Reviewerly/exHarmony huggingface dataset:
A Survey of Encryption Standard and Potential Impact Due to Quantum Computing

The Intelligence Community, DOD, financial, and commercial entities rely on encryption to protect its most sensitive information. Public Key Encryption (PKE) method is used to encrypt information using the principle that it is difficult to factor a very large number into its primes unless one has access to a private key that was used to encrypt the information. Using this technology, decrypting a message using a 128-bit long key would take a classical supercomputer about 1 billion billion years to decrypt. However, in the mid-1990s, mathematicians Shor and Grover developed algorithms that have proved to be efficient in factoring large numbers into primes using a quantum computer. The development of quantum hardware and algorithms therefore threaten encryption protection. In 2019, Google announced that it established quantum supremacy, meaning that its quantum computer solved a problem that no classical computer could. The authors of this paper conducted a study to describe the latest encryption research and development, methods used, and the potential vulnerability due to quantum computing. The authors further established the timeframes in which pre-quantum encryption method will become vulnerable under three scenarios. All the research for this study was derived from unclassified open source materials.
Selective, Structural, Subtle: Trilinear Spatial-Awareness for Few-Shot Fine-Grained Visual Recognition

Few-shot learning aims to recognize the novel categories from a few examples. However, most of the existing approaches usually focus on general image classification and fail to handle subtle differences between images. To alleviate this issue, we propose a trilinear spatial-awareness network for few-shot-grained visual recognition, called S3Net, which is composed of a spatial selection module, structural pyramid descriptor, and subtle difference mining module. Specifically, we first build the global relation to strengthen the features by spatial selection module. The structural pyramid descriptor then constructs a multi-scale representation for enhancing the rich contextual information by exploiting different receptive fields in the same feature layer. Furthermore, a similarity loss based on local descriptors and a global classification loss is design to help the network learn discrimination capability by handling subtle differences in confused or near-duplicated samples. Extensive experiments on 4 few-shot fine-grained benchmarks demonstrate that our proposed approach is effective and outperforms state-of-the-art models by large margins.
Artificial Intelligence and Emerging Technologies in Travel

Over the past four decades Operations Research (OR) has played a key role in solving complex problems in airline planning and operations. More recently, Artificial Intelligence (AI) has seen rapid adoption in travel that covers everything from robotic process automation, cognitive insight to cognitive engagement. This chapter discusses the role of AI in travel, its potential to address travel complexity, solve a range of problems, and create new value propositions. This chapter also covers the role of Big Data and block chain technology in travel. Big Data and blockchain are explained with a range of use cases.
Transfer Learning with Bayesian Filtering for Object Tracking Under Varying Conditions

We propose an algorithm that integrates Bayesian filtering with transfer learning to track a moving object under unknown time-varying environmental conditions. In order to account for measurement noise intensity variations in the primary source, we use multiple learning sources with labeled measurements. For each source, the measurement likelihood is modeled using Gaussian mixtures whose parameters are learned from conjugate priors. Weighted basis combinations of the multiple learned information are then used to model the measurement likelihood of the primary source; the basis weights are learned using a Dirichlet distribution prior. The improved tracking performance of the proposed algorithm is demonstrated for both low and high noise scenarios.
Non-Random Weight Initialisation in Deep Convolutional Networks Applied to Safety Critical Artificial Intelligence

This paper presents a non-random weight initialisation scheme for convolutional neural network layers. It builds upon previous work that was limited to perceptron layers, but in that work repeatable determinism was achieved with equality in categorisation accuracy between the established random scheme and a linear ramp non-random scheme. This work however, is in Convolutional layers and are the layers that have been responsible for better than human performance in image recognition. The previous perceptron work found that number range was more important rather than the gradient. However, that was due to the fully connected nature of dense layers. Although, in convolutional layers by contrast, there is an order direction implied, and the weights relate to filters rather than image pixel positions, so the weight initialisation is more complex. However, the paper demonstrates a better performance, over the currently established random schemes with convolutional layers. The proposed method also induces earlier learning through the use of striped forms, and as such has less unlearning of the traditionally speckled random forms. That proposed scheme also provides a higher performing accuracy in a single learning session, with improvements of: 3.35% un-shuffled, 2.813% shuffled in the first epoch and 0.521% over the 5 epochs of the model. Of which the first epoch is more relevant as it is the epoch after initialisation. Also the proposed method is repeatable and deterministic, which is also a desirable quality for safety critical applications within image classification. The proposed method is also robust to He initialisation values too, and scored 97.55% accuracy compared to 96.929% accuracy with the Glorot/ Xavier in the traditional random forms, of which the benchmark model was originally optimised with.
Improved Dark Channel Prior Algorithm Based on Wavelet Decomposition for Haze Removal in Dynamic Recognition

Environmental perception and precise positioning of area targets are key technologies in dynamic recognition. However, the perceptual information that is dynamically acquired in some haze weather cannot provide an accurate basis for decision-making and dynamic planning. In addition, under some outdoor condition, such as in the bright light, the quality of the perception information obtained by the system is usually low, and the robustness of the area target is relatively poor. In this paper, the implementation strategy of haze removal for images is comprehensively studied, and an improved dark channel prior algorithm is accordingly proposed by introducing the wavelet decomposition. The related experimental research is further carried out in the MATLAB development environment. As a result, haze can be effectively removed, and the real time of the improved dark channel prior algorithm can be largely enhanced.
Parallel compression for large collections of genomes

Summary With the development of genome sequencing technology, the cost of genome sequencing is continuously reducing, while the efficiency is increasing. Therefore, the amount of genomic data has been increasing exponentially, making the transmission and storage of genomic data an enormous challenge. Although many excellent genome compression algorithms have been proposed, an efficient compression algorithm for large collections of FASTA genomes, especially can be used in the distributed system of cloud computing, is still lacking. This article proposes two optimization schemes based on HRCM compression method. One is MtHRCM adopting multi‐thread parallel technology. The other is HadoopHRCM adopting distributed computing parallel technology. Experiments show that the schemes recognizably improve the compression speed of HRCM. Moreover, BSC algorithm instead of PPMD algorithm is used in the new schemes, the compression ratio is improved by 20% compared with HRCM. In addition, our proposed methods also perform well in robustness and scalability. The Java source codes of MtHRCM and HadoopHRCM can be freely downloaded from https://github.com/haicy/MtHRCM and https://github.com/haicy/HadoopHRCM .
Unveiling a Definition of the Blockchain Technology and Plotting the Landscape of the ICO Marketplace using the Text Mining Approach

Bitcoins and other cryptocurrencies are still generating opposing views ranging from being a fraud to being the source of a new economy. Those differences are stemming from confusion, bias and vested interest. This paper is an attempt to generate an unbiased, unified market definition to fill a literature gap and to provide guidance using a bottom-up approach to enable a better understanding of the cryptocurrency’s potentials using the market own lens.
Memory Layout Extraction and Verification Method for Reliable Physical Memory Acquisition

Physical memory acquisition is a prerequisite when performing memory forensics, referring to a set of techniques for acquiring and analyzing traces associated with user activity information, malware analysis, cyber incident response, and similar areas when the traces remain in the physical RAM. However, certain types of malware have applied anti-memory forensics techniques to evade memory analysis strategies or to make the acquisition process impossible. To disturb the acquisition process of physical memory, an attacker hooks the kernel API, which returns a map of the physical memory spaces, and modifies the return value of the API, specifically that typically used by memory acquisition tools. Moreover, an attacker modifies the kernel object referenced by the kernel API. This causes the system to crash during the memory acquisition process or causes the memory acquisition tools to incorrectly proceed with the acquisition. Even with a modification of one byte, called a one-byte modification attack, some tools fail to acquire memory. Therefore, specialized countermeasure techniques are needed for these anti-memory forensics techniques. In this paper, we propose a memory layout acquisition method which is robust to kernel API hooking and the one-byte modification attack on NumberOfRuns, the kernel object used to construct the memory layout in Windows. The proposed acquisition method directly accesses the memory, extracts the byte array, and parses it in the form of a memory layout. When we access the memory, we extract the _PHYSICAL_MEMORY_DESCRIPTOR structure, which is the basis of the memory layout without using the existing memory layout acquisition API. Furthermore, we propose a verification method that selects a reliable memory layout. We realize the verification method by comparing NumberOfRuns and the memory layout acquired via the kernel API, the registry, and the proposed method. The proposed verification method guarantees the reliability of the memory layout and helps secure memory image acquisition through a comparative verification with existing memory layout acquisition methods. We also conduct experiments to prove that the proposed method is resistant to anti-memory forensics techniques, confirming that there are no significant differences in time compared to the existing tools.
On CSS Unsatisfiability Problem in the Presense of DTDs

Cascading Style Sheets (CSS) is a popular language for describing the styles of XML documents as well as HTML documents. To resolve conflicts among CSS rules, CSS has a mechanism called specificity. For a DTD D and a CSS code R, due to specificity R may contain “unsatisfiable” rules under D, e.g., rules that are not applied to any element of any document valid for D. In this paper, we consider the problem of detecting unsatisfiable CSS rules under DTDs. We focus on CSS fragments in which descendant, child, adjacent sibling, and general sibling combinators are allowed. We show that the problem is coNP-hard in most cases, even if only one of the four combinators is allowed and under very restricted DTDs. We also show that the problem is in coNP or PSPACE depending on restrictions on DTDs and CSS. Finally, we present four conditions under which the problem can be solved in polynomial time.
Framework-Agnostic Optimization of Repeated Skewed Joins at Massive Scale

The join operation is a common but expensive operation in many data analysis processes. Despite lots of work on optimizing distributed joins in data-parallel processing platforms, there is still room to improve its performance, especially in repeated join operations and skewed data. Repeated join is an operation used in many services, where the same join operation is repeated multiple times by different users. Moreover, due to skewed data, joins may create uneven load distribution over the workers that harms the performance. Our goal is to improve the performance of repeated distributed joins on massive data with respect to execution time and network traffic. To this end, we present SMBJOIN, a framework-agnostic join operation that reduces execution time and network traffic by 50% and 17%, respectively, compared to the distributed hash-join operation after five joins. We also present SMBJOINSKEWED, a variant of SMBJOIN, which is particularly robust for different degrees of skewness, and show that it outperforms the distributed hash-join operation. For example, for a medium degree of skewness, SMBJOINSKEWED reduces execution time and network traffic by 34% and 5%, respectively, compared to the distributed hash-join operation after five joins.
Correcting the Defects of the Default Tone Mapping Operators Implemented in the JPEGXT HDR Image Compression Standard

This short article reports two imperfections or implementation bugs of the default tone mapping operators (TMOs) of the JPEGXT HDR image compression standard. In JPEGXT, profiles A and C use the same default TMO, while profile B uses a different default TMO. The first imperfection is with the default TMO of profile A and profile C. Specifically, the default TMO leads to a reduced dynamic range of the tone-mapped LDR images. Very often, the reduction can be severe. The mechanism of causing the dynamic range reduction stems from an implementation bug. To resolve the issue, the correction of adding the missed final normalization step to linearly stretch the dynamic range needs to be made. The second imperfection is the highlights clipping in the LDR images from the default TMO of profile B. The clipping could lead to unusable LDR images. Our suggestion is to unify the default TMO within JPEGXT, i.e., to use the corrected TMO of profiles A and C for profile B.
Systolic Processor Core for Finite-Field Multiplication and Squaring in Cryptographic Processors of IoT Edge Devices

Internet of Things (IoT) edge devices’ security is one of the main barriers to use IoT applications on a large scale. Securing these devices is mainly based on using primitive cryptographic algorithms. The hardware implementation of the cryptographic algorithms should be managed to be suitable for these resource-constrained devices. Finite-field arithmetic operations are at the heart of the cryptographic algorithms, and their efficient implementation directly affects the whole performance of the cryptographic algorithm. Filed multiplication operation is the core of the most finite-field arithmetic operations, such as squaring, inversion, and division. Therefore, this article mainly concentrates on efficiently implementing a resource-constrained unified processor core that simultaneously performs multiplication and squaring operations to reduce hardware resources. The offered processor core has a digit-serial systolic structure providing the designer with flexibility to manage the area, delay, and consumed energy to be suitable for IoT edge devices. ASIC results of the developed design and the reported efficient ones indicate that the proposed structure has a meaningful saving in the area and consumed energy for all embedded word-sizes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula> . The area achieves a reduction varying from 44.7% to 97.46% at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v=8$ </tex-math></inline-formula> , 35.7% to 95.4% at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v=16$ </tex-math></inline-formula> , and 57.1% to 95.6% at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v=32$ </tex-math></inline-formula> . Also, the energy realizes a reduction ranging from 24.0% to 97.7% at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v=8$ </tex-math></inline-formula> , 7.6% to 97.2% at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v=16$ </tex-math></inline-formula> , and 25.4% to 98.1% at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$v=32$ </tex-math></inline-formula> . That makes it more suitable for embedded and IoT applications that impose more restrictions on the area and consumed energy.
Robotic Motion Planning Using Probabilistic Collision Hidden Markov Model (PCHMM) for Contactless Drug Delivery

Due to situations like COVID-19 pandemic, it is preferred that human contact should be avoided in the healthcare center to control the spread of the disease. For this situation, we simulated a scenario of the hospital floor, where a robot delivers drugs to the patient, so that human contact is minimized. We considered real-time motion planning in a dynamic situation so that the robot can reach its destination by tackling all the static and dynamic obstacles. We have used a combination of probabilistic collision (PC) and Hidden Markov Model (HMM) to develop the Probabilistic Collision Hidden Markov Model (PCHMM), due to which we achieved the safest motion planning (a feature of PC) and optimal result in limited space (a feature of HMM) together. We simulated a Hospital with 8 beds to be served by a drug delivery robot, considering static 9 walls and 5 dynamically moving obstacles. We found the PCHMM can support an average motion speed of 1.215 m/sec which is roughly 69 \% of the max speed for the robot. We expect user PCHMM can be a very useful method not only for hospital robots but for any mobile robot.
Analysis of Individual Purchase Decision Based on Bayesian Personalized Ranking

In recent years, text-based transaction data has been highly valued by merchants. It not only reflects the degree of consumer preference on goods, but also reflects the consumer's satisfied and potential needs. In this paper, we used sentiment analysis, Naive Bayes Algorithm and other methods to build a personalized recommendation model based on texts and time. The paper will conduct model assumptions and tests from the two main bodies: Users and items.
Burstable resource compatible general resource scheduling for stochastic demands in heterogeneous cloudsU+2605;

A new kind of virtual machine instance named burstable instance is recently introduced in leading cloud providers, which can help cloud user handle fluctuating demands with reduced cost. There is an impending need to incorporate burst instances in common scheduling. Due to the time varying provisioning of burstable resources, the stochastic demand oriented scheduling can be a better choice where the demands are represented using distribution functions instead of mean values. However, little has been done to the scheduling of the burstable resources with stochastic demands. To address the problem, we present the first stochastic revenue model for the burstable resources, and propose a bounded delta-solution based differential evolution algorithm, which exploits inherent information of the problem to accelerate the searching of optimal solution. Experiments using simulated and real-world data indicate that the algorithm outperforms the existing representative algorithms and can increase the revenue significantly.
Sequential Recommendation with Temporal Context via Convolutional Sequence Embedding

Human behavior consists to a large extent of repeated temporal patterns. What a person is going to do next often depends on what time it is as well as what things she/he has interacted with in recent past. Often times, in many fields it is required to predict what actions or items a user is most likely to engage with in near future. To solve this problem, there are many deep learning approaches such as RNN, LSTM [2], [3] but they are usually computationally more demanding as well as difficult to train. Other sequential recommendation methodologies like CASER [1] does not incorporate temporal context. To overcome these limitations, this paper has proposed a convolutional neural network based deep learning approach to solve this problem. The experiments on public dataset demonstrated that our model which takes in account the temporal context performs better than CASER [1] without temporal context. Also, this research work has demonstrated the application of the proposed model as an on-device solution to enable voice assistants or other host applications to proactively provide recommendations and suggestions based on users' past activity, routine and current timestamp.
Transfer Learning Based Credit Scoring

Financial institutions often encounter cold-start problems when building credit scoring models. This paper applies transfer learning to credit scoring predictions to help solve this problem. According to the characteristics of the credit scoring task, we make the following two innovations. The first one is to propose a SPY-Transfer model. We transform the SPY algorithm in Positive-Unlabeled(PU) field to enable it to select more valuable samples from the source data and fill them into the target data, thus implement a sample-based migration learning method. The second one is to propose a SPY-TrAdaBoost model, which uses the idea of SPY algorithm and the calculation method of TrAdaBoost algorithm to select more suitable samples from the source data to fill in the target data. In the credit score prediction experiment, the transfer learning method proposed in this paper has better performance than the cold-start modeling. In the same task, compared with the classic transfer learning method TrAdaBoost algorithm, the model proposed in this paper also has a greater performance advantage.