Reviewerly/exHarmony huggingface dataset:
A first offering of software engineering

This paper describes our first offering of a project-based software engineering course for undergraduate seniors. The course was given to 72 undergraduates, mostly seniors majoring in computer engineering. Our project taught the full engineering cycle with a narrative based on supporting the re-use of software. In two parts spanning 13 weeks, successful teams deployed a web service. We identify lessons learned and opportunities for improvement.
Posterior Refinement on Metric Matrix Improves Generalization Bound in Metric Learning

Deep metric learning (DML) attempts to learn a representation model as well as a metric function with a limited generalization gap, so that the model trained on finite known data can achieve similitude performance on infinite unseen data. While considerable efforts have been made to bound the generalization gap by enhancing the model architecture and training protocol a priori in the training phase, none of them notice that a lightweight posterior refinement operation on the trained metric matrix can significantly improve the generalization ability. In this paper, we attempt to fill up this research gap and theoretically analyze the impact of the refined metric matrix property on the generalization gap. Based on our theory, two principles, which suggest a smaller trace or a smaller Frobenius norm of the refined metric matrix, are proposed as guidance for the posterior refinement operation. Experiments on three benchmark datasets verify the correctness of our principles and demonstrate that a pluggable posterior refinement operation is potential to significantly improve the performance of existing models with negligible extra computation burden.
TrustlessNAS: Towards Trustless Network Architecture Search

Machine learning (ML) and artificial intelligence (AI) technologies are becoming intrinsic in our every-day life. These technologies are present in our vehicles, our customer service departments, the applications in our smart-phones, our hospitals, to name a few. The tasks they are trying to solve have grown in complexity, thus, it is imperative we can trust not just the accuracy of the models, but the process used to design, train, and deploy them. This paper introduces the idea of TrustlessNAS, a platform for decentralized Network Architecture Search (NAS), which serves as the root-of-trust for the design, training, and validation process of NAS-based models. We propose an architecture that relies on permissioned blockchain technology to make the entire design process transparent, auditable, and trusted without the need to fully trust all the participants.
Non-Causal Integrated Navigation for Airborne SAR Sytems

A non-causal nonlinear navigation algorithm is proposed for CONAE’s Airborne Synthetic Aperture Radar (SARAT). Compared with a previous causal algorithm, the new approach provides higher ex-post navigation accuracy with reduced navigation data. Moreover, and importantly, it avoids the abrupt jumps occurring with causal navigation each time an innovation is processed -these jumps having a deleterious impact on the SAR image focusing process. Straight leveled uniform flight, required for SAR image acquisitions, traduces into low excitability and subsequent low estimability (see [1]) of those variables not directly measured by the navigation instruments (magnetometers, inertial measurement units, GNSS etc.). As such, information gathered outside the acquisition interval becomes crucial. A clear advantage of a non causal algorithm is that it exploits past as well as future data with respect to any flight interval of interest. The algorithm is evaluated with real flight navigation data. The study proposes criteria to choose the shortest flight path enabling the best navigation performance over any given SAR acquisition interval. Those criteria may also be used to optimize the pilot’s maneuvers before and after any acquisition interval.
Understanding Low-Code or No-Code Adoption in Software Startups: Preliminary Results from a Comparative Case Study

Low-code or no-code application development is a new jargon in the software development community. In response, large and medium-sized companies, are seen triggered to join the bandwagon. Existing research on why small and innovative companies, like software startups, apply this paradigm is limited. The current literature shows that software startups are different from established software companies in terms of their focus on innovation, market-driven context, limited resources, and uncertainty. Therefore, in this paper, we study and report our initial understanding of why software startups apply low-code or no-code. We studied two cases, in the first phase, to address the research question. Our preliminary results show that software startups apply this paradigm in an ad-hoc manner and use it for experimentation, prototyping, and idea validation. On the flip side, large companies enjoy a stable workflow of low-code or no-code development. The motivations include achieving rapid product development, fast feedback, and empowering business users. These results provide a good starting point for discussion and demand for further research. Including additional data, particularly, more cases, therefore, is our essential next step to get a deeper understanding and report final results.
HCL: Hybrid Contrastive Learning for Graph-based Recommendation

Graph-based collaborative filtering for recommendation has attracted great attention recently, due to its effectiveness of capturing high-order proximity among users and items. To further improve its model robustness and alleviate label-sparsity issue, contrastive learning has been introduced to polish user and item representation by contrasting different views of user/item nodes, learning necessary and robust representation for recommendation. However, we argue that prior contrastive learning approaches only explore its unsupervised intrinsic nature as a plug-in without leveraging available user-item interactions, failing to exploit the huge potential of contrastive learning. In this paper, to alleviate the above issues, we propose Hybrid Contrastive Learning for graph-based recommendation that integrates unsupervised and supervised contrastive learning. Specifically, to improve model robustness, we first present bipartite graph augmentation operations from the perspectives of node attributes and topology to generate incomplete and noisy graph views. Then, we propose a hybrid contrastive learning module that conducts unsupervised and supervised contrastive learning together. Last, we present an approach to perform hybrid contrastive learning permutationally among multiple views. Extensive experiments show that our proposed model not only outperforms state-of-the-art baselines significantly on two public datasets and one internal dataset, but also demonstrates superiority regarding to model robustness over other strong baselines.
AtNet: A Novel Anti-tracking Network with Multi-Party Judgement Capability Based on Cross-Domain Small-World Topology

Anti-tracking network plays an important role to protect the participants from being associated in a conversation. Centralized networks are probably blocked or leak users’ communication privacy. So anti-tracking network is usually a decentralized P2P overlay network. It needs good network robustness. The existing anti-tracking networks are lack of defense capability. A malicious node can easily find the key points or even all participants in the network systems. To address these problems, we propose AtNet, a novel anti-tracking network based on cross-domain small-world topology. First, we generate a small-world topology with more uniform domain distribution based on greedy thought. Then we show a recursive requesting method to maintain the network’s properties. Last, we explain the multi-party judgement mechanism. With the collaboration of near nodes, a node’s abnormal requests can be found. Compared with three state-of-the-art anti-tracking network topologies, AtNet obviously has better robustness. Moreover, we implement the prototype system and evaluate the maintenance effect and defense effect. The experiment results show that AtNet can keep good robustness, anti-tracking capability, small-world property and high clustering when nodes randomly join in or exit the network, and a node can only averagely detect 7.71 nodes in the network with 1000 nodes when allowing nodes to lie twice.
Research on Data Sufficiency and Model Validity toward AI for Critical Systems

Deep learning is used in many fields, but it has not been used in critical systems because testing that it works correctly is difficult. There are no comprehensive methods to ensure deep learning quality. Our research introduced effective test methods for deep learning in terms of the data sufficiency and model validity based on user requirements. To determine the data sufficiency, we proposed a method using the new concept of extrapolation and interpolation to classify out-of-domain uncertainty, domain-shift uncertainty, in-domain uncertainty, and data uncertainty. This method allows us to indicate what training data are missing. This paper proposes methods to visualize a basis for the model's decisions to determine the model's validity. We explain how to visualize the region of interest in an image and explain the basis for deep learning decisions from only human-understandable explanations using the combination of intermediate output and a decision tree.
Investigating the transaction Log file to detect malicious transactions

many advantages have been provided to organizations and people by using Database applications. It provides many services and benefits to companies and people. Therefore, these organizations and users have begun using Database systems in all their works. On the other hand, they are worried about their works when they use wrong information to organize and process some jobs. The wrong and invalid information comes when someone changes or enters invalid information to a database. In this paper, a technique has been proposed to investigating the transaction Log file to detect malicious transactions. In addition, the technique uses Functional dependency between the attributes to detect the malicious transactions which are wrong information. The technique begins by studying the Log file and defining all the transactions and their attributes which are used in each transaction. In addition, frequent pattern growth technique from data mining field is used to find the malicious transactions. Frequent pattern growth technique is used to be more efficient to detect the malicious transactions.
CNN Image Recognition is Mainly Based on Local Features

We show that image classification with Convolutional Neural Networks (CNNs) follows a different strategy than how humans recognize objects: while a human may consider the global shape or a 3D picture of the object, a CNN’s decision is based mainly on recognizing local-patterns without taking into account their relative positions. We show this by feeding a CNN an image that has been cut into many squares and randomly reassembled. We observe that the original object is still recognized in the scrambled image with high probability up to a certain point. This allows us to estimate the size of the patterns used for recognizing objects by the CNN. We also show that for this analysis to make sense, we need to introduce an alternative class, "other" which contains a very vast amount of different motives, too vast for the CNN to learn.
Updatable Hybrid Encryption Scheme with No-Directional Key Update for Cloud Storage

Updatable encryption (UE) plays an important role in mitigating the impact of key compromise on ciphertext in the scenario of cloud storage. Nevertheless, most of the existing UE schemes focus on making symmetric encryption updatable. In this paper, to give play to advantages of symmetric and asymmetric encryption, we present an updatable hybrid encryption (UHE) scheme. By setting the REACT transform as the underlying encryption scheme and using the indistinguishable obfuscation to convert the private key into a public key encryption program, the synchronous update of public/private key pair and ciphertext can be realized. More importantly, the generation and update of private key are stored locally in hybrid encryption, the update token is not used to derive the private key, and thus the no-directional key update can be achieved. Furthermore, considering for the adversarial attack behaviors, we build a reasonable security model. Finally, since the decryption ability of the punctured keys is limited at the punctured points, the adversary can be allowed to access to all oracles at the full epochs, and then under the proposed security framework the confidentiality of the proposed scheme can be proven based on the indistinguishability between pseudorandom functions, where the confidentiality can capture forward and post-compromise security.
Fractional Two-Dimensional Linear Discriminant Analysis

Two-dimensional linear discriminant analysis (2DLDA) is a dimensional reduction technique widely applied to image recognition, particularly to the task of human facial recognition. Despite this, one of the potential limitations of the standard 2DLDA is its inability to model a discriminative feature subspace with the appearance of undesirable variations (illumination changes, face expressions, etc.) in the original image samples. To address this limitation, fractional 2DLDA (F2DLDA) is suggested in this paper. F2DLDA uses fractional transformed images-as-matrices to generate a projection matrix through maximization of the fractional 2D Fisher’s criterion. In this manner, the constructed feature subspace by F2DLDA reflects the most discriminative information of the image samples and is comparatively less sensitive to undesirable variations. Results from a set of experiments affirm the viability of the proposed F2DLDA in face recognition.
Is Inner Source the Next Stage in the Agile Revolution?

Modern business leaders know it is important to be able to work fast, adapt to change, and build on existing solutions. This is especially true of digital business, where the high rate of change creates a continuous stream of new problems and new opportunities. Many have called for business leaders to adopt more ‘agile’ structures and processes that embrace change, transparency, and employee empowerment. Now a new complementary approach called ‘inner source’ is emerging to help address this. Inner source does not only embrace speed and change; it also lays out techniques for globally distributed agile teams to systematically store and reuse solutions in a shared, organization-wide repository. This paper explores this new management concept – where it came from, what it means – and illustrates its value in large organizations. Building on a number of illustrative cases, we explain how certain business leaders have brought the principles and practices of inner source to their respective organizations.
Human Action Recognition Based on 2D Poses and Skeleton Joints

With the growing capacity of current technologies to store and process data at extremely fast speed, video processing and its diverse applications have been studied and several researches have emerged using videos as objects of study. One of them is human action recognition, which seeks to identify in a given video what actions the people present in it are performing, whether for recreational purposes or for monitoring public places for people’s safety. Being able to extract the necessary characteristics to perform the action classification is a complex task, because, unlike traditional image classification problems, the recognition of human actions requires that the solution works with spatio-temporal characteristics, which represent a pattern of movements performed by the person in both the spatial and temporal aspects along the frames that make up the action. One way to generate information that describes human movement is to identify the skeleton joints and limbs and use them to perform the classification, and the extraction of this data can be done by using 2D pose estimation algorithms, capable of tracking parts of the human body and returning their coordinates. This paper proposes a method for human action recognition that uses skeleton joints and limb information as features and a CNN together with an LSTM network for classification. The method was assessed on a public dataset (Weizmann dataset) and obtained superior recognition rates when compared to other action recognition methods of literature.
Chinese Short Text Entity Linking Model Based on PET

Existing Chinese short text entity link models are less, and the short text is limited and handled by the context missing and the processing noise. There is still a lot of space to improve the accuracy. This paper proposes a Chinese short text entity linking model, encoding the mention and entity representation of Pattern-Exploiting Training (PET), and learning the potential relationship between the entities in the knowledge base, based on contrastive learning. Our Chinese short text model experiments on Duel2.0 dataset and improves the result.
SLIT: Achieving Fast Bandwidth Isolation Across Virtual Machines

Network performance guarantee in the cloud is one of the hottest research topics recently. However, current approaches either lack scalability or fail to achieve high performance. To avoid the hard tradeoff between the scalability and performance, we adopt a hybrid approach to get the best of both worlds. In this paper, we follow the stateless design principle and propose SLIT, which provides fast VM-level network performance isolation and maintains core-stateless characteristics. Specifically, at the end host, we develop a novel network-adaptive labeling mechanism and it computes the correct scheduling priority for each hop. At the switch, packets are scheduled based on their labels and we develop a novel straggler detection algorithm to find the new incoming flow. Our evaluation results show that SLIT can approximate the optimal performance of Weighted Fair Queuing (WFQ) effectively.
RGIS.Online: Geophysical Data Processing and Integrated Analysis Platform in Cloud Mode

Geophysical software can not only promotes the development of geophysics, but is also an indispensable tool for the processing and interpretation of geophysical data. Existing geophysical software solves people's data processing and visualization needs, but there are still some problems, mainly in the lack of intelligence and collaboration in the software and the lack of ease of solving geophysical system problems.Considering these deficiencies, “Cloud-based” is one of the ways to solve the existing problems, besides enabling better remote team collaboration, it has more optimized computing performance, and higher processing efficiency. Based on the analysis of domestic and international online data processing tools platforms, the authors have realized the convenient cloud-based data processing software modules on the desktop with few changes and configurations, and we also realized the rapid integration of functional modules under the unified standard through the unified data computing interface standard and low-code UI development technology. Based on the analysis of the shortcomings of traditional cloud computing such as insufficient real-time and broadband, the authors go further to realize the “cloud + end” model, providing both cloud and local services.
An Incremental Clustering Algorithm Based on CFS

With the development of emerging technologies such as Internet of things, cloud computing and social networks, data is growing and accumulating at an unprecedented rate. It is necessary to effectively analyze the growing data. Clustering, as an important data analysis method, can discover the hidden patterns of data. However, the previous clustering methods can only deal with the static data. To address this challenge, an incremental clustering algorithm (ICFS) based on CFS (clustering by fast search and find of density peaks) is proposed to dynamically partition the growing data. ICFS utilizes the traditional CFS algorithm to divide the initial data to clusters, and then utilizes cluster creation and cluster merging strategies to divide the new data continuously acquired, which ensures the dynamic adjustment of clustering structure. The expensive experiment demonstrate the excellent performance of the proposed method with low time consumption in comparison with the existing incremental clustering algorithms.
Improved Transformer-Based Implicit Latent GAN with Multi-headed Self-attention for Unconditional Text Generation

Generative Adversarial Network (GAN) is widely used in computer vision, such as image generation and other tasks. In recent years, GAN has also been developed in the field of unconditional text generation. In this work, we improve TILGAN for unconditional text generation by refactoring the generator. In short, we use Multi-headed Self-attention to replace the Linear layer and BN layer to endow the generator with better text generation capabilities. Our model consists of three components: a transformer autoencoder, a Multi-headed Self attention based generator and a linear based discriminator. The encoder in transformer autoencoder is used to generate the distribution of real samples, and the decoder is used to decode real or generated sentence vector into text. The loss functions for autoencoder and GAN are cross entropy and KL divergence, respectively. On the MS COCO dataset, the proposed model has achieved a better BLEU score than TILGAN. Our ablation experiments also proved the effectiveness of the proposed generator network for unconditional text generation.
Low-Power Fall Detection System with Location Versatility

In response to the low power consumption and comfortable wearing requirements of wearable fall detection technology, a fall detection algorithm triggered by interrupt was proposed in this paper. The acceleration and angular velocity measured by MPU6050 were utilized to track different stages of the fall, which achieved the purpose of identifying falls. Software and hardware were combined to reduce power consumption. A dataset composed of 1400 simulated falls and 900 ADLs has been used to train the fall detection algorithm, and it was tested in real scenarios. The results show that the algorithm can be applied in different locations, including chest, back, waist, left and right pockets of pants. The best location is the waist, and the accuracy reaches 100%. At the same time, the best sensitivity, best specificity and best accuracy achieve by the overall location are 100%, 100%, and 99.43% respectively. In addition, it is estimated that the maximum service life of the equipment is 50 hours. Compared with the existing fall detection system, this system has the advantages of low power consumption, high accuracy, and position versatility.
Applications Classification of VPN Encryption Tunnel Based on燬AE-2dCNN燤odel

How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protocols make the feature engineering of machine learning extremely difficult.Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification.This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model.SAE can effectively reduce the dimensionality of the data, which not only improves the training efficiency of 2dCNN, but also extracts more precise features and improves accuracy.This paper uses the most common VPN encryption data in the real network to train and test the model.The test results verify the effectiveness of the SAE-2dCNN model.