Reviewerly/exHarmony huggingface dataset:
Anonymous Feature Processing for Efficient Onboard Navigation

Recent developments using “anonymous features” permit feature-based navigation techniques, such as lidar retroreflector tracking or vision-based terrain relative navigation, to be performed without the need of a step that explicitly identifies the observed features, thereby eliminating sensitivity to misidentification errors and reducing the reliance upon potentially computationally burdensome identification techniques. This paper seeks to further advance this “anonymous” approach to feature-based navigation by preparing a formulation tailored for flight applications, i.e. focusing upon computational efficiency and numerical stability, to further assess and improve its applicability to real flight applications. In particular, this work explores some of the key details necessary to implement the new anonymous feature processing techniques in square-root and UDU factorized filter formulations to widen its applicability to systems constructed around factorized covariance representations.
Skew Handling Technique for Scheduling Huge Data Mapper with High End Reducers in MapReduce Programming Model

In recent years, to handle huge amount of data, MapReduce Programming Model has been used in parallel which ensures distributed processing. However, data skew is an important issue in MapReduce model which occur when the data are unevenly distributed between the mapper and the reducer which increase the waiting time of the reducer with small sized partition. Several Skew Handling techniques were proposed which tune this process by repartitioning the data into equal sized chunks and distribute them to the reducers. Repartitioning will be suitable for only independent datasets and will degrade the accuracy in dependent datasets. Time Series datasets such as Online Sales, weather and sensor data are dependent data where the prediction of future depends on the historical data. In such case, repartitioning will reduce the prediction accuracy and makes the decision process tedious. A skew handling technique is proposed, where the mapper with huge sized data will be assigned with high-end reducers dynamically.
SGXPy: Protecting Integrity of Python Applications with Intel SGX

Python is the programming language of choice for many data scientists, and thus widely used in cloud computing platforms. Untrusted cloud environments have imposed challenges to the security of Python applications. Intel SGX (Intel Software Guard eXtensions) provides an encrypted enclave for securing applications, and a library OS technology can be adopted to run legacy applications inside these enclaves. However, this technology has some limitations: (i) It is difficult to ensure the integrity of Python applications as a result of the complex dependencies among modules. (ii) Python applications often spawn new processes, and file access permissions need to be handled separately in the parent-child process. To address these limitations, we present SGXPy (SGX Python), an integrity preserving tool for Python applications. The design of SGXPy makes it possible to obtain dependencies of applications and assign file access permissions among processes automatically: (i) During the build stage, SGXPy constructs dependency manifests of Python applications based on the ptrace mechanism. (ii) To enhance access control among processes, SGXPy utilizes process introspection to cascading manifests for each process. With the proposed framework, sophisticated Python applications such as NumPy and a web server can now run unmodified with the library OS. We present a series of experiments to evaluate performance overheads of Python applications in SGX. Our evaluation of NumPy submodules shows SGXPy can pass 97.60% of unit testing, even with the isolated environment and limited memory of SGX.
Weighted Voting Based Ensemble Classification with Hyper-parameter Optimization

Ensemble learning is one of the most popular research fields in machine learning and pattern recognition due to its contribution to the performance of a classification system. Voting based ensemble methods employs multiple learning algorithms and make the classification model more robust. Weighted voting based ensemble methods provide more flexible and fine-grained way to predict actual output classes compared to the unweighted (majority) voting based ensemble methods. However, the main drawback of weighted methods is to decide which values to be used. In this study, two hyper-parameter optimization strategies, namely Random Search and TPE, are used for optimizing weights of voting based ensemble classification. Experimental results based on seven machine learning datasets demonstrate the effectiveness of using hyper-parameter optimization for the purpose of finding optimal values for weighted voting based ensemble classification.
Topic Sensitive User Clustering Using Sentiment Score and Similarity Measures

Social media data (SMD) is driven by statistical and analytical technologies to obtain information for various decisions. SMD is vast and evolutionary in nature which makes traditional data warehouses ill suited. The research aims to propose and implement novel framework that analyze tweets data from online social networking site (OSN; i.e., Twitter). The authors fetch streaming tweets from Twitter API using Apache Flume to detect clusters of users having similar sentiment. Proposed approach utilizes scalable and fault tolerant system (i.e., Hadoop) that typically harness HDFS for data storage and map-reduce paradigm for data processing. Apache Hive is used to work on top of Hadoop for querying data. The experiments are performed to test the scalability of proposed framework by examining various sizes of data. The authors' goal is to handle big social data effectively using cost-effective tools for fetching as well as querying unstructured data and algorithms for analysing scalable, uninterrupted data streams with finite memory and resources.
Generalized Meet in the Middle Cryptanalysis of Block Ciphers With an Automated Search Algorithm

Meet in the middle (MITM) attack is one of the most important and applicable methods for cryptanalysis of block ciphers. In this paper, a more generalized method for MITM attack is considered. For this purpose, a notion, namely cut-set, is utilized by which several numbers of MITM attacks can be performed. However, manual investigation on these cases is time-consuming and sometimes not error-free. Therefore, a new search algorithm is also provided to obtain proper attacks in a timely manner. For examination, this new search algorithm, which could make an automated attack along with some certain ideas, is applied on HIGHT, Piccolo-128, CRAFT and AES-128 block ciphers. The least time complexities which are obtained by generalized MITM attack on full HIGHT, Piccolo-128, CRAFT and AES-128 are 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">125.08</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">126.78</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">123.25</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">125.53</sup> , respectively. The results on full-round CRAFT are, to the best of our knowledge, the first cryptanalysis results in the single-key model except the designers' investigations. In addition, the results show some improvements for complexities of all the attacks, especially on HIGHT.
Robust boosting via self-sampling

Boosting is a widely used ensemble meta-algorithm due to its excellent performance in combining weak learners into a strong learner. However, vanilla boosting methods are proved to be sensitive to noise due to the lack of restriction to the always misclassified samples during iterations. In this work, we present a new aspect to overcome the sensitiveness problem by combining the self-sampling learning framework, which can pursue reliable samples and smooth the training process based on the sample reliability measurement designed for the boosting procedure. Experimental results on the synthetic data and several real-world datasets show that the self-sampling regime can automatically optimize an appropriate training subset in different noise environments, and robust boosting algorithms we proposed outperform the state-of-the-art methods.
A Modified Least Significant Bit Randomized Embedding Method based on Image Partitioning and Columnar Transposition with Encryption

While traditional embedding methods which are usually based on LSBs provide security to hidden messages in image-based Steganography, steganalyses have shown these methods to be vulnerable to detection and extraction, thus compromising the security of embedded messages. In this study, a new embedding method based on LSB is introduced to address this vulnerability. By using a one-time pad encryption method, the security of secret messages before embedding is ensured. A partitioning method based on columnar transposition and RGB color plane scattering technique is employed to improve scattering and ensure randomization during embedding. The experimental result shows an excellent outcome of the Peak-signal-to-noise Ratio (PSNR), Mean square Error (MSE), and Structural Similarity (SSIM) index. The PSNR value of the 5 test images results ranges from 53.2122 to 60.8716 all are higher than the acceptable value of PSNR, which is 30db.The average MSE result is0.2766 for all color planes, which also provide an outstanding result since the lower the MSE means the accumulative squared error between the stego image and the original one is lower. The average SSIM obtained in the testing is 0.9792for the 5 test images, which is very close to 1. The result supports the claim that the resulting stego images have low distortion which means that the cover image is almost the same with the stego image and that the modified LSB embedding algorithm provides excellent image quality with high imperceptibility and provides added security layers.
Marine Biometric Recognition Algorithm Based on YOLOv3-GAN Network

With the rise of the marine ranching field, the object recognition applications on underwater catching robots have become more and more popular. However, due to the influence of uneven underwater light, the underwater images are easily encountered with problems such as color distortion and underexposure, which seriously affects the accuracy of underwater object recognition. In this work, we propose a marine biometric recognition algorithm based on YOLOv3-GAN network, which jointly optimizes the training of image enhancement loss (LossGAN) and classification and location loss (LossYOLO) in the network, and it is different from the traditional underwater object recognition approaches which usually consider image enhancement and object detection separately. Consequently, our proposed algorithm is more powerful in marine biological identification. Moreover, the anchor box is further modified by k-means method to cluster the object box size in the network detection part, which makes the anchor box more in line with the object size. The experimental results demonstrate that the mean Average Precision (mAP) and the Recall of the YOLOv3-GAN network are above 6.4% and 4.8% higher than that of the YOLOv3 network. In addition, the image enhancement part in the YOLOv3-GAN network can provide high quality images which benefit for other applications in the marine surveillance field.
Implementation of the Concept of “Smart Laboratory” in the Framework of Digitization of the University

The structure of the “smart laboratory” as a complex of human interaction, laboratory equipment and information-measuring system using the Internet of things and cloud computing is considered.
Nonlinear Systems for Unconventional Computing

The search for new computational machines beyond the traditional von Neumann architecture has given rise to a modern area of nonlinear science—development of unconventional computing—requiring the efforts of mathematicians, physicists and engineers. Many analogue physical systems including nonlinear oscillator networks, lasers, and condensates were proposed and realised to address hard computational problems from various areas of social and physical sciences and technology. The analogue systems emulate spin Hamiltonians with continuous or discrete degrees of freedom to which actual optimisation problems can be mapped. Understanding of the underlying physical process by which the system finds the ground state often leads to new classes of system-inspired or quantum-inspired algorithms for hard optimisation. Together physical platforms and related algorithms can be combined to form a hybrid architecture that may one day compete with conventional computing. In this chapter, we review some of the systems and physically-inspired algorithms that show such promise.
Space mission design ontology: extraction of domain-specific entities and concepts similarity analysis

Expert Systems, computer programs able to capture human expertise and mimic experts' reasoning, can support the design of future space missions by assimilating and facilitating access to accumulated knowledge. To organise these data, the virtual assistant needs to understand the concepts characterising space systems engineering. In other words, it needs an ontology of space systems. Unfortunately, there is currently no official European space systems ontology. Developing an ontology is a lengthy and tedious process, involving several human domain experts, and therefore prone to human error and subjectivity. Could the foundations of an ontology be instead semi-automatically extracted from unstructured data related to space systems engineering? This paper presents an implementation of the first layers of the Ontology Learning Layer Cake, an approach to semi-automatically generate an ontology. Candidate entities and synonyms are extracted from three corpora: a set of 56 feasibility reports provided by the European Space Agency, 40 books on space mission design publicly available and a collection of 273 Wikipedia pages. Lexica of relevant space systems entities are semi-automatically generated based on three different methods: a frequency analysis, a term frequency-inverse document frequency analysis, and a Weirdness Index filtering. The frequency-based lexicon of the combined corpora is then fed to a word embedding method, word2vec, to learn the context of each entity. With a cosine similarity analysis, concepts with similar contexts are matched.
Public Data Integrity Checking for Cloud Storage with Privacy Preserving

Cloud storage service makes people easily to maintain and manage amounts of data with lower cost. In order to ensure the cloud server honestly keeps the client's data, some remote data integrity checking (RDIC) schemes have been presented. However, in many schemes the original data is not protected at all so that public verifier is able to obtain the data through the integrity checking. To address this issue, we propose an efficient RDIC scheme with data privacy-preserving. In our scheme, we utilize the data masking technique to randomize the original data before generating the final proof which makes the verifier can not get any knowledge about the stored data. Furthermore, experiment result exhibits that our scheme is very efficient and feasible for real applications.
Mining Spatial Co-location Patterns by the Fuzzy Technology

The main purpose of co-location pattern mining is to mine the set of spatial features whose instances are frequently located together in space. Because a single distance threshold is chosen in the previous methods when generating the neighbourhood relationships, some interesting spatial colocation patterns can't be extracted. In addition, previous methods don't take the neighborhood degree into consideration and they depend upon the PI (participation index) to measure the prevalence of the co-locations, which these methods are very sensitive to PI and also lead to the absence of co-location patterns. In order to overcome these limitations of traditional co-location pattern mining, considering that the neighbor relationship is a fuzzy concept, this paper introduces the fuzzy theory into co-location pattern mining, a new fuzzy spatial neighborhood relationship measurement between instances and a reasonable feature proximity measurement between spatial features are proposed. Then, a novel algorithm based on fuzzy C-medoids clustering algorithm, FCB, is proposed, extensive experiments on synthetic and real-world data sets prove the practicability and efficiency of the proposed mining algorithm, it also proves that the algorithm has low sensitivity to thresholds and has high robustness.
A New MRF-Based Lossy Compression for Encrypted Binary Images

Although there exist many researches on the compression of original non-encrypted binary images, few approaches focus on the compression of encrypted binary images.As binary images like contract, signature, halftone images are still used widely in practice, how to compress efficiently encrypted binary images in a lossy way deserves further exploration.To this end, this paper develops a lossy compression scheme for encrypted binary images by exploiting the Markov random field (MRF) model.Considering that the third-party in scenarios of cloud or distributed computing cannot access to the encryption key, we develop the concatenated down-sampling and LDPC-based encoding to perform the compression, in which four different down-sampling methods are designed to facilitate improving the quality of reconstructed image.In reconstruction, we first formulate the lossy reconstruction from the encrypted and compressed binary image as an optimization problem, and then build a joint factor graph involving the LDPC-decoding, decryption, and MRF to solve this optimization problem, in which the MRF is exploited to well infer pixels discarded in the down-sampling process.By adapting the sum-product algorithm (SPA) to the constructed joint factor graph for lossy reconstruction (JFG-LR) and running the adapted SPA on the JFG-LR, we thus recover the original binary image in a lossy way.By integrating the stream-cipher-based encryption, the down-sampling and LDPC-based compression, and the JFG-LR-involved reconstruction, we thus propose a new lossy compression scheme for encrypted binary images.Experimental results show that the proposed scheme achieves desirable compression efficiency, which is comparable to or even better than that of the JBIG2 with the original unencrypted binary image as input.
A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space

We prove that, for any finite set X in Euclidean space of any dimension and for any fixed $$\varepsilon >0$$, there exists a polynomial-cardinality set of points in this space which can be constructed in polynomial time and which contains $$(1+\varepsilon )$$-approximations of all the points of space in terms of the distances to every element of X. The proved statement allows to approximate a lot of clustering problems which can be reduced to finding optimal cluster centers in high-dimensional space. In fact, we construct a polynomial-time approximation-preserving reduction of such problems to their discrete versions, in which the desired centers must be selected from a given finite set.
Implementing an Asset Trading System Based on Blockchain and Game Theory

With the development of the Internet and e-commerce, an asset trading system has become more and more complicated. There are security obstacles such as the untrustworthy third-party service in the centralized management system of asset transactions. Thanks to the advanced features of openness, immutability and decentralization of blockchain technology. It is expected to solve the trust problem between buyers and sellers. Based on the Hyperledger Fabric platform, we first present an asset trading system that implements smart contracts for asset release and asset trading. Moreover, we evaluate the performance of the system using Hyperledger Caliper and test functions of system by Postman. To address issues of matching buyers and sellers, a matching model based on game theory is proposed. In this process, the user's reputation value and buyer's bid are used to determine the seller's matchable range, which aims to find the optimal equilibrium solution for buying and selling profit. Our experimental result proves that this system's average latency is less than 0.27S and the event response success rate is 1 basically, when the maximum throughput of the system is not exceeded.
How QBF Expansion Makes Strategy Extraction Hard

In this paper we show that the QBF proof checking format QRAT (Quantified Resolution Asymmetric Tautologies) by Heule, Biere and Seidl cannot have polynomial time strategy extraction unless P=PSPACE. In our proof, the crucial property that makes strategy extraction PSPACE-hard for this proof format is universal expansion, even expansion on a single variable. While expansion reasoning used in other QBF calculi can admit polynomial time strategy extraction, we find this is conditional on a property studied in proof complexity theory. We show that strategy extraction on expansion based systems can only happen when the underlying propositional calculus has the property of feasible interpolation.
Design of low power NFSR for RFID system with irregular clock pulse

This Paper describes the Low Power Non linear Feedback Shift Register (NFSR) for Radio Frequency Identification (RFID) System. RFID systems are widely used in many places for product tracking, monitoring the objects and more. The RFID tag stores its distinctive Electronic Product Code (EPC) with related product information within the tag's memory and encrypts this information before its send to the reader. The RFID tags encrypt the data using Pseudo random numbers. Mostly Linear Feedback Shift Register (LFSR) are used to generating pseudo-random sequences which is less security. Nonlinear Feedback Shift Registers (NFSR) is getting to be more famous in recent years because of the insecurity of LFSR. The output sequence of the LFSR is a linear function of the previous stage, it is easily predictable by intruders. Because of this, NFSR is used in many security systems for generating Pseudo-random numbers. The output sequence of NFSR is irrelevant to the previous stage. In this paper, we proposed a new architecture for NFSR, in this model NFSR is controlled by an LFSR with irregular clocking to generate maximal length sequences. The proposed model is designed using 16 nm CMOS technology and operated in the sub-threshold region. The examination is done using Tanner EDA-Industry Standard design environment. The simulation results demonstrate that the irregular clocking architecture reduces the total power consumption by 30 percent.