Reviewerly/exHarmony huggingface dataset:
DAKRS: Domain Adaptive Knowledge-Based Retrieval System for Natural Language-Based Vehicle Retrieval

Given Natural Language (NL) text descriptions, NL-based vehicle retrieval aims to extract target vehicles from a multi-view multi-camera traffic video pool. Due to inherent distinctions between textual and visual data, this is a challenging multi-modal retrieval task that requires robust feature extractors (e.g. neural network) to well-align the abstract representations of texts and images in the same domain. However, solutions to the problem have been challenged by the high data complexities of not only the multi-view, multi-camera attributes of visual data and the diverse range of textual descriptions but also a lack of high-volume datasets in this relatively new field, alongside a prominently large domain gap between training and test sets. Many existing approaches have developed computationally expensive models to separately extract the subspaces of language and vision before blending into the same shared representation space while only focusing on single-modal information and ignoring much of the multi-modal information to deal with the aforementioned issues. Hence, we propose a Domain Adaptive Knowledge-based Retrieval System (DAKRS) to effectively and efficiently align multi-modal knowledge in a setting of limited labels. Our contributions are threefold: (i) An efficient extension of Contrastive Language-Image Pre-training (CLIP)’s transfer learning into a baseline text-to-image multi-modular vehicle retrieval framework; (ii) A data enhancement module to create pseudo-vehicle tracks from the traffic video pool by leveraging the robustness of baseline retrieval model combine with background subtraction; and (iii) A SSDA (SSDA) scheme to engineer pseudo-labels for adapting model parameters to the target domain distribution. Experimental results are benchmarked on the Cityflow-NL dataset, illustrating our competitiveness against state-of-the-art performances in terms of effectiveness and efficiency without needing further post-processing or ensembling.
Applications to Computing: Representing Functions in Quantum and Reversible Computing

In the previous two chapters, we showed how ideas of decision making under uncertainty can be applied to the current data processing algorithms. However, for many practical problems, the existing algorithms take too long a time. It is therefore necessary to explore the possibility of faster computations.
Adaptive Fragment-Based Parallel State Recovery for Stream Processing Systems

Today, large-scale cloud organizations are deploying datacenters and “edge” clusters globally to provide low-latency access to services. Running stream applications across geo-distributed sites are emerging as a daily requirement. However, existing efforts have dominantly centered around <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stateless stream processing</i> , leaving another urgent trend- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stateful stream processing</i> -much less explored. A driving need is to store and update states during processing, and most importantly, successfully recover large distributed states when faults and failures happen. Existing studies exhibit major limitations including: (1) they mostly inherit MapReduce's “single master/many workers” architecture, where the central master can easily become ascalability bottleneck; (2) they offer state recovery mainly through three approaches: replication recovery, checkpointing recovery, and DStream-based lineage recovery, which are either slow, resource-expensive or failing to handle multiple failures; and (3) they are not adaptive to heterogeneous hardware settings. We present A-FP4S, a novel adaptive fragments-based parallel state recovery mechanism for stream processing systems. A-FP4S organizes stream operators into a distributed hash table based peer-to-peer overlay and divides each node's local state into many fragments. These fragments are periodically stored in node's multiple neighbors, ensuring different sets of available fragments can reconstruct failed states in parallel. This mechanism is extremely scalable to the lost state, significantly reduces failure recovery time, and can tolerate multiple node failures. A-FP4S is adaptive to heterogeneous hardware settings by automatic parameter tuning over phases. Compared to Apache Storm, A-FP4S achieves 31.8% to 50.5% reduction in recovery latency. Large-scale experiments using real-world datasets demonstrate A-FP4S's attractive scalability and adaptivity properties.
Improvement of Makespan and TCTime in Dynamic Job Ordering and Slot Utilization for MapReduce Workloads

The amount of data generated in today's environment is increasing at an exponential rate. These data are structured, semi-structured, or unstructured in some cases. Processing vast amounts of data is a difficult task. MapReduce is a technique for processing large amounts of data. For storing big data sets, Hadoop data file system (HDFS) is employed. A MapReduce workload is made up of a number of jobs, each of which has multiple map tasks and numerous reduce tasks. In this paper, we propose the Shortest Task Ordering (SJA) algorithm for optimising Mkspan (Makespan) and TCTime (Total completion time) for MapReduce Workloads using dynamic job ordering and slot design. We conducted a comparison analysis on data sets of various sizes. By comparing objective measurements such as Mkspan and TCTime, the experimental results show that performance in terms of speed has improved. Each set of jobs, such as WordCount, CharCount, LineCount, and Anagram, displays comparative improvement in our work. When compared to previous algorithms such as MkJR and MkTctJR, the results demonstrate an improvement in time efficiency, slot usage, and execution speed. In terms of Mkspan and TCTime, the Shortest Task Assigned (SJA) method for job ordering yielded results that were up to 95% better than MkJR. In comparison to the previous algorithms MkSfJR and MkTctSfJR, there is also an increase in slot usage. In terms of Mkspan and TCTime, the Shortest Job Assigned (SJA) algorithm achieved results that were up to 150 percent better than MkSfJR.
Privately Policing Dark Patterns

Lawmakers around the country are crafting new laws to target “dark patterns”—user interface designs that trick or coerce users into enabling cell phone location tracking, sharing browsing data, initiating automatic billing, or making whatever other choices their designers prefer. Dark patterns pose a serious problem. In their most aggressive forms, they interfere with human autonomy, undermine customers’ evaluation and selection of products, and distort online markets for goods and services. Yet crafting legislation is a major challenge: Persuasion and deception are difficult to distinguish, and shifting tech trends present an ever-moving target. To address these challenges, this Article proposes leveraging state private law to define and track dark patterns as they evolve. Judge-crafted decisional law can respond quickly to new techniques, flexibly define the boundary between permissible and impermissible designs, and bolster state and federal regulatory enforcement efforts by quickly identifying those designs that most undermine user autonomy.
Why Immediate Repetition is Good for Short-Time Learning Results But Bad for Long-Time Learning: Explanation Based on Decision Theory

InTeachingrepetition Short-term learning results Teachingshort-term results Long-term learning results Teachinglong-term results this chapter, we apply decision making under uncertainty to decide how to teach. Specifically, it is well known that repetition enhances learning; the question is: when is a good time for this repetition? Several experiments have shown that immediate repetition of the topic leads to better performance on the resulting test than a repetition after some time. Recent experiments showed, however, that while immediate repetition leads to better results on the test, it leads to much worse performance in the long term, i.e., several years after the material have been studied. In this chapter, we use decision theory to provide a possible explanation for this unexpected phenomenon.
Distributed Learning Over a Wireless Network With Non-Coherent Majority Vote Computation

In this study, we propose an over-the-air computation (OAC) scheme to calculate the majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge devices (EDs) transmit the signs of local stochastic gradients, i.e., votes, by activating one of two orthogonal resources. The MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the accumulations on the resources. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. In this study, we analyze various gradient-encoding strategies through the weight functions and waveform configurations over orthogonal frequency division multiplexing (OFDM). We show that specific weight functions that enable absentee EDs (i.e., hard-coded participation with absentees (HPA)) or weighted votes (i.e., soft-coded participation (SP)) can substantially reduce the probability of detecting the incorrect MV. By taking path loss, power control, cell size, and fading channel into account, we prove the convergence of the distributed learning for a non-convex function for HPA. Through simulations, we show that the proposed scheme with HPA and SP can provide high test accuracy even when the time-synchronization and the power control are not ideal under heterogeneous data distribution scenarios.
Sampling Propagation Attention With Trimap Generation Network for Natural Image Matting

Natural image matting aims to precisely separate foreground objects from backgrounds using alpha mattes. Fully automatic natural image matting without external annotations is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as an extra input while the performance of automatic trimap generation method, e.g., erosion/dilation manipulation on foreground segmentation, fluctuates with segmentation quality. Therefore, we argue that how to produce a high-quality trimap using coarse segmentation is a major issue in automatic matting. In this paper, we present a two-stage trimap-free natural image matting pipeline that does not need trimap and background as input. Specifically, guided by a coarse segmentation, Trimap Generation Network (TGN) estimates a trimap where the coarse segmentation can be produced by segmentation/salient object detection/matting approaches, which enables more flexibility for matting to adapt into different scenarios. Then, with an estimated trimap as guidance, our Sampling Propagation Attention Matting Network (SPAMattNet) estimates an alpha matte. Different from previous propagation-based matting networks, inspired by traditional sampling/propagation matting approaches, we propose Sampling Propagation Attention (SPA) for matting network to incorporate sampling and propagation procedures in deep learning based manner for network explainability and performance improvement. It explicitly investigates local spatial and global semantic relationships to reconstruct alpha features. To better harvest sampling/propagation and local/global information, a Cross-Fusion Contextual Module (CFC) is introduced to aggregate features from different sources. Extensive experiments are conducted to show that our matting approach is competitive compared to other state-of-the-art methods in both trimap-free and trimap-needed aspects on several challenging matting benchmarks.
VALS: Supporting Visual Data Analysis in Longitudinal Clinical Studies

Visual data analysis helps to understand different types of phenomena by allowing experts to explore for relationships, patterns, outliers, unexpected changes, and more. Experts need tools that help them find useful and actionable information in the data so they can test their hypotheses and come up with new ones. This need becomes more evident in cases of longitudinal studies, where there are usually a large number of variables and the process being analyzed can be complex as well. We present VALS (Visual Analytics in Longitudinal Studies), a framework for visually exploring longitudinal clinical data. VALS includes a data model, a task categorization model, and an approach to guidance through feature engineering techniques and interactive visualizations, all to help analysts perform their analysis tasks. VALS has been designed in collaboration with healthcare experts with experience in longitudinal studies. We have also developed a tool prototype for a case study using real-world datasets. The evidence collected in the case study shows how useful a VALS-based visual analytics tool can be.
A Real-Time Object Detection Processor With xnor-Based Variable-Precision Computing Unit

Object detection algorithms using convolutional neural networks (CNNs) achieve high detection accuracy, but it is challenging to realize real-time object detection due to their high computational complexity, especially on resource-constrained mobile platforms. In this article, we propose an algorithm-hardware co-optimization approach to designing a real-time object detection system. We first develop a compact object detection model based on a binarized neural network (BNN), which employs a new layer structure, the DenseToRes layer, to mitigate information loss due to deep quantization. We also propose an efficient object detection processor that runs object detection with high throughput using limited hardware rescources. We develop a resource-efficient processing unit supporting variable precision with minimal hardware overheads. Implemented in field-programmable gate array (FPGA), the object detection processor achieves 64.51 frames/s throughput with 64.92 mean average precision (mAP) accuracy. Compared to prior FPGA-based designs for object detection, our design achieves high throughput with competitive accuracy and lower hardware implementation costs.
Virtual Reality Haptic Device for Mental Illness Treatment

Schizophrenia is a mental disorder that alters mental functioning and can cause hallucinations, mental disorientation, and a variety of other symptoms, which results in a separation of schizophrenics from society. Despite the fact that the condition has been known about for a long time, there is still an urgent need for research and testing to develop better therapies. Virtual reality (VR) has had interesting outcomes when used to treat illnesses, and it is gradually emerging as a viable technical choice for the healthcare sector due to its immersion, which offers better and more intense user experiences. Once linked to a serious game, it can introduce the user to a number of situations that, when combined with medical assistance, aid in their rehabilitation and treatment. The use of haptic devices enhances VR immersion by enabling users to comprehend virtual environments with greater nuance, which increases their level of believing in the new environment they are in. When used in conjunction with VR, it can improve the efficacy of the treatment, making its use viable for the treatment of psychic diseases. The goal of this research is to develop haptic vest interactions using a three-dimensional virtual testing scenario to support and improve the use of VR for rehabilitation and therapy for schizophrenia. The established functionalities can be applied to a further serious game with the same focus.
Understanding Issues and Challenges of DFR Implementation in SDN Platform

Software-Defined Networking (SDN) is an evolutionary networking paradigm that offers simplified and agile network configuration and management capabilities. However, embracing this new and futuristic paradigm requires the understanding of Digital Forensics (DF) limitations that it presents. Studies show that the dynamism of SDN architecture impedes the preservation of Potential Digital Evidence (PDE) during a Digital Forensic Readiness (DFR) process. Therefore, the identification and acquisition of viable PDE in SDN platforms largely depends on the thorough understanding of the issues and challenges affecting the application of DFR in SDN platforms. For this reason, this study leverages a case study research methodology to empirically underline the forensic limitations and provide level of specificity with which these limitations affect the DFR process. The results of the case study combined with existing literature are used to expose the issues and challenges in a typical SDN testbed. The knowledge acquired from the state-of-the-art with respect to conducting DFR in an SDN platform addresses the knowledge gap of understanding these limitations.
Characterisation of the first group of participants in the Capacid@de Digital initiative

One of the pressing problems of the current time is the digital inequalities caused by the population's lack of technological knowledge and the accelerated technological transformation. These inequalities are increasing as the need for training for digital inclusion of the adult population. Concerned about the digital inequality of our region's adult population, Escola Superior de Tecnologia e Gestão do Politécnico do Porto introduced an initiative, denoted [email protected] Digital. This initiative helps to capacitate the adult population with the use of technology through a series of training actions. These training actions are guaranteed by students integrated into the institution volunteer network and supervised by the academic staff. The challenge proposed to our students allows them to gain a new set of competencies that would be otherwise hard to obtain at this point in their academic paths. In this paper, presents the characterization of the digital illiteracy of one the municipalities of the Portuguese region Tâmega e Sousa, namely Baião.
A Novel model to adapt CMMI Level 2 by Assessing the Local SMEs of Bangladesh

While software process improvement (SPI) aims at optimizing software processes to produce high-quality software, SPI efforts in software development are still inert in practice, especially in developing countries. In this work, we make an experimental investigation aiming at developing a model for applying SPI of CMMI level 2 practice based on the views and experiences of small and medium-sized software firms in Bangladesh. The primary goal of this study is to determine how commonly a given CMMI practice is adopted to build a new maturity model within specific exercises. We use questionnaire-based survey sessions as the main tool to collect data from 9 SMEs in Bangladesh. Practitioners were asked to choose and rank CMMI level 2 practices based on the five classes of evaluations. Afterward, based on CMMI Level 2 practices from the acquired data, we develop a maturity model for adopting SPI.The paper proposes a model to improve those particular practices to build successful CMMI-based SPI and increase the success rate of CMMI-based SPI initiatives.
ChevroCrypto – Blockchain Cryptographic File System Prototype

The Internet of Things technologies are progressively being incorporated into the different difficulties that occur and ensuring data protection from these systems is becoming increasingly vital. There are various technologies available to help developers with data protection but comprehending worldwide obligatory regulations as well as a comprehensive set of key qualities that should be addressed while carrying out projects is required. This project was created to help developers who may lack knowledge, are unfamiliar with the region, and want a high level of security in their system.
Blockchain Enabled Smart Metering Solutions: Challenges and Opportunities

The applications of smart energy meters have been increasing in power systems, such as real-time monitoring, remote control, online billing, theft detection, energy trading, energy forecasting, and demand response by using the Internet of Things (IoT). IoT based smart metering system is centralized in nature, which has data security and privacy issues. Since blockchain is secured, authentic, confidential, and data integrity properties, hence blockchain enabled smart metering is an effective solution. This paper reviews a broad understanding of blockchain enabled smart metering systems and opportunities in various segments. This paper highlights the shortcomings in smart metering systems, which effectively address the privacy and security gaps, discusses blockchain-based energy trading and the existing pilot project of blockchain technology in India.
Assessment of Efficiency for Refurbished 3⌽ Squirrel Cage Induction Motor using No-load Field Data

Three phase Squirrel Cage Induction Motors (SCIM) are widely used as industrial drives due to their self-starting, reliable and economical feature. The estimation of the refurbished/rewound SCIM’s working efficiency is given priority in this work. The conventional test set up used by manufacturing industry may not be feasible solution for estimating the efficiency of refurbished/rewound SCIM due to transportation and other issues. Current Vector Method (CVM) and Modified IEEE Standard 112 Equivalent Circuit Method (Modified IEEE Std. ECM) are used based no-load availability of the data. The measured stray load losses are accounted in estimating the efficiency. Hence, the expensive dynamometer test procedure is removed. The proposed methods are simple, more accurate and less expensive which will give minimum possible error. Efficiency estimation using different methods are being calculated, verified and compared for three different rating of three phase SCIM. Mainly focused on the 110kW SCIM carried out in this research work.
Visualising Digital Pathology Research : A Bibliometric Analysis from 1991-2021

Introduction: Digital pathology encompasses the acquisition, management, sharing and interpretation of pathology information in a digital environment. Bibliometric analysis is a quantitative method to examine scholarly publications including the number of publications, citations, co-authorships, and collaboration network. Aim of this study is to provide a bibliometric analysis of academic documents on digital pathology (DP) from 1991-2021. Methods: The literature on digital pathology were obtained from the Scopus database. Frequency, percentage, data visualisation and citation metric were analysed using Microsoft Excel 365 and VOSviewer. Results: A total of 1848 documents from the Scopus database were analysed. There is a continuous growth of publications on DP with a total of 28330 citations. The United States was the most productive contributor to the publications followed by the United Kingdom and European countries, whilst University of Pittsburgh Medical Center, US produced the most publications. Progress in Biomedical Optics and Imaging Proceedings of SPIE was the largest source title while the Medical Image Analysis was the most prestigious journal. The keyword analysis suggests that DP research is mainly a medical imaging and engineering research domain with application in the histopathology subject. Conclusion: Digital Pathology research and publications continue to grow and concentrated in the Western countries. The publications focused on the image analysis, machine learning and engineering research domain in histopathology subject. Potential research areas include the implementation, validation of use and impact of DP to the pathology services and health care with exploration in other pathology subjects such as haematology.