Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenario-Simplified Successive Cancellation Decoding of Polar Codes for Channel With Deletions

Successive cancellation-based decoding algorithm and corresponding polarization theorems for polar codes over channels with deletions have been proposed recently. In that decoding algorithm, each node in the conventional successive cancellation decoding trellis is divided into many different scenarios according to different deletion patterns. The number of scenarios increases with the square of the number of deletion errors <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> which results in high decoding complexity. In this paper, to reduce the decoding complexity, we propose the scenario-simplified successive cancellation decoding algorithm for the polar codes over the deletion channel. In the proposed decoding algorithm, we use exact upper and lower bounds to identify the feasible scenarios of each node in the decoding trellis and avoid calculating the impossible scenarios. And by rearranging the scenario index table, the operations of calculating indices of scenarios can be simplified. We also investigate the joint-weight for each scenario. By setting a threshold <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> to prune the scenarios with low joint-weight probabilities, the complexity can be reduced further. For polar codes of length <inline-formula> <tex-math notation="LaTeX">$N=512$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$d = 10$ </tex-math></inline-formula>, we can reduce 42.5% stored scenarios and 46.8% computed scenarios when <inline-formula> <tex-math notation="LaTeX">$\tau = 10^{-5}$ </tex-math></inline-formula> with a negligible performance loss.
Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.
ARCA-IoT: An Attack-Resilient Cloud-Assisted IoT System

Putting trust in the world of the Internet of Things, where served and serving entities are often unknown, is very hard especially when personal and business information is often being exchanged for providing and consuming services. Moreover, the issues of interoperability and scalability of billions of heterogeneous things in the IoT systems require more attention. A user-centric model of complex interconnected things must be designed in a way that not only makes things trustworthy for common people but it also provides the solution for interoperability and scalability. ARCA-IoT is such a system which not only identifies the attributes (including quality of service) essential for trust but also presents a user-centric model that is robust enough to tackle the attacks made by dishonest entities to manipulate the trustworthiness. For scalability and interoperability, a cloud-assisted environment is introduced in the ARCA-IoT. An intuitive Naive Bayes approach is used to train the ARCA-IoT in a way that it calculates the probabilities of the trustworthiness of the entities and then identifies various types of attacks with the support of three proposed algorithms. The approach is validated with a specifically designed simulated environment. Based on our simulation results, the ARCA-IoT demonstrates the effectiveness in term of performance metrics such as accuracy, sensitivity, specificity, and precision. Besides this, the system outperforms the existing related approaches in terms of a qualitative analysis based on different parametric metrics such as interoperability, scalability, context-awareness, and a human-like decision.
Neural Network Localization With TOA Measurements Based on Error Learning and Matching

Due to the widespread application of location information, the neural network localization method with the advantage of high localization accuracy has received significant interests in recent years. In this paper, we present two new neural network localization methods with time-of-arrival (TOA) measurements. In order to deal with three types of error about TOA measurements such as measurement error, non-line of sight (NLOS) error, and synchronization error, the proposed methods contain an offline training stage and an online localization stage. In the offline stage, the artificial neural network (ANN) or radial basis function (RBF) neural network is utilized to train the range measurements with the output of range errors rather than the position of the mobile terminal (MT). Moreover, due to the unknown signal propagation condition whether it is the line of sight or NLOS propagation, the $k$ -mean clustering algorithm is used to classifying the range errors into different clusters. In the online stage, the range errors are predicted and updated, and then, the linear least square algorithm with the adjusted range measurements is applied for the position estimate of MT. Comparing with the ANN or RBF neural network localization methods, the simulation results show that the proposed methods can effectively reduce the localization error, especially when the training sample is not adequate. In addition, they are insensitive to measurement error, synchronization error, and the distribution of NLOS error. Finally, the memory requirement and computational complexity about different algorithms are analyzed and compared.
Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition

This paper reviews evidence for the idea that much of human learning, perception, and cognition may be understood as information compression and often more specifically as “information compression via the matching and unification of patterns” (ICMUP). Evidence includes the following: information compression can mean selective advantage for any creature; the storage and utilisation of the relatively enormous quantities of sensory information would be made easier if the redundancy of incoming information was to be reduced; content words in natural languages, with their meanings, may be seen as ICMUP; other techniques for compression of information—such as class-inclusion hierarchies, schema-plus-correction, run-length coding, and part-whole hierarchies—may be seen in psychological phenomena; ICMUP may be seen in how we merge multiple views to make one, in recognition, in binocular vision, in how we can abstract object concepts via motion, in adaptation of sensory units in the eye of Limulus, the horseshoe crab, and in other examples of adaptation; the discovery of the segmental structure of language (words and phrases), grammatical inference, and the correction of over- and undergeneralisations in learning may be understood in terms of ICMUP; information compression may be seen in the perceptual constancies; there is indirect evidence for ICMUP in human cognition via kinds of redundancy such as the decimal expansion of π which are difficult for people to detect; much of the structure and workings of mathematics—an aid to human thinking—may be understood in terms of ICMUP; and there is additional evidence via the SP Theory of Intelligence and its realisation in the SP Computer Model. Three objections to the main thesis of this paper are described, with suggested answers. These ideas may be seen to be part of a “Big Picture” with six components, outlined in the paper.
Student Engagement is Key to Broadening Participation in CS

The Mobile CS Principles (Mobile CSP) course is one of the NSF-supported, College Board-endorsed curricula for the new Computer Science Principles AP course. Since 2013, the Mobile CSP project has trained more than 700 teachers, and the course has been offered to more than 20,000 students throughout the United States. The organizing philosophy behind the Mobile CSP course is that student engagement in the classroom is the key to getting students, especially those traditionally underrepresented in CS, interested in pursuing further study and careers in CS. The main strategies used to engage Mobile CSP students are: (1) a focus on mobile computing throughout the course, taking advantage of current student interest in smartphones; (2) an emphasis on getting students building mobile apps from day one, by utilizing the highly accessible App Inventor programming language; and (3) an emphasis on building creative, 'socially useful' apps to get students thinking about ways that computing can help their communities. In this paper we present and summarize two years of data of various types (i.e., student surveys, teacher surveys, objective assessments, and anecdotal reports from students and teachers) to support the hypothesis that engagement of the sort practiced in the Mobile CSP course not only helps broaden participation in CS among hard-to-reach demographics, but also provides them with a solid grounding in computer science principles and practices.
A Triple-Mode Performance-Optimized Reconfigurable Incremental ADC for Smart Sensor Applications

This paper proposes a triple-mode discrete-time incremental analog-to-digital converter (IADC) employing successive approximation register (SAR)-based zooming and extended counting (EC) schemes to achieve programmable trade-off capability of resolution and power consumption in various smart sensor applications. It mainly consists of an incremental delta–sigma modulator and the proposed SAR-EC sub-ADC for alternate operation of the coarse SAR conversion and EC. They can be reconfigured to operate separately depending on the application requirements. The SAR-based zooming structure allows the IADC to have better linearity and resolution, and additional activation of the EC function gives the further resolution. During this reconfigurable conversion process, pipelined reusing operation of sub-blocks reduces the silicon area and the number of cycles for target resolutions. A prototype ADC is fabricated in a 180-nm CMOS process, and its triple-mode operation of high-resolution, medium-resolution, and low-power is experimentally verified to achieve 116.1-, 109.4-, and 73.3-dB dynamic ranges, consuming 1.60, 1.26, and 0.39 mW, respectively.
Opinion Polarization during a Dichotomous Electoral Process

Political polarization can emerge on electoral campaigns where the population faces a dichotomous decision, with only two voting alternatives. In this paper, we analyze the Twitter conversation around the second round of the 2017 Chilean elections, where voters had to choose between the final two candidates. First, we have estimated the opinions of Twitter users obtaining a distribution of opinions for each day. Next, we have measured the resulting political polarization from the cited opinion distributions and track its evolution during a full week that includes the voting day. We found the conversation to be highly polarized, reaching its maximum during the previous day to the election and significantly decreasing the day after due to the presence of new users who only participated during that day.
Optimization for Nonlinear Uncertain Switched Stochastic Systems with Initial State Difference in Batch Culture Process

Based on the deterministic description of batch culture expressed in form of switched ordinary differential equations, we introduce a switched stochastic counterpart system with initial state difference together with uncertain switching instants and system parameters to model the process of glycerol biodissimilation to 1,3-propanediol (1,3-PD) induced byKlebsiella pneumoniae(K. pneumoniae). Important properties of the stochastic system are discussed. Our aim is to obtain the unified switched instants and system parameters under the condition of different initial states. To do this, we will formulate a system identification problem in which these uncertain switched instants and system parameters are regarded as decision variables to be chosen such that the relative error between experimental data and computational results is minimized. Such problem governed by the stochastic system is subject to continuous state inequality constraints and box constraints. By performing a time-scaling transformation as well as introducing the constraint transcription and local smoothing approximation techniques, we convert such problem into a sequence of approximation subproblems. Considering both the difficulty of finding analytical solutions and the complex nature of these subproblems, we develop a parallelized differential evolution (DE) algorithm to solve these approximation subproblems. From an extensive simulation, we show that the obtained optimal switched instants and system parameters are satisfactory with initial state difference.
On the Design of Broadband Hybrid Amplifiers Using Non-Uniform Transmission Lines as Impedance Matching Networks

In this paper, an approach for the practical design of broadband amplifiers in hybrid technology is presented. It is based on the use of non-uniform transmission lines to implement impedance matching networks, which are synthesized exploiting the powerful optimization tools available in the most common computer-aided design software packages. The versatility of the proposed technique makes it suitable for the design of a wide variety of broadband amplifiers. Furthermore, it is easily implementable in most microwave simulators and allows a considerable design time reduction, since it can be applied in a systematic way and avoids the use of external tools. To validate the technique, it is applied to the design of a low noise amplifier using a single encapsulated transistor. A prototype was implemented, providing 10-dB flat gain from 1 to 12 GHz, noise figure under 2.5 dB, and acceptable input and output matching, which agrees with the simulation data.
Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation

ABSTRACT PC is a prototypical constraint-based algorithm for learning Bayesian networks, a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg, was developed to make the skeleton phase order independent. In return, it has notably increased execution time. In this paper, we clarify that the PC algorithm the skeleton phase of PC is indeed order independent. The modification we propose outperforms pcalg’s variant of the PC in terms of returning correct networks of better quality as is less prone to errors and in some cases it is a lot more computationally cheaper. In addition, we show that pcalg’s variant does not return valid acyclic graphs.
Pervasive and Personalized Ambient Parameters Monitoring: A Wearable, Modular, and Configurable Watch

An innovative, small, compact, light-weighted, configurable, and low power consumption wrist-worn prototype in the area of ambient parameters monitoring, in five physical layers, is introduced. The prototype is based on the multi-layer multi-sensor approach and is capable of measuring a wide range of toxic/hazardous gases, physical ambient parameters, and motion tracking. The proposed device operates in the stand-alone (BLE disconnected) and configurable (BLE connected) modes. The stand-alone mode supports real-time data monitoring on the display and data storage in an integrated external memory. The logged data are retrieved once BLE is resumed based on the user decision. In the configuration mode, sensors are activated and configured by the user via sending a command from the smartphone. The chemical parameters monitoring includes three hazardous gases (SO2, NO2, and CO one at each time). The toxic gas sensor along with the gas sensor driver forms the gas sensor node which is located at the top of the proposed device. Hardware flex interface is utilized as the second layer to facilitate the display and sound module connected to the host platform. The main platform that is expandable from both the sides in $z$ -axis hosts the integrated sensors, microcontroller, and the chip antenna. Notification driver for the early user warning of abnormal status and battery holder is placed at the bottom of the device in two separate layers. Furthermore, the adjustable sensor sampling rate according to the battery level is applied in order to manage power consumption. Device configuration, prototype validation, data storage, power dissipation, and experimental results are discussed in detail at the end of this paper.
A methodology for part classification with supervised machine learning

Abstract In this paper, we report on a data analysis process for the automated classification of mechanical components. In particular, here, we describe, how to implement a machine learning system for the automated classification of parts belonging to several sub-categories. We collect models that are typically used in the mechanical industry, and then we represent each object by a collection of features. We illustrate how to set-up a supervised multi-layer artificial neural network with an ad-hoc classification schema. We test our solution on a dataset formed by 2354 elements described by 875 features and spanned among 15 sub-categories. We state the accuracy of classification in terms of average area under ROC curves and the ability to classify 606 unknown 3D objects by similarity coefficients. Our parts’ classification system outperforms a classifier based on the Light Field Descriptor, which, as far as we know, actually represents the gold standard for the identification of most types of 3D mechanical objects.
Human motion analysis based on extraction of skeleton and dynamic time warping algorithm using RGBD camera

Human action analysis is an extremely popular topic in the field of computer vision. It has wide application prospects in intelligent monitoring, virtual reality, pedestrian tracking, etc. This paper present an algorithm for human motion analysis based on extraction of skeleton and dynamic time warping algorithm using RGBD camera. First, to solve the problem of the limitation of information in two-dimensional space, the RGBD camera is adopted to obtain the three-dimensional spatial information of the human body. Then, in the process of feature extraction, 11 skeleton points are selected from the depth image and the relative distance of the space is calculated, reducing the computational complexity significantly. Furthermore, an optimisation algorithm of dynamic time warping is introduced so that the adverse effects of time difference are decreased. Finally, the human motion analysis is studied. The experimental results show that the proposed method can recognise single action and double action effectively.
Medium Access Control Protocols for Power Line Communication: A Survey

In the literature, the majority of research efforts in power line communication (PLC) has been focused on the physical layer in order to deal with issues such as the time varying behavior of loads in electric power systems, the presence of high power impulsive noise, the occurrence of impedance mismatching, the widespread use of unshielded power cables, and the existence of coupling losses. While some works have also been carried out on the PLC medium access control (MAC) sublayer, there are scopes for further research to address the novel demands associated with cyber physical systems that need to mitigate unfairness in resource sharing, collisions and starvation, among other issues which may degrade data communication quality. In this paper, we provide a comprehensive survey regarding the state-of-the-art of MAC protocols for PLC systems, including an overview of existing PLC MAC research results and an organization of current PLC MAC protocols in terms of type of protocols, applications, and main research focus. Moreover, we present modern PLC technologies and standards, highlighting their MAC sublayer characteristics and providing a detailed comparative analysis of PLC MAC protocols in the context of current and emerging PLC applications. Finally, we identify future trends within the scope of the MAC sublayer for PLC systems with a view to stimulating additional research efforts on PLC MAC design.
Security Challenges in Control Network Protocols: A Survey

With the ongoing adoption of remotely communicating and interacting control systems harbored by critical infrastructures, the potential attack surface of such systems also increases drastically. Therefore, not only the need for standardized and manufacturer-agnostic control system communication protocols has grown, but also the requirement to protect those control systems’ communication. There have already been numerous security analyses of different control system communication protocols; yet, these have not been combined with each other sufficiently, mainly due to three reasons: First, the life cycles of such protocols are usually much longer than those of other Internet and communication technologies, therefore legacy protocols are often not considered in current security analyses. Second, the usage of certain control system communication protocols is usually restricted to a particular infrastructure domain, which leads to an isolated view on them. Third, with the accelerating pace at which both control system communication protocols and threats against them develop, existing surveys are aging at an increased rate, making their re-investigation a necessity. In this paper, a comprehensive survey on the security of the most important control system communication protocols, namely Modbus, OPC UA, TASE.2, DNP3, IEC 60870-5-101, IEC 60870-5-104, and IEC 61850 is performed. To achieve comparability, a common test methodology based on attacks exploiting well-known control system protocol vulnerabilities is created for all protocols. In addition, the effectiveness of the related security standard IEC 62351 is analyzed by a pre- and post-IEC 62351 comparison.
Fast and robust copy-move forgery detection using wavelet transforms and SURF

Most of the images today are stored in digital format. With the advent of digital imagery, tampering of images became easy. The problem has become altogether intensified due to the availability of image tampering softwares. Moreover there exist cameras with different resolutions and encoding techniques. Detecting forgery in such cases becomes a challenging task. Furthermore, the forged image may be compressed or resized which further complicates the problem. This article focuses on blind detection of copy-move forgery using a combination of an invariant feature transform and a wavelet transform. The feature transform employed is Speeded Up Robust Features (SURF) and the wavelet transforms employed are Discrete Wavelet Transform (DWT) and Dyadic Wavelet Transform (DyWT). A comparison between the performances of the two wavelet transforms is presented. The proposed algorithms are different from the previously proposed methods in a way that they are applied on the whole image, rather than after dividing the image in to blocks. A comparative study between the proposed algorithm and the previous block-based methods is presented. From the results obtained, we conclude that these algorithms perform better than their counterparts in terms of accuracy, computational complexity and robustness to various