Miracle-dz/UniFAR-FTUs huggingface dataset:
ORF10: Molecular Insights into the Contagious Nature of Pandemic Novel Coronavirus 2019-nCoV

This article stems from the author's observations that ORF10, a protein with no sequence and structural conservation, provides with a high number of potentially immunogenic, promiscuous epitopes. With high degree of epitope conservation across CTL and HTL epitopes of ORF10, its existence in the 2019-nCoV genome is largely seen as particularly contributing to the highly contagious nature of this virus.
Genetic variation via SSR polymorphic information content and ecological distribution of Nigerian sesame

Sesame (Sesamum indicum L.) of the family Pedaliaceae is an important and old oil crop that is cultivated mainly in the tropical and subtropical regions of Asia and Africa for several economic values. Several molecular markers have been employed to study the genetic diversity of this important crop. The study focused on the genetic diversity through polymorphism information content (PIC) by the use of Simple Sequence Repeat markers among randomly collected 22 Sesame germplasm across 2 ecological zones stored in NACGRAB seed Genebank, Nigeria. The extraction procedure followed Cetyltrimethylammonium Bromide (CTAB) and the recovered DNA were good with average concentration of 337.00 ng/µL and average quality of 1.795. 30 primers were designed but only 12 with highest genome coverage were used to analyzed the genetic data with NTSYS pc ver.2. 02 and Power Marker ver.3. 5. The PIC ranged between 0.36 in Primer OTO2 and 0.76 in Primer OTO5. The evolutionary relationship was constructed based on the polymorphic primers and according to their ecological locations. The populations were divided to 2 major clads. Of the 2 ecological zones, the more diverse ecological zone is the derived Savana with 13 accessions, while the less diverse is the humid forest with 9 accessions. Key words: Sesame, genetic diversity, polymorphism information content (PIC), ecological distribution, microsatellite.
A Finitely Axiomatized Formalization of Predicate Calculus with Equality

We present a formalization of ﬁrst-order predicate calculus with equality which, unlike traditional systems with axiom schemata or substitution rules, is ﬁnitely axiomatized in the sense that each step in a formal proof admits only ﬁnitely many choices. This formalization is primarily based on the inference rule of condensed detachment of C. A. Meredith. The usual primitive notions of free variable and proper substitution are absent, making it easy to verify proofs in a machine-oriented application. Completeness results are presented. The example of Zermelo-Fraenkel set theory is shown to be ﬁnitely axiomatized under the formalization. The relationship with resolution-based theorem provers is brieﬂy discussed. A closely related axiomatization of traditional predicate calculus is shown to be complete in a strong metamathematical sense.
Impact of Board Characteristics, Ownership Structure and Growth opportunity on Corporate Risk Taking. Evidence from Pakistan listed firms

Corporate risk taking is important for the firms for maximization of value. However, excessive risk may lead towards crisis. The primary objective of this study is to explore the relationship of board characteristics, ownership structure and growth opportunity with corporate risk-taking behavior in listed firms of Pakistan. This study uses data of 90 non-financial firms listed in PSX-100 index for the period of 2013 to 2017. In this study, quantitative methods such as, Pooled OLS, Fixed Effect and Random Effectpanel regression method are used to examine the relationship between variables. Overall results suggest that firm size,Board diligence, Family ownership, ownership concentration, the presence of the independent director in board composition and total number of the independent directors in board is negatively associated with risk taking. No relationship is found between CEO duality and risk taking. Whereas profitability has significant relationship with risk taking, it means that profitability of firms consistently helpful in mitigation of the risk behavior in consideration for uprising the business activities.Growth opportunity has significant positive relationship with risk taking, it means that firms with growth opportunity has less efficient risk management system. In previous research government ownership, private ownership and foreign ownership was taken but family ownership and ownership concentration were not taken. In this research board characteristics, family ownership and ownership concentration are taken to examine the relationship. My suggestion is that some further study should be conducted on corporate governance other factors such as board expertise and knowledge, gender diversity and board qualification that make strong corporate governance.
Aquaporin-1 Protein Levels Elevated in Fresh Urine of Renal Cell Carcinoma Patients: Potential Use for Screening and Classification of Incidental Renal Lesions

Introduction and Objectives. There are over 65,000 new cases of renal cell carcinoma (RCC) each year, yet there is no effective clinical screening test for RCC. A single report claimed no overlap between urine levels of aquaporin-1 (AQP1) in patients with and without RCC (Mayo Clin Proc. 85:413, 2010). Here, we used archived and fresh RCC patient urine to validate this report. Methods. Archived RCC, fresh prenephrectomy RCC, and non-RCC negative control urines were processed for Western blot analysis. Urinary creatinine concentrations were quantified by the Jaffe reaction (Nephron 16:31, 1976). Precipitated protein was dissolved in 1x SDS for a final concentration of 2 μg/µL creatinine. Results. Negative control and archived RCC patient urine failed to show any AQP1 protein by Western blot analysis. Fresh RCC patient urine is robustly positive for AQP1. There was no signal overlap between fresh RCC and negative control, making differentiation straightforward. Conclusions. Our data confirms that fresh urine of patients with RCC contains easily detectable AQP1 protein. However, archival specimens showed an absence of detectable AQP1 indistinguishable from negative control. These findings suggest that a clinically applicable diagnostic test for AQP1 in fresh urine may be useful for detecting RCC.
Enhanced Network Intrusion Detection System for Imbalance Dataset based on Feature Selection and Classification Algorithms

—: With rapid development and extensive growth of information systems, security problems increasingly bring into prominence.IDS are trained for classes: normal and attacks and further for types of attacks. A data set is called imbalanced when its elements are not evenly divided among the classes and this problem is a difficult challenge faced by machine learning and data mining researchers. Sampling techniques are one of the solutions to this problem. This paper is an attempt to provide an efficient method for imbalanced dataset classification with effect of sampling and feature selection. Proposed class balancing approach improve the performance of the classifier, shows better accuracy and reduced false alarm rate when compared with existing approach. Our proposed approach has been tested using KDD CUP'99 dataset.
Trial-Based Heuristic Tree Search for MDPs with Factored Action Spaces

MDPs with factored action spaces, i.e., where actions are described as assignments to a set of action variables, allow reasoning over action variables instead of action states, yet most algorithms only consider a grounded action representation. This includes algorithms that are instantiations of the Trial-based Heuristic Tree Search (THTS) framework, such as AO* or UCT. To be able to reason over factored action spaces, we propose a generalization of THTS where nodes that branch over all applicable actions are replaced with subtrees that consist of nodes that represent the decision for a single action variable. We show that many THTS algorithms retain their theoretical properties under the generalised framework, and show how to approximate any state-action heuristic to a heuristic for partial action assignments. This allows to guide a UCT variant that is able to create exponentially fewer nodes than the same algorithm that considers ground actions. An empirical evaluation on the benchmark set of the probabilistic track of the latest International Planning Competition validates the benefits of the approach.
Face Recognition with Sub-Sampled Images

Deep learning (DL) has proven to be a power paradigm in the field of imaging and computer vision. Many DL-based techniques have been developed to upsample images for human consumption. In this work, we have developed a CNN-based model that optimizes instead for another DL mode, the FaceNet. Our model takes as input aggressively sub-sampled images (⅛ of the original resolution in both imaging dimensions). We demonstrate that our model outperforms several other non-DL based upsampling filters in certain dataset. We identify the areas of improvement of our model and insights obtained from this work. If this technology is developed successfully, it can extend the application range of the FaceNet model for a given camera hardware configuration.
Cutting Down “Fluff” A Twist on Text Summarization

In this paper we explore the task of making text concise, which we deﬁne as keeping all ideas of a text intact in as few words as possible. We built a dataset of 2,225 pairs mapping cluttered text to their concise version, which we used to train our constructed extractive model and already established abstractive summarization model. Our models performed well on the test set, obtaining ROUGE F-Scores of more than 90. The results indicate that using deep-learning to assist humans in writing concisely is worth further exploration
Employing Tasks to Improve Argumentative Essay Writing of EFL Teachers: A Case of Interactionist versus Interventionist Dynamic Assessment

Dynamic Assessment (DA), which stems from Vygotsky’s sociocultural theory, invests in the mediation of teacher to improve learners’ Zone of Proximal Development (ZPD). This study investigated the impact of DA on the development of argumentative essay writing of EFL teachers and consisted of a pretest, mediation, a post-test, and a transcendence test (TRT). Accordingly, 66 novice EFL teachers were selected based on convenience sampling and were randomly assigned to two experimental groups and a control group (n1=n2=n3=22). The interactionist DA group (IA-DAG) and the interventionist DA group (IV-DAG) received non-standardized and standardized feedback types, respectively. However, the control group practiced non-DA writing instruction. Subsequently, a series of writing tasks were used for teaching writing. After nine sessions, the groups took a post-test, and after two weeks, TRT was administered. Appropriately, several statistical procedures were conducted to analyze data. First, repeated-measure ANOVAs were performed to examine the beneficial effects of the instructions in the groups. Second, the mixed between-within-subjects ANOVA was run and the results revealed that there were main effects for treatment types and time. Also, the result of one-way ANOVA on the post-test showed the outperformance of the IA-DAG over the other groups. Besides, the results of the one-way ANOVA on the TRT indicated no significant differences between the IA-DAG and IV-DAG. However, both groups had significantly higher mean scores than the non-DA group. The study has implications for L2 writing teachers, material developers, and educators.
A reflective journal as learning process and contribution to quality and validity in interpretative phenomenological analysis

Using selected, contemporaneous illustrations from the reflective journal of a doctoral student undertaking data analysis for the first time, this article examines the relationship between journaling as a learning process when undertaking computer-assisted qualitative data analysis and establishing quality and validity in interpretative phenomenological analysis. The writing of the journal is shown both to enact some potential validity criteria (e.g. in producing an audit trail) whilst also recording and reflectively prompting the process of learning, interpretation and bracketing, thus evidencing transparency. By using a journal inside the software package and alongside the stages of the interpretative phenomenological analysis, analysis within the software package, it is argued that quality and validity become dynamic, not static constructs. These constructs are intimately linked to the researcher-learning-process and permit a critical stance to be taken.
When does the pooled variance t-test fail?

The pooled variance t-tests used prominently for comparing means between two groups is usually restricted with the assumptions of normality and homogeneity of variances. However, the violation of the assumptions happens in many real world data. In this study, the conditions where the pooled variance t-test would fail were investigated. The performance of the t-test was evaluated under different conditions. They were sample sizes, type of distributions (normal or non-normal), and unequal group variances. The Type I error rates and power of the pooled variance t-tests for different designs were obtained and compared. The results showed that the test failed dramatically when the group sample sizes were small and unequal with slight departure from homogeneity of variances. Key words: Pooled variance, power, t-test, type 1 error.
Heart Disease Prediction Evaluation using Numerous Machine Gaining Knowledge Strategies

: The human offerings enterprise is determined with huge degree of rich information but absence of records to address the big information if you want to get the great effects. Analyze of heart contamination in starting instances is one of the problems for these expert via adjusting ongoing AI techniques which diminishes the amount of check to be occur and moreover decrease the amount of test to be arise and furthermore reduce the quantity of death from the heart contamination. Information mining gives the everyday techniques to deal with these troubles and type out with the efficient system to foresee the heart infection at beginning periods utilizing special AI predication calculation. This paper outline specific AI calculation related to searching out higher execution in heart contamination determination, so analysts can pick out the proper machine for extra therapeutic critical thinking with excessive exactness.
Automatic trajectory extraction and validation of scanned handwritten characters

A well-established task in forensic writer identification is the comparison of prototypical character shapes (allographs) present in the handwriting. Using elastic matching techniques like Dynamic Time Warping (DTW), comparison results can be made that are plausible and understandable to the human expert. Since these techniques require the dynamics of the handwritten trace, the “online” dynamic allograph trajectories need to be extracted from the “offline” scanned documents. We have implemented an algorithm that can automatically extract this information from scanned images. The algorithm makes a list of all possible trajectories. Using a number of traditional techniques and DTW for evaluation, the best trajectory is selected. To be able to make a quantitative assessment of our techniques, rather than a qualitative discussion of a small number of examples, we tested the performance on two large datasets, one containing online and the other containing offline data. Two different methods (one for the online, and one for the offline dataset) are used to validate the generated trajectories. The results of the experiments show that DTW can significantly improve the performance of trajectory extraction when compared to traditional techniques.
The Effect of Fee Shifting on Litigation: Evidence from a Policy Innovation in Intermediate Cost Shifting

We study the effect of fee shifting rules on litigation. First, we build a model to study the theoretical effect of a change in cost-recovery rules on case filings, (postfiling) settlement, win rates, and plaintiffs’ average litigation expenditures. We then undertake an empirical analysis of the introduction of an intermediate cost shifting rule that falls between the English and American Rules: a reform that limits the size of fee awards to successful litigants in cases decided by the Intellectual Property Enterprise Court (IPEC), one of two venues where IP cases may be filed in England and Wales. Our empirical analysis takes advantage of heterogeneity among case types and compares IPEC cases with intellectual property cases litigated at the PHC of England and Wales, which was not subject to this reform. We find that patent case filings increased following the IPEC’s shift from a pure English Rule to a rule that caps costs awards. Consistent with our model’s predictions, we also find evidence that smaller plaintiffs both won less often and settled more often postreform, as well as evidence that larger plaintiffs spent less on litigation postreform.
Multi-modal Model for Sentence Level Captioning

Captioning refers to the transcription of speech into time-coded blocks. Traditional approaches to captioning have relied on transcribing audio data using End-to-End Automatic Speech Recognition models (ASR) (DeepSpeech citation). However the performance of ASR models in real world settings is limited due to factors such as high noise and speaker distance. Other approaches have relied on transcribing visual data using lipreading models (LipNet citation). However, the performance of lipreading models have been variable with limited spatio-temporal robustness. To build on the existing state of the art, we developed a multi-modal model for captioning that integrates both auditory and visual cues. Our model splits video data into visual and audio data and makes use of a DeepSpeech ASR Model and LipNet, an end-to-end sentence-level lipreading model, which are fused together with fully connected softmax and Connectionist Temporal Classiﬁcation (CTC) layers.
Detecting Release Decade From Song Lyrics

The way people speak changes over time, and can be reﬂected in pop cultural media such as books or songs. In this project, I apply a couple of different neural network models - one simple, and one based on Long Short-Term Memory (LSTM) models - in order to try to classify the decade a song was released in solely by its lyrics. While I did not achieve signiﬁcant success with my models, my approach shows potential for success using more robust techniques
Supplement for Estimating Mixed Memberships with Sharp Eigenvector Deviations

In this document we present technical details and accompanying lemmas which are necessary for the main results in the main manuscript. When we make references to equations or theorems etc. in the main document, we follow the numbering scheme of the main document, and the references do not have any alphabets in them.
Assistance for Target Selection in Mobile Augmented Reality

Mobile augmented reality – where a mobile device is used to view and interact with virtual objects displayed in the real world – is becoming more common. Target selection is the main method of interaction in mobile AR, but is particularly difficult because targets in AR can have challenging characteristics such as being moving or occluded (by digital or real world objects). Because target selection is particularly difficult and error prone in mobile AR, we conduct a comparative study of target assistance techniques. We compared four different cursor-based selection techniques against the standard touch-to-select interaction, finding that a newly adapted Bubble Cursor-based technique performs consistently best across five different target characteristics. Our work provides new findings demonstrating the promise of cursor-based target assistance in mobile AR.
Design patterns for mixed-method research in HCI

In this paper we discuss mixed-method research in HCI. We report on an empirical literature study of the NordiCHI 2012 proceedings which aimed to uncover and describe common mixed-method approaches, and to identify good practices for mixed-methods research in HCI. We present our results as mixed-method research design patterns, which can be used to design, discuss and evaluate mixed-method research. Three dominant patterns are identified and fully described and three additional pattern candidates are proposed. With our pattern descriptions we aim to lay a foundation for a more thoughtful application of, and a stronger discourse about, mixed-method approaches in HCI.
Deep Learning for In-Game NFL Predictions

This project explores deep learning methods for predicting in-game NFL play outcomes. Systems that predict play outcomes in-game may help NFL team play callers improve their strategies at play-call time. I focus on two important outcomes, the yardage outcome of a play and the offensive play call (pass or run). My models combine pre-snap “situational data” with pre-snap images of the play. I run an array of learning models, including shallow CNNs and transfer learning, with and without the image data. The models show that while the features have no signal for yardage outcomes, offensive play calls can be predicted with much higher accuracy than benchmark models. However, the marginal value of the image data and sophisticated deep learning models is very low for both tasks
EFFECT OF SEASONAL VARIATION ON ZINC CONTENT OF SOIL, FORAGE, WATER, FEED, AND SMALL RUMINANTS GRAZING THE NATIVE AND IMPROVED PASTURE DURING DIFFERENT SEASONS IN THE SEMI-ARID REGION OF PAKISTAN

An investigation was conducted to evaluate the zinc status of three different classes of grazing goats on the basis of zinc concentrations in pasture and animal samples as affected by season and class of animals in central Punjab, Pakistan. Soil, water, plasma, urine, and fecal zinc levels of lactating and male goats did not show seasonal differences while forage, feed and milk zinc concentrations in lactating and faeces in non lactating animals had seasonal variations with greater concentrations in winter than in summer except feed which was higher in summer than that in winter. Mean soil, forage and plasma zinc concentrations were adequate for plants and animals requirements during both seasons. Milk concentration of zinc showed seasonal variation with being consistently higher in winter than in summer. In both seasons, most milk samples had lower/higher zinc concentrations than reported reference values of goat milk. The overall zinc status of these goats based on pasture and animal samples may be considered adequate mainly due to supplemental feed provided containing different amount of zinc, since forage zinc concentration were deficient particularly in summer season.
Operationalizing Dialogue Games for Collaborative Modeling

In our ongoing attempt to create and support workable "rules of play" for effective, goal-driven conversations in the context of collaborative modeling, we have now reached a stage in which a first operational setup for such 'dialogue games' has been developed and tested. In this paper we present the basic, generic structure of a game-like procedure description, based on a prototype game for conceptual (information) modeling. The procedure has been tested and refined in a modest design cycle and has been demonstrated to work realistically. Its generic structure is meant to be applicable for different types of conceptual modeling, e.g. process modeling, goal modeling, task modeling, and so on (requiring specific rules and configurations for the different types). However, in this paper we merely aim to present our basic approach. An illustrative example is provided.