Reviewerly/exHarmony huggingface dataset:
Don’t Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation

Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i.e. daytime, overcast) but performance degrades quickly and often catastrophically when input conditions change. In this work, we present a domain adaptation system that uses light-weight input adapters to pre-processes input images, irrespective of their appearance, in a way that makes them compatible with off-the-shelf computer vision tasks that are trained only on inputs with ideal conditions. No fine-tuning is performed on the off-the-shelf models, and the system is capable of incrementally training new input adapters in a self-supervised fashion, using the computer vision tasks as supervisors, when the input domain differs significantly from previously seen domains. We report large improvements in semantic segmentation and topological localization performance on two popular datasets, RobotCar and BDD.
Building Large-Scale Deep Learning System for Entity Recognition in E-Commerce Search

Named-Entity-Recognition (NER) or Item Aspect Recognition task is fundamental to e-commerce marketplace. A structured listing (i.e. item aspect and catalog) is critical to the success of the marketplace - it helps the seller to put their listings to the right catalog and aspects so that the buyers can easily find the most relevant and accurate listings they are looking for. For e-commerce search engine, item aspects (brand, color, size, texture, etc.) should be automatically recognized from users' shopping queries in order to accurate understand their shopping intent. An accurate query and item aspect understanding helps to match seller's listing to buyer's purchase intend. However, in practice, this still remains challenge in e-commerce marketplace due to a couple reasons, e.g. the sparsity of the data - hundreds of millions of item aspect-name and aspect-value pairs (e.g. brand=Nike); noisy (low quality), not scalable but expensive label data that are obtained via human labeling \citeb12 effort; the lack of context in generally very short search queries; the disperse of context and dis-orderedness in listing title or descriptions, etc. among others. All those imposes a big challenge that affect the performance of aspect recognition for e-commerce practice. In this paper, we introduce an end-to-end machine learning system to build an effective aspect recognition system for search engine that leverages the generated label data from different legacy systems and effort, without any extra human-label effort (and thus no incurred cost). The framework is constructed with multiple machine learning components that are built to optimize the search relevance and conversion as an end goal. We show that the proposed aspect recognition machine learning system improved search results quality and relevance greatly compared to the existing already strong baseline system for e-commerce search.
Simple background subtraction constraint for weakly supervised background subtraction network

Recently, background subtraction based on deep convolutional neural networks has demonstrated excellent performance in change detection tasks. However, most of the reported approaches require pixel-level label images for training the networks. To reduce the cost of rendering pixel-level annotation data, weakly supervised learning approaches using frame-level labels have been proposed. These labels indicate if a target class is present. Frame-level supervised learning is challenging because we cannot use location information for training the networks. Therefore, some constraints are introduced for guiding foreground locations. Previous works exploit prior information on foreground sizes and shapes. In this work, we propose two constraints for weakly supervised background subtraction networks. Our constraints use binary mask images generated by simple background subtraction. Unlike previous works, our approach does not require prior information on foreground sizes and shapes. Moreover, our constraints are more suitable for change detection tasks. We also present an experiment verifying that our constraints can improve foreground detection accuracy compared to other methods, which do not include them.
Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%–~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.
A Unified and Composable Take on Ratcheting

Ratcheting, an umbrella term for certain techniques for achieving secure messaging with strong guarantees, has spurred much interest in the cryptographic community, with several novel protocols proposed as of lately. Most of them are composed from several sub-protocols, often sharing similar ideas across different protocols. Thus, one could hope to reuse the sub-protocols to build new protocols achieving different security, efficiency, and usability trade-offs. This is especially desirable in view of the community's current aim for group messaging, which has a significantly larger design space. However, the underlying ideas are usually not made explicit, but rather implicitly encoded in a (fairly complex) security game, primarily targeted at the overall security proof. This not only hinders modular protocol design, but also makes the suitability of a protocol for a particular application difficult to assess. In this work we demonstrate that ratcheting components can be modeled in a composable framework, allowing for their reuse in a modular fashion. To this end, we first propose an extension of the Constructive Cryptography framework by so-called global event histories, to allow for a clean modularization even if the component modules are not fully independent but actually subtly intertwined, as in most ratcheting protocols. Second, we model a unified, flexibly instantiable type of strong security statement for secure messaging within that framework. Third, we show that one can phrase strong guarantees for a number of sub-protocols from the existing literature in this model with only minor modifications, slightly stronger assumptions, and reasonably intuitive formalizations. When expressing existing protocols' guarantees in a simulation-based framework, one has to address the so-called commitment problem. We do so by reflecting the removal of access to certain oracles under specific conditions, appearing in game-based security definitions, in the real world of our composable statements. We also propose a novel non-committing protocol for settings where the number of messages a party can send before receiving a reply is bounded.
Trajectory Following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation

In this paper we proposed the circle trajectory assembly algorithm to control the multi-UAVs circular assembly formation.CTFAP solution provides rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance.Proposed Distributed model prediction control framework improves the optimization ability and reduces the computation consumption with the better convergence ability of the UAV formation.Firstly, a circular trajectory following algorithm with an adaptive parameter is proposed to complete the rapid formation of UAVs on a circular orbit and solve the problem of large scattering distance during formation forming.Then, in the stage of formation reconfiguration, with distributed model prediction control framework (DMPC), the proposed method gets the prediction information of DMPC to optimize the population of classical differential evolution (DE) algorithm and improve the iterative optimization ability of DE algorithm.Experiments show that the proposed differential evolution algorithm greatly improves the efficiency of solving the formation reconfiguration problem under the DMPC framework and overcomes the disadvantages of random population of classical DE.For the proposed rapid forming method, assembly range is reduced by 41% compared with direct linearly formation assembly, and the formation forming time is reduced by approximately 21%.Compared with other optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and DE, the proposed differential evolution algorithm reduces instruction response time of single-drones by 16%-30%.
SEAL: Sealed-Bid Auction Without Auctioneers

We propose the first auctioneer-free sealed-bid auction protocol with a linear computation and communication complexity O(c), c being the bit length of the bid price. Our protocol, called Self-Enforcing Auction Lot (SEAL), operates in a decentralized setting, where bidders jointly compute the maximum bid while preserving the privacy of losing bids. In our protocol, we do not require any secret channels between participants. All operations are publicly verifiable; everyone including third-party observers is able to verify the integrity of the auction outcome. Upon learning the highest bid, the winner comes forward with a proof to prove that she is the real winner. Based on the proof, everyone is able to check if there is only one winner or there is a tie. While our main protocol works with the first-price sealed-bid, it can be easily extended to support the second-price sealed-bid (also known as the Vickrey auction), revealing only the winner and the second highest bid, while keeping the highest bid and all other bids secret. To the best of our knowledge, this work establishes to date the best computation and communication complexity for sealed-bid auction schemes without involving any auctioneer.
Twitter Based Outcome Predictions of 2019 Indian General Elections Using Decision Tree

Social Media is a huge corpus of raw data available to process and analyze the mood of the people. Various institutions and corporations use social media data to understand market response of their products. Twitter data is widely available one for this purpose. Similar to market analysis, this data is used to map political mood of the general public. Most of the existing methodologies use tweets downloaded on a particular criterion. There is very limited study done on daily mood mapping of political views among people. This paper addresses a methodology to predict the outcome of the 2019 Indian general elections using the sentiment analysis of twitter data. Decision tree classifier is used to train and test data and the predicted outcome is found to be close to that of the actual outcome and most of the pre poll analysis done so far. The experiments reported in this paper are only on tweets in English language and having the most number of retweets by the users. This methodology is efficient enough to map the mood of people over a timely basis across various phases of polls.
Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China’s international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009–2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China’s AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009–2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013–2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016–2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China’s artificial intelligence.
Collusion Resistant Watermarking Schemes for Cryptographic Functionalities

A cryptographic watermarking scheme embeds a message into a program while preserving its functionality. Recently, a number of watermarking schemes have been proposed, which are proven secure in the sense that given one marked program, any attempt to remove the embedded message will substantially change its functionality. In this paper, we formally initiate the study of collusion attacks for watermarking schemes, where the attacker's goal is to remove the embedded messages given multiple copies of the same program, each with a different embedded message. This is motivated by practical scenarios, where a program may be marked multiple times with different messages. The results of this work are twofold. First, we examine existing cryptographic watermarking schemes and observe that all of them are vulnerable to collusion attacks. Second, we construct collusion resistant watermarking schemes for various cryptographic functionalities (e.g., pseudorandom function evaluation, decryption, etc.). To achieve our second result, we present a new primitive called puncturable functional encryption scheme, which may be of independent interest.
Automated Grading of Class Diagrams

Drawing UML diagrams, such as class diagrams, is an essential learning task in many software engineering courses. In course assignments, students are tasked to draw models that describe scenarios, model requirements, or system designs. The course instructor usually grades the diagrams manually by comparing a student's solution model with a template solution model made by the instructor. However, modelling is not an exact science, and multiple correct solutions or variants may exist. This makes grading UML assignments a cumbersome task, especially when there are many assignments to grade. Therefore, there is a need for an automated grading tool that aids the instructor in the grading process. This paper presents an approach for automated grading of UML class diagrams. We propose a metamodel that establishes mappings between the instructor solution and all the solutions for a class. The approach uses a grading algorithm that uses syntactic, semantic and structural matching to match a student's solutions with the template solution. We evaluate the algorithm on a real assignment for modeling a Flight Ticketing domain model for a class of 20 students and report our findings.
Practical Volume-Based Attacks on Encrypted Databases

Recent years have seen an increased interest towards strong security primitives for encrypted databases (such as oblivious protocols) that hide the access patterns of query execution and reveal only the volume of results. However recent work has shown that even volume leakage can enable the reconstruction of entire columns in the database. Yet existing attacks rely on a set of assumptions that are unrealistic in practice for example they (i) require a large number of queries to be issued by the user or (ii) assume certain distributions on the queries or underlying data (e.g. that the queries are distributed uniformly at random or that the database does not contain missing values). In this work we present new attacks for recovering the content of individual user queries assuming no leakage from the system except the number of results and avoiding the limiting assumptions above. Unlike prior attacks our attacks require only a single query to be issued by the user for recovering the keyword. Furthermore our attacks make no assumptions about the distribution of issued queries or the underlying data. Instead our key insight is to exploit the behavior of real-world applications. We start by surveying 11 applications to identify two key characteristics that can be exploited by attackers-(l) file injection and (ii) automatic query replay. We present attacks that leverage these two properties in concert with volume leakage independent of the details of any encrypted database system. Subsequently we perform an attack on the real Gmail web client by simulating a server-side adversary. Our attack on Gmail completes within a matter of minutes demonstrating the feasibility of our techniques. We also present three ancillary attacks for situations when certain mitigation strategies are employed.
Exploration of Various Cloud Security Challenges and Threats

Most of the organizations are stuck in a situation to cloudify due to concerns regarding security of sensitive data. Cloud computing provides many aids to the users and organizations in accordance with expenditure and savings. Other than these benefits, cloud computing has some hurdles that result in restriction of it's usage. Cloud security is the bigger hurdle which is regularly taken into consideration. This paper will give a broad view of major threats challenges in security which are encountered in cloud computing. Cloud computing entities include cloud user, cloud provider, and data owner. The study is carried out based on selection of open-source cloud offerings. This paper can be an exploration tool for any IT person to gain knowledge into security-related risks and challenges which are concerned with cloud computing.
A Hybrid Approach of Solar Power Forecasting Using Machine Learning

Photovoltaic (PV) power generation is prone to fluctuations and it is affected by different weather conditions. Therefore, accurate forecasting becomes vital for grid operators to manage grid operations. In this study, multiple machine learning models are used, and different weather parameters are analyzed for PV forecasting. The performance of different models is compared. The proposed forecasting models are tested with 4 kWp PV device installed at our institute, fortiss GmbH and the comparison of the models is done through error analysis. The accuracy is evaluated using historical weather data. The study proposes a novel approach of PV forecasting where a model is built based on the principle of pipelining clustering, classification and regression algorithms. Clustering and classification algorithms group the data-points with similar weather conditions. Subsequently, segmented regression is applied to these groups. Weather forecast of the next day is utilized to determine the models used for forecasting. The results show that the proposed forecasting model for PV systems is effective and promising.
Virtual Machine Placement Using JAYA Optimization Algorithm

Cloud computing has become more popular with the ability to run HPC applications on cloud infrastructures. Improving the energy efficiency of these data centers become important for all the cloud providers. We observe that bin-packing heuristics such as Best-Fit Decreasing for energy-aware virtual machine (VM) allocation could not provide the optimal solution to minimize the total energy consumption of the data center. In this work, we explore the virtual machines provisioning considering multi-dimensional resources and energy consumption of the data center. We propose to use JAYA algorithm for optimal placement and minimizing the energy consumption of the data center. Our simulation results show the proposed algorithm could reduce the total energy consumption up to 34% and SLA by 15% compared with the Particle Swarm Optimization and power-aware best-fit decreasing.
Big Data Forecasting Applied Nearest Neighbor Method

Nearest neighbor is a method of approaching the case by calculating the proximity between the new and the old case in order to make the forecasting process. The value of the approach used in Nearest Neighbor itself is a classification approach that searches all training data that is relatively similar to the data test. Each approach calculates all new and old data so that in the process of searching the value of proximity needs testing of Big data in order to find how much time to produce in forecasting based on old cases. This study had been experimented by using 2,500 cases with 1 minute and 52 seconds, 18,000 of which required longer time i.e. 16 minutes and 30 seconds. The differences in the number of the old cases influence the process of time calculation.