Miracle-dz/UniFAR-FTUs huggingface dataset:
Low frequency keyword and keyphrase extraction from meeting transcripts with sentiment classification using unsupervised framework

Various kinds of audio and video data are generated everyday like audio and video chatting, blog posts, e-communities, social networks, customer reviews on wide range of products and online audio and video helpline for different technical problems. Providing keywords for these audio files, thus allow the users to quickly grab the gist of the lengthy recordings and helps information access effectively. Nowadays online reviews are having greater impact on consumers and companies compared to the traditional data. New methodologies are available for automated sentiment analysis and discovering the hidden knowledge from unstructured audio and video data. Among various sentiment analysis tasks, one of them is sentiment classification, ie. , identifying whether the input of the given text is positive or negative. In this paper it is proposed to combine both keyword extraction and sentiment classification into a single model which will perform both the works at a single time.
Intelligent dispatching in dynamic stochastic job shops

Dispatching rules are common method to schedule jobs in practice. However, they consider only limited factors which influence the priority of jobs. This limited consideration narrows the rules' scope of application. We develop a new hierarchical dispatching approach based on two types of factors: local factors and global factors, where each machine has its own dispatching rule setup. According to the global factors, the dispatchers divide the state of the manufacturing system into several patterns, and parameterize a neural network for each pattern to map the relationships between the local factors and the priorities of jobs. When making decisions, the dispatchers determine which pattern the current state belongs to. Then the appropriate neural network computes priorities according to the jobs' local factors. The job with the highest priority will be selected. Finally, the proposed approach is introduced on a manufacturing line and the performance is compared to classical dispatching rules.
A pleckstrin homology‐related domain in SHIP1 mediates membrane localization during Fcγ receptor‐induced phagocytosis

SH2 domain‐containing inositol‐5′‐phosphatase‐1 (SHIP1) inhibits inflammation by hydrolyzing phosphoinositide‐3′‐kinase generated membrane phosphatidylinositol‐3,4,5‐trisphosphate (PIP3). Bioinformatic analysis of SHIP1 from multiple species revealed a pleckstrin homololgy‐related (PH‐R) domain, which we hypothesize mediates SHIP1′s association with the membrane, a requirement for its biological function. Recombinant murine SHIP1 PH‐R domain was subjected to biophysical and biochemical analysis. Residues K370 and K397 were found to be important for PH‐R domain association with membrane PIP3. Wild‐type PH‐R domain bound PIP3 with 1.9 ± 0.2 nM affinity, while the affinity of a K370A/K397A substituted mutant was too low to measure. Wild‐type (but not the K370A/K397A substituted) full‐length SHIP1 protein, reconstitutes normal inhibition of Fcγ receptor‐mediated phagocytosis when introduced into SHIP1−/− murine macrophages, reducing the number of phagocytic events by 2‐fold as compared to SHIP1−/− cells. In fact, the PH‐R‐mediated membrane interaction appears to be a major mechanism by which SHIP1 is recruited to the membrane, since the K370A/K397A substitution reduced the recruitment of both full‐length SHIP1 and the PH‐R domain by ≥2‐fold. We have previously shown that SHIP1 enzyme activity can be targeted for therapeutic purposes. The current studies suggest that molecules targeting the PH‐R domain can also modulate SHIP1 function. —Ming‐Lum, A. , Shojania, S. , So, E. , McCarrell, E. , Shaw, E. , Vu, D. , Wang, I. , McIntosh, L. P., Mui, A. L. F. A pleckstrin homology‐related domain in SHIP1 mediates membrane localization during Fcγ receptor‐induced phagocytosis. FASEB J. 26, 3163–3177 (2012). www.fasebj. org
Impact of Checkpoint Latency on Overhead Ratio of a Checkpointing Scheme

Checkpointing reduces loss of computation in the presence of failures. Two metrics characterize a checkpointing scheme: checkpoint overhead and checkpoint latency. The paper shows that a large increase in latency is acceptable if it is accompanied by a relatively small reduction in overhead. Also, for equidistant checkpoints, optimal checkpoint interval is shown to be typically independent of checkpoint latency.
Value, Challenges, and Solutions in Incorporating Victim Impact Awareness in Offender Rehabilitation: The Results of Qualitative Interviews with Stakeholders

Abstract Research has failed to provide strong evidence linking victim impact awareness (VIA) with reduced offending. However, this research has serious methodological limitations. Further, issues in the implementation of correctional interventions targeting VIA, which may impact their outcomes, have not been explored in the literature. The current study addresses this second issue. In-depth semistructured interviews with key stakeholders in VIA activities (e.g., program facilitators, victims’ advocates) elucidated implementation issues, such as challenges surrounding offender readiness specific to VIA activities. Informants’ recommendations to address these challenges provide valuable insight to practitioners in this popular and growing area of correctional intervention.
Shortening of 3′UTRs Correlates with Poor Prognosis in Breast and Lung Cancer

A major part of the post-transcriptional regulation of gene expression is affected by trans-acting elements, such as microRNAs, binding the 3′ untraslated region (UTR) of their target mRNAs. Proliferating cells partly escape this type of negative regulation by expressing shorter 3′ UTRs, depleted of microRNA binding sites, compared to non-proliferating cells. Using large-scale gene expression datasets, we show that a similar phenomenon takes place in breast and lung cancer: tumors expressing shorter 3′ UTRs tend to be more aggressive and to result in shorter patient survival. Moreover, we show that a gene expression signature based only on the expression ratio of alternative 3′ UTRs is a strong predictor of survival in both tumors. Genes undergoing 3′UTR shortening in aggressive tumors of the two tissues significantly overlap, and several of them are known to be involved in tumor progression. However the pattern of 3′ UTR shortening in aggressive tumors in vivo is clearly distinct from analogous patterns involved in proliferation and transformation.
Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.
Improving Accuracy of Constraint-Based Structure Learning

Hybrid algorithms for learning the structure of Bayesian networks combine techniques from both the constraintbased and search-and-score paradigms of structure learning. One class of hybrid approaches uses a constraintbased algorithm to learn an undirected skeleton identifying edges that should appear in the final network. This skeleton is used to constrain the model space considered by a search-and-score algorithm to orient the edges and produce a final model structure. At small sample sizes, the performance of models learned using this hybrid approach do not achieve likelihood as high as models learned by unconstrained search. Low performance is a result of errors made by the skeleton identification algorithm, particularly false negative errors, which lead to an over-constrained search space. These errors are often attributed to “noisy” hypothesis tests that are run during skeleton identification. However, at least three specific sources of error have been identified in the literature: unsuitable hypothesis tests, lowpower hypothesis tests, and unexplained d-separation. No previous work has considered these sources of error in combination. We determine the relative importance of each source individually and in combination. We identify that low-power tests are the primary source of false negative errors, and show that these errors can be corrected by a novel application of statistical power analysis. The result is a new hybrid algorithm for learning the structure of Bayesian networks which produces models with equivalent likelihood to models produced by unconstrained greedy search, using only a fraction of the time.
Forensic “Lie Detection”

Abstract This paper provides a critical overview of the scientific status of the control question test (CQT), the type of polygraph test most likely to be used in forensic settings. The CQT is based on an implausible set of assumptions that makes it biased against innocent individuals and easy for guilty persons to defeat using countermea-sures. Due to serious methodological problems that characterize research on CQT validity, it is not possible to use the existing literature to provide a satisfactory error rate estimate. Scientists, including members of the Society for Psychophysiological Research and APA Fellows, hold negative views about the CQT. They do not believe that it is based on sound theory, that it has adequate psychometric properties, or that it should be used as evidence in court.
Filter Forests for Learning Data-Dependent Convolutional Kernels

We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context. FF can be used for general signal restoration tasks that can be tackled via convolutional filtering, where it attempts to learn the optimal filtering kernels to be applied to each data point. The model can learn both the size of the kernel and its values, conditioned on the observation and its spatial or temporal context. We show that FF compares favorably to both Markov random field based and recently proposed regression forest based approaches for labeling problems in terms of efficiency and accuracy. In particular, we demonstrate how FF can be used to learn optimal denoising filters for natural images as well as for other tasks such as depth image refinement, and 1D signal magnitude estimation. Numerous experiments and quantitative comparisons show that FFs achieve accuracy at par or superior to recent state of the art techniques, while being several orders of magnitude faster.
CKIP at the NTCIR-13 STC-2 Task

In recent years, LSTM-based sequence-to-sequence model have been applied successfully in many fields, including short text conversation and machine translation. The inputs and outputs of the models are usually word sequences. However, for a fixed-size training corpus, a word sequence or even part of it is unlikely to repeat many times, thus in natural, data sparseness problem could be an obstacle for training of sequence-to-sequence model. To address this issue, through this task, we propose the idea of using LSTM with concept sequence. That is, given input word sequence, we first predict the concept for each word of the word sequence and thus form a concept sequence as the input of the LSTM model. At training phase, the output remains the form of word sequence. So during testing phase, given a generated concept sequence, LSTM model is able to directly output the corresponding response in a form of word sequence. Although our results are not among top systems in this task, the experimental results still show the potential of this idea through the comparison among our submitted runs.
The memory stone: a personal ICT device in health care

New technology enables novel ways of sharing information between health care recipients and providers. In this study, however, we found that the medical information for pregnant women in Denmark is located in a number of different places, that midwives and doctors spend a considerable amount of time administrating data, and that consultations are felt to be rather inefficient. This paper describes these problems and some solutions. We explore the idea of providing each woman with a digital artifact, called the Memory Stone. The goal is to supply them with tools to collect and review clinical and personal information concerning their pregnancies. The paper discusses: (1) the user-centered methodology for development of a personal device for health care information, (2) the design and evaluation of prototypes, and (3) critical issues concerning the introduction of novel personal ICT in a health care setting. The main focus is on the experiences and interests of the individual pregnant woman in the study. Several insights were gained into more general pervasive health care issues, including technical and ethical ones as well as safety and security concerns.
The evaluation of sensory processing: a validity study using contrasting groups.

The purpose of this study was to examine the validity of a sensory history questionnaire using contrasting groups. Specifically, the goal was to identify items on the Evaluation of Sensory Processing that differentiate between parent ratings of children with sensory integrative dysfunction and parent ratings of typically developing children. Data were collected from 30 parents of children with sensory integrative dysfunction and 59 parents of typically developing children. Items were analyzed using a Wilcoxon signed rank test to detect the difference between the parent ratings for the 30 children with sensory integrative dysfunction and 30 of the typically developing children who were matched to the dysfunctional children on age, ethnicity, gender, geographic location, and socioeconomic status. Eighty-four of the 200 items significantly (p < .05) distinguished between parents' ratings of children with and without sensory integrative dysfunction.
Nonlinear mixed effects pharmacokinetic/pharmacodynamic analysis of the cholinesterase inhibitor pyridostigmine bromide in Chinese males

Pyridostigmine is a reversible inhibitor of acetylcholinesterase (AChE). The objective of the present analysis was to characterise the population pharmacokinetics / pharmacodynamics (PK/PD) of pyridostigmine given as pyridostigmine bromide. Fifty healthy Chinese males received 7 doses of 30 mg of pyridostigmine bromide each every 8 hours orally. Plasma concentrations of pyridostigmine and red blood cell (RBC) AChE activity were determined at various times within 8 hours after the first and the seventh doses. The resulting PK data were fit to a single compartment open model with first order absorption and elimination. The PD was modelled using an inhibitory Emax model. The potential influence of demographic and biological covariates on the model parameters was investigated. Modelling was performed using NONMEM VI. The apparent clearance and volume of distribution as well as absorption rate constant of plasma pyridostigmine were estimated to be 136 L/hr, 130 L and 0.68 1/hr respectively. The maximum RBC AChE activity decrease (Emax) and plasma pyridostigmine concentration producing 50% of this reduction (EC50) were estimated to be 9.32 AChE units per gram haemoglobin and 51.9 ng/ml respectively. None of the tested covariates explained any additional variability in either PK or PD. Dosing simulations suggested that 30 mg repeated every 6 hours might be needed to achieve steady-state trough percentage inhibition above the recommended 10% in healthy Chinese male adults.
Extraction of Blood Vessels and Optic Disc Segmentation for Retinal Disease Classification

The retina is the only tissue in human body from which the information of blood vessel can be unswervingly obtained. The information of retinal vessel plays an important role in the diagnosis and treatment of various diseases such as glaucoma, age-related macular degeneration, degenerative myopia, diabetic retinopathy etc. The morphology of the retinal blood vessel and the optic disk is an important structural indicator for assessing the presence and severity of retinal diseases such as diabetic retinopathy, hypertension, glaucoma, hemorrhages, vein occlusion, and neovascularization. However, to assess the diameter and tortuosity of the retinal blood vessel or the shape of the optic disk, manual planimetry has commonly been used by ophthalmologists, which is generally time consuming and prone to human error, especially when the vessel structures are complicated or a large number of images are acquired to be labeled by hand. Therefore, a reliable automated method for retinal blood vessel and optic disk segmentation, which preserves various vessel and optic disk characteristics, is attractive in computer-aided diagnosis. However here implement a new competent method for the detection diseases using the retinal fundus image. In this anticipated work first step is the extraction of retinal vascular tree using graph cut technique. The blood vessel information is then use to calculate approximately the position of optic disc. These results are given to an ANN classifier for the detection and classification of diseases. By robotically identify the disease from normal images, the workload and its costs will be reduced.
Novel Chimpanzee/Human Monoclonal Antibodies That Neutralize Anthrax Lethal Factor, and Evidence for Possible Synergy with Anti-Protective Antigen Antibody

ABSTRACT Three chimpanzee Fabs reactive with lethal factor (LF) of anthrax toxin were isolated and converted into complete monoclonal antibodies (MAbs) with human γ1 heavy-chain constant regions. In a macrophage toxicity assay, two of the MAbs, LF10E and LF11H, neutralized lethal toxin (LT), a complex of LF and anthrax protective antigen (PA). LF10E has the highest reported affinity for a neutralizing MAb against LF (dissociation constant of 0.69 nM). This antibody also efficiently neutralized LT in vitro, with a 50% effective concentration (EC50) of 0.1 nM, and provided 100% protection of rats against toxin challenge with a 0.5 submolar ratio relative to LT. LF11H, on the other hand, had a slightly lower binding affinity to LF (dissociation constant of 7.4 nM) and poor neutralization of LT in vitro (EC50 of 400 nM) and offered complete protection in vivo only at an equimolar or higher ratio to toxin. Despite this, LF11H, but not LF10E, provided robust synergistic protection when combined with MAb W1, which neutralizes PA. Epitope mapping and binding assays indicated that both LF10E and LF11H recognize domain I of LF (amino acids 1 to 254). Although domain I is responsible for binding to PA, neither MAb prevented LF from binding to activated PA. Although two unique MAbs could protect against anthrax when used alone, even more efficient and broader protection should be gained by combining them with anti-PA MAbs.
Membaca Al Fatihah Reflektif Intuitif untuk Menurunkan Depresi dan Meningkatkan Imunitas

This study aimed to reduce depression and increase immunity through training of reading of the reflective intuitive Al-Fatihah. Researchers used an experimental method with pretest-posttest control group design. Each experimental group and control group consisted of 10 students of Islamic Boarding School. To measure stress degree, Depression Anxiety Stress Scale (DASS) was used. Immunity was measured with microscopic slide method using hematology analyzer. Quantitative analysis was done through a mixed design anova statistical test. The results showed that the training could reduce depression (F=15.34, p<0.05) and increase immunity (F=25.6, p<0.05). This study defines that Reading of Reflective Intuitive Al Fatihah can change a subject’s perception toward any problem of life. The subjects of this research could see what was going on in their lives through the anchor perspective. The experimental subjects who had secured their anchors onto Allah could significantly decrease their depression and improve their immunity. Such state of calmness cannot be separated from Quran reading. Keywords: Al Quran, imun system, stress Penelitian ini bertujuan untuk menurunkan depresi dan meningkatkan imunitas melalui pelatihan membaca Al Fatihah reflektif intuitif. Peneliti menggunakan metode eksperimen dengan pretest-posttest control group design. Kelompok eksperimen dan kelompok kontrol masing-masing terdiri dari 10 siswa pondok pesantren. Pengukuran tingkat stres menggunakan Depression Anxiety Stress Scale (DASS) dan pengukuran imunitas menggunakan alat hematology analyzer dengan metode slide mikroskopis. Analisis kuantitatif menggunakan mixed design anova. Hasil penelitian menunjukkan bahwa membaca Al Fatihah reflektif intuitif dapat menurunkan depresi (F=15.34, p<0.05) dan meningkatkan imunitas (F=25.6, p<0.05). Penelitian menunjukkan bahwa membaca Al Fatihah reflektif intuitif dapat mengubah persepsi subjek terhadap permasalahan dalam hidupnya. Subjek mulai melihat apa yang sedang terjadi pada diri mereka dengan menggunakan perspektif anchor Allah. Subjek eksperimen yang berhasil memegang teguh anchor Allah, mengalami penurunan depresi dan peningkatan imunitas secara signifikan. Perubahan tersebut tidak dapat dipisahkan dari rasa tenang sesudah membaca Al Quran. Kata kunci: Al Quran, sistem imunitas, stres
Non-cycle-accurate Sequential Equivalence Checking

We present a novel technique for sequential equivalence checking (SEC) between non-cycle-accurate designs. The problem is routinely encountered in verifying the correctness of a system-level model versus an RTL design which has been derived from the former either manually or through high-level synthesis. The existing state-of-the-art in formal verification/SEC does not provide an efficient mechanism to perform such an equivalence check. Our technique reduces the SEC problem to a cycle-accurate equivalence-checking problem by constructing a pair of normalized cycle-accurate designs from the original designs, on which standard equivalence-checking techniques can then be deployed. We report the results of deploying our techniques on several industrial examples.