Reviewerly/exHarmony huggingface dataset:
Spatial Photonic Ising Machine with Time/Space Division Multiplexing

Abstract The spatial photonic Ising machine (SPIM) is an unconventional computing architecture based on parallel propagation/processing with spatial light modulation. SPIM enables the handling of an Ising model using light as a pseudospin. This chapter presents SPIMs with multiplexing to enhance their functionality. Handling a fully connected Ising model with a rank-2 or higher spin-interaction matrix becomes possible with multiplexing, drastically improving its applicability in practical applications. We constructed and examined systems based on time- and space-division multiplexing to handle Ising models with ranks of no less than one while maintaining high scalability owing to the features of spatial light modulation. Experimental results with knapsack problems demonstrate that these methods can compute the Hamiltonian consisting of objective and constraint terms, which require multiplexing, and can determine the ground-state spin configuration. In particular, in space-division multiplexing SPIM, the characteristics of the solution search vary based on the physical parameters of the optical system. A numerical study also suggested the effectiveness of the dynamic parameter settings in improving the Ising machine performance. These results demonstrate the high capability of SPIMs with multiplexing.
Optimal control and ultimate bounds of 1:2 nonlinear quantum systems

Using optimal control, we establish and link the ultimate bounds in time (referred to as the quantum speed limit) and energy of two- and three-level quantum nonlinear systems which feature 1:2 resonance. Despite the unreachable complete inversion, by using the Pontryagin maximum principle, we determine the optimal time, pulse area, or energy for a given arbitrary accuracy. We show that the third-order Kerr terms can be absorbed in the detuning in order to lock the dynamics to the resonance. In the two-level problem, we determine the nonlinear counterpart of the optimal $\ensuremath{\pi}$-pulse inversion for a given accuracy. In the three-level problem, we obtain an intuitive pulse sequence similar to the linear counterpart but with different shapes. We prove the (slow) logarithmic increasing of the optimal time as a function of the accuracy.
Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving

Despite the widespread popularity of dynamically typed languages such as Python, it is well known that they pose significant challenges to code optimization due to the lack of concrete type information. To overcome this limitation, many ahead-of-time optimizing compiler approaches for Python rely on programmers to provide optional type information as a prerequisite for extensive code optimization. Since few programmers provide this information, a large majority of Python applications are executed without the benefit of code optimization, thereby contributing collectively to a significant worldwide wastage of compute and energy resources. In this paper, we introduce a new approach to concrete type inference that is shown to be effective in enabling code optimization for dynamically typed languages, without requiring the programmer to provide any type information. We explore three kinds of type inference algorithms in our approach based on: 1) machine learning models including GPT-4, 2) constraint-based inference based on SMT solving, and 3) a combination of 1) and 2). Our approach then uses the output from type inference to generate multi-version code for a bounded number of concrete type options, while also including a catch-all untyped version for the case when no match is found. The typed versions are then amenable to code optimization. Experimental results show that the combined algorithm in 3) delivers far superior precision and performance than the separate algorithms for 1) and 2). The performance improvement due to type inference, in terms of geometric mean speedup across all benchmarks compared to standard Python, when using 3) is 26.4× with Numba as an AOT optimizing back-end and 62.2× with the Intrepydd optimizing compiler as a back-end. These vast performance improvements can have a significant impact on programmers’ productivity, while also reducing their applications’ use of compute and energy resources.
An Explanation Method for Models of Code

This paper introduces a novel method, called WheaCha, for explaining the predictions of code models. Similar to attribution methods, WheaCha seeks to identify input features that are responsible for a particular prediction that models make. On the other hand, it differs from attribution methods in crucial ways. Specifically, WheaCha separates an input program into "wheat" (i.e., defining features that are the reason for which models predict the label that they predict) and the rest "chaff" for any given prediction. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show that (1) HuoYan is efficient — taking on average under twenty seconds to compute wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to make predictions is predominantly comprised of simple syntactic or even lexical properties (i.e., identifier names); (3) neither the latest explainability methods for code models (i.e., SIVAND and CounterFactual Explanations) nor the most noteworthy attribution methods (i.e., Integrated Gradients and SHAP) can precisely capture wheat. Finally, we set out to demonstrate the usefulness of WheaCha, in particular, we assess if WheaCha’s explanations can help end users to identify defective code models (e.g., trained on mislabeled data or learned spurious correlations from biased data). We find that, with WheaCha, users achieve far higher accuracy in identifying faulty models than SIVAND, CounterFactual Explanations, Integrated Gradients and SHAP.
Validating IoT Devices with Rate-Based Session Types

We develop a session types based framework for implementing and validating rate-based message passing systems in Internet of Things (IoT) domains. To model the indefinite repetition present in many embedded and IoT systems, we introduce a timed process calculus with a periodic recursion primitive. This allows us to model rate-based computations and communications inherent to these application domains. We introduce a definition of rate based session types in a binary session types setting and a new compatibility relationship, which we call rate compatibility. Programs which type check enjoy the standard session types guarantees as well as rate error freedom --- meaning processes which exchanges messages do so at the same rate. Rate compatibility is defined through a new notion of type expansion, a relation that allows communication between processes of differing periods by synthesizing and checking a common superperiod type. We prove type preservation and rate error freedom for our system, and show a decidable method for type checking based on computing superperiods for a collection of processes. We implement a prototype of our type system including rate compatibility via an embedding into the native type system of Rust. We apply this framework to a range of examples from our target domain such as Android software sensors, wearable devices, and sound processing.
Building Dynamic System Call Sandbox with Partial Order Analysis

Attack surface reduction is a security technique that secures the operating system by removing the unnecessary code or features of a program. By restricting the system calls that programs can use, the system call sandbox is able to reduce the exposed attack surface of the operating system and prevent attackers from damaging it through vulnerable programs. Ideally, programs should only retain access to system calls they require for normal execution. Many researchers focus on adopting static analysis to automatically restrict the system calls for each program. However, these methods do not adjust the restriction policy along with program execution. Thus, they need to permit all system calls required for program functionalities. We observe that some system calls, especially security-sensitive ones, are used a few times in certain stages of a program’s execution and then never used again. This motivates us to minimize the set of required system calls dynamically. In this paper, we propose , which gradually disables access to unnecessary system calls throughout the program’s execution. To accomplish this, we utilize partial order analysis to transform the program into a partially ordered graph, which enables efficient identification of the necessary system calls at any given point during program execution. Once a system call is no longer required by the program, can restrict it immediately. To evaluate , we applied it to seven widely-used programs with an average of 615 KLOC, including web servers and databases. With partial order analysis, restricts an average of 23.50, 16.86, and 15.89 more system calls than the state-of-the-art Chestnut, Temporal Specialization, and the configuration-aware sandbox, C2C, respectively. For mitigating malicious exploitations, on average, defeats 83.42% of 1726 exploitation payloads with only a 5.07% overhead.
Assessing Precinct Consolidation Strategies Through Simulation Optimization

The Supreme Court ruling on Shelby County v. Holder, 570 U.S. 529 (2013) provided states and local jurisdictions with increased discretion when operating elections. Since this decision, voters across the United States have experienced a reduction in polling locations through precinct consolidation and polling location closures. Further, passed and pending legislation will restrict vote-by-mail use and early voting in future elections. For many, these changes will restructure the in-person voting experience as more voters will vote in-person on Election Days at fewer locations. While scholarship has demonstrated the effects of polling location consolidation on voter turnout and voter disenfranchisement, less research has investigated the effects on system performance and voter wait times. This analysis applies a simulation optimization to assess the consequences of three polling location consolidation strategies on voter wait time. We find that allocating resources in combination with the consolidation strategy directly affects voter wait times. Our results suggest that consolidating precincts and polling locations requires deliberate resource allocation planning to distribute voting equipment efficiently.
Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial Network

Automatically transforming developers' natural language descriptions into source code has been a longstanding goal in software engineering research. Two types of approaches have been proposed in the literature to achieve this: code generation, which involves generating a new code snippet, and code search, which involves reusing existing code. However, despite existing efforts, the effectiveness of the state-of-the-art techniques remains limited. To seek for further advancement, our insight is that code generation and code search can help overcome the limitation of each other: the code generator can benefit from feedback on the quality of its generated code, which can be provided by the code searcher, while the code searcher can benefit from the additional training data augmented by the code generator to better understand code semantics. Drawing on this insight, we propose a novel approach that combines code generation and code search techniques using a generative adversarial network (GAN), enabling mutual improvement through the adversarial training. Specifically, we treat code generation and code search as the generator and discriminator in the GAN framework, respectively, and incorporate several customized designs for our tasks. We evaluate our approach in eight different settings, and consistently observe significant performance improvements for both code generation and code search. For instance, when using NatGen, a state-of-the-art code generator, as the generator and GraphCodeBERT, a state-of-the-art code searcher, as the discriminator, we achieve a 32% increase in CodeBLEU score for code generation, and a 12% increase in mean reciprocal rank for code search on a large-scale Python dataset, compared to their original performances.
NIEG-PNet: noise intensity estimation guided progressive network for hyperspectral image denoising

How to remove various kinds of noises of hyperspectral images (HSIs) simultaneously is an important issue that we face, which will directly affect the subsequent application of HSIs. Many HSI denoising techniques have been proposed for this purpose and deep convolutional neural network (CNN) is the most effective approach in recent years. However, existing HSI denoising methods are usually unsatisfactory in removing mixed noises and preserving more details. In this paper, we propose a novel noise intensity estimation guided progressive network (NIEG-PNet) to address these problems. To be specific, we design three core modules to improve the HSI denoising accuracy. First, we propose a noise intensity estimation module (NIEM) to capture multiscale noises and structural characteristics of HSI, which can also be a noise prior to guide the network learning. Second, we propose a recurrent feedback grouped denoising module (RFGDM) to fully capture the spectral correlation. Moreover, we design a dual self-attention module including a position self-attention module and a channel self-attention module to exploit the local and global spatial and spectral correlations and alleviate the problem of small available training HSIs. Last, we propose a global spatial-spectral consistency module (GSSCM), which designs a new parallel structure to combine the two-dimensional and the three-dimensional convolutions more effectively. Moreover, it can explore the relationship between the spectral and the horizontal or vertical direction of the spaces. The experimental results on both synthetic and real-data experiments show the superiority of the proposed NIEG-PNet compared to other traditional and advanced HSI denoising methods.
Semantic segmentation of remote sensing images combined with attention mechanism and feature enhancement U-Net

Target segmentation of remote sensing images has always been a hotspot in image processing. This paper proposes a new semantic segmentation technology for remote sensing images, which uses Unet as the backbone and combines attention mechanism and feature enhancement module. The feature enhancement module can enlarge the information of the region of interest (ROI) to improve the contrast of the image; the attention mechanism includes spatial and channel attention modules, which can obtain more detailed information of the desired target while suppressing other useless information. This paper improves the loss function of the traditional Unet. On the basis of the sparse categorical cross-entropy function, the mean squared logarithmic error function is added, which can effectively improve the accuracy of semantic segmentation. The experimental results show that the algorithm has higher computational accuracy than Unet, DeepLabV3, SegNet, PSPNet, CBAM and DAnet while having the computational speed of FCN and Unet in model testing and validation.
Functional Prototype Design for a Programmable Equipment in Personalized Football Training

Abstract Assembly and testing are the last stages in the design phase to obtain proof of concept for a certain product. Although it is time and cost consuming, assembly fit and test are crucial for the future development of any complex product, unveiling potential consequences in the future product life cycle. The current paper addresses the main assembly stages, testing strategies, feedback implementation, and redesign decisions involved in the development of a programmable equipment in personalized football training. Demountable subassemblies are used for the manufacture of the final prototype, encouraging interchangeability of parts, and modularity of the final product. Coupling, gear, key, and bearing assemblies are mainly used to physically obtain the programmable equipment prototype. Since some components are bespoke and are 3D printed specifically for this product, fit tests are undertaken to ensure proper functioning of all mechanical and electronic components. Software and electronic testing are also done to both calibrate and ensure proper code functioning. Optimization of the prototype is implemented in all three main systems that make up the personalized football training equipment: mechanical, electronic, and software. The optimized functional prototype of the equipment will be subjected to endurance tests in order to evaluate the life expectancy of individual components, subassemblies, and, finally, of the general product.
Emoji and visual complexity in health information design: A moderated serial mediation model

Social media is a valuable tool that enables public health organizations to communicate effectively. To enhance the reach of health communication on social media, scholars have proposed that emoji be used to convey scientific information. The current study explored the influence of emoji on the effectiveness of health communication on social media. Automated content analysis revealed that the presence of emoji in online health information resulted in higher levels of social media engagement (SME) than the absence of emoji did. Additionally, a 2 (emoji: present versus absent) × 3 (visual complexity of information design: low versus medium versus high) online experiments revealed that the presence of emoji in health information sequentially increased perceived enjoyment and perceived interactivity, thereby promoting SME. However, this effect is influenced by the visual complexity of health information designs. The presence of emoji is only effective in increasing SME with health information presented using a design with low or medium visual complexity. This study provides theoretical and practical insights into visual health communication and health information design.
TLS Guard for TLS 1.3 zero round-trip time (0-RTT) in a distributed environment

One of the most vastly used protocols to protect services traffic like the web and email is Transport Layer Security (TLS). In 2018 the Internet Engineering Task Force (IETF) standardized the latest version TLS 1.3, Introduced a significant enhancement in the protocol performance, and presented some new features. One of the new features is zero round trip-time (0-RTT) handshake mode, where the client can send the application data to the server within the handshake messages before the TLS connection is fully established. However, this application data is replayable. The replay attacks in 0-RTT handshake mode have been studied, and several mechanisms are proposed for protection, these proposed mechanisms are not able to prevent replay attacks when the server functionality is provided by a distributed environment such as multiple servers in a cloud computing environment or a CDN. This paper introduces TLS Guard as a new mechanism that extends TLS 1.3 to prevent replay attacks in a distributed environment. We evaluated a prototype TLS Guard implementation in controlled experiments and showed that its effect comes at the cost of some CPU overhead. More importantly, we showed that TLS Guard is scalable and tolerates faults without any required changes on the client side. © 2017 Elsevier Inc. All rights reserved.
Approaches and Methodology of Forming the Worldview of High School Students: The Experience of Socialization of Donbass Children in the Summer Profile Shift

The study deals with the problem of forming a worldview among high school students. The author tried to identify with the help of which tools schoolchildren from the territories of Gorlovka and Mariupol managed to find a common language and change their understanding of each other, as well as change their picture of the world. The developed recommendations can be used in working with teenagers by employees of regional authorities and municipalities.
Development and Applied Research of Korean-Index of Phonetic Complexity-Revision (K-IPC-R)

Objectives: In this paper, the Korean-Index of phonetic complexity-revision (K-IPC-R) was revised and supplemented to use the Korean-Index of phonetic complexity (K-IPC; Lee, Han, Shim, 2004) as a phonological evaluation index of the independent analysis method. Methods: Speech samples of 20 typically developing children of 18 to 23 months, 24 to 29 months, and 30 to 35 months of age were collected from each child during spontaneous play. The analizing and scoring criteria of K-IPC-R were devised on the basis of the phonological characteristics found in the speech samples and the results of previous studies related to speech sound development. Results: The K-IPC-R was composed of nine sub-indices: consonant by place class, consonant by manner class, consonant by phonation type, vowel type, shape of syllables, length of syllables, singleton consonants by place variegation, presence of consonant chain, and consonant chain type. The scoring criteria were determined to weight 1 point to velars, fricatives, affricates, liquids, aspirated, diphthongs, closed syllable words, and three or more syllable words. And 1 point is weighted when two or more consonants with different place of articulation are present within a word. Also, 1 point is weighted if a consonant chain appeared and another 1 point is weighted if the place of articulation of the chained consonants are different. Conclusion: This study is meaningful in proposing the K-IPC-R as a phonological evaluation index of an independent analysis method that can evaluate the phonological ability of children at the initial level of phonological development, whose phonological ability is difficult to evaluate with a relational analysis method.
How Emerging Technologies Spawn New Social Engineering Paradigms

Cybersecurity criminals often use social engineering techniques to meet their objectives. The most egregious cybersecurity attacks may be designed to get an organization to override defenses, change standards, or convince governing bodies to change laws and regulations through political pressure, espionage, or litigation. By creating cultures of trust or mistrust, organizational leaders can be persuaded to declassify information or make changes that allow such adversaries to gain access to things that are unavailable to them. Through time and strategy, cybercriminals can weaken cybersecurity defense capabilities and awareness. Organizations people trust may use social engineering against consumers through online advertisements, cookies, and spyware to gather data, like IP addresses, location histories, and browsing preferences, for gain and profit. This study highlights areas where social engineering tools are designed into devices and activities to influence behavior in personal and professional settings.
Artificial Intelligence

Artificial intelligence's (AI) importance in customer service has significantly expanded in our daily lives. Businesses and customers are already swiftly adjusting to the new reality thanks to AI's simplicity of use. Artificial intelligence is useful in many areas and corrects common mistakes in conventional customer service. The three most prevalent problems customers have with customer service involve delays in responses, a lack of contact control, and the difficulty to receive help outside of typical business hours. The good news is that AI can handle these problems with ease, especially when it comes to customer service. Since AI is ultimately dependent on the data provided, and today data is available in abundance and an effective analysis of that data is required in order to gain competitive advantage over rivals and only with the help of AI can this be achieved, it may be essential for businesses to employ AI-enabled customer experience solutions that capture and model data in order to improve both customer satisfaction and the overall health of the company.