nils-herrmann/clean_scirepeval huggingface dataset:
An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems

In the last two decades, we have witnessed that Interval Type-2 Fuzzy Logic Systems (IT2-FLSs) have been successfully implemented in various engineering areas. In this paper, we will introduce a free open source Mat lab/Simulink toolbox for the development of IT2-FLSs for a wider accessibility to users beyond the type-2 fuzzy logic community. The presented IT2-FLS toolbox allows intuitive implementation of IT2-FLSs where it is capable to cover all the phases of its design. In order to allow users to easily construct IT2-FISs, a GUI is developed which is similar to that of Matlab® Fuzzy Logic Toolbox. We have embedded various Type Reduction (TR) methods in the toolbox since the TR method is the most important operation that must be taken into consideration. This gives the opportunity to the user to examine the performance of his/her IT2-FLS design with respect to the TR methods. We have also developed an IT2-FLS Simulink library so that the designer can perform various simulation analyses and can also investigate the effect of TR method on the performance of the IT2-FLS. Moreover, the developed IT2-FLS Mat lab/Simulink toolbox contains an automatic connection between Matlab and Simulink environments. Once the user has finished the design of the IT2-FLS via the GUI, it is possible to export his/her design directly to Simulink via an automatic Simulink file generation. We believe that the availability of the developed free and open-source IT2-FLS Mat lab/Simulink toolbox will be an important step for a wider deployment and development of IT2-FLSs.
ActiveCDN: Cloud Computing Meets Content Delivery Networks

Content delivery networks play a crucial role in today’s Internet. They serve a large portion of the multimedia on the Internet and solve problems of scalability and indirectly network congestion (at a price). However, most content delivery networks rely on a statically deployed configuration of nodes and network topology that makes it hard to grow and scale dynamically. We present ActiveCDN, a novel CDN architecture that allows a content publisher to dynamically scale their content delivery services using network virtualization and cloud computing techniques.
Combination 830-nm and 633-nm light-emitting diode phototherapy shows promise in the treatment of recalcitrant psoriasis: preliminary findings.

Psoriasis is one of the major problems facing dermatologists worldwide. Planar arrays of light-emitting diodes (LEDs) have recently attracted attention in the treatment of difficult dermatological entities, 830 nm in near infrared (near-IR) and 633 nm in visible red. This study was designed to assess the efficacy of combination 830-nm and 633-nm LED phototherapy in the treatment of recalcitrant psoriasis.



Nine informed and consenting patients with psoriasis were enrolled in this preliminary study, (3 men, 6 women, mean age 34.3, skin types I to IV). All had chronic psoriasis, which in most cases had proved resistant to conventional treatments. They were treated sequentially with LED arrays delivering continuous-wave 830 nm (near-IR) and 633 nm (red) in two 20-min sessions over 4 or 5 weeks, with 48 h between sessions (830 nm, 60 J/cm(2); 633 nm, 126 J/cm(2)).



All patients completed their LED regimens (4 requiring 1 regimen, 5 requiring a second). Follow-up periods were from 3 to 8 months, except in two patients who were lost to follow-up. Clearance rates at the end of the follow-up period ranged from 60% to 100%. Satisfaction was universally very high.



The antiinflammatory effects of LED energy at 830 nm and 633 nm have been well documented, as has their use in wound healing. LED phototherapy is easy to apply, pain free and side-effect free, and is well tolerated by patients of all skin types. The promising results of this preliminary study warrant a proper controlled double-blind study with a larger patient population.
THE UBISENSE SMART SPACE PLATFORM

Ubisense has developed a platform for building Smart Space applications. The platform addresses the key requirements for building Smart Spaces: accurate 3D positioning, scalable realtime performance, development and deployment tools. This paper deepens the key requirements and describes how the Ubisense platform components meets them. The demonstration exemplifies the smart space platform by tracking players in a game application. The Ubisense Smart Space Platform Ubisense has developed a platform for building Smart Space applications. Our platform addresses the key requirements for building Smart Spaces: Accurate 3D positioning supports applications that can perceive the physical relationships between people and objects in the environment Scalable real-time performance enables arbitrary numbers of applications, used by arbitrary numbers of people over an arbitrarily-large area Development and deployment tools make it easy to design, implement, and manage Smart Space applications. The demonstration shows a Smart Space containing applications that integrate with external software (a simple game that users control by moving around), and devices (a PTZ camera that keeps users in shot while they are playing the game) Fig. 1 Smart space demonstration setup 1 Ubisense, St Andrews House, 90 St Andrews Road, Chesterton, CB4 1DL, United Kingdom, http://www.ubisense.net
A systematic review of readability and comprehension instruments used for print and web-based cancer information.

Adequate functional literacy skills positively influence individuals' ability to take control of their health. Print and Web-based cancer information is often written at difficult reading levels. This systematic review evaluates readability instruments (FRE, F-K, Fog, SMOG, Fry) used to assess print and Web-based cancer information and word recognition and comprehension tests (Cloze, REALM, TOFHLA, WRAT) that measure people's health literacy. Articles on readability and comprehension instruments explicitly used for cancer information were assembled by searching MEDLINE and Psyc INFO from 1993 to 2003. In all, 23 studies were included; 16 on readability, 6 on comprehension, and 1 on readability and comprehension. Of the readability investigations, 14 focused on print materials, and 2 assessed Internet information. Comprehension and word recognition measures were not applied to Web-based information. None of the formulas were designed to determine the effects of visuals or design factors that could influence readability and comprehension of cancer education information.
Hard and Easy Distributions of SAT Problems

We report results from large-scale experiments in satis-ability testing. As has been observed by others, testing the satissability of random formulas often appears surprisingly easy. Here we show that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satissability testing is quite diicult. Our results provide a benchmark for the evaluation of satissability-testing procedures.
An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation

During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image.
Automated leukemia detection in blood microscopic images using statistical texture analysis

Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagnostic prediction of ALL. Other shape features are also extracted to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.
Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction.

Would people exhibit similarity-attraction and consistency-attraction toward unambiguously computer-generated speech even when personality is clearly not relevant? In Experiment 1, participants (extrovert or introvert) heard a synthesized voice (extrovert or introvert) on a book-buying Web site. Participants accurately recognized personality cues in text to speech and showed similarity-attraction in their evaluation of the computer voice, the book reviews, and the reviewer. Experiment 2, in a Web auction context, added personality of the text to the previous design. The results replicated Experiment 1 and demonstrated consistency (voice and text personality)-attraction. To maximize liking and trust, designers should set parameters, for example, words per minute or frequency range, that create a personality that is consistent with the user and the content being presented.
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games

Poker is a family of card games that includes many variations. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representation. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold’em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competitive player against human experts. The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a CNN based learning model that can effectively learn the patterns in three different games, and (3) a selftrained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.
Collaborative Joint Training With Multitask Recurrent Model for Speech and Speaker Recognition

Automatic speech and speaker recognition are traditionally treated as two independent tasks and are studied separately. The human brain in contrast deciphers the linguistic content, and the speaker traits from the speech in a collaborative manner. This key observation motivates the work presented in this paper. A collaborative joint training approach based on multitask recurrent neural network models is proposed, where the output of one task is backpropagated to the other tasks. This is a general framework for learning collaborative tasks and fits well with the goal of joint learning of automatic speech and speaker recognition. Through a comprehensive study, it is shown that the multitask recurrent neural net models deliver improved performance on both automatic speech and speaker recognition tasks as compared to single-task systems. The strength of such multitask collaborative learning is analyzed, and the impact of various training configurations is investigated.
Cost-effective low-loss flexible optical engine with microlens-imprinted film for high-speed on-board optical interconnection

There is a strong demand for optical interconnection technology to overcome bandwidth bottlenecks in high-end server systems. The interconnection speed in present systems is approaching 10 Gb/s, and higher-speed interconnections over 25 Gb/s are being discussed. To achieve such optical interconnections in commercial production, it is necessary to develop lower-cost and higher-speed optical transceiver modules. We propose a flexible printed circuit optical engine (FPC-OE) with a microlens-imprinted film and a polymer waveguide to achieve low-cost and high-speed operation. The microlens-imprinted film can be produced at low cost by using nanoimprint technology and can drastically reduce the optical loss of the FPC-OE with polymer waveguide. We successfully demonstrated error-free operation at 25 Gb/s with the fabricated optical transceiver that contains an FPC-OE, microlens-imprinted film, and a polymer waveguide.
Specific Impairment of Smiling Increases the Severity of Depressive Symptoms in Patients with Facial Neuromuscular Disorders

Depressive symptoms and related emotional distress are prevalent among patients with facial neuromuscular disorders, and the psychological distress impacts the functional disabilities associated with the facial impairment. A specific impairment in the ability to smile may elevate the risk for depression, with patients experiencing a reduced physiological feedback associated with smiling as well as the social consequences of the inability to communicate positive emotion. We tested the hypothesis that specific impairments in the ability to smile increase the severity of depressive symptoms in patients with facial neuromuscular disorders. Twenty-nine consecutive patients (mean age, 50.2 years; SD, 17.0 years; range, 18–81 years) with a facial neuromuscular disorder, who volunteered and completed all of the assessment measures participated. Facial neuromuscular impairments were assessed using multiple measures of facial motility and dysfunction, and emotional functioning was assessed using self-report measures of depression, anxiety, and positive and negative affect. Severity of global facial impairment was statistically controlled in evaluating the association between specific impairment in smiling and the degree of depressive symptoms. Separate hierarchical linear regression analyses indicated the specific impairment of smiling contributed to the prediction of depression (R 2= .41, df = 3,25, p= .00) and anxiety (R 2= .35, df = 3,25, p= .00), controlling first for the contribution of global impairment and facial physical disability. The specific impairment of smiling did not contribute to the prediction of positive emotional experience. Specific impairment of smiling and physical disability, but not global impairment of facial motion, were key predictors of depression in patients with facial neuromuscular disorders. The results emphasize the need to assess and treat depression and anxiety in patients with a facial neuromuscular disorder.
A Survey of Design Issues in Spatial Input

We present a survey of design issues for developing effective free-space three-dimensional (3D) user interfaces. Our survey is based upon previous work in 3D interaction, our experience in developing free-space interfaces, and our informal observations of test users. We illustrate our design issues using examples drawn from instances of 3D interfaces.
For example, our first issue suggests that users have difficulty understanding three-dimensional space. We offer a set of strategies which may help users to better perceive a 3D virtual environment, including the use of spatial references, relative gesture, two-handed interaction, multisensory feedback, physical constraints, and head tracking. We describe interfaces which employ these strategies.
Our major contribution is the synthesis of many scattered results, observations, and examples into a common framework. This framework should serve as a guide to researchers or systems builders who may not be familiar with design issues in spatial input. Where appropriate, we also try to identify areas in free-space 3D interaction which we see as likely candidates for additional research.
An extended and annotated version of the references list for this paper is available on-line through mosaic at address http://uvacs.cs.virginia.edu/~kph2q/.
Computer- vs. paper-based tasks: are they equivalent?

In 1992, Dillon published his critical review of the empirical literature on reading from paper vs. screen. However, the debate concerning the equivalence of computer- and paper-based tasks continues, especially with the growing interest in online assessment. The current paper reviews the literature over the last 15 years and contrasts the results of these more recent studies with Dillon's findings. It is concluded that total equivalence is not possible to achieve, although developments in computer technology, more sophisticated comparative measures and more positive user attitudes have resulted in a continuing move towards achieving this goal. Many paper-based tasks used for assessment or evaluation have been transferred directly onto computers with little regard for any implications. This paper considers equivalence issues between the media by reviewing performance measures. While equivalence seems impossible, the importance of any differences appears specific to the task and required outcomes.
Feasibility of Embedded DRAM Cells on FinFET Technology

In this paper, we analyze the suitability of implementing embedded DRAM (eDRAM) cells on FinFET technology compared to classical planar MOSFETs. The results show a significant improvement in overall cell performance for multi-gate devices. While pFinFET-based memories showed better cell behavior and variability robustness, mixed n/pFinFET cells had the highest working frequency and a negligible impact on degradation. Finally, we show that a multiple fin-height strategy can be used to reduce the layout area of the eDRAM cells (>10%).
A Survey of Inter-Vehicle Communication ∗

As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry of, notably, US, EU, and Japan. The most important feature of IVC is its ability to extend the horizon of drivers and on-board devices (e.g., radar or sensors) and, thus, to improve road traffic safety and efficiency.  IVC with respect to key enabling technologies ranging from physical radio frequency to group communication primitives and security issues. The mobility models used to evaluate the feasibility of these technologies are also briefly described. We focus on the discussion of various MAC protocols that seem to be indispensable components in the network protocol stack of IVC. By analyzing the application requirements and the protocols built upon the MAC layer to meet these requirements, we also advocate our perspective that ad hoc routing protocols and group communication primitives migrated from wired networks might not be an efficient way to support the envisioned applications, and that new coordination algorithms directly based on MAC could be designed for this purpose.
Company event popularity for financial markets using Twitter and sentiment analysis

The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies.
Bayesian Nonparametric Methods for Learning Markov Switching Processes

In this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP.
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects

An image is not just a collection of objects, but rather a graph where each object is related to other objects through spatial and semantic relations. Using relational reasoning modules, allowing message passing between objects, can therefore improve object detection. Current schemes apply such dedicated modules either on a specific layer of the bottom-up stream, or between already-detected objects. We show that the relational process can be better modeled in a coarse to fine manner and present a novel framework, applying a non-local module sequentially to increasing resolution feature-maps along the top-down stream. In this way, the inner relational process can naturally pass information from larger objects to smaller related ones. Applying the modules to fine feature-maps also allows message passing between the small objects themselves, exploiting repetitions of instances from of the same class. In practice, due to the expensive memory utilization of the non-local module, it is unfeasible to apply the module as currently used to highresolution feature-maps. We efficiently redesigned the non local module, improved it in terms of memory and number of operations, allowing it to be placed anywhere along the network. We also incorporated relative spatial information into the module, in a manner that can be incorporated into our efficient implementation. We show the effectiveness of our scheme by improving the results of detecting small objects on COCO by 1.5 AP over Faster RCNN and by 1 AP over using non-local module on the bottom-up stream.
Insights into Human Behavior from Lesions to the Prefrontal Cortex

The prefrontal cortex (PFC), a cortical region that was once thought to be functionally insignificant, is now known to play an essential role in the organization and control of goal-directed thought and behavior. Neuroimaging, neurophysiological, and modeling techniques have led to tremendous advances in our understanding of PFC functions over the last few decades. It should be noted, however, that neurological, neuropathological, and neuropsychological studies have contributed some of the most essential, historical, and often prescient conclusions regarding the functions of this region. Importantly, examination of patients with brain damage allows one to draw conclusions about whether a brain area is necessary for a particular function. Here, we provide a broad overview of PFC functions based on behavioral and neural changes resulting from damage to PFC in both human patients and nonhuman primates.