Reviewerly/exHarmony huggingface dataset:
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition

End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly take advantage of the feature interaction between the two tasks. In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer encoder with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism to explicitly guide text localization through recognition loss. The straightforward design results in a concise framework that requires neither additional rectification module nor character-level annotation for the arbitrarily-shaped text. Qualitative and quantitative experiments on multi-oriented datasets RoIC13 and ICDAR 2015, arbitrarily-shaped datasets Total-Text and CTW1500, and multi-lingual datasets ReCTS (Chinese) and VinText (Viet-namese) demonstrate SwinTextSpotter significantly outperforms existing methods. Code is available at https://github.com/mxin262/SwinTextSpotter.
Dynamic Generation of Dilemma-Based Interactive Narratives

This paper presents a system which automatically generates interactive stories. These are focused on dilemmas in order to create dramatic tension. The system is provided with knowledge of generic story actions and dilemmas based on those cliches encountered in many of today&amp;rsquo;s soap operas. The story designer is only required to provide genre specific storyworld knowledge, such as information on characters and their relations, locations and actions. These dilemmas and story actions are instantiated for the given storyworld and a story planner creates sequences of actions that each lead to a dilemma for a character (who can be the user). The user interacts with the story by making decisions on relevant dilemmas and by freely choosing their own actions. Using this input the system chooses and adapts future story lines according to the user's past behaviour.
Translation of patient information leaflets: Trained translators and pharmacists-cum-translators – a comparison

Numerous studies have shown that Patient Information Leaflets (PILs) are generally difficult to understand for ordinary people and that this may be one of the reasons why a high percentage of patients fail to take their medication correctly. A study by Askehave and Zethsen (2002), based on textual analysis and relying on comprehensive extratextual procedural knowledge, has shown that translated Danish PILs were, without exception, more complex than their STs. But why is this so? One possible explanation could be that PILs are very frequently translated by pharmacists, who do not possess the linguistic tools and translational knowledge necessary for expert-to-layman translation or interlingual translation. This article reports on an empirical study that falls into two parts. The first aims to identify possible differences in the translations of these two types of translator in terms of lay-friendliness. The second aims to describe the nature of the differences found between these two types of translator, and discusses whether they could potentially be detrimental to lay-friendliness in PILs.
How successfully do users search the Web? One real-time site allows you to “spy” on searchers

Visit of the McKinley Search Voyeur Web site, a real-time site that allows people to view a sample of searches currently entered by users of the McKinley Magellan searc engine
Dynamic Potential Search — A New Bounded Suboptimal Search

Potential Search (PS) is an algorithm that is designed to solve bounded cost search problems. In this paper, we modify PS to work within the framework of bounded suboptimal search and introduce Dynamic Potential Search (DPS). DPS uses the idea of PS but modifies the bound to be the product of the minimal f-value in OPEN and the required suboptimal bound. We study DPS and its attributes. We then experimentally compare DPS to WA* and to EES on a variety of domains and study parameters that affect the behavior of these algorithms.In general we show that in domains with unit edge costs (e.g., many standard benchmarks) DPS significantly outperforms WA* and EES but there are exceptions.
Assessing Data Management Support Needs of Bioengineering and Biomedical Research Faculty

<strong>Objectives</strong>:<strong> </strong>This study explores data management knowledge, attitudes, and practices of bioengineering and biomedical researchers in the context of the National Institutes of Health-funded research projects. Specifically, this study seeks to answer the following questions: <ol> <li>What is the nature of biomedical and bioengineering research on the Illinois campus and what kinds of data are being generated?</li> <li>To what degree are biomedical and bioengineering researchers aware of best practices for data management and what are the actual data management behaviors?</li> <li>What aspects of data management present the greatest challenges and frustrations?</li> <li>To what degree are biomedical and bioengineering researchers aware of data sharing opportunities and data repositories, and what are their attitudes towards data sharing?</li> <li>To what degree are researchers aware of campus services and support for data management planning, data sharing, and data deposit, and what is the level of interest in instruction in these areas?</li> </ol> <strong>Methods</strong>: Librarians on the University of Illinois at Urbana Champaign campus conducted semi-structured interviews with bioengineering and biomedical researchers to explore researchers’ knowledge of data management best practices, awareness of library campus services, data management behavior and challenges managing research data. The topics covered during the interviews were current research projects, data types, format, description, campus repository usage, data-sharing, awareness of library campus services, data reuse, the anticipated impact of health on public and challenges (interview questions are provided in the Appendix). <strong>Results</strong>: This study revealed the majority of researchers explore broad research topics, various file storage solutions, generate numerous amounts of data and adhere to differing discipline-specific practices. Researchers expressed both familiarity and unfamiliarity with DMP Tool. Roughly half of the researchers interviewed reported having documented protocols for file names, file backup, and file storage. Findings also suggest that there is ambiguity about what it means to share research data and confusion about terminology such as “repository” and “data deposit”. Many researchers equate publication to data sharing. <strong>Conclusions</strong>: The interviews reveal significant data literacy gaps that present opportunities for library instruction in the areas of file organization, project workflow and documentation, metadata standards, and data deposit options. The interviews also provide invaluable insight into biomedical and bioengineering research in general and contribute to the authors’ understanding of the challenges facing the researchers we strive to support.
Consistent order estimation for nonparametric hidden Markov models

We consider the problem of estimating the number of hidden states (the order) of a nonparametric hidden Markov model (HMM). We propose two different methods and prove their almost sure consistency without any prior assumption, be it on the order or on the emission distributions. This is the first time a consistency result is proved in such a general setting without using restrictive assumptions such as a priori upper bounds on the order or parametric restrictions on the emission distributions. Our main method relies on the minimization of a penalized least squares criterion. In addition to the consistency of the order estimation, we also prove that this method yields rate minimax adaptive estimators of the parameters of the HMM - up to a logarithmic factor. Our second method relies on estimating the rank of a matrix obtained from the distribution of two consecutive observations. Finally, numerical experiments are used to compare both methods and study their ability to select the right order in several situations.
From Mechanics to Meaning and Back Again: Exploring Techniques for the Contextualisation of Code

Code generation is a promising new area for the automatic production of mechanics and systems in games. Generated code alone is not sufficient for inclusion in a rich, fully-designed game, however - it lacks context to bind the functionality of code to the metaphorical setting of the game. In this paper we explore potential solutions to this problem, both in terms of creative systems which co-operate with human content, and the possibility for contextual meaning in autonomous, human-free creative systems as well.
Experiences With Agile Teaching In Project Based Courses

In this paper we describe an agile teaching methodology as applied to project-based software engineering courses.We take the term "agile" from the popular software development methodology that emphasizes short feedback cycles, flexibility, and direct involvement of the customer.Our software engineering courses are goal-driven, we include structural mechanisms to support feedback, and we design the projects around frequent checkpoints.The course content is adaptable to student needs and changing situations.After giving an overview of agile teaching, we address specific issues of course structure for supporting feedback, how we act on student feedback, and the mechanisms for collecting just-in-time feedback.The paper concludes with a discussion of the impact on instructors and students, as well as of results from polling our colleagues on their teaching practices in similar courses.
Characters Who Speak Their Minds: Dialogue Generation in Talk of the Town

The Expressive Intelligence Studio is developing a new approach to freeform conversational interaction in playable media that combines dialogue management, natural language generation (NLG), and natural language understanding. In this paper, we present our method for dialogue generation, which has been fully implemented in a game we are developing called Talk of the Town. Eschewing a traditional NLG pipeline, we take up a novel approach that combines human language expertise with computer generativity. Specifically, this method utilizes a tool that we have developed for authoring context-free grammars (CFGs) whose productions come packaged with explicit metadata. Instead of terminally expanding top-level symbols — the conventional way of generating from a CFG — we employ an unusual middle-out procedure that targets mid-level symbols and traverses the grammar by both forward chaining and backward chaining, expanding symbols conditionally by testing against the current game state. In this paper, we present our method, discuss a series of associated authoring patterns, and situate our approach against the few earlier projects in this area.
Robust Multi-Agent Path Finding

In the multi-agent path-finding (MAPF) problem, the task is to find a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. We explore the notion of k-robust MAPF, where the task is to find a plan that can be followed even if a limited number of such delays occur. k-robust MAPF is especially suitable for agents with a control mechanism that guarantees that each agent is within a limited number of steps away from its pre-defined plan. We propose sufficient and required conditions for finding a k-robust plan, and show how to convert several MAPF solvers to find such plans. Then, we show the benefit of using a k-robust plan during execution, and for finding plans that are likely to succeed.
The streaming <i>k</i>-mismatch problem

We consider the streaming complexity of a fundamental task in approximate pattern matching: the k-mismatch problem. In this problem, we must compute Hamming distances between a pattern of length n and all length-n substrings of a text for which the Hamming distance does not exceed a given threshold k. In our problem formulation, we report not only the Hamming distance but also, on demand, the full mismatch information, that is the list of mismatched pairs of symbols and their indices. The twin challenges of streaming pattern matching derive from the need both to achieve small working space and also to guarantee that every arriving input symbol is processed quickly.We present a streaming algorithm for the k-mismatch problem which uses bits of space and spends time on each symbol of the input stream. In our formulation, the pattern is also in the stream, arriving directly before the text. The running time almost matches the classic offline solution [5] and the space usage is within a logarithmic factor of optimal. Our new algorithm therefore effectively resolves and also extends a problem first introduced in FOCS'09 [38]. En route to this solution, we also give a deterministic -bit encoding of all the alignments with Hamming distance at most k of a length-n pattern within a text of length O(n). This secondary result provides an optimal solution to a natural encoding problem which may be of independent interest.
Deep Learning for Real-Time Heuristic Search Algorithm Selection

Real-time heuristic search algorithms are used for creating agents that rely on local information and move in a bounded amount of time making them an excellent candidate for video games as planning time can be controlled. Path finding on video game maps has become the de facto standard for evaluating real-time heuristic search algorithms. Over the years researchers have worked to identify areas where these algorithms perform poorly in an attempt to mitigate their weaknesses. Recent work illustrates the benefits of tailoring algorithms for a given problem as performance is heavily dependent on the search space. In order to determine which algorithm to select for solving the search problems on a map the developer would have to run all the algorithms in consideration to obtain the correct choice. Our work extends the previous algorithm selection approach to use a deep learning classifier to select the algorithm to use on new maps without having to evaluate the algorithms on the map. To do so we select a portfolio of algorithms and train a classifier to predict which portfolio member to use on a unseen new map. Our empirical results show that selecting algorithms dynamically can outperform the single best algorithm from the portfolio on new maps, as well provide the lower bound for potential improvements to motivate further work on this approach.
Local Critic Training for Model-Parallel Learning of Deep Neural Networks

In this article, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks. The main network is divided into several layer groups, and each layer group is updated through error gradients estimated by the corresponding local critic network. We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In addition, we demonstrate that the proposed method is guaranteed to converge to a critical point. We also show that trained networks by the proposed method can be used for structural optimization. Experimental results show that our method achieves satisfactory performance, reduces training time greatly, and decreases memory consumption per machine. Code is available at https://github.com/hjdw2/Local-critic-training.
Fine-grained Complexity Meets IP = PSPACE

In this paper we study the fine-grained complexity of finding exact and approximate solutions to problems in P. Our main contribution is showing reductions from an exact to an approximate solution for a host of such problems.As one (notable) example, we show that the Closest-LCS-Pair problem (Given two sets of strings A and B, compute exactly the maximum LCS(a, b) with (a, b) ∊ A × B) is equivalent to its approximation version (under near-linear time reductions, and with a constant approximation factor). More generally, we identify a class of problems, which we call BP-Pair-Class, comprising both exact and approximate solutions, and show that they are all equivalent under near-linear time reductions.Exploring this class and its properties, we also show:•Under the NC-SETH assumption (a significantly more relaxed assumption than SETH), solving any of the problems in this class requires essentially quadratic time.•Modest improvements on the running time of known algorithms (shaving log factors) would imply that NEXP is not in non-uniform NC1.•Finally, we leverage our techniques to show new barriers for deterministic approximation algorithms for LCS.A very important consequence of our results is that they continue to hold in the data structure setting. In particular, it shows that a data structure for approximate Nearest Neighbor Search for LCS (NNSLCS) implies a data structure for exact NNSLCS and a data structure for answering regular expression queries with essentially the same complexity.At the heart of these new results is a deep connection between interactive proof systems for bounded-space computations and the fine-grained complexity of exact and approximate solutions to problems in P. In particular, our results build on the proof techniques from the classical IP = PSPACE result.
Bayesian query expansion for multi-camera person re-identification

Person re-identification (re-ID) is challenging because pedestrians may exhibit distinct appearance under different cameras. Given a query image, previous methods usually output the person retrieval results directly, which may perform badly due to the limited information provided by the single query image. To mine more query information, we add an expansion step to post-process the initial ranking list. The intuition is that a true match in the gallery may be difficult to be found by the query alone, but it can be easily retrieved by other true matches in the initial ranking list. In this paper, we propose the Bayesian Query Expansion (BQE) method to generate a new query with information from the initial ranking list. The Bayesian model is used to predict true matches in the gallery. We apply pooling on the features of these “true matches” to get a single vector, i.e., the expanded new query, with which the retrieval process is performed again to obtain the final results. We evaluate BQE with various feature extraction methods and distance metric learning methods on four large-scale re-ID datasets. We observe consistent improvement over all the baselines and report competitive performances compared with the state-of-the-art results.
Reducing redundancy of test cases generation using code smell detection and refactoring

In software development life cycle (SDLC), the testing phase is important to test the functionalities of any software. In this phase, test cases are generated to test software functionalities. This paper presents an approach on how to detect and refactor code smells from the source codes of an Android application in order to reduce the redundancy in test case generation. Refactoring is one of the vital activities in software development and maintenance. Refactoring is a process of code alteration that aims to make structural modifications to the source code without altering any external functionality. These changes often improve software quality such as readability, execution time and maintainability. The proposed approach is then implemented and evaluated in order to determine its effectiveness in reducing the redundancy of test case generation.
Layered Object-Oriented Programming: Advanced VTable Reuse Attacks on Binary-Level Defense

Vtable reuse attack, as a novel type of code reuse attacks, is introduced to bypass most binary-level control flow integrity enforcement and vtable integrity enforcement. So far, two binary-level defenses (TypeArmor and vfGuard) are proposed to defend against vtable reuse attacks. Both techniques use semantic information as the control flow integrity enforcement policy, i.e., TypeArmor and vfGuard utilize argument register count and dispatch offset at virtual callsite as the signature to check the validity of target functions, respectively. In this paper, we propose layered object-oriented programming (LOOP), an advanced vtable reuse attack, to show that the coarse-grained control flow integrity strategies are still vulnerable to vtable reuse attacks. In LOOP, we introduce argument expansion gadgets and transfer gadgets to, respectively, bypass TypeArmor and vfGuard. We generalize the characteristics of both gadgets and develop a tool to discover them at the binary level. We demonstrated that under the protection of TypeArmor and vfGuard, Firefox, Adobe Flash Player, and Internet Explorer are all vulnerable to LOOP attacks. Furthermore, we show the availability of argument expansion gadgets and transfer gadgets in common software or libraries.
Building a Trustworthy Execution Environment to Defeat Exploits from both Cyber Space and Physical Space for ARM

The rapid evolution of Internet-of-Things (IoT) technologies has led to an emerging need to make them smarter. However, the smartness comes at the cost of multi-vector security exploits. From cyber space, a compromised operating system could access all the data in a cloud-aware IoT device. From physical space, cold-boot attacks and DMA attacks impose a great threat to the unattended devices. In this paper, we propose TrustShadow that provides a comprehensively protected execution environment for unmodified application running on ARM-based IoT devices. To defeat cyber attacks, TrustShadow takes advantage of ARM TrustZone technology and partitions resources into the secure and normal worlds. In the secure world, TrustShadow constructs a trusted execution environment for security-critical applications. This trusted environment is maintained by a lightweight runtime system. The runtime system does not provide system services itself. Rather, it forwards them to the untrusted normal-world OS, and verifies the returns. The runtime system further employs a page based encryption mechanism to ensure that all the data segments of a security-critical application appear in ciphertext in DRAM chip. When an encrypted data page is accessed, it is transparently decrypted to a page in the internal RAM, which is immune to physical exploits.
Research on the intelligent diagnosis method of the server based on thermal image technology

Balancing the uneven temperature distribution of data centers can reduce cooling energy consumption significantly, as the overheated surface of servers is one of the root causes of the uneven distribution. This paper presents a method for intelligently diagnosing server operating status based on the heat distribution of the server surface. Five types of server operation can be diagnosed: normal status, main fan failure, vice-fan failure, air vent blockage and low-load status. The method involves signal processing and pattern recognition techniques such as thermal image enhancement, region segmentation and image classification. First, thermal images of server outlets in running status are captured as data; second, the images are preprocessed for standardization; third, after homomorphic filtering enhancement, the images are subjected to one-dimensional maximum entropy segmentation to obtain server hotspot images; fourth, morphological features, texture features and statistical features are extracted from hotspot images; finally, the server status is diagnosed by a support vector machine. Experiment results show that the method achieves a diagnosis accuracy of 91.111%. This method can solve the problem of uneven heat distribution in data centers effectively.