Reviewerly/exHarmony huggingface dataset:
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks

Transfer learning has emerged as a powerful technique for improving the performance of machine learning models on new domains where labeled training data may be scarce. In this approach a model trained for a source task, where plenty of labeled training data is available, is used as a starting point for training a model on a related target task with only few labeled training data. Despite recent empirical success of transfer learning approaches, the benefits and fundamental limits of transfer learning are poorly understood. In this paper we develop a statistical minimax framework to characterize the fundamental limits of transfer learning in the context of regression with linear and one-hidden layer neural network models. Specifically, we derive a lower-bound for the target generalization error achievable by any algorithm as a function of the number of labeled source and target data as well as appropriate notions of similarity between the source and target tasks. Our lower bound provides new insights into the benefits and limitations of transfer learning. We further corroborate our theoretical finding with various experiments.
Asymptotic Analysis of Blind Reconstruction of BCH Codes Based on Consecutive Roots of Generator Polynomials

In the military surveillance and security information systems, correct blind reconstruction of signal parameters from unknown signals is very important. Especially, many blind reconstruction methods of error-correcting codes have been proposed, and their theoretical performance analysis is essential for both the defender who wants to prevent information leakage and the challenger who wants to extract information from the intercepted signals. However, a proper performance analysis of most blind reconstruction methods has not been performed yet. Among many blind reconstruction methods of BCH codes proposed so far, the blind reconstruction method based on consecutive roots of generator polynomials proposed by Jo, Kwon, and Shin, called the JKS method, shows the best performance under the unknown channel information. However, the performance of the JKS method is only evaluated through simulation without performing theoretical analysis. In this paper, the JKS method is asymptotically analyzed under the binary symmetric channel with the cross-over probability $p$ . Since the blind reconstruction performance heavily depends on how many and which received codewords are used even for the same channel environment, sufficiently many codewords are assumed to perform asymptotic analysis. More specifically, an asymptotic threshold on $p$ , up to which blind reconstruction is successful, is derived when the number of received codewords is sufficiently large, which can be used as a new performance metric for blind reconstruction methods. Finally, the validity of the asymptotic analysis is confirmed through simulation.
Regularizing Black-box Models for Improved Interpretability

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regularizes a model for explanation quality at training time. Importantly, these regularizers are differentiable, model agnostic, and require no domain knowledge to define. We demonstrate that post-hoc explanations for ExpO-regularized models have better explanation quality, as measured by the common fidelity and stability metrics. We verify that improving these metrics leads to significantly more useful explanations with a user study on a realistic task.
The demographic profile of victims of investment fraud: an update

Purpose This study aims to examine the demographic factors of investors, contributing to financial victimization that occurs in Canada from June of 2008 to December of 2019. Design/methodology/approach In all 235 cases disclosing the details of financial crime victims are collected from the Industry Regulatory Organization of Canada (IIROC) enforcement platform between June of 2009 and December of 2019 for the analysis. The study used a descriptive analysis to showcase the demographic characteristics of investors who have been victims of financial crimes in Canada. Findings The findings indicate that these investors of age 60 and above were more likely to fall prey to various types of financial crime. The results also disclosed that retirees and investors with limited investment knowledge increase the probability of being vulnerable to the perpetrators than others. Research limitations/implications Overall, the study helps regulators in the securities industry gain insights into demographic portraits of the more vulnerable investors. Hence, more precautionary measures could pitch into these concerns to protect specific subsets of investors from investment fraud. Originality/value Individuals who are more vulnerable to investment fraud might not be entirely comparable with the stereotypical victims that most studies portray. The research gap could cause individual investors who appear to be at lower risk to unconsciously fall prey to investment fraud. The IIROC study, detailing the demographic factors of victims, can fill the gap and improve understanding of the tendency of victims.
Mobile Application with Cloud-Based Computer Vision Capability for University Students’ Library Services

This paper presents the development of the smart device mobile application “Book’s Recognition”. The app recognizes the text of library book titles at the library of the Universidad Politécnica Salesiana in the city of Guayaquil, Ecuador. Through a service stored in Amazon Web Service (AWS), Mobil Vision’s algorithms for text recognition, and Google’s API on the Android platform, the app “Book’s Recognition” allows its user to recognize the text of the title of a physical book in an innovative and effective way, showing the user basic information about the book in real time. The application can be offered as a service of the library. The purpose of this development is to awaken university student’s interest about new and creative forms of intelligent investigation with resources from the university’s main library, and furthermore to facilitate the investigative process by providing information on non-digitalized, and digitalized, books that hold valuable and relevant information for all generations. The mobile app can be downloaded the following website: https://github.com/seimus96/mobile_vision .
A Review on Task Scheduling in Cloud Computing using parallel Genetic Algorithm

This Cloud computing (CC) infrastructure has many issues including scheduling, budgeting & load balancing (LB). Among them, the biggest challenge for load balancing is a cloud platform. In task scheduling environment generally, the occurrence of load imbalance tends to uncertainty and complexity. Cloud computing is growing Internet-based computing platform & innovative that is emerging & one of its biggest tasks. The goal is to use resources efficiently & decrease resource consumption in the cloud environment. This can be achieved by increasing the LB rate when selecting the best resources for low work failure rates with low lead times. This paper discusses load balancing based on advanced genetic algorithms in the cloud computing platform.
A Dataset &amp; Methodology for Computer Vision based Offside Detection in Soccer

Offside decisions are an integral part of every soccer game. In recent times, decision-making in soccer games, including offside decisions, has been heavily influenced by technology. However, in spite of the use of a Video Assistant Referee (VAR), offside decisions remain to be plagued with inconsistencies. The two major points of criticism for the VAR have been extensive delays in providing final decisions and inaccurate decisions arising from human errors. The visual nature of offside decision-making makes Computer Vision techniques a viable option for tackling these issues, by automating appropriate aspects of the process. However, the lack of a computational algorithm that captures all aspects of the offside rule, lack of an established methodology to computationally represent soccer match scenes in a way that can be utilized by such an algorithm, and the absence of a diverse, comprehensive dataset for testing these methods have stood in the way of research efforts for this problem. This paper precisely addresses each one of these obstacles, in an effort to facilitate further research in this area. The paper presents a computational offside decision algorithm for soccer match images. The methodology for creating a quantitative representation of soccer match images for this offside algorithm has also been presented as a pipeline of Computer Vision tasks. A novel dataset for evaluating this methodology has been presented, which contains a curated selection of soccer match scenes that represent the various challenges that can be faced by a system that aims to aid or automate the task of making offside decisions. Finally, this paper also details the performance of a specific set of Computer Vision tasks used in the presented pipeline, on the given dataset. The proposed system achieves an F1 score of 0.85 on the dataset. The drawbacks and areas of improvements for these methods have also been discussed in an attempt to focus future research on this task. The presented dataset and pipeline implementation code is available at: https://github.com/Neerajj9/Computer-Vision-based-Offside-Detection-in-Soccer
Blockchain for Smart Cities: A Systematic Literature Review

We use a systematic literature review methodology to answer the following questions pertaining to smart cities and blockchain: (i) Why was blockchain chosen as the solution? (ii) What blockchains are being considered for use in smart cities and why? and (iii) What blockchain based applications are being researched for smart cities? Our results - based on 45 peer-reviewed academic studies all published in journals that met pre-defined search criteria - show that increased security, privacy, and trust are the reasons most cited in the literature for the use of blockchain for smart cities. Consortium, Hybrid, Private, and Public blockchains are discussed with respect to their suitability for smart cities applications, and finally, we discuss smart cities blockchain applications from the literature using a taxonomy based on the framework defined by Silva et al. [40]. In conclusion, this study highlights the current blockchain challenges and future research opportunities, including the need to change the current mindset of centralized control and trusted third parties to a more participative engagement model across smart cities.
A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.
Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication

In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems. Specifically, we propose a solution towards robust cooperation, which enables the multi-agent system to maintain high performance in the presence of anonymous non-cooperative agents that communicate faulty, misleading or manipulative information. In pursuit of this goal, we propose a communication architecture based on Graph Neural Networks (GNNs), which is amenable to a novel Gaussian Process (GP)-based probabilistic model characterizing the mutual information between the simultaneous communications of different agents due to their physical proximity and relative position. This model allows agents to locally compute approximate posterior probabilities, or confidences, that any given one of their communication partners is being truthful. These confidences can be used as weights in a message filtering scheme, thereby suppressing the influence of suspicious communication on the receiving agent's decisions. In order to assess the efficacy of our method, we introduce a taxonomy of non-cooperative agents, which distinguishes them by the amount of information available to them. We demonstrate in two distinct experiments that our method performs well across this taxonomy, outperforming alternative methods. For all but the best informed adversaries, our filtering method is able to reduce the impact that non-cooperative agents cause, reducing it to the point of negligibility, and with negligible cost to performance in the absence of adversaries.
Contemplating Security Challenges and Threats for Smart Cities

Smart City technologies cannot be disentangled from the end-user but the debate of Smart City development is mostly lacking privacy, protection and information and cyber security concerns. It is vital to apply the countermeasures to eradicate these privacy and security ethnic issues for sustainability of Smart City. It is a dilemma of central importance both at individual and government levels since the development and implementation of a Smart City has the aim of interconnecting eventually everything on the Internet, but with lesser emphasis on the security. In this paper, the major privacy and security concerns, limitations, challenges and their consequences in the Smart City infrastructure have been discussed and the multiple dimensions in the area of information and cyber security along with privacy concerns are listed that also provide an insight into the gap in research. The study is carried out in the domain of contemplate security and privacy concerns linked to Smart City user corresponding to various technologies. The countermeasures for achieving privacy protection are elaborated in the light of studied literature.
Robust Neural Networks Inspired by Strong Stability Preserving Runge-Kutta Methods

Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler method of numerical discretization. From the numerical discretization perspective, Strong Stability Preserving (SSP) methods are more advanced techniques than the explicit Euler method that produce both accurate and stable solutions. Motivated by the SSP property and a generalized Runge-Kutta method, we proposed Strong Stability Preserving networks (SSP networks) which improve robustness against adversarial attacks. We empirically demonstrate that the proposed networks improve the robustness against adversarial examples without any defensive methods. Further, the SSP networks are complementary with a state-of-the-art adversarial training scheme. Lastly, our experiments show that SSP networks suppress the blow-up of adversarial perturbations. Our results open up a way to study robust architectures of neural networks leveraging rich knowledge from numerical discretization literature.
Incremental Cryptography Revisited: PRFs, Nonces and Modular Design

This paper gives the first definitions and constructions for incremental pseudo-random functions (IPRFs). The syntax is nonce based. (Algorithms are deterministic but may take as input a non-repeating quantity called a nonce.) The design approach is modular. First, given a scheme secure only in the single-document setting (there is just one document on which incremental updates are being performed) we show how to generically build a scheme that is secure in the more realistic multi-document setting (there are many documents, and they are simultaneously being incrementally updated). Then we give a general way to build an IPRF from (1) an incremental hash function with weak collision resistance properties and (2) a symmetric encryption scheme. (This adapts the classic Carter-Wegman paradigm used to build message authentication schemes in the non-incremental setting.) This leads to many particular IPRFs. Our work has both practical and theoretical motivation and value: Incremental PRFs bring the benefits of incrementality to new applications (such as incremental key derivation), and the movement from randomized or stateful schemes to nonce based ones, and from UF (unforgeability) to PRF security, bring incremental symmetric cryptography up to speed with the broader field of symmetric cryptography itself.
Examining virtual reference services in academic libraries

Virtual reference services, once considered an alternative to traditional in-person reference services, are now the norm. Most academic libraries offer chat and email reference services, and many offer options for texting as well. The following articles examine many facets of virtual reference services. The results of these studies reveal the effectiveness of virtual services, how to improve them, and why users prefer one method of virtual reference services to another.
The Financial Document Causality Detection Shared Task (FinCausal 2023)

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING'2020), Barcelona, Spain on September 12, 2020.
Multi-factor Authentication

Today, there are over 3.5 billion people online and have access to many online services such as banking, shopping, student records, and social media. In order to access these services, they have to carry out and pass a login or authentication mechanism, which usually requires a username and a password. In the previous chapters, we have learned a username and a password are not the only credentials that can be used during the login process.
Second-Order Camera-Aware Color Transformation for Cross-Domain Person Re-identification

In recent years, supervised person re-identification (person ReID) has achieved great performance on public datasets, however, cross-domain person ReID remains a challenging task. The performance of ReID model trained on the labeled dataset (source) is often inferior on the new unlabeled dataset (target), due to large variation in color, resolution, scenes of different datasets. Therefore, unsupervised person ReID has gained a lot of attention due to its potential to solve the domain adaptation problem. Many methods focus on minimizing the distribution discrepancy in the feature domain but neglecting the differences among input distributions. This motivates us to handle the variation between input distributions of source and target datasets directly. We propose a Second-order Camera-aware Color Transformation (SCCT) that can operate on image level and align the second-order statistics of all the views of both source and target domain data with original ImageNet data statistics. This new input normalization method, as shown in our experiments, is much more efficient than simply using ImageNet statistics. We test our method on Market1501, DukeMTMC, and MSMT17 and achieve leading performance in unsupervised person ReID.
Multi-client Oblivious RAM with Poly-logarithmic Communication

Oblivious RAM enables oblivious access to memory in the single-client setting, which may not be the best fit in the network setting. Multi-client oblivious RAM (MCORAM) considers a collaborative but untrusted environment, where a database owner selectively grants read access and write access to different entries of a confidential database to multiple clients. Their access pattern must remain oblivious not only to the server but also to fellow clients. This upgrade rules out many techniques for constructing ORAM, forcing us to pursue new techniques. MCORAM not only provides an alternative solution to private anonymous data access (Eurocrypt 2019) but also serves as a promising building block for equipping oblivious file systems with access control and extending other advanced cryptosystems to the multi-client setting. Despite being a powerful object, the current state-of-the-art is unsatisfactory: The only existing scheme requires $$O(\sqrt{n})$$ communication and client computation for a database of size n. Whether it is possible to reduce these complexities to $$\mathsf {polylog}(n)$$ , thereby matching the upper bounds for ORAM, is an open problem, i.e., can we enjoy access control and client-obliviousness under the same bounds? Our first result answers the above question affirmatively by giving a construction from fully homomorphic encryption (FHE). Our main technical innovation is a new technique for cross-key trial evaluation of ciphertexts. We also consider the same question in the setting with N non-colluding servers, out of which at most t of them can be corrupt. We build multi-server MCORAM from distributed point functions (DPF), and propose new constructions of DPF via a virtualization technique with bootstrapping, assuming the existence of homomorphic secret sharing and pseudorandom generators in NC0, which are not known to imply FHE.
An hybridization of global-local methods for autonomous mobile robot navigation in partially-known environments

This paper deals with the navigation problem of an autonomous non-holonomic mobile robot in partially-known environment. In this proposed method, the entire process of navigation is divided into two phases: an off-line phase on which a distance-optimal reference trajectory enables the mobile robot to move from an initial position to a desired target which is planned using the B-spline method and the Dijkstra algorithm. In the online phase of the navigation process, the mobile robot follows the planned trajectory using a sliding mode controller with the ability of avoiding unexpected obstacles by the use of fuzzy logic controller. Also, the fuzzy logic and fuzzy wall-following controllers are used to accomplish the reactive navigation mission (path tracking and obstacle avoidance) for a comparative purpose. Simulation results prove that the proposed path planning method (B-spline) is simple and effective. Also, they attest that the sliding mode controller track more precisely the reference trajectory than the fuzzy logic controller (in terms of time elapsed to reach the target and stability of two wheels velocity) and this last gives best results than the wall-following controller in the avoidance of unexpected obstacles. Thus, the effectiveness of our proposed approach (B-spline method combined with sliding mode and fuzzy logic controllers) is proved compared to other techniques.