Reviewerly/exHarmony huggingface dataset:
Application-Platform Co-design for Serverless Data Processing

“Application-platform co-design” refers to the phenomenon of new platforms being created in response to changing application needs, followed by application design and development changing due to the emergence (and the specifics, limitations) of the new platforms, therefore creating, again, new application and platform requirements. This continuous process of application and platform (re-)design describes an engineering and management responsibility to constantly evaluate any given platform for application fit and platform-specific application design, and to consider a new or evolutionary platform development project due to evolving and changing application needs.In this paper, we study this phenomenon in the context of serverless computing and (big) data processing needs, and thus, for application-platform co-design for serverless data processing (SDP). We present an analysis of the state-of-the-art of function-as-a-service (FaaS) platforms, which reveals several configuration, deployment, execution, and measurement differences between popular platforms happening at-speed. These differences indicate already ongoing platform (re-)design processes resulting in more specialized serverless platforms and new, platform-specific challenges for application design. We discuss data processing needs of applications using the serverless model and present common initial (and undesirable) workaround solutions on the application level, giving additional argument to the creation of new SDP platforms. We present critical SDP requirements and possible new platform augmentations, but identify the need for engineering methods and tooling to better guide application-platform co-design. We argue to pay appropriate attention to the phenomenon of continuous application-platform co-design to better anticipate and to control future platform and application developments.
Multiview Inherent Graph Hashing for Large-Scale Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) is one of the most challenging tasks in remote sensing (RS) community. With the volume of RS images increases explosively, conventional exhaustive search is often infeasible in real applications. Recently, hashing has attracted increasing attention for RSIR due to significant advantage in terms of computation and storage. Hashing first generates a set of short compact hash codes to encode RS images, and then applies hash codes for effective RSIR. Multiview hashing usually achieves promising RSIR performance by fusing multiples kinds of RS image features. Conventional multiview hashing simply predefines graph Laplacian in each view, which cannot effectively explore underlying similarity structures among RS images. To address this issue, this article proposes a novel multiview inherent graph hashing (MvIGH) for RSIR. MvIGH captures the latent similarities among RS images, and adaptively learns weights of each view to characterize its contribution. In addition, MvIGH further minimizes the quantization errors. We develop an efficient alternating algorithm to solve the formulated optimization problem. The experiments on three public RS image datasets demonstrate the superiority of the proposed method over the existing multiview hashing methods in RSIR tasks.
Artificial Intelligence and Personalized Banking

AI has begun making its presence felt in every industry and now across the financial services industry as well. This chapter examines and presents the use of AI in banks for better customer service giving them a personalized experience. This chapter explains how banks are getting future-ready for their financial services by means of AI and are delivering financial offerings seamlessly. This research primarily focuses on the concept of AI in the field of banking, how AI has revolutionized personalized banking and made banking operations more efficient and successful. AI innovations are an integral part of Industry 5.0 which aims at integrating automation and human intelligence. This chapter aims to study and describe the current applications of AI in the banking industry and its impact on the banking sector. The study also gives a description of the banks employing AI to facilitate an exceedingly personalized customer journey with the banks.
Generating Sequence Diagram from Natural Language Requirements

Model-driven requirements engineering is gaining enormous popularity in recent years. Unified Modeling Language (UML) is widely used in the software industry for specifying, visualizing, constructing, and documenting the software systems artifacts. UML models are helpful tools for portraying the structure and behavior of a software system. However, generating UML models like Sequence Diagrams from requirements documents often expressed in unstructured natural language, is time consuming and tedious. In this paper, we present an automated approach towards generating behavioral models as UML sequence diagrams from textual use cases written in natural language. The approach uses different Natural Language Processing (NLP) techniques combined with some rule based decision approaches to identify problem level objects and interactions. Additionally, different quality metrics are defined to assess the validity of generated sequence diagrams in terms of expected behaviour from a given use case. The criteria we established to assess the quality of analysis sequence diagrams can be applied to similar experiments. We evaluate our approach using different case studies concerning correctness and completeness of the generated sequence diagrams using those metrics. In most situations, we attained an average accuracy factor of over 85% and average completeness of over 90%, which is encouraging.
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Single-Source Domain Generalization

Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of the training data. The issue can be mitigated by using unlabeled data from the target domain at training time, but because data distributions can change dynamically in real-life applications once a learned model is deployed, it is critical to create networks robust to unknown and unforeseen domain shifts. In this paper we focus on one of the reasons behind the inability of neural networks to be so: deep networks focus only on the most obvious, potentially spurious, clues to make their predictions and are blind to useful but slightly less efficient or more complex patterns. This behaviour has been identified and several methods partially addressed the issue. To investigate their effectiveness and limits, we first design a publicly available MNIST-based benchmark to precisely measure the ability of an algorithm to find the "hidden" patterns. Then, we evaluate state-of-the-art algorithms through our benchmark and show that the issue is largely unsolved. Finally, we propose a partially reversed contrastive loss to encourage intra-class diversity and find less strongly correlated patterns, whose efficiency is demonstrated by our experiments.
BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis

Vision-and-language (V&L) models take image and text as input and learn to capture the associations between them. Prior studies show that pre-trained V&L models can significantly improve the model performance for downstream tasks such as Visual Question Answering (VQA). However, V&L models are less effective when applied in the medical domain (e.g., on X-ray images and clinical notes) due to the domain gap. In this paper, we investigate the challenges of applying pre-trained V&L models in medical applications. In particular, we identify that the visual representation in general V&L models is not suitable for processing medical data. To overcome this limitation, we propose BERTHop, a transformer-based model based on PixelHop++ and VisualBERT, for better capturing the associations between the two modalities. Experiments on the Openl dataset, a commonly used thoracic disease diagnosis benchmark, show that BERTHop achieves an average Area Under the Curve (AUC) of 98.12% which is 1.62% higher than state-of-the-art (SOTA) while it is trained on a 9x smaller dataset.
Device-Oriented Keyword-Searchable Encryption Scheme for Cloud-Assisted Industrial IoT

Massive physical devices are deployed in the Industrial Internet of Things (IoT) to collect ambiance data while heavy storage and communication cost are imposed on these IoT devices. To overcome this constraint, cloud-assisted technologies are introduced to store and manage the collected data. In order to protect data quality and security, encryption is required before uploading data to remote clouds. Consequently, a search function is added to cloud services to find the specific data. However, traditional data searching schemes are constructed in user-oriented systems, where the search function is mainly involved with the relationship between data and users rather than data and devices. As a result, traditional search schemes are not suitable to find special IoT devices. On the other hand, the status of these devices is described by many attributes, e.g., temperature, clean water storage, and machine speed in an early warning system for industrial sewage disposal equipment. Hence, multi-keyword conjunctive queries for partial attributes should be introduced so as to find the target device more accurately and more efficiently. To address these challenges, we propose a new universal device-oriented keyword searchable encryption (Do-KSE) scheme for cloud-assisted IoT in this paper. Furthermore, the functions of a single and conjunctive keyword search are maintained to handle the device search requirement of partial attributes. We conduct extensive experiments to evaluate the proposed scheme. Experimental results show that our scheme has excellent performance because of the lightweight index and query trapdoor.
Recent Advances in Transform and Hybrid Domain Digital Watermarking Techniques—A Survey

In the older days, watermarking has been extensively used in different forms in diverse fields, mainly to recognize an entity's ownership. At the early stages, its analog rendition has been utilized, like stamps or currencies, perceptible to the users. In the present scenario, the situation changed from analog-to-digital watermarking; mostly, the watermarks are imperceptible to the users, requiring some processing steps to figure out the entity's possession. The processing steps may vary from the type or the application area of the watermark that has been utilized. This paper aims to review the basic idea of digital watermarking techniques, current trends, and future scopes that have been occurred in this area during recent years.
HORNET

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the language understanding abilities of deep language models by leveraging the rich semantic knowledge from knowledge graphs, other than plain pre-training texts. However, previous efforts mostly use homogeneous knowledge (especially structured relation triples in knowledge graphs) to enhance the context-aware representations of entity mentions, whose performance may be limited by the coverage of knowledge graphs. Also, it is unclear whether these KEPLMs truly understand the injected semantic knowledge due to the "black-box'' training mechanism. In this paper, we propose a novel KEPLM named HORNET, which integrates Heterogeneous knowledge from various structured and unstructured sources into the Roberta NETwork and hence takes full advantage of both linguistic and factual knowledge simultaneously. Specifically, we design a hybrid attention heterogeneous graph convolution network (HaHGCN) to learn heterogeneous knowledge representations based on the structured relation triplets from knowledge graphs and the unstructured entity description texts. Meanwhile, we propose the explicit dual knowledge understanding tasks to help induce a more effective infusion of the heterogeneous knowledge, promoting our model for learning the complicated mappings from the knowledge graph embedding space to the deep context-aware embedding space and vice versa. Experiments show that our HORNET model outperforms various KEPLM baselines on knowledge-aware tasks including knowledge probing, entity typing and relation extraction. Our model also achieves substantial improvement over several GLUE benchmark datasets, compared to other KEPLMs.
Comparative Explanations of Recommendations

As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system's. Unfortunately, little research attention has yet been paid on such comparative explanations. In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. For each recommended item, we first extract one sentence from its associated reviews that best suits the desired comparison against a set of reference items. Then this extracted sentence is further articulated with respect to the target user through a generative model to better explain why the item is recommended. We design a new explanation quality metric based on BLEU to guide the end-to-end training of the extraction and refinement components, which avoids generation of generic content. Extensive offline evaluations on two large recommendation benchmark datasets and serious user studies against an array of state-of-the-art explainable recommendation algorithms demonstrate the necessity of comparative explanations and the effectiveness of our solution.
Get Rich or Keep Tryin’ Trajectories in dark net market vendor careers

Dark net markets are a competitive environment. As these anonymous markets enable criminals to trade illicit goods or services, this causes vendors to operate under pseudonyms, rather than real-world identities. The constant battle between market admins and law enforcement makes the typical lifespan of a market two years. When a market disappears, active vendors migrate to other markets with the intention to continue their business, or have already pro-actively done so in an effort to ensure business continuity. To secure their reputation across markets, they can try to obtain the same pseudonym on multiple markets, but other individuals could beat them to the punch. A much safer method therefore, is to generate a PGP-key and use the public key as identification across markets. This way, vendors signal their continued trustworthy and reputable service on markets to buyers. In this paper, we leverage the use of PGP-keys to map careers of dark net market vendors. We parse and analyze scraped data from over 90 dark net markets (2011-2015), and discern 2,925 unique careers. By employing group based trajectory modelling, a type of latent class analysis, we infer three different career trajectories - differentiating ‘established’, ‘challenger’ and ‘failed’ vendor careers. We show that these trajectories are heavily unbalanced in terms of longevity and success. We find that on average 80% of careers last just four months and generate very little sales. Only a small group (∼2%) of highly successful vendors have a long and uninterrupted career that lasts years and spans multiple markets. This group is also responsible for at least 31% of the total revenue in our data.
Tackling Requirements Uncertainty in Software Projects: A Cognitive Approach

In this era of dynamic technological phase shift, management and estimation of client's requirements in software project development has been established as one of the most crucial steps from the point of view of research. Ever changing user's environment make it more difficult to capture precise requirements and hinder successful software development process due to faulty capture leading to potentially live and abrupt threats. However, this situation can be averted by efficient management which tapers to the essence of this article. In this study, we have tried to narrow down to specific uncertainty types, their sources, relationships and their impact and finally getting down to the nitty-gritty in the form of a theoretical framework to manage the requirement uncertainty. This article also contributes in outlining good practices efficient handling of software project uncertainties and mitigating uncertainties through adequate methods and techniques.
Collaborative Recommender System (CRS) Using Optimized SGD - ALS

Matrix factorization (MF), dimensional reduction techniques are broadly used in recommender systems (RS) to retrieve the preference of user from explicit ratings. However, the interactions are not always consistent due to the influence of numerous elements on users on a product, including friend's recommendation and business publicizing. In comparison, traditional MF is not able to find consistent ratings. Find the exact prediction/ratings of a product/item is essential for further improvement of the performance of the collaborative recommender framework. To find the exact prediction, we propose the parameter optimizing stochastic gradient descent (SGD) and alternate least square (ALS) over MF. Furthermore, we examine the deviation of prediction error after setting each parameter over a general parameter distribution of both techniques (SGD and ALS). To evaluate the performance of the proposed model, we use two well-known datasets. The exploratory outcomes reveal that our approach gets significant improvement over the base model.
Feature-Option-Action: A domain adaption transfer reinforcement learning framework

Transfer reinforcement learning (TRL) algorithms have achieved success on alleviating the resource-consumption and sample-insufficiency problem in reinforcement learning (RL). Existing works of cross-domain TRL mainly focus on designing a mapping between the state-action space of source and target domains. We, however, propose a novel TRL framework, Feature-Option-Action (FOA), with novel neural network architecture in this work, to avoid the design of explicit mapping functions between source and target domain. FOA learner is normally trained in the source domain, and the parameters of the option components in the neural network would then be used to initialize the learners in target domain. Empirical evidences have shown that our technique could significantly improve the performance of learners in target domains. Meanwhile, we train FOA models with the model updating methods (in our works, we call it step-update) used in Option-Critic, and illustrate that this method can improve the exploration ability of FOA models by increasing the diversity of options. We also compare step-update with other model updating methods, and the results show that step-update method performs better for FOA model to make transfer training faster and smoother.
Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective

In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sensing devices, their energy and computational constraints, and their collective (collaborative) dimension. These constraints have a fundamental impact on the final activity recognition models as the quality of the data, its availability, and its reliability, among other things, are not ensured during model deployment in real-world configurations. Current approaches for activity recognition rely on the activity recognition chain which defines several steps that the sensed data undergo: This is an inductive process that involves exploring a hypothesis space to find a theory able to explain the observations. For activity recognition to be effective and robust, this inductive process must consider the constraints at all levels and model them explicitly. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity, dynamicity, or stochastic effects, it is essential to understand their substantial impact on the quality of the data and ultimately on activity recognition models. This study highlights the need to exhibit the different types of biases arising in real situations so that machine learning models, e.g., can adapt to the dynamicity of these environments, resist sensor failures, and follow the evolution of the sensors' topology. We propose a metamodeling approach in which these biases are specified as hyperparameters that can control the structure of the activity recognition models. Via these hyperparameters, it becomes easier to optimize the inductive processes, reason about them, and incorporate additional knowledge. It also provides a principled strategy to adapt the models to the evolutions of the environment. We illustrate our approach on the SHL dataset, which features motion sensor data for a set of human activities collected in real conditions. The obtained results make a case for the proposed metamodeling approach; noticeably, the robustness gains achieved when the deployed models are confronted with the evolution of the initial sensing configurations. The trade-offs exhibited and the broader implications of the proposed approach are discussed with alternative techniques to encode and incorporate knowledge into activity recognition models.
Organic matter estimation of surface soil using successive projection algorithm

Abstract Soil organic matter (SOM) affects circulation and stability of the soil C pool, and plays a key role in global C change and local ecological balance. Timely and accurate acquisition of large‐scale farmland soil organic carbon (SOC) information is of great significance to the study of SOC reserves and distribution, farmland soil ecological security, and scientific crop management. In this study, we have evaluated the potentiality of visible‐near‐infrared spectroscopy to along with machine learning algorithms to predict SOM content. At present, soil characterization is mainly focused on hyperspectral data measured by remote‐sensing spectral index and spectrometer. This article aims to establish a statistical machine‐learning model to study the efficiency of spectral data to comprehensively evaluate SOM. The results show that after transforming the different forms of the original spectrum and combining with successive projection algorithm (SPA), the characteristic wavelength of organic matter at 350–2,500 nm can be effectively extracted. A combination of spectroscopy and chemometrics techniques can be used as a practical, rapid, low‐cost, and quantitative method to evaluate SOM. Based on SPA, partial least squares regression, support vector machine, random forest, a SOM prediction model with optimal characteristic wavelength is constructed. The best spectral processing method is continual removal combined with SPA, the validation coefficient of determination, root mean square error prediction, and ratio of performance deviation values are 0.82, 1.12, and 2.87, respectively.