional Autoencoder (RC-AE) is built to explore the spatial-temporal features derived from the high-dimensional behavior data. Second, Reconstruct Error analysis of the autoencoder and one-class support vector machine method are both applied to identify anomalous lane-changing behavior in the learned feature space by autoencoder. Last, we employ T-Distributed Stochastic Neighbor Embedding (T-SNE) for data visualization in the anomaly detection. Based on the kernel density estimation analysis, anomalous and normal lane-changing sample groups display distinct difference over probability distributions. The findings contribute to a better understanding on drivers' natural lane-changing behavior, and can provide important insight into real-time personalized unusual lane-changing behavior monitoring system development.
Micro - GAGE: A Low-power Compact GAGE Hash Function Processor for IoT Applications

The emergence of Internet of Things (IoT) applications presents a wide range of opportunities in areas such as automotive, consumer, energy, smart city and many others. Since many of these systems operate in environments with heightened sensitivity and privacy, they require trusted level of security. In addition, the implemented security measures must fit within strict power and area budgets, yet deliver maximum performance mandated by the application. In this paper, we propose and evaluate the area, power and performance of a novel compact low-power GAGE hash function processor (MICRO-GAGE). The results show Micro- Gageis 28× more power-efficient than the reported implementation of GAGE hash function on AVR ATMEGA 328p micro-controller. Micro- Gageneeds approximately 2.6 times less power than several comparable LWC designs. In addition, the proposed processor performs approximately 2.6 times longer than the other LWC designs when powered by typical Ni-Cd battery.