Reviewerly/exHarmony huggingface dataset:
Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Software piracy is one of the concerns in the IT sector. Pirates leverage the debugger tools to reverse engineer the logic that verifies the license keys or bypass the entire verification process. Anti-debugging techniques are used to defeat piracy using self-healing codes. However, anti-debugging methods can be defeated when the licensing protections are limited to CPU-based implementation by writing custom codes to deactivate the anti-debugging methods. In the paper, we demonstrate how GPU implementation can prevent pirates from deactivating the anti-debugging methods by using the limitations of debugging on GPU. Generally, GPUs do not support debugging directly on the hardware, and therefore all the debugging is limited to CPU-based emulation. Also, a process running on CPU generally does not have any visibility on codes running on GPU, which comes as an added benefit for our work. We provide an implementation on GPU to show the feasibility of our method. As GPUs are getting widespread with the raise in popularity of gaming software, our technique provides a method to protect against piracy. Our method thwarts any attempts to bypass the license verification step thus offering a better anti-piracy mechanism.
A Push-Pull Train for Safety of Autonomous Vehicle Software

A new software engineering framework is provided for assessing and enhancing the safety of artificial-intelligence (AI) based software for controlling autonomous vehicles. The new framework is based on specifying and sharing datasets used for training AI-based systems.
Virtual Machine Placement in Non-Cooperative Cloud Federation-Alliance

Many inter-cloud organizations have been proposed to overcome the current limitations of cloud computing, such as service interruption, lack of interoperability, and degradation of services. One such multi-cloud architecture is the cloud federation, where multiple geographically distributed autonomous service providers voluntarily agree to share resources governed by a well-defined set of rules. Although federation offers numerous benefits for the service providers, sharing resources is supervised using a strict set of protocols offering limited flexibility. Hence, this paper proposes a relaxed resources sharing model for the service providers called cloud federation-alliance. The formation of the alliance is modeled as a non-cooperative game among the service providers. The game's stability and the alliance's performance loss are also studied using price-of-stability (PoS) and price-of-anarchy (PoA), respectively. A modified best-fit placement strategy is focused on reducing the power consumed. To assess the performance of the alliance placement, we compare its performance with random and worst-fit allocation techniques. This work aims to build a stable, sustainable, multi-cloud federation alliance and address this structure's critical issues. Extensive simulation results show stability between 2% and 30% with varying workloads.
Detecting Sensitive Information from Unstructured Text in a Data-Constrained Environment

For an enterprise, it is important to handle sensitive customer data properly because any data breach or violation can lead to hefty penalties. Past work has looked at various techniques for detecting sensitive data in free-flowing text for a given regulation. However, most of them either produce many false positives or are very specific to certain types of data, for example, email, account number, or social security number. Moreover, machine learning-based methods are difficult to use as finding large amounts of labeled data for training a supervised model poses a serious challenge. In this work, we aim to address the issue of sensitive data discovery in a data-constrained environment by utilizing pre-trained models. We compare their effectiveness in the financial and health domains. Further, we improve the performance of pre-trained models by employing morphological-level features and propose a hybrid model architecture. Our experimental results show that pre-trained models in a data-constrained environment can reduce the turnaround time for sensitive data discovery, thus saving money and effort.
Integrated secure distance bounding and hardware-based security: A case study for the insurance claim verification of farmers during COVID-19

<ns3:p>Given the rapidly evolving developments in Fintech, Insurtech, Open Banking, and Mobile Money business models in recent years, the capability for ensuring strong authentication remains the most pressing need for the protection of security and privacy of data in this sector as in many other areas.</ns3:p><ns3:p> The security-integrity of insurance and financial transactions and workflows is vitally dependent on access control mechanisms to deliver strong multi-factor authentication (MFA) with operationally acceptable latency and throughput to support real-time response, particularly as demanded by the increasing online and mobile financial service models. </ns3:p><ns3:p> </ns3:p><ns3:p> The Critical-Chains Project was motivated by the above objectives as underpinned by the overarching commitment to accountability engineering as required by the operational logic. This must be crucially supported by real-time hardware-enabled services comprising authentication (including Distance Bounding and Prover’s Proximal Location Presence Verification), hardware security and cryptography (AUTH-as-a-Service, Hardware-Security-as-a-Service, Cryptography-as-a-Service) as delivered through the Critical-Chains main framework.</ns3:p><ns3:p> </ns3:p><ns3:p> This paper reports on the development and evaluation of the innovative Hardware-enabled authentication and security capabilities of the Critical-Chains framework which is successfully validated in the context of financial services, specifically the insurance claim settlement application domain, and can also be deployed in any other domains where trusted authentication and specific location-time bound prover’s presence verification is required.</ns3:p>
BCMIX: A Blockchain-Based Dynamic Self-Reconfigurable Mixnet

The increasing awareness of privacy preservation has led to a strong focus on mix networks (mixnets) protecting anonymity. As an efficient mixnet, cMix greatly reduces the latency, but brings privacy leakage risks due to the use of presetting mix nodes controlled by service providers. Besides, cMix is susceptible to blocking attacks that paralyze the anonymous service. In this paper, we combine blockchain with cMix, and come up with a BlockChain-based dynamic self-reconfigurable MIXnet (BCMIX) approach for anonymous communication. In BCMIX, we design PoW voting, IP sharding and VRF voting algorithms to select mix nodes from blockchain miners. With these voting algorithms, BCMIX can mitigate blocking attacks and blockchain Sybil attacks. Then we present an additive homomorphic mixnet protocol altered from cMix. Furthermore, we design a transaction-based key-exchange protocol for users and mix nodes to negotiate keys with each other, which resists Man-in-the-Middle attacks that exist in cMix. We also demonstrate the security and anonymity of BCMIX under formal security models. To evaluate BCMIX, we leverage the distribution of mining pools in the real-world to test the system’s performance and ability to resist the mentioned attacks. The results show that with the proper mining difficulty, the probability of an attacker launching collusion attacks and Sybil attacks is negligible. Compared with the existing anonymous systems, BCMIX provides better resilience to known attacks while enabling low-latency anonymous communication without requiring significant bandwidth or storage resources.
An Efficient Identity-Based Encryption with Equality Test in Cloud Computing

Identity-based encryption with equality test (IBEET) provides a feasible way for cloud server partitioning or searching on ciphertexts in the cloud. In that case, the server can judge if two different ciphertexts encrypt the same plaintexts or not. In response to threats posed by quantum computers, lattice-based IBEET schemes have been proposed to make cloud service post-quantum secure. However, those schemes are inefficient and can hardly meet the needs of resource-constrained devices. In this article, an efficient post-quantum IBEET scheme is introduced, which achieves testability by embedding the hash value of plaintext into testing trapdoor instead of encrypting it directly and doubling the size of ciphertext. We also prove that, with the Learning With Errors (LWE) problem assumption, the new scheme is one-way secure against selective identity and chosen ciphertext attacks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OW-sID-CCA</i> ) in quantum secure model. Furthermore, we evaluate the performance of the new construction and demonstrate its efficiency by showing that it only costs about half storage comparing with other lattice-based IBEET schemes. The execution time of encryption and decryption phrases in the new scheme reduce by 50%, while the computational cost in test algorithm keeps the same. Therefore, the new proposed IBEET is much more practical for working in post-quantum cloud computing scenarios.
Snowball: Energy Efficient and Accurate Federated Learning With Coarse-to-Fine Compression Over Heterogeneous Wireless Edge Devices

Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterogeneous edge devices. In this paper, we propose an energy-efficient learning framework, named Snowball, that enables edge devices to incrementally upload their model updates in a coarse-to-fine compression manner. To this end, we first design a fine-grained compression scheme that enables a nearly continuous compression rate. After that, we investigate the Snowball optimization problem to minimize the energy consumption of parameter transmission with learning performance constraints. By leveraging the theoretical insights of the convergence analysis, the optimization problem is transformed into a tractable form. Following that, a water-filling algorithm is designed to solve the problem, where each device is assigned a personalized compression rate according to the status of the locally available resource. Experiments indicate that, compared to state-of-the-art FL algorithms, our learning framework can save five times the required energy of uplink communication to achieve a good global accuracy.
Joint Optimization of Dimension Reduction and Mixed-Precision Quantization for Activation Compression of Neural Networks

Recently, deep convolutional neural networks (CNNs) have achieved eye-catching results in various applications. However, intensive memory access of activations introduces considerable energy consumption, resulting in a great challenge for deploying CNNs on resource-constrained edge devices. Existing research utilizes dimension reduction (DR) and mixed-precision (MP) quantization separately to reduce computational complexity without paying attention to their interaction. Such naïve concatenation of different compression strategies ends up with suboptimal performance. To develop a comprehensive compression framework, we propose an optimization system by jointly considering DR and MP quantization, which is enabled by independent groupwise learnable MP schemes. Group partitioning is guided by a well-designed automatic group partition mechanism that can distinguish compression priorities among channels, and it can deal with the tradeoff between model accuracy and compressibility. Moreover, to preserve model accuracy under low bit-width quantization, we propose a dynamic bit-width searching technique to enable continuous bit-width reduction. Our experimental results show that the proposed system reaches 69.03%/70.73% with average 2.16/2.61 bits per value on Resnet18/MobileNetV2, while introducing only approximately 1% accuracy loss of the uncompressed full-precision models. Compared with individual activation compression schemes, the proposed joint optimization system reduces 55%/9% (−2.62/−0.27 bits) memory access of DR and 55%/63% (−2.60/−4.52 bits) memory access of MP quantization, respectively, on Resnet18/ MobileNetV2 with comparable or even higher accuracy.
Design and Research of Multi-user Distributed Configuration Management Based on Zookeeper

The aerospace system can be divided into multiple types of users according to the division of functions. Different users maintain a set of their own configuration files and store them on different servers. Since there may be logical crossover between services between users, there are situations where different users maintain the same set of configuration files. For the problem of fast synchronization of the same configuration file among multiple servers and simultaneous modification of the same configuration file by multiple users, using the Zookeeper distributed synchronization mechanism and the multi-user dismantling and marking method of configuration files, we propose a multi-user distributed configuration management scheme based on Zookeeper. Store the content of the configuration file in Zookeeper according to the node, use the Zookeeper publish/subscribe mechanism to synchronize the configuration file between the configuration management center and each user application center, realize version management through Bitkeeper technology, and use B/S architecture to realize daily configuration file maintenance. The synchronization performance is tested by synchronizing time, and the test results show that the solution optimizes the configuration file management process and improves the efficiency of configuration file management and maintenance.
Multi-Robot Trajectory Planning With Feasibility Guarantee and Deadlock Resolution: An Obstacle-Dense Environment

This letter presents a multi-robot trajectory planning method which not only guarantees optimization feasibility and but also resolves deadlocks in obstacle-dense environments. The method is proposed via formulating a recursive optimization problem, where a novel safe corridor is generated online to ensure obstacle avoidance in trajectory planning. A dynamic-priority mechanism is combined with the right-hand rule to handle potential deadlocks that are much harder to resolve due to static obstacles. Comparisons with other state-of-the-art results are conducted to validate the improved safety and success rate. Additional hardware experiments are carried out with up to eight nano-quadrotors in various cluttered scenarios.
An Improved Density Peak Clustering Algorithm Based on Gravity Peak

Density peak clustering (DPC) algorithm has been cited and further improved by many researchers since it was put forward. In the aspect of cluster centers discovery, the locations of these points need manually judged by classical decision graph or improved decision graph. However, this way of manual participation in decision-making significantly reduces the efficiency of the algorithm and trades the cost of efficiency for accuracy. Although, relevant scholars have made some improvements on the decision graph to automatically determine the truncation distance or cluster center. To solve this problem, an improved density peak algorithm based on gravitational peak named IDPC-GP is proposed. In this approach, the gravitational dimension is introduced into the data space in order to better grasp the data distribution, and KNN similarity is used for extended clustering. Then, the cluster centers can be quickly found and clustered. Experiments verifies the superiority of the algorithm in comprehensive performance of the IDPC-GP algorithm.
Application of Frequency Domain Filtering in Edge Detection

Image edge extraction is a widely studied problem in the field of digital image processing. When performing edge extraction on noisy images, in order to avoid that the spatial domain filtering cannot preserve the edge information to the greatest extent while suppressing the noise, the frequency domain band-stop filter is used instead of the spatial domain low-pass filter to preprocess the image. In this way, part of the high-frequency information generated by the edge is preserved, and the accuracy of the detection result is improved. Based on standard frequency domain band-stop filter, a special filter is constructed for the edge in a specific direction to further improve the edge detection quality in this direction. In addition, in the edge extraction algorithm, the double-threshold Otsu adaptive threshold detection algorithm is integrated into the Canny algorithm in the step of edge selection. Experiments show that frequency domain filtering can better detect edges and is more convenient to design specific filters for specific problems.
A Method to Measure the Quality of a Cloud Network Across Multiple Resource Pools

The digital transformation of enterprises has brought about the rapid growth of cloud usage and also promoted the reform of network architecture. Cloud network is the development direction and inevitable trend of the information and communication industry in the future. Creating intelligent cloud network is an essential base for digital transformation. The cloud network is also a connection to the digital economy, and its quality directly affects data transmission and user experience. In the face of these challenges, vendors adjust the cloud network architecture in virtualization, intelligence and automation, which improves the quality of the cloud network to a certain extent. However, due to the lack of technology maturity, there are still many problems affecting user experience to be solved. How to evaluate and guarantee the user experience quality of cloud services will directly affect users’ choice of cloud service providers and operators, thus determining the development of new users and retention of existing users of cloud services. This paper proposes a method to measure cloud network quality based on cloud computing service experience, which divides the user experience index of cloud computing service into three quantitative levels: experience KQI index, business KPI index and network KPI index. At the same time, the conditions and model details that should be paid attention to in the concrete implementation of this method are introduced, which provides a new idea for the study of cloud network quality.
A Fast Online Planning Under Partial Observability Using Information Entropy Rewards

Motion planning in an unknown environment is a common challenge because of the existing uncertainties. Representatively, the partially observable Markov decision process (POMDP) is a general mathematical framework for planning in uncertain environments. Recent POMDP solvers generally adopt the sparse reward scheme to solve the planning under uncertainty problem. Subsequently, the robot's exploration may be hindered without immediate rewards, resulting in excessively long planning time. In this article, a POMDP method, information entropy determinized sparse partially observation tree (IE-DESPOT), is proposed to explore a high-quality solution and efficient planning in unknown environments. First, a novel sample method integrating state distribution and Gaussian distribution is proposed to optimize the quality of the sampled states. Then, an information entropy based on sampled states is established for real-time reward calculation, resulting in the improvement of robot exploration efficiency. Moreover, the near-optimality and convergence of the proposed algorithm are analyzed. As a result, compared with general-purpose POMDP solvers, the proposed algorithm exhibits fast convergence to a near-optimal policy in many examples of interest. Furthermore, the IE-DESPOT's performance is verified in real mobile robot experiments.
A New Multi-instance Learning Algorithm Integrated with TF-IDF for Entity Relation Extraction in Electronic Medical Records

Electronic medical record (EMR) is the core data which generated in the process of clinical treatment. By extracting the record text’s entity relationship, we can catch a large amount of information closely related to the patient’s condition. Deep learning is widely used in the field of entity relationship extraction in EMR. Among them, seq-to-seq is the most popular network structure for text sequence, but it can’t distinguish the influence between different words. Therefore, multi-instance learning which are improved by attention mechanism can adjust the weight of words is proposed and achieved outstanding results. However, the reason for the creation of the attention mechanism parameter is always puzzling: it lacks interpretability and it is difficult for us to understand the origin of the numbers. Term frequency–inverse document frequency (TF-IDF) is an algorithm to calculate the importance of words according to their occurrence frequency, so we think integrating it into the construction of neural network may increase model’s interpretability. In this paper, we propose a new multi-instance learning algorithm, which integrates the TF-IDF into the construction of deep learning model. Compared with the network without TF-IDF, our model achieves an improvement of nearly 2% and the convergence rate is reduced by 5% on average.
Research on Method of On-Orbit Evolution for Aerospace Control Software

This paper summarizes the development of software evolution technology. Based on the fundamental concepts of software evolution, we deeply analyzed the motivation and evolution process of the on-orbit evolution for aerospace control software. Five key technologies in on-orbit evolution of aerospace control software are emphasized, including architecture modeling, unified description of adaptive elements, construction of on-orbit evolution knowledge base, on-orbit evolution requirement perception and event identification mechanism, and policy-based adaptive decision-making mechanism. In conclusion, we point out the challenges and development trends of the on-orbit evolution of aerospace control software.
Anomaly Interpretation Method Based on Data Mining for Remote Sensing Satellite Payload

Because of the rapid development of earth observation technology and artificial intelligence technology, high-resolution optical imaging satellites have emerged continuously, and the era of remote sensing big data has arrived. Due to the limitations of huge image size, sparse target distribution, rich variation of target time domain and insufficient labeling training samples, the current automatic detection of abnormal targets in satellite remote sensing images is inefficient and easy to generate false alarms. This paper proposes a data mining-based joint interpretation method for remote sensing satellite load data, which realizes automatic and fast accurate interpretation of remote sensing image abnormal targets and significantly improves the efficiency of camera data quality assessment. It is the first time to realize a major technical leap from visual interpretation to machine full adaptive interpretation of remote sensing image data, which can provide reference for subsequent adaptive interpretation of remote sensing satellite images.