Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples

In Machine Learning as a Service, a provider trains a deep neural network and gives many users access. The hosted (source) model is susceptible to model stealing attacks, where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust method to determine whether a suspect model is a surrogate of their model. We propose a fingerprinting method for deep neural network classifiers that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs. These inputs are a subclass of transferable adversarial examples which we call conferrable adversarial examples that exclusively transfer with a target label from a source model to its surrogates. We propose a new method to generate these conferrable adversarial examples. We present an extensive study on the irremovability of our fingerprint against fine-tuning, weight pruning, retraining, retraining with different architectures, three model extraction attacks from related work, transfer learning, adversarial training, and two new adaptive attacks. Our fingerprint is robust against distillation, related model extraction attacks, and even transfer learning when the attacker has no access to the model provider's dataset. Our fingerprint is the first method that reaches a ROC AUC of 1.0 in verifying surrogates, compared to a ROC AUC of 0.63 by previous fingerprints.
VisualGPT: Data-efficient Image Captioning by Balancing Visual Input and Linguistic Knowledge from Pretraining

In this paper, we aim to improve the data efficiency of image captioning. We propose VisualGPT, a data-efficient image captioning model that leverages the linguistic knowledge from a large pretrained language model (LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining.We designed a novel self-resurrecting encoder-decoder attention mechanism to quickly adapt the pretrained LM as the language decoder on a small amount of in-domain training data. The pro-posed self-resurrecting activation unit produces sparse activations but is not susceptible to zero gradients. When trained on 0.1%, 0.5% and 1% of MSCOCO and Conceptual Captions, the proposed model, VisualGPT, surpasses strong image captioning baselines. VisualGPT outperforms the best baseline model by up to 10.8% CIDEr on MS COCO and up to 5.4% CIDEr on Conceptual Captions.We also perform a series of ablation studies to quantify the utility of each system component. To the best of our knowledge, this is the first work that improves data efficiency of image captioning by utilizing LM pretrained on unimodal data. Our code is available at: this https URL.
Key Technologies of Distributed Transactional Database Storage Engine

With the development of information technology at home and abroad and the rapid popularization of mobile Internet, the data of business systems has increased exponentially. The processing and storage capabilities of traditional centralized databases are facing great challenges. Distributed databases have become the only way for enterprises to transform their digitalization. At the same time, in order to break through the market monopoly of traditional database vendors, it is a strong demand for the domestic database market to realize the localization of distributed database brands. This technical research paper constitutes a storage engine for distributed databases by studying consistent hash distributed storage, data sharding technology and transaction consistency. On the basis of the above technical research, further discussion on the hierarchical design of distributed storage engines, distributed system expansion and data rebalancing, provides theoretical and practical basis for creating mature and stable distributed database innovative products.
Application Analysis of Artificial Intelligence in Library Network Security

Abstract In the information age, the rapid development of computer network technology and artificial intelligence technology has created conditions for the development of traditional libraries to digital libraries. While computer network technology and artificial intelligence technology continue to promote the development of digital libraries, library network security issues also arise. The purpose of this paper is to explore the application analysis of artificial intelligence technology in library network security. This article puts forward the problem of library network system security, describes the concept and characteristics of library network system security, and aims at the basic factors affecting the security of digital library network system, constructs artificial intelligence-based books from the perspective of management and technology Preventive mechanism for library network system security. In terms of situation prediction, this article uses artificial intelligence technology and analytic hierarchy process to evaluate the network security situation. This method can quickly extract parameters and can quickly evaluate the current network, thereby improving the real-time performance of the situation assessment. The optimized support vector machine method overcomes the subjectivity of support vector machine parameter selection, thereby improving the prediction accuracy of the network security situation. The simulation experiment verifies that the optimized support vector machine prediction model has a higher value for the network security situation. Forecast accuracy. Experimental research shows that the PSO-SVR prediction model used in this article can predict the future situation value relatively closely, help library management network administrators analyze the future network situation, and prompt library management network administrators to adjust defense strategies in time based on threat assessment to maintain library management network Safe and healthy operation.
Performance modeling for secure migration processes of legacy systems to the cloud computing

Data migration is a type of process which is used to send the data from a cloud server to another end server. The target cloud where the data is sent can be divided into three types: public, private, and hybrid cloud. The data can be sent in multiple ways, such as organization to organization or cloud to cloud. This chapter proposes a framework which is used to verify all the details of the user with their security clearance. The information has to be extracted from the source, remodeled and loaded into the target throughout the information migration method. In the present scenario, the security of data migration is the major concern for users who use the cloud to move their data and application. Cloud migration is the peak point where the cloud manager meets with significant problems at the time of data migration from one company’s server to another server. The cloud behaves as an interface through which companies access their data on the virtual network. Cloud computing allows access control as part of the cryptographic algorithm. The starting point of predicate-based encryption (PBE) is identity-based encryption (IBE). PBE is a mixture of IBE and ABE (attribute-based encryption). It is used to design decryption keys and encrypt simple text messages. Amazon SimpleDB stores key-value pairs within the Amazon Web Services. The information manipulation in key-value pairs is done on the single node only. Results in key-value pairs can scale to a billion values with horizontal partition. Key-value storage systems duplicate the data on a number of product servers, guaranteeing high accessibility and low latency. In this chapter, a framework for data migration over cloud computing will develop in which migrated data is secure and it will measure the performance parameters of the data over the cloud. The computed result is compared with the existing framework, and the result is analyzed using different parameters.
WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach

The huge number of modern social network users has made the web a fertile ground for the growth and development of a plethora of recommender systems. To date, recommending a new user profile X to a given user U that could be interested in creating a relationship with X has been tackled using techniques based on content analysis, existing friendship relationships and other pieces of information coming from different social networks or websites. In this paper we propose a recommending architecture - called WhoSNext (WSN) - tested on Twitter and which aim is promoting the creation of new relationships among users. As recent researches show, this is an interesting recommendation problem: for a given user U, find which other user might be proposed to U as a new friend. Instead of conducting a study based on a semantic approach (e.g. analyzing tweet content), the proposed algorithm exploits a graph created from a set of Twitter users called seeds. In this work - and, to the best of our knowledge, for the first time - this issue is addressed using only user ID for building a particular Spreading Activation Network. This network was firstly trained and then tested on a set consisting of over 400,000 real users. Experimental results show that this approach outperforms the results obtained from many well-known state-of-the-art systems, which are much more expensive in terms of either data preprocessing or computational resources.
The Method of Construction of Logical Neural Networks on the Basis of Variable-Valued Logical Functions

In this paper, we suggest a method of constructing logical neural networks on the basis of variable-valued logical functions. We prove the theorem on a possibility of representation of any logical function as a logical neural network. The proof given in the paper contains an algorithm of the construction of the logical neural network. We point out the possibility of the generalization of the result obtained to the case of fuzzy logic.
Removing Undesirable Feature Contributions Using Out-of-Distribution Data

Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have clear limitations in terms of availability of UID data and dependence of algorithms on pseudo-labels. Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues. We show how to improve generalization theoretically using OOD data in each learning scenario and complement our theoretical analysis with experiments on CIFAR-10, CIFAR-100, and a subset of ImageNet. The results indicate that undesirable features are shared even among image data that seem to have little correlation from a human point of view. We also present the advantages of the proposed method through comparison with other data augmentation methods, which can be used in the absence of UID data. Furthermore, we demonstrate that the proposed method can further improve the existing state-of-the-art adversarial training.
Management of electronic resources by management institute in Pune City: a survey

Purpose The present study was conducted to find what practices Pune city's management institutes are following for managing their online databases. Essentially, the purpose of the research was to study librarians' awareness about the life cycle of electronic resources and how to manage their life span, so that the data in use do not go obsolete. Design/methodology/approach This experimentative study was based on a structured questionnaire designed to discover whether librarians of Pune's management institute followed the recommended six-step process in managing the life cycle of institutes' electronic resources as proposed by Jill Emery and Graham Stone in their Techniques in Electronic Resource Management (TERMS). Findings Our study revealed that the librarians are not following any fixed protocol and are managing electronic resources in an ad hoc manner as per their own discretion. A majority are not even aware of the six steps the TERMS model prescribed for managing electronic databases. Research limitations/implications The limitation of the study is to find the current practices of management of electronic resources at management institutes / colleges affiliated to Savitiribai Phule Pune University. Institutes which are not subscribing any database other than databases provided by UGC-INFONET thus got excluded from the study. Originality/value A comprehensive literature review revealed that although such studies have been carried out elsewhere in the world, there is nothing specific to the Indian scenario. This study aims to plug that information gap.
Algorithms and Complexity on Indexing Founder Graphs

Abstract We study the problem of matching a string in a labeled graph. Previous research has shown that unless the Orthogonal Vectors Hypothesis (OVH) is false, one cannot solve this problem in strongly sub-quadratic time, nor index the graph in polynomial time to answer queries efficiently (Equi et al. ICALP 2019, SOFSEM 2021). These conditional lower-bounds cover even deterministic graphs with binary alphabet, but there naturally exist also graph classes that are easy to index: For example, Wheeler graphs (Gagie et al. Theor. Comp. Sci. 2017) cover graphs admitting a Burrows-Wheeler transform -based indexing scheme. However, it is NP-complete to recognize if a graph is a Wheeler graph (Gibney, Thankachan, ESA 2019). We propose an approach to alleviate the construction bottleneck of Wheeler graphs. Rather than starting from an arbitrary graph, we study graphs induced from multiple sequence alignments (). Elastic degenerate strings (Bernadini et al. SPIRE 2017, ICALP 2019) can be seen as such graphs, and we introduce here their generalization: elastic founder graphs . We first prove that even such induced graphs are hard to index under OVH. Then we introduce two subclasses, repeat-free and semi-repeat-free graphs, that are easy to index. We give a linear time algorithm to construct a repeat-free (non-elastic) founder graph from a gapless , and (parameterized) near-linear time algorithms to construct a semi-repeat-free (repeat-free, respectively) elastic founder graph from general . Finally, we show that repeat-free founder graphs admit a reduction to Wheeler graphs in polynomial time.
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms

In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds. Specifically, we first show that the simple combination of divide-and-conquer technique and random features can achieve the same statistical accuracy as the exact KRR in expectation requiring only O(|D|) memory and O(|D|1.5) time. Then, beyond the generalization bounds in expectation that demonstrate the average information for multiple trails, we derive generalization bounds in probability to capture the learning performance for a single trail. Finally, we propose an effective communication strategy to further improve the performance of DKRR-RF, and validate the theoretical bounds via numerical experiments.
Cheat Detection in a Multiplayer First-Person Shooter Using Artificial Intelligence Tools

The use of cheating software in video games to gain an unfair advantage has required the use of anti-cheat software and deterrents such as account bans. Anti-cheat software is, however, always a step behind the opposition and as such new and innovative solutions are required. This paper considers AI driven tools as one such approach and compared decision trees, SVMs and Naïve Bayes classifiers in an attempt to classify cheating and honest player behaviour. The results of the research highlighted the potential for mouse dynamics as a measure of player behaviour, and decision trees as the most accurate detector of honest player behaviour.
Modified Model-Agnostic Meta-Learning

Meta-learning, an idea of "learning to learn," is a machine learning field that applies a learning algorithm to train a model for performing various tasks. This paper extends the current version of meta-learning by applying a modified model-agnostic algorithm so that the model becomes capable of performing the tasks just upon being trained on a few shots of samples. Moreover, the proposed algorithm also helps facilitate problems like over-fitting.
Batch Reinforcement Learning Through Continuation Method

Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions. Policy optimization under this setting is extremely challenging as: 1) the geometry of the objective function is hard to optimize efficiently; 2) the shift of data distributions causes high noise in the value estimation. In this work, we propose a simple yet effective policy iteration approach to batch RL using global optimization techniques known as continuation. By constraining the difference between the learned policy and the behavior policy that generates the fixed trajectories, and continuously relaxing the constraint, our method 1) helps the agent escape local optima; 2) reduces the error in policy evaluation in the optimization procedure. We present results on a variety of control tasks, game environments, and a recommendation task to empirically demonstrate the efficacy of our proposed method.
Mobile-based Decision Support System for Poultry Farmers: A Case of Tanzania

Poultry farms in Tanzania are characterized by inadequate management practices which are mainly caused by the lack of adequate systems to guide the small-scale poultry farmers in decision making.It is well-established that information is a key factor in making effective decisions in numerous sectors including poultry farming.Furthermore, various researchers have identified the use of mobile decision support tools to be an effective way of aiding farmers in making informed decisions.In this paper, we present a mobile-based decision support system that will aid rural and small-scale poultry farmers in Tanzania to obtain reliable information that is crucial for making proper decisions in their farming activities.In this context, a mobile-based decision support system was achieved through a mobile application integrated with a chatbot assistant to provide a solution to various poultry farming-related problems and simplify their decision-making process.We used a data-driven approach towards developing an informational chatbot assistant for Android smartphones that is capable of interacting with small-scale poultry farmers through natural conversations by utilizing the RASA framework.
Improved Predictive System for Soil Test Fertility Performance Using Fuzzy Rule Approach

Decision making in agriculture affects both crops in all their phases (planting, management, costs, etc.) as well as the distribution and organization of crop production systems. Generating rules and connecting the rules into the fuzzy inference system for decision making is a very delicate task when associated with soil fertility test. Some of the constraints with previous study is the problem of uncertainty and imprecise complexity of experimenting and interpreting DSS system in real-life. The application of fuzzy-logic provides an effective decision-making tool for dealing the aforementioned challenges. This paper proposed a Mandani-type fuzzy inference System for testing soil fertility and increasing overall crop yield. This study was conducted in FUNAAB farm and seven inputs (nitrogen, phosphorous, potassium, organic matter, boron, zinc, and PH of water) with their nutrients percentage were fed into a fuzzy inference system. Three major inputs were varied based on their major role in soil constituents (nitrogen, phosphorous and potassium (NPK)). Our experimental result showed that irrespective of the level of the three major inputs (NPK) at every given time, the values of the output did not significantly change, it remained at 0.5 and it represents an average output on the crops considered for this purpose of this work. This study concludes that the application of fuzzy logic rule (mandani –type FIS model) in soil fertility test for cultivating four crops (Yam, Cassava, Maize and Cocoyam) have proven to be effective measure and thereby ensuring effective decision making.
Gas-Theft Suspect Detection Among Boiler Room Users: A Data-Driven Approach

The natural gas tightly correlates with our everyday life. However, driven by gray incomes, some users are prone to stealing gas by refitting the equipment without permission. Especially for the boiler room users in winter, this phenomenon appears more rampant. Traditional gas-theft detection methods highly rely on the on-site inspection, where exists ineffective and randomness. With the rapidly deployed IoT sensors, we can collect real-time gas consumption data to analyze users’ behavior patterns, where the gas-theft suspects could be discovered early and accurately. In this paper, we propose a data-driven approach, named SVOC, to detect gas-theft suspects among boiler room users. Our approach consists of a scenario-based data quality detection algorithm, a deformation-based normality detection algorithm, and an One-Class Support Vector Machine (OCSVM) based anomaly detection algorithm. Specifically, considering the temporal proximity between the gas consumption and the outdoor temperature, the normality detection algorithm adopts a similarity-based deformation correlation to detect normal boiler room users out of abnormal ones. Then, we employ OCSVM as the anomaly detection algorithm to capture various features across multiple data sources, aiming to distinguish gas-theft suspects from the remaining irregular users. Here, the detected normal and abnormal users are fed into the OCSVM for training and prediction, respectively, which can overcome the label scarcity problem. We conduct extensive experiments on a real-world dataset during one heating season. The results demonstrate distinct advantages of our approach over various baselines. We have developed a real-time system on the cloud, providing daily gas-theft suspects for gas companies.
A collaborative ide for graphics programming

We present a collaborative coding environment that helps the teaching of introductory computergraphics courses, with the goal of to do teaching graphics fundamentals more effectively and lowering theexcessive difficult of initiate into 3D graphics programming. Traditionally, the OpenGL Library is used forteaching computer graphics courses, since there are bindings for the most popular languages, including Python,Ruby, Java and the web (WebGL). In particular, our proposed framework provides a web IDE for collaborativecoding, between classmates or workgroups, and the professor can help them or interact on it. The secondcomponent is the use of the Emscripten library, which allows the program with OpenGL on the web in the sameway as it is done in a local mode. Compared with WebGL, our proposal has better benefits since the code couldbe used in any computer with OpenGL support, which has greatly benefited our students. It also was proposedto diminish the complexity of creating websites with 3D graphics support and better online games, which hasbeen motivating students to learn 3D computer graphics, build educational graphical applications and articles,host them online. Compared to other online coding platforms, oriented to mainstream graphics educationalmaterials and graphics in general, the collaborative IDE for programming computer graphics that we havedeveloped offers the possibility of students could learn OpenGL in a fast way for both inside and outside ourclassrooms with the C++ language.