Reviewerly/exHarmony huggingface dataset:
A social robot empowered with a new programming language and its performance in a laboratory

For the last years, social robots have been used to serve as tour-guide robots in museums, laboratories, trade fairs, etc. So they are intended to perform tasks which will be affected by the interaction with people. In order to perform these tasks, a task planner must be developed in order to integrate the several modules that the robot contains in its software architecture. In this paper a new programming language was developed in order to command the actions of the robot over the place where the social robot is going to perform its actions. Lexical, syntactical and semantical analyzers were developed based on a simple language syntax with the possibility to create events. These events can come from different sensors installed like the RFID, cameras, etc. The testbed is Doris, an interactive robot developed in the Centre of Automation and Robotics. The results show that the new language can merge all the modules from Doris, executing the actions (trajectories, speeches and actions based on events) specified in the program provided by the developer without making any changes in the lower layers of the framework.
THE VANISHING EFFECT OF FINANCE ON ECONOMIC DEVELOPMENT

This paper examines the mechanisms that determine the “vanishing effect of finance” on economic growth found in recent studies. Based on both current (171 countries, 1960–2014) and historical (21 OECD countries, 1870–2009) data, the results show that financial development promotes growth in poorer countries by increasing education and investment, and by decreasing fertility. The relevance of these transmission channels declines when countries become richer. The growth effect of the financial sector in high-income countries primarily depends on new ideas and potentials for innovation projects. Consequently, the major decline in factor productivity growth since the early 2000s has contributed to the reduction in the financial sector’s average effect on growth.
An Energy-Efficient Accelerator with Relative- Indexing Memory for Sparse Compressed Convolutional Neural Network

Deep convolutional neural networks (CNNs) are widely used in image recognition and feature classification. However, deep CNNs are hard to be fully deployed for edge devices due to both computation-intensive and memory-intensive workloads. The energy efficiency of CNNs is dominated by off-chip memory accesses and convolution computation. In this paper, an energy-efficient accelerator is proposed for sparse compressed CNNs by reducing DRAM accesses and eliminating zero-operand computation. Weight compression is utilized for sparse compressed CNNs to reduce the required memory capacity/bandwidth and a large portion of connections. Thus, ReLU function produces zero-valued activations. Additionally, the workloads are distributed based on channels to increase the degree of task parallelism, and all-row- to-all-row non-zero element multiplication is adopted for skipping redundant computation. The simulation results over the dense accelerator show that the proposed accelerator achieves 1.79x speedup and reduces 23.51%, 69.53%, 88.67% on-chip memory size, energy, and DRAM accesses of VGG-16.
Path Planning for Anti-Ship Missile Using Tangent Based Dubins Path

Path planning is a significant component to achieve successful operation of anti-ship missile in dynamic battlefield environment. A threat-free path planning algorithm based on geometric tangent Dubins path is proposed in this paper. First, the constraints need to be satisfied for threat-free path planning are presented, and the problem model of missile path planning is constructed. Then, the geometric tangent between two threat circles is taken advantage of guiding the missile to avoid the threat, and the basic turn and straight line path is generated based on Dubins path. At last, the adaptability of the proposed algorithm in complex battlefield environment is verified by simulations. The results show that the Dubins path avoidance method based on geometric tangent can meet the requirements of flight path, obstacle avoidance and real-time planning, which make it have a strong application prospect.
Residual Magnifier: A Dense Information Flow Network for Super Resolution

Recently, deep learning methods have been successfully applied to single image super-resolution tasks. However, some networks with extreme depth failed to achieve better performance because of the insufficient utilization of the local residual information extracted at each stage. To solve the above question, we propose a Dense Information Flow Network (DIF-Net), which can fully extract and utilize the local residual information at each stage to accomplish a better reconstruction. Specifically, we present a Two-stage Residual Extraction Block (TREB) to extract the shallow and deep local residual information at each stage. The dense connection mechanism is introduced throughout the model and within TREBs to dramatically increase the information flow. Meanwhile this mechanism prevents the shallow features extracted earlier from being diluted. Finally, we propose a lightweight subnet (residual enhancer) to efficiently recycle the overflow residual information from the backbone net for detail enhancement of the residual image. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods with relatively-less parameters.
Computational technology for solution of the reverse problem of filtration theory for oil fields with an aquifer

In this paper are proposes a technology for solving the inverse problem of identifying parameters of the aquifer for an oil field. The solution is to determine the boundary conditions, including the definition of their type, and spatial location. On the example of one of the fields, the computing technology of solving the inverse problem was tested to restore the parameters of the aquifer. Using the proposed algorithm, maps and graphs of aquifer activity were plotted.
An Accurate Phase Measuring Deflectometry Method for 3D Reconstruction of Mirror-Like Specular Surface

Measurement of specular surface shape is still a challenging task for existing 3D scanning techniques. In this paper, an accurate phase measuring deflectometry method is investigated for 3D reconstruction of mirror surfaces. To calibrate the system parameters, a standard flat mirror is used. By the reflected checkerboard pattern displayed by the LCD screen, the camera intrinsic parameters are calibrated firstly. The last orientation of calibration mirror is aligned as the reference plane. With such a calibration manner, intrinsic camera parameters, lens distortion and the LCD position can be determined efficiently. By displaying both horizontal and vertical Gray code plus sinusoidal shifting patterns on the LCD screen, dense surface gradient field is calculated with the proposed slope calculation method, and then integrated to retrieve precise 3D surface shapes. The experiments conducted with both flat and concave mirror surface demonstrate that high precision measurement results can be obtained.
Development of Machine Learning Based Recommendation Engine for Embedded Programmer

In this paper, we propose a machine learning based recommendation engine that can assist embedded programmers to quickly search and query related code segments and instructions. In the design, we build a code database and each code segment is auto-classified and multi-labeled with the chip model, application scenario, function module, and register name. The similarity of each level is calculated by Hamming Distance, Cosine similarity, and Euclidean distance approaches separately. Based on the different similarities of each level, we come up with a dynamic machine learning model to train the weights of each similarity. The final similarity scores of different codes are generated and sorted, and then the related embedded codes are recommended to programmer according to the final similarity score. Comparisons are performed against single similarity methodology. The experimental results show proposed methodology outperforms the single similarity approach and could assist embedded programmers find the related code more accurately.
Learning a Deep Representative Saliency Map With Sparse Tensors

The past few years have witnessed the prosperity of establishing deep architectures for modeling complex structured data. In this paper, motivated by the hierarchical, multi-scale and sparse characteristics of Human Visual System (HVS), we advance a new Deep and Sparse Representative (DSR) saliency detection approach for natural images. Under the assumption that salient regions are considered as sparse representatives of an image, we gradually select the salient regions via a deep and hierarchical sparse coding model. Moreover, in order to well preserve image structures and achieve more efficient coding, we formulate image patches as tensors and propose a Sparse Tensor Coding based DSR (STC-DSR) approach to find representatives. A generalized Laplacian regularizer is further cast on the coding to refine the coefficients. Finally, the saliency map is formulated by a spatial aggregation and multi-scale fusion of multilayer representatives. The proposed STC-DSR is investigated on the synthetic and public datasets and the experimental results prove its efficiency and superiority to its counterparts.
Program Logics Based on Algebras with the Composition of Predicate Complement

The formalism of program logics is the main instrument for software verification. Many such logics reflecting different properties of software systems were proposed. Floyd-Hoare logic and its variants are popular for program verification. But this logic is oriented on total pre- and post-conditions (predicates) and becomes unsound in the case of partial predicates. In our previous works we considered different methods to extend Floyd-Hoare logic for partial predicates. One of the methods involves program algebras with the composition of predicate complement. Introduction of this composition permits to modify rules of the logic making them sound, but the obtained logic becomes more complicated because it involves undefinedness conditions. In this paper we continue our research of such logics. We introduce special consequence relation and prove the soundness and completeness theorems for the case of logic of propositional level with the composition of predicate complement.
Impact of Android Phone Rooting on User Data Integrity in Mobile Forensics

Modern cellular phones are potent computing devices, and their capabilities are constantly progressing. The Android operating system (OS) is widely used, and the number of accessible apps for Android OS phones is unprecedented. The increasing capabilities of these phones imply that they have distinctive software, memory designs, and storage mechanisms. Furthermore, they are increasingly being used to commit crimes at an alarming rate. This aspect has heightened the need for digital mobile forensics. Because of the rich user data they store, they may be relevant in forensic investigations, and the data must be extracted. However, as this study will show, most of the available tools for mobile forensics rely greatly on rooted (Android) devices to extract data. Rooting, as some of the selected papers in this research will show, poses a key challenge for forensic analysts: user data integrity. Rooting per se, as will be seen, is disadvantageous. It is possible for forensic analysts to extract useful data from Android phones via rooting, but the user data integrity during data acquisition from Android devices is a prime concern. In suggesting an alternative rooting technique for data acquisition from an Android handset, this paper determines whether rooting is forensically sound. This is particularly due to the device's modification, which a root often requires, that may violate the data integrity.
Quantitative research of convolutional neural network and FPGA deployment

Convolutional neural networks have become indispensable in the field of artificial intelligence. It is very important to implement the convolutional neural networks in FPGAs especially for some specific applications. However, the characteristics of convolutional neural networks such as large model and multiple parameters pose great challenges to the deployment of that in FPGAs. Typically, the convolutional neural network model need to be quantized and compressed while deploying it in FPGAs. In this paper, we trained the weight binary network and implemented it in the FPGA platform. The HDL descriptions are mainly converted from C++ high-level language with XILINX tool. Experimental results show that the implemented scheme has low power consumption, high precision and performance.
EarlGrey: iOS UI Automation Testing Framework

In this paper, we outline the challenges faced in scalable UI testing of applications on Apple's iOS platform. We analyze how different existing UI testing Tools have provided solutions for them and highlight EarlGrey, Google's in-house UI testing tool. We delve into the technical aspects of EarlGrey 2.0, a multi-process UI testing tool in regards to these challenges.
Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative Filtering

Recommender systems are ubiquitous in the modern internet, where they help users find items they might like. A widely deployed recommendation approach is item-based collaborative filtering. This approach relies on analyzing large item cooccurrence matrices that denote how many users interacted with a pair of items. The potentially quadratic number of items to compare poses a scalability bottleneck in analyzing such item cooccurrences. Additionally, this problem intensifies in real world use cases with incrementally growing datasets, especially when the recommendation model is regularly recomputed from scratch. We highlight the connection between the growing cost of item-based recommendation and densification processes in common interaction datasets. Based on our findings, we propose an efficient incremental algorithm for item-based collaborative filtering based on cooccurrence analysis. This approach restricts the number of interactions to consider from 'power users' and 'ubiquitous items' to guarantee a provably constant amount of work per user-item interaction to process. We discuss efficient implementations of our algorithm on a single machine as well as on a distributed stream processing engine, and present an extensive experimental evaluation. Our results confirm the asymptotic benefits of the incremental approach. Furthermore, we find that our implementation is an order of magnitude faster than existing open source recommender libraries on many datasets, and at the same time scales to high dimensional datasets which these existing recommenders fail to process.
Restoration of Underwater Distorted Image Sequence Based on Generative Adversarial Network

Underwater images will be distorted due to the influence of turbulence, and images will appear geometric distortion since the light is refracted by the turbulence, which makes task of image recognition difficult. In order to improve image recognition underwater, this paper proposes an image restoration method using underwater distorted image sequence through deep learning technique. Considering the complexity of dynamics motion, image sequence is more feasible to realize task of restoration, which contains enough information of water turbulence. Generative adversarial network as a deep neural network has proved to be an appropriate method applying to the field of image processing, which is used to restore the distorted image. Experiment shows the proposed method has fine ability of using distorted image sequence to realize image restoration.
Quantum-enhanced estimation of the optical phase gradient by use of image-inversion interferometry

We show that the quantum Cram\'er-Rao bound on the error of measurement of the optical phase gradient with a beam of finite width (or the wavefront tilt within a finite aperture) is consistent with the Fourier-transform uncertainty principle for the single-photon state, and is a factor of $N$ lower for the maximally entangled $N$-photon state. This fundamental bound therefore governs the tradeoff between quantum sensitivity and spatial resolution. Error bounds for a structured configuration using binary projective-field measurements implemented by an image-inversion (II) interferometer are higher, and the factor of $N$ advantage attained by the $N$-photon maximally entangled state is reduced and eventually washed out as the beam width or the phase gradient increases. This reduction is more rapid for larger $N$, so that the quantum advantage is more vulnerable. The precision of the II interferometer is greater than that based on a split detector placed in the focal plane of a lens.
Visualizing Structures in Confocal Microscopy Datasets Through Clusterization: A Case Study on Bile Ducts

Three-dimensional datasets from biological tissues have increased with the evolution of confocal microscopy. Hepatology researchers have used confocal microscopy for investigating the microanatomy of bile ducts. Bile ducts are complex tubular tissues consisting of many juxtaposed microstructures with distinct characteristics. Since confocal images are difficult to segment because of the noise introduced during the specimen preparation, traditional quantitative analyses used in medical datasets are difficult to perform on confocal microscopy data and require extensive user intervention. Thus, the visual exploration and analysis of bile ducts pose a challenge in hepatology research, requiring different methods. This paper investigates the application of unsupervised machine learning to extract relevant structures from confocal microscopy datasets representing bile ducts. Our approach consists of pre-processing, clustering, and 3D visualization. For clustering, we explore the density-based spatial clustering for applications with noise (DBSCAN) algorithm, using gradient information for guiding the clustering. We obtained a better visualization of the most prominent vessels and internal structures.
Collaborative Filtering Recommendation Algorithm Based on Element-Wise Alternating Least Squares and Time Weight

Aiming at the problem of lack of time information in traditional collaborative filtering recommendation algorithm, the dynamic characteristics of user preference drift over time are analyzed. An improved scheme of collaborative filtering algorithm based on time characteristic is proposed. In calculating the predictive score, taking into account the characteristics of user interest decay over time, the time decay function is introduced as the weight of the loss function, the lower the weight for the interaction farther from the present time, the closer the interaction is with the higher weight, Element-wise Alternating Least Squares (eALS) is used to improve model training speed when updating models. Using the public dataset Yelp to conduct experiments, compared with the traditional RCD method, the predicted hit rate using the eALS method is significantly improved, and the T-eALS method with the added time information makes the hit rate increase significantly, and the experimental results show that this method can effectively integrate time information and provide users with more accurate recommendations.
Disposable dynamic accumulators: toward practical privacy-preserving mobile eIDs with scalable revocation

Providing methods to anonymously validate user identity is essential in many applications of electronic identity (eID) systems. A feasible approach to realize such a privacy-preserving eID is the usage of group signature protocols or pseudonym-based signatures. However, providing a revocation mechanism that preserves privacy is often the bottleneck for the scalability of such a system. In order to bridge this gap between practicability and privacy, we propose a new pseudonym-based mobile eID signature scheme suitable for smart cards and secure elements that also enables efficient and scalable revocation checks. By using a pseudorandom function, we derive one-time verification tokens used for identity verification as well as revocation checks and generate proofs of validity using a new method referred to as disposable dynamic accumulators. Our scheme preserves unlinkability and anonymity of the eID holder even beyond revocation and does not require online connectivity to a trusted party for verification and revocation checks.
Introducing Data-Centric Engineering to Instrumented Infrastructure

A variety of methods exist in the structural health monitoring literature that aim to combine the observed data and predicted outputs from physics-based models (e.g.model updating and calibration).Typically implemented on a case-by-case basis, there is currently no unifying procedure or method for formally synthesising this combination of information.Data-centric engineering (DCE) is an emerging class of analytical approaches that is aimed at studying engineered systems and assets through the synthesis of various data-driven and physicsbased models.DCE may also involve procedures for fusing sensor data from both experimental and operational systems.This paper introduces a DCE-based approach using data collected from experimental and operational railway structures (bridges and sleepers), which have been instrumented with advanced fibre optic sensors (FOS).This study provides a high-level definition of DCE approaches to studying instrumented infrastructure and discusses some of the challenges in implementing these methods in practice.An example of an implementation of a Gaussianprocess based DCE method is provided, with the goal of predicting the response of operational instrumented rail infrastructure (i.e.concrete sleepers) over time.Some practical issues of this implementation relate to the measurement systems, data acquisition rate, efficiently batch processing the data and accounting for uncertainty in the response predictions.In leveraging both the information gained from real-time measurement data and from traditional analytics or physics-based methods, a DCE-based modelling approach can provide unique a set information and insights into the operational performance of infrastructure.
Iterative Differential Characteristic of TRIFLE-BC

TRIFLE is a Round 1 candidate of the NIST Lightweight Cryptography Standardization process. In this paper, we present an interesting 1-round iterative differential characteristic of the underlying block cipher TRIFLE-BC used in TRIFLE, which holds with probability of $$2^{-3}$$. Consequently, it allows to mount distinguishing attack on TRIFLE-BC for up to 43 (out of 50) rounds with data complexity $$2^{124}$$ and time complexity $$2^{124}$$. Most importantly, with such an iterative differential characteristic, the forgery attack on TRIFLE can reach up to 21 (out of 50) rounds with data complexity $$2^{63}$$ and time complexity $$2^{63}$$. Finally, to achieve key recovery attack on reduced TRIFLE, we construct a differential characteristic covering three blocks by carefully choosing the positions of the iterative differential characteristic. As a result, we can mount key-recovery attack on TRIFLE for up to 11 rounds with data complexity $$2^{63}$$ and time complexity $$2^{104}$$. Although the result in this paper cannot threaten the security margin of TRIFLE, we hope it can help further understand the security of TRIFLE.