Miracle-dz/UniFAR-FTUs huggingface dataset:
Blind Segmentation of Speech Using Non-Linear Filtering Methods

Automated segmentation of speech into phone-sized units has been a subject of study for over 30 years, as it plays a central role in many speech processing and ASR applications. While segmentation by hand is relatively precise, it is also extremely laborious and tedious. This is one reason why automated methods are widely utilized. For example, phonetic analysis of speech (Mermelstein, 1975), audio content classification (Zhang & Kuo, 1999), and word recognition (Antal, 2004) utilize segmentation for dividing continuous audio signals into discrete, non-overlapping units in order to provide structural descriptions for the different parts of a processed signal. In the field of automatic segmentation of speech, the best results have so far been achieved with semi-automatic HMMs that require prior training (see, e.g., Makhoul & Schwartz, 1994). Algorithms using additional linguistic information like phonetic annotation during the segmentation process are often also effective (e.g., Hemert, 1991). The use of these types of algorithms is well justified for several different purposes, but extensive training may not always be possible, nor may adequately rich descriptions of speech material be available, for instance, in real-time applications. Training of the algorithms also imposes limitations to the material that can be segmented effectively, with the results being highly dependent on, e.g., the language and vocabulary of the training and target material. Therefore, several researchers have concurrently worked on blind speech segmentation methods that do not require any external or prior knowledge regarding the speech to be segmented (Almpanidis & Kotropoulos, 2008; Aversano et al., 2001; Cherniz et al., 2007; Esposito & Aversano, 2005; Estevan et al., 2007; Sharma & Mammone, 1996). These so called blind segmentation algorithms have many potential applications in the field of speech processing that are complementary to supervised segmentation, since they do not need to be trained extensively on carefully prepared speech material. As an important property, blind algorithms do not necessarily make assumptions about underlying signal conditions whereas in trained algorithms possible mismatches between training data and processed input cause problems and errors in segmentation, e.g., due to changes in background noise conditions or microphone properties. Blind methods also provide a valuable tool for investigating speech from a basic level such as phonetic research, they are language independent, and they can be used as a processing step in self-learning agents attempting to make sense of sensory input where externally supplied linguistic knowledge cannot be used (e.g., Rasanen & Driesen, 2009; Rasanen et al., 2008).
Tapping the Tacit Knowledge of Playfield Swings to Learn Physics: A Case Study of Childhood Reflections by Pre-service Teachers

Children experience physical phenomena and formulate an epistemological worldview before contact with school science. School science extends thought about the same phenomena to deeper levels but does not always link with out-of-school experiences. This study explored preservice teachers’ indigenous knowledge or tacit knowledge about the playground swing and its use to teach the physics of the simple pendulum. The preservice teachers’ indigenous knowledge experiences with playground swings may be a pivot for linking their physics teaching to out-of-school experiences. A phenomenological research design was used to explore experiences of eight preservice teachers. Data were collected using focus group interviews and personal interviews and were thematically analysed. Findings show that young players become aware of relationships between variables; they are attracted to the swings for specific reasons; they connect experiences at swings with physical laws. However, pre-service teachers are not able to connect this tacit knowledge with formal science. Yet teachers should realise that school physics explains everyday experiences which they know in their vernacular. The lack of such a link may disadvantage learners in understanding ideas expressed in English, which is not their mother tongue. Thus, teachers need to link indigenous tacit knowledge with school physics in order to develop effective teaching strategies.
Memory Detection 2.0: The First Web-Based Memory Detection Test

There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262) tried to hide 2 high salient (birthday, country of origin) and 2 low salient (favourite colour, favourite animal) autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.
BigDAWG : a Polystore for Diverse Interactive Applications

Interactive analytics requires low latency queries in the presence of diverse, complex, and constantly evolving workloads. To address these challenges, we introduce a polystore, BigDAWG, that tightly couples diverse database systems, data models, and query languages through use of semantically grouped “Islands of Information”. BigDAWG, which stands for the Big Data Working Group, seeks to provide location transparency by matching the right system for each workload using black-box model of query and system performance. In this paper we introduce BigDAWG as a solution to diverse web-based interactive applications and motivate our key challenges in building BigDAWG. BigDAWG continues to evolve and, where applicable, we have noted the current status of its implementation.
Spatial structure of tetrapeptide N-AC-Ser-Phe-Val-Gly-OMe in "protein-micelle of sodium dodecyl sulfate" complex and in solid state by NMR spectroscopy

In the present paper the applicability of a structure determination for a four amino acid residues containing oligopeptide N-Ac-Ser-Phe-Val-Gly-OMe was investigated. The spatial structure of the “tetrapeptide (N-Ac-Ser-Phe-Val-Gly-OMe) - sodium dodecyl sulfate micelle” complex in aqueous solution was studied by 2D nuclear magnetic resonance (NMR) spectroscopy. The complexation was confirmed NOEs signs and values in the presence of sodium dodecyl sulfate. The spatial structure of the tetrapeptide in the complex was determined by 2D NOESY NMR spectroscopy. In present paper by the comparison 13 C NMR chemical shifts was shown that tetra peptide’s spatial structure in solid state and tetrapeptide structure in “peptide-micelle” complex are identical.
NEURAL NETWORK WITH PSO BASED TRAINING TO DETECT SPOOFING WEBSITES

With the advent of Internet, various online attacks were increased among them and the most well-known is a spoofing attack. Web spoofing is a type of spoofing in which fake and spoofing websites made by means of fraudsters to duplicate real websites. Spoofing websites represent legitimate websites which attract users into visiting fake websites to steal users sensitive, personal information or install malwares in their devices. The scammers will use the stolen information for illegal purposes. The specific intention of this paper is to build a new intelligent system that detects and recognize between trusted and spoofing websites which try to mimic the trusted sites because it is very difficult to visually recognize whether they are spoofing or legitimate. This paper deals with the detection of spoofing websites using Neural Network (NN) trained with Particle Swarm Optimization (PSO) algorithm. An Information gain algorithm is used for feature selection, which was a useful step to remove the unnecessary features and reduce time. The Information gain seems to improve the classification accuracy via reducing the number of extracted features and used as an input for training the NN using PSO. Training neural network using PSO provides less training time and high accuracy which achieved 99.18% compared to NN trained with back propagation algorithm which takes more time for training and less accuracy which was 98.20%. The proposed technique is evaluated with a dataset of 2500 spoofing sites and 2500 legitimate sites. The results show that the technique can detect over 99.18% spoofing sites with NN trained using PSO.
Evaluating SWAT Snowmelt Parametersand Simulating Spring Snowmelt NonpointSource Pollution in the Source Areaof the Liao River

Snowmelt hydrology becomes an important component of the soil and water assessment tool (SWAT) when spring flows are dominated by melting snow. The objective of this study was to evaluate the performance of the SWAT model’s snowmelt parameters by simulating nonpoint source pollution in the source area of the Liao River – a cold area in China. The seven snowmelt parameters for the simulation were adjusted. The exports rule of nonpoint source pollution for the spring snowmelt period was analyzed. The result show that SFTMP, SMTMP, SMFMX, SMFMN, and TIMP were sensitive parameters, while SNOCOVMX and SNO50COV were insensitive. In addition, two common statistics (Nash-Sutcliffe coefficient, and coefficient of determination) were used to evaluate the model. This evaluation indicated that the SWAT model had good performance simulating the seasonal nonpoint source pollution with large snowmelt. According to the analysis, rainfall and snowmelt is a main driver of nonpoint source pollution in the cold area, and that summer and spring are high-risk seasons. The study provides a new and suitable method of nonpoint source pollution simulation for the cold areas.
Fooling PRNU-Based Detectors Through Convolutional Neural Networks

In the last few years, forensic researchers have developed a wide set of techniques to blindly attribute an image to the device used to shoot it. Among these techniques, those based on photo response non uniformity (PRNU) have shown incredibly accurate results, thus they are often considered as a reference baseline solution. The rationale behind these techniques is that each camera sensor leaves on acquired images a characteristic noise pattern. This pattern can be estimated and uniquely mapped to a specific acquisition device through a cross-correlation test. In this paper, we study the possibility of leveraging recent findings in the deep learning field to attack PRNU-based detectors. Specifically, we focus on the possibility of editing an image through convolutional neural networks in a visually imperceptible way, still hindering PRNU noise estimation. Results show that performing such an attack is possible, even though an informed forensic analyst can reduce its impact through a smart test.
Sparse inpainting and isotropy

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.
Target Site—Based and Non—Target Site Based Resistance to ALS Inhibitors in Palmer Amaranth (Amaranthus palmeri)

Resistance to acetolactate synthase (ALS)-inhibitor herbicides due to continuous and repeated selection is widespread in many troublesome weed species, including Palmer amaranth, throughout the United States. The objective of this research was to investigate the physiological and molecular basis of resistance to ALS inhibitors in a chlorsulfuron-resistant Palmer amaranth population (KSR). Our results indicate that the KSR population exhibits a high level of resistance to chlorsulfuron compared with two known susceptible populations, MSS and KSS from Mississippi and Kansas, respectively. MSS is highly susceptible to chlorsulfuron, whereas KSS is moderately sensitive. Dose—response analysis revealed that KSR was more than 275-fold more resistant compared with KSS. Nucleotide sequence analysis of the ALS gene from the plants that survived chlorsulfuron treatment revealed the possibility of evolution of both target site—based and non—target site based resistance to ALS inhibitors in the KSR population. The most common mutation (Pro-197-Ser) in the ALS gene associated with resistance to the sulfonylureas in many weed species was found only in 30% of the KSR population. A preliminary malathion study showed that the remaining 70% of resistant plants might have cytochrome P450—mediated non—target site resistance. This is the first report elucidating the mechanism of resistance to ALS inhibitors in Palmer amaranth from Kansas. Presence of both target site— and non—target site based mechanisms of resistance limits the herbicide options to manage Palmer amaranth in cropping systems. Nomenclature: Chlorsulfuron; Palmer amaranth, Amaranthus palmeri S. Wats. AMAPA
1 , 25 ( OH ) 2 D 3 enhances PTH-induced Ca 2 ÷ transients in preosteoblasts by activating L-type Ca 2 + channels

During the month of Iune, 1997, the final month of this contract, Dr. Wei Li continued his investigation of the "priming" effect of vitamin D 3 on parathyroid hormone (PTH)-induced elevations in cytosolic free Ca 2÷ in cultured osteoblasts. Unlike the vitamin D steroid, neither estrogen nor progesterone were either to prime the cells' response to PTH. Wei also confirmed previous experimental results suggesting that vitamin D_ alone does not change cytoplasmic Ca 2* levels.
Investigation of Critical Thinking Attitudes and Reading Habits of Teacher Candidates.

This study aims to define reading habits and critical thinking levels of pre-service teachers, who study at departments of classroom and physical education and sports teaching, and presenting the differences between these. The variables of the research were designed in accordance with descriptive research model. In 2016 to 17 academic year fall semester, the universe of the research consists of 250 students. All of these students study at Mugla Sitki Kocman University, and their departments are Classroom and Physical Education and Sports Teaching. The variables of the present research were tested for normality, and it was found that the variables were non-normally distributed. The differences between variables were tested (p=0.05) by using Mann-Whitney U test is one of the nonparametric tests. In order to define their reading habit and critical thinking levels, the attitude scale towards reading habit and California critical thinking dispositions inventory (CCTDI) were employed. As a result of this study, pre-service classroom and physical education teachers have medium level critical thinking; pre-service classroom teachers’ level of critical thinking have higher than pre-service physical education and sports teachers; and significant difference among genders from the point of reading habit levels of teacher candidates were found. Key words: Teacher candidates, pre-service teachers, physical education and sports teaching, reading Habits, critical thinking
Hospitalization Pattern and Treatment Outcome Among Diabetic Patients Admitted to a Teaching Hospital in Ethiopia: A Prospective Observational Study

Diabetes mellitus is associated with frequent hospitalization and high morbidity, mortality and health care expenditure . However, there is scarcity of data on hospitalization pattern and treatment outcome among diabetic patients in Ethiopia. Objective : to assess reasons for hospitalization, treatment outcome and its predictors among diabetic patients admitted at a teaching hospital in Ethiopia Method : A hospital based prospective observational study was conducted at medical and surgical wards of Jimma University Specialized Hospital from March to June, 2015. All adult patients with the diagnosis of diabetes who met the inclusion criteria were included in the study. Data was collected using pretested abstracting format. Logistic regression was employed for statistical analysis and statistical significance was considered at p value<0.05. Result : A total of 89 patients were included in the analysis; 59.6% were male, with the mean age 46.9±15.5 years. Type 2 diabetic patients accounted for 74.2%. One third (33.7%) of patients were hospitalized with diabetic ketoacidosis. Other common reasons for hospitalization were infections (19.1%) and cardiovascular diseases (18.0%). Median duration of hospital stay was 9 days ranged from 1 to 88 days. Seventy six percent of patients were discharged with improvement and hospital mortality rate was 11.2%. Patients admitted due to diabetic complications had favorable treatment outcome (AOR=5.7, 95%CI: 1.4-22.9). Conclusion : This study identified that the most common reasons for hospitalization of diabetic patients were diabetic complications, infections and cardiovascular diseases. Despite majority of patients discharged with improvement, mortality rate is still high which urges attention to the care of hospitalized diabetic patients and the need to enhance outpatient care of diabetic patients to minimize hospitalization due to preventable causes. Keywords : reasons for hospitalization, diabetic patients, treatment outcome, Jimma University
Non-orthogonal Multiple Access in Hybrid Heterogeneous Networks

In this paper, the application of non-orthogonal multiple access (NOMA) into K-tier heterogeneous networks (HetNets) is investigated. A new promising transmission framework is proposed, in which massive multiple-input multipleoutput (MIMO) is employed in macro cells and NOMA is adopted in small cells. For maximizing the biased average received power at mobile users, a massive MIMO and NOMA based user association scheme is developed. In an effort to evaluate the performance of the proposed framework, analytical expressions for the spectrum efficiency of each tier are derived using stochastic geometry. Simulation results are presented to verify the accuracy of the proposed analytical derivations and confirm that NOMA is capable of enhancing the spectrum efficiency of the network compared to the orthogonal multiple access (OMA) based HetNets.
Green Synthesis of TiO 2 Nanoparticle Using Moringa Oleifera Leaf Extract

The present work describes synthesis and characterization of Titanium dioxide (TiO2) nanoparticles. TiO2 nanoparticles prepared by eco-friendly green synthesis method using Moringa oleifera leaves extract. The synthesized TiO2 nanoparticles were characterized by X-ray diffraction (XRD). Anatase phase TiO2 nanoparticles with mean crystalline size 12.22 nm and Band gap of 3.9 eV was obtained by UV-Visible spectroscopy.
Using N-grams and Word Embeddings for Twitter Hashtag Suggestion

Many approaches have been taken in prior research to develop accurate Twitter hashtag suggestion systems. Most of these systems involve longer known NLP-techniques like topic distribution and tf-idf representations. However, none of these approaches make use of the more state-of-the-art machine learning techniques involving artificial neural networks (ANNs). This thesis examines the possibility of using word embeddings features, which are trained using an ANN-approach, to develop a system which suggests appropriate hashtags for given tweets. The experiments demonstrated that adding word embeddings to the n-gram baseline indeed lead to a better overall performance of the system. For the best performing baseline (character 3-grams), the F1-score went up from 0.17 to 0.18 when adding word embeddings features. For word unigrams, the F1-score went op from 0.07 to 0.13 when adding word embeddings features.
The ameliorative potential of Sophora alopecuroides essential oil on CCl4-induced hepatotoxicity in mice; a stereological study

Background and objectives: Sophora alopecuroides, one of the most important herbal medicines is widely grown in west of Iran, and has a long history for treatment of gastrointestinal diseases, leucorrhea, eczema, and psoriasis. The aim of this study was to investigate the ameliorative effect of Sophora alopecuroides essential oil on CCl4-induced hepatotoxicity in mice. Methods: Thirty five male mice were divided into five groups; group I as the negative control, received olive oil intraperitoneally and distilled water orally. Group II as the positive control, received CCl4 mixed with olive oil in the ratio of 5:5, intraperitoneally and distilled water orally. Group III, IV and V received CCl4 and 200, 800 and 1600 µg/kg of S. alopecuroides essential oil through gavages for 45 consecutive days. Results: The results showed that low and intermediate doses of S. alopecuroides essential oil significantly decreased the raised levels of Alanine aminotrasferase and Aspartate aminotransaminase toward control levels (p<0.05). Alkaline phosphatase was improved with tree examined doses of S. alopecuroides essential oil. The volume and weight of the liver, as well as the volume of hepatocytes and sinusoids which had increased significantly in the positive control group (p<0.05), decreased significantly following treatment by low dose of S. alopecuroides essential oil (p<0.05). Conclusion: It was concluded that although Sophora alopecuroidesessential could protect liver against CCl4-induced toxicity at lower doses, further studies would be needed to define the selective dose of this plant against CCl4-induced hepatotoxicity.
An Optimization Approach To EO Phase Retrieval

This paper describes an optimization approach to optical phase retrieval. Our method casts the probl em of estimating a complex image from measurements of ima ge and pupil intensities as a classical estimation problem . To this end, we first define a non-linear error function and compute its gradient. These expressions enable a quasi-Newton optimizatio n to find a stationary point, which corresponds to the final complex image estimate. One benefit of the optimization approach is that, unlike the classical Gerchberg-Saxton approach, the framew ork is easily extended to additional measurements by simply addin g terms to the objective function. We illustrate the algorithm on collected laboratory data with images of approximate size × , for a total of million unknown image coefficients. Keywords—phase retrieval; optimization