Miracle-dz/UniFAR-FTUs huggingface dataset:
Super-Resolution using Combination of Wavelet Transform and Interpolation Based Method

Super-resolution is a technique of producing a high-resolution (HR) image from one or more lowresolution (LR) images. Classical interpolation based magnification techniques like nearest-neighbor, bilinear and bicubic interpolation results in a larger image along with undesirable artifacts like blurring, aliasing and ringing effects. So the aim of super-resolution is to provide a larger image with good quality (quality means an image with less undesirable artifacts). Previous super-resolution techniques are based on using multiple images and learning based methods but the idea here is to use a single image in the super-resolution process. Here we have used the combination of wavelet transform and interpolation based technique to achieve the super-resolution using a single image. First the edges of the image are enhanced using wavelet transform and then the magnification is done using an interpolation based method. A comparison of this algorithm with other technique is also done to provide the quantitative and qualitative result to prove the effectiveness of the methods.
Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull

The purpose of the present theoretical study was to examine the spatial resolution of electroencephalography (EEG) by means of the accuracy of the inverse cortical EEG solution. The study focused on effect of the amount of measurement noise and the number of electrodes on the spatial resolution with different resistivity ratios for the scalp, skull and brain. The results show that if the relative skull resistivity is lower than earlier believed, the spatial resolution of different electrode systems is less sensitive to the measurement noise. Furthermore, there is then also greater advantage to be obtained with high-resolution EEG at realistic noise levels
Fault-Tolerant Topology Control for All-to-One and One-to-All Communication in Wireles Networks

This paper introduces the problem of fault-tolerant topology control for all-to-one and one-to-all communication in static wireless networks with asymmetric wireless links. This problem is important in both theoretical and practical aspects. We investigate two approaches, namely, the minimum-weight-based approach and the augmentation-based approach, to address this problem. Furthermore, we prove that the minimum-weight-based approach has a k-approximation algorithm for all-to-one fault-tolerant topology control, where k is the number of node-disjoint paths. When k = 1, this approach solves the minimum power sink tree problem. To the best of our knowledge, this paper is the first to study the fault-tolerant topology control for all-to-one and one-to-all communication in asymmetric static wireless networks and is also the first to demonstrate that the minimum power sink tree problem has a polynomial time optimal solution.
Automatic detection of anomalies in blood glucose using a machine learning approach

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data. We study the problem of automatically detecting anomalies in the measured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.
Advanced MudPIT as a next step toward high proteome coverage

We present a simple, time‐ and cost‐efficient approach to tackle the proteome of prokaryotic organisms. To obtain large data sets of complex biological experiments high‐throughput and time‐ and cost‐efficient methods still have to be developed and refined. In this study, we combined well‐approved techniques, namely elevated chromatographic temperatures, long RP columns and the multidimensional protein identification technology MudPIT to achieve high proteome coverage. The advanced MudPIT approach has been evaluated and delivered very comprehensive results for Gram‐positive as well as Gram‐negative bacteria (53% proteome coverage for Corynebacterium glutamicum and 46% proteome coverage for Escherichia coli). Also, a high identification rate for the challenging integral membrane proteins was achieved. The competitiveness of the advanced MudPIT technology is strengthened by the fact that in this approach only two fractions were analyzed with both, simple and time‐efficient sample preparation, and a moderate data acquisition time.
Bisphosphonate and risk of cancer recurrence: protocol for a systematic review, meta-analysis and trial sequential analysis of randomised controlled trials

Introduction No consensus exists on the associations between adjuvant treatment with bisphosphonates and cancer recurrence risk among patients with primary early-stage cancers. We plan to perform a comprehensive systematic review, study-level meta-analysis and trial sequential analysis of randomised controlled trials to comprehensively summarise evidence of the bisphosphonate treatment for difference cancers. Methods and analyses We will report our results according to the PRISMA guideline. The primary outcomes include any cancer recurrence and bone metastasis and secondary outcomes include events of local recurrence, regional recurrence or non-skeletal distant metastasis, disease-free survival and overall survival. We will perform systematic electronic searches and other manual searches. To be conservative, all statistical analyses will be conducted with random-effects models. Cumulative meta-analyses and trial sequential analyses will be performed to assess whether and when firm evidence is reached. Various sensitivity analyses and rigid publication bias analyses will be performed to challenge the consistency and robustness of results. We will also grade the quality of evidence with the GRADE system. Ethics and dissemination Ethical approval is not required in this study. The findings will be submitted for publication in a peer-reviewed journal and also presented at relevant national and international conferences. Trial registration number PROSPERO CRD42014014699.
Strengths and Weaknesses of Immunotherapy for Advanced Non-Small-Cell Lung Cancer: A Meta-Analysis of 12 Randomized Controlled Trials

Background Lung cancer is one of the leading causes of cancer death worldwide. Non-small-cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers. Immunotherapy has yielded no consistent benefit to date for those patients. Assessing the objective efficacy and safety of immunotherapy for advanced NSCLC patients will help to instruct the future development of immunotherapeutic drugs. Methodology and Principal Findings We performed a meta-analysis of 12 randomized controlled trials including 3134 patients (1570 patients in the immunotherapy group and 1564 patients in the control group) with histologically confirmed stage IIIA, IIIB, or IV NSCLC. The analysis was executed with efficacy end points regarding overall survival (OS), progression-free survival (PFS), complete response (CR), partial response (PR), and total effective rate. Overall unstratified OS, PFS, PR, and total effective rate were significantly improved in advanced NSCLC patients in the immunotherapy group (P = 0.0007, 0.0004, 0.002, 0.003, respectively), whereas CR was not improved (P = 0.97). Subgroup analysis showed that monoclonal antibody (mAb) immunotherapy significantly improved the PFS, PR, and total effective rate and showed a trend of improving OS of advanced NSCLC patients compared with the control group, with one kind of adverse event being significantly dominant. Compared with the control group, the vaccine subgroup showed no significant difference with regard to serious adverse events, whereas cytokine immunotherapy significantly induced three kinds of serious adverse events. Conclusions Immunotherapy works efficiently on advanced NSCLC patients. Of several immunotherapies, mAb therapy may be a potential immunotherapy for advanced NSCLC patients, and become a standard complementary therapeutic approach in the future if the issues concerning toxicity and allergenicity of mAbs have been overcome.
Effects of inorganic phosphate analogues on stiffness and unloaded shortening of skinned muscle fibres from rabbit.

1. We examined the effects of aluminofluoride (AlFx) and orthovanadate (Vi), tightly binding analogues of orthophosphate (Pi), on the mechanical properties of glycerinated fibres from rabbit psoas muscle. Maximum Ca(2+)‐activated force, stiffness, and unloaded shortening velocity (Vus) were measured under conditions of steady‐state inhibition (up to 1 mM of inhibitor) and during the recovery from inhibition. 2. Stiffness was measured using either step or sinusoidal (1 kHz) changes in fibre length. Sarcomere length was monitored continuously by helium‐neon laser diffraction during maximum Ca2+ activation. Stiffness was determined from the changes in sarcomere length and the corresponding changes in force. Vus was measured using the slack test method. 3. AlF chi and Vi each reversibly inhibited force, stiffness and Vus. Actively cycling cross‐bridges were required for reversal of these inhibitory effects. Recovery from inhibition by AlF chi was 3‐ to 4‐fold slower than that following removal of V1. 4. At various degrees of inhibition, AlF chi and Vi both inhibited steady‐state isometric force more than either Vus or stiffness. For both AlF chi and Vi, the relatively greater inhibition of force over stiffness persisted during recovery from steady‐state inhibition. We interpret these results to indicate that the cross‐bridges with AlF chi or Vi bound are analogous to those which occur early in the cross‐bridge cycle.
Identification of steel bars immersed in reinforced concrete based on experimental results of eddy current testing and artificial neural network analysis

This paper presents an experimental research on the use of eddy current testing (ECT) and artificial neural networks (ANNs) in order to identify the gauge and position of steel bars immersed in concrete structures. The paper presents details of the ECT probe and concrete specimens constructed for the tests, and a study about the influence of the concrete on the values of measured voltages. After this, new measurements were done with a greater number of specimens, simulating a field condition and the results were used to generate training and validation vectors for multilayer perceptron ANNs. The results show a high percentage of correct identification with respect to both, the gauge of the bar and of the thickness of the concrete cover.
Using neuromuscular electrical stimulation for pseudo-haptic feedback

This paper focuses at the usage of neuromuscular electrical stimulation (NMES) for achieving pseudo-haptic feedback. By stimulating the motor nerves, muscular contractions can be triggered that can be matched to a haptic event. Reflecting an initial user test, we will explain how this process can be realized, by investigating the physiological processes involved. Relating the triggered feedback to general haptics, its potential in future interfaces will be identified and laid out in a development roadmap.
Underwater superoleophobic palygorskite coated meshes for efficient oil/water separation

Oil/water separation has recently become a global challenging task due to frequent oil spill accidents and increasing industrial oily waste water. Here, we demonstrate for the first time underwater superoleophobic palygorskite coated meshes, which were fabricated by spraying palygorskite and polyurethane mixtures on copper mesh substrates. The underwater superoleophobic meshes were then used to study gravity driven oil/water separation for a series of oil/water mixtures, where only the water from the oil/water mixture is allowed to permeate through the mesh. A separation efficiency of up to 99.6% could be achieved through the coated mesh for the kerosene–water mixture. In addition, the palygorskite coated mesh still maintained a high separation efficiency of over 99.0% and stable recyclability after 50 separation cycles with the surface morphology of the palygorskite coated mesh nearly unchanged. Furthermore, the palygorskite coated meshes exhibit excellent environmental stability under a series of harsh conditions, which are used for the separation of mixtures of oil and various corrosive and active aqueous solutions, including strong acidic, alkaline, or salt aqueous solutions, even hot water. The fabrication approach presented here can be applied for coating large surface areas and to develop a large-scale oil/water separation facility for oil and various corrosive and active aqueous mixtures.
Efficient Collective Entity Linking with Stacking

Entity disambiguation works by linking ambiguous mentions in text to their corresponding real-world entities in knowledge base. Recent collective disambiguation methods enforce coherence among contextual decisions at the cost of non-trivial inference processes. We propose a fast collective disambiguation approach based on stacking. First, we train a local predictor g 0 with learning to rank as base learner, to generate initial ranking list of candidates. Second, top k candidates of related instances are searched for constructing expressive global coherence features. A global predictor g 1 is trained in the augmented feature space and stacking is employed to tackle the train/test mismatch problem. The proposed method is fast and easy to implement. Experiments show its effectiveness over various algorithms on several public datasets. By learning a rich semantic relatedness measure between entity categories and context document, performance is further improved.
Challenges In Deeply Networked System Survivability

Deeply networked systems are formed when embedded computing systems gain connectivity to each other and to larger enterprise systems. New functionality also brings new survivability challenges, including security across the embedded/enterprise interface. Addressing the needs of deeply networked system survivability is an open challenge that will require new approaches beyond those used for enterprise systems.
Investigate the Effects of Information Security Climate and Psychological Ownership on Information Security Policy Compliance

Currently, information security policy compliance research mainly investigates information security compliant behaviors of employees from general deterrence theory or protection motivation theory. However, these studies focus on the discussions of security specifications in organization and the motivations of individuals’ behaviors but omit the influences of contextual effects on employees’ psychological states and employees’ information security policy compliance. To fill this gap, we consider information security climate as background factors and psychological ownership as personal factors to investigate their impacts on information security policy compliance intentions. We will collect data from employees working in the high-tech industry in Taiwan to explore the relationships proposed in the research model. We conclude by explicating the implications of this research for academics and practitioners, along with compelling future research possibilities.
Direct binding of SWAP-70 to non-muscle actin is required for membrane ruffling

Membrane ruffling induced by growth factor stimulation is caused by actin remodeling, which is mediated by various signaling molecules including Rac. We have shown that SWAP-70, which binds phosphatidylinositol trisphosphate, is one such molecule required for membrane ruffling in mouse kidney cells. Here, we show that SWAP-70 directly binds to F-actin. The bacterially expressed C-terminal region of SWAP-70 co-sedimented with non-muscle F-actin, suggesting direct binding of SWAP-70 to F-actin. The binding was much weaker in muscle F-actin. A truncated mutant of SWAP-70 containing only the C-terminal region readily colocalizes with F-actin, supporting this idea. Full-length SWAP-70 does not colocalize with F-actin unless cells are stimulated with growth factors, suggesting the presence of a stimuli-dependent regulatory mechanism for actin-binding activity in vivo. Overexpression of the mutant SWAP-70 lacking this binding domain inhibits the membrane ruffling induced by epidermal growth factor stimulation in COS7 cells. This dominant-negative effect is also observed in membrane ruffling induced by a dominant-active Rac, suggesting that SWAP-70 cooperates with Rac. These results suggest that the binding activity of SWAP-70 to non-muscle F-actin is required for membrane ruffling.
Change in the Cortical Complexity of Spinocerebellar Ataxia Type 3 Appears Earlier than Clinical Symptoms

Patients with spinocerebellar ataxia type 3 (SCA3) have exhibited cerebral cortical involvement and various mental deficits in previous studies. Clinically, conventional measurements, such as the Mini-Mental State Examination (MMSE) and electroencephalography (EEG), are insensitive to cerebral cortical involvement and mental deficits associated with SCA3, particularly at the early stage of the disease. We applied a three-dimensional fractal dimension (3D-FD) method, which can be used to quantify the shape complexity of cortical folding, in assessing cortical degeneration. We evaluated 48 genetically confirmed SCA3 patients by employing clinical scales and magnetic resonance imaging and using 50 healthy participants as a control group. According to the Scale for the Assessment and Rating of Ataxia (SARA), the SCA3 patients were diagnosed with cortical dysfunction in the cerebellar cortex; however, no significant difference in the cerebral cortex was observed according to the patients’ MMSE ratings. Using the 3D-FD method, we determined that cortical involvement was more extensive than involvement of traditional olivopontocerebellar regions and the corticocerebellar system. Moreover, the significant correlation between decreased 3D-FD values and disease duration may indicate atrophy of the cerebellar cortex and cerebral cortex in SCA3 patients. The change of the cerebral complexity in the SCA3 patients can be detected throughout the disease duration, especially it becomes substantial at the late stage of the disease. Furthermore, we determined that atrophy of the cerebral cortex may occur earlier than changes in MMSE scores and EEG signals.
A-Book: A Feedback-Based Adaptive System to Enhance Meta-Cognitive Skills during Reading

In the digital era, tech devices (hardware and software) are increasingly within hand’s reach. Yet, implementing information and communication technologies for educational contexts that have robust and long-lasting effects on student learning outcomes is still a challenge. We propose that any such system must a) be theoretically motivated and designed to tackle specific cognitive skills (e.g., inference making) supporting a given cognitive task (e.g., reading comprehension) and b) must be able to identify and adapt to the user’s profile. In the present study, we implemented a feedback-based adaptive system called A-book (assisted-reading book) and tested it in a sample of 4th, 5th, and 6th graders. To assess our hypotheses, we contrasted three experimental assisted-reading conditions; one that supported meta-cognitive skills and adapted to the user profile (adaptive condition), one that supported meta-cognitive skills but did not adapt to the user profile (training condition) and a control condition. The results provide initial support for our proposal; participants in the adaptive condition improved their accuracy scores on inference making questions over time, outperforming both the training and control groups. There was no evidence, however, of significant improvements on other tested meta-cognitive skills (i.e., text structure knowledge, comprehension monitoring). We discussed the practical implications of using the A-book for the enhancement of meta-cognitive skills in school contexts, as well as its current limitations and future developments that could improve the system.
EFFECTS OF OIL DROPLET AND AGAR CONCENTRATION ON GEL STRENGTH AND MICROSTRUCTURE OF O/W EMULSION GEL

The effects of oil droplet size and agar concentration on gel strength and microstructure of emulsion gels were investigated by compression test and cryoscanning electron microscope (Cryo-SEM). At all agar concentrations, the compressive stress values of emulsion gels were lower than those of the oil-free gels. Compressive stress and energy were significantly affected by oil droplet size and agar concentration, but compressive strain was not. SEM observation revealed that the overall volume of void spaces decreased and strand compactness increased with increasing agar concentration. Gels containing oil droplets had some void spaces between the gel network and the oil droplets. The strands of emulsion gels did not cover the oil globules completely, a phenomenon which was also observed for the gel with high agar concentration.
Improving Visual Gaze Estimation by Saliency

In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowl- edge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gaz- ing at, in order to find the most probable location. The pro- posed system helps in correcting the fixations which are er- roneously estimated by the gaze estimation device by em- ploying a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze esti- mation systems.
HMM-Based Speaker Emotional Recognition Technology for Speech Signal

In emotion classification of speech signals, the popular features employed are statistics of fundamental frequency, energy contour, duration of silence and voice quality. However, the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified. In this paper, a text independent method of emotion classification of speech is proposed. The proposed method makes use of short time log frequency power coefficients(LFPC) to represent the speech signals and a discrete Hidden Markov Model (HMM) as the classifier. The category labels used are, the archetypal emotions of anger, joy, sadness and neutral. Results show that the proposed system yields an average accuracy of 82.55%and the best accuracy of 94.4% in the classification of 4 emotions. Results also reveal that LFPC is a better choice as feature parameters for emotion classification than the traditional feature parameters.