Reviewerly/exHarmony huggingface dataset:
Visualization of emergency department clinical data for interpretable patient phenotyping

Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual patient disposition, typically admission to the hospital or discharge home. EHR data are high-dimensional and complex, but offer the opportunity to discover and characterize underlying data-driven patient phenotypes. Data-driven phenotypes are intended to relieve reliance on weak labels like diagnosis codes and to aid in identifying populations of existing patients that are most similar to a specific patient. These phenotypes will enable improved, personalized therapeutic decision making and prognostication. In this work, we focus on the challenge of two-dimensional patient projections. A low dimensional embedding offers visual interpretability lost in higher dimensions. While linear dimensionality reduction techniques such as principal component analysis are often used towards this aim, they are insufficient to describe the variance of patient data. This linear reduction does not account for higher order, non-linear interactions of variables. In this work, we employ the newly-described non-linear embedding technique called uniform manifold approximation and projection (UMAP). UMAP seeks to capture both local and global structures in high-dimensional data. We then use Gaussian mixture models to identify clusters in the embedded data and use the adjusted Rand index (ARI) to establish stability in the discovery of these clusters. This technique is applied to five common clinical chief complaints from a real-world ED EHR dataset, describing the emergent properties of discovered clusters. We observe clinically-relevant cluster attributes, suggesting that visual embeddings of EHR data using non-linear dimensionality reduction is a promising approach to reveal data-driven patient phenotypes. In the five chief complaints, we find between 2 and 6 clusters, with the peak mean pairwise ARI between subsequent training iterations to range from 0.35 to 0.74.
Optical Character and Font Recognizer

Optical Character and Font Recognizer focuses primarily on building a complete model for document processing. The proposed system recognizes the font style along with the text from an image of certain resolution. The system uses principles of both machine learning and image processing to obtain the desired results. The model uses Contour selection for character extraction and K-Nearest Neighbor approach for character and font recognition. With the assistance of the proposed system using the mentioned techniques, scanned documents can be altered or the font style of a particular document can be known as desired. Many models that perform character recognition are present but a model that performs both character and font recognition with good accuracy is difficult to find. The experiment resulted in 87% overall accuracy for detection of characters.
Challenges Classification of Software Requirements Interaction Management Using Search-Based Methods

Requirements engineering is an important process in software engineering. An important task in the area is to select and optimize the requirements by considering requirement interaction management. Actually, the main goal of this area is to make best and most optimal choices among all the possible requirements, taking into account the dependencies between requirements. In this way, there are challenges and threats at all stages, including requirements engineering, search-based software engineering, requirements interactive management, and selection and optimization of requirements. The Identification and classification of challenges help to better understand the problem and find better solutions. We intend to examine and classify the main challenges in the papers in this area. Our goal in this article is to classify the challenges in this area from a new perspective.
Parametrized bar recursion: a unifying framework for realizability interpretations of classical dependent choice

Abstract During the last 20 years or so, a wide range of realizability interpretations of classical analysis have been developed. In many cases, these are achieved by extending the base interpreting system of primitive recursive functionals with some form of bar recursion, which realizes the negative translation of either countable or countable dependent choice. In this work, we present the many variants of bar recursion used in this context as instantiations of a parametrized form of backward recursion, and give a uniform proof that under certain conditions this recursor realizes a corresponding family of parametrized dependent choice principles. From this proof, the soundness of most of the existing bar recursive realizability interpretations of choice, including those based on the Berardi–Bezem–Coquand functional, modified realizability and the more recent products of selection functions of Escardó and Oliva, follows as a simple corollary. We achieve not only a uniform framework in which familiar realizability interpretations of choice can be compared, but show that these represent just simple instances of a large family of potential interpretations of dependent choice principles.
Developments in Concert Hall Acoustics in the 1960s: Theory and Practice

After the war, there was a general understanding of reverberation time (RT), including how to measure it and its significance, as well as its link to a state of diffusion. Reverberation refers to a property of late sound; there was an appreciation that early sound must be significant, but in what way? Research had begun in the 1950s using simulation systems in anechoic chambers, with the Haas effect of 1951 being the most prominent result. Thiele’s Deutlichkeit, or early energy fraction, was important from 1953 and indirectly found expression in Beranek’s initial time delay gap (ITDG) from 1962. The 1960s produced a possible explanation for RTs in halls being shorter than calculations predicted, the importance of early sound for the sense of reverberation (EDT), the nature of directional sensitivity, conditions for echo disturbance, and the importance of early lateral reflections. Much of the research in the 1960s laid the foundations for research investigating the relative importance of the various subjective effects for concert hall listening. Important concert halls built during the period include Philharmonic Hall, New York (1962); Fairfield Hall, Croydon, London (1962); the Philharmonie, Berlin (1963); and De Doelen Hall, Rotterdam (1966). The parallel-sided halls of the past were rarely copied, however, due to architectural fashion. These various halls will be discussed as they make a fascinating group.
An Experiment In Web Based Registration For New Students

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 1353 An Experiment in Web-Based Registration for New Students In the College of Engineering and Applied Science James C. Sherman University of Colorado at Boulder Abstract For the Fall Semester 1999, new freshmen and new transfer students entering the College of Engineering and Applied Science at the University of Colorado at Boulder, were advised to register for their courses via a web-based registration system. This registration was completed prior to meeting with a faculty or staff advisor, and prior to an on-campus orientation program. Previously, the College utilized a telephone-based registration system with mixed results. Today the development of a web-based system provides a more visual and user-friendly registration system. This study comes from a College interest in securing a better understanding of the new student registration experience. A telephone log was maintained for this study by faculty and staff advisors and a follow-up e-mail survey was conducted of our new students once the Fall Semester was underway. This survey secured new student comments on the registration experience and their level of satisfaction with the web-based registration system. Overall, most students registered successfully and were satisfied with this registration process. However, a few students did find it necessary to meet with an advisor to answer specific questions prior to registration. Also, some problems were encountered early on the first day of new freshman registration, when the web-based registration system was overloaded and new freshmen were directed to the original telephone-based registration system. A small number of our new students also found our registration instructions to be vague and incomplete in select areas, prompting a number of calls to departmental faculty and staff advisors. Minor new student schedule adjustments were required once these new students were on campus for the Fall Semester. 1. Past Registration Practice Some colleges at the University of Colorado at Boulder have elected to offer multiple on- campus registration and orientation programs for their new students during the summer prior to the Fall Semester. The College of Engineering and Applied Science has not participated in these summer programs because of concern over the potential cost for new students, difficulties in arranging these programs to accommodate student schedules, faculty and staff summer work schedules, and an assumption that these two-day programs were not needed because of our structured engineering curricula. For the decade prior to the Fall Semester 1999, engineering students registered via telephone or while attending the New Student Orientation Program, offered immediately prior to the Fall Semester.
Diverse Depth-First Search in Satisificing Planning

Diverse best-first search (DBFS) is a successful algorithm for satisficing planning that is built on top of greedy best-first search and uses stochastic search techniques to select the next ``best'' state that is not always in agreement with the heuristic function. DBFS thus considers a diversity of search directions and avoids search plateaus caused by inaccurate heuristic estimation. However, the scalability of DBFS is still limited by the available memory resource, as in any planning system that uses best-first search at its core. This paper presents the diverse depth-first search (DDFS) algorithm that overcomes the memory bottleneck of DBFS. DDFS requires much less memory than DBFS yet it successfully incorporates DBFS' randomized node selection scheme for effective search diversification. Additionally, DDFS is enhanced with a transposition table to avoid duplicate search effort commonly found in depth-first search. Experimental results in solving problems used in previous International Planning Competitions show that DDFS can solve instances that remain unsolvable by DBFS due to its excessive memory requirements.
9. Semantic underspecification

This article reviews semantic underspecification, which has emerged over the last three decades as a technique to capture several readings of an ambiguous expression in one single representation by deliberately omitting the differences between the readings in the semantic descriptions. After classifying the kinds of ambiguity to which underspecification can be applied, important properties of underspecification formalisms will be discussed that can be used to distinguish subgroups of these formalisms. The remainder of the article then presents var ious motivations for the use of underspecification, and expounds the derivation and further processing of underspecified semantic representations.
Design And Implementation Of Audio Steganography On Fpga

As the emergence of latest hacking programs and attention of hackers in hacking more and more data, the need arises to make an integrated circuit that can help hiding the message in such a form that no one can hack it easily. The audio Steganographer is a device or an integrated circuit that facilitates the user to hide a massage in the ongoing digital audio stream. It also ensures the purity of the audio upon playback. The integrated circuit consists of various blocks like Encoder/ Decoder, Encryption/Decryption, Pseudo Random Number generator, Register Control Unit etc. The Report talks on the design aspects of all the listed blocks in detail. The report also discusses the various protocol developed during the design phase. It is obvious that the digital audio stream will definitely be distorted by stegenography. The care has been taken that the audio stream is not damaged to an extent that it cannot be recovered back. The last part of the report discusses the verification of the design. The development of the test bench and the simulation results are also indicated. Keywords: Steganography, PRN, Register Control Logic
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A Model Driven Analysis of the 802.11 CSMA/CA Protocol through SD2PN

Unified Modelling Language (UML) has been conferred as the de facto standard in modeling by majority in the softwaresystem development community. Among the various types of diagrams that exist under the umbrella of UML is Sequence Diagram. Sequence Diagrams are capable of modeling interactional behaviours as well as dynamic happenings in a system, and as such are generally used in the modeling of complex software systems. However in this paper, Sequence Diagrams are used in the modeling of the IEEE 802.11 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. The Sequence Diagram representing this protocol will then be used for formal, mathematical analysis by first transforming the Sequence Diagram throughthe MDA model transformation tool called SD2PN, and performing analysis such as liveness analysis, boundedness analysis and reachability analysis of the resulting Petri Net.
Adaptive Compiler Strategies for Mitigating Timing Side Channel Attacks

Existing compiler techniques can transform code to make its timing behavior independent of sensitive values to prevent information leakage through time side channels. Those techniques are hampered, however, by their static nature and dependence on details of the processor targeted during the compilation. This paper presents a dynamic compiler approach based on offline profiles and JIT compiler strategies. This approach reduces overhead significantly and enables a trade-off between provided protection and overhead. Furthermore, it supports adaptive policies in which the protection adapts to run-time changes in the requirements. A prototype implementation in the Jikes Research VM is evaluated on RSA encryption, HMAC key verification, and IDEA encryption.
FAST: Facilitated and Accurate Scene Text Proposals through FCN Guided Pruning

Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition.
Message Integrity Protection Over Wireless Channel: Countering Signal Cancellation via Channel Randomization

Physical layer message integrity protection and authentication by countering signal-cancellation has been shown as a promising alternative to traditional pure cryptographic message authentication protocols, due to the non-necessity of neither pre-shared secrets nor secure channels. However, the security of such an approach remained an open problem due to the lack of systematic security modeling and quantitative analysis. In this paper, we first establish a novel signal cancellation attack framework to study the optimal signal-cancellation attacker's behavior and utility using game-theory, which precisely captures the attacker's knowledge using its correlated channel estimates in various channel environments as well as the online nature of the attack. Based on theoretical results, we propose a practical channel randomization approach to defend against signal cancellation attack, which exploits state diversity and swift reconfigurability of reconfigurable antenna to increase randomness and meanwhile reduce correlation of channel state information. We show that by proactively mimicking the attacker and placing restrictions on the attacker's location, we can bound the attacker's knowledge of channel state information, thereby achieve a guaranteed level of message integrity protection in practice. Besides, we conduct extensive experiments and simulations to show the security and performance of the proposed approach. We believe our novel threat modeling and quantitative security analysis methodology can benefit a wide range of physical layer security problems.
Stochastic scheduling for variation-aware virtual machine placement in a cloud computing CPS

As the most promising computing paradigm, cloud computing opens up new horizons for the area of high-performance distributed computing. Cyber-physical Systems (CPSs) present novel digital systems, which integrate computation, communication and the control of physical resource. Applied CPSs architecture in cloud computing can provide real-time and scalable resource monitoring and offer time-critical applications. With unrivaled scalability and flexibility, the CPSs based cloud services brings significant convenience to customers in need of elastic computing power. The quality of CPSs based cloud services is, to an large extent, determined by the performance of Virtual Machine (VM) placement algorithm for the data center. VM placement also effect the communication between applications and physical resource distribution in cloud computing CPSs. The traditional VM placement algorithm is built upon the two-tier architecture. With the presence of multi-media applications, the application level controller cannot accurately quantify the varying amount computing resources required by VMs at runtime. Consequently, lacking accurate resource demand for each VM, controller at the data center level cannot generate the VM placement with satisfactory feasibility. This architecture no longer fits the modern data centers. In this paper, the two tier VM placement framework is proposed to resolve this technical challenge. Our LP-based variation-unaware VM placement algorithm generates the VM placement with minimized energy consumption. On the other hand, our feasibility driven stochastic VM placement (FDSP) algorithm works seamlessly with the LP-based algorithm to achieve desirable feasibility of the placement. Our experimental results show that the LP-based variation unaware VM placement algorithm improves the energy consumption by 15.3% on average from the baseline algorithm. For test cases with resource request variations, the FDSP algorithm saves 15.7% energy cost compared to the “worst case scenario” of the traditional VM placement paradigm. On the other hand, it improves the feasibility by 50.0% compared to the “best case scenario”.
Hemisystems of the Hermitian surface

We present a new method for the study of hemisystems of the Hermitian surface U3 of PG(3,q2). The basic idea is to represent generator-sets of U3 by means of a maximal curve naturally embedded in U3 so that a sufficient condition for the existence of hemisystems may follow from results about maximal curves and their automorphism groups. In this paper we obtain a hemisystem in PG(3,p2) for each p prime of the form p=1+16n2 with an integer n. Since the famous Landau's conjecture dating back to 1904 is still to be proved (or disproved), it is unknown whether there exists an infinite sequence of such primes. What is known so far is that just 18 primes up to 51000 with this property exist, namely 17,257,401,577,1297,1601,3137,7057,13457,14401,15377,24337,25601,30977,32401,33857,41617,50177. The scarcity of such primes seems to confirm that hemisystems of U3 are rare objects.
Hierarchical Image Segmentation Ensemble for Objectness in RGB-D Images

Objectness has recently become a standard step in many computer vision tasks. Among various techniques, those based on hierarchical image segmentation play a fundamental role for developments in new data modalities. In this paper, we address the problem of objectness in RGB-D images and propose a novel and effective approach, namely, hierarchical image segmentation ensemble (HISE). Different from existing image segmentation based methods that generate object segments or proposals largely by heuristics or empirical rules, HISE learns superpixel mergings with a hierarchical tree-structured ensemble, where individual merging models of the ensemble are formed by traversing different paths of the tree, and where both the merging accuracy and proposal diversity are emphasized. Furthermore, we use efficient feature measurements that support easy integration of additional clues. Extensive experiments conducted on the benchmark NYU-v2 RGB-D and SUN RGB-D data sets show the competency of our proposed method.
Coordinating Workload Scheduling of Geo-Distributed Data Centers and Electricity Generation of Smart Grid

With the rapidly increasing computing demand, data centers become more and more power-hungry, which incurs substantial electricity cost. Meanwhile, due to the time-dependent demand preference, power grid is suffering high load variations, which results in a large profit loss. In this paper, we consider a cost-efficient workload scheduling with a coordination between a cloud service provider operating multiple geo-distributed data centers and smart grids. The aim is to explore the flexibility of data center power demands to reduce the cost of the cloud service provider and smooth the load variations of smart grids simultaneously. We first present the penalty model of the computation workload scheduling at each data center, and introduce the cost model of smart grids, including power generation cost and the cost due to the power load variations. To jointly minimize the cost of smart grids and penalty of the cloud service provider resulted from workload scheduling, we formulate the objective function as a weighted sum of the cost and the penalty to study the tradeoffs, and obtain the optimal offline solution by the dual decomposition technique. In order to make the coordination implemented in an online fashion, we propose a Receding Horizon Control (RHC) based online algorithm to obtain the suboptimal workload management based on the predicted information, including the future amounts of interactive workload, batch workload, and power load, in the prediction horizon. The simulation results show that with the coordination between the cloud service provider and smart grids, the cost of smart grids can be significantly reduced, by up to 20 percent, and the load variations of smart grids can be well smoothed simultaneously.