Miracle-dz/UniFAR-FTUs huggingface dataset:
Expert System Diagnosis of Cataract Eyes Using Fuzzy Mamdani Method

Cataracts are eye diseases characterized by cloudy or opacity of the lens of the eye by changing the colour of black into grey-white which slowly continues to grow and develop without feeling pain and pain that can cause blindness in human vision. Therefore, researchers make an expert system of cataract eye disease diagnosis by using Fuzzy Mamdani and how to care. The fuzzy method can convert the crisp value to linguistic value by fuzzification and includes in the rule. So this system produces an application program that can help the public in knowing cataract eye disease and how to care based on the symptoms suffered. From the results of the design implementation and testing of expert system applications to diagnose eye disease cataracts, it can be concluded that from a trial of 50 cases of data, obtained test results accuracy between system predictions with expert predictions obtained a value of 78% truth.
Towards automating protocol synthesis and analysis

In OSI (Open Systems Interconnection) Reference Model, a communication protocol is a set of rules governing the interaction and coordination between a number of communicating entities at a certain protocol layer. The purpose of these rules is to provide some intended communication services to the service users, which constitute the next higher layer above the concerned one. The first part of this research is with regard to automating the process of deriving protocol specifications from service specifications. However, the protocol derivation process for an arbitrary communication service appears to be formidably difficult. As a result, we concentrate on a class of communication services whose behavior can be described by a set of directly coupled Finite State Machines (FSMs). For a service specified in this state-transition model, we provide a protocol algorithm which produces the protocol specification from the given service specification automatically once some additional information about the decision options and initiation option is provided by the protocol designer. The provision of the additional information is to ensure that the produced protocol specification is indeed intended by the protocol designer. To enable our algorithm to deal with underlying error-prone communication services, we further devise an error-recovery transformation procedure. The error-recovery transformation procedure consists of three transformation rules applicable to three different patterns of transitions in the synchronizing protocol FSM produced by the protocol derivation algorithm from a service specification. Since every protocol synthesis technique, including ours, has its own limitations imposed by the underlying model and assumptions, protocol analysis (validation and verification) is still needed to ensure the correctness of protocols beyond the expressive power of the underlying model. One of the most effective and mechanizable protocol validation/verification techniques is reachability analysis. However, the global state explosion problem restricts the applicability of reachability analysis to protocols whose global state graphs are not huge. As a result, global state graph reduction techniques are needed for alleviating this problem. In the second part of this research, we propose two such techniques for protocol specification in the Extended Finite State Machine (EFSM) model. The impact of the first global state graph reduction technique on the incremental protocol validation is also examined.
A New Backward-Compatible Web Transport

4 Section 1: Understanding Internet Communication 4 1.1 An Internet Communication Model 4 1.2 The Internet Service Model 5 1.3 The Modern Internet 6 1.3.1 Middleboxes in the Internet 6 1.3.2 Implications: Hurdles in Deploying New Transport Services 6 Section 2: The Web and TCP 7 2.1 Non-Persistent and Persistent Connections 8 2.2 Problems with Multiple TCP Connections 8 3.1 uTCP: Breaking Out of the Transport Logjam 9 3.1.1 Modifications to the TCP Stack and the TCP API: 9 3.1.2 Limitations of uTCP 10 3.2 uCOBS: Building Unordered Messages 11 3.2.1 COBS (Consistent-Overhead Byte Stuffing) 11 3.3 uHTTP: Using an Unordered Transport for the Web 12 Section 4: Implementation of Our New Transport 13 4.1 uTCP Implementation 13 4.2 uCOBS Implementation 13 4.3 uHTTP Implementation 14 4.4 Implementation Challenges 15 Section 5: Results 15 5.1 Basic Functionality of uTCP 15 5.2 Bandwidth and CPU Costs 17 Section 5: Related Work 18 Section 6: Conclusions and Future Work 18 Section 7: References 19
PKUTM participation in TAC2011

In this paper, we present details of the participation of PKUTM in the Guided Summarization and AESOP tracks at TAC 2011. For the Guided Summarization task, we develop two extractive summarization systems based on manifold ranking and Integer Linear Programming (ILP) respectively. In our first system, we score the sentences by linearly combining manifold ranking scores and scores based on other surface features. In our second system, we extend the traditional ILP approach to Tolerated ILP (T-ILP), where certain degree of redundancy is tolerated in the optimization process. We also introduce Wikipedia as domain knowledge into the concept weighting step of the T-ILP framework. For post-processing, we revise our sentence ordering scheme proposed in our last year’s participation. In the AESOP track, we submit for the no model task. We combine the Rouge scores by training regression models on the TAC 2010’s summarization evaluation data and apply the regression model to predict the TAC 2011’s system performance.
Streaming Data Algorithm Design for Big Trajectory Data Analysis

Trajectory streams consist of large volumes of time-stamped spatial data that are constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectorystreamssuchasgatheringandcompaniesneedstoprocesseachrecordwhenitarrivesand correlatesacrossmultiplerecordsnearreal-time. Thustechniquesforhandlinghigh-speedtrajectorystreamsshouldscaleondistributedclustercomputing. Themainissuesencapsulatethreeaspects, namely a data model to represent the continuous trajectory data, the parallelism of a discovery algorithm, and end-to-end performance improvement. In this thesis, I propose two parallel discovery methods,namelysnapshotmodelandslotmodelthateachconsistsof1)amodelofpartitioningtrajectoriessampledondifferenttimeintervals;2)deﬁnitionondistancemeasurementsoftrajectories; and 3) a parallel discovery algorithm. I develop these methods in a stream processing workﬂow. I evaluate our solution with a public dataset on Amazon Web Services (AWS) cloud cluster. From parallelization point of view, I investigate system performance, scalability, stability and pinpoint principle operations that contribute most to the run-time cost of computation and data shufﬂing. I improve data locality with ﬁne-tuned data partition and data aggregation techniques. I observe that both models can scale on a cluster of nodes as the intensity of trajectory data streams grows. Generally, snapshot model has higher throughput thus lower latency, while slot model produce more accurate trajectory discovery.
2004 IEEE International Workshop on Biomedical Circuits & Systems AN ON/OFF SPIKING PHOTORECEPTOR FOR ADAPTIVE ULTRAFASTKJLTRAWIDE DYNAMIC RANGE VISION CHIPS

In this work we present an adaptable spike generator circuit for intelligent vision chips. Our aim is to realize an adaptable vision chip which can -adjust for localized gain control, wide dynamic range, high temporal response, high spatial resolution and low power. To this end we have developed pulse frequency modulation spike encoder which is capable of providing very high dynamic ranges with power consumption similar to animal retina At lower dynamic ranges, frequency responses of over 2kHz is possible. Our circuit is based on the on-off opponency algorithm used by the human eye, and mows for integration of other functions such as tomographic increase in spatial resolution and information compression. Both silicon area and power consumption are kept to a minimum. This paper discusses the algorithm, its implementation and simulated results describing its response and power consumption.
A modified version of the SAR speckle filter based on structure detection

A modified version of the structure detection filter (SDF), originally developed by Nezry et al. for speckle reduction in SAR images, is presented. The original SDF first detects edges and thin linear structures, then applies the maximum a posteriori (MAP) filter equation for gamma distributed scene (Gamma MAP) based on local statistics extracted from rectangular masks. The modified-SDF proposed in this paper exploits the edge detection step of the SDF to perform image segmentation. The new filter computes local statistics over areas that are subsets of image segments having any possible shape. The modified-SDF procedure is effective in speckle removal and its computation time is estimated to be of the same magnitude as the original SDF. <<ETX>>
Hierarchical Motion-Compensated Frame Rate Up-Conversion Using Gaussian Pyramid and Guidance Motion Vector

This paper proposes fast and accurate frame rate up-conversion algorithm for both software and hardware. The proposed algorithm first construct Gaussian pyramid structure using 3×3 Gaussian mask. After constructing Gaussian pyramid, forward and backward unilateral motion estimation is implemented at the top level of the pyramid. Then, motion vector field that is obtained at the top level is projected onto the middle level and the bottom level of the pyramid with unilateral motion vector refinement. Next, on the bottom level, we implement motion vector smoothing and proposed motion vector shifting with bilateral motion vector refinement. By shifting unilateral motion vector to bilateral motion vector, we could avoid generating holes and overlapped regions. Finally, we select final MV between forward and backward MVs according to their reliability. After all the processes, we interpolate new frames by using overlapped block motion compensation. Experimental results show that proposed algorithm is up to 80 times faster than conventional algorithm with 0.1dB peak signal-to-noise gain in average.
Big-Data Processing Techniques and Their Challenges in Transport Domain

This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-data processing as batch- based, stream- based, graph- based,DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropriate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems.
A 6.25-GHz low DC power low-noise amplifier in SiGe

A 6.25-GHz monolithic low-noise amplifier (LNA) with a minimum noise figure of 2.2 dB and an associated gain of 20.4 dB implemented in a standard SiGe bipolar technology is presented. The 50-ohm noise figure is 3.5 dB with S21 of 18.3 dB. The circuit dissipates 9.4 mW from a 2.5-V supply (6.4 mW in the gain stages). The LNA's figure of merit gain/(P/sub DC//spl times/NF) of 0.56 mW/sup -1/ exceeds those of recently published 5 to 6 GHz GaAs MESFET and HBT LNA's.
Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

PROBABILISTIC PERSONALIZED RECOMMENDATION MODELS FOR HETEROGENEOUS SOCIAL DATA by VINEETH RAKESH MOHAN August 2017 Advisor: Dr. Chandan K. Reddy and Dr. Harpreet Singh Major: Computer Engineering Degree: Doctor of Philosophy Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this Thesis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks.
Fundamentals of deep learning

Businesses worldwide are using artificial intelligence (AI) to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.
Providing Database Encryption as a Scalable Enterprise Infrastructure Service

As databases become networked in more complex multi-tiered applications, their vulnerability to external attack grows. We address scalability as a particularly vital problem and propose alternative solutions for data encryption as an enterprise IT infrastructure component. In this paper, we explore a new approach for data privacy and security in which a security administrator protecting privacy at the level of individual fields and records, and providing seamless mechanisms to create, store, and securely access databases. Such a model alleviates the need for organizations to purchase expensive hardware, deal with software modifications, and hire professionals for encryption key management development tasks. Although access control has been deployed as a security mechanism almost since the birth of large database systems, many still look at database security as a problem to be addressed as the need arises - this is often after threats to the secrecy and integrity of data have occurred. Instead of building walls around servers or hard drives, a protective layer of encryption is provided around specific sensitive data items or objects. This prevents outside attacks as well as infiltration from within the server itself. This also allows the security administrator to define which data stored in databases are sensitive and thereby focusing the protection only on the sensitive data, which in turn minimizes the delays or burdens on the system that may occur from other bulk encryption methods. Encryption can provide strong security for data at rest, but developing a database encryption strategy must take many factors into consideration. Different stored data encryption strategies are outlined, so you can decide the best practice for each situation, and each individual field in your database, to handle different security and operating requirements. Application code and database schemas are sensitive to changes in the data type and data length. the paper presents a policy driven solution that allows transparent data level encryption that does not change the data field type or length.
Sequential Learning with Similarities

This thesis studies several extensions of multi-armed bandit problem, where a learner sequentially selects an action and obtains the reward of the action. Traditionally, the only information the learner acquires is about the obtained reward while information about other actions is hidden from the learner. This limited feedback can be restrictive in some applications like recommender systems, internet advertising, packet routing, etc. Usually, these problems come with structure, similarities between users or actions, additional observations, or any additional assumptions. Therefore, it is natural to incorporate these assumptions to the algorithms to improve their performance. This thesis focuses on multi-armed bandit problem with some underlying structure usually represented by a graph with actions as vertices. First, we study a problem where the graph captures similarities between actions; connected actions tend to grand similar rewards. Second, we study a problem where the learner observes rewards of all the neighbors of the selected action. We study these problems under several additional assumptions on rewards (stochastic, adversarial), side observations (adversarial, stochastic, noisy), actions (one node at the time, several nodes forming a combinatorial structure in the graph). The main contribution of this thesis is to design algorithms for previously mentioned problems together with theoretical and empirical guarantees. We also introduce several novel quantities, to capture the difficulty of some problems, like effective dimension and effective independence number.
Improving Communication Performance in GPU-Accelerated HPC Clusters

In recent years, GPUs have been adopted in many High-Performance Computing (HPC) clusters due to their massive computational power and energy efficiency. The Message Passing Interface (MPI) is the de-facto standard for parallel programming. Many HPC applications, written in MPI, use parallel processes and multiple GPUs to achieve higher performance and GPU memory capacity. In such applications, efficiently performing GPU inter-process communication is the key in the application performance. In this dissertation, we present proposals to improve the GPU inter-process communication in HPC clusters using novel GPU-aware designs, efficient and scalable algorithms, topology-aware designs, and hardware features. Specifically, we propose various approaches to improve the efficiency of MPI communication routines in GPU clusters. We also propose designs that evaluate the total application inter-process communication and provide solutions to improve its efficiency. First, we propose efficient GPU-aware algorithms to improve MPI collective performance. We show the importance of minimizing CPU intervention on GPU collective performance. We also utilize GPU features to enhance both collective communication and computation. As inter-process communications scale to across multi-GPU nodes and clusters,
Biomimetic Design and Experimental Methods Towards a Biologically Inspired, Soft Bodied, Peristaltic, Esophageal Swallowing Robot

Natural and biological phenomena continue to be a strong source of inspiration in the robotics field, as scientists and engineers aim to solve synonymous problems. The capabilities of traditional robots have seen significant development, where a new frontier into more soft and compliant actuation and sensation structures has seen recent exploration and the emergence of a new branch of research: soft-robotics. The intrinsic nature of these new technologies is that they are capable of soft and continuous interaction with their environment, which facilitates new actuation, transport, and locomotion modes. The main aim of this research initiative was to develop complementary actuation and sensation technologies, inspired by the esophageal phase of swallowing in man, to replicate biological swallowing. It is motivated by the differing swallow capability of individuals, to understand swallow efficacy as a symptom mitigation method for patients suffering from dysphagia, difficulty with safe swallowing. The peristaltic transport process in the esophagus has been previously investigated by a range of medical, mathematical, food scientific, and engineering methods. However, each of these techniques has their own unique caveats. The processes of abstraction, specification, and subsequent conceptual development of a physical swallowing system external to the human body have taken these limitations as a set of input constraints. The swallowing robot and sensation technologies embody the interdisciplinary knowledge in this field, and facilitate a complementary physical evaluation technique to augment and support the alternative avenues of investigation. This thesis examines the methods towards delivery of a soft, biomimetic, peristaltic, swallowing robot, consisting of actuation, trajectory generation, and sensation elements, to achieve clinically significant swallowing trajectories for the purpose of physical food bolus investigation external to the human body. By demonstrating the capability of these systems independently, and in their integrated final state, the research provides a unique platform for biomimetic swallowing that brings the diverse knowledge from the medical, mathematical, food science, and engineering fields together. It presents new opportunities to investigate the relationships between these domains, towards developing foods, which exhibit intrinsic swallow safety in man. Dedicated to my parents
Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions

The 45th Annual Meeting of the Association for Computational Linguistics, Posters and Demonstrations session was held between the 25th to 27th June 2007 in Prague. This year we had 113 submissions out of which 61 were selected for presentation, resulting in a 54% acceptance rate. The criteria for acceptance of posters were to describe original work in progress, and to present innovative methodologies used to solve problems in computational linguistics or NLP. 48 posters were accepted. For demonstrations the criterion for acceptance was the implementation of mature systems or prototypes in which computational linguistics or NLP technologies are used to solve practically important problems. 13 demonstrations were accepted.