Reviewerly/exHarmony huggingface dataset:
Information-Based Heuristics for Learned Goal-Driven Exploration and Mapping

In this paper, we propose a novel heuristics function for evaluating and selecting intermediate points in a goal-driven autonomous exploration and mapping system where navigation is performed by a learned neural network. The function calculation takes into consideration the training setting of the deep reinforcement learning-based network and combines it with distance information towards the global goal and the map information. The candidate with the minimum score is selected as the current intermediate goal. This allows the navigation system to be guided towards the global goal in an informed manner. Experiments in simulation and real-world settings show the benefit of the proposed approach over similar heuristic candidate point evaluation methods.
A Modulation Recognition Algorithm of Communication Signals and Implementation Based on Digital Signal Processor

Based on the existing modulation recognition algorithms, this paper proposes a modulation recognition algorithm of communication signals based on decision tree theory by analyzing and extracting multiple signal characteristic parameters. This algorithm will find the best decision threshold by training the characteristic parameters of different signals. The research results show that the recognition accuracy of the algorithm can reach 93% when the signal noise ratio (SNR) is 10 dB, and it can recognize twelve modulation signals of CW, AM, FM, LSB, USB, 2FSK, 4FSK, BPSK, QPSK, 2ASK, 4ASK and 16QAM. The algorithm is processed in parallel on the TMS320C6678 platform to realize signal modulation identification in the frequency band of 1 to 3 GHz.
Decentralized Knowledge Transfer on Edge Networks for Detecting Cancer in Images

The proliferation of edge networks creates an opportunity to utilize extremely low latency servers for offloading computation. In the healthcare domain, these will allow for more significant opportunities for personalized medicine and health monitoring solutions that will be directly connected to a health-care provider where patient data will be analyzed. Optimally, a model fitted to local data could be shared between providers to benefit from data that is not available locally, whether to debias a model or to gain better insight on a rarely seen condition. Since patient data privacy is of utmost importance, we require a method for transferring knowledge from sources containing data of interest without exposing or sharing local data. We propose such a method based on knowledge distillation that can transfer knowledge between models in real-time. Our approach results in lower training and converging time in addition to the ability to classify targets that are rarely or never seen locally accurately. Our method emphasizes transferring knowledge without sacrificing the insights gained from local data. We show our method works on skin cancer classification, detecting tumors in brain MRI scans, and other image datasets. All assuming data is non-i.i.d.
Design and Execution of Highly Adaptable Elliptic Curve Cryptographic Processor and Algorithm on FPGA using Verilog HDL

Cryptography is the science or process used for the encryption and decryption of data that helps the users to store important information or share it across networks where it can be read only by the intended user. In this paper, Elliptic Curve Cryptography (ECC) has been proposed because of its small key size, less memory space and high speed. Elliptic curve scalar multiplication is an important part of elliptic curve systems. Here, the scalar multiplication is performed with the help of hybrid Karatsuba multiplier as the area utilization of Karatsuba multiplier is less. An alternative of digital signature algorithm, that is, Elliptic Curve Digital Signature Algorithm (ECDSA) along with the primary operations of elliptic curves have also been discussed in this paper.
An Anonymous Trust-Marking Scheme on Blockchain Systems

During the Coincheck incident, which recorded the largest damages in cryptocurrency history in 2018, it was demonstrated that using Mosaic token can have a certain effect.Although it seems attractive to employ tokens as countermeasures for cryptocurrency leakage, Mosaic is a specific token for the New Economy Movement (NEM) cryptocurrency and is not employed for other blockchain systems or cryptocurrencies.Moreover, although some volunteers tracked leaked NEM using Mosaic in the CoinCheck incident, it would be better to verify that the volunteers can be trusted.Simultaneously, if someone (e.g., who stole cryptocurrencies) can identify the volunteers, then that person or organization may be targets of them.In this paper, we propose an anonymous trust-marking scheme on blockchain systems that is universally applicable to any cryptocurrency.In our scheme, entities called token admitters are allowed to generate tokens adding trustworthiness or untrustworthiness to addresses.Anyone can anonymously verify whether these tokens were issued by a token admitter.Simultaneously, only the designated auditor and no one else, including nondesignated auditors, can identify the token admitters.Our scheme is based on accountable ring signatures and commitment, and is implemented on an elliptic curve called Curve25519, and we confirm that both cryptographic tools are efficient.Moreover, we also confirm that our scheme is applicable to Bitcoin, Ethereum, and NEM.
Privacy-Preserving Collaborative SDR Networks for Anomaly Detection

By offering unprecedented flexibility for wireless communications, Software-Defined Radio (SDR) is an enabling technology for distributed platforms such as Internet-of-Things (IoT) and cognitive radio. We propose a network of SDR devices with sensing and computation capabilities without relying on a centralized architecture to monitor and develop a detailed representation of network behavior in the radio frequency spectrum. This network representation provides a foundation for the task of anomaly detection for security purposes, in which SDRs collect the data needed to identify adversarial actors in a selected environment. We present and evaluate a novel distributed anomaly detection scheme for SDRs that applies Round Robin Learning (RRL) and Random Exchange Learning (REL) strategies to a type of machine learning model known as an autoencoder. In doing so, participating devices fill in gaps in a single model’s data representation, in order to build a model closer to that achieved through centralized training, while maintaining the advantages of distributed systems. The distributed architecture developed improves network robustness, decreases the amount of intranetwork communication, and preserves privacy of individual SDR subnetworks. Furthermore, it was found to consistently raise the average anomaly detection performance for groups of participating devices without exchanging any training data.
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes

We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. Our approach predicts absolute scale depth maps over the entire scene consisting of a static background and multiple moving people, by training on dynamic scenes. Since it is difficult to collect dense depth maps from crowded indoor environments, we design our training framework without requiring depths produced from depth sensing devices. Our network leverages RGB images and sparse depth maps generated from traditional 3D reconstruction methods to estimate dense depth maps. We use two constraints to handle depth for non-rigidly moving people without tracking their motion explicitly. We demonstrate that our approach offers consistent improvements over recent depth estimation methods on the NAVERLABS dataset, which includes complex and crowded scenes.
Clustering-based rule generation methods for fuzzy classifier using Autonomous Data Partitioning algorithm

Abstract In this paper, clustering-based rule generation methods for fuzzy classifier using non-parametric Autonomous Data Partitioning algorithm have been proposed. ADP-algorithm is used to determine the number of clusters for use in various k-means-like clustering algorithms. Proposed method contributes to solving the problem of determining optimal number of clusters/rules. The efficiency of fuzzy classifiers with rules constructed by the specified algorithms has been tested on data sets from the KEEL repository. Experimental results show that proposed method outperforms baseline algorithm (the extremums rulebase generation algorithm) both in terms of classification accuracy and geometric mean metrics.
SAR image ship detection based on improved YOLOv4

Ship target detection is an important means to supervise and protect maritime rights and interests and is widely used in the marine monitoring field. To improve the detection accuracy of ship targets in SAR (Synthetics Aperture Radar) images, this paper proposes a SAR image ship target detection algorithm on the improved YOLOv4. The advantages of various improvement techniques of the YOLOv4 detection algorithm are analyzed, and the YOLOv4 improvement techniques that are most suitable for this data set are selected through experiments. Then adjust the anchor box of the original YOLOv4 algorithm based on the K-means clustering algorithm to solve the detection accuracy drop caused by the mismatch between the original anchor box and the ship target size ratio. Finally, in the open SSDD data sets for the improved algorithm was trained and tested. The experimental results show that, under the condition of ensuring the detection efficiency, the detection accuracy of the method in this paper is improved by 2.87% compared with the original YOLOv4 algorithm.
Activated: Decentering activism in and with academia

Who is an activist? With the advent of activism-related scholarship in HCI and CSCW, the current challenge involves thinking about what activism is, who an activist is, and the opportunities and limitations of activism. Recently, researchers in academia and industry, such as Timnit Gebru, demonstrate a commitment to stay activated for the structural changes we need, for example, diverse and inclusive scholarship, to address overlapping problems, e.g., sexism, racism, and tokenism. Additionally, there is a continuing dominance of Western, formally educated, industrialized, rich and democratic (WEIRD) perspectives. Countering this requires collective efforts in citational justice and decolonial computing, among others. But such complex issues do not yet cover the inner conflicts that we face, such as mental health struggles while dismantling the prejudices stemming from the ivory tower, locating our privileges as academics while traversing less privileged locales of research sites, or the dilemmas on whether we are doing enough to fulfill our responsibilities to the people who have trusted us enough to work with us in the face of "publish or perish" culture. This workshop explores what activism means within the CSCW community and how we can remain activated while harboring doubts and hopes in calling ourselves "activists".
Characterizing Crisp Simulations and Crisp Directed Simulations between Fuzzy Labeled Transition Systems by Using Fuzzy Modal Logics

We formulate and prove logical characterizations of crisp simulations and crisp directed simulations between fuzzy labeled transition systems with respect to fuzzy modal logics that use a general t-norm-based semantics. The considered logics are fragments of the fuzzy propositional dynamic logic with the Baaz projection operator. The logical characterizations concern preservation of existential (respectively, positive) modal formulas under crisp simulations (respectively, crisp directed simulations), as well as the Hennessy-Milner property of such simulations.
Analytical method for selection an informative set of features with limited resources in the pattern recognition problem

Feature selection is one of the most important issues in Data Mining and Pattern Recognition. Correctly selected features or a set of features in the final report determines the success of further work, in particular, the solution of the classification and forecasting problem. This work is devoted to the development and study of an analytical method for determining informative attribute sets (IAS) taking into account the resource for criteria based on the use of the scattering measure of classified objects. The areas of existence of the solution are determined. Statements and properties are proved for the Fisher type informativeness criterion, using which the proposed analytical method for determining IAS guarantees the optimality of results in the sense of maximizing the selected functional. The relevance of choosing this type of informativeness criterion is substantiated. The universality of the method with respect to the type of features is shown. An algorithm for implementing this method is presented. In addition, the paper discussed the dynamics of the growth of information in the world, problems associated with big data, as well as problems and tasks of data preprocessing. The relevance of reducing the dimension of the attribute space for the implementation of data processing and visualization without unnecessary difficulties is substantiated. The disadvantages of existing methods and algorithms for choosing an informative set of attributes are shown.
EnNetForens: An Efficient Proactive Approach For Network Forensic

The number of digital crimes is increased rapidly with technology. There might be sure crimes that don't break network security strategies however might be legitimately indicted. These crimes can be taken care of by network forensic. Network forensics plays a significant part in the cybercrime investigation measure. It is a science that manages the catch, copying, and examination of network data for recognizing interruptions and discovering them. In our paper, we used the proactive approach for network forensic which provides us a live investigation. For that, we must capture live network packets and then encrypt the capture network data to achieve integrity of data so that it will not modify by the third person and only used by an investigator for further analysis. the data is classified based on different parameters like port number, protocol, packet size, arrival time, etc. Utilizing data mining procedures that help to improve and quicken the decision-making process.
Photoelastic characterization of shear‐bands in mechanically stretched polymeric fibers

In this work, the shear bands formed during the stretching of isotactic polypropylene fibers are studied. The digital photoelasticity technique is applied to provide full-field visualization of the stress of the shear bands regions. The mechanical testing device is modified to be suitable for mechanical measurements. The effect of different parameters, that is, the stretching rates and the ambient temperature on the formation of shear bands is investigated. Finally, the influence of heating pre-existed shear bands is studied. Photoelastic patterns of the shear bands at these conditions are included for illustration.
Beyond Reasonable Doubt

Prior work on fairness in machine learning has focused on settings where all the information needed about each individual is readily available. However, in many applications, further information may be acquired at a cost. For example, when assessing a customer's creditworthiness, a bank initially has access to a limited set of information but progressively improves the assessment by acquiring additional information before making a final decision. In such settings, we posit that a fair decision maker may want to ensure that decisions for all individuals are made with similar expected error rate, even if the features acquired for the individuals are different. We show that a set of carefully chosen confidence thresholds can not only effectively redistribute an information budget according to each individual's needs, but also serve to address individual and group fairness concerns simultaneously. Finally, using two public datasets, we confirm the effectiveness of our methods and investigate the limitations.
A Movement Adjustment Method for DQN-Based Autonomous Aerial Vehicle

The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms, which uses deep neural network structure to estimate the Q-value in Q-learning. In the previous work, we designed and implemented a DQN-based Autonomous Aerial Vehicle (AAV) testbed and proposed a Tabu List Strategy based DQN (TLS-DQN). In this paper, we propose a movement adjustment method for decreasing the movement fluctuations caused by TLS-DQN during autonomous movement control. The performance evaluation results show that the proposed method can decrease the movement fluctuations.
A MAP-based Performance Analysis on 5G-powered Cloud VR Streaming

Despite that cloud virtual reality (VR) is the most promising service in 5G networks, a reasonable traffic model for it is still unknown. Based on statistics of real cloud VR traces, we justify that the Markovian arrival process (MAP) can well characterize the inter-arrival times (IATs) among packets. Moreover, we discuss possible methods to efficiently estimate parameters for flow aggregation. Since MAPs are analytically tractable, we quantify the performance of delivering cloud VR traffic from a queueing theory perspective. Aiming at supporting quantile metrics, we provide distributions of queue-length and latency for an arbitrary packet. The accuracy of estimated latency is validated by comparing with measured latency on an industrial 5G platform. The MAP-based traffic model will potentially serve as an input for performance evaluation and network planning for assuring high requirements of user experience.
Query-By-Object Based Video Synopsis

In this paper, we propose a framework for query-by-object(s) based video synopsis. Video Synopsis aims to create a summary of video by retaining the important activities/events present in the input video. We propose creating a shorter video by selecting salient frames based on the important objects present in the video. We train the Yolov3 model with surveillance videos for object detection. Select the frames as salient based on the importance of objects present in a frame and generate the video synopsis with the salient frames. We demonstrate the proposed method on the Summe and TV Sum dataset and own dataset captured from the surveillance camera. We obtain the average F1 score as 93% and average accuracy as 94%. And also show that proposed method gives better results as compared to VASNET model.
IoT Based Warning System from the Threat of Wild Animals

Wildlife has recently become very common when it comes to inhabited areas. As humans encroach on trees, the habitat for wild animals is diminishing. When wild animals invade communities, it causes a great loss of property and lives. To build a warning system of potential animals exiting the park, this research work utilizes the newest developments in technologies such as the Internet of Things (IoT) and the notification will also be sent to Smartphone users to alert them. To do this, the low cost motion detectors and Passive Infrared sensors are used. In order to take more measures, the proposed system relay on the information about certain movements of a control room. Also, this research work involves in the creation of noisy noise by speakers in which animals are unable to reach the land. IoT's fundamental concept is to link numerous sensors and to create connectivity and to provide services as well. In this post, to build an alarm system, multiple IoT devices are utilized at the periphery of the natural reserve. This device can also be used to identify smugglers and other individuals entering the forest illegally.
A Framework to Implement Blockchain in Higher Education Institutions

This paper presents a framework to implement business solutions based on Blockchain technology (BCT) for the higher education institutions (HEIs). The first part of the paper provides an overview of the Blockchain technology, its implementation in the sector, and its advantages. The second part discusses the common challenges of implementing technology and then identifies higher education stakeholders' insights. Getting stakeholders' insights is the root of creating a comprehensive framework to help higher education institutions successfully implement the solutions based on BCT.
MADDPG Algorithm for Coordinated Welding of Multiple Robots

This paper deals with the coordinated welding problem of multi-robot systems by applying a deep reinforcement learning algorithm which is called multi-agent deep deterministic policy gradient (MADDPG). It is assumed that the states and actions of robots are continuous and each robot can only get local information of its neighbors. A novel reward composed of trajectory optimization, coordinated welding and collision avoidance is designed, which yield multi-robot systems to arrive the welding targets quickly, achieve the simultaneous welding for a weld line and avoid collision among robots. Simulation results are provided to demonstrate the effectiveness of the proposed control algorithm.
A comparison of domain integral and multi-parameter methods for the strain intensity factor estimation of rigid line inclusion using digital image correlation

A comparative study is carried out between two methods to estimate the strain intensity factor of a rigid line inclusion using the displacement and strain fields from digital image correlation (DIC). The two methods are (i) a proposed hybrid domain integral method and (ii) the linear over-deterministic least-squares technique involving multi-parameter displacement field equations (multi-parameter method). The multi-parameter method uses the displacement data obtained from the DIC experiments to find the unknowns in the multi-parameter equation. The proposed hybrid methodology uses a domain integral method to calculate the strain intensity factor using the full-field displacement and strain data obtained from DIC. The strain intensity factor estimated using the proposed domain integral method, and the multi-parameter method agrees with the analytical estimate. The influence of the size of the annular region and the mesh sensitivity is also reported. The sensitivity to the variance and bias errors in measurement for both the methods is investigated. To investigate the effect of variance error, a Gaussian noise is introduced over the reference and deformed images before post-processing, and its influence on the strain intensity factor estimate is discussed. The effect of random perturbations of displacements and strains on the strain intensity factor estimates is also investigated. The domain integral method is less sensitive to the variance error when compared to the multi-parameter solution approach. Further, the bias errors due to uncorrected lens distortions, over-smoothing of data, and DIC algorithm adopted are introduced into the measurements, and its effect on both the methods is investigated. The multi-parameter method is less sensitive to bias errors when compared to the domain integral method. Therefore, it is recommended that the domain integral method may be used for the experimental determination of strain intensity factor in measurements with variance errors and the multi-parameter method for measurements with bias errors.