Reviewerly/exHarmony huggingface dataset:
Research on LED lightweight cryptographic algorithm based on RFID tag of Internet of things

In recent years, with the rapid development of Internet of things, RFID tags have been widely used, in due to the chip used in radio frequency identification (RFID) tags is more demanding for resources, which also brings a great threat to the safety performance of cryptographic algorithms in differential power analysis (DPA). For this purpose, it is necessary to study the LED lightweight cryptography algorithm of RFID tags in the Internet of things, so as to explore a lightweight and secure cryptographic algorithm which can be applied to RFID Tags. In this paper, through the combination of Piccolo cryptographic algorithm and the new DPA protection technology threshold, we propose a LED lightweight cryptographic algorithm which can be applied to the RFID tag of the Internet of things. With the help of improve d exhaustive search and Boolean expression reconstruction, the two methods share the implementation of the S -box and the InvS-box, thereby effectively solves the burr threat problem of the S-box and the InvS-box in the sharing implementation process, the security performance of the algorithm is evaluated by the DPA attack of FPGA. The results show that the algorithm can achieve lightweight and security performance at the same time, can effectively meet the light and security requirements of RFID tag chip of Internet of things for cryptographic algorithms.
Trajectory Tracking Control of a 7-Axis Robot Arm Using SMCSPO

Multi-axis-robots have been wildly used in industry for soldering, plasma cutting with an indirect contact without high disturbance. However, recently, the multi axis robot is designed for a direct contact work which may generate a large reaction force from environment. The complex dynamics and high payload can lead to the control instability. In this paper, we proposed a method focusing on the trajectory tracking control for an electrical motor actuated 7-axis robot which is the sliding mode control with sliding perturbation observer. Sliding perturbation observer is used to estimate the dynamic uncertainties, errors occurred in modeling, the main payload and the disturbance from environment. Stability analysis is derived by the Lyapunov stability. The tracking performance is demonstrated by a real experiment.
A crime script analysis of the Madoff Investment Scheme

The Madoff Investment Scheme was a long-term and large-scale fraud which involved both an affinity fraud and a Ponzi scheme. Numerous studies have examined financial and legal aspects of the case, but relatively few have explored the case from a criminological perspective. This study applies crime script analysis (CSA) to the Madoff case in order to conceptualise the procedural elements of complex fraud offences and to identify crime prevention opportunities for investigators and regulatory bodies. CSA identifies 12 steps in the offence, occurring in three phases which create a relationship between earlier and later victims, with earlier victims providing credibility to the scheme and later victims generating the resources to sustain it. The study finds that crime prevention techniques which harden targets and extend guardianship could reduce criminal opportunities for offenders and techniques which strengthen formal surveillance and assist natural surveillance could control the capabilities required to perpetrate similar offences.
Thermal to Visual Face Recognition using Transfer Learning

Inter-modality face recognition refers to the matching of face images between different modalities and is done usually by taking visual images as source and one of the other modalities as a target. Performing facial recognition between thermal to visual is a tough task because of nonlinear spectral characteristics of thermal and visual images. However, this is a desirable requirement for night-time security applications and military surveillance. In this paper, we propose a method to improve the thermal classifier accuracy by using transfer learning and as a result, the accuracy of thermal to visual face recognition gets increased. The proposed method is tested on RGB-D-T dataset (45900 images) and UND-Xl collection (4584 images). Experimental results show that the overall accuracy of thermal to visual face recognition by transferring the knowledge gets increased to 94.32% from 89.3% on RGB-D-T dataset and from 81.54% to 90.33% on UND-Xl dataset.
Encoding Distortions for Multi-task Full-Reference Image Quality Assessment

Most existing image quality assessment models focus on evaluating the image quality score, however, the quality score alone is not enough to characterize the degeneration. In this paper, we propose a full reference framework named Mask Gated Convolutional Network (MGCN) for evaluating the image quality score and identifying distortions simultaneously. Observing the fact that the reference images are distorted by various distortions in pixel space, we design an encoder module to capture the transformation between reference images and distorted images as low level features. Further higher level features are extracted from the low level features and shared by both the regression and the classification tasks. Instead of simply cropping patches to augment data, we mask the high level feature map in the spatial domain to randomly sample patches from the image and learn to assign the image quality score to the patch set. The proposed method achieves the state-of-the-art performance on LIVE2, TID2008 and TID2013 datasets.
No-three-in-line problem on a torus: Periodicity

Let τm,n denote the maximal number of points on the discrete torus (discrete toric grid) of sizes m×n with no three collinear points. The value τm,n is known for the case where gcd(m,n) is prime. It is also known that τm,n≤2gcd(m,n). In this paper we generalize some of the known tools for determining τm,n and also show some new. Using these tools we prove that the sequence (τz,n)n∈N is periodic for all fixed z>1. In general, we do not know the period; however, if z=pa for p prime, then we can bound it. We prove that τpa,p(a−1)p+2=2pa which implies that the period for the sequence is pb, where b is at most (a−1)p+2.
Usage Identification of Anomaly Detection in an Industrial Context

Abstract The use of flexible and autonomous robotics systems is the solution for the automation task of the production and intra-logistics environments. This dynamic context requires the robot to be aware of its surroundings through the whole task, also after accomplishing the gripping action. We present an anomaly detection approach based on unsupervised learning and reconstruction fidelity of image data. We design our method to enhance the dynamic environment perception of robotics systems and apply it in a palletizing robot, in order to perceive and detect changes to its surrounding and process after the gripping step. Our proposed approach achieves the performance targeted by the considered industrial requirements.
A High Performance Full-Word Barrett Multiplier Designed for FPGAs with DSP Resources

Modular multiplication with large integers is the fundamental operation in public-key cryptosystems. In this paper, a high performance, full-word Barrett modular multiplier design utilizing the DSP resources in modern FPGAs is presented. The operand size of the multiplier is multiples of 528 bits. Proposed design consists of 48×48 bit multiplier blocks built from the DSP slices which perform 24×16 bit multiplications and a carry select accumulator built from the DSP slices which perform 48 bit additions. The proposed design first multiplies operands and accumulate the result and then, reduces the accumulated result using Barrett's method. A Xilinx Virtex-7 implementation of the proposed hardware takes 0.49 us and 1.88 us for 528 bit and 1056 bit modular multiplications for any modulus respectively. To the best of authors' knowledge, this is the first work which gives the detailed implementation results for full-word Barrett modular multiplier targeting FPGAs with DSP resources.
Hierarchical Schema Representation for Text-to-SQL Parsing With Decomposing Decoding

Most of existing studies on parsing natural language (NL) for constructing structured query language (SQL) do not consider the complex structure of database schema and the gap between NL and SQL query. In this paper, we propose a schema-aware neural network with decomposing architecture, namely HSRNet, which aims to address the complex and cross-domain Text-to-SQL generation task. The HSRNet models the relationship of the database schema with a hierarchical schema graph and employs a graph network to encode the information into sentence representation. Instead of end-to-end generation, the HSRNet decomposes the generation process into three phases. Given an input question and schema, we first choose the column candidates and generate the sketch grammar of the SQL query. Then, a detail completion module fills the details based on the column candidates and the corresponding sketch. We demonstrate the effectiveness of our hierarchical schema representation by incorporating the information into different baselines. We further show that the decomposing architecture significantly improves the performance of our model. Evaluation of Spider benchmark shows that the hierarchical schema representation and decomposing architecture improves our parser result by 14.5% and 4.3% respectively.
Multi-rotor Robot Learning to Fly in a Bio-inspired Way Using Reinforcement Learning

Bio-inspired artificial intelligence-reinforcement learning, artificial neural network and bio-inspired methods- is used in this paper in order to design an algorithm capable of learning to flight autonomously.In this paper, we introduced and explained about our algorithm called FE-TRPO (Flight Enhanced TRPO) and tested it in Gazebo simulator by an AscTec Pelican simulated model where the multi-rotor shows the capability of learning to fly by learning from its own actions and the rewards receiving from the environment in an autonomous way, where the only controller is the reinforcement learning based algorithm and no conventional controllers such as PID, MPC, etc is used. Finally, our algorithm learns to fly using only the data generated from IMU. As a result, our algorithm can be used by a multi-rotor at any unknown environment where no guiding system such as Vicon exist. A video clip of our work can be viewed in: https://www.youtube.com/watch?v=Jj42A5q-YA.
StreamFlowVR: A Tool for Learning Methodologies and Measurement Instruments for River Flow Through Virtual Reality

Virtual Reality potentialities can be exploited for several types of contexts beyond the mainstream video game industry. One of the most interesting application fields concerns its use in education and teaching especially in the contexts where security and safety are essential elements. The measurement of river flow is required for river management purposes including water resources planning, pollution prevention, and, flood control. The teaching of this complex task requires to transfer well-known methodologies as well as careful attention while performing the training in situ. In this paper, we propose a virtual reality tool called StreamFlowVR to improve the learning process for the river flow instruments and measuring methodologies. The basic idea is to create a rich user experience in a secure environment where students can understand the correct methodologies. We believe that the use of virtual reality benefits the students in learning hydraulic phenomenon faster and more confident respect to the traditional approach on a real river.
Performance Comparison of Different CNN models for Indian Road Dataset

Recent advancement in the field of computer vision and development of Deep Neural Network based object detection led researchers and industries to focus on autonomous vehicles. This paper aims to find how accurately previously proposed CNN architectures detect on-road obstacles in Indian road scenarios in the context of autonomous vehicle. We have compared three different convolution neural networks trained with COCO dataset for detecting autorickshaws in Indian road. We undertook statistical hypothesis testing to find effect of these three models, i.e. YOLOv3, Mask R-CNN, and RetinaNet on detection accuracy rate. While measuring accuracy, we have noted that detection accuracy rate of RetinaNet is significantly better than other two CNN architectures. Although there is no significant difference between other two networks in context of detection rate. The accuracy rate shows the performance of RetinaNet invariant to autorickshaws' color and shape, and different climatic and complex background scenarios.
Performance of Data Enhancements and Training Optimization for Neural Network: A Polyp Detection Case Study

Deep learning using neural networks is becoming more and more popular.It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition.In this respect, the learning and training process usually requires a lot of data.However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract.Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells.Detection and removal of polyps can therefore prevent the development of cancer.Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures.Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate.We also experiment with how various training techniques can be used to increase performance.Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no and even a negative effect.
Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques

Schizophrenia is a severe chronic mental disorder, which affects the behavior, the perception and the thinking of the patient. The purpose of this research is to develop a predictive system to preemptively diagnose Schizophrenia Disease using computational intelligence-based techniques. The system will show the possibilities of getting the disease at an early stage, which will improve the health state of the patients. This will be done using machine learning techniques. The used dataset has 86 records, which was obtained from the Machine Learning for Signal Processing (MLSP) 2014 Schizophrenia Classification Kaggle Challenge. The used techniques in this paper are Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Naive Bayesian (NB). The highest accuracy was 90.6977% reached by using SVM, RF, and NB techniques while ANN technique reached 88.3721% accuracy. The obtained accuracies are reached by using 204 features. Therefore, we conclude that using SVM, RF, and NB techniques are better in this particular problem.
Acquisition of weak GPS signals using wavelet-based de-noising methods

Various factors cause GPS signals to be weakened in their path from satellites to receiver. This phenomenon leads to some problems in the normal stages of the positioning process, including acquisition stage. The wavelet transform is one of the tools that have been employed in several methods to improve receiver's sensitivity in confronting weak GPS signals. Choosing an appropriate threshold plays a key role in the performance of this de-noising method. This study presents two methods for selecting an appropriate threshold. In the first method, the threshold is determined based on the statistical information extracted from the signal. On the other hand, the threshold in the second method is determined by type II fuzzy sets. The results show that employment of these two methods increases SNR gain as well as the range of acquirable SNR which makes signals acquisition with minimum post-correlation SNR of −6.5dB in a GPS receiver possible.
Faster constrained linear regression via two-step preconditioning

In this paper, we study the large scale constrained linear regression problem and propose a two-step preconditioning method, which is based on some recent developments on random projection, sketching techniques and convex optimization methods. Combining the method with (accelerated) mini-batch SGD, we can achieve an approximate solution with a time complexity lower than that of the state-of-the-art techniques for the low precision case. Our idea can also be extended to the high precision case, which gives an alternative implementation to the Iterative Hessian Sketch (IHS) method with significantly improved time complexity. Experiments on benchmark and synthetic datasets suggest that our methods indeed outperform existing ones considerably in both the low and high precision cases.
Where are the People? A Multi-Stream Convolutional Neural Network for Crowd Counting via Density Map from Complex Images

Crowd counting is a challenging task that aims to compute the number of people present in a single image. The problem has a significant impact on various applications, for instance, urban planning, forensic science, surveillance and security, among others. In this paper, we propose and evaluate a multi-stream convolutional neural network that receives an image as input, generates a density map as output that represents the spatial distribution of people in an end-to-end fashion, and then we estimate the number of people in the image from the density map. The network architecture employs receptive fields with different size filters for each stream in order to deal with extremely unconstrained scale and perspective changes, which are complex issues in the crowd counting context. Although simple, the proposed architecture achieves effective results on the two challenging UCF_CC_50 and ShanghaiTech datasets.
Design of a digit-serial multiplier over GF(2<i><sup>m</sup></i>) using a karatsuba algorithm

A Karatsuba algorithm (KA) is used for highly accurate multiplication using a divide and conquer approach. A new approach to a polynomial digit-serial multiplier that uses an optimal digit size (d) for KA decomposition has recently been proposed. In this study, the proposed architecture uses three small multipliers to derive an optimal digit size (d) for the case of trinomial based fields. Using the proposed KA decomposition, this study establishes five types of sub-quadratic multipliers, which are, the recombined m-bit exponentiation multipliers using a KA. The theoretical results show that the proposed polynomial exponentiation multipliers that use a KA have a value of (d × m)/2 and involve significantly less time and area complexity than existing digit-serial multipliers. The simulation results for the proposed method demonstrate a respective 68.20%, 77.37%, 72%, 83.18%, 36.66% decrease in area × time over GF(236), GF(284), GF(2126), GF(2204) and GF(2340).
A Collaborative filtering Method based on Forgetting Theory And Neural Item Embedding

The collaborative filtering algorithm (CF) is one of the most important algorithms in the recommendation system. Recently, the use of neural word embedding methods to learn the latent representation of words has become a mature method in the field of natural language processing (NLP). Inspired by the SGNS algorithm in the NLP field, we propose f-item2vec as a new method to learn the latent representation in item vector space, and based on this vector to calculate the similarity between items. In addition, according to the forgetting process, we propose a new user preference model to accurately identify the user's short-term preferences and long-term preferences. The experimental results show that the proposed method is effective and superior to the traditional algorithm in predicting the score and generating the recommendation list.
Refined Spatial Network for Human Action Recognition

Effective video representation is a key ingredient in action recognition, but how to learn effective spatial features remains a fundamental and challenging task. The existing CNN-based methods apply low-resolution feature maps to get the high-level semantic labels. However, the slenderer spatial information for action representation has lost. In this paper, we propose a novel stacked spatial network (SSN), which integrates multi-layer feature maps in an end-to-end manner. Spatial features extraction network based on encoder-decoder is firstly used to obtain multi-level and multi-resolution spatial features under the supervision of high-level sematic labels. The multi-level features are aggregated through a stacked spatial fusion layer, which intrinsically refines the traditional convolutional neural network. Then, refined spatial network (RSN) is proposed to aggregate spatial network and SSN. Particularly, the learned representation of RSN comprises two components for representing semantic label information and local slenderer spatial information. Extensive experimental results on UCF-101 and HMDB-51 datasets demonstrate the effectiveness of the proposed RSN.
Smart Wheelchair Maneuvering Among People

The advancement of technology is increasing with several applications on robotics like smart wheelchairs providing autonomous functions for severely impaired users with less need of caregiver support. One of the main issues in robotic wheelchair research is autonomous pedestrian avoidance for safety and smooth maneuvering. However, this is difficult because most of the pedestrians change their motion abruptly. Thus we need a fully autonomous smart wheelchair that can avoid collisions with individual or multiple pedestrians safely and with user comfort in mind for crowded environments like train stations. This paper presents a method for our smart wheelchair to maneuver through individual and multiple pedestrians by detecting and analyzing their interactions and predicted intentions with the wheelchair. Our smart wheelchair can obtain head and body orientations of pedestrians by using OpenPose. Using a single camera, we infer the walking directions or next movements of pedestrians by combining face pose and body posture estimation with our collision avoidance strategy in real-time. For an added layer of safety, we also use a LiDAR sensor for detection of any obstacles in the surrounding area to avoid collisions in advance.
New Authentication Algorithm Based on Verifiable Encryption with Digital Identity

We propose a new authentication algorithm for small internet of things (IoT) devices without key distribution and secure servers. Encrypted private data are stored on the cloud server in the registration step and compared with incoming encrypted data without decryption in the verification step. We call a set of encryptions that can verify two encrypted data items without decryption a verifiable encryption (VE). In this paper, we define VE, and claim that several cryptosystems belong to the VE class. Moreover, we introduce an authentication algorithm based on VE, and show an example of the algorithm and discuss its performance and security. As the algorithm neither shares any secret keys nor decrypts, its computation time becomes very small.