Reviewerly/exHarmony huggingface dataset:
Counterfeit Currency Detection using Deep Learning

Currency is the backbone of a country's economy and banknotes are the primary form of currency used in the Indian Economy. Counterfeit currency is an imitation of a real currency with an idea to deceive. With advancement in technology the practice of counterfeiting currency has become more refined and circulation of such counterfeit notes gives a huge blow to the economy. Hence detecting them is of utmost importance. There exist many commercial techniques for fake note detection however they are not accessible by common man. Individuals who innocently acquire counterfeit currency are victimized and there are usually no government policies to reimburse them for counterfeit notes that are seized. This paper suggests a system that is deployable on smartphones and hence can be used by common people to check whether the possessed currency is fake or real.
Bi-directional Attention Feature Enhancement for Video Instance Segmentation

As a recently proposed task, video instance segmentation (VIS) can classify, detect, segment and track each instance in a given video, which is very useful for driving environment perception of autonomous vehicles. In this paper, we propose a novel method called bi-directional attention feature enhancement (BAFE) to make up for the lack of feature processing in existing works of VIS. BAFE contains a top-down attention branch and a bottom-up attention branch. It uses attention to perform two-way information transmission between high-level, semantic features and low-level, local features to combine them efficiently and can be utilized in any network that uses both high-level and low-level features. We add BAFE before the mask-specialized regression branch of the latest work, spatial information preservation for VIS (SipMask- VIS), to enhance features for mask generating. Besides, we introduce modified path aggregation network (Mod-PAN) to further enhance features. Different from existing works which first train their models on image datasets and then use the pre-trained models to finish the VIS task, we obtain VIS results in an end-to-end way without any pre-training. Our method outperforms SipMask-VIS by an absolute gain of 2.5%, which strongly proves its effectiveness.
Efficient Model Based Grid Intersection Detection for Single-Shot 3D Reconstruction

The crucial role of pixel correspondences in the process of projector-camera calibration and 3D reconstruction is to determine the relationship between camera and projector view. Consequently, acquiring accurate pixel correspondences with a robust detection method can result in accurate 3D reconstruction for a given scene. Single-shot structured light (SSSL) binary patterns are now commonly projected onto target scenes, where they usually consist of white grid lines separating different square tags ordered in such a way to encode the projector pixels. This paper introduces a new technique for detecting the intersection of grid lines to obtain the pixel correspondences of a given SSSL system, where ultimately the detected intersections represent the pixel correspondences. Our SSSL projected pattern contains horizontal and vertical grid lines which allows us to parameterize the lines fitting the grid lines. Experimental results show that the proposed method offers at least 50 times faster computation than a recent connected component analysis-based method at the cost of sparser pixel correspondences and a slight decrease in the accuracy of the 3D reconstructed point cloud.
Modeling the Effects of Global Variables in Data-Flow Analysis for C/C++

Global variables make software systems hard to maintain and debug, and break local reasoning. They also impose a non-trivial challenge to static analysis which needs to model its effects to obtain sound analysis results. However, global variable initialization, codes of corresponding constructors and destructors as well as dynamic library code executed during load and unload not only affect control flows but data flows, too. The PhASAR static data-flow analysis framework does not handle these special cases and also does not provide any functionalities to model the effects of globals. Analysis writers are forced to model the desired effects in an ad-hoc manner increasing an analysis’ complexity and imposing an additional repetitive task. In this paper, we present the challenges of modeling globals, elaborate on the impact they have on analysis information, and present a suitable model to capture their effects, allowing for an easier development of global-aware static data-flow analyses. We present an implementation of our model within the PhASAR framework and show its usefulness for an IDE-based linear-constant propagation that crucially requires correct modeling of globals for correctness.
Business premises capacity manager project for covid compliance using Android applications with geolocation services

The global pandemic has affected how restaurants and other businesses provide service to their customers. This is especially true of the hospitality industry. Customers are wary of visiting businesses due to crowds and adherence to covid 19 regulations, which requires businesses to operate at a certain capacity to ensure social distancing is followed. The solutions to these problems should be done in such a way that customers of the hospitality industries etc. are able to choose and attend premises knowing that capacity and social distancing is being followed. Results will show this using mobile app technology, with real-time information from businesses on their capacity. There is a gap in the research as the global pandemic has only happened recently. We have exhibited an open-source application to aid customers and businesses in the social distancing era.
Proposal of method for generating musical compositions of different genres

Abstract The work is dedicated to the research and development of methods and algorithms that automate and support the process of technical creativity, from the point of automated creation of musical compositions with different genres. The method is based on the use of neural networks to predict composition, and also suggests using various models that have been trained by songs in certain genres to improve the quality of the resulting musical composition. This work describes the algorithm itself, the process of collecting and classifying data for training, the process of training a neural network. In addition, it describes the process of choosing a neural network architecture. Also, the work proposes a system architecture for the automated generation of compositions using the proposed algorithm.
Toward Automated Audit of Client-Side Vulnerability Against Cross-Site Scripting

There are many types of web attacks on the Internet. While cross-site scripting (XSS) is one of the most popular among attackers, it is also one of the most underrated attack vectors. The typical use of XSS attacks is to steal cookies and expose sensitive information. An XSS attack occurs when the attacker tricks a legitimate web application to accept a malicious request. In this context, XSS is a server-side vulnerability. Hence, many previous studies had focused on evaluating server-side vulnerability against XSS attacks. Some studies have focused on evaluating client-side vulnerability against XSS attacks. The latest version of web browsers, plugins, and operating systems is a basic countermeasure against XSS attacks. However, keeping the latest updates on all computers requires time and effort. Furthermore, this does not reveal the actual impact of vulnerability. In this paper, we propose an automated audit method of client-side vulnerability against XSS. Our method is based on Browser Exploitation Framework (BeEF), which is designed to provide effective client-side attack vectors and to exploit any potential vulnerabilities in the web browser. Our method automates the penetration testing process using the RESTful API. The experimental result shows that our method provides a remote testing option for client computers and evaluates the actual impact of XSS vulnerability.
Simulation and experimental approach for optimal path planning of UAV using A* and MEA* algorithms

Over the past decades, Unmanned Aerial Vehicle (UAV) have been effectively adapted to perform disaster missions, agricultural and various societal applications. The path planning plays a crucial role in bringing autonomy to the UAVs to attain the designated tasks by avoiding collision in the obstacles prone regions. Optimal path planning of UAV is considered to be a challenging issue in real time navigation during obstacle prone environments. The present article focused on implementing a well-known A* and variant of A* namely MEA* algorithm to determine an optimal path in the varied obstacle regions for the UAV applications which is novel. Simulation is performed to investigate the performance of each algorithm with respect to comparing their execution time, total distance travelled and number of turns made to reach the source to target. Further, experimental flight trails are made to examine the performance of these algorithms using a UAV. The desired position, velocity and yaw of UAV is obtained based on the waypoints of optimal path planned data and effective navigation is performed. The simulation and experimental results are compared for confirming the effectiveness of these algorithms.
Analytic Form Fitting in Poor Triangular Meshes

Fitting of analytic forms to point or triangle sets is central to computer-aided design, manufacturing, reverse engineering, dimensional control, etc. The existing approaches for this fitting assume an input of statistically strong point or triangle sets. In contrast, this manuscript reports the design (and industrial application) of fitting algorithms whose inputs are specifically poor triangular meshes. The analytic forms currently addressed are planes, cones, cylinders and spheres. Our algorithm also extracts the support submesh responsible for the analytic primitive. We implement spatial hashing and boundary representation for a preprocessing sequence. When the submesh supporting the analytic form holds strict C0-continuity at its border, submesh extraction is independent of fitting, and our algorithm is a real-time one. Otherwise, segmentation and fitting are codependent and our algorithm, albeit correct in the analytic form identification, cannot perform in real-time.
Artificial Intelligence and Entertainment Science Workshop: Towards Empathic Entertainment Technology

The intersection of artificial intelligence and entertainment science via games had led to fruitful development in both fields. Empathic technology in the entertainment medium is a promising trend that bridges both worlds. This workshop provides the discussion space for those interested in its application, societal issues, risks, and best practices .
Privacy information verification of homomorphic algorithm for aggregated data based on fog layer structure

Becoming a vital position in the interconnection industry of the Internet of Things, IIoT has promoted the conversion of traditional industries to intelligent industries. However, it is necessary further to solve the security and privacy threats in IIoT while reducing communication bandwidth and computing resource consumption to carry out research. In order to solve the problem of user privacy leakage caused by the access structure, a fog computing-oriented access control structure hiding scheme is proposed in the paper. The Paillier homomorphic filter algorithm is introduced in the fog calculation. The Paillier homomorphic algorithm hides the mapping function in the access structure during the data upload process, achieving the effect of completely hiding the access structure. Moreover, the ciphertext is stored separately from the access structure, and the Paillier homomorphic algorithm is run through the fog node during the decryption process to detect whether the attributes of the data user exist in the hidden access structure. If it exists, reconstruct the mapping function and send it to the data user, who then downloads and decrypts the ciphertext. If it does not exist, it means that the data user does not meet the access conditions of the data, and there is no need to download and decrypt the ciphertext. Meanwhile, a simulation experiment platform is constructed in the paper to distinguish the performance of the method proposed in the paper from other similar methods, proving the efficiency and practicability of the scheme.
QuAX

Frequently Asked Questions (FAQ) are a form of semi-structured data that provides users with commonly requested information and enables several natural language processing tasks. Given the plethora of such question-answer pairs on the Web, there is an opportunity to automatically build large FAQ collections for any domain, such as COVID-19 or Plastic Surgery. These collections can be used by several information-seeking portals and applications, such as AI chatbots. Automatically identifying and extracting such high-utility question-answer pairs is a challenging endeavor, which has been tackled by little research work. For a question-answer pair to be useful to a broad audience, it must (i) provide general information -- not be specific to the Web site or Web page where it is hosted -- and (ii) must be self-contained -- not have references to other entities in the page or missing terms (ellipses) that render the question-answer pair ambiguous. Although identifying general, self-contained questions may seem like a straightforward binary classification problem, the limited availability of training data for this task and the countless domains make building machine learning models challenging. Existing efforts in extracting FAQs from the Web typically focus on FAQ retrieval without much regard to the utility of the extracted FAQ. We propose QuAX: a framework for extracting high-utility (i.e., general and self-contained) domain-specific FAQ lists from the Web. QuAX receives a set of keywords from a user, and works in a pipelined fashion to find relevant web pages and extract general and self-contained questions-answer pairs. We experimentally show how QuAX generates high-utility FAQ collections with little and domain-agnostic training data, and how the individual stages of the pipeline improve on the corresponding state-of-the-art.
The Importance of Rural Library Services Based on Social Inclusion in Indonesia

Libraries provide services for anyone, regardless of their social or economic interests, so that they have a non-discriminatory nature in providing good services in the library. Rural library into the container provision of reading materials as a source of education and community empowerment. Facing COVID-19 is not easy. It becomes a challenge for community to stay active and productive. Social inclusion has been introduced in many public libraries in Indonesia. Through this social inclusion, the library wants to further improve the welfare of community, especially in the rural areas. This chapter discusses the conceptualization of information for development which accommodates the concepts of advancement of literacy and development information for the community. The purpose of this service is to be able to provide services to rural communities so that it is easier to access information.
Visual Analytics to Support Industrial Vehicle Fleet Planning

Data analytics is becoming more important due to the data generated by the immensity of systems driven by widespread use of internet and mobile devices. Getting useful information from a lot of data is a challenge that visual analytics can be an alternative to. Visual analytics promotes information visualization and data mining techniques to consolidate and extract insightful information from databases. This paper aims to apply visual analytics concepts to help new logistic solutions prospection for optimization of industrial vehicles fleet. As a result, an artifact prototype able to assess the fleet behavior in terms of operational and available vehicles was developed. The prototype was evaluated based on information visualization and visual analytics practices.
Control-Flow Integrity for Real-Time Operating Systems: Open Issues and Challenges

The pervasive presence of smart objects in almost every corner of our everyday life urges the security of such embedded systems to be the point of attention. Memory vulnerabilities in the embedded program code, such as buffer overflow, are the entry point for powerful attack paradigms such as Code-Reuse Attacks (CRAs), in which attackers corrupt systems’ execution flow and maliciously alter their behavior. Control-Flow Integrity (CFI) has been proven to be the most promising approach against such kinds of attacks, and in the literature, a wide range of flow monitors are proposed, both hardware-based and software-based. While the formers are hardly applicable as they impose design alteration of underlying hardware modules, on the contrary, software solutions are more flexible and also portable to the existing devices. Real-Time Operating Systems (RTOS) and their key role in application development for embedded systems is the main concern regarding the application of the CFI solutions.This paper discusses the still open challenges and issues regarding the implementation of control-flow integrity policies on operating systems for embedded systems, analyzing the solutions proposed so far in the literature, highlighting possible limits in terms of performance, applicability, and protection coverage, and proposing possible improvement directions.
An Efficient Interaction Protocol Inference Scheme for Incompatible Updates in IoT Environments

Incompatible updates of IoT systems and protocols give rise to interoperability problems. Even though various protocol adaptation and unknown protocol inference schemes have been proposed, they either do not work where the updated protocol specifications are not given or suffer from inefficiency issues. In this work, we present an efficient protocol inference scheme for incompatible updates in IoT environments. The scheme refines an active automata learning algorithm, L*, by incorporating a knowledge base of the legacy protocol behavior into its membership query selection procedure for updated protocol behavior inference. It also infers protocol syntax based on our previous work that computes the most probable message field updates and adapts the legacy protocol message accordingly. We evaluate the proposed scheme with two case studies with the most popular IoT protocols and prove that it infers updated protocols efficiently while improving the L* algorithm’s performance for resolving the incompatibility.
Lightweight Object Tracking Algorithm Based on Siamese Network with Efficient Attention

Siamese networks have drawn great attention in visual tracking in recently years because they have a good balance between accuracy and speed. However, in most Siamese trackers, their backbone networks used as feature extractor are relatively shallow and narrow like AlexNet, which does not take full advantage of deep neural networks. In this paper, we propose a lightweight Siamese network object tracking algorithm based on efficient attention mechanism to enhance tracking robustness and accuracy. Firstly, we modify MobileNetV2 and use it as our backbone network, it can reduces the parameters and calculation amount drastically and upgrades the speed of training and testing. Secondly, attention mechanism weighted the feature maps in channels and spatial use for distributing the contribution of the different response maps. Thirdly, different level features are fused for the purpose of obtaining more robust results. The experiments show that our tracker can improve both the accuracy and speed on three benchmarks, including OTB2015, VOT2018 and TrackingNet.
Identifying the Alterations in the Microbiome Using Classification and Clustering Analysis: A Path Towards Microbiome Bio-Tech Innovations

Understanding the various factors how the infections among the humans are provoked when any disease affects the microbiome of the human body which then leads to certain alterations. Microbiome studies are drawing an expanding interest, particularly in person’s wellbeing approach, where their usage in illness anticipating, indication and treatments can affect quality of life. In our investigation, we created the operational taxonomic unit (OTU) which contains moving pictures of microbiome of humans. The consequences obtained from OUT table encouraged us to select Random Forest (RF) classifier and Spectral grouping (SC). The Random Forest Classifier has performed well in terms of accuracy and precision. After the analysis we have observed that the alterations in the microbiome. This leads to development of certain module of invention or model of technology for detecting different diseases and to take preventing measure.
Real Time Monocular Vehicle Velocity Estimation using Synthetic Data

Vision is one of the primary sensing modalities in autonomous driving. In this paper we look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. Contrary to prior methods that train end-to-end deep networks that estimate the vehicles&#x0027; velocity from the video pixels, we propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity from the tracked bounding boxes. Surprisingly, we find that this still achieves state-of-the-art estimation performance with the significant benefit of separating perception from dynamics estimation via a clean, interpretable and verifiable interface which allows us distill the statistics which are crucial for velocity estimation. We show that the latter can be used to easily generate synthetic training data in the space of bounding boxes and use this to improve the performance of our method further.
Engineering Trust-Aware Decentralized Applications with Distributed Ledgers

Decentralized Applications (dApps) are software applications where different parties interact over the Internet to achieve a common business goal. In contrast to centralized applications, not a singular entity has control over the whole system. The different parties interact in a peer-to-peer fashion. Without a centralized intermediary with the power to resolve conflicts or counteract malicious behavior by its users, dApps often lack trust-building mechanisms for their users. Blockchain and distributed ledger technologies promise to solve trust issues in dApps. This chapter explores in a three-step approach systematically how to build trust-aware dApps by design with distributed ledger technologies. Therefore, business process models with a trust layer are used as a tool to visualize and analyze trust issues of the dApps underlying process systematically as a first step. Secondly, different distributed ledger trust patterns are selected and used to mitigate these trust issues. Finally, the trust-enhanced dApp can be implemented. This chapter helps system engineers systematically to create trust-aware dApps with distributed ledgers.