Reviewerly/exHarmony huggingface dataset:
Novel GF($2_m$) Digit-Serial PISO Multipliers for the Self-Dual Gaussian Normal Bases

Security protocols such as Transport Layer Security implement the Elliptic curve digital signature algorithm (ECDSA) over different binary extension fields defined by the National Institute of Standards and Technology (NIST). Specifically, such multiple cipher-suite support is a security recommendation. Binary extension field arithmetic processors are expensive, especially if more than one field is supported. In this context, this article introduces a novel lightweight digit-serial parallel-in-serial-out (DS-PISO) design for versatile multiplication (DS-VPISO) targeting the NIST-like fields in resource constrained embedded systems where the crypto module allocation is limited. The proposed DS-VPISO multiplier offers competitive area (up to 40 percent area savings) compared to existing multiple field multiplier schemes based on results conducted on bit-serial implementations using Intel's Field programmable gate arrays. The article first presents a DS-PISO self-dual Gaussian normal basis multiplication architecture based on the trace mapping. After this, the article extends the new trace based DS-PISO multiplier to construct an architecture for the first versatile DS-PISO multiplication (DS-VPISO) targeting NIST's binary fields. The latter extension to versatile multiplication is based on novel architectures for versatile cyclic shifts and versatile multiplication by normal elements.
Track-Before-Detect Labeled Multi-Bernoulli Smoothing for Multiple Extended Objects

For the evaluation of autonomous driving systems, this paper provides a new approach of generating reference data for multiple extended object tracking. In our approach, we apply a forward-backward smoother for objects with star-convex shapes based on the Labeled Multi-Bernoulli (LMB) Random Finite Set (RFS) and recursive Gaussian processes. We further propose to combine a robust birth policy with a backward filter to solve the conflict between robustness and completeness of tracking. Thereby, cluster candidates are evaluated based on a quality measure to only initialize objects from more reliable clusters in the forward pass. Missing states will then be recovered by the backward filter through post-processing the unassociated data after the smoothing process. Simulations and real-world experiments demonstrate superior performance of the proposed method in both cardinality and individual state estimation compared to naive LMB filter and smoother for extended objects.
Design and Analysis of Secure One-way Functions for the Protection of Symmetric Key Cryptosystems

Security is a major concern for internet users and industries when it comes to data processing, message transmission, electronic transaction etc. One important feature of secure communication is modern cryptography. It allows the implementation of major cryptographic objectives: confidentiality, authentication, data integrity and non-repudiation. Most NIST-recommended crypto algorithms are highly reliable, faster and powerful, but are still susceptible to attacks such as brute-force attack, algebraic attacks, linear attacks, etc. Hash functions are the most widespread primitives of cryptography that are widely used in information security applications. The main objective of this paper is to design the hardware Intellectual Property (IP) core of the secured one-way hash functions (SHA-256 and MD5) and analyze the fully pipelined optimized hardware architecture. The key feature of this improved implementation is to generate a secure and unique key for the protection of symmetric key crypto algorithms. The proposed work is implemented at high-level language C and converted into Register Transfer Level (RTL) IP using the Xilinx Vivado HLS on ZedBoard. The robustness and randomness of the algorithm are evaluated in the form of the Hamming Distance and Avalanche Effect. The results show that the proposed design offers a higher level of security and implementation flexibility with small design overhead and low power consumption. The resulting SHA-256 architecture operates at 127.22MHz with a throughput of 15Gbps, while MD5 architecture operates at 123MHz with a throughput of 15Gbps.
Fourier-plane Vortex Laser Holography for Robust, Small-brain Machine Learning and Image Classification

We show that the optical vortex illumination of objects undergoing diffraction performs simultaneous corner detection and image compression to be exploited in machine learning applications immune to adversarial attacks and robust to background noise.
A Composite Cost-Sensitive Neural Network for Imbalanced Classification

Imbalanced data with skewed class distributions and different misclassification costs is common in many real-world applications. Traditional classification approach does not work well for imbalanced data, because they assume equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true original distributions of samples. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Whats more, even instances in the same class may have different misclassification costs. As an extension of class-dependent costs, this paper presents a composite cost-sensitive deep neural network (CCS-DNN) for imbalanced classification. A specifically-designed cost-sensitive matrix, which is composed of example-dependent costs and class-dependent costs, is embedded into the loss function to improve the classification performance. And the parameters of both the cost-sensitive matrix and the network are jointly optimized during training. The results of comparative experiments on some benchmark datasets indicate that the CCS-DNN performs better than other baseline methods.
A Hybrid Spherical Evolution and Particle Swarm Optimization Algorithm

A metaheuristic algorithm called spherical evolution (SE) is proposed recently, and the SE innovatively adopts a novel spherical search mechanism instead of the conventional hypercube search mechanism. SE has shown superior performance over other metaheuristic algorithms. However, it still suffers from low search performance and low convergence speed. In this paper, we for the first time propose a hybrid spherical evolution and particle swarm optimization algorithm aimed to leverage strengths of two different search mechanism in a hybrid algorithm, and design a search mechanism control rule based on the fitness of individuals. Experimental results based on 30 benchmark functions of IEEE CEC2017 and results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.
An Empirical Study of Web API Quality Formulation

This paper presents an empirical study on one of the most popular web API repositories, www.programmableweb.com . The study is to ascertain the impact of the structure and formulation of external web API quality factors on the overall web API quality. The study is based on the hypothesis that, in such a multi-factor quality measurement, the structure and formulation of the quality factors can make a substantial difference in its quantification. Specifically, we employ statistical tools such as exploratory factor analysis, to determine the latent factors that contributes to web API quality. We subsequently determine the loading of each latent factors to propose a new quality model for web API quality computation.
Microtargeting or Microphishing? Phishing Unveiled

Online advertisements delivered via social media platforms function in a similar way to phishing emails. In recent years there has been a growing awareness that political advertisements are being microtargeted and tailored to specific demographics, which is analogous to many social engineering attacks. This has led to calls for total bans on this kind of focused political advertising. Additionally, there is evidence that phishing may be entering a more developed phase using software known as Phishing as a Service to collect information on phishing or social engineering, potentially facilitating microphishing campaigns. To help understand such campaigns, a set of well-defined metrics can be borrowed from the field of digital marketing, providing novel insights which inform phishing email analysis. Our work examines in what ways digital marketing is analogous to phishing and how digital marketing metric techniques can be used to complement existing phishing email analysis. We analyse phishing email datasets collected by the University of Houston in comparison with Corporate junk email and microtargeting Facebook Ad Library datasets, thus comparing these approaches and their results using Weka, URL mismatch and visual metrics analysis. Our evaluation of the results demonstrates that phishing emails can be joined up in unexpected ways which are not revealed using traditional phishing filters. However such microphishing may have the potential to gather, store and analyse social engineering information to be used against a target at a later date in a similar way to microtargeting.
Trajectory generation for quadrotor while tracking a moving target in cluttered environment

In this paper, we present a trajectory generation method for quadrotor while tracking a moving target with relative pattern. Compared to existing methods, safe flying zone, vehicle’s physical limits and smoothness are considered to ensure obstacles avoidance and real tracking flight. To tackle with the cluttered environment, a parallel Particle Swarm Optimization algorithm is applied to calculate the safe feasible waypoints for quadrotor with consideration of target’s predicted state and the realistic tracking pattern, as well as safety. Then, a multiple waypoints constraints motion planning method is applied and embed into a cost function for solving the problem of trajectories generation and robust tracking for quadrotor via convex optimization approach, ensuring the real-time planning, with the geometrical constraint of safe flight corridor for quadrotor. The effectiveness of proposed method is verified by numerical simulation experiments.
A Study on Evaluation Board Requirements for Assessing Vulnerability of Cryptographic Modules to Side-Channel Attacks

We studied for specifying requirements for side-channel attack (SCA) vulnerability evaluation boards. SCAs are a potential threat to cryptographic modules mounted in electronics products. Cryptographic modules are required to be evaluated in terms of vulnerability by using a test board. However, no reasonable requirements for such test boards have been specified. In this paper, we investigated the signal-to-noise ratio (SNR) of side-channel leakage and the transfer impedance from the side-channel leakage source to an observation port where side-channel leakage is probed in an evaluation board. We examined two models of existing SCA vulnerability evaluation boards that implemented the Advanced Encryption Standard (AES). Results suggest that cryptographic modules need to provide SNRs of side-channel leakage greater than 2 dB for implementations of the cryptographic algorithm involving no SCA countermeasures. It is also inferred that the transfer impedance needs to satisfy two conflicting requirements. The transfer impedance should be as large as possible, considering the ease of evaluation. At the same time, the transfer impedance needs to be low enough to suppress fluctuations in the power supply voltage and guarantee evaluation in a state equivalent to the actual operating state of the IC. Coaxial connectors would be recommended to be implemented for increasing the SNR and for reproducibility of measurements.
Two-stage Sequential Recommendation via Bidirectional Attentive Behavior Embedding and Long/Short-term Integration

In E-commerce applications, to predict what users will buy next is a crucial mission of sequential recommendation. Most frontier researches build end-to-end training models for sequential recommendation tasks via RNNs, CNNs or attentive models. However, a user's historical behavior sequence carries more complex contextual information than words. In this paper, we propose a two-stage user modeling framework for sequential recommendation, which is consisted by a Bidirectional Self-attentive Behavior Embedding and a Long/Short-term Sequential Behavior Predictor. Firstly, in order to expand perceivable information, a novel self-attentive behavior embedding method is proposed to learn semantic representations not only for items, but also for other important contextual factors (e.g. actions, categories and time). Then, with the pre-trained behavior embeddings, we propose a personalized memory network for Top-N recommendation. We use recurrent network to encode the short-term intent and learn the personalized long-term memory by a self-attention block. To integrate the long/short-term preferences, we generate the predicted behavior representation by using the present intent as a query to match with user's historical preferences via attentive memory reader. Finally, we conduct extensive experiments on two benchmark datasets provided by Tmall and Amazon. Compared with state-of-the-art techniques, experimental results demonstrate the effectiveness of our proposed framework.
Multi-Classification of Rainfall Weather Based on Deep Learning-Mod

Rain is one of the common weather conditions in the meteorological field, and at the same time it will have an important impact on people's daily life, industry and agricultural development. Rainfall is related to physical parameters of weather conditions, such as temperature, saturated water pressure, water vapor density, temperature and humidity. This paper constructs a rainfall multi-classification model based on physical quantities and deep learning model DBNs. Firstly, to resolve the problem of uneven distribution of the samples of the original data, some rainfall samples were synthesized using the SMOTE algorithm. Secondly, the observation data encrypted hourly by the automatic ground monitoring station and physical quantities commonly used in weather forecast analysis are used to construct a deep learning model containing a Gauss Boltzmann machine to extract the intrinsic characteristics of the original data. Finally, a multi-class identification model of DBNs rainfall weather was realized. Experiments show that compared with other methods, the model can more accurately identify rainfall weather, and the POD, FAR and CSI indicators all perform well.
Efficient Hybrid &lt;i&gt;GF&lt;/i&gt;(2&lt;i&gt;&lt;sup&gt;m&lt;/sup&gt;&lt;/i&gt;) Multiplier for All-One Polynomial Using Varied Karatsuba Algorithm

The PCHS (Park-Chang-Hong-Seo) algorithm is a varied Karatsuba algorithm (KA) that utilizes a different splitting strategy with no overlap module. Such an algorithm has been applied to develop efficient hybrid GF(2m) multipliers for irreducible trinomials and pentanomials. However, compared with KA-based hybrid multipliers, these multipliers usually match space complexity but require more gates delay. In this paper, we proposed a new design of hybrid multiplier using PCHS algorithm for irreducible all-one polynomial. The proposed scheme skillfully utilizes redundant representation to combine and simplify the subexpressions computation, which result in a significant speedup of the implementation. As a main contribution, the proposed multiplier has exactly the same space and time complexities compared with the KA-based scheme. It is the first time to show that different splitting strategy for KA also can develop the same efficient multiplier.
Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images

Saliency has been widely studied in relation to image quality assessment (IQA). The optimal use of saliency in IQA metrics, however, is nontrivial and largely depends on whether saliency can be accurately predicted for images containing various distortions. Although tremendous progress has been made in saliency modelling, very little is known about whether and to what extent state-of-the-art methods are beneficial for saliency prediction of distorted images. In this paper, we analyse the ability of deep learning versus traditional algorithms in predicting saliency, based on an IQA-aware saliency benchmark, the SIQ288 database. Building off the variations in model performance, we make recommendations for model selections for IQA applications.
Hybrid Deep Neural Network based on SDAE and GRUNN

Stacked autoencoder (SAE) is hard to achieve satisfactory performance, when input data are complex and non-stationary. Besides, the identification performance of recurrent neural network (RNN) may decrease rapidly under noisy environment. In order to deal with these problems, a novel hybrid deep neural network (DNN) based on stacked denoising autoencoder (SDAE) and gated recurrent unit neural network (GRUNN) is presented. First, the structure of the presented hybrid DNN is given. The hybrid DNN contains a SDAE, a GRUNN, and a softmax classifier. Then, the training algorithm based on action discovery (AD) is proposed to train the presented hybrid DNN. The experimental studies indicate the presented hybrid DNN processes strong anti-noise ability and adaptability to time-varying signals.
CausalBatch

Deep neural networks consisting of a combination of convolutional feature extractor layers and Long Short Term Memory (LSTM) recurrent layers are widely used models for activity recognition from wearable sensors ---referred to as DeepConvLSTM architectures hereafter. However, the subtleties of training these models on sequential time series data is not often discussed in the literature. Continuous sensor data must be segmented into temporal 'windows', and fed through the network to produce a loss which is used to update the parameters of the network. If trained naively using batches of randomly selected data as commonly reported, then the temporal horizon (the maximum delay at which input samples can effect the output of the model) of the network is limited to the length of the window. An alternative approach, which we will call CausalBatch training, is to construct batches deliberately such that each consecutive batch contains windows which are contiguous in time with the windows of the previous batch, with only the first batch in the CausalBatch consisting of randomly selected windows. After a given number of consecutive batches (referred to as the CausalBatch duration τ), the LSTM states are reset, new random starting points are chosen from the dataset and a new CausalBatch is started. This approach allows us to increase the temporal horizon of the network without increasing the window size, which enables networks to learn data dependencies on a longer timescale without increasing computational complexity.
A Multi-objective Performance Optimization Approach for Self-adaptive Architectures

This paper presents an evolutionary approach for multi-objective performance optimization of Self-Adaptive Systems, represented by a specific family of Queuing Network models, namely SMAPEA QNs. The approach is based on NSGA-II genetic algorithm and it is aimed at suggesting near-optimal alternative architectures in terms of mean response times for the different available system operational modes. The evaluation is performed through a controlled experiment with respect to a realistic case study, with the aim of establishing whether meta-heuristics are worth to be investigated as a valid support to performance optimization of Self-Adaptive Systems.
Variable Sampling Domain and Map Compression Based on Greedy RRT Algorithm for Robot Path Planning

The traditional rapidly-exploring random tree (RRT) algorithm and the improved algorithms use a fixed sampling domain in each iteration. This way of sampling will result in much repeated exploration. Also, a fixed step causes the inefficiency of expansion in open areas and the incremental algorithm consumes much time in collision detection. In order to solve these problems, this paper proposes an improved RRT algorithm based on a variable sampling domain and a map compression algorithm along with a strategy of greedy growing (VSDGC-RRT). A variable sampling domain and the greedy strategy increase the utilization of the sample node and reduce the number of iterations. The map compression algorithm reduces time consumption in collision detection. These improvements can make the tree expand more rapidly to the target. The simulation results indicate that the improved algorithm can effectively reduce the quantity of iterations and take less time while retaining the probabilistic integrity of the traditional RRT.
Dioxin Emission Concentration Forecasting Model for MSWI Process with Random Forest-Based Transfer Learning

The dioxin (DXN) emission concentration is an important indicator related to the stability and harmless operation of municipal solid waste incinerator (MSWI) process, and also one of the key parameters for MSWI process to realize optimal operation control. In the actual MSWI process, the process variables such as furnace temperature, grate speed and air pressure are hundreds. However, the modeling samples containing DXN emission concentrations are small, which makes it impossible to establish accurate DXN emission concentration forecasting model. In this study, a forecasting model construction method by using random forest (RF)-based transfer learning was proposed. At first, initial weights are assigned to the source domain and target domain samples. Then, an RF-based DXN emission concentration transfer learning model is established, and the prediction errors are used as the indicator to calculate the weight adjustment coefficients. Finally, the sample weights are adjusted through iteration loop in terms of increase the source domain instance weights that related to the target domain. The method proposed in this paper can transfer the source domain sample to enhance the prediction performance of the DXN emission concentration forecasting model. The experimental results based on the actual industrial data show the effectiveness of the proposed method.
Knowledge Base Enhanced Topic Modeling

Topic models, such as Latent Dirichlet Allocation (LDA), are successful in learning hidden topics and has been widely applied in text mining. There are many recently developed augmented topic modeling methods to utilize metadata information. However, the effect of topic models is still not comparable to humans. We think one key point is that humans have background knowledge, which is essential for topic understanding. Inspired by this, we propose a knowledge base enhanced topic model in this paper. We take knowledge bases as good presentations of human knowledge, with huge collections of entities and their relations. We assume that documents with related entities tend to have similar topic distributions. Based on this assumption, we compute document similarity information via the linked entities and then use it as a constraint for LDA. More specifically, we embed entities in a low-dimensional space via DeepWalk and use Entity Movers Distance to efficiently and effectively measure the similarities between documents. The results of experiments over two real-world datasets show that our method boosts the LDA model on the document classification while no supervision information is needed.