Reviewerly/exHarmony huggingface dataset:
Fast Heuristics for Mixed Fleet Capacitated Multiple TSP with Applications to Location Based Games and Drone Assisted Transportation

The Travelling Salesman Problem (TSP) is one of the most known problems in combinatorics and consists of finding a route so that a salesman visits all destination points exactly once with minimum cost. Applications of the problem have long been studied in various fields of computer science, however, with the modernization of transportation means as manifested by autonomous vehicles and drone based delivery, variations of TSP have seen renewed interest. Simultaneously, in location based games such as Pokemon GO, the problem of defining optimal routes in order to direct a single player or a team of players into visiting a set of locations, leads to solving TSP variations. Inspired by the above, in this paper we tackle the problem of Mixed Fleet Capacitated Multiple TSP (mfcmTSP), whereby a set of couriers (using different vehicle types or drones) must deliver a set of homogeneous parcels to predefined destinations with minimum makespan. All transportation means have capacity measured as the number of parcels they can carry. We motivate mfcmTSP considering a scenario whereby all parcels are available at a single base station and a fleet of one truck with unlimited capacity, a motorbike with k capacity and a drone with capacity of one parcel are available to fulfill delivery orders. We present a fast heuristic to obtain good solution quality with negligible running time overhead and compare it against a yardstick strategy whereby only the truck is used. Experiments demonstrate the merits of our approach.
Synthesizing Face Images from Match Scores

In this paper, we consider the problem of generating a face image based on its match scores with other face images. Such an exercise is not only useful in understanding the relationship between face images, but it can also be used to understand the degree of privacy associated with match scores. We address the problem using two approaches. The first mixes face images to deduce the appearance of a missing face image and the second uses a convolutional auto-encoder to further enhance the mixed face image. Experiments suggest the potential of the proposed approaches in generating a missing face image in a database by utilizing its relationship with other images in the database.
Modified Transformer Architecture to Explain Black Box Models in Narrative Form

The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to interpret for a non-expert user. Here, the use of natural language generation (NLG)-based techniques can help to represent explanations in a human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to the unavailability of a relevant annotated dataset for development and testing, the authors also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.
Role of Blockchain in IoT Enabled Power and Energy Related Healthcare System-Platform for the Development of IoT Security

In the concept of IoT technology, various physical objects, things, machines and even peoples can communicate with each other through the internet. Predictably, millions of devices would be connected and shared in the coming future. In this technology, there are various types of sensors attached to these devices. These sensors can use different types of connections like RFID, Zigbee, Bluetooth, Wi-Fi and the internet via wireless and wired network configurations. There are many IoT enabled applications like smart home, intelligent traffic, smart energy, innovative healthcare, smart agriculture, etc., and these applications enhance the existing infrastructure a lot. There are many challenges with this technology, such as data privacy and security, user authentication, attacks countermeasure, easy deployment and self-maintenance. IoT enabled healthcare system is one of the applications which can be boost by integrated blockchain technology. Although blockchain technology is based on expensive computation, it involves the overhead of high bandwidth and delay. Therefore, it is not entirely fit for resource-constrained IoT devices suggested smart healthcare system (Dwivedi et al. in Sensors, 2019; Dorri et al. in Blockchain in Internet of Things: Challenges and Solutions). The key objective of this chapter is to investigate and proposed a modified power and energy-efficient architecture to integrate the IoT and the blockchain in the application of digital healthcare information system to eliminate the overhead of the blockchain technology security and privacy benefits. This integration might be beneficial not only for the patients but also for the doctors, medical caregivers, insurance companies, pharmacies and hospitals. In this chapter, we have discussed the power and energy-efficient architecture of IoT enabled healthcare system using blockchain and its application in numerous fields of the healthcare system (Srivastava in A Light and Secure Healthcare Blockchain for IoT Medical Devices. IEEE, CCECE, Edmonton, AB, Canada, 2019). In the last section, we discussed various benefits, challenges, opportunities, various security challenges in IoT enabled healthcare with a Blockchain system with the solution and currently ongoing research along with future directions.
SNI Field Blocking and Internet Censorship

The government tried to close illegal sites and conduct a copyright protection campaign intensively. However, it takes a lot of time to prevent illegal sites to block, and there is a limitation when internet browsers used a secure protocol. In order to implement effective policies, DNS servers and SNI fields were blocked since 2019. But SNI field blocking is not a solution of effective blocking because of technical problems. The risk of invading Internet users' access to information has also increased due to the strengthening of access blocking technology and excessive review by government agencies. The government's method of blocking illegal sites is technically circumvented, so there is a problem in its implementation, and it is necessary to start again from collecting public opinion due to the effectiveness and lack of transparency in terms of control. International judicial assistance should be activated to allow direct disposal of the server operating overseas, not to block access to domestic telecommunication network.
Introduction to Chaotic Dynamics’ Forecasting

Chaotic dynamics are the paradigm of complex and unpredictable evolution due to their built-in feature of amplifying arbitrarily small perturbations. The forecasting of these dynamics has attracted the attention of many scientists since the discovery of chaos by Lorenz in the 1960s. In the last decades, machine learning techniques have shown a greater predictive accuracy than traditional tools from nonlinear time-series analysis. In particular, artificial neural networks have become the state of the art in chaotic time series forecasting. However, how to select their structure and the training algorithm is still an open issue in the scientific community, especially when considering a multi-step forecasting horizon. We implement feed-forward and recurrent architectures, considering different training methods and forecasting strategies. The predictors are evaluated on a wide range of problems, from low-dimensional deterministic cases to real-world time series.
Application of Fuzzy Evaluation Method Based on D-S Evidence Theory

Main transformer of thermal power plant is the key electrical equipment. And it is important to evaluate the state of transformer. However, with a large number of features, it is difficult to properly fuse evaluation information and evaluate the state of transformer accurately. This paper proposed a fuzzy evaluation method based on D-S evidence theory. First, transformer testing data was collected and classified into four components: body, casing, oil and chromatography. Then, establish an evaluation model according to existing standards and expert database, which included indicator evaluation method based on fuzzy membership degree and weight calculation method based on analytic hierarchy process. After that, the component states were obtained by integrating the indicators and weights of each state quantity with evidence theory. Finally, the evaluation results of each component are fused to get the overall evaluation results of the equipment. Besides, a confirmed multi-evidences case was made to test the applicability of two improvement methods of D-S evidence theory: Yager and improved Yager. The results shows that improved Yager method has better performance in conflict evidence fusion and more suitable for equipment condition evaluation. This method can effectively judge the case of transformer oil damp defect. It is proved that this method can solve the problem that evaluation indicators are difficult to integrate. And a damp oil case was evaluated correctly, which proves the feasibility of the method.
Poster: Data Collection for ML Classification of Encrypted Messaging Applications

Network traffic classification is used to identify the nature of traffic on a network. Entities capable of monitoring net-work traffic use classification for all manner of reasons, including identification of mobile applications being used on the network. It is possible that the usage of encrypted messaging applications by users on these networks can be detected, betraying elements of their privacy.In this paper, we describe a system that leverages campus network resources to generate real-world data alongside a more curated dataset captured from Android application traffic. We also explore the ability of machine learning (ML) models to accurately classify traffic from these encrypted messaging applications. Understanding what is revealed from network data is important given that the use of these applications is meant to maximize privacy in the first place.
Entity Resolution in Dissimilarity Spaces

In this paper we propose a dissimilarity-based entity resolution framework that imposes a new efficient object representation scheme. This representation relies on the embedding of the dissimilarity space of pairs of objects to the space of distances of objects from a set of prototypes. These prototypes are selected among the input objects as the centers of clusters which are identified through an efficient clustering technique. An accurate object similarity metric that takes into consideration the rank correlation of distances from the prototypes is utilized to overcome the curse of dimensionality problem. Our methodology proposes the use of the generalized Hausdorff distance metric to deal with those cases where only partially ranked data is available in the representation domain of objects. Finally a locality sensitive hashing approach for partially ranked data is applied to reduce the high complexity of the similarity search for approximate duplicates.
Speech Denoising Based on Wavelet Transform of Correlation Function

Speech signal is a non-stationary short-time transient signal. The useful signal is in the same frequency range as the noise. The traditional de-noising method in the form of filter can not separate the noise effectively. Wavelet transform has the characteristics of time-frequency local analysis. By decomposing the noisy signal, separating the noisy signal and reconstructing the useful signal, the noise can be effectively removed. White noise is a stationary random signal, and the wavelet transform on different scales is irrelevant. Combined with the basic idea of wavelet de-noising, a method of speech de-noising based on wavelet transform of correlation function is proposed. Through MATLAB simulation, the de-noising method determined by correlation function can effectively remove the white noise of speech signal.
Dual-Anchor Metric Learning for Blind Image Quality Assessment of Screen Content Images

The artificial portion of screen content images (SCIs) destroys the natural scene statistics of natural scenes. It is also impractical to obtain an accurate statistical model due to the variable composition of the artificial and natural parts in SCIs. To resolve this problem, this paper presents a dual-anchor metric learning (DAML) method that is inspired by metric learning to obtain discriminative statistical features, and further to identify complex distortions, and predict SCI image quality. First, two available statistical models with prior data are constructed as the target anchors of the statistical model from two uncorrelated pristine databases, which can effectively enhance the metrical discrimination of the mapping relation between the feature representation and quality degradation by conditional probability analysis. Then, the distances of high-order statistics are softly aggregated to conduct metric learning between local features and clusters of each target statistical model. Through empirical analysis and experimental verification, only variance differences are used as quality-aware features to benefit the balance of complexity and effectiveness. Finally, mapping model between the target distances and subjective quality can be obtained by support vector regression. To validate the performance of DAML, multiple experiments are carried out on three public databases, and the results confirm the superior performance of the proposed method compared to existing relevant blind image quality assessment methods with SCIs.
A Performance Metric for Multiple Target Tracking with Temporal Dimension Specifics

With the great development of multi-target tracking (MTT) technologies, many MTT algorithms have been proposed with their own advantages and disadvantages. Due to the fact that requirements to MTT algorithms vary from the application scenarios, a metric is significant to select an appropriate MTT algorithm for the specific application scenario. In this paper, we proposed a metric on the trajectory sets with temporal dimension specifics to compare the estimated trajectories with the true trajectories. The proposed metric evaluates the estimate results of an MTT algorithm in terms of tracking accuracy, continuity and clarity. Furthermore, the metric computation is based on a multi-dimensional assignment problem, which is formulated as a computable form using linear programming. To enhance the influence of recent estimated states of the trajectories in the metric, an attention function is used to reweighted the trajectory errors at different time steps. Finally, simulation results show that the proposed metric is able to evaluate many aspects of the MTT algorithms. These evaluations are worthy for selecting suitable MTT algorithms in different application scenarios.
Symmetrical Performance Assessment of Large-Scaled Data Using Meta-heuristic Approach

Considering the present situation of enhancement of computing resources, the processing as well as flow of large scaled data can be deployed towards several data intensive applications to prioritize the scalability as well as consistency of data even if in virtual platform. In such situation, the technologies can be provisioned with specific paradigms to minimize the cost linked with computing resources. The provision in virtual platform can also be able to manage the specific infrastructure obtaining the security and privacy with the applications. The association of physical components as well as the storage components in this situation can have support on resource abstraction with control while generating applications in virtual platforms. In specific term, it can be understood that the management as well as support in virtual platform should be provisioned with portability as well as interoperability requirements. During the applications in virtual machines, it has also been observed that the specified techniques somehow generate massive heterogeneous large scaled data which sometimes a big challenge as many virtual platforms are typically acquainted with homogeneous linked data along with resources. Considering the performance of virtual machines, it is observed that the heterogeneity of the databases linked with virtual machines can be sequenced and the processes associated with the services can be implemented easily through these virtual machines. Based on all the situations, in this work, the meta-heuristic techniques, i.e., ant colony optimization and firefly approach have been applied to these large-scaled data towards symmetrical performance assessment.
Hybrid Art: fraught relationships with nature and technology

Hybrid Art: fraught relationships with nature and technology is a series of installations produced in 2021 for the “Hybrid Culture” exhibitions (3 different ones in distinct locations as part of “Viver ao Vivo” project) and for the “Contingency” exhibition (ARTECH 2021).
Shared Filtering-Based Advice of Online Group Voting

Public voting in online social networks is a recent feature. It presents special problems and suggestion possibilities. In order to suggest the social voting method, we create a series of matrix factorization (MF) systems and neighboring NN (RSs) systems to explore social network users and community membership details. Via experimentation with actual traces of social voting, the accuracy of popularity-based voting recommendations is greatly improved by social network and group membership information, and social network information in NN-funded approaches exceeds group membership information. We can see that the input provided by social and community users is much more useful than for heavy users. In our tests, basic Meta track based NN models exceed hot-voting recommended computational MF models, while MF models can help mitigate user interest in non-hot ballot. We also offer a hybrid RS that offers various single approaches to achieve the best hit rate.
Poisson Noise-Adapted Total Variation-Based Filter for Restoration and Enhancement of Mammogram Images

In this manuscript, we introduce a total variation to eradicate noise from an image which the Poisson noise has corrupted. This method regulates the total variation in order to preserve edges. Apart from that, it employs the fidelity of the data term that is better suited to the Poisson noise. A diversity of approaches for denoising mammogram pictures has been described, each with its own assumptions, advantages, and limitations. The efficacy of the filters has also been compared using criteria such as MSE and PSNR. Like the Poisson noise, the result of this regularization is also signaling dependent. The design steps include preprocessing. The preprocessing techniques include manual cropping of original mammograms to remove background details, quantum noise reduction, and contrast enhancements. A modified TV-based filter with the Poisson noise adaptation is utilized to reduce quantum noise.
SC-K9: A Self-synchronizing Framework to Counter Micro-architectural Side Channels

Side channels within the processor mi-croarchitecture are notorious for their ability to leak information without leaving any physical traces for forensic examination. Most prior detection frame-works typically choose to continuously sample a select subset of hardware events without attempting to understand the mechanics behind the side channel activity. In this work, we propose SC-K9, a novel framework that synchronizes its sampling frequency with that of the adversary, thereby improving the detection accuracy even when the frequency of attack operations vary with specific implementations. We then deploy a hardware-based deception strategy to trick the adversary and annul its observations from the side channel activities. We illustrate our design and demonstrate its effectiveness in identifying some of the potent side channels exposed by recent speculative execution attacks. Our experimental results show that SC-K9 can effectively spot adversaries at different operational modes, and incurs very low rate of false alarms among the benign workloads.
Intelligent Traffic Management System

We propose here intelligent traffic management system for heavily denser junctions to provide the smart traffic operations. The manual operation is efficient only for certain extent hence to achieve complete efficiency, technology need to be involved. The main reason for traffic jam is violating traffic rules. Using Image processing and raspberry pi as processor in real-time will calculate the denser lane and set the green light duration in the articular lane. Zebra crossing is for pedestrian crossing, but, drivers block the way by standing on it, hence by using Arduino micro-controller we ill incorporate the barricade system which will open and close on necessary conditions. To warn the drivers on open and closure of the barricades, buzzers are used. Certain places still uses manual operation of street lights that will lead to loss of electricity, human intervention and many other drawbacks. To overcome those drawbacks photo-resistors are used. These technology will reduce complexity in the present system and will easier the traffic density.
Research on the Design of Mapping Sorting Algorithm Based on Information Processing

In the computer information library processing work, the two most critical issues are speed and occupy space, the former refers to the speed of information processing and information query speed, and the latter refers to how to reduce the occupation of storage space. From the practical point of view, speed and space in computer technology research is a contradiction. In many cases, storage will be traded for speed, and the design will depend on the situation. Therefore, on the basis of understanding the current situation of computer information processing, according to the mapping technology and related algorithms in sorting, the design of mapping sorting algorithm with information processing as the core is systematically understood, and the final results prove that in information processing, the use of mapping sorting algorithm has effectiveness.
Applications of Cloud Computing in Modern Marketing

Many cloud-based marketing programs, ranging from CRM systems to marketing automation solutions, are already widely used. These services let marketers monitor campaigns and activities across mobile, social, and Web platforms, as well as customer interactions. As Internet usage has spread across devices, there are more ways to engage potential consumers in this modern era - but it is also more challenging to attract their attention. Customers desire stuff that is unique, organic, engaging, and personalized. Marketers may employ cloud technology to generate new data-driven strategies and more tailored and targeted marketing. These tools will very definitely be combined with one of the digital marketing components listed below.