Reviewerly/exHarmony huggingface dataset:
Processing Systems for Deep Learning Inference on Edge Devices

Deep learning models are taking place at many artificial intelligence tasks. These models are achieving better results but need more computing power and memory. Therefore, training and inference of deep learning models are made at cloud centers with high-performance platforms. In many applications, it is more beneficial or required to have the inference at the edge near the source of data or action requests avoiding the need to transmit the data to a cloud service and wait for the answer. In many scenarios, transmission of data to the cloud is not reliable or even impossible, or has a high latency with uncertainty about the round-trip delay of the communication, which is not acceptable for applications sensitive to latency with real-time decisions. Other factors like security and privacy of data force the data to stay in the edge. With all these disadvantages, inference is migrating partial or totally to the edge. The problem, is that deep learning models are quite hungry in terms of computation, memory and energy which are not available in today’s edge computing devices. Therefore, artificial intelligence devices are being deployed by different companies with different markets in mind targeting edge computing. In this chapter we describe the actual state of algorithms and models for deep learning and analyze the state of the art of computing devices and platforms to deploy deep learning on edge. We describe the existing computing devices for deep learning and analyze them in terms of different metrics, like performance, power and flexibility. Different technologies are to be considered including GPU, CPU, FPGA and ASIC. We will explain the trends in computing devices for deep learning on edge, what has been researched, and what should we expect from future devices.
Automatic Segmentation of Liver Tumor in Multiphase CT Images by Mask R-CNN

In computer-aided diagnosis of liver tumors, tumor detection and segmentation are essential preprocessing steps. In this study, we proposed a method based on mask R-CNN to detect and segment the liver tumor in a multi-phase CT image simultaneously. Triple phase images are used as three different channels just like a color RGB image. The segmentation accuracy (Dice value) is about 0.60 ~ 0.66 for a single phase CT image, while the accuracy can be improved to about 0.73 by using the multi-phase CT image.
An Autonomous Courses Recommender System For Undergraduate Using Machine Learning Techniques

A University admission process is a complex process that needs a tremendous amount of time and labor to allocate course(s) to the prospective applicants. The study aimed at tackling the wrong placement of applicants into courses and to also address the wastage of admission vacancies by recommending the appropriate course(s). About 8,700 data for three academic sessions were collected from two different universities for training and testing the system. The features used are the results of Senior Secondary School subjects and the average score of UTME and Post-UTME. The proposed system employed five (5) classification models, which include Linear Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Decision Tree Algorithm. The Linear Regression Model has the Root Mean Square Error (RMSE) as 2. 614 e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-14</sup> The other four (4) classification models are also found to be efficient with an efficiency of at least 90% sequel to the dimensionality reduction in the dataset. The result shows that both Naive Bayes Classifier and Support Vector Machine have achieved the highest recommendation accuracy of approximately 99.94%, which outperformed Decision Tree and K-Nearest Neighbor algorithms with an accuracy of 98.10% and 99.87% respectively. The system can be adjusted with the change of admission criteria in Nigerian Universities. In consideration of the high degree of prediction accuracy, flexibility is an advantage, as the system can predict suitable courses that match the students' grades. Therefore, the system is adaptive and good in making the prediction and can be used as a framework for further research on the admission recommender system.
AdaPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Model-free policy learning has enabled good performance on complex tasks that were previously intractable with traditional control techniques. However, this comes at the cost of requiring a perfectly accurate model for training. This is infeasible due to the very high sample complexity of model-free methods preventing training on the target system. This renders such methods unsuitable for physical systems. Model mismatch due to dynamics parameter differences and unmodeled dynamics error may cause suboptimal or unsafe behavior upon direct transfer. We introduce the Adaptive Policy Transfer for Stochastic Dynamics (AdaPT) algorithm that achieves provably safe and robust, dynamically-feasible zero-shot transfer of RL-policies to new domains with dynamics error. AdaPT combines the strengths of offline policy learning in a black-box source simulator with online tube-based MPC to attenuate bounded dynamics mismatch between the source and target dynamics. AdaPT allows online transfer of policies, trained solely in a simulation offline, to a family of unknown targets without fine-tuning. We also formally show that (i) AdaPT guarantees bounded state and control deviation through state-action tubes under relatively weak technical assumptions and, (ii) AdaPT results in a bounded loss of reward accumulation relative to a policy trained and evaluated in the source environment. We evaluate AdaPT on 2 continuous, non-holonomic simulated dynamical systems with 4 different disturbance models, and find that AdaPT performs between 50 and $$300\%$$ better on mean reward accrual than direct policy transfer.
Probabilistic Logic Graph Attention Networks for Reasoning

Knowledge base completion, which involves the prediction of missing relations between entities in a knowledge graph, has been an active area of research. Markov logic networks, which combine probabilistic graphical models and first order logic, have proven to be effective on knowledge graph tasks like link prediction and question answering. However, their intractable inference limits their scalability and wider applicability across various tasks. In recent times, graph attention neural networks, which capture features of neighbouring entities, have achieved superior results on highly complex graph problems like node classification and link prediction. Combining the best of both worlds, we propose Probabilistic Logic Graph Attention Network (pGAT) for reasoning. In the proposed model, the joint distribution of all possible triplets defined by a Markov logic network is optimized with a variational EM algorithm. This helps us to efficiently combine first-order logic and graph attention networks. With the goal of establishing strong baselines for future research on link prediction, we evaluate our model on various standard link prediction benchmarks, and obtain competitive results.
Fast Algorithms for the Shortest Unique Palindromic Substring Problem on Run-Length Encoded Strings

For a string S, a palindromic substring S[i..j] is said to be a shortest unique palindromic substring (SUPS) for an interval [s,t] in S, if S[i..j] occurs exactly once in S, the interval [i,j] contains [s,t], and every palindromic substring containing [s,t] which is shorter than S[i..j] occurs at least twice in S. In this paper, we study the problem of answering SUPS queries on run-length encoded strings. We show how to preprocess a given run-length encoded string RLES of size m in O(m) space and $O(m \log \sigma _{\mathit {RLE}_{S}} + m \sqrt {\log m / \log \log m})$ time so that all SUPSs for any subsequent query interval can be answered in $O(\sqrt {\log m / \log \log m} + \alpha )$ time, where α is the number of outputs, and $\sigma _{\mathit {RLE}_{S}}$ is the number of distinct runs of RLES. Additionaly, we consider a variant of the SUPS problem where a query interval is also given in a run-length encoded form. For this variant of the problem, we present two alternative algorithms with faster queries. The first one answers queries in $O(\sqrt {\log \log m /\log \log \log m} + \alpha )$ time and can be built in $O(m \log \sigma _{\mathit {RLE}_{S}} + m \sqrt {\log m / \log \log m})$ time, and the second one answers queries in $O(\log \log m + \alpha )$ time and can be built in $O(m \log \sigma _{\mathit {RLE}_{S}})$ time. Both of these data structures require O(m) space.
The State of the Art on Secure Software Engineering

Secure Software Development (SSD) is becoming a major challenge, due to the increasing complexity, openness and extensibility of Information and Communication Technologies (ICTs). These make the overall security requirements analysis very difficult. Many techniques have been theoretically developed, however, there is a lack of empirical evidence of its application in building secure software system. A Systematic Mapping Study (SMS) has been conducted in this paper to examine the existence of software security frameworks, models and methods. In total, we selected 116 primary studies. After examining the selected studies, we identified 37 Secure Software Engineering (SSE) paradigms/frameworks/models. The results show that the most frequently used SSE frameworks/models are "Microsoft Software Development Life Cycle (MS-SDL)", "Misuse case modeling", "Abuse case modeling", "Knowledge Acquisition for Automated Specification", "System Security Engineering-Capability Maturity Model (SSE-CMM)" and "Secure Tropos Methodology". This work will help organizations in the development of software to better understand existing security initiatives used in the development of secure software. It can also provide researchers with a basis for designing and developing new methods of software security and identifying new axis of research.
Analysis of chaotic characteristics of trigonometric function system

Chaos, as an important subject of nonlinear science, plays an important role in solving problems in both natural sciences and social sciences such as the fields of secure communications, fluid motion, particle motion and so on. Aiming at this problem, this paper proposes a nonlinear dynamic system composed of product trigonometric functions and studies its chaotic characteristics. Through the mathematical derivation of the system’s period, the analysis of the necessary conditions at the fixed point, the experimental drawing of the Lyapunov exponential graph and the branch graph of the system, it is proved that the system has larger chaotic interval and stronger chaotic characteristics. The parameters of the proposed dynamic system are generated randomly, and then the chaotic sequence can be generated. The chaotic sequence is used to encrypt the digital image, a good encryption effect is obtained, and there is a large key space. At the same time, the motion of the particles in the space magnetic field is simulated, which further proves that the trigonometric system has strong chaotic characteristics.
Cascade architecture with rhetoric long short-term memory for complex sentence sentiment analysis

In a sentiment analysis task, it is essential to differentiate the various and sometimes even contradictory emotions in each segment and to judge the underlying true emotion of the whole sentence. Rhetorical structure theory hierarchically structures the relationships between segments and describes the effects of relations. This study proposes a flexible cascade architecture: the lower unit divides the sentence into segments and obtains their distributed representation vector; the upper rhetoric-based long short-term memory unit aggregates the information of every segment and applies the concrete hierarchical relation information to perform sentiment analysis. Auxiliary techniques, namely, data augmentation and relation clustering, are also proposed for preventing overfitting. The experiment results prove that the proposed cascade architecture and auxiliary techniques improve the traditional approaches in most cases, which shows 3.17% accuracy growth in the fine-grained classification and 1.41% in the binary tasks at most. Furthermore, the cascade architecture is flexible enough, which could be easily extended by combining the Rhetoric-LSTM unit with the state-of-the-art classification or pre-trained models if necessary.
Deep Color Transfer for Color-Plus-Mono Dual Cameras

A few approaches have studied image fusion using color-plus-mono dual cameras to improve the image quality in low-light shooting. Among them, the color transfer approach, which transfers the color information of a color image to a mono image, is considered to be promising for obtaining improved images with less noise and more detail. However, the color transfer algorithms rely heavily on appropriate color hints from a given color image. Unreliable color hints caused by errors in stereo matching of a color-plus-mono image pair can generate various visual artifacts in the final fused image. This study proposes a novel color transfer method that seeks reliable color hints from a color image and colorizes a corresponding mono image with reliable color hints that are based on a deep learning model. Specifically, a color-hint-based mask generation algorithm is developed to obtain reliable color hints. It removes unreliable color pixels using a reliability map computed by the binocular just-noticeable-difference model. In addition, a deep colorization network that utilizes structural information is proposed for solving the color bleeding artifact problem. The experimental results demonstrate that the proposed method provides better results than the existing image fusion algorithms for dual cameras.
Visual object tracking using sparse context-aware spatio-temporal correlation filter

This paper presents a novel sparse context-aware spatio-temporal correlation filter tracker (SCAST) method for robust visual object tracking. Different from the existing trackers, this paper introduce an l1 multi-scale regularization parameter-based correlation filter that reduces the boundary effect due to partial occlusions, illumination and scale variations. At each iteration, the l1 regularization parameter is updated through spatial knowledge of each correlation filter coefficient. Besides, the contextual information acquired from the target region can lead to determining the accurate localization of the target. Moreover, contextual information has combined with spatio-temporal factor to achieve the better performance. Further, an objective function is designed with system constraints to ensure the applicability of the model and the optimal solution is derived by utilizing the alternating direction method of multiplier, which leads to low computational cost. Finally, the feasibility and superiority of proposed tracker algorithm is evaluated through three benchmark dataset: OTB-2013, OTB-2015, and TempleColor-128.
A Time Bank System Design on the Basis of Hyperledger Fabric Blockchain

This paper presents a blockchain-based time bank system on the basis of the Hyperledger Fabric framework, which is one of the permissioned blockchain networks. Most of the services provided by existing Time Bank systems were recorded and conducted manually in the past; furthermore, jobs for matching services with receivers were managed by people. Running a time bank in this way will cost lots of time and human resources and, worse, it lacks security. This work designs and realizes a time bank system enabling all the service-related processes being executed and recorded on a blockchain. The matching between services’ supply-and-demand tasks can directly be done through autonomous smart contracts. Building a time bank system on blockchain benefits the transaction of time credit which plays the role of digital currency on the system. In addition, the proposed time bank also retains a grading system, allowing its members to give each other a grade for reflecting their degrees of satisfaction about the results provided by the system. This grading system will incentivize the members to provide a better quality of service and adopt a nicer attitude for receiving a service, which may positively endorse the development of a worldwide time bank system.
Stream Data Preprocessing: Outlier Detection Based on the Chebyshev Inequality with Applications

A novel method of outlier detection for preprocessing stream data in the conditions of uncertainty when the data means and standard errors as the only available summary information about the initial data statistics is proposed. As neither the initial data samples nor their sample sizes are known, the classical methods of outlier detection, including nonparametric methods of statistics, cannot be applied in this case. The principal idea of the proposed approach to outlier detection is based on the use of the classical Gauss-Chebyshev type probability inequalities-the corresponding confidence intervals constructed on these inequalities allow to set up the problems of hypotheses testing similar to the classical settings as the problems of minimizing the upper bound of the Bayesian risk and maximizing the lower bound of the test power in the Neyman-Pearson sense. The results of the processing of the real-life data (Lunar Laser Ranging data) and the model data manifest unexpectedly good outlier detection performance.
Blind Stereo Image Quality Assessment Based on Binocular Visual Characteristics and Depth Perception

The quality prediction of stereo images has great challenges without reference images. In this paper, we propose a novel no-reference stereo image quality assessment (NR-SIQA) model based on binocular visual characteristics and depth perception, which can effectively evaluate the quality of symmetric distortion and asymmetric distortion images. To be specific, we discriminate the different binocular behaviors by analyzing binocular visual characteristics, and construct the corresponding cyclopean view instead of single cyclopean view to simulate different binocular behaviors. Then, we extract monocular and binocular visual features from the left view, the right view and the synthetic cyclopean view. Furthermore, in order to evaluate the depth quality of the stereo image accurately, we extract the depth perception features from the weighted disparity map and the longitudinal correlation coefficient map. Finally, we construct the mapping relationship model from quality perception feature domain to quality score domain by training an adaptive enhancement algorithm based on support vector regression (SVR). We evaluate the performance of the proposed algorithm on four stereo image databases. The experimental results show that compared with the state-of-the-art full reference(FR), reduced reference(RR) and NR-SIQA algorithms, the proposed algorithm achieves highly competitive performance for both symmetric and asymmetric distortions.
Optimal texture image reconstruction method for improvement of SAR image matching

Image matching is an important step that is taken in most synthetic aperture radar (SAR) images applications. In this study, a method is proposed for texture image reconstruction to improve SAR image matching. This method consists of three major steps. First, textural features are extracted. Second optimal texture images (OTIs) are created using extracted features. Texture optimisation is conducted using a cat swarm optimisation algorithm. The cost function is the number of correct matched points that are obtained from speeded-up robust features (SURFs) image matching algorithm. Finally, corresponding points on stereo OTI pairs are matched. Experiments were conducted on spaceborne SAR image pairs consist of RADARSAT-2, ALOS-PALSAR, and Sentinel-1. Results demonstrate that the proposed method has better results in terms of the number of matched points compared to the result of the original SURF and scale-invariant feature transform methods as two common matching methods.
Scene-Edge GRU for Video Caption

Recurrent neural networks for video caption have recently attracted widespread attention. It is essential for the video captioning task as it is involved in both the encoding phase and the text description generation phase of the video. However, the traditional encoding-decoding method ignores the scene switching in the video during the encoding phase. In this paper, we propose a video encoding scheme that can discover the structure of a video scene, so as to achieve variable length of the flexible encoding for the video. Unlike the classic encoding-decoding scheme, we propose a new GRU unit that recognizes discontinuities between video frames and enables end-to-end training without the need for additional annotation information. We evaluated our approach on two large datasets: the MPII movie description dataset, and the MSVD dataset. Experiments have shown that our method can find the appropriate level representation of the video and improve the best results of the movie description dataset.
Towards Controllable Explanation Generation for Recommender Systems via Neural Template

It has been commonly agreed that the explanation associated with recommendation can be effective in increasing the recommender systems (RS)'s transparency and thus users' satisfaction and acceptance. Among the various types of explanation in RS, the commonly used textual explanation can be roughly classified into two categories, i.e., template-based and generation-based. As for the former, the fixed template may lose flexibility, while, though the latter may enrich the explanation, it may produce less useful content due to the lack of controllability. In this work, we combine the advantages of the two types of method by developing a neural generation approach named Neural Template (NETE) whose explanations are not only flexible but also controllable and useful. Our human evaluation results confirm that the explanations from our model are perceived helpful by users. Furthermore, our case study illustrates that the explanation generation process is controllable. To demonstrate the controllability of our model, we present a demo that can be easily viewed on a Web browser.
High-resolution, ultrafast, wide-field retinal eye-tracking for enhanced quantification of fixational and saccadic motion

We introduce a novel, noninvasive retinal eye-tracking system capable of detecting eye displacements with an angular resolution of 0.039 arcmin and a maximum velocity of 300°/s across an 8° span. Our system is designed based on a confocal retinal imaging module similar to a scanning laser ophthalmoscope. It utilizes a 2D MEMS scanner ensuring high image frame acquisition frequencies up to 1.24 kHz. In contrast with leading eye-tracking technology, we measure the eye displacements via the collection of the observed spatial excursions for all the times corresponding a full acquisition cycle, thus obviating the need for both a baseline reference frame and absolute spatial calibration. Using this approach, we demonstrate the precise measurement of eye movements with magnitudes exceeding the spatial extent of a single frame, which is not possible using existing image-based retinal trackers. We describe our retinal tracker, tracking algorithms and assess the performance of our system by using programmed artificial eye movements. We also demonstrate the clinical capabilities of our system with in vivo subjects by detecting microsaccades with angular extents as small as 0.028°. The rich kinematic ocular data provided by our system with its exquisite degree of accuracy and extended dynamic range opens new and exciting avenues in retinal imaging and clinical neuroscience. Several subtle features of ocular motion such as saccadic dysfunction, fixation instability and abnormal smooth pursuit can be readily extracted and inferred from the measured retinal trajectories thus offering a promising tool for identifying biomarkers of neurodegenerative diseases associated with these ocular symptoms.