Reviewerly/exHarmony huggingface dataset:
Video Face Recognition with Audio-Visual Aggregation Network

With the continuing improvement in deep learning methods in recent years, face recognition performance is starting to surpass human performance. However, current state-of-the-art approaches are usually trained on high-quality still images and do not work well in unconstrained video face recognition. We propose to use audio information in the video to aid in the face recognition task with mixed quality inputs. We introduce an Audio-Visual Aggregation Network (AVAN) to aggregate multiple facial and voice information to improve face recognition performance. To effectively train and evaluate our approach, we constructed an Audio-Visual Face Recognition dataset. Empirical results show that our approach significantly improves the face recognition accuracy on unconstrained videos.
Type-2 Code Clone Detection for Dockerfiles

This paper proposes a Type-2 code clone detection technique for Dockerfiles.Docker is a platform for realizing containerized virtual environments that has been attracting significant attention as a technology support for server infrastructures.In Docker, the procedure to realize a virtual environment is described in the form of a source code called a Dockerfile.Therefore, code clones such as repetitions and duplications with similar structures are often included.In this study, we propose a Type-2 code clone detection technique for deriving patterns within Dockerfiles.More specifically, our technique detects code clones by normalizing tokens properly, separating Docker syntax and shell syntax, and then adopting suffix array algorithm for each syntax.We conducted application experiments on approximately 5,000 Dockerfiles available on GitHub and found that our technique could detect Type-2 code clones with a high precision.Our technique derived some patterns in Dockerfiles, such as those used for building a new distribution on a container.
Hybrid Architecture to Achieve Semantic Interoperability for Engineering Oil and Gas Industry Process

In the new era of Information & Communication Technologies, the interoperability between systems has been gaining importance in the industry, becoming the key to achieve better information management, production control, decision making, failure control, and risk management, significantly reducing the production costs. Studies to provide interoperability between systems have been focused on computational architectures, including conceptual models and implementations. In this work, a novel proposed architecture is presented. It is based on paradigms like Polystores and Ontology-based Data Access (OBDA), and streaming data processing over micro-services. Also, an implementation of the proposed architecture was developed. Finally, the approach was evaluated over a case study that integrates engineering data from different data sources in the oil and gas industry context.
Building a Part of Speech tagger for the Tamil Language

Identifying the lexical category or Part of Speech (POS) of words is critical for developing Natural Language Processing (NLP) systems. Existing Tamil POS taggers are either not publicly available, inaccurate or use a non-standard POS tagset. In this paper, we present a state-of-the-art, contextual neural POS tagger named ThamizhiPOSt. It tags words in a sentence with their lexical category, using the Universal Part of Speech tagset. Each tag is based on the word's context in the sentence. ThamizhiPOSt also integrates a tokeniser and a sentence segmenter so that it can handle raw Tamil text. This paper introduces this POS tagger and compares it with existing tools and resources for Tamil POS tagging. ThamizhiPOSt reports an accuracy of 93.27% for unseen data, which constitutes the best score in comparison to the publicly available Tamil POS taggers. ThamizhiPOSt and its associated resources are all made public. Further, ThamizhiPOSt has been published as a Python library for others to build upon.
JRP analysis of synchronization loss between signals recording during bubble departures

Abstract The synchronization between the air pressure fluctuations and the depth of liquid penetration into the nozzle during bubble departures was investigated using joint recurrence quantification analysis. In the experiment, the bubbles were generated from a glass nozzle into distilled water. During the analysis, the recurrent rate coefficients were calculated for the depth of liquid penetration into the glass nozzle and pressure changes in the gas supply system. The study was conducted by two air volume flow rates, i.e. 0.023 l/min and 0.026 l/min. The air volume flow rates were selected so that the appearance and disappearance of period bubble departures were clearly visible. It has been shown that the synchronization of the pressure changes and the depth of liquid penetration appears when periodic changes in the depth of liquid penetration occur in a relatively long period of time. The process of changing the distance between the extremes of liquid penetration into the nozzle and pressure changes in the gas supply system was observed. It has been found that the decrease in the distance between these extremes is responsible for the appearance of periodic bubble departures. This behaviour has not been reported in previous papers. This process was modelled by numerical simulations.
Bounded Collusion ABE for TMs from IBE

We give an attribute-based encryption system for Turing Machines that is provably secure assuming only the existence of identity-based encryption (IBE) for large identity spaces. Currently, IBE is known to be realizable from most mainstream number theoretic assumptions that imply public key cryptography including factoring, the search Diffie-Hellman assumption, and the Learning with Errors assumption.
Visual Decision-Support for Live Digital Forensics

Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts’ experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.
Sequential Item Recommendation in the MOBA Game Dota 2

Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.
Visual adversarial attacks and defenses

Visual adversarial examples are images and videos purposefully perturbed to mislead machine learning models. This chapter presents an overview of methods that craft adversarial perturbations to generate visual adversarial examples for image classification, object detection, motion estimation and video recognition tasks. We define the key properties of an adversarial attack and the types of perturbations that an attack generates. We then analyze the main design choices for methods that craft adversarial attacks for images and videos, and discuss the knowledge they use of the target model. Finally, we review defense mechanisms that increase the robustness of machine learning models to adversarial attacks or detect manipulated input data.
On the Experience of Using NI ELVIS III in Remote Laboratory Practice During Pandemic Lockdown

By the beginning of COVID pandemic, remote education technology have was being implemented at some level by almost every educational institution, but the following lockdown forced the world to accelerate this process. In these circumstances, a lack of laboratory practice in online engineering education was revealed. The existing lab equipment, even quite modern, was focused on offline laboratory experience, and required enough efforts to be reachable from the outside. The International Scientific and Educational Center "National Instruments – Polytechnic" possessed a number of virtual instrumentation lab stations called NI ELVIS II, which had been being used in education for more than 10 years and allowed the replacement outdated bulky instruments with computer-based all-in-one virtual measurement solutions. But even these devices required additional development to allow students to work remotely, did not allow them to work in groups and manage access control. The new product applied by our professors called NI ELVIS III appeared to be a perfect solution to establish online lab practice. We managed to run several labs in electronics course with almost zero configuration, group collaboration and possible real-time teacher supervision. The paper describes the experience of applying NI ELVIS III in remote electronics lab, the benefits and caveats of using this platform.
[Heavy Metal Concentration Characteristics and Health Risks of Farmland Soils in Typical Pyrite Mining Area of the Central Zhejiang Province, China].

In order to ascertain the impact of pyrite mining on the surrounding farmland soil environment and human health, 42 surface soil samples (from 0-20 cm) were collected around the pyrite mining area in Longyou county. In this study, the concentrations of eight heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) and the pH in the topsoil were analyzed, and the concentration characteristics of heavy metals, source analysis, and human health risks assessment were studied using statistical analysis (SA), geo-accumulation index (Igeo), positive matrix factorization (PMF), and the health risk model. The average of ω(Cd), ω(Cu), ω(Pb), and ω(Zn) concentrations exceeded the background values of soils in Zhejiang province and China. According to the agricultural land pollution risk screening values (GB 15618-2018), Cd, Cu, Pb, and Zn were up to 82%, 49%, 42%, and 31%, respectively. The Igeo shows that the major pollutant element in the soils was Cd, followed by Cu, Pb, and Zn. The PMF analysis indicates that nature sources (As, Cr, and Ni), comprehensive pollution sources caused by high geological background and mining of ore-forming geological bodies (Cd, Cu, Pb, and Zn), and anthropogenic sources (Hg) were the three major sources of heavy metals in the study area, with contributions of 32%, 46%, and 22%, respectively. The results of the health risk assessment indicate that the major non-carcinogenic factor triggering risks was the ingestion of Pb; Cr exposure had carcinogenic risk for adults, and Cr and As exposure had carcinogenic risk for children.
A Lightweight Flow Feature-Based IoT Device Identification Scheme

Internet of Things (IoT) device identification is a key step in the management of IoT devices. The devices connected to the network must be controlled by the manager. For this purpose, many schemes are proposed to identify IoT devices, especially the schemes working on the gateway. However, almost all researchers do not pay close attention to the cost. Thus, considering the gateway’s limited storage and computational resources, a new lightweight IoT device identification scheme is proposed. First, the DFI (deep/dynamic flow inspection) technology is utilized to efficiently extract flow-related statistical features based on in-depth studies. Then, combined with symmetric uncertainty and correlation coefficient, we proposed a novel filter feature selection method based on NSGA-III to select effective features for IoT device identification. We evaluate our proposed method by using a real smart home IoT data set and three different ML algorithms. The experimental results showed that our proposed method is lightweight and the feature selection algorithm is also effective, only using 6 features can achieve 99.5% accuracy with a 3-minute time interval.
Projectile trajectory estimation: performance analysis of an Extended Kalman Filter and an Imperfect Invariant Extended Kalman Filter

This paper compares two nonlinear Kalman filters to estimate a projectile trajectory: an Extended Kalman Filter (EKF) and an Imperfect Right-Invariant Extended Kalman Filter (Imperfect R-IEKF). For this purpose, only an Inertial Measurement Unit (IMU), composed by an accelerometer and a gyrometer embedded in the projectile is considered. In addition, a misalignment between the IMU and the projectile is used as an observation. Both filters share the same evolution and measurement models.A nonlinear observability analysis is performed and suggests that the EKF creates a false observability contrary to the Imperfect R-IEKF. Furthermore, evaluated on 100 mortar fire simulations, the Imperfect R-IEKF is considerably more accurate than the EKF to estimate a projectile trajectory. These observations on accuracy and observability are explained by the Imperfect R-IEKF design, i.e. an EKF based on a nonlinear error and an update step based, in part, on an exponential application.
An Image Encryption Algorithm Based on a New Hybrid Power Exponent Chaotic System

Aiming at the problem of a small parameter value range when a one-dimensional chaotic system presents a chaotic state, this paper proposes a new type of hybrid power exponential chaotic system (HPECS). HPECS combines the classic one-dimensional Sine chaotic system to form a new chaotic system (HPECS-SS). Experiments show that the obtained new chaotic system has better chaotic performance, a more extensive parameter value range, and higher sensitivity. Simultaneously, on the basis of HPECS-SS, a new image encryption algorithm is proposed. The algorithm uses the key generated by the SHA-512 algorithm and HPECS-SS to iteratively output the chaotic sequence, SFY algorithm combines the chaotic sequence to perform two rounds of scrambling on the plaintext sequence to obtain the scrambling sequence, and finally, through the modulus operation to diffuse the scrambling sequence to form the encryption matrix of the plaintext image, simulation experiment analysis shows that the algorithm has a large key space, good encryption effect, and security; the pixel change rate (NPCR) and the normalized average change intensity (UACI) are close to ideal values which can resist various cryptanalysis and attacks.
Nonlocal Low-Rank Point Cloud Denoising for 3-D Measurement Surfaces

3-D imaging devices (e.g., depth cameras and optical and laser scanners) are frequently used to measure outdoor/indoor scenes. The measurement data represented by 3-D point clouds are, however, usually noisy and should be denoised to facilitate subsequent applications. Existing point cloud denoising methods typically perform: 1) point position updating directly or 2) point normal filtering followed by point position updating, and seldom consider the correlation between position updating and normal filtering, leading to less desirable denoised results. This article proposes a nonlocal low-rank point cloud denoising framework (NL-PCD) to handle 3-D measurement surfaces with different-scale and -type noise. We first design a rotation-invariant feature descriptor, called height and normal patch (HNP), to encode the position and normal information of each point, and search nonlocal yet geometrically similar HNPs in the whole point cloud. Similar HNPs are then grouped and packed into a noisy matrix which exhibits high rank due to the existence of the noise. Finally, we remove the noise from the noisy matrix through low-rank matrix recovery by making use of nonlocal similarities among HNPs. In such a way, we can optimize both point positions and normals (i.e., dual geometry domains) in a joint framework to fully exploit the correlation between the two domains for point cloud denoising. Experimental results on synthetic and real-world data demonstrate that our NL-PCD outperforms both traditional and deep learning-based denoising methods in terms of noise removal and feature preservation.
Improving Data Hiding Methods in Network Steganography Based on Packet Header Manipulation

In this paper, internet is among the basic necessities of life. Internet has changed each and everybody's lives. So confidentiality of messages is very important over the internet. Steganography is the science of sending secret messages between the sender and intended receiver. It is such a technique that makes the exchange of covert messages possible. Each time a carrier is to be used for achieving steganography. The carrier plays a major role in establishing covert communication channel. This survey paper introduces steganography and its carriers. This paper concentrates on network protocols to be used as a carrier of steganograms. There are a number of protocols available to do so in the networks. Network steganography describes various methods used for transmitting data over a network without it being detected. Most of the methods proposed for hiding data in a network do not offer an additional protection to the covert data as it is sent as plain text. This paper presents a framework that offers the protection to the covert data by encrypting it and compresses it for gain in efficiency.
AGNet: Attention Guided Sparse Depth Completion Using Convolutional Neural Networks

Sparse depth completion generates a dense depth image from its sparse measurement with the guidance of RGB image.In this paper, we propose attention guided sparse depth completion using convolutional neural networks, called AGNet.We adopt attention learning to get geometric cues for depth regression from RGB image and capture multi-scale depth structures.First, we use RGB image and valid binary mask from the input sparse depth image as input to generate an initial coarse depth image and its confidence map.Then, we generate attention map for depth refinement using a cross spatial attention module (CSAM).CSAM separately takes the RGB image with valid mask and invalid mask as input to make full use of color information in attention map.Next, we build a multi-scale learning network to encode the sparse depth image with different scales, thus leading to accurate depth completion.AGNet takes advantage of the input sparse depth image for encoding coarse features with a moderate model size.Experimental results show that AGNet achieves comparable performance with state-of-the-art methods for depth completion in NYU v2 dataset.
Heuristic Evaluation of a Mobile Game Developed to Help Battle the Pandemic

As the COVID-19 storms the globe, there are many efforts to battle it. This paper looks at the development of one such an effort in a form of a mobile game. The game is a 2D arcade game called Unus Terra. It is intended to be entertaining but aims to influence people to partake in social distancing through several mechanics. An early version of the game is evaluated by five usability and user experience experts using heuristics and a few other selected methods. In this paper, we present the results from these evaluations along with our findings from using this type of evaluation method. The use of heuristics is considered from the perspectives of academics and practitioners.
Hypotheses Disambiguation in Retrospective

Robust perception is a key required capability in robotics and AI when dealing with scenarios and environments that exhibit some level of ambiguity and perceptual aliasing. In this work we consider such a setting and contribute a framework that enables to update probabilities of externally-defined data association hypotheses from some past time with new information that has been accumulated until current time. In particular, we show appropriately updating probabilities of past hypotheses within this smoothing perspective potentially enables to disambiguate these hypotheses even when there is no full disambiguation of the mixture distribution at the current time. Further, we develop an incremental algorithm that re-uses hypotheses’ weight calculations from previous steps, thereby reducing computational complexity. In addition we show how our approach can be used to enhance current-time hypotheses pruning, by discarding corresponding branches in the hypotheses tree. We demonstrate our approach in simulation, considering an extremely aliased environment setting.
Firmblock: A Scalable Blockchain-Based Malware-Proof Firmware Update Architecture with Revocation for IoT Devices

In recent years, the smart city paradigm continues to receive major advancements which is helping to improve the quality of life of people within the environment. The Internet of Things (IoT) which represents the backbone of the Smart City paradigm is receiving exponential growth. This exponential growth is also companied by some challenge which need to be addressed to further support the ever-growing demand of the IoT devices. Secure firmware update and distribution mechanisms is a major stage in the lifecycle of IoT devices management. Although the Internet Engineering Task Force (IETF) Software Updates for Internet of Things (SUIT) have started preparing software update models for IoT devices, scalability of secure firmware update distribution and centralization exists as challenges for the current model. In this paper, we propose a blockchain based firmware update architecture for IoT devices. The proposed architecture ensures secure distribution of firmware updates, malware-proof and solves the author-disappearing issue. We introduced a key revocation mechanism to secure the IoT environment from malicious devices. We further secure centralized entities that are susceptible to targeting attacks and single point of failure problem that is critical to the system by integrating all activities into the blockchain as transactions. The proposed model in this paper achieves effective and efficient security for IoT device update, as well as addressing the targeting attack and the author-disappearing issue in IoT device management.