Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving diagnostic accuracy in clinically ambiguous paediatric appendicitis: a retrospective review of ultrasound and pathology findings with focus on the non-visualised appendix.

OBJECTIVE To compare pre-operative ultrasound to histopathological results and retrospectively assess the diagnostic accuracy of ultrasound in paediatric appendicitis. METHODS 5 year review of all appendectomies performed in patients <16 years old in a tertiary referral university hospital. 983 patients had an appendicectomy over the time period while 189 patients had a preoperative ultrasound. We retrospectively reviewed all of the preoperative imaging in conjunction with the reports for the 189 patients; our aim was to determine the sensitivity of preoperative ultrasound for the diagnosis of acute appendicitis. RESULTS Of the 189 patients who had an ultrasound, 102 had histology positive for appendicitis and 87 had normal histology. Sensitivity overall was 72.55% [95% confidence interval (CI) 62.82 to 80.92] and specificity was 77.01% (95% CI 66.75 to 85.36). A suggested ultrasound diagnosis of appendicitis made positive pathology three times more likely and a normal ultrasound made positive pathology three times less likely [positive-predictive value 3.16 (95% CI 2.11 to 4.72) negative predictive value 0.36 (95% CI 0.25 to 0.50)]. 77% (67/87) of the patients whose pathology was ultimately normal had an ultrasound which was either normal or suggested an alternative diagnosis. However, in the 33 (17%) of patients with a non-visualised appendix, no secondary signs of inflammation or alternative diagnosis 16 (48%) had pathologically confirmed appendicitis. CONCLUSION Ultrasound has the potential to improve diagnostic accuracy in clinically ambiguous appendicitis. Advances in knowledge: This paper furthers the evidence on the efficacy of ultrasound as a diagnostic tool in acute appendicitis in children, especially when the diagnosis is clinically equivocal. It also sheds further light on the "non-visualized appendix" with almost half of these patients having pathologically confirmed appendicitis; meaning advanced imaging with CT or MR may be indicated in this cohort.
The Virtual Team Performance in Solving Teamwork Conflict Problems

This study aimed to know the ability of Binus Online Learning (BOL) students as a virtual team in solving their teamwork conflict problems, especially when they have to complete team assignments. This study uses Tuckman theory of teamwork (known as team stages model or FSNP) as the main theoretical basis for reference [1, 2]. This study uses a quantitative method with interview randomly among the students to clarify the subject matter mainly focuses on 131 students as the respondents which are identified as the millennial generation (born 1981-2000, based on Reeves and Oh, 2008)[3] considering their fluency in using Information and Communication Technology (ICT). To measure the ability of respondents in solving teamwork conflict problems based on online learning system in BOL, Likert scale is used on a scale of 1 to 5 with corrected item-total correlations 0.49, and α 0.81 (> 0.5). The result shows scale 1 was chosen by 17.20%, scale 2 was 15.53%, scale 3 was 31.90%, scale 4 was 24.99%, and scale 5 was selected by 10.28% of respondents. It concluded that the virtual team performance of BOL in solving teamwork conflict problems is average (tend to be moderate to high).
Relationship between Body Mass Composition, Bone Mineral Density, Skin Fibrosis and 25(OH) Vitamin D Serum Levels in Systemic Sclerosis

A reduced bone mineral density (BMD) is observed in several rheumatic autoimmune diseases, including Systemic Sclerosis (SSc); nevertheless, data concerning the possible determinants of bone loss in this disease are not fully investigated. The aim of this study is to evaluate the relationship between BMD, body mass composition, skin sclerosis and serum Vitamin D levels in two subsets of SSc patients. 64 post-menopausal SSc patients, classified as limited cutaneous (lcSSc) or diffuse cutaneous (dcSSc) SSc, were studied. As control, 35 healthy post-menopausal women were recruited. Clinical parameters were evaluated, including the extent of skin involvement. BMD at lumbar spine, hip, femoral neck and body mass composition were determined by dual-energy X-ray absorptiometry. Serum calcium, phosphorus, alkaline phosphatase, urine pyridinium cross-links, intact parathyroid hormone and 25-hydroxyvitamin D (25OHD) were measured. BMD at spine, femoral neck and total hip was significantly lower in SSc patients compared to controls. In dcSSc subset, BMD at spine, femoral neck and total hip was significantly lower compared to lcSSc. No differences in both fat and lean mass were found in the three study groups even if patients with dcSSc showed a slightly lower total body mass compared to healthy controls. Total mineral content was significantly reduced in dSSc compared to both healthy subjects and lcSSc group. Hypovitaminosis D was observed both in healthy post-menopausal women and in SSc patients, but 25OHD levels were significantly lower in dcSSc compared to lcSSc and inversely correlated with the extent of skin thickness. These results support the hypothesis that the extent of skin involvement in SSc patients could be an important factor in determining low circulating levels of 25OHD, which in turn could play a significant role in the reduction of BMD and total mineral content.
Probabilistic analysis of onion routing in a black-box model

We perform a probabilistic analysis of onion routing. The analysis is presented in a black-box model of anonymous communication that abstracts the essential properties of onion routing in the presence of an active adversary that controls a portion of the network and knows all a priori distributions on user choices of destination. Our results quantify how much the adversary can gain in identifying users by exploiting knowledge of their probabilistic behavior. In particular, we show that a user uâ s anonymity is worst either when the other users always choose the destination u is least likely to visit or when the other users always choose the destination u chooses. This worst-case anonymity with an adversary that controls a fraction b of the routers is comparable to the bestcase anonymity against an adversary that controls a fraction pb.
A Novel Cloud-Based Advanced Distribution Management System Solution

This paper proposes a novel advanced distribution management system (ADMS) solution based on a common operation technology platform implemented on a cloud infrastructure. It introduces a virtualization methodology that is used to transition and deploy a traditional, monolithic ADMS in a cloud environment. First, typical ADMS functional blocks (FBs) are identified and their performance is evaluated with respect to four major metrics: processor, memory, network, and storage utilization. Next, a cloud infrastructure-based ADMS solution is presented, based on a collection of FBs mapped to a set of virtual machines. Finally, the proposed architecture is validated via deployment on a physical hardware platform with two representatively sized distribution networks and emulated workloads. Obtained results show that an ADMS, as a mission-critical system, can be deployed on a cloud infrastructure, providing many benefits of virtualized solutions, without negative impact on system performance.
Image Inpainting Based on a Novel Criminisi Algorithm

In view of the problem of image inpainting error continuation and the deviation of finding best match block, an improved Criminisi algorithm is proposed. The improvement was mainly embodied in two aspects. In the repairing order aspect, we redefine the calculation formula of the priority. In order to solve the problem of error continuation caused by local confidence item updating, the mean value of Manhattan distance is used for replace the confidence item. In the matching strategy aspect, finding the best match block not only depend on the difference of the two pixels, but also consider the matching region. Therefore, Euclidean distance is introduced. Experiments confirm that the improved algorithm can overcome the insufficiencies of the original algorithm. The repairing effect has been improved, and the results have a better visual appearance.
A Distributed Semiasynchronous Algorithm for Network Traffic Engineering

In this paper, we consider the traffic engineering problem in a large-scale hierarchical network arising in the next-generation cloud-based wireless networks. We propose a distributed semiasynchronous algorithm for this problem based on the so-called block successive upper bound minimization method of multipliers (BSUM-M). Theoretically, we show that the proposed algorithm converges to the global optimal solution under some assumptions on the degree of network asynchrony. We illustrate the effectiveness and efficiency of the proposed algorithm by comparing it with the state-of-the-art commercial solvers in a networked environment.
How does citing behavior for a scientific article change over time? A preliminary study

This study is to investigate how citing behavior for a scientific article changes over time. A highly cited article is chosen to collect citation content from its 902 citing articles. Natural language processing and content analysis are adopted to encode citation content and complete the statistics of citation behavior. Citation content analysis indexes including citation mention, length, and location, are used to describe the changes of citation behavior over time. The experimental results show that there is correlation between citing time with most of the indexes and that citation mention and citation length are declining while the number of the citation co‐occurrences is increasing.
Mystic: Mystifying IP Cores Using an Always-ON FSM Obfuscation Method

The separation of manufacturing and design processes in the integrated circuit industry to tackle the ever increasing circuit complexity and time to market issues has brought with it some major security challenges. Chief among them is IP piracy by untrusted parties. Hardware obfuscation which locks the functionality and modifies the structure of an IP core to protect it from malicious modifications or piracy has been proposed as a solution. In this paper, we develop an efficient hardware obfuscation method, called Mystic (Mystifying IP Cores), to protect IP cores from reverse engineering, IP overproduction, and IP piracy. The key idea behind Mystic is to add additional state transitions to the original/functional FSM (Finite State Machine) that are taken only when incorrect keys are applied to the circuit. Using the proposed Mystic obfuscation approach, the underlying functionality of the IP core is locked and normal FSM transitions are only available to authorized chip users. The synthesis results of ITC99 circuit benchmarks for ASIC 45nm technology reveal that the Mystic protection method imposes on average 5.14% area overhead, 5.21% delay overhead, and 8.06% power consumption overheads while it exponentially lowers the probability that an unauthorized user will gain access to or derive the chip functionality.
Opening the Black Boxes in Data Flow Optimization

Many systems for big data analytics employ a data flow abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally, query optimization is applied to queries with known algebraic semantics. In this work, we find that a handful of properties, rather than a full algebraic specification, suffice to establish reordering conditions for data processing operators. We show that these properties can be accurately estimated for black box operators by statically analyzing the general-purpose code of their user-defined functions. We design and implement an optimizer for parallel data flows that does not assume knowledge of semantics or algebraic properties of operators. Our evaluation confirms that the optimizer can apply common rewritings such as selection reordering, bushy join-order enumeration, and limited forms of aggregation push-down, hence yielding similar rewriting power as modern relational DBMS optimizers. Moreover, it can optimize the operator order of nonrelational data flows, a unique feature among today's systems.
Grasp Stiffness Control in Robotic Hands Through Coordinated Optimization of Pose and Joint Stiffness

This letter presents a novel controller for robotic hands, which regulates the grasp stiffness by manipulating the pose and the finger joint stiffness of hands with multiple degrees of freedom while guaranteeing grasp stability. The proposed approach is inspired by the observations of human motor behavior, which reveal a coordinated pattern of stiffening among the hand fingers, along with a predictive selection of the hand pose to achieve a reliable grasp. The first adjusts the magnitude of the grasp stiffness, while the latter manipulates its overall geometry (shape). The realization of a similar control approach in robotic hands can result in a reduction of the software complexity and also promote a novel mechanical design approach, in which the finger stiffness profiles of the hand are adjusted by only one active component. The proposed control is validated with a fully actuated Allegro Hand, while trying to achieve predefined grasp stiffness profiles or modifications of an initial one.
Endothelial Vasodilation After a High-Volume Training Load and Tapered Training in Collegiate Female Swimmers.

Weihl, FM and Van Guilder, GP. Endothelial vasodilation after a high-volume training load and tapered training in collegiate female swimmers. J Strength Cond Res XX(X): 000-000, 2018-High-volume endurance training loads have been linked to adverse remodeling of the heart and large arteries; yet, data on the vascular endothelial function are unclear. Moreover, although collegiate-level endurance athletes often perform high-volumes of vigorous endurance training and resistance training as part of their strength and conditioning programs, it is unknown whether they also experience vascular abnormalities, particularly changes in endothelial function. The aim of this study was to verify the impact of a high-volume training load phase followed by low-volume tapered training on endothelial vasodilator function in National Collegiate Athletic Association (NCAA) Division I competitive female swimmers. Microvascular endothelial vasodilation was assessed by pulse arterial tonometry that provides a reactive hyperemia index in 10 female NCAA Division 1 swimmers after 4 weeks of a high-volume training load, and subsequently, after 3 weeks of low-volume tapered training as part of preparation for annual conference championships. The reactive hyperemia index was calculated as the ratio of the pulse volume amplitude after 5 minutes of left-arm brachial artery ischemia to the baseline amplitude, divided by same ratio in the contralateral arm. The high-volume training load included a 4-week block of dual-day sessions (120 minutes per practice) consisting of vigorous intensity endurance and high-intensity interval/sprint swim training, coupled with 5K running, resistance training, and Olympic weightlifting. Tapered training consisted of 3 weeks of 3-5 swims per week at ∼50% V[Combining Dot Above]O2max for 60 minutes per practice (∼4,000 minutes per practice). The reactive hyperemia index (1.73 ± 0.50) was low in athletes after the high-volume training load with 8 athletes demonstrating endothelial dysfunction. However, after tapered training, the reactive hyperemia index was ∼33% higher (2.29 ± 0.43; 95% confidence interval [CI]: 1.98-2.60, p = 0.0223 vs. the high-volume training load). Effect size, as expressed by the partial eta (0.46) and Cohen's dz (1.1923; 95% CI: 0.1687-2.4643) with tapered training, was large. These results demonstrate distinct differences in endothelial vasodilation after 4 weeks of a high-volume training load compared with a 3-week taper in NCAA Division I female swimmers.
Fatigue Status Recognition in a Post-Stroke Rehabilitation Exercise with sEMG Signal

Exercise therapy is considered as one of the main rehabilitation treatments for post-stroke patients, especially by utilizing modern technologies, such as virtual and/or augmented reality. However, in order to design an appropriate exercise program, which prolongs the exercise duration and maximize the patient's improvement, the fatigue status needs to be detected and used for the program adjustment. In the previous fatigue recognition works, only exercises for healthy and athlete subjects have been taken into account. In this paper, fatigue status classification has been accomplished in a rehabilitation exercise for poststroke patients. To do so, the reaching task, as a basic rehabilitation exercise, was performed by post-stroke patients, utilizing Xbox Kinect; and surface EMG signal and Maximum voluntary contraction (MVC) of the subjects were collected during the exercises. The MVC values were used as the reference for fatigue status. Several features were determined and extracted from the sEMG and finally, classification of fatigue status on the sE MG was performed by two well-known classifiers: Hidden Markov Model (HMM) and Artificial Neural Network (ANN). An accuracy of 95.3% was achieved by HMM, which is a promising step toward an automated fatigue status recognition system in post-stroke rehabilitation exercises.
Machine Learning Promoting Extreme Simplification of Spectroscopy Equipment

The spectroscopy measurement is one of main pathways for exploring and understanding the nature. Today, it seems that racing artificial intelligence will remould its styles. The algorithms contained in huge neural networks are capable of substituting many of expensive and complex components of spectrum instruments. In this work, we presented a smart machine learning strategy on the measurement of absorbance curves, and also initially verified that an exceedingly-simplified equipment is sufficient to meet the needs for this strategy. Further, with its simplicity, the setup is expected to infiltrate into many scientific areas in versatile forms.
Neural Networks for Mobile Robot Inverse Kinematics

Inverse kinematics refers to determining the forces that must be applied to a particular system to result in a desired configuration of the system. In robotics, inverse kinematics means calculating the robot actuator movements necessary to make a robot perform a specific task. Calculating the inverse kinematics using traditional methods is a complex and time consuming task. This paper reports on a novel approach to predicting the inverse kinematics of a mobile robot using Neural Networks (NNs). The main advantage of using artificial intelligence to determine inverse kinematics is that minimal human input and intervention is required. This research makes use of Feedforward NNs to predict the motor velocities and the time that they must be maintained to make the robot reach a specified destination. Inertia and friction are automatically incorporated into the NN predictions. Experimental evidence is presented which shows that the proposed approach can successfully produce commands which allow the robot to traverse a given path.
Reverberation-Robust Localization of Speakers Using Distinct Speech Onsets and Multichannel Cross Correlations

Many speaker localization methods can be found in the literature. However, speaker localization under strong reverberation still remains a major challenge in the real-world applications. This paper proposes two algorithms for localizing speakers using microphone array recordings of reverberated sounds. To separate concurrent speakers, the first algorithm decomposes microphone signals spectrotemporally into subbands via an auditory filterbank. To suppress reverberation, we propose a novel speech onset detection approach derived from the speech signal and impulse response models, and further propose to formulate the multichannel cross-correlation coefficient of encoded speech onsets in each subband. The subband results are combined to estimate the directions-of-arrival of speakers. The second algorithm extends the generalized cross-correlation phase transform method by using redundant information of multiple microphones to address the reverberation problem. The proposed methods have been evaluated under adverse conditions using not only simulated signals (reverberation time <inline-formula><tex-math notation="LaTeX">$T_{60}$</tex-math> </inline-formula> of up to <inline-formula><tex-math notation="LaTeX">$1$</tex-math></inline-formula>s) but also recordings in a real reverberant room (<inline-formula><tex-math notation="LaTeX">$T_{60} \approx 0.65$</tex-math> </inline-formula>s). Comparing with some state-of-the-art localization methods, experimental results confirm that the proposed methods can reliably locate static and moving speakers, in presence of reverberation.
Volatile Memory Forensics Acquisition Efficacy: A Comparative Study Towards Analysing Firmware-Based Rootkits

Firmware-based malware is an emerging threat with few obvious mechanisms for detection. There have been multiple cases where the presence of firmware-based malware has been confirmed or strongly suspected, and current mitigations strategies have little or no recourse. Volatile memory forensics may be one of the few technologies that can be employed to detect the presence of modified firmware, through ROM shadowing. However, the majority of volatile memory forensic tools were not designed with this use-case in mind and may not be suited to the capture of protected memory regions. This work performs experimental analysis to determine which, if any, memory acquisition tools are able to collect evidence pertaining to firmware-based rootkits or malware.
Freeing Testers from Polluting Test Objectives

Testing is the primary approach for detecting software defects. A major challenge faced by testers lies in crafting efficient test suites, able to detect a maximum number of bugs with manageable effort. To do so, they rely on coverage criteria, which define some precise test objectives to be covered. However, many common criteria specify a significant number of objectives that occur to be infeasible or redundant in practice, like covering dead code or semantically equal mutants. Such objectives are well-known to be harmful to the design of test suites, impacting both the efficiency and precision of testers’ effort. This work introduces a sound and scalable formal technique able to prune out a significant part of the infeasible and redundant objectives produced by a large panel of white-box criteria. In a nutshell, we reduce this challenging problem to proving the validity of logical assertions in the code under test. This technique is implemented in a tool that relies on weakest-precondition calculus and SMT solving for proving the assertions. The tool is built on top of the Frama-C verification platform, which we carefully tune for our specific scalability needs. The experiments reveal that the tool can prune out up to 27% of test objectives in a program and scale to applications of 200K lines of code.
Exact Analysis of k-Connectivity in Secure Sensor Networks with Unreliable Links

The Eschenauer--Gligor (EG) random key predistribution scheme has been widely recognized as a typical approach to secure communications in wireless sensor networks (WSNs). However, there is a lack of precise probability analysis on the reliable connectivity of WSNs under the EG scheme. To address this, we rigorously derive the asymptotically exact probability of $k$-connectivity in WSNs employing the EG scheme with unreliable links represented by independent on/off channels, where $k$-connectivity ensures that the network remains connected despite the failure of any $(k-1)$ sensors or links. Our analytical results are confirmed via numerical experiments, and they provide precise guidelines for the design of secure WSNs that exhibit a desired level of reliability against node and link failures.