Reviewerly/exHarmony huggingface dataset:
On-Chip Instruction Generation for Cross-Layer CNN Accelerator on FPGA

Convolutional neural networks (CNN) are gaining popularity in the field of computer vision. CNN-based methods are computational-intensive and resource-consuming, thus are hard to be integrated into embedded systems and applied to real-time task scenarios. Many FPGA based CNN accelerators have been proposed to get higher performance. Cross-layer CNN accelerator is designed to reduce the data transfer by fusing several layers. However, the instruction size that needs to be transferred is usually considerable, leading to a performance drop of cross-layer accelerators. In this study, we develop an on-chip instruction generation method based on the cross-layer accelerator to reduce the total instruction size transferred to the chip. We design the corresponding hardware module and modify existing object detection models according to the hardware structure to improve the accuracy of object detection tasks. The evaluation results show that in the same calculation process, our accelerator can achieve 35% data transfer reduction on the VGG16 network. The average instruction size and compilation time are reduced by 95% using our instruction generation method. The performance of the accelerator reaches 1414 GOP/s.
Influence of Different Sampling Techniques on The Real-time Crash Risk Prediction Model

In Active Traffic Management (ATM) system, realtime crash risk prediction is the basis for managing and improving traffic safety. The paper focuses upon the impact of training set on the cash risk prediction model and explores how to preprocess training set in order to improve the quality of the model in continuous data environment, based on the traffic data collected by loop detectors on 237 road segments of Shanghai Urban Expressway. The study operates full set of data, random undersampling data, and matched case-control data as the training set to build crash risk prediction models separately and compares the prediction effect of these models to determine a best sampling mechanism. In addition, we also apply the oversampling method SMOTE (Synthetic Minority Oversampling Technique) to the training set mentioned above to expand the crash data and then compared the prediction effects of models before and after expansion to explore if the oversampling method can improve the prediction accuracy of a real-time crash risk prediction model in continuous data environment. The commonly used logistic regression is used as the modeling algorithm, and the area under the ROC (Receiver Operating Characteristic) curve (AUC) value is employed as the evaluation index. The experimental results show that under the continuous data environment, the random undersampling data model has the best prediction effect with ratio of crash to non-crash 1:5, and the SMOTE method has little effect on the prediction accuracy improvement.
Design, Development and Experimental Validation of Multi -Target Tracking Framework for Passive Radar

In this paper we present the multi-target tracking (MTT) framework for passive radar (PR). We address the problem of target filtering in the presence of clutter and measurement uncertainty by employing a two-stage cascaded logic for track initiation. We discuss target association in both plot-to-plot and plot-to-track association regimes, and give practical solutions for efficient data association in multi-target environment. We demonstrate the effectiveness of proposed framework using Kalman filter (KF) based range-doppler (RD) tracking of multiple targets, and verify the system performance against truth data in both bi-static and cartesian domain. Radar measurements have been taken using a mobile PR system, developed at Prince Sultan Defense Studies and Research Center (PSDSARC), Saudi Arabia. Experiments conducted with arbitrary transmitter-target-receiver geometries, exploiting a single non-cooperative FM broadcast channel in bi-static configuration, show that the radar is able to reliably track aerial targets at bi-static ranges of beyond 300 Km, corresponding to ranges up to 150 Km from the receiver.
A Particle Filter for Track-before-Detect in GSM Passive Coherent Location

This paper describes a Track-before-Detect method using a Particle Filter that operates on raw unthresholded sensor data provided by a Passive Coherent Location system using GSM signals as illuminators of opportunity. The classical approach of establishing tracks from preprocessed/thresholded data often fails to successfully track low SNR targets. By moving the detection step after a first tracking stage and hence virtually integrating and tracking raw sensor data over time weak targets previously hidden in noise can be successfully unmasked. The method is adapted to GSM signals and through simulations its potential is shown.
Supercritical sequences, and the nonrationality of most principal permutation classes

We prove that for any fixed n, and for most permutation patterns q, the number Avn,ℓ(q) of q-avoiding permutations of length n that consist of ℓ skew blocks is a monotone decreasing function of ℓ. We then show that this implies that for most patterns q, the generating function ∑n≥0Avn(q)zn of the sequence Avn(q) of the numbers of q-avoiding permutations is not rational. Placing our results in a broader context, we show that for rational power series F(z) and G(z) with nonnegative real coefficients, the relation F(z)=1∕(1−G(z)) is supercritical, while for most permutation patterns q, the corresponding relation is not supercritical.
High-Order Factorization Machine Based on Cross Weights Network for Recommendation

Factorization machines are important methods for discovering complex and evolving relationships among data entities by crafting combinatorial features automatically. Because of good data representation ability, recently Deep Neural Networks (DNNs) have been applied to factorization machines to learn high-order interactions. However, the interactions with DNNs are generated implicitly. In this paper, we propose a novel factorization machine named High-order Cross Factorization Machine (HCFM), which is an efficient and explicit high-order feature interaction factorization machine. The main component that we design for HCFM is an explicit cross network: Cross Weights Network (CWN). CWN considers both the weights of different feature combinations and interaction orders, which can capture the inherent correlations of real-word data. In CWN, cross and compression layers can learn different weights of feature interactions efficiently. Different weights of interaction orders can be learned by the weights pooling layer. CWN not only retains the important interaction features but also avoids the complex interaction computations. Comprehensive experiments are conducted on three real-world recommendation datasets to verify the validity of the model. The experiment results have demonstrated its superiority over the other state-of-art algorithms, in terms of both accuracy and space complexity.
Vehicle tire (tyre) detection and text recognition using deep learning

This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires in motion are acquired using roadside cameras. Firstly, the tire circularity is detected using Circular Hough Transform (CHT) with dynamic radius detection. The tire is then unwarped into a rectangular patch and a cascade of convolutional neural network (CNN) classifiers is applied for text recognition. We introduce a novel proposal generator for localizing the tire code by combining Histogram of Oriented Gradients (HOG) with a CNN. The proposals are then filtered using a deep network. After the code is localized, character detection and recognition are carried out using two separate deep CNNs. The system presents impressive accuracy and efficiency proving its suitability for the intended industrial application.
Innovative Marketing Strategies in Academic Libraries

Advancements of Science and Technology have made a tremendous impact on all walks of life. Library and Information centers are no exception. The ICT-based marketing techniques are employed in libraries in an innovative way to maximize the usage. A similar approach can be followed in academic libraries, especially in higher educational institutions with an intention to maximize the use library resources to user's satisfaction. In this context, the chapter analyzes the various facets of marketing techniques in academic libraries. This chapter highlights the intention of academic libraries to use the resources at maximum level. The library home page in the institutional websites and links provided in them to the variety of e-resources play a pivotal role. Social Medias, blogs, and other promotional materials are extensively used as innovative marketing strategies in academic libraries. Efforts to familiarize the library professionals in adopting different marketing strategies help to use the resources at optimum level.
Thai Sign Language Recognition Using 3D Convolutional Neural Networks

Translating sign language is a complicating and challenging task due to complexity of sign language structures including hand shapes, hand orientation, hand movements, and facial expression. Existing approaches generally used complex handcrafted features to recognize a sign language. However, to build a model based on those features is a difficult task. To deal with this problem, we proposed a model of Thai sign language recognition using 3D Convolutional Neural Network (3DCNN) which can automatically learn both temporal and spatial features from data. Our data is a collection of 64 isolated Thai signs language vocabulary as video stream using Microsoft Kinect to acquire information of color, depth, skeleton, hand shapes, and whole body movement. To evaluate our proposed approach, different kind of stream and information are tested with the best empirical model. The selected model is 3DCNN with kernel 3×3×3. The experimental results demonstrated that the accuracy of different input and information is as high as the highest 97.7% which belong to skeleton dataset.
Variational approach for privacy funnel optimization on continuous data

Here we consider a common data encryption problem encountered by users who want to disclose some data to gain utility but preserve their private information. Specifically, we consider the inference attack, in which an adversary conducts inference on the disclosed data to gain information about users’ private data. Following privacy funnel (Makhdoumi et al., 2014), assuming that the original data X is transformed into Z before disclosing and the log loss is used for both privacy and utility metrics, then the problem can be modeled as finding a mapping X→Z that maximizes mutual information between X and Z subject to a constraint that the mutual information between Z and private data S is smaller than a predefined threshold ϵ. In contrast to the original study (Makhdoumi et al., 2014), which only focused on discrete data, we consider the more general and practical setting of continuous and high-dimensional disclosed data (e.g., image data). Most previous work on privacy-preserving representation learning is based on adversarial learning or generative adversarial networks, which has been shown to suffer from the vanishing gradient problem, and it is experimentally difficult to eliminate the relationship with private data Y when Z is constrained to retain more information about X. Here we propose a simple but effective variational approach that does not rely on adversarial training. Our experimental results show that our approach is stable and outperforms previous methods in terms of both downstream task accuracy and mutual information estimation.
Tiny Face Detection Based on Deep Learning

This paper proposed an improved strategy of tiny-face detection applied to images, videos, and real-time scenarios. First, six feature layers which are more conducive to the detection of tiny faces are integrated on the basis of the Single Shot MultiBox Detector (SSD) framework, and the prediction box scale generated by the algorithm is adapted to the shape of the face. Then, a hierarchical training strategy is put forward. Different scales are set to divide the ground truth box (GT box) into different levels according to the area of pixels, and are sent to the network training in descending order. Finally, in the prediction stage, the new soft non maximum suppression (new-softnms) algorithm is proposed to accurately suppress the generated redundant prediction box. And the continuous Gaussian function is combined with the hard-threshold screening method of nms. The proposed solution of this paper obtains 93.7% and 82.6% average accuracy respectively in the FDDB and Wider Face datasets. In the detection of video and real-time scenarios, the minimum face size of 200 pixels can be detected while obtaining 45 frames per second (FPS).
Confidence map based KCF object tracking algorithm

Tracking with kernelized correlation filters (KCF) is an excellent object tracking algorithm, which is widely concerned. In KCF, each candidate patch in tracked region corresponds to a confidence ratio indicating the probability of containing the target, and the patch with the maximum confidence ratio is the output. In traditional KCF, its tracking performance decreases in complex scenes and the model is liable to be contaminated due to updated every frame. To overcome these limitations, we combine all available confidence ratios to form a confidence map, then by analyzing the confidence map, we infer the tracking scene and adopt different tracking strategies. For complex scenes, we dynamically improve KCF to enhance its tracking performance. In addition, we propose an innovative model update mechanism to reduce the computational complexity and model contamination. The experimental results show that compared with the conventional KCF algorithm, the proposed approach improves success rate and precision by 7% and 8% respectively.
Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks

Artificial intelligence and machine learning are becoming increasingly important in science and society. In image processing, they are mainly used for object classification. The aim of this paper is the comparison of classical supervised machine learning methods with innovative deep learning (DL) approaches in terms of performance, which is described by the calculated accuracy. Classifiers of different characteristics are used. These are the Support Vector Machines, Random Forest, k-Nearest-Neighbor, and Naive Bayes. They are compared to two not pre-trained and four pre-trained neural networks. The former neural network are based on LeNet, the second ones include AlexNet, GoogleNet and ResNet provided by Matlab as well as a pre-trained neural network provided by MVTec HALCON. Comparisons were made using the recognition rates achieved with five real data sets from industrial applications. The results showed that not pre-trained neural networks produce worse results than classical classifiers with the given small amounts of data for training. On the other hand, the pre-trained networks achieved surpassing recognition rates. However, if there are features that describe the classes very well, the recognition performance of classical machine learning methods little differs from that of deep learning algorithms.
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies

This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DRL policies to distributional shifts in state transitions. Building on this approach, we propose two RL-based techniques for quantitative benchmarking of adversarial resilience and robustness in DRL policies against perturbations of state transitions. We demonstrate the feasibility of our proposals through experimental evaluation of resilience and robustness in DQN, A2C, and PPO2 policies trained in the Cartpole environment.
An Adaptive Wordpiece Language Model for Learning Chinese Word Embeddings

Word representations are crucial for many natural language processing tasks. Most of the existing approaches learn contextual information by assigning a distinct vector to each word and pay less attention to morphology. It is a problem for them to deal with large vocabularies and rare words. In this paper we propose an Adaptive Wordpiece Language Model for learning Chinese word embeddings (AWLM), as inspired by previous observation that subword units are important for improving the learning of Chinese word representation. Specifically, a novel approach called BPE+ is established to adaptively generates variable length of grams which breaks the limitation of stroke n-grams. The semantical information extraction is completed by three elaborated parts i.e., extraction of morphological information, reinforcement of fine-grained information and extraction of semantical information. Empirical results on word similarity, word analogy, text classification and question answering verify that our method significantly outperforms several state-of-the-art methods.
History of the Geological Research in Egypt

This chapter documented the history of the geological research in Egypt. It exhibits the ancient literature written and complied on the geology of Egypt including Hume’s book and Geology of Egypt books of Rushdi Said that were issued in 1962 and 1990. In addition to literature, the chapter covers the history of geological mapping in Egypt and also a review on geological remote sensing publications including statistics on satellite sensors and techniques. Monitoring spatiotemporal variability in Egypt’s Ground water resources using Grace data and the history of geochronological measurements are also considered.
Workshop on explainable AI in automated driving

With the increasing use of automation, users tend to delegate more tasks to the machines. Such complex systems are usually developed with "black box" Artificial Intelligence (AI), which makes these systems difficult to understand for the user. This assumption is particularly true in the field of automated driving since the level of automation is constantly increasing via the use of state-of-the-art AI solutions. We believe it is important to investigate the field of Explainable AI (XAI) in the context of automated driving since interpretability and transparency are key factors for increasing trust and security. In this workshop, we aim at gathering researchers and industry practitioners from different fields to brainstorm about XAI with a special focus on human-vehicle interaction. Questions like "what kind of explanation do we need", "which is the best trade-off between performance and explainability" and "how granular should the explanations be" will be addressed in this workshop.
Word Length Distribution in German Texts during the 17<sup>th</sup>-19<sup>th</sup> Century

Word length in German texts has been a frequently discussed issue in the field of quantitative linguistics. Taking an overall view of the existing research data, however, most of the research focuses on literary texts and private letters and the size of data corpus for each research is relatively small. This paper provides a time- and genre-based analysis of word length distribution in German using 360 texts originated between the 17th and 19th centuries, aiming to find a probability distribution that can capture well the German word length distribution from a diachronic perspective and to reveal the relationship between the word length distribution and boundary conditions such as the genre and the creation time of text. Results indicate that the word length distribution in German texts written in different eras abides by the 1-displaced hyper-Poisson distribution, whose parameters (a, b) are interconnected with boundary conditions. This study corroborates that the word length distribution of a certain language is consistent, due to the constraint of the cognitive mechanism. Besides, the parameters of probability distribution can be good indicators of the writing style as well as the creation time of text.
Adaptive appearance modeling via hierarchical entropy analysis over multi-type features

The descriptiveness of visual models is crucial for many image processing applications, however, it is still challenging to adaptively formulate such models. This paper systematically advocates a generic and adaptive appearance modeling method. For object-specific instances in images, it can adaptively generate a descriptive codebook by exploring the maximum discriminability of multi-type features. The key idea is to define feature-independent information entropy as a unified criterion to measure different features in a common entropy space. Towards this goal, a hierarchical maximum entropy (HME) model is proposed to conduct multi-feature selection based on the random forest. Specifically, the improved random forest replaces space-specific expression “distance similarity” with the statistical concept “entropy”. Thus, the random forest could integrate the subspace clustering results from different feature spaces. Such integration can not only afford adaptive feature selection and cross-feature error control but also be robust to possible weak/inconsistent feature expressions. To effectively construct a class-specific appearance model, a sparse codebook model, consisting of a series of weak learners, is proposed to further explore the maximum discriminative subspaces of each object class. Finally, a maximum entropy model is proposed to formulate appearance model by optimizing the probabilistic distributions of all the codebook words’ response parameters. To verify the efficacy and effectiveness of the proposed model, it is applied to multi-class image classification. We conduct extensive experiments and make comprehensive evaluations w.r.t several state-of-the-art methods over PASCAL VOC 2007, VOC 2012, Caltech 101 and Caltech 256 datasets. All the results demonstrate the advantages of the our method in terms of precision, robustness, flexibility, and versatility.
Feature-Trace

Operational Profiles provide quantitative information about how the software will be used, which supports highlighting those software components more sensitive to reliability based on their profile usage. However, the generation of Operational Profiles usually requires a considerable team effort to liaise requirements specification until their reification into expected software artifacts. In this sense, it becomes paramount in the software life cycle the ability to seamlessly or efficiently perform traceability from requirement to code, embracing the testing process as a means to ensure that the requirements are satisfiably covered and addressed. In this work, we propose the Feature-Trace approach which merges the advantages of the Operational Profile and the benefits of the requirements-to-code traceability present in the BDD (Behavior-Driven Development) approach. The primary goal of our work is to use the BDD approach as an information source for the semi-automated generation of the Operational Profile. The proposed approach was evaluated on the Diaspora software, on a GitHub open source software. The case study revealed that the Feature-Trace approach is capable of extracting the operational profile seamlessly from the specified Diaspora's BDD features as well as obtaining and presenting vital information to guide the process of test cases prioritization.