Reviewerly/exHarmony huggingface dataset:
Profanity Detection and Removal in Videos using Machine Learning

With the increase in content being uploaded daily on video sharing platforms, the need to moderate the material uploaded becomes mandatory. Traditional profane removal mechanisms follow a prolonged approach. This is not recommended since the audience already had access to the media before it was identified of having potential profane content. There has been extensive work in the domain focusing on profanity removal from text. There arises a need for automated video moderation technique to handle the media which is being uploaded at the initial stage itself so as to provide a profane free platform to the viewers which ranges from kids to the adults. With a view to detect and remove profanity from the video-sharing platforms, this paper suggests an approach to silence the audio and pixelate the lips present if any in the video-segments containing profanity using machine learning.
Web Controlled Raspberry Pi Robot Surveillance

Security is a major thing to focus on during this modern era as it is very important to secure your surroundings for the well being of oneself and his family, But there are many drawbacks of using conventional security surveillance cameras as they have to be set in a particular angle for good visual and they do not cover a large area, conventional security cameras can only be used from a particular device and cannot alert the user during an unforeseen circumstance. Hence we require a much more efficient device for better security a web controlled surveillance robot is much more practical device to be used compared to conventional security surveillance, this system needs a single camera to perform its operation and the user can monitor a wide range of area, any device with a wireless connection to the internet can be used to operate this device. This robot can move to any location within the range of the network and can be accessed globally from anywhere and as it uses only one camera to secure a large area it is also cost-efficient. At the core of the system lies Raspberry-pi which is responsible for all the operation of the system and the size of the device can be engineered according to the area it is to be used.
A Platform for Disease Intervention Planning

The research and development of new tools and strategies for disease intervention planning requires resources, data, and computation spread across multiple institutions and individuals. Whether this is towards an objective such as drug discovery or informing intervention policy, it should be possible for these tools to be flexibly deployed to meet the decision support needs of the Global Health community. In this work we introduce a new platform to demonstrate the utility of a scalable computational infrastructure, blockchain based validation and machine learning (ML) algorithms, to assist in the generation of validated novel policies for malaria control. We have conducted preliminary tests in the generation of simulation-based evidence to guide policy level decision making. Specifically to assess the performance of; the scalable infrastructure under different simulation complexities and distributed compute; Hyperledger Fabric to provide validation of shared information within our application; and ML approaches to generate novel policy insights. Finally the components of the platform may be leveraged via a non-technical user or policy-maker through an Interactive Dashboard, bridging the gap between research and immediate needs in Disease Intervention Planning.
Deep Graph-Based Character-Level Chinese Dependency Parsing

Character-level Chinese dependency parsing has been a concern of several studies that naturally handle word segmentation, POS (Part of Speech) tagging and dependency parsing jointly in an end-to-end way. Previous work mostly concentrates on a transition-based framework for this task because of its easy adaption, which is extremely important when feature representation relies heavily on the decoding strategy, particularly under the traditional statistical setting. Recently, on the one hand, sophisticated deep neural networks and deep contextualized word representations have greatly weakened the dependence between feature representation and decoding. On the other hand, (first-order) graph-based models, especially the biaffine parsers, are straightforward for dependency parsing, and meanwhile they can yield competitive parsing performance. In this paper, we make a comprehensive investigation of the deep graph-based character-level dependency parsing for Chinese. We start from an extension of a standard graph-based biaffine parser, and then exploit Chinese BERT as well as our improved encoders based on transformers to enhance the character-level dependency parsing model. We conduct a series of experiments on the Chinese benchmark datasets, showing the performances of various graph-based character-level models and analyzing the advantages of the character-level dependency parsing under the deep neural setting.
Artificial seeding for micro-computed tomography image contrast enhancement for digital volume correlation

Digital volume correlation (DVC) can be implemented via micro-computed tomography (μ-CT) to resolve 3D strains. For proper correlation, microstructure detail is required. This study aims to provide a methodology for obtaining optimal DVC image datasets for composite materials by adding contrast particles to an epoxy resin. Image segmentation was utilized to ensure each particle met analysis DVC baselines. Particles with sizes of 10, 25, 50 and 200 μm were investigated. The 25 μm copper particles provided the greatest accuracy. These particles were embedded into an epoxy sample with a hole and subjected to uniaxial compression. Downsampled μ-CT and full-resolution datasets were compared to demonstrate processing strategies for DVC analysis.
Virtual Reality for Developing Intercultural Communication Skills of Engineering Students

Nowadays, virtual reality (VR) systems gain popularity in various applications ranging from space to entertainment. Education took up this challenge thus promoting virtual reality into the learning environment, first of all, in teaching science and engineering. Courses in humanities, however, are often ignored, though they have a great potential of applying VR techniques to develop students’ soft skills, intercultural communication among them. To bridge this gap, the paper aims at analyzing the demand for VR, the experience in virtual reality and readiness of Master’s degree engineering students for taking courses with immersion into virtual reality, using the example of intercultural communication courses. The authors analyzed recent publications in research journals and conference proceedings on the use of VR tools in higher education paying special attention to engineering curricula. The authors also conducted a survey among 87 MSc students focusing on the degree to which students are familiar with VR and ready to study through immersion into VR. The overall analysis of the survey results revealed that the students have a limited experience of using VR in any applications but most of them are ready for this experience in education. Based on these interpretations of the survey results, the authors made an attempt to develop a draft course to introduce elements of VR into EFL teaching for engineering students. Further research can be done to disseminate the initial experience of VR applications and to update the course developed.
Matching Sensor Ontologies with Simulated Annealing Particle Swarm Optimization

In recent years, innovative positioning and mobile communication techniques have been developing to achieve Location-Based Services (LBSs). With the help of sensors, LBS is able to detect and sense the information from the outside world to provide location-related services. To implement the intelligent LBS, it is necessary to develop the Semantic Sensor Web (SSW), which makes use of the sensor ontologies to implement the sensor data interoperability, information sharing, and knowledge fusion among intelligence systems. Due to the subjectivity of sensor ontology engineers, the heterogeneity problem is introduced, which hampers the communications among these sensor ontologies. To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms. Among all ontology matching technologies, Particle Swarm Optimization (PSO) can represent a contributing method to deal with the low-quality ontology alignment problem. For the purpose of further enhancing the quality of matching results, in our work, sensor ontology matching is modeled as the meta-matching problem firstly, and then based on this model, aiming at various similarity measures, a Simulated Annealing PSO (SAPSO) is proposed to optimize their aggregation weights and the threshold. In particular, the approximate evaluation metrics for evaluating quality of alignment without reference are proposed, and a Simulated Annealing (SA) strategy is applied to PSO’s evolving process, which is able to help the algorithm avoid the local optima and enhance the quality of solution. The well-known Ontology Alignment Evaluation Initiative’s benchmark (OAEI’s benchmark) and three real sensor ontologies are used to verify the effectiveness of SAPSO. The experimental results show that SAPSO is able to effectively match the sensor ontologies.
Using Multi-objective Grammar-Based Genetic Programming to Integrate Multiple Social Theories in Agent-Based Modeling

Different theoretical mechanisms have been proposed for explaining complex social phenomena. For example, explanations for observed trends in population alcohol use have been postulated based on norm theory, role theory, and others. Many mechanism-based models of phenomena attempt to translate a single theory into a simulation model. However, single theories often only represent a partial explanation for the phenomenon. The potential of integrating theories together, computationally, represents a promising way of improving the explanatory capability of generative social science. This paper presents a framework for such integrative model discovery, based on multi-objective grammar-based genetic programming (MOGGP). The framework is demonstrated using two separate theory-driven models of alcohol use dynamics based on norm theory and role theory. The proposed integration considers how the sequence of decisions to consume the next drink in a drinking occasion may be influenced by factors from the different theories. A new grammar is constructed based on this integration. Results of the MOGGP model discovery process find new hybrid models that outperform the existing single-theory models and the baseline hybrid model. Future work should consider and further refine the role of domain experts in defining the meaningfulness of models identified by MOGGP.
Cryogenic Benchmarks of Embedded Memory Technologies for Recurrent Neural Network based Quantum Error Correction

Even at deep cryogenic temperature ~20 milli-Kevin, the qubit is fragile, therefore a feedback loop is needed to perform the quantum error correction (QEC). It is highly desirable to operate the QEC at 4K to minimize the thermal heat transfer between the physical qubits and the peripheral control circuitry. In this work, we propose implementing the surface code QEC circuitry with compute-in-memory (CIM) based recurrent neural network accelerator at 4K. To serve this purpose, we develop Cryo-NeuroSim, a device-to-system modeling framework that calibrate the transistor and interconnect parameters with experimental data at cryogenic temperature. Then we benchmark the QEC circuitry with SRAM technologies and optimize its energy-delay-product (EDP) with reengineered threshold voltage and supply voltage.
Experimental Quantum Message Authentication with Single Qubit Unitary Operation

We have developed a quantum message authentication protocol that provides authentication and integrity of an original message using single qubit unitary operations. Our protocol mainly consists of two parts: quantum encryption and a correspondence check. The quantum encryption part is implemented using linear combinations of wave plates, and the correspondence check is performed using Hong–Ou–Mandel interference. By analyzing the coincidence counts of the Hong–Ou–Mandel interference, we have successfully proven the proposed protocol experimentally, and also showed its robustness against an existential forgery.
A Vehicular Queue Length Measurement System in Real-Time Based on SSD Network

Abstract Vehicular queue length measurement is an important parameter to detect the traffic congestion, which is resulted from several issues such as traffic lights, accidents, and poor roads infrastructures. In this paper, a system in real-time is proposed to detect and measure the vehicular queue length at intersections. The proposed system consists of two main steps: the first step is the detection of queue by using frames differencing method to detect the motion in the target areas. If there is no a motion, then the second step is implemented to detect the vehicles in these areas by using Single Shot Multibox Detector (SSD) algorithm. If there are vehicles, that means the queue exists and the measurement process begins. Some modifications are applied on SSD algorithm to fit with in our system and to improve the accuracy of the vehicle detection process. The system is applied on videos obtained by stationary cameras. The experiments demonstrate that this system is able to accurately detect and measure the vehicular queue length.
Transparent Data Dealing: Hyperledger Fabric Based Biomedical Engineering Supply Chain

Blockchain is a decentralized and immutable technology which offers transparency for the digital world, where habitual technology does not. The novel blockchain technology may be employed by many sectors, i.e., healthcare, bank, government services, and supply chain. In particular, Biomedical Engineering Supply Chain (BESC) is a significant part of the medical sector that supplies equipment for the medical sector i.e., Covid-19 testing kit, PPE (Personal Protection Equipment), and medicine. The biomedical product should be able to be traced and the data secured; otherwise, the initial data may be modified and potentially risking patients and the public. Nevertheless, the conventional centralized technology creates a leakage point and as such, compromises data security. This paper proposes a new data dealing approach with using Hyperledger Fabric Blockchain-based BESC to alleviate the centralized controllable and operational issues. The blockchain-based BESC is a novel approach, which can control the users and subsequently eliminate the possibility of tampering within the blockchain system when stored.
A methodology for domain and culture-oriented heuristics creation and validation

Culture fit is an essential consideration while designing websites in this technologically evolving era, yet it is too often ignored in current usability evaluation methods. The heuristic evaluation is one such method, in which, generally, experts use a set of heuristics as a guide. These heuristics are well designed to pinpoint the general usability design faults; however, they limit identifying potential usability problems for specific domains and cultures. This article presents a formal methodology with a sequence of steps of how domain and culture-specific heuristics can be designed. This methodology is validated and applied in the context of websites for universities in Pakistan.
Knowledge-Based Hierarchical POMDPs for Task Planning

The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state. In robotics, this is particularly difficult because actions usually have several possible results, and sensors are prone to produce measurements with error. Partially observable Markov decision processes (POMDPs) are commonly employed, thanks to their capacity to model the uncertainty of actions that modify and monitor the state of a system. However, since solving a POMDP is computationally expensive, their usage becomes prohibitive for most robotic applications. In this paper, we propose a task planning architecture for service robotics. In the context of service robot design, we present a scheme to encode knowledge about the robot and its environment, that promotes the modularity and reuse of information. Also, we introduce a new recursive definition of a POMDP that enables our architecture to autonomously build a hierarchy of POMDPs, so that it can be used to generate and execute plans that solve the task at hand. Experimental results show that, in comparison to baseline methods, by following a recursive hierarchical approach the architecture is able to significantly reduce the planning time, while maintaining (or even improving) the robustness under several scenarios that vary in uncertainty and size.
Research on Sports Training Decision Support System Based on Improved Association Rules Algorithm

In my country, college students’ physical exercise is mainly through physical exercise and amateur sports, which is basically in a state of disorder. Based on the improved association rules, this paper introduces the auxiliary decision support system for college students’ sports training. First, we introduced a network security method and designed a sports training decision support system based on network security. Secondly, based on the Apriori algorithm, we have realized the organic integration of all aspects of college students’ sports, applied scientific training theories and advanced training methods to the management of college students’ sports training, and generated new knowledge rules through data mining technology, enriching knowledge. Finally, according to user input, select the corresponding model and combine with the rules in the knowledge base to generate a reasonable exercise training plan. After model simulation and example verification, the sports training decision support system based on the improved association rule prior algorithm designed in this paper has good applicability and efficiency.
Secure decision tree twin support vector machine training and classification process for encrypted <scp>IoT</scp> data via blockchain platform

Summary A secure decision tree twin support vector machine (DT‐TSVM) multi‐classification algorithm has been proposed in this paper for improving the reliability and security of the collected IoT data from multiple data providers. The multiclass secure DT‐TSVM algorithm has been employed to train a machine learning model using the encrypted training dataset. The training dataset is collected via a blockchain platform. A blockchain method has been adopted to construct a secure and reliable distributed platform among dataset providers. The Paillier homomorphic cryptosystem has been applied for encrypting the IoT dataset. Then, the dataset has been recorded on the distributed ledger. The secure DT‐TSVM algorithm's‐based train model effectiveness has been compared with the other two available algorithms, namely the multiclass binary support vector machine (MBSVM) and one‐to‐one SVM algorithms. The experiment results showed that the privacy‐preserving multiclass secure DT‐TSVM‐based model did not reduce the accuracy, but it increased the average precision and recall by 0.53% and 0.44% than MBSVM and 0.82% and 0.71% than one‐to‐one SVM, respectively. Further, the time consumption of data providers and data analysts did not change significantly with the increase of number of data provider.
A Construction of Minimal Linear Codes From Partial Difference Sets

In this paper, we study a class of linear codes defined by characteristic functions of certain subsets of a finite field. We derive a sufficient and necessary condition for such a code to be a minimal linear code by a character-theoretical approach. We obtain new three-weight or four-weight minimal linear codes that do not satisfy the Ashikhmin-Barg condition by using partial difference sets. We show that our construction yields minimal linear codes that do not arise from cutting vectorial blocking sets, and also discuss their applications in secret sharing schemes.
Enhancing Open-Set Recognition using Clustering-based Extreme Value Machine (C-EVM)

In real-world deployments, machine learning applications find challenges when accessing ever-increasing volumes of data - the real world is open and often presents data from classes not seen in training. Open-set recognition is a growing area of machine learning addressing such problems. This research work advances the state-of-the-art in open-set recognition, the Extreme Value Machine (EVM), with a novel clustering-based extension (C-EVM) during training to improve the end-to-end prediction performance. The C-EVM combines Density-based spatial clustering of applications with noise (DBSCAN)-based clustering with a novel Nearby Clusters (NC) algorithm during model fitting to reduce computation while improving accuracy. Our experiments show a statistically significant improvement of 5-10% in macro F1-score over the state-of-the-art EVM on open-set testing using the KDD CUP-99 data set. Past work on open set recognition often traded improved open-set robustness for a decrease in closed-set accuracy, whereas C-EVM outperforms the EVM in both closed-set and open-set recognition. Testing on subsets of ImageNet-2012 with varying numbers of classes, the C-EVM statistically significantly out performs EVM when using deep features. A parameterless Hierarchical DBSCAN (HDBSCAN)-based C-EVM variant is introduced as part of this work that scales well for large data sets. Finally, both EVM and C-EVM can operate as kernel-free incremental learners, enabling these open-set multi-class classifiers to be useful for streaming and big data applications.
Identification of Spoofed Emails by applying Email Forensics and Memory Forensics

Email forensics is the subdomain of network forensics, and email spoofing is the most common type of email attack. Email spoofing is a process of creating a forged message by manipulating the sender’s email address so that it appears to the recipient that the originating email is coming from a genuine sender. Spoofed email attack and its detection is a challenging problem in email forensic investigation. Research in the past has tried to address email detection by different mechanisms. This paper tries to improve and fill some of the research gaps from the base paper of R.P Iyer [11]. In our work, we detect spoofed emails received by the user by applying memory forensic approach. Instead of capturing the complete memory dump, we only capture the browser’s live running processes from memory and extract the email header for analysis. This reduces the size of the memory dump and makes detection fast. Also proposed detection algorithm overcomes messageID based detection failures by applying nslookup to fetch MX record to identify the genuine emails. The advantage of memory forensic application for spoofed email detection is that we get guaranteed non-repudiation of the user’s digital footprint in physical memory. The results of the performance analysis show that the entire task can be completed in approximately 1 min with high accuracy with minimum false positives. The proposed method detects spoofed emails without disrupting the regular operation of the testing machine.