Reviewerly/exHarmony huggingface dataset:
Detile: Fine-Grained Information Leak Detection in Script Engines

Memory disclosure attacks play an important role in the exploitation of memory corruption vulnerabilities. By analyzing recent research, we observe that bypasses of defensive solutions that enforce control-flow integrity or attempt to detect return-oriented programming require memory disclosure attacks as a fundamental first step. However, research lags behind in detecting such information leaks. In this paper, we tackle this problem and present a system for fine-grained, automated detection of memory disclosure attacks against scripting engines. The basic insight is as follows: scripting languages, such as JavaScript in web browsers, are strictly sandboxed. They must not provide any insights about the memory layout in their contexts. In fact, any such information potentially represents an ongoing memory disclosure attack. Hence, to detect information leaks, our system creates a clone of the scripting engine process with a re-randomized memory layout. The clone is instrumented to be synchronized with the original process. Any inconsistency in the script contexts of both processes appears when a memory disclosure was conducted to leak information about the memory layout. Based on this detection approach, we have designed and implemented Detile (\underline{det}ection of \underline{i}nformation \underline{le}aks), a prototype for the JavaScript engine in Microsoft's Internet Explorer 10/11 on Windows 8.0/8.1. An empirical evaluation shows that our tool can successfully detect memory disclosure attacks even against this proprietary software.
Research Guides: Research Data Management : Cite Data

This guide will help researchers with managing research data as well as preparing for data management plans required by funders.
Coverage of an Environment Using Energy-Constrained Unmanned Aerial Vehicles

We study the problem of covering an environment using an Unmanned Aerial Vehicle (UAV) with limited battery capacity. We consider a scenario where the UAV can land on an Unmanned Ground Vehicle (UGV) and recharge the onboard battery. The UGV can also recharge the UAV while transporting the UAV to the next take-off site. We present an algorithm to solve a new variant of the area coverage problem that takes into account this symbiotic UAV and UGV system. The input consists of a set of boustrophedon cells -- rectangular strips whose width is equal to the field-of-view of the sensor on the UAV. The goal is to find a coordinated strategy for the UAV and UGV that visits and covers all cells in minimum time, while optimally finding how much to recharge, where to recharge, and when to recharge the battery. This includes flight time for visiting and covering all cells, recharging time, as well as the take-off and landing times. We show how to reduce this problem to a known NP-hard problem, Generalized Traveling Salesperson Problem (GTSP). Given an optimal GTSP solver, our approach finds the optimal coverage paths for the UAV and UGV. Our formulation models multi-rotor UAVs as well as hybrid UAVs that can operate in fixed-wing and Vertical Take-off and Landing modes. We evaluate our algorithm through simulations and proof-of-concept experiments.
Digital Contact Tracing : Software Architecture

The covid-19 crisis is providing a lot of impetus to the search for innovative technological solutions to solve major problems of tracking and containment of the pandemic. The major cornerstones of testing, isolation, contact tracing and quarantine are well understood and agreed upon at a general level. In this paper, the software architecture for implementing successful automated digital contact tracing applications is elaborated. The goal of contact tracing is to proactively identify the infection chain of the population including asymptomatic people yet to be tested positive, i.e., to avoid asymptomatic people from spreading the disease without any intention. The entire ecosystem of contact tracing is explained so that the real challenges of integrating the key healthcare components are appreciated.
An Experimental Study on the Behavioural Tendencies of Objects Classified As Hot and Cold by a Java Virtual Machine Garbage Collector

A constitutive hypothesis of the Java Virtual Machine garbage collector ”ThinGC”, presented by Mingkun Yang et al. [1], an extension of Oracle’s ”ZGC”, is that capitalising on possible temporal loc ...
Specialists in Kazakh Сulture of the Petersburg’s Kunstkamera: Vadim Petrovich Kurylev (1928—2008) and His Museum and Scientific Heritage

The article is devoted to Vadim Petrovich Kurylev, researcher of the MAE RAS. He was the head of the Department of Ethnography of Central Asia from 1983 to 2002. The author sequentially considers the activities of V. P. Kurylyov both in the scientific and in the museum sphere. The analysis of his scientific heritage was carried out against the background of the history of the formation of the ethnographic study of the Kazakhs in the St. Petersburg Kunstkamera. The area of scientific interests of V. P. Kurylev is directly related to the main priority areas of Russian science of that time. He actively elaborated the topics of community land relations, social organization of traditional and modern Kazakh society. Field work was one of the most important areas of Kurylev’s scientific activity; he introduced a significant body of field ethnographic materials. The scientific heritage of the Soviet time is being reevaluated now. From the second half of the 20th century humanitarian science begins to recognize the value of a modern source, the everyday life of an ordinary person becomes an object of close study. We have an idea of the ritual and everyday life of peoples during the modernization of the traditional way of life due to researchers of the Soviet time. Despite the ideological orientation of many of their works, they were able to create and maintain for us an excellent base of sources. V. P. Kurylev is a prominent member of the Leningrad school of studying the ethnography of Central Asian peoples, based on strong scientific traditions laid down in the times of V. V. Bartold, V. V. Radlov, A. N. Samoilovich, I. I. Zarubin.
Vector quantization based CPRI compression algorithm

With increasing demand of Internet and availability of handsets supporting high speed data rate. Technological evolution is to take place. 4G & 5G are the current technologies being used to cater the demand of high speed data download. These technologies are not only confined to high speed download or upload, but also they are different with their preceding technologies because of their ability to provide low latency. High Speed data, such as 10 Gbps in downlink & 1 Gbps in uplink and Low latency such as 1ms or less would make 5G as preferred and widely accepted technology in coming days. To provide high speed data and low latency a technique called cloud RAN (C-RAN) has been invented. In C-RAN base band processing unit and Radio units are kept away from each other. And these Hardware units are connected via a common public radio interface called (CPRI) instead of Coax or copper wire. In order to send the large amount of data, it needs to be compressed, which is the enhancement of CPRI. To compress the data, vector quantization-based algorithm is used for CPRI links, making use of Lloyd algorithm. For optimal performance methods, vector I/Q samples and enhanced initialization of the Lloyd algorithm for the Codebook training need to be investigated further.
Design of Light Weight Encryption Algorithm for Data Privacy in Light Weight Devices Using 128 Bit Key

Internet of things (IoT), internetworking of smart devices, embedded with sensors, software, electronics and network connectivity that enables to communicate with each other to exchange and collect data through an uncertain wireless medium. Recently IoT devices are dominating the world by providing it's versatile functionality and real-time data communication. Apart from versatile functionality of IoT devices, they are very low battery powered, small and sophisticated, and experience lots of challenges due to unsafe communication medium. Despite the fact of many challenges, the energy issue is now becoming the prime concern. Optimization of algorithms in terms of energy consumption has not been explored specifically; rather most of the algorithms focus on hardware area to minimize it extensively and to maximize it on security issue as possible.
OBU Library: Online Library Services: Curbside Pickup

This guide provides information regarding the library's remote services such as links to online resources and research assistance tools.
Research on Fault Diagnosis Method of High Voltage Circuit Breaker Based on gray Relational Analysis and D-S Evidence Theory

Abstract In this paper, a high-voltage circuit breaker diagnosis method based on gray correlation analysis and DS evidence theory is proposed for the fault diagnosis of high-voltage circuit breakers. The fault diagnosis calculation results of a high voltage circuit breaks using this method are compared with those using only gray correlation analysis. The comparison result shows that the proposed method has higher accuracy and reliability, and can accurately determine the type of faults occurring in the high-voltage circuit breaker through the monitored characteristic parameters, avoiding more subjective judgments and improving the reliability of the results.
Top-Level Design of Intelligent Video Surveillance System for Abandoned Objects

In the area of public safety and video surveillance, it is particularly important to monitor suspicious abandoned objects and pedestrian abandonment in public places. By analyzing the characteristics of abandonment behavior, this paper put forward the theory of life cycle of abandoned objects, which describes the feature of abandoned objects and abandonment behaviors overtime, and proposed the top-level architecture and workflow of the abandoned objects intelligent monitoring system based on this theory. The system tracks pedestrians entering the video scene, captures offloading behaviors, extracts motion features and outputs suspicious images. It also captures static images of abandoned objects through video surveillance, extract image feature and monitor object carriers. Finally, images, videos and other structured data is collected and as the input of a multi-characteristic risk assessment model, which outputs the suspicious level of the abandonment event. This paper fills the blank in the theoretical research on abandonment behavior and gives solutions to the shortcomings of traditional remnant monitoring systems.
Challenges in Designing Exploit Mitigations for Deeply Embedded Systems

Memory corruption vulnerabilities have been around for decades and rank among the most prevalent vulnerabilities in embedded systems. Yet this constrained environment poses unique design and implementation challenges that significantly complicate the adoption of common hardening techniques. Combined with the irregular and involved nature of embedded patch management, this results in prolonged vulnerability exposure windows and vulnerabilities that are relatively easy to exploit. Considering the sensitive and critical nature of many embedded systems, this situation merits significant improvement. In this work, we present the first quantitative study of exploit mitigation adoption in 42 embedded operating systems, showing the embedded world to significantly lag behind the general-purpose world. To improve the security of deeply embedded systems, we subsequently present {\mu}Armor, an approach to address some of the key gaps identified in our quantitative analysis. {\mu}Armor raises the bar for exploitation of embedded memory corruption vulnerabilities, while being adoptable on the short term without incurring prohibitive extra performance or storage costs.
Application of post-stack Rune Inversion for reservoir rock properties estimation in comparison with conventional seismic attribute analysis: a case study from the Paleocene formation, Middle Indus basin, onshore Pakistan

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
The naming process of the Ikea products: Analysis of the names of the early catalogs (1950—1953)

The texts in this book are based on a selection of the paper presentations and Keynote lectures held during the Centre for Scandinavian Studies Copenhagen – Lund’s (CSS) second biannual international conference in the field of Scandinavian Studies.&nbsp; The conference took place in Lund, at the joint Faculties of Humanities and Theology of Lund University between May 16-18, 2019.&nbsp; This year’s theme was “Scandinavian Languages and Literatures Worldwide – Prospects and Challenges” and the central topics being researched and discussed were: Scandinavian Literature in the World Translations of Scandinavian Literature Scandinavian Children’s Literature in the World Linguistic and literary constructions of the North The Framing of Scandinavian Studies Worldwide Nordic Languages in the World / World Languages in the Nordic Countries The conference had three Keynote lectures: Språk och psykologiskt lokalsinne, by Sigrid Combüchen (Author and former Senior Lecturer at the Centre for Languages and Literature, Lund University) [see below] Region. Verden. Arkipelag – Nordisk litteratur og Tomas Tranströmers “Östersjöar”, by Dan Ringgaard (Professor at the School of Communication and Culture - Scandinavian Studies, Aarhus University) [see below] Beyond Noma, Björk and Bergman – How the Nordic Countries could increase their Soft Power, by Francisco Beltran (Senior Lecturer at the Department of Political Science, University of Toronto)&nbsp; Following long established international praxis in the field of Scandinavian Studies the texts are written in any of the Nordic languages or in English.
Load Forecasting Algorithm on Weekdays Using Solar Radiation Weight

For efficient operation of the electric power system, it is important to increase the accuracy of load forecasting. Recently, small capacity solar photovoltaic(PV) generation in South Korea is the majority of Behind-The-Meter (BTM) generation, which is one of the main factors that change the gross load. Solar radiation is a weather factor that is highly correlated with solar PV generation and closely related to BTM in South Korea. So far, solar radiation is not systematically reflected in the load forecasting algorithm, so the load forecasting error is increasing due to the BTM solar PV generation. Therefore, it is necessary to analyze the impact of the BTM and to study the load forecasting algorithm considering the BTM. As a method to reflect the impact of BTM solar PV generation on the load forecasting, the load sensitivity by cloud amount is calculated. To reflect the characteristics of solar radiation that changes monthly due to earth"s revolution, the load sensitivity by cloud amount with monthly solar radiation weighting is calculated. The load sensitivity by cloud amount with monthly solar radiation weighting is applied to the weekday load forecasting, and the accuracy is better than the load forecasting result applied only load sensitivity by temperature.
Learning to Compose Relational Embeddings in Knowledge Graphs

Knowledge Graph Embedding methods learn low-dimensional representations for entities and relations in knowledge graphs, which can be used to infer previously unknown relations between pairs of entities in the knowledge graph. This is particularly useful for expanding otherwise sparse knowledge graphs. However, the relation types that can be predicted using knowledge graph embeddings are confined to the set of relations that already exists in the KG. Often the set of relations that exist between two entities are not independent, and it is possible to predict what other relations are likely to exist between two entities by composing the embeddings of the relations in which each entity participates. We introduce relation composition as the task of inferring embeddings for unseen relations by combining existing relations in a knowledge graph. Specifically, we propose a supervised method to compose relational embeddings for novel relations using pre-trained relation embeddings for existing relations. Our experimental results on a previously proposed benchmark dataset for relation composition ranking and triple classification show that the proposed supervised relation composition method outperforms several unsupervised relation composition methods.
EvilCoder: Automated Bug Insertion

The art of finding software vulnerabilities has been covered extensively in the literature and there is a huge body of work on this topic. In contrast, the intentional insertion of exploitable, security-critical bugs has received little (public) attention yet. Wanting more bugs seems to be counterproductive at first sight, but the comprehensive evaluation of bug-finding techniques suffers from a lack of ground truth and the scarcity of bugs. In this paper, we propose EvilCoder, a system to automatically find potentially vulnerable source code locations and modify the source code to be actually vulnerable. More specifically, we leverage automated program analysis techniques to find sensitive sinks which match typical bug patterns (e.g., a sensitive API function with a preceding sanity check), and try to find data-flow connections to user-controlled sources. We then transform the source code such that exploitation becomes possible, for example by removing or modifying input sanitization or other types of security checks. Our tool is designed to randomly pick vulnerable locations and possible modifications, such that it can generate numerous different vulnerabilities on the same software corpus. We evaluated our tool on several open-source projects such as for example libpng and vsftpd, where we found between 22 and 158 unique connected source-sink pairs per project. This translates to hundreds of potentially vulnerable data-flow paths and hundreds of bugs we can insert. We hope to support future bug-finding techniques by supplying freshly generated, bug-ridden test corpora so that such techniques can (finally) be evaluated and compared in a comprehensive and statistically meaningful way.
Snippets, Templates, and More …

Thus far we have developed and discussed extensions on custom commands, tool windows, infobars, code generation, code editor, code analysis, and refactoring. However, as a developer, we spend most of our time coding, developing, and debugging, so in this chapter, we will look at ways by which we can enhance our productivity while coding and debugging. We will begin with a discussion on code snippets, then move on to project and item templates. Next we will have a look at adding a connected service provider. Finally, will discuss a generic approach to debug an extension. Let’s get things underway!
Steroids for DOPed Applications: A Compiler for Automated Data-Oriented Programming

The wide-spread adoption of system defenses such as the randomization of code, stack, and heap raises the bar for code-reuse attacks. Thus, attackers utilize a scripting engine in target programs like a web browser to prepare the code-reuse chain, e.g., relocate gadget addresses or perform a just-in-time gadget search. However, many types of programs do not provide such an execution context that an attacker can use. Recent advances in data-oriented programming (DOP) explored an orthogonal way to abuse memory corruption vulnerabilities and demonstrated that an attacker can achieve Turing-complete computations without modifying code pointers in applications. As of now, constructing DOP exploits requires a lot of manual work. In this paper, we present novel techniques to automate the process of generating DOP exploits. We implemented a compiler called Steroids that compiles our high-level language SLANG into low-level DOP data structures driving malicious computations at run time. This enables an attacker to specify her intent in an application- and vulnerability-independent manner to maximize reusability. We demonstrate the effectiveness of our techniques and prototype implementation by specifying four programs of varying complexity in SLANG that calculate the Levenshtein distance, traverse a pointer chain to steal a private key, relocate a ROP chain, and perform a JIT-ROP attack. Steroids compiles each of those programs to low-level DOP data structures targeted at five different applications including GStreamer, Wireshark, and ProFTPd, which have vastly different vulnerabilities and DOP instances. Ultimately, this shows that our compiler is versatile, can be used for both 32- and 64-bit applications, works across bug classes, and enables highly expressive attacks without conventional code-injection or code-reuse techniques in applications lacking a scripting engine.
Hybrid Approach for Evaluation of Visibility of Platform Display: Subjective Ratings Combined with Gaze Behavior Elicited from Eye Movement Data

In the present paper, we propose a hybrid approach for visibility evaluation based on both of conventional subjective ratings and eye tracking data analysis. In the hybrid approach, we obtain responses to questionnaire consisting of questions of visibility and aesthetic impressions. Additionally, we record eye movement data during seeing platform displays shown in a standard train station environment. From eye movement data, various indices representing how difficult it is to find a specific display and how much readable the texts on a specific display are calculated. By combining both of the data, interpretations regarding visibility are given. The proposed approach was applied to comparative visibility evaluations between three platform displays. Results of visibility evaluations as well as consistency between perceived visibility and gaze behavior are discussed.
EXPLORING THE IMPACT OFLEADERSHIP STYLESON ORGANIZATIONAL COMMITMENT- A STUDY ININDIAN BPO SECTOR

The present study examinesthe impact of transformational and transactional leadership styles onthree different types of organizational commitment in Indian BPO industry. For this purpose, data was collectedfrom 400 respondents through a structured questionnaire. The relationship between leadership styles andorganizational commitment was examined by applying Statistical Package for Social Sciences.Findings of thestudy revealed that there is positive and significant correlation between leadership styles and organizationalcommitment. Specifically, continuance commitment has higher correlation with both the leadership styles,affective commitment has high correlation with transformational leadership and low correlation withtransactional leadership but normative commitment has low correlation with transformational leadership andhigh correlation with transactional leadership.It was also found that there is a significant impact oftransformational and transactional leadership styles on affective, continuance and normative commitment. Thepresent research provides a strong evidence on how the leadership styles contribute towards organizationalcommitment of employees of BPO sector in Indian context.