nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing Patient Surgical Management Using WhatsApp Application in the Italian Healthcare System.

Smartphones changed the method by which doctors communicate with each other, offer modern functionalities sensitive to the context of use, and can represent a valuable ally in the healthcare system. Studies have shown that WhatsApp™ application can facilitate communication within the healthcare team and provide the attending physician a constant oversight of activities performed by junior team members. The aim of the study was to use WhatsApp between two distant surgical teams involved in a program of elective surgery to verify if it facilitates communication, enhances learning, and improves patient care preserving their privacy.



We conducted a focused group of surgeons over a 28-month period (from March 2013 to July 2015), and from September 2014 to July 2015, a group of selected specialists communicated healthcare matters through the newly founded "WhatsApp Surgery Group." Each patient enrolled in the study signed a consent form to let the team communicate his/her clinical data using WhatsApp. Communication between team members, response times, and types of messages were evaluated.



Forty six (n = 46) patients were enrolled in the study. A total of 1,053 images were used with an average of 78 images for each patient (range 41-143). 125 h of communication were recorded, generating 354 communication events. The expert surgeon had received the highest number of questions (P, 0.001), while the residents asked clinical questions (P, 0.001) and were the fastest responders to communications (P, 0.001).



Our study investigated how two distant clinical teams may exploit such a communication system and quantifies both the direction and type of communication between surgeons. WhatsApp is a low cost, secure, and fast technology and it offers the opportunity to facilitate clinical and nonclinical communications, enhance learning, and improve patient care preserving their privacy.
Improving the detection and prediction of suicidal behavior among military personnel by measuring suicidal beliefs: an evaluation of the Suicide Cognitions Scale.

Newer approaches for understanding suicidal behavior suggest the assessment of suicide-specific beliefs and cognitions may improve the detection and prediction of suicidal thoughts and behaviors. The Suicide Cognitions Scale (SCS) was developed to measure suicide-specific beliefs, but it has not been tested in a military setting.



Data were analyzed from two separate studies conducted at three military mental health clinics (one U.S. Army, two U.S. Air Force). Participants included 175 active duty Army personnel with acute suicidal ideation and/or a recent suicide attempt referred for a treatment study (Sample 1) and 151 active duty Air Force personnel receiving routine outpatient mental health care (Sample 2). In both samples, participants completed self-report measures and clinician-administered interviews. Follow-up suicide attempts were assessed via clinician-administered interview for Sample 1. Statistical analyses included confirmatory factor analysis, between-group comparisons by history of suicidality, and generalized regression modeling.



Two latent factors were confirmed for the SCS: Unloveability and Unbearability. Each demonstrated good internal consistency, convergent validity, and divergent validity. Both scales significantly predicted current suicidal ideation (βs >0.316, ps <0.002) and significantly differentiated suicide attempts from nonsuicidal self-injury and control groups (F(6, 286)=9.801, p<0.001). Both scales significantly predicted future suicide attempts (AORs>1.07, ps <0.050) better than other risk factors.



Self-report methodology, small sample sizes, predominantly male samples.



The SCS is a reliable and valid measure that predicts suicidal ideation and suicide attempts among military personnel better than other well-established risk factors.
Privacy-preserving data integration and sharing

Integrating data from multiple sources has been a longstanding challenge in the database community. Techniques such as privacy-preserving data mining promises privacy, but assume data has integration has been accomplished. Data integration methods are seriously hampered by inability to share the data to be integrated. This paper lays out a privacy framework for data integration. Challenges for data integration in the context of this framework are discussed, in the context of existing accomplishments in data integration. Many of these challenges are opportunities for the data mining community.
Decoupled Direct Memory Access: Isolating CPU and IO Traffic by Leveraging a Dual-Data-Port DRAM

Memory channel contention is a critical performance bottleneck in modern systems that have highly parallelized processing units operating on large data sets. The memory channel is contended not only by requests from different user applications (CPU access) but also by system requests for peripheral data (IO access), usually controlled by Direct Memory Access (DMA) engines. Our goal, in this work, is to improve system performance byeliminating memory channel contention between CPU accesses and IO accesses. To this end, we propose a hardware-software cooperative data transfer mechanism, Decoupled DMA (DDMA) that provides a specialized low-cost memory channel for IO accesses. In our DDMA design, main memoryhas two independent data channels, of which one is connected to the processor (CPU channel) and the other to the IO devices (IO channel), enabling CPU and IO accesses to be served on different channels. Systemsoftware or the compiler identifies which requests should be handled on the IO channel and communicates this to the DDMA engine, which then initiates the transfers on the IO channel. By doing so, our proposal increasesthe effective memory channel bandwidth, thereby either accelerating data transfers between system components, or providing opportunities to employ IO performance enhancement techniques (e.g., aggressive IO prefetching)without interfering with CPU accessesWe demonstrate the effectiveness of our DDMA framework in two scenarios: (i) CPU-GPU communication and (ii) in-memory communication (bulk datacopy/initialization within the main memory). By effectively decoupling accesses for CPU-GPU communication and in-memory communication from CPU accesses, our DDMA-based design achieves significant performanceimprovement across a wide variety of system configurations (e.g., 20% average performance improvement on a typical 2-channel 2-rank memory system).
Accelerating Haskell array codes with multicore GPUs

Current GPUs are massively parallel multicore processors optimised for workloads with a large degree of SIMD parallelism. Good performance requires highly idiomatic programs, whose development is work intensive and requires expert knowledge.
 To raise the level of abstraction, we propose a domain-specific high-level language of array computations that captures appropriate idioms in the form of collective array operations. We embed this purely functional array language in Haskell with an online code generator for NVIDIA's CUDA GPGPU programming environment. We regard the embedded language's collective array operations as algorithmic skeletons; our code generator instantiates CUDA implementations of those skeletons to execute embedded array programs.
 This paper outlines our embedding in Haskell, details the design and implementation of the dynamic code generator, and reports on initial benchmark results. These results suggest that we can compete with moderately optimised native CUDA code, while enabling much simpler source programs.
Single-Site Colectomy With Miniature In Vivo Robotic Platform

There has been a continuing push to reduce the invasiveness of surgery by accessing the abdominal cavity through a single incision, such as with laparoendoscopic single-site (LESS) surgery. Although LESS procedures offer significant benefits, added complexities still inhibit the procedures. Robotic surgery is proving to be an excellent option to overcome these limitations. This paper presents the experimental results of the single-incision in vivo surgical robot (SISR), a multifunctional, dexterous, two-armed robot capable of performing surgical tasks while overcoming the issues associated with manual LESS operations. In vivo surgical procedures have been used to demonstrate the efficacy of using a robotic platform over traditional laparoscopic tools. The most recent experimental test resulted in the first successful in vivo robotic LESS colectomy utilizing a robot completely contained within the abdominal cavity. In this test, SISR showed significant benefits including access to all quadrants in the peritoneal cavity and improved dexterity.
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems

Recent growing interest in predicting and influencing consu mer behavior has generated a parallel increase in research efforts on Recommend er Systems. Many of the state-of-the-art Recommender Systems algorithms rely on o btaining user ratings in order to later predict unknown ratings. An underlying assumpt ion in this approach is that the user ratings can be treated as ground truth of the user’s t aste. However, users are inconsistent in giving their feedback, thus introducing an un known amount of noise that challenges the validity of this assumption. In this paper, we tackle the problem of analyzing and charact e izing the noise in user feedback through ratings of movies. We present a user st udy aimed at quantifying the noise in user ratings that is due to inconsistencies. We m easure RMSE values that range from0.557 to 0.8156. We also analyze how factors such as item sorting and time of rating affect this noise.
A New Generalized Error Path Algorithm for Model Selection

Model selection with cross validation (CV) is very popular in machine learning. However, CV with grid and other common search strategies cannot guarantee to find the model with minimum CV error, which is often the ultimate goal of model selection. Recently, various solution path algorithms have been proposed for several important learning algorithms including support vector classification, Lasso, and so on. However, they still do not guarantee to find the model with minimum CV error. In this paper, we first show that the solution paths produced by various algorithms have the property of piecewise linearity. Then, we prove that a large class of error (or loss) functions are piecewise constant, linear, or quadratic w.r.t. the regularization parameter, based on the solution path. Finally, we propose a new generalized error path algorithm (GEP), and prove that it will find the model with minimum CV error for the entire range of the regularization parameter. The experimental results on a variety of datasets not only confirm our theoretical findings, but also show that the best model with our GEP has better generalization error on the test data, compared to the grid search, manual search, and random search.
Directional texture transfer

A texture transfer algorithm modifies the target image replacing the high frequency information with the example source image. Previous texture transfer techniques normally use such factors as color distance and standard deviation for selecting the best texture from the candidate sets. These factors are useful for expressing a texture effect of the example source in the target image, but are less than optimal for considering the object shape of the target image.
 In this paper, we propose a novel texture transfer algorithm to express the directional effect based on the flow of the target image. For this, we use a directional factor that considers the gradient direction of the target image. We add an additional energy term that respects the image gradient to the previous fast texture transfer algorithm. Additionally, we propose a method for estimating the directional factor weight value from the target image. We have tested our algorithm with various target images. Our algorithm can express a result image with the feature of the example source texture and the flow of the target image.
Multi-Residential Demand Response Scheduling With Multi-Class Appliances in Smart Grid

In this paper, we study a multi-residential electricity load scheduling problem with multi-class appliances in smart grid. Compared with the previous works in which only limited types of appliances are considered or only single residence grids are considered, we model the grid system more practically with jointly considering multi-residence and multi-class appliance. We formulate an optimization problem to maximize the sum of the overall satisfaction levels of residences which is defined as the sum of utilities of the residential customers minus the total cost for energy consumption. Then, we provide an electricity load scheduling algorithm by using a PL-Generalized Benders Algorithm which operates in a distributed manner while protecting the private information of the residences. By applying the algorithm, we can obtain the near-optimal load scheduling for each residence, which is shown to be very close to the optimal scheduling, and also obtain the lower and upper bounds on the optimal sum of the overall satisfaction levels of all residences, which are shown to be very tight.
Designing Acceptable Assisted Living Services for Elderly Users

With today’s technology, elderly users could be supported in living independently in their own homes for a prolonged period of time. Commercially available products enable remote monitoring of the state of the user, enhance social networks, and even support elderly citizens in their everyday routines. Whereas technology seems to be in place to support elderly users, one might question the value of present solutions in terms of solving real user problems such as loneliness and self-efficacy. Furthermore, products tend to be complex in use and do not relate to the reference framework of elderly users. Consequently, acceptability of many present solutions tends to be low. This paper presents a design vision of assisted living solutions that elderly love to use. Based on earlier work, five concrete design goals have been identified that are specific to assisted living services for elderly users. The vision is illustrated by three examples of ongoing work; these cases present the design process of prototypes that are being tested in the field with elderly users. Even though the example cases are limited in terms of number of participants and quantitative data, the qualitative feedback and design experiences can serve as inspiration for designers of assisted living services.
Measuring online social bubbles

Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive dataset of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed — Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information.
Maternal immune activation: Implications for neuropsychiatric disorders

Epidemiological evidence implicates maternal infection as a risk factor for autism spectrum disorder and schizophrenia. Animal models corroborate this link and demonstrate that maternal immune activation (MIA) alone is sufficient to impart lifelong neuropathology and altered behaviors in offspring. This Review describes common principles revealed by these models, highlighting recent findings that strengthen their relevance for schizophrenia and autism and are starting to reveal the molecular mechanisms underlying the effects of MIA on offspring. The role of MIA as a primer for a much wider range of psychiatric and neurologic disorders is also discussed. Finally, the need for more research in this nascent field and the implications for identifying and developing new treatments for individuals at heightened risk for neuroimmune disorders are considered.
A word-order based graph representation for relevance identification

In this paper we propose a new word-order based graph representation for text. In our graph representation vertices represent words or phrases and edges represent relations between contiguous words or phrases. The graph representation also includes dependency information. Our text representation is suitable for applications involving the identification of relevance or paraphrases across texts, where word-order information would be useful. We show that this word-order based graph representation performs better than a dependency tree representation while identifying the relevance of one piece of text to another.
Graphene transfer: key for applications.

The first micrometer-sized graphene flakes extracted from graphite demonstrated outstanding electrical, mechanical and chemical properties, but they were too small for practical applications. However, the recent advances in graphene synthesis and transfer techniques have enabled various macroscopic applications such as transparent electrodes for touch screens and light-emitting diodes (LEDs) and thin-film transistors for flexible electronics in particular. With such exciting potential, a great deal of effort has been put towards producing larger size graphene in the hopes of industrializing graphene production. Little less than a decade after the first discovery, graphene now can be synthesized up to 30 inches in its diagonal size using chemical vapour deposition methods. In making this possible, it was not only the advances in the synthesis techniques but also the transfer methods that deliver graphene onto target substrates without significant mechanical damage. In this article, the recent advancements in transferring graphene to arbitrary substrates will be extensively reviewed. The methods are categorized into mechanical exfoliation, polymer-assisted transfer, continuous transfer by roll-to-roll process, and transfer-free techniques including direct synthesis on insulating substrates.
A three-phase approach for operating theatre schedules.

In this paper we develop a three-phase, hierarchical approach for the weekly scheduling of operating rooms. This approach has been implemented in one of the surgical departments of a public hospital located in Genova (Genoa), Italy. Our aim is to suggest an integrated way of facing surgical activity planning in order to improve overall operating theatre efficiency in terms of overtime and throughput as well as waiting list reduction, while improving department organization. In the first phase we solve a bin packing-like problem in order to select the number of sessions to be weekly scheduled for each ward; the proposed and original selection criterion is based upon an updated priority score taking into proper account both the waiting list of each ward and the reduction of residual ward demand. Then we use a blocked booking method for determining optimal time tables, denoted Master Surgical Schedule (MSS), by defining the assignment between wards and surgery rooms. Lastly, once the MSS has been determined we use the simulation software environment Witness 2004 in order to analyze different sequencings of surgical activities that arise when priority is given on the basis of a) the longest waiting time (LWT), b) the longest processing time (LPT) and c) the shortest processing time (SPT). The resulting simulation models also allow us to outline possible organizational improvements in surgical activity. The results of an extensive computational experimentation pertaining to the studied surgical department are here given and analyzed.
Machine Learning and Software Engineering

Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in software engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks and use some software development and maintenance tasks as examples to show how they can be formulated as learning problems and approached in terms of learning algorithms.
DIVISION ALGORITHM DESIGN USING FIELD PROGRAMMABLE GATE ARRAY

This project is to design eight bit division algorithm program by using Xilinx ISE 10.1 software for simulation algorithm circuit partitioning through hardware Field Programmable Gate Array (FPGA). The algorithms are divide 8-bit dividend by 8-bit divisor for input and get the result 16-bit for the output. Circuit partitioning algorithms eight bits used to implement the distribution process for each program using the arithmetic and logic unit operations, called (ALU). All these operations using Verilog language in a program to be displayed on (LED) using the FPGA board. FPGA is a semiconductor device containing programmable logic components called "logic blocks", and programmable. Logic block can be programmed to perform the functions of basic logic gates such as AND, and XOR, or more complex combination of functions such as decoders or simple mathematical functions such as addition, subtraction, multiplication, and division (+, -, x, ÷). Finally, this project outlines the design and implementation of a new hardware divisor for performing 8-bit division. The error probability function of this division algorithm is fully characterized and contrasted against existing hardware division algorithms.
Software Reuse: Architecture, Process and Organization for Business Success

Object technology is believed to be crucial in achieving the long sought-after goal of widespread reuse. This goal is the most frequently stated reason for adopting OT. Unfortunately, many people naively equate reuse with objects, expecting it to “automatically” ensure reuse, but often do not get much reuse. Based on my experience with reuse at HP, Objectory and Rational, and with many customers, I know that without extensive changes to support component-based development and systematic reuse, OT as used today will not succeed in giving users reuse. Without an explicit reuse agenda, and a systematic approach to the design and use of reusable components and frameworks, 00 reuse will not succeed. In almost all cases of successful reuse, architecture, a dedicated component development and support group, management support, and a stable domain were the keys to success. These largely non-technical issues seem to be more important to successful reuse than the specific language or design chosen.