Reviewerly/exHarmony huggingface dataset:
Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval

The goal of cross-domain retrieval (CDR) is to search for instances of the same category in one domain by using a query from another domain. Existing CDR approaches mainly consider the standard scenario that the cross-domain data for both training and testing come from the same categories and underlying distributions. However, these methods cannot be well extended to the newly emerging task of universal cross-domain retrieval (UCDR), where the testing data belong to the domain and categories not present during training. Compared to CDR, the UCDR task is more challenging due to (1) visually diverse data from multi-source domains, (2) the domain shift between seen and unseen domains, and (3) the semantic shift across seen and unseen categories. To tackle these problems, we propose a novel model termed Structure-Aware Semantic-Aligned Network (SASA) to align the heterogeneous representations of multi-source domains without loss of generalizability for the UCDR task. Specifically, we leverage the advanced Vision Transformer (ViT) as the backbone and devise a distillation-alignment ViT (DAViT) with a novel token-based strategy, which incorporates two complementary distillation and alignment tokens into the ViT architecture. In addition, the distillation token is devised to improve the generalizability of our model by structure information preservation and the alignment token is used to improve discriminativeness with trainable categorical prototypes. Extensive experiments on three large-scale benchmarks, i.e., Sketchy, TU-Berlin, and DomainNet, demonstrate the superiority of our SASA method over the state-of-the-art UCDR and ZS-SBIR methods.
Deep Learning based Prediction of Solar Surface Irradiance with Geostationary Satellite Images

Solar energy has been expanding its scope for use in various systems, such as power generation, portable power, and power for eco-friendly moving objects and space power of various sizes depending on the purpose. However, since solar energy can be affected by meteorological and environmental variables, it is difficult to predict and manage energy production. Recently, artificial intelligence techniques have been applied to optimize predictive models and improve predictive performance of renewable solar energy. In this paper, a deep neural network-based prediction model is presented to predict the amount of solar energy potentials using geostationary satellite image data in units of one hour for over 7 years. In most of the previous studies, only a short-time prediction has been possible using only the daytime information, but in this model, the prediction performance is improved by using the image information including the cloud movement during the nighttime. In order to combine images of different characteristics of solar surface irradiance (SSI) and infrared (IR) in successive time and integrate them into one predictive model, after learning the basic structure to solve the trade-off problem between localization and context in the deep neural network structure, a generative model-based learning model is connected for matching between images of the different characteristics. In addition, in order to preserve the value of the target region that occupies a relatively small portion of the image, the performance of the model is supplemented using the region of interest (RoI) mask As a result, a model with improved predictive performance is presented when predicting using both daytime and nighttime image information the previous day.
Risk-Sensitive Deep Neural Learning to Rank

Learning to Rank (L2R) is the core task of many Information Retrieval systems. Recently, a great effort has been put on exploring Deep Neural Networks (DNNs) for L2R, with significant results. However, risk-sensitiveness, an important and recent advance in the L2R arena, that reduces variability and increases trust, has not been incorporated into Deep Neural L2R yet. Risk-sensitive measures are important to assess the risk of an IR system to perform worse than a set of baseline IR systems for several queries. However, the risk-sensitive measures described in the literature have a non-smooth behavior, making them difficult, if not impossible, to be optimized by DNNs. In this work we solve this difficult problem by proposing a family of new loss functions -- \riskloss\ -- that support a smooth risk-sensitive optimization. \riskloss\ introduces two important contributions: (i) the substitution of the traditional NDCG or MAP metrics in risk-sensitive measures with smooth loss functions that evaluate the correlation between the predicted and the true relevance order of documents for a given query and (ii) the use of distinct versions of the same DNN architecture as baselines by means of a multi-dropout technique during the smooth risk-sensitive optimization, avoiding the inconvenience of assessing multiple IR systems as part of DNN training. We empirically demonstrate significant achievements of the proposed \riskloss\ functions when used with recent DNN methods in the context of well-known web-search datasets such as WEB10K, YAHOO, and MQ2007. Our solutions reach improvements of 8% in effectiveness (NDCG) while improving in around 5% the risk-sensitiveness (\grisk\ measure) when applied together with a state-of-the-art Self-Attention DNN-L2R architecture. Furthermore, \riskloss\ is capable of reducing by 28% the losses over the best evaluated baselines and significantly improving over the risk-sensitive state-of-the-art non-DNN method (by up to 13.3%) while keeping (or even increasing) overall effectiveness. All these results ultimately establish a new level for the state-of-the-art on risk-sensitiveness and DNN-L2R research.
A Comparative Cost Analysis of Organizational Network Security Test Lab Setup on Cloud Versus Dedicated Virtual Machine

The global network infrastructure spectrum is witnessing its fastest growth since the last decade with concurrent rise in cloud computing, Internet of things (IoT), and edge computing. There has been a multitude of heterogeneous networking devices spanning different configurations and using a variety of access methods. A parallel evolution of the network infrastructure security is happening with increasing attempts to exploit the security vulnerabilities in mission critical cyber-assets of organizations. Several organizations invest heavily in security research using lengthy and cryptic mathematical models while ignoring the practical network implementation situation and focus only on the monetary implications of the attack and defense. Attack tree has evolved as a convenient and cost effective way of plotting the network in which an attack may take place and can also help organizations understand the way it can be defended. Attack trees combined with the MITRE ATT&CK framework are widely used for crown jewels risk assessment globally. However, the major challenge for information security experts using the attack tree methodology lies in manually creating the attack tree and plotting all the crown jewels and perimeter network so that it can be defended from attackers. We propose a test lab setup for simulation and attack tree generation, which can be used in conjunction with the MITRE ATT&CK framework and allow us to create and assess various attack scenarios while providing flexibility in subnet configuration and movement, addition or removal of networking devices. The lab can be cloud hosted with a popular cloud hosting on Microsoft Azure or may be created on a VM within a dedicated high-resource machine to be used as a portable testbed. The results indicate that both services have their own pros and cons based on the hours of usage, and the dedicated resource VM testbed may perform better in a low-risk potential small network while the cloud-based approach is useful for the scalable organizations with high-threat potential.
AGE AND GENDER PREDICTION USING MACHINE LEARNING

The development of intelligent systems capable of effectively learning and recognising objects is the major goal of the very active research fields of pattern recognition and automatic classification. The usage of biometrics, which is generally utilised for security considerations, is integral to these applications. In the realm of research, the face modality has grown in importance as a basic biometric technique. The objective of this work is to create a system for estimating age and gender from a facial image or real-time video using convolutional neural networks. In this study, three CNN network models with various architectures (number of filters, number of convolution layers, etc.) were built and validated using data from the IMDB and WIKI. The results revealed that CNN networks significantly increase the system's performance and recognition accuracy.
A blockchain enhanced book lending system for college library

The limited storage space of the library cannot support the continuous expansion of the book stock. Based on the contradiction between the collection and use of books, "collecting books for the people" may be a solution. A discrete lending model is built by blockchain technology to break through the library space for book lending. The system automatically executes the lending incentive mechanism on the smart contracts and uses the tokens in the blockchain platform as assets to complete the transaction. An auction mechanism model is proposed based on tokens and the discrete space of users to solve the problem of discretized borrowing of readers. Meanwhile, a queuing model is used to verify the feasibility of the system. Results show that the blockchain-based lending system shortens the average waiting time than traditional lending services. The blockchain lending method helps solve the shortage of library space resources and improves users' lending experience.
Verification of ORM-based controllers by summary inference

In this work we describe a novel approach for modeling, analysis and verification of database-accessing applications that use the ORM (Object Relational Mapping) paradigm. Rather than directly analyze ORM code to check specific properties, our approach infers a general-purpose relational algebra summary of each controller in the application. This summary can then be fed into any off-the-shelf relational algebra solver to check for properties or specifications given by a developer. The summaries can also aid program understanding, and may have other applications. We have implemented our approach as a prototype tool that works for 'Spring' based MVC applications. A preliminary evaluation reveals that the approach is efficient, and gives good results while checking a set of properties given by human subjects.
A blockchain-based certifiable anonymous E-taxing protocol

The security of the tax system is directly related to the development of a country. The conventional process of tax payment laborious steps, so this process becomes a cause of irregularities among taxpayers and tax authorities, increasing the rate of corruption in tax collection. Blockchain, as a distributed ledger technology, its unique advantages and promising applications in taxation offer an effective solution to the problems of electronic taxation. However, the transparency of blockchain exists the risk of privacy disclosure, the high degree of anonymity brings the problem of lack of user supervision. Therefore, for balancing the contradiction of taxpayer privacy and supervision, we propose a blockchain-based self-certified and anonymous e-taxing scheme, which uses blockchain as the underlying support, and utilizes cryptography technology such as self-certified public key, Diffie-Hellman, to reduce the taxpayer's reliance on the certificate authority, and protects the taxpayer's anonymity while realizing the tracking of the real identity of malicious taxpayers. The security analysis proves that the scheme has the properties such as anonymity, conditional privacy and unforgeability, etc. Finally, performance analysis shows that compared with similar schemes, the scheme significantly improves the registration efficiency, proving its practicability and implementability.
A novel adaptive boundary weighted and synthetic minority oversampling algorithm for imbalanced datasets

In recent years, imbalanced data learning has attracted a lot of attention from academia and industry as a new challenge. In order to solve the problems such as imbalances between and within classes, this paper proposes an adaptive boundary weighted synthetic minority oversampling algorithm (ABWSMO) for unbalanced datasets. ABWSMO calculates the sample space clustering density based on the distribution of the underlying data and the K-Means clustering algorithm, incorporates local weighting strategies and global weighting strategies to improve the SMOTE algorithm to generate data mechanisms that enhance the learning of important samples at the boundary of unbalanced data sets and avoid the traditional oversampling algorithm generate unnecessary noise. The effectiveness of this sampling algorithm in improving data imbalance is verified by experimentally comparing five traditional oversampling algorithms on 16 unbalanced ratio datasets and 3 classifiers in the UCI database.
Scorpius: Proactive Code Preparation to Accelerate Function Startup

Massive enterprises deploy their applications on public clouds to relieve infrastructure management burden. However, applications are faced with highly fluctuating workloads, while clouds provision exclusive resources at coarse time granularity, resulting in severely low resource efficiency. Function-as-a-Service (FaaS) platform enables fine-grained resource multiplexing, which has the potential to improve efficiency. However, FaaS platforms could consume several seconds to start functions and the long startup latency can severely hurt the performance of applications. In this paper, we measure the FaaS platforms and find that most startup latency is occupied by code preparation. To reduce the code preparation latency with little resource overhead, we propose Scorpius, a FaaS platform that proactively prepares code based on the historical data of functions. It combines two optimization categories: (1) To reduce the code size, Scorpius proposes to proactively prepare partial libraries over servers and run functions on the server with most library sharing. (2) To advance the start time, Scorpius proposes to predict the function overload with a simple model and proactively scale code to more servers. We have implemented a prototype of Scorpius and conducted extensive experiments. Evaluation results demonstrate that compared with state-of-the-art methods, Scorpius can reduce the code preparation latency by 87.6% with only 9.3% storage overhead.
Transformer-based image captioning by leveraging sentence information

Although the autoregressive image captioning methods yield good-quality image descriptions, their sequential structures slow down the speed of sentence generation processes. With a view to overcome these shortcomings, some nonautoregressive models have been proposed, but the quality of sentences produced by them is lower than those obtained in autoregressive methods. We have designed a new structure based on nonautoregressive methods to not only find better relations between sentence words and image salient objects but also combine this information with some positional information, extracted from the sentence, to generate a more qualified target sentence. The experimental results on the standard benchmark show that our proposed model achieves performance better than general nonautoregressive captioning models.
Adaptive Dialogue Management for Conversational Information Elicitation

Information elicitation conversations, for example, when a medical professional asks about a patient's history or a sales agent tries to understand their client's preferences, often start with a set of routine questions. The interviewer asks a predetermined set of questions conversationally, adapting them to the unique characteristics and context of an individual. Multiple-choice questionnaires are commonly used as a screening tool before the client sees the professional for more efficient information elicitation [5]. However, recent proof-of-concept studies show that users are more likely to report their symptoms to an embodied conversational agent (ECA) than on a pen-and-paper survey [3], and rate ECAs highly on user experience [4]. Chatbots allow the user to give free-form responses and ask clarification questions instead of having to interpret and choose from a list of given options. They can also keep the user engaged by sharing relevant information and offering empathetic acknowledgments when appropriate. However, many of the technical challenges involved in building such a conversational agent remain unsolved.
Consensus Algorithm of Proof-of-Stake Based on Credit Model

Blockchain has attracted widespread attention from scholars due to its decentralization characteristics. Consensus algorithm is one of the core technologies of blockchain. The proposal of the Proof-of-Stake consensus algorithm solves the problem of wasted hashrate in Proof-of-Work. Aiming at the problem of interest centralization in Proof-of-Stake we propose an improved PoS scheme based on credit model. Design the credit evaluation model, using the credit evaluation mechanism to quantify the credit status of each node in the network, and adjust the hash difficulty according to the credit ranking. Introduce the concept of Gini coefficient to analyze the centralization of stake of the algorithm. Simulation experiments show that the PoS improvement scheme based on the credit model effectively relieves the power centralization, guarantees the block chain decentralization.
Analysis of Diversified Radio and Television Data Based on Adaptive Least Squares Support Vector Machine

Under the current big data background, the training mode of radio and television director technology is obsolete, and the technical means do not meet the needs of modern development. In this article, a self-adaptive multivariate data statistical model of radio and television directors based on the least squares support vector machine is proposed, which combines the students' views with the diversified teaching methods and teaching contents needed by university teachers in the process of vocational education and television education. This article applies the technology integration degree measurement, market integration degree measurement, business integration degree measurement, and integration degree comprehensive analysis to analyze the data of major video websites and major radio and television media. It is found that the market share of major radio and television media is increasing, and the number of broadcasts of major video online stores is also excellent.
Multi-View Learning for Material Classification

Material classification is similar to texture classification and consists in predicting the material class of a surface in a color image, such as wood, metal, water, wool, or ceramic. It is very challenging because of the intra-class variability. Indeed, the visual appearance of a material is very sensitive to the acquisition conditions such as viewpoint or lighting conditions. Recent studies show that deep convolutional neural networks (CNNs) clearly outperform hand-crafted features in this context but suffer from a lack of data for training the models. In this paper, we propose two contributions to cope with this problem. First, we provide a new material dataset with a large range of acquisition conditions so that CNNs trained on these data can provide features that can adapt to the diverse appearances of the material samples encountered in real-world. Second, we leverage recent advances in multi-view learning methods to propose an original architecture designed to extract and combine features from several views of a single sample. We show that such multi-view CNNs significantly improve the performance of the classical alternatives for material classification.
The Seventh Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI'22)

The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.
Multi-label Masked Language Modeling on Zero-shot Code-switched Sentiment Analysis

In multilingual communities, code-switching is a common phenomenon and code-switched tasks have become a crucial area of research in natural language processing (NLP) applications. Existing approaches mainly focus on supervised learning. However, it is expensive to annotate a sufficient amount of code-switched data. In this paper, we consider zero-shot setting and improve model performance on code-switched tasks via monolingual language datasets, unlabeled code-switched datasets, and semantic dictionaries. Inspired by the mechanism of code-switching itself, we propose multi-label masked language modeling and predict both the masked word and its synonyms in other languages. Experimental results show that compared with baselines, our method can further improve the pretrained multilingual model's performance on code-switched sentiment analysis datasets.
CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users

Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.