Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

Due to advancement in technology, enormous amount of data is generated every day. One of the main challenges of large amount of data is user overloaded with huge volume of data. Hence effective methods are highly required to help user to comprehend large amount of data. This research work proposes effective methods to extract and represent the data. The summarization is applicable to obtain a brief overview of the text and sentiment analysis can obtain emotions expressed in the text computationally. The combined text summarization and sentiment analysis is proposed on BBC news articles. A pronoun replacement based text summarization method is developed and VADER sentiment analyzer is used to determine sentiment information. The 3-D visualization schemes have been provided to represent the sentiment information. The sentiment analysis and classification are performed on original BBC news articles as well as on summarized articles using classifiers, such as Logistic Regression, Random Forest and Adaboost. On original news articles highest classification rate of 84.93%, using summarization of ratio 25%, 50% and 75% highest classification rates of 78.73%, 83.06% and 83.23%, respectively are observed.
The Protective Effect of Spironolactone and Role of the Na /K -ATPase Pump on Intestinal Ischemia/Reperfusion Injury

The aim of this study was to evaluate the possible protective effect of spironolactone (SPL) and role of the Na-K ATPase pump on intestinal ischemia/reperfusion injury. In our study, the period of ischemia was established by clamping the mesenteric artery for 45 minunder anesthesia in Wistar albino rats and the animals left for reperfusion at the end of this period were decapitated after one hour. Spironolactone (20 mg kg-1) was administered orally for three days before ischemia, 30 minbefore ischemia. The control group rats were subjected to the Sham operation and administered saline solution. TNF-α and IL-1β levels were measured in the serum samples. Ileal Na+/K+-ATPase, myeloperoxidase (MPO) analysis were performed. Structural injury was assessed histopathologically. Ischemia/reperfusion increased serum TNF-α and IL-1β levels together with MPO activity, whereas these values were maintained at the control group levels through SPL activation. However, ischemia/reperfusion decreased Na+/K+ATPase activity in ileal tissues; however, these parameters were found to be significantly increased with SPL activation. The protective effect of SPL against ischemia/reperfusion injury by different mechanisms, mainly the activity of the Na+/K+-ATPase pump, suggests that this nontoxic agent may constitute a new clinical therapeutic principle.
Damage Identification Using Sub-Microstrain FBG Data from a Pre-Stressed Concrete Beam During Progressive Damage Testing

Vibration-based damage identification can constitute a successful approach for Structural Health Monitoring (SHM) of civil structures. It is a non-destructive condition assessment method, dependent on the identification of changes in the modal characteristics of a structure that are related to damage. However, the damage identification from the modal characteristics of existing structures currently suffers from a low sensitivity of eigenfrequencies and mode shapes to certain types of damage. Furthermore, the sensitivity of eigenfrequencies to environmental influences may be sufficiently high to completely mask the effect even of severe damage. Modal strains and curvatures are more sensitive to local damage, but the direct monitoring of these quantities is challenging when the strain level is very low. In the present work, the identification of the modal strains of a pre-stressed concrete beam, subjected to a progressive damage test, is performed. Dynamic measurements are conducted on the beam at the beginning of each cycle and its response is recorded with multiplexed Fiber-optic Bragg Grating (FBG) strain sensors. Bending, lateral and torsional modes are accurately identified from dynamic strains of the sub-microstrain level. The evolution of the modal characteristics of the beam after each loading cycle is investigated. Changes of the eigenfrequency values, the amplitude and the curvature of the strain mode shapes are observed. The changes in the strain mode shapes appear at the locations where the damage is induced, and are already identified from an early damaged state.
Shocks and Shock Absorbers in Japanese Banks and Bond Markets During the Global Financial Crisis

During the global financial crisis (GFC), Japan and the U.S. differed in at least two important ways: (1) while markets were stable in Japan then, the bubbles in the U.S. housing and financial markets burst and (2) while Japanese banks suffered few losses, huge losses badly weakened U.S. banks. Unlike the U.S., Japan requires firms' balance sheets to show, not only their total debt, but also the components of their debt: bonds, loans, and other debt. We used these components of debt for listed, non-financial, Japanese firms to investigate whether the 2008 “Lehman shock” in the U.S. disrupted Japanese bond markets. We estimated how much the Lehman shock affected Japanese firms’ bonds outstanding and bonds issued. We also estimated how much, in turn, the Lehman shock to credit markets led Japanese firms to get more loans from Japanese banks. We discovered important cross-currents in firms’ borrowings during the crisis. While the crisis reduced bond issuance, additional bank loans more than offset lower bond issuance. During the crisis, both bonds outstanding and bond issuance responded less to prior shortfalls of bonds outstanding and responded less to maturing bonds. Second, while it greatly reduced bonds issued and outstanding at smaller, listed firms, the crisis boosted both at the very largest firms. In contrast, bank loans rose more at smaller firms. Third, bank loans rose more, and bonds outstanding fell more, at firms with greater exposure to foreign sales. Fourth, the crisis led firms, and especially larger firms, to hold more cash and fewer securities and to reduce costs. Fifth, capital expenditures generally declined during the crisis, but declined significantly less at larger firms.
Multi-task Learning by Maximizing Statistical Dependence Supplemental Material

We present a discussion on embedding and visualizing multiple tasks using the FSIC similarity measure, along with additional experimental results including the regression performance on two regression benchmark datasets (RF1 and ENB described in the main paper) as well as adaptations of Pentina et al.’s curriculum learning approach (Sec. 2) to ranking and regression settings . We reproduce some content of the main paper to make this supplemental self-contained.
A Review of Inverse Gas Chromatography and its Development as a Tool to Characterize Anisotropic Surface Properties of Pharmaceutical Solids

Surface properties can profoundly impact the bulk and interfacial behavior of pharmaceutical solids, and also their manufacturability, processability in drug product processes, dissolution kinetics and mechanism in drug delivery. Variation in the interand intra-molecular interactions gives rise to anisotropic surface properties of crystalline solids which display direction-dependent characteristics relative to the orientation of the crystal unit structure. Despite its establishment since the 1950s, inverse gas chromatography (IGC) is still an evolving technology in the field of pharmaceutical R&D. In this review, the principles behind IGC as a physicochemical technique to measure the surface properties of solids are presented. The introduction is followed by an overview of its utility in pharmaceutical R&D, spanning a variety of applications including batch-to-batch variability, solid-solid transitions, physical stability, interfacial behavior in powder processing, and more. For anisotropic materials, IGC has been utilized to characterize the heterogeneity of materials using adsorption and energy distribution functions. Recent development and applications of IGC at finite concentration (IGC-FC) to determine the surface heterogeneity distribution of solids are presented. This methodology overcomes a number of limitations associated with traditional experiments.
Exploring the Use of the Timed Up and Go Test to Identify Patient Fall Risk in an Inpatient Geriatric Psychiatry Unit

Background: Patient falls during hospitalization influence healthcare costs and quality, and hospitals are mandated to complete fall risk assessments on all patients. Inpatient geriatric psychiatry units have the highest fall rates in the acute care setting, and most falls in this population occur during the mobility tasks of transfers and ambulation. The Timed Up and Go (TUG) test includes these two specific functional tasks, and has been used to predict falls in other geriatric populations, but has never been tested in an inpatient geriatric psychiatry unit. The purpose of this study was to determine if the TUG is a predictive tool to identify high fall risk patients in the inpatient geriatric psychiatry setting. Methods: The study was a retrospective chart review using a between groups design. The sample was obtained from patients admitted to one inpatient geriatric psychiatry unit during the four month study period. Results: The total sample size was N = 62 and included 33 non-fallers and 29 fallers. The mean age of fallers (M = 75.79, SD = 9.60) was not significantly different from the age of non-fallers (M = 74.03, SD = 7.62), p = . 424. Both groups had higher proportions of female subjects; non-faller 75.8% (n = 25) female and faller 69.0% (n = 20) female. Most non-fallers (84.8%) completed the TUG testing without an assistive device, while most fallers (48.3%) used a walker. A significant difference was found between the TUG times of non-fallers and fallers, U = 737.00, z = 3.65, p = <. 001, r = . 46. Fallers took longer to complete the TUG test (Mdn = 26.48) than non-fallers (Mdn = 13.56). The TUG time predictor variable was statistically significant, p = . 002. Increasing TUG times were associated with an increased likelihood of patient falls (OR = 1.10). The optimal TUG cut-off score was 16.46 seconds, with 79.3% sensitivity and 72.7% specificity. Conclusions: The TUG was found to be a predictive tool to identify high fall risk patients in the
Swelling and mucoadhesive behavior with drug release characteristics of gastoretentive drug delivery system based on a combination of natural gum and semi-synthetic polymers

Using ramipril as a model active pharmaceutical ingredient, the focus of the present study was to fabricate low-cost controlled release tablets using combinations of biopolymer and semi-synthetic polymers. Cellulose derivatives are more viscous where biopolymers form gels more easily. Xanthan gum can’t shape a solid gel, result in fragmentation of gel around the tablets so that depend upon high concentration. Therefore, a combination was used to formulate tablet by direct compression. This combination of polymer provides low cost product with higher potentiality. Ingredients as per formulations were mixed in small polybag through hand blending. Then tablets were compressed one by one tablet in a hydraulic press. Prepared tablets were evaluated for hardness, weight variation, content uniformity, friability, surface pH and in-vitro dissolution studies. ANOVA was used to analyze the differences between release data. Swelling index, mucoadhesive strength and in-vitro residence time was studied to show gastroretention of the tablets. Formulated products exhibit sufficient quality and strength to formulate as a mucoadhesive tablet. Significant differences were found in drug release among different formulations (p ˂0.05) in all cases. Among all of formulations, F11, F12 and F13 containing different ratio of xanthan gum and guar gum showed promising mucoadhesive strength and in-vitro residence time.
Egocentric Activity Recognition on a Budget

Recent advances in embedded technology have enabled more pervasive machine learning. One of the common applications in this field is Egocentric Activity Recognition (EAR), where users wearing a device such as a smartphone or smartglasses are able to receive feedback from the embedded device. Recent research on activity recognition has mainly focused on improving accuracy by using resource intensive techniques such as multi-stream deep networks. Although this approach has provided state-of-the-art results, in most cases it neglects the natural resource constraints (e.g. battery) of wearable devices. We develop a Reinforcement Learning model-free method to learn energy-aware policies that maximize the use of low-energy cost predictors while keeping competitive accuracy levels. Our results show that a policy trained on an egocentric dataset is able use the synergy between motion and vision sensors to effectively tradeoff energy expenditure and accuracy on smartglasses operating in realistic, real-world conditions.
Using Sab-Iomha for an Alpha Channel based Image Forgery Detection

Digital images are a very popular way of transfer-ring media. However, their integrity remains challenging because these images can easily be manipulated with the help of software tools and such manipulations cannot be verified through a naked-eye. Although there exist some techniques to validate digital images, but in practice, it is not a trivial task as the existing approaches to forgery detection are not very effective. Therefore, there is need for a simple and efficient solution for the challenge. On the other hand, digital image steganography is the concealing of a message within an image file. The secret message can be retrieved afterwards by the author to check the image file for its veracity. This research paper proposes Sabiomha, an image forgery technique that make use of image steganography. The proposed technique is also supported by a software tool to demonstrate its usefulness. Sabiomha works by inserting an invisible watermark to certain alpha bits of the image file. The watermark we have used to steganograph an image is composed of a combination of text inputs the author can use to sign the image. Any attempts to tamper the image would distort the sequence of the bits of the image pixel. Hence, the proposed technique can easily validate originality of a digital image by exposing any tampering. The usability of our contribution is demonstrated by using the software tool we developed to automate the proposed technique. The experiment which we performed to further validate our technique suggested that Sabimoha could be flawlessly applied to image files.
Jerk-Aware Video Acceleration Magnification

Video magnification reveals subtle changes invisible to the naked eye, but such tiny yet meaningful changes are often hidden under large motions: small deformation of the muscles in doing sports, or tiny vibrations of strings in ukulele playing. For magnifying subtle changes under large motions, video acceleration magnification method has recently been proposed. This method magnifies subtle acceleration changes and ignores slow large motions. However, quick large motions severely distort this method. In this paper, we present a novel use of jerk to make the acceleration method robust to quick large motions. Jerk has been used to assess smoothness of time series data in the neuroscience and mechanical engineering fields. On the basis of our observation that subtle changes are smoother than quick large motions at temporal scale, we used jerk-based smoothness to design a jerk-aware filter that passes subtle changes only under quick large motions. By applying our filter to the acceleration method, we obtain impressive magnification results better than those obtained with state-of-the-art.
EEG-Based Emotion Recognition using 3D Convolutional Neural Networks

Emotion recognition is a crucial problem in Human-Computer Interaction (HCI). Various techniques were applied to enhance the robustness of the emotion recognition systems using electroencephalogram (EEG) signals especially the problem of spatiotemporal features learning. In this paper, a novel EEG-based emotion recognition approach is proposed. In this approach, the use of the 3-Dimensional Convolutional Neural Networks (3D-CNN) is investigated using a multi-channel EEG data for emotion recognition. A data augmentation phase is developed to enhance the performance of the proposed 3D-CNN approach. And, a 3D data representation is formulated from the multi-channel EEG signals, which is used as data input for the proposed 3D-CNN model. Extensive experimental works are conducted using the DEAP (Dataset of Emotion Analysis using the EEG and Physiological and Video Signals) data. It is found that the proposed method is able to achieve recognition accuracies 87.44% and 88.49% for valence and arousal classes respectively, which is outperforming the state of the art methods.
Effective Teaching and Learning: A Business Education Focus

The need to understand the process of student learning in order to improve the quality of that learning has been identified in the education literature (for example, see Biggs, 1978, 1987a, 1987b, Malton and Saljo, 1976, Ramsden, 1992). In addition, the outcomes of this learning have been identified in quantitative, qualitative or attitudinal terms (Biggs, 1990; Marton and Booth, 1997). To this end there have been a number of models of student approaches to learning (Biggs, 1988; Kember and Gow, 1989; Marton and Saljo, 1976; Zhang, 2000). Each model has considered the antecedents, and by way of application, the effectiveness ofvarious learning approaches.
Coding Kendall's Shape Trajectories for 3D Action Recognition

Suitable shape representations as well as their temporal evolution, termed trajectories, often lie to non-linear manifolds. This puts an additional constraint (i.e., non-linearity) in using conventional machine learning techniques for the purpose of classification, event detection, prediction, etc. This paper accommodates the well-known Sparse Coding and Dictionary Learning to the Kendall's shape space and illustrates effective coding of 3D skeletal sequences for action recognition. Grounding on the Riemannian geometry of the shape space, an intrinsic sparse coding and dictionary learning formulation is proposed for static skeletal shapes to overcome the inherent non-linearity of the manifold. As a main result, initial trajectories give rise to sparse code functions with suitable computational properties, including sparsity and vector space representation. To achieve action recognition, two different classification schemes were adopted. A bi-directional LSTM is directly performed on sparse code functions, while a linear SVM is applied after representing sparse code functions using Fourier temporal pyramid. Experiments conducted on three publicly available datasets show the superiority of the proposed approach compared to existing Riemannian representations and its competitiveness with respect to other recently-proposed approaches. When the benefits of invariance are maintained from the Kendall's shape representation, our approach not only overcomes the problem of non-linearity but also yields to discriminative sparse code functions.
End-to-End Convolutional Semantic Embeddings

Semantic embeddings for images and sentences have been widely studied recently. The ability of deep neural networks on learning rich and robust visual and textual representations offers the opportunity to develop effective semantic embedding models. Currently, the state-of-the-art approaches in semantic learning first employ deep neural networks to encode images and sentences into a common semantic space. Then, the learning objective is to ensure a larger similarity between matching image and sentence pairs than randomly sampled pairs. Usually, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are employed for learning image and sentence representations, respectively. On one hand, CNNs are known to produce robust visual features at different levels and RNNs are known for capturing dependencies in sequential data. Therefore, this simple framework can be sufficiently effective in learning visual and textual semantics. On the other hand, different from CNNs, RNNs cannot produce middle-level (e.g. phrase-level in text) representations. As a result, only global representations are available for semantic learning. This could potentially limit the performance of the model due to the hierarchical structures in images and sentences. In this work, we apply Convolutional Neural Networks to process both images and sentences. Consequently, we can employ mid-level representations to assist global semantic learning by introducing a new learning objective on the convolutional layers. The experimental results show that our proposed textual CNN models with the new learning objective lead to better performance than the state-of-the-art approaches.
Ankle joint stiffness during phases of human walking

Joint stiffness relates the dynamic relationship between joint position and the torque acting about it, and thus is one of the factors that characterises the mechanical properties of a joint. Understanding the biomechanics of human ankle joint in dynamic conditions allows insight into design of ankle prostheses which could theoretically provide a functionality similar to that of a healthy limb. 11 non-disabled subjects (24-27 years) participated in the study. Subjects walked on a treadmill while perturbations (single displacement pulses) were applied to the ankle and the resulting torque was measured. Ankle joint stiffness was investigated during walking conditions in three phases of gait cycle, and in standing conditions, where subjects’ leg postures isometrically matched the different phases of the dynamic trials.Stiffness estimates were generated using a multi-segment algorithm, with position and torque used to characterise the dynamic system non–parametrically. Results showed a non–significant difference between the mean values of joint stiffness during dynamic and isometric trials, and a statistically significant difference in mean ankle stiffness was found between the three phases of gait cycle at plantarflexion [P < 0.0005].
Arabic Chatbots: A Survey

A Chatbot is a programmed entity that handles human-like conversations between an artificial agent and humans. This conversation has attracted the attention of researchers who are interested in the interaction between humans and machines to make the conversation more rational and hence pass the Turing test. The available research done in the field of Arabic chatbots is comparably scarce. This paper presents a review of the published Arabic chatbots studies to identify the gap of knowledge and to highlight the areas that needs more study and research. This study concluded the rarity of available research on Arabic chatbots and that all available works are retrieval based.
Evidence and Implications of Regime Shifts: Time‐Varying Effects of the United States and Japanese Economies on House Prices in Hawaii

We show that local house prices may be driven almost entirely by the demands of one identifiable group for several years and then by demands of another group at other times. We present evidence that house prices in Hawaii were subject to such regime shifts. Prices responded to demands associated with U.S. incomes and wealth for most years from 1975 through 2008. For about a decade starting in the middle of the 1980s, after the Japanese yen appreciated dramatically and Japanese housing and stock market wealth soared, however, house prices in Hawaii responded to Japanese incomes and wealth. Estimated models with these regime shifts outperformed conventional, constant‐coefficient models. The regime‐shifting model helps explain why, when and by how much the volatility and the elasticities of house prices in Hawaii with respect to the incomes and wealth of the United States and Japan varied over time.