Reviewerly/exHarmony huggingface dataset:
Two-Step Affine Transformation Prediction for Visual Object Tracking

The main drawback of correlation filter scheme is that it can only predict translation of the object, ignoring the other affine transformations such as rotation, aspect ratio change, and scale change. This paper tries to address this problem by a two-step affine transformation prediction method. The first step is to predict coarse target translation by the correlation filter model with adaptive model update rate generation. Compared with fixed update rate in existing correlation filter based trackers, the presented method generates the update rate by Long Short-Term Memory model with inputs of historical target templates. The second step maps well-designed features to affine transformation parameters. The designed features contain the information of affine transformation which makes the learning of non-linear mapping function possible. The training samples for on-line filter model update are transformed to the same pose, and they help the learnt filter to represent the object better than non-aligned samples. The proposed network is trained from end to end and achieves competitive performance when comparing with state-of-the-arts trackers on four benchmarks, OTB100, UAV123, VOT2018, and VOT2020.
BA-TLS: Blockchain Authentication for Transport Layer Security in Internet of Things

Traditional security solutions that rely on public key infrastructure present scalability and transparency challenges when deployed in Internet of Things (IoT). In this paper, we develop a blockchain based authentication mechanism for IoT that can be integrated into the traditional transport layer security protocols such as Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). Our proposed mechanism is an alternative to the traditional Certificate Authority (CA)-based Public Key Infrastructure (PKI) that relies on x.509 certificates. Specifically, the proposed solution enables the modified TLS/DTLS a viable option for resource constrained IoT devices where minimizing memory utilization is critical. Experiments show that blockchain based authentication can reduce dynamic memory usage by up to 20%, while only minimally increasing application image size and time of execution of the TLS/DTLS handshake.
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs

The problem of knowledge graph (KG) reasoning has been widely explored by traditional rule-based systems and more recently by knowledge graph embedding methods. While logical rules can capture deterministic behavior in a KG, they are brittle and mining ones that infer facts beyond the known KG is challenging. Probabilistic embedding methods are effective in capturing global soft statistical tendencies and reasoning with them is computationally efficient. While embedding representations learned from rich training data are expressive, incompleteness and sparsity in real-world KGs can impact their effectiveness. We aim to leverage the complementary properties of both methods to develop a hybrid model that learns both high-quality rules and embeddings simultaneously. Our method uses a cross feedback paradigm wherein an embedding model is used to guide the search of a rule mining system to mine rules and infer new facts. These new facts are sampled and further used to refine the embedding model. Experiments on multiple benchmark datasets show the effectiveness of our method over other competitive standalone and hybrid baselines. We also show its efficacy in a sparse KG setting.
PoliceChain: Blockchain-Based Smart Policing System for Smart Cities

This paper proposes a novel blockchain-based smart policing system which increases accountability, transparency, and trust concerning the storage, safeguarding, and sharing of evidence and intelligence related to ongoing investigations, criminal cases, and justice information among the stakeholders. The system allows various stakeholders, such as citizens of the country, law enforcement agencies, intelligence agencies, forensic departments, government bodies, and judges, to participate and to provide crucial services, such as filing FIR, initiating investigations, adding forensic reports, delivering justice, etc., related to smart policing. We present a proof of concept of our proposal using Hyperledger Fabric, adopting the Attribute-Based Access Control (ABAC) policy, and we perform an experimental evaluation to demonstrate the performance of the system. To the best of our knowledge, this is the first proposal on a blockchain-based smart policing system in the literature.
A Utilization of Information Technology on Education in Indonesia (2017-2020): A Systematic Literature Review

Abstract This research aims to demonstrate a theoretical review of information technology used on education in Indonesia in since 2017 to 2020 and review the areas that have been researched to give an overview of the potential subsequent studies. areas of study that have been researched to give an overview of the potential subsequent studies. This study used a qualitative methods of systematic literature review of Denyer and Tranfield models which is divided into 5 systematic steps. From the documents found, demonstrated that the utilization of information technology in the field of education in Indonesia throughout the year 2017-2020 has experienced a surge in innovation in the field of development of learning media and education governance facilities. These developments are mostly solutions to solve contextual problems in research approach, dominance of development and survey is inversely proportional to the case study method, action research phenomenology, and evaluation. In the research object, policy evaluation sector, learning evaluation, and quality assurance have not been lifted as a research object. These sectors have the potential to be objects in future research.
See-Through Vision with Unsupervised Scene Occlusion Reconstruction

Among the greatest of the challenges of minimally invasive surgery (MIS) is the inadequate visualisation of the surgical field through keyhole incisions. Moreover, occlusions caused by instruments or bleeding can completely obfuscate anatomical landmarks, reduce surgical vision and lead to iatrogenic injury. The aim of this paper is to propose an unsupervised end-to-end deep learning framework, based on fully convolutional neural networks to reconstruct the view of the surgical scene under occlusions and provide the surgeon with intraoperative see-through vision in these areas. A novel generative densely connected encoder-decoder architecture has been designed which enables the incorporation of temporal information by introducing a new type of 3D convolution, the so called 3D partial convolution, to enhance the learning capabilities of the network and fuse temporal and spatial information. To train the proposed framework, a unique loss function has been proposed which combines feature matching, reconstruction, style, temporal and adversarial loss terms, for generating high fidelity image reconstructions. Advancing the state-of-the-art, our method can reconstruct the underlying view obstructed by irregularly shaped occlusions of divergent size, location and orientation. The proposed method has been validated on in vivo MIS video data, as well as natural scenes on a range of occlusion-to-image (OIR) ratios. It has also been compared against the latest video inpainting models in terms of image reconstruction quality using different assessment metrics. The performance evaluation analysis verifies the superiority of our proposed method and its potential clinical value.
TradAO: A Visual Analytics System for Trading Algorithm Optimization

With the wide applications of algorithmic trading, it has become critical for traders to build a winning trading algorithm to beat the market. However, due to the lack of efficient tools, traders mainly rely on their memory to manually compare the algorithm instances of a trading algorithm and further select the best trading algorithm instance for the real trading deployment. We work closely with industry practitioners to discover and consolidate user requirements and develop an interactive visual analytics system for trading algorithm optimization. Structured expert interviews are conducted to evaluate TradAO and a representative case study is documented for illustrating the system effectiveness. To the best of our knowledge, previous financial data visual analyses have mainly aimed to assist investment managers in investment portfolio analysis but have neglected the need of traders in developing trading algorithms for portfolio execution. TradAO is the first visual analytics system that assists users in comprehensively exploring the performances of a trading algorithm with different parameter settings.
Canonical Huffman Decoder on Fine-grain Many-core Processor Arrays

Canonical Huffman codecs have been used in a wide variety of platforms ranging from mobile devices to data centers which all demand high energy efficiency and high throughput. This work presents bit-parallel canonical Huffman decoder implementations on a fine-grain many-core array built using simple RISC-style programmable processors. We develop multiple energy-efficient and area-efficient decoder implementations and the results are compared with an Intel i7-4850HQ and a massively parallel GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 324x and 2.7x greater on average than the i7 and GT 750M respectively. In addition, the many-core implementations yield a scaled energy efficiency (bytes decoded per energy) that is 24.1x and 4.6x greater than the i7 and GT 750M respectively.
Wearable Device Monitoring Exercise Energy Consumption Based on Internet of Things

Computer technology and related Internet of things technology have penetrated into people’s daily life and industrial production; even in competitive sports training and competition, the Internet of things technology has also been a large number of applications. Traditional intelligent wearable devices are mainly used to calculate the steps of athletes or sports enthusiasts, corresponding physical data, and corresponding body indicators. The energy consumption calculated by these indexes is rough and the corresponding error is large. Based on this, this paper will design a wearable device which can accurately calculate and monitor sports energy consumption based on relevant sensors and Internet of things technology. The corresponding core algorithm is the step counting algorithm, which can accurately calculate the relationship between human motion and the corresponding energy consumption and feed back to the intelligent device. In the experiment, the wearable device designed in this paper is compared with the traditional intelligent device. The experiment shows that the wearable device proposed in this paper is more accurate in energy consumption estimation than the traditional device, and its corresponding energy consumption is relatively small.
Analysis on the Privacy of DID Service Properties in the DID Document

Decentralized Identifiers (DIDs) are a technology that allows individuals to realize self-sovereign identity. The advantage of the DIDs is that individuals can decide what information to disclose and transparently review the details of the disclosed information. In the core architecture of DIDs, a DID document associated with a DID is derived, and this document is open to the public to access. However, DID service properties in the DID document raises a dataveillance problem. In this paper, we analyze the risks of leaking sensitive information that may be included in the DID authentication process through a scenario.
Divergence Family Contribution to Data Evaluation in Blockchain Via Alpha-EM and Log-EM Algorithms

This study interrelates three adjacent topics in data evaluation. The first is the establishment of a relationship between Bregman divergence and probabilistic alpha-divergence. In particular, we demonstrate that square-root-order probability normalization enables the unification of these two divergence families. This yields a new alpha-divergence, which can be used to jointly derive the alpha-EM algorithm (alpha-expectation-maximization algorithm) and the traditional log-EM algorithm. The second topic is the application of the alpha-EM algorithm in the evaluation of graders scoring raw data over a network. We estimate multinomial mixture distributions in this evaluation problem. We note that the convergence speed of the alpha-EM algorithm is significantly higher than that of the log-EM algorithm. Finally, the third topic is the use of this increase in convergence speed to assign the winning evaluator and miner in a blockchain environment. This is achieved by proof-of-review using evaluation scores, which is a class of proof-of-stake. In the second and third topics, we select terminology from wine tasting for brevity in the exposition. However, this formulation can be applied to a broader class of data in a network environment comprising blockchains.
NMN-VD: A Neural Module Network for Visual Dialog

Visual dialog demonstrates several important aspects of multimodal artificial intelligence; however, it is hindered by visual grounding and visual coreference resolution problems. To overcome these problems, we propose the novel neural module network for visual dialog (NMN-VD). NMN-VD is an efficient question-customized modular network model that combines only the modules required for deciding answers after analyzing input questions. In particular, the model includes a Refer module that effectively finds the visual area indicated by a pronoun using a reference pool to solve a visual coreference resolution problem, which is an important challenge in visual dialog. In addition, the proposed NMN-VD model includes a method for distinguishing and handling impersonal pronouns that do not require visual coreference resolution from general pronouns. Furthermore, a new Compare module that effectively handles comparison questions found in visual dialogs is included in the model, as well as a Find module that applies a triple-attention mechanism to solve visual grounding problems between the question and the image. The results of various experiments conducted using a set of large-scale benchmark data verify the efficacy and high performance of our proposed NMN-VD model.
Application of fault detection using distributed sensors in smart cities

This paper proposes a multi-sensor fault detection algorithm based on intelligent sensing systems. The algorithm establishes equations considering physical constraints and then uses the constraints between adjacent nodes to calculate the Kalman filter estimation value with state constraints, for fault diagnosis and data checking. The sensor structure propose herein is distributed, and the algorithm assumes the form of sensor network nodes, each node having its own processing system, without any central nodes or central communication facilities. Therefore, the proposed algorithm is fully distributed, enabling independent calculations between multiple measurement nodes. This paper discusses the algorithm derivation process in detail, as well as the verification of the parallelism, accuracy, and stability of the algorithm through software simulation and hardware testing.
A System for Automatic Regional Accent Classification

Automated identification of a speaker's native language is carried out using non-native English speech data spoken by native speakers of languages Kannada, Tamil, Telugu. Dataset consists of speech in text-dependent and text-independent modes. Experiments are conducted using Mel-frequency cepstral coefficients (MFCCs), and classifiers Gaussian Mixture Models (GMM), GMM with Universal Background Model (UBM) and i-vector. A prototype system for Automated Classification of Speakers based on Regional Accent (ACSRA) is developed. Best results are obtained using the i-vector based classifier. Accuracy at 93.9% is obtained for native regional language identification (NLI), using the text-independent speech data. It is observed that distinguishing the native language is better using the text-independent data than with text-dependent data. Further analysis indicated better classification accuracy for female non-native speakers (87%), than for male speakers (80%). It is found that identifying English speakers of Kannada is easier than that of Telugu or Tamil languages.
Gaussian Mixture Model for Estimating Solar Irradiance Probability Density

The increasing penetration of photovoltaic generation resources make it imperative for power network designers to assess the available resources by obtaining accurate estimates of solar irradiance at a given site/geographical area. The parametric Beta distribution has long been a popular choice in such studies; however, the use of parametric functions for probability density estimation (such as the Beta distribution) can be problematic and may lead to model misspecification. The Gaussian Mixture Model (GMM) is proposed in this paper to provide a more robust estimation of solar irradiance probability density at a certain site. Multi-year solar data from eight locations in the United States is utilized to evaluate the accuracy of the GMM estimate and compare its performance with the popular Beta distribution. Assessments are carried out using three standard measures of error, coefficient of determination, and the Kolmogorov-Smirnov goodness-of-fit test for distributional adequacy. Results demonstrate that the GMM estimate produces a more robust estimation with better performance metrics when compared with the Beta distribution.
On two-weight codes

We consider q-ary (linear and nonlinear) block codes with exactly two distances: d and d+δ. We derive necessary conditions for existence of such codes (similar to the known conditions in the projective case). In the linear (but not necessary projective) case, we prove that under certain conditions the existence of such linear 2-weight code with δ>1 implies the following equality of greatest common divisors: (d,q)=(δ,q). Upper bounds for the maximum cardinality of such codes are derived by linear programming and from few-distance spherical codes. Tables of lower and upper bounds for small q=2,3,4 and qn<50 are presented.
An Efficient Quality Assessment Method for Screen Content Image Based on Gabor

The goal of an objective quality assessment metric is to use a computational model to measure the image visual quality, which consistent with the subjective judgment of the human being. Accurately evaluating the visual quality of the image is an important task under many application scenarios, such as image compression, image reconstruction, and image enhancement. In this paper, we present an accurate full-reference image quality assessment (IQA) metric for screen content (SC) images. Compared to the other classical algorithms, the proposed method is based on convolution operations, which extremely reduced computational complexity. First, the Gabor filter is used to capture the image structure information, usually composed of edges and contours, which is used to understand the image. And then we detect the visual sensitive areas that can attract more attention from the human visual system (HVS). Finally, the map of visual sensitive areas is used for the weighting strategy. Extended experiments on the SIQAD database validate the superiority of the proposed method. Specifically, it is more consistent with subjective evaluation results than the most popular and state-of-the-art IQA metrics and requires lower computational complexity <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Estimation of Unknown Phase Shift Between Synthesized Speckle Interferograms Using Pearson Correlation Coefficient

A sample Pearson correlation coefficient (SPCC) is proposed to calculate an unknown phase shift (UPS) between speckle fringe patterns (speckle interferograms (SIs)) of the same test object. The SI sequences of an initial flat optically rough surface and the SI sequences of a deformed surface were synthesized to estimate the systematic errors of the UPS extraction. The low level of these errors in the range from 0 to 180 degrees proves the effectiveness of using the SPCC to extract the UPS.
DExIE - An IoT-Class Hardware Monitor for Real-Time Fine-Grained Control-Flow Integrity

The Dynamic Execution Integrity Engine (DExIE) is a lightweight hardware monitor that can be flexibly attached to many IoT-class processor pipelines. It is guaranteed to catch both inter- and intra-function illegal control flows in time to prevent any illegal instructions from touching memory. The performance impact of attaching DExIE to a core depends on the concrete pipeline structure. In some especially suitable cases, extending a processor with DExIE will have no performance penalty. DExIE is real-time capable, as it causes only very few and then perfectly predictable pipeline stalls. It is often faster than software-based monitoring and often smaller than a separate guard processor. We present not just the hardware architecture, but also the automated programming flow, and discuss compact adaptable storage formats for fine-grained control flow information.
AppArmor For Health Data Access Control: Assessing Risks and Benefits

The AppArmor Linux Security Module (LSM) is widely used on Linux operating systems as it, among other things, provides mandatory access control (MAC) and isolates processes. This isolation helps meet the privacy requirements for critical applications. These application security policies are defined with profiles loaded into the Linux system kernel. However, these access control mechanisms are far from covering all the rising demands for confidentiality enforcement regarding critical applications. This paper conducts a risks and benefits analysis to assess whether a healthcare infrastructure can safely rely on the AppArmor LSM to protect its sensitive data. Thus, the general architecture of AppAmor comes to be detailed. Then, a static code analysis is performed to study the data structures found in the LSM. Finally, the outbreak of would-be side-channel attacks from userspace is discussed while offering mitigation methods. The result of this analysis shows that the AppArmor LSM is susceptible to side-channel attacks and should be used as part of a more comprehensive defense-in-depth strategy.
Signal-Dependent Noise Estimation for a Real-Camera Model via Weight and Shape Constraints

Most computer vision algorithms require parameter adjustment according to the image noise level. Conventionally, the additive white Gaussian noise (AWGN) model is widely used in most noise estimation algorithms; however, this assumption does not hold in the real world where the noise from cameras is more complex, and it is more appropriate to assume the signal-dependent noise (SDN) model. In this paper, we focus on the SDN model while considering the nonlinear radiometric calibration inside an actual camera, and propose an algorithm to efficiently estimate the noise level function (NLF), which is defined as the noise standard deviation with respect to image intensity. First, the input image is divided into overlapping patches, and noise samples are estimated in the linear transform domain. The confidence levels of the noise samples and the prior of the camera response function are then employed as constraints for the recovery of the NLF. Finally, the noise samples and constraints are represented in a convex optimization problem. The experimental results using both real and synthetic noisy images demonstrate the superiority of the proposed method. In addition, the estimated NLFs are incorporated into two well-known denoising schemes, non-local means and BM3D, and shows significant improvements in denoising SDN-polluted images.