Reviewerly/exHarmony huggingface dataset:
On Decision Diagrams for Maximally Asymmetric Functions

Maximally asymmetric functions are multiple-valued functions such that “asymmetry” defined by the minimum Hamming distance from symmetric functions is maximum. While many studies on symmetric functions have been reported, few studies on maximally asymmetric functions have been reported. It is said that maximally asymmetric functions can be promising for applications such as CDMA communication and encryption, due to randomness of the functions. To promote related studies, providing benchmark maximally asymmetric functions in compact form is important. This paper focuses on decision diagrams as compact form of maximally asymmetric functions, and shows sizes of popular decision diagrams for the functions. This paper also presents a method to randomly generate benchmark maximally asymmetric functions represented by the decision diagrams.
Cloud Based QR Code for Confidential Message

The QR code was created to be used for data storage and high-speed reading applications. QR codes are used to connect important world products that have been tagged with a QR code to the internet. A new QR code with two storage cloud layers is proposed: a private cloud and a public cloud. The private cloud is imperceptible to typical QR code perusers. It can also be utilized for archive verification. It comprises data encoded utilizing QR code with a blunder revision limit, which assists with further developing the first record from a duplicate and the QR code storage limit. The public cloud is, in many cases, perused by many. While the public level is created by replacing dark modules with clear finished designs, the private level is created by replacing dark modules with clear finished designs. This confirmation is addressed by being aware of the print-and-scan process's used finish. The acknowledgement method is commonly used in both private message exchange and confirmation situations. The capacity limit is frequently worked on by increasing the number of code letters in order Q or increasing the size of a two-level QR code for secret messages. When the coding alphabet QR is increased or the pattern size is increased, the storage capacity is frequently significantly increased.
Library Document Resource Construction and Data Sharing Based on Semantic Web Technology

With the deepening of the reform and development of higher education, many colleges and universities have taken the merger as an opportunity to promote the leap-forward development of their schools. Some colleges and universities that have not merged have also taken the newly-built campus as a new growth point to promote the rapid development of their schools. The library’s collection structure reflects a library’s document support ability, and the formation of the collection structure depends on the accumulation of document resources. Based on semantic web technology, this paper studies the construction of library literature resources. Among the institutions that choose to give priority to the development of library literature resources in the development strategy of main literature resources, the proportion of long-term preservation practice has reached 65.23%, compared with 57.42% of all other institutions. Due to the small number of samples, we cannot make a simple statistical inference, but it may reflect from the other side that the institutions that pay attention to digital resources are slightly more active in implementing preservation activities than other institutions. Based on the semantic web technology, the integrity and continuity of important documents and characteristic resources were kept and we paid attention to the collection of publications and academic documents related to our school. Under the double restriction of document resources guarantee, the acquisition behavior of guarantee systems at all levels will gradually strengthen self-control, actively establish the concept of coordination and sharing, and gradually move from randomness to standardization.
Direction Estimation of Drone Collision Using Optical Flow for Forensic Investigation

Nowadays drones have an important role to help people in several aspects. For example, drones are very beneficial in military sector. In this sector, drones may have a collision with different reasons, such as human error and a malfunctioning system. Therefore, forensic investigation helps the authority to find out drone collision cause. One indication before having collision is a change of direction of the drone. This cause can be detected by analyzing drone flying direction which changes irregularly. In this paper, we propose to use direction estimation method to assist the forensic investigation of a drone collision. We apply optical flow methods, specifically Lucas-Kanade, Horn-Schunck, and Gunnar-Farnerback. Based on the experimental results, Lucas-Kanade technique can achieve the best direction estimation providing a specific motion vector and also a filter to reduce noise.
Coherence in inquisitive first-order logic

Inquisitive first-order logic, InqBQ, is a conservative extension of classical first-order logic with questions. Formulas of InqBQ are interpreted with respect to information states—essentially, sets of relational structures over a common domain. It is unknown whether entailment in InqBQ is compact, and whether validities are recursively enumerable. In this paper, we study the semantic property of finite coherence: a formula of InqBQ is finitely coherent if in order to determine whether it is satisfied by a state, it suffices to check substates of a fixed finite size. We show that finite coherence has interesting implications. Most strikingly, entailment towards finitely coherent conclusions is compact. We identify a broad syntactic fragment of the language, the rex fragment, where all formulas are finitely coherent. We give a natural deduction system which is complete for InqBQ entailments with rex conclusions, showing in particular that rex validities are recursively enumerable. On the way to this result, we study approximations of InqBQ obtained by restricting to information states of a fixed cardinality. We axiomatize the finite approximations and show that, in contrast to the situation in the propositional setting, InqBQ does not coincide with the limit of its finite approximations, settling a question posed by Sano [19].
Environmental Effects on the Optimal Performance of Photovoltaic Power Systems

This paper investigates the effect of the different climatic parameters such as accumulated dust, high temperature, humidity, and solar radiation on the PV system performance. This study is carried out using 20 kW roof-top photovoltaic (PV) system located in Cairo city (capital of Egypt). The practical results are recorded for two years (2017-2018). The experiments and the processes of collecting data have been performed according to a normal cleaning scheduled scheme. The essential contribution of this research is the usage of the collected practical data for developing an empirical formula used for detecting the energy loss due to dust accumulation, high temperature, humidity, and cleaning pattern. Therefore, the proposed formula can be used for detecting the energy produced from any solar power plant in this area according to the used cleaning pattern. The validity of the proposed model is investigated by comparing the output energy from the proposed model with the real measured data at different operating conditions during the period of study.
Penerapan Metode Fuzzy Mamdani dan Metode Fuzzy Sugeno dalam Penentuan Jumlah Produksi

This study examines the application of the Mamdani fuzzy method and Sugeno fuzzy method in determining the amount of jenang production, based on the variables of demand, supply, raw materials and production. The purpose of this study was to apply the Mamdani fuzzy and Sugeno fuzzy methods, and to find out which method is more accurate between the two methods in determining the amount of jenang production based on the MAPE value. Data collection was obtained at PJ Menara Kudus with Jenang Kombinasi and Jenang Wijen products in January 2020 to March 2021. The results of data processing obtained MAPE values for jenang kombinasi products using the Mamdani fuzzy method is 7,5% while the Sugeno fuzzy method is 7,1%, and the MAPE value for jenang wijen product using Mamdani fuzzy method is 21,7% while the Sugeno fuzzy method is 8,2%. Based on the result obtained, the MAPE value of Sugeno fuzzy method is less than Mamdani fuzzy method. So it can be concluded that Sugeno fuzzy method more accurate than Mamdani fuzzy method in determining the amount of jenang production at PJ Menara Kudus.
Features of Continuous Education of the Adult Population

The article deals with the issues of continuing education of the adult population. The analysis of statistical data on continuous education of the adult population in Russia and abroad is carried out. The main trends in obtaining continuous education of the adult population have been identified.
Management of Ungraded Educational Organizations in the Digital Transformation of Education

This research discusses the problems of managing ungraded educational organizations (UEOs) integrated into a digital learning environment (DLE). The educational and managerial aspects of this environment are examined. One of the critical challenges addressed in this environment is the sustainability of the educational system of the UEO, which is highly susceptible to a variety of external influences, including in the management process. The research proposes to consider the region’s UEOs in the information nexus that forms a unified digital educational environment. The stability of this environment can be assessed using system criteria. One of the critical tasks to be solved within the DLE of the system of UEOs is the organization of the communication system to optimize the document flow of the UEO.
An Input-Output Model to Determine the Operability and Economic Impacts of IT on Interdependent Industries

Disasters, such as hurricanes and terrorist attacks, are damaging to various critical infrastructure systems. They disrupt supply chains and have adverse effects on the productivity of economic sectors. The IT sector is a highly vulnerable and critical infrastructure in the United States. This sector is susceptible to various forms of malicious attacks. Previous publications focus on security shortfalls existing within the IT system architecture and organizational policy. This paper asserts the need for a broader exploration of IT security as a macroeconomic system engineering problem. In this paper, we conducted a study to evaluate the inoperability impact and economic losses experienced by various industries due to a direct attack on the IT sector.
Hidden in Plain Sight - Persistent Alternative Mass Storage Data Streams as a Means for Data Hiding With the Help of UEFI NVRAM and Implications for IT Forensics

This article presents a first study on the possibility of hiding data using the UEFI NVRAM of today's computer systems as a storage channel. Embedding and extraction of executable data as well as media data are discussed and demonstrated as a proof of concept. This is successfully evaluated using 10 different systems. This paper further explores the implications of data hiding within UEFI NVRAM for computer forensic investigations and provides forensics measures to address this new challenge.
A moment in the sun

Solar energy is now the cheapest form of electricity in history. Unfortunately, significantly increasing the electric grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more difficult. While thermal generators' ramp rate---the maximum rate at which they can change their energy generation---is finite, solar energy's ramp rate is essentially infinite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warnings to adjust thermal generator output in response to variations in solar generation to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning.
Captcha me if you can: Imitation Games with Reinforcement Learning

Since their inception, Captchas have been widely used as reverse Turing tests for combating bot proliferation on the web. This has resulted in an arms race between bot developers that automate Captcha solvers and Captcha services that adjust the challenges accordingly or come up with new ones altogether. Ultimately, older generations could be bypassed consistently, and thus in the third version of reCAPTCHA, Google offers zero user friction. The intent in the new system is not only to avoid interrupting user experience but to also obfuscate the nature of the challenge itself, being much less prominent than a text or image recognition task. We introduce a methodology that learns through interaction how to evade detection, while collecting and analyzing reCAPTCHA v3 scores over fifteen months and various web environments. With reinforcement learning as the backbone, we build models that can simulate human-like web browsing behaviour by using the returned score as an informative signal. Our study exposes an important vulnerability: while the score is influenced by a multitude of undisclosed factors, it is easily accessible and it enables adversaries to learn and perfect evasive models. Notably, we demonstrate that our automation models, which integrate general web browsing capabilities, transfer between websites with an evasion rate up to 99.6%.
Evaluating asynchronous speed scaling policies in high-performance data centres with heavy tails

Complex scientific problems are solved by high-performance computing (HPC) data centres. To reduce their energy consumption, recently the concept of dynamically scaling the voltage and frequency (DVFS) of the processor has been adopted for HPC systems. In this paper, we address the energy-performance trade-off of those systems using the queuing theory methodology, to study the opportunity of speed scaling technique in HPC data centres. To this end, we consider a real-world environment of 8 servers as well as develop a simulator based on the generalised semi-Markov processes. After validating the correctness of the simulator against the configured real-world environment, the underlying simulator is used to derive optimal asynchronous speed scaling policies. We show that by setting the probabilities of switching the frequency of the processor at the arrival and departure epochs of the jobs (customers) to 0.65 and 0.7 respectively, an optimal point is achieved for the heterogeneous case with truncated Pareto distribution of the amount of work.
Towards Fair and Robust Classification

Robustness and fairness are two equally important issues for machine learning systems. Despite the active research on robustness and fairness of ML recently, these efforts focus on either fairness or robustness, but not both. To bridge this gap, in this paper, we design Fair and Robust Classification (FRoC) models that equip the classification models with both fairness and robustness. Meeting both fairness and robustness constraints is not trivial due to the tension between them. The trade-off between fairness, robustness, and model accuracy also introduces additional challenge. To address these challenges, we design two FRoC methods, namely FRoC-PRE that modifies the input data before model training, and FRoC-IN that modifies the model with an adversarial objective function to address both fairness and robustness during training. FRoC-IN is suitable to the settings where the users (e.g., ML service providers) only have the access to the model but not the original data, while FRoC-PRE works for the settings where the users (e.g., data owners) have the access to both data and a surrogate model that may have similar architecture as the target model. Our extensive experiments on real-world datasets demonstrate that both FRoC-IN and FRoC-PRE can achieve both fairness and robustness with insignificant accuracy loss of the target model.
Revisiting Performance Measures for Cross-Modal Hashing

Recently, cross-modal hashing has attracted much attention due to its low storage cost and fast query speed. Mean Average Precision (MAP) is the most widely used performance measure for cross-modal hashing. However, we found that the MAP scores do not fully reflect the quality of the top-K results for cross-modal retrieval because it neglects multi-label information and overlooks the label semantic hierarchy. In view of this, we propose a new performance measure named Normalized Weighted Discounted Cumulative Gains (NWDCG) by extending Normalized Discounted Cumulative Gains (NDCG) using co-occurrence probability matrix. To verify the effectiveness of NWDCG, we conduct extensive experiments using three popular cross-modal hashing schemes over two publically available datasets.
Compositional Information Flow Monitoring for Reactive Programs

To prevent applications from leaking users' private data to attackers, researchers have developed runtime information flow control (IFC) mechanisms. Most existing approaches are either based on taint tracking or multi-execution, and the same technique is used to protect the entire application. However, today's applications are typically composed of multiple components from heterogenous and unequally trusted sources. The goal of this paper is to develop a framework to enable the flexible composition of IFC enforcement mechanisms. More concretely, we focus on reactive programs, which is an abstract model for event-driven programs including web and mobile applications. We formalize the semantics of existing IFC enforcement mechanisms with well-defined interfaces for composition, define knowledge-based security guarantees that can precisely quantify the effect of implicit leaks from taint tracking, and prove sound all composed systems that we instantiate the framework with. We identify requirements for future enforcement mechanisms to be securely composed in our framework. Finally, we implement a prototype in OCaml and compare the effects of different compositions.
A Dynamic Obfuscation Framework for Security and Utility

Obfuscation can be used by dynamic systems to ensure private and secure communication over networks vulnerable to eavesdroppers. Balancing the utility of sending information to intended recipients and privacy by hiding information from unintended recipients presents an interesting challenge. We propose a new framework for obfuscation that includes an inference interface to allow intended recipients to interpret obfuscated information. We model the security of the obfuscation with opacity, a formal notion of plausible deniability. Using techniques from distributed reactive synthesis, we show how to automatically design a privacy-enforcing obfuscator along with the inference interface that is given to intended recipients to use as a “key”. We demonstrate this approach by enforcing privacy while maintaining utility in a contact tracing model.
Weakly Supervised Fine-grained Recognition based on Combined Learning for Small Data and Coarse Label

Learning with weak supervision already becomes one of the research trends in fine-grained image recognition. These methods aim to learn feature representation in the case of less manual cost or expert knowledge. Most existing weakly supervised methods are based on incomplete annotation or inexact annotation, which is difficult to perform well limited by supervision information. Therefore, using these two kind of annotations for training at the same time could mine more relevance while the annotating burden will not increase much. In this paper, we propose a combined learning framework by coarse-grained large data and fine-grained small data for weakly supervised fine-grained recognition. Combined learning contains two significant modules: 1) a discriminant module, which maintains the structure information consistent between coarse label and fine label by attention map and part sampling, 2) a cluster division strategy, which mines the detail differences between fine categories by feature subtraction. Experiment results show that our method outperforms weakly supervised methods and achieves the performance close to fully supervised methods in CUB-200-2011 and Stanford Cars datasets.
Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost

Traditionally, distributed machine learning takes the guise of (i) different nodes training the same model (as in federated learning), or (ii) one model being split among multiple nodes (as in distributed stochastic gradient descent). In this work, we highlight how fog- and IoT-based scenarios often require combining both approaches, and we present a framework for flexible parallel learning (FPL), achieving both data and model parallelism. Further, we investigate how different ways of distributing and parallelizing learning tasks across the participating nodes result in different computation, communication, and energy costs. Our experiments, carried out using state-of-the-art deep-network architectures and large-scale datasets, confirm that FPL allows for an excellent trade-off among computational (hence energy) cost, communication overhead, and learning performance.
An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

The service network is capable of addressing large-scale service composition. However, existing service network works still have several limitations. Prior knowledge, such as expert-defined service chains, is not incorporated into the service network. QoS constraints are less considered in the service network, and thus the generated service chain does not always satisfy the optimal QoS constraints. Additionally, some basic services also require outputs to be used directly as inputs, which the service network cannot provide. To address these limitations, this paper proposes a geospatial service web (GSW) model named SR-QoS-GSW that incorporates service semantic relationships and QoS information. The SR-QoS-GSW model consists of atomic services and composite services that consider QoS, processing services, data services, and relationships among them. A SR-QoS-GSW prototype was developed using 570 atomic services and 27 composite services and evaluated using two case studies—a river network extraction and an urban housing selection. Then, the information entropy and time complexity between SR-QoS-GSW and the existing service network were compared. The results show that geospatial service chains can be created more efficiently by incorporating existing service chains as composite services. Integrating QoS information into the GSW would allow service composition algorithms to generate service chains that satisfy optimal QoS constraints. The outputs of services used as new inputs with additional self-matching relationships also give the service network greater flexibility. Finally, the analysis of the information entropy and time complexity verified the increased diversity and decreased the search space of the SR-QoS-GSW.