Reviewerly/exHarmony huggingface dataset:
PyTFHE: An End-to-End Compilation and Execution Framework for Fully Homomorphic Encryption Applications

Fully Homomorphic Encryption (FHE) is a powerful cryptographic scheme that enables computation on encrypted data, which allows clients to offload computation to an untrusted third party without compromising data privacy. However, FHE has not yet been widely adopted due to its enormous computational overhead. Further, cryptographic software development requires specialized expertise and presents a significant challenge when applying FHE to a broad range of applications. We present PyTFHE, a framework that tackles these difficulties by enabling highly productive FHE application development and orders of magnitude more efficient FHE application execution. PyTFHE is built on top of the TFHE (Fast Fully Homomorphic Encryption over the Torus) scheme, which is an FHE scheme that supports gate-level evaluation and arbitrary depth of boolean circuits. PyTFHE is designed in a TFHE-specific approach, allowing state-of-the-art optimizations for TFHE applications. Specifically, PyTFHE features ChiselTorch, the first compiler that allows easy generations of privacy-preserving deep neural network models with PyTorch-compatible APIs. PyTFHE is also the first FHE framework that employs a powerful backend enabling efficient execution of TFHE applications on distributed CPU systems and high-performance GPUs. We demonstrate the effectiveness of PyTFHE by benchmarking the framework using VIP-Bench and implementing privacy-preserving deep neural networks and evaluating their performance on various systems. We compare the performance of our generated TFHE program execution with three existing frameworks, Google Transpiler, Cingulata, and E3. We show that PyTFHE achieves one to two orders of magnitude performance advantage.
Discovering Emerging Applications of Multi-Valued Logic: Protocols for Human-Autonomy Teaming

This paper discovers an emerging area of application of the advanced Multivalued Logic (MVL), – a collaboration between humans and autonomous machines in the first responder teams (firefighters, search-and-rescue operators, disaster response personnel, and military special operations forces). We developed an approach to formalization of the collaboration protocols (also known as contagion protocols) that model the human-machine teaming in emergency missions. MVL techniques satisfy the requirements of encoding the states of stress and trust, and their propagation between the teammates. Advanced MVL techniques are also useful for other applications, such as modeling the contagion of infection, or the spread of rumors, ideas, panic, and fear.
Quick Computation of the Lower Bound on the Gate Count of Toffoli-Based Reversible Logic Circuits

We present a time-efficient lower bound $\tilde \sigma $ on the number of gates in Toffoli-based reversible circuits that represent a given reversible logic function. For the characteristic vector s of a reversible logic function, the value of $\tilde \sigma ({\mathbf{s}})$ is almost the same as σ-lb (s), which is known as a relatively-efficient lower bound in terms of the evaluation time and the tightness. By slightly sacrificing the tightness of the lower bound, $\tilde \sigma $ achieves fast computation. We prove that $\tilde \sigma $ is a lower bound on σ-lb. Next, we show $\tilde \sigma $ can be calculated faster than σ-lb. The time complexity of $\tilde \sigma ({\mathbf{s}})$ is О(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), where n is the dimension of s. Experimental results to compare $\tilde \sigma $ and σ-lb are also given. The results demonstrate that the values of $\tilde \sigma ({\mathbf{s}})$ are equal to those of σ-lb (s) for most reversible functions and that the computation time of $\tilde \sigma ({\mathbf{s}})$ is much shorter than that of σ-lb(s).
Exploring the Efficiency of Data-Oblivious Programs

Data-oblivious programs have gained popularity due to their application in security, but are often dismissed because of anticipated performance loss. In order to better understand these performance concerns, this paper details the first performance characterization of data-oblivious programs. We study mechanical data-oblivious transformations applied to twenty workloads from the VIP-Bench benchmark suite and find that, overall, performance overheads vary widely, with a geomean slowdown of 7.4×. This variance can be attributed to whether or not the data-oblivious transformations affect the workload’s asymptotic complexity. Performance overheads are much lower for the fourteen workloads whose complexity is unaffected, at 1.9× geomean. Further, by reducing control hazards, we find that dataoblivious transformations often result in improved per-instruction performance (e.g., better branch and memory performance) and increase the number of instructions the processor can execute in parallel (e.g., IPC). Leveraging lessons from analyzing these overheads, we study four notably slow data-oblivious workloads and show how algorithmic changes can significantly improve performance–achieving an average 86.4× speedup over the mechanically produced baseline programs. While data-oblivious program execution often incurs overheads, the contributions of this paper show that these overheads can be overcome by compiler and algorithmic optimizations, bringing us closer to achieving efficient and widely-used data-oblivious programs.
STRATEGIC ORGANIZATIONAL CHALLENGES AND SUSTAINABLE MANAGING HUMAN RESOURCES IN JORDAN SME LOGISTICS COMPANIES

Organizational development is a natural phenomenon in the world of administrative organizations. As it plays a significant role in enhancing the effectiveness and functionality of job performance. The human and organizational side is regarded as one of the most crucial components of a successful renaissance in the life of administration in general and human resources management. However, this paper aims to investigate the impact of strategic organizational challenges on managing human resources in the light of entrepreneurship as a moderate variable in Jordanian SME logistics companies. Smart PLS4 was used to analyze the data that collected via questionnaire. Probability sampling were used. A total of 527 questionnaires were collected from the respondents. The result of missing value analysis showed that there are 193 responses are missing. Moreover, the findings indicate that strategic organizational challenges positively and significantly affect the entrepreneurship capabilities and competitive advantage. In addition, entrepreneurship capabilities positively and significantly affect competitive advantage and human resources performance. Entrepreneurship capabilities have a mediator effect between variables. However, this study contributes to providing decision makers and policy makers; where adopting sustainable HR practices can benefit decision makers in Jordan SME logistics companies by improving employee retention, productivity, efficiency, reputation, and talent acquisition, while mitigating risks associated with legal, regulatory, and ethical issues.
Executives vs. Chatbots: Unmasking Insights through Human-AI Differences in Earnings Conference Q&amp;amp;A

A significant portion of information shared in earnings calls is conveyed through verbal communication by corporate managers. However, quantifying the extent of new information provided by managers poses challenges due to the unstructured nature of human language and the difficulty in gauging the market’s existing knowledge. In this study, we introduce a novel measure of information content (Human-AI Differences, HAID) by exploiting the discrepancy between answers to questions at earnings calls provided by corporate executives and those given by several context-preserving Large Language Models (LLM) such as ChatGPT, Google Bard, and an open source LLM. HAID strongly predicts stock liquidity, abnormal returns, number of analysts’ forecast revisions, analyst forecast accuracy following these calls, and propensity of managers to provide management guidance, consistent with HAID capturing new information conveyed by managers. Overall, our results highlight the importance of using LLM as a tool to help investors unveil the veiled – penetrating the information layers and unearthing hidden insights.
Actor–Critic or Critic–Actor? A Tale of Two Time Scales

We revisit the standard formulation of tabular actor-critic algorithm as a two time-scale stochastic approximation with value function computed on a faster time-scale and policy computed on a slower time-scale. This emulates policy iteration. We observe that reversal of the time scales will in fact emulate value iteration and is a legitimate algorithm. We provide a proof of convergence and compare the two empirically with and without function approximation (with both linear and nonlinear function approximators) and observe that our proposed critic-actor algorithm performs on par with actor-critic in terms of both accuracy and computational effort.
Defying Gravity and Gadget Numerosity: The Complexity of the Hanano Puzzle

Using the notion of visibility representations, our paper establishes a new property of instances of the Nondeterministic Constraint Logic (NCL) problem (a $$\textrm{PSPACE}$$ -complete problem that is very convenient to prove the $$\textrm{PSPACE}$$ -hardness of reversible games with pushing blocks). Direct use of this property introduces an explosion in the number of gadgets needed to show $$\textrm{PSPACE}$$ -hardness, but we show how to bring that number from 32 down to only three in general, and down to two in a specific case! We propose it as a step towards a broader and more general framework for studying games with irreversible gravity, and use this connection to guide an indirect polynomial-time many-one reduction from the NCL problem to the Hanano Puzzle—which is $$\textrm{NP}$$ -hard—to prove it is in fact $$\textrm{PSPACE}$$ -complete.
A Scalable and Secure Framework for 5G Networks Applications

In the fifth-generation (5G) networks and the beyond, communication latency and network bandwidth will be no more bottleneck to mobile users. Thus, almost every mobile device can participate in the distributed learning. That is, the availability issue of distributed learning can be eliminated. However, the model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients amongst multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this chapter proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE. To prove the feasibility of the proposed PIRATE, we implemented a prototype. A case-study shows how the proposed PIRATE contributes to the distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine-resilient learning framework.
Blockchain Scalability Fundamentals

Blockchains have been deeply diving into multiple applications, such as blockchain-assisted Internet of Things (IoT), business applications, security-enabled solutions, and many other applications in diverse fields. However, we think that a comprehensive survey towards the essentials of blockchains by exploiting the state-of-the-art theoretical modelings, analytic models, and useful experiment tools is still missing. To fill this gap, we identify and classify the most recent high-quality research outputs that are closely related to the theoretical findings and essential mechanisms of blockchain systems and networks. Several promising open issues are also summarized finally for future research directions. We wish our work can serve as a useful guideline for researchers, engineers, and educators about the cutting-edge development of blockchains in the perspectives of theories, modelings, and tools.
Yield Curve Forecasting using Deep Learning Nelson-Siegel Model

I propose a deep learning term structure forecasting model by introducing a Nelson-Siegel layer that generates smooth yield curve forecasts while explaining the stylized facts of the term structure of interest rates. It is flexible enough to easily incorporate time-varying or time-invariant decay parameters into the deep learning framework. In the empirical analysis using U.S. yield data, the proposed model outperforms the benchmark DNS model in terms of lower RMSEs for 1-, 3-, 6-, and 12-month out-of-sample forecasts with statistical significance at 1, 3, and 6 months. Interestingly, it prefers the time-invariant decay parameter over the time-varying one and produces relatively smoother and less volatile latent yield factor estimates, which is interpreted as related to an attempt to reduce overfitting for the unseen yield curve. Finally, some relevant hyperparameters need to vary with the forecast horizon rather than uniformly in order to improve forecasting performance.
Risk Factors in Cryptocurrency Investments and Feasible Solutions to Mitigate Them

This systematic literature review and bibliometric analysis are intended to provide qualitative and quantitative knowledge on our research domain—risk factors affecting cryptocurrency market returns. Numerous review papers and research papers are considered for this study, published during 2019–2021. Out of these most influential papers were considered for thorough content analysis. These papers were segregated based on the resulting outcome of bibliometric analysis. Three major themes were identified using the clustering technique viz. cryptocurrency technology, markets and business aspects of cryptocurrency, security and governance. These clusters identified prominent keywords in this research domain and helped identify major and broader issues related to cryptocurrency markets. Furthermore, a detailed study was conducted on stablecoin, one of the highly proposed solutions for mitigating the risk of cryptocurrencies, as suggested by most of the authors we studied during this research (mentioned in the clusters under study). The correlation of AI with cryptocurrency is reviewed in prospects of pattern analysis of cryptocurrency price drivers. A research gap has also been identified, giving researchers a pathway to follow and help mitigate the crypto market risks.
Security Standards for Classical and Modern Cryptography

Today in this world of data, we transmit a huge amount of data on the network on daily basis. We are not sure that this transmission of data over the network is safe or not. This arises a need of security. We must ensure that the data that we are transmitting is secure, valid, and is reaching to its destination without getting altered. The best way to secure the data is to encrypt the data and then send it over the network. Encryption is the method by which information is converted into secret code that hides the information’s true meaning. We can use different Encryption standards(rules) to achieve this. This chapter introduces us to such various Encryption standards as such as Data Encryption Standard (DES), Triple DES and modes of operation well as some ciphers like Stream Cipher and some mathematical background, Abstract Algebra and Number Theory. This chapter will explain how the concepts are working with some easy calculations and examples. Hence, this chapter provides theoretical concepts with its working and examples which will help readers thoroughly understand how the things are working behind every encryption standard algorithm and will be able to understand the mathematical concepts.
ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image Recognition

Recent advances in deep Convolutional Neural Networks (CNNs) have established them as a premier technique for a wide range of classification tasks, including object recognition, object detection, image segmentation, face recognition, and medical image analysis. However, a significant drawback of utilizing CNNs is the requirement for a large amount of annotated data, which may not be feasible in the context of historical document analysis. In light of this, we present a novel CNN-based architecture ResPho(SC)Net, to recognize handwritten word images in a zero-shot learning framework. Our method proposes a modified version of the Phosc(Net) architecture with a much lesser number of trainable parameters. Experiments were conducted on word images from two languages (Norwegian and English) and encouraging results were obtained.
Calibration of Non-Central Conical Catadioptric Systems from Parallel Lines

Most camera calibration approaches require the use of calibration patterns like checkerboards. This also applies to Omnidirectional Catadioptric Systems. In this paper, we propose a new calibration method for Non-Central Conical Catadioptric Systems which only requires long and parallel lines which are plentiful in man-made environments. It is based on the extraction of lines from a single omnidirectional image, where we impose parallelism restrictions to obtain the analytical solution of the calibration parameters. However, the calibration of this kind of non-central catadioptric systems is noise sensitive. The proposal has been tested with simulations and then with synthetic and real images to evaluate the accuracy of the proposed method.
The ETS2 Dataset, Synthetic Data from Video Games for Monocular Depth Estimation

In this work, we present a new dataset for monocular depth estimation created by extracting images, dense depth maps, and odometer data from a realistic video game simulation, Euro Truck Simulator 2 $$^\textrm{TM}$$ . The dataset is used to train state-of-the-art depth estimation models in both supervised and unsupervised ways, which are evaluated against real-world sequences. Our results demonstrate that models trained exclusively with synthetic data achieve satisfactory performance in the real domain. The quantitative evaluation brings light to possible causes of domain gap in monocular depth estimation. Specifically, we discuss the effects of coarse-grained ground-truth depth maps in contrast to the fine-grained depth estimation. The dataset and code for data extraction and experiments are released open-source.
Can Representation Learning for Multimodal Image Registration be Improved by Supervision of Intermediate Layers?

Multimodal imaging and correlative analysis typically require image alignment. Contrastive learning can generate representations of multimodal images, reducing the challenging task of multimodal image registration to a monomodal one. Previously, additional supervision on intermediate layers in contrastive learning has improved biomedical image classification. We evaluate if a similar approach improves representations learned for registration to boost registration performance. We explore three approaches to add contrastive supervision to the latent features of the bottleneck layer in the U-Nets encoding the multimodal images and evaluate three different critic functions. Our results show that representations learned without additional supervision on latent features perform best in the downstream task of registration on two public biomedical datasets. We investigate the performance drop by exploiting recent insights in contrastive learning in classification and self-supervised learning. We visualize the spatial relations of the learned representations by means of multidimensional scaling, and show that additional supervision on the bottleneck layer can lead to partial dimensional collapse of the intermediate embedding space.
Asymmetric Key Cryptography

Asymmetric encryption is a brand-new, highly advanced encryption method that employs two keys. Public and private keys are the names given to these keys. As the name suggests, anyone who wants to send an encrypted communication can obtain the "public key." The other is the "private key," which the holder of the public key or the person encrypting keeps secret. First, with the aid of a public key and a certain algorithm, information is encrypted. The encrypted information will then be decrypted using the receiver's private key. Both encoding and decoding will employ the same algorithm. There are various algorithms related to asymmetric key cryptography - Diffie-Hellman, RSA, ElGamal, Digital Signature etc. Also, some protocols that make use of asymmetric key algorithms included, i.e., S/MIME, PGP, IPsec, etc.
CCLM: Class-Conditional Label Noise Modelling

The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics - Noise Division Accuracy and Classiness.
Design of Ground Pressure Safety Monitoring System Based on SaaS

Sudden ground pressure disasters caused by deep mining such as rock burst and cave-in are major hidden dangers affecting safety production underground mine. The safe and stable operation of ground pressure safety monitoring platform is of great significance for mine safety production. However, many small and medium-sized mining enterprises make slow progress in the construction of the online monitoring platform for ground pressure safety due to the limitations of enterprise scale, capital capacity and other factors. With the development of The Times, SaaS model (software as a service model) develops rapidly with the advantages of Internet, multiple leasing, service and scalability. Through SaaS model, it effectively solves the problem of building a low-cost and high-reliability online ground pressure monitoring platform for Chinese small and medium-sized mining enterprises.