Miracle-dz/UniFAR-FTUs huggingface dataset:
Naive Bayes Classifier for Positive Unlabeled Learning with Uncertainty

Existing algorithms for positive unlabeled learning (PU learning) only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. In this paper, based on positive naive Bayes (PNB), which is a PU learning algorithm for certain data, we propose an algorithm to handle uncertain data. However, it requires the prior probability of positive class and in real-life applications it is generally difficult for the users to provide this parameter, which is a drawback inherited from traditional PNB algorithm. We improve it by selecting the value of the prior probability of positive class automatically that can make the obtained classifier achieved optimal performance on the validation set. The conducted experiments show that the proposed algorithm yields good performance without user-specified the prior probability of positive class and has satisfactory performance even on highly uncertain data. Copyright
Endothelial dysfunction as a nexus for endothelial cell-cardiomyocyte miscommunication

Most studies of the heart focus on cardiomyocytes (CM) at the exclusion of other cell types such as myocardial endothelial cells (EC). Such mono-cellular approaches propagate the presumption that EC provide a mere “passive lining” or supportive role. In fact, EC contribute to a dynamic network regulating vascular tone, cardiac development, and repair. Two distinct EC types, vascular EC and epicardial EC, possess important structural and signaling properties within both the healthy and diseased myocardium. In this review, we address EC-CM interactions in mature, healthy myocardium, followed by a discussion of diseases characterized by EC dysfunction. Finally, we consider strategies to reverse EC-CM “miscommunication” to improve patients' outcomes in various cardiovascular diseases.
A Novel Algorithm of Image Vector Quantization and VLSI Implementation Architecture

One of the main drawbacks of vector quantization (VQ) image compression is computation complexity in encoding process. A novel algorithm based on cross mean order codebook, is presented to reduce codeword search computation, and its VLSI implementation architecture of VQ encoder is proposed. The FPGA implementation results show that the proposed architecture will reduce three-quarter coding computation complexity and be capable of real-time coding rate of 45 frames per second at 50 MHz system clock.
The Intervening Effects of Affective Commitment on HRM Practices and Turnover Intention Relationship: An Evidence from Non-Western Context

The present study was mainly designed to examine the intervening effects of affective commitment on the relationship between certain human resource practices and turnover intention in the context of private university in Bangladesh. The direct relationship between human resource practices and turnover intention was also investigated. A total of 317 faculty members participated in the questionnaire survey. The response rate was 57% for this study. Multiple regression analyses were used to test the hypotheses of the study. The results revealed that all the human resource practices and affective commitment were significantly and negatively related to turnover intention. The study also found that affective commitment fully and partially mediated the proposed relationship between studied human resource practices and turnover intention except job autonomy practice. Limitations and suggestions for future research are forwarded.
Multi-Criteria Evaluation in User-Centric Distributed Scheduling Agents

Distributed scheduling systems have used a variety of mechanisms to maximize the quality of the global solution. Techniques include negotiation frameworks based on market economies [Kis et al. 1996], game theoretic algorithms, and global or shared evaluation functions [Modi et al. 2005]. Our position is that these techniques do not adequately address the situation where self-interested cooperative agents maintain schedules on behalf of users operating in unbounded environments. Here, satisfaction is more important than optimality [Palen 1999], and personalized preferences are paramount to users [Berry et al. 2005a; Faulring and Myers 2005]. In our approach, global utility is secondary; the agent aims to maximize the utility of its user, but folds the positive utility of cooperating with others into that utility. Thus, we treat the utility of others as a single component of a personalized and context-dependent multi-criteria evaluation function, which the agent learns through interactions with its user.
RF energy harvesting two-way cognitive DF relaying with transceiver impairments

This paper analyzes the impact of transceiver impairments on outage probability (OP) and throughput of decode-and-forward two-way cognitive relay (TWCR) networks, where the relay is self-powered by harvesting energy from the transmitted signals. We consider two bidirectional relaying protocols namely, multiple access broadcast (MABC) protocol and time division broadcast (TDBC) protocol, as well as, two power transfer policies namely, dual-source (DS) energy transfer and single-fixed-source (SFS) energy transfer. Closed-form expressions for OP and throughput of the network are derived in the context of delay-limited transmission. Numerical results corroborate our analysis, thereby we can quantify the degradation of OP and throughput of TWCR networks due to transceiver hardware impairments. Under the specific parameters, our results indicate that the MABC protocol achieves asymptotically a higher throughput by 0.65 [bits/s/Hz] than the TDBC protocol, while the DS energy transfer scheme offers better performance than the SFS policy for both relaying protocols.
Evaluation of Anomaly Detection techniques for SCADA communication resilience

Attacks on critical infrastructures' Supervisory Control and Data Acquisition (SCADA) systems are beginning to increase. They are often initiated by highly skilled attackers, who are capable of deploying sophisticated attacks to exfiltrate data or even to cause physical damage. In this paper, we rehearse the rationale for protecting against cyber attacks and evaluate a set of Anomaly Detection (AD) techniques in detecting attacks by analysing traffic captured in a SCADA network. For this purpose, we have implemented a tool chain with a reference implementation of various state-of-the-art AD techniques to detect attacks, which manifest themselves as anomalies. Specifically, in order to evaluate the AD techniques, we apply our tool chain on a dataset created from a gas pipeline SCADA system in Mississippi State University's lab, which include artefacts of both normal operations and cyber attack scenarios. Our evaluation elaborate on several performance metrics of the examined AD techniques such as precision; recall; accuracy; F-score and G-score. The results indicate that detection rate may change significantly when considering various attack types and different detections modes (i.e., supervised and unsupervised), and also provide indications that there is a need for a robust, and preferably real-time AD technique to introduce resilience in critical infrastructures.
Getting up Motion Planning using Mahalanobis Distance

This paper presents a motion planner of getting up motion using Mahalanobis distance. It is an indispensable function for humanoid robots to get up by itself and some humanoid robots are able to get up by themselves, but the motion can start only from the states specified a prior. The robots have to get up from an arbitrary lying state which may result after an unexpected falling. The proposed method (l) determines the degree of similarity between the current falling state and predefined falling states using Mahalanobis distance, (2) generates a collision-free motion to the most similar state, and (3) plans a sequence of motions using a state transition graph.
Suppression of mode conversion by decreasing path difference by using an asymmetrically tapered bend in differential transmission lines

A tightly coupled asymmetrically tapered bend to suppress differential-to-common-mode conversion (caused by bend discontinuity in a pair of differential transmission lines) is proposed. A tightly coupled tapered bend has been proposed to suppress the mode conversion by decreasing the path difference in the bend. The tightly coupled bend with symmetric tapers makes the path difference shorter so that the differential transmission lines are coupled more tightly, but a path difference of twice the line separation still remains. To decrease the remaining path difference, a pair of asymmetric tapers is proposed. A full-wave simulation demonstrated that the amount of differential-to-common-mode conversion was decreased by 29 dB compared to that of the symmetrically tapered bend. To suppress differential-mode reflection, furthermore, a tightly coupled bend with sectionally divided asymmetric tapers is suggested.
Accelerating early design phase differential power analysis using power emulation techniques

The personal banking and ID sector has seen a tremendous change in recent years, partially caused by the widespread introduction of smart-cards. Because of the extensive implications of a successful attack on these devices, a wide range of practical as well as purely academic attacks has been developed during the last years. These attacks have unveiled weaknesses in hardware as well as software implementations of several different, partially widely used cryptographic algorithms. An especially powerful method, the differential power analysis (DPA), extracts secret information from power consumption and electro-magnetic emission profiles. The efficiency of a DPA attack significantly depends on the quality of the cryptographic algorithm implementation. These traces currently can only be generated using real hardware or simulation-based approaches. Depending on the chosen simulation accuracy these evaluations result in time-consuming RTL and SPICE simulations often limiting the maximum amount of available execution traces. This paper introduces a novel high-speed methodology for early security evaluations of integrated processor systems using power emulation. First, the usage of power emulation hardware allows for the estimation of attack effort that an adversary will have to invest to gain secret information from an algorithm's execution profile. Second, countermeasures against differential power analysis attacks can be quickly evaluated in terms of effectiveness. The shown approach uses semi-automatic characterization techniques and fully synthesizable emulation hardware to reduce the designer's dependency on time-consuming simulation runs.
Classification of Brain MR Images Using Corpus Callosum Shape Measurements

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.
Heuristics for Area Minimization in LUT-Based FPGA Technology Mapping

In this paper, an iterative technology-mapping tool called IMap is presented. It supports depth-oriented (area is a secondary objective), area-oriented (depth is a secondary objective), and duplication-free mapping modes. The edge-delay model (as opposed to the more commonly used unit-delay model) is used throughout. Two new heuristics are used to obtain area reductions over previously published methods. The first heuristic predicts the effects of various mapping decisions on the area of the final solution, and the second heuristic bounds the depth of the mapping solution at each node. In depth-oriented mode, when targeting five lookup tables (LUTs), IMap obtains depth optimal solutions that are 44.4%, 19.4%, and 5% smaller than those produced by FlowMap, CutMap, and DAOMap, respectively. Targeting the same LUT size in area-oriented mode, IMap obtains solutions that are 17.5% and 9.4% smaller than those produced by duplication-free mapping and ZMap, respectively. IMap is also shown to be highly efficient. Runtime improvements of between 2.3times and 82times are obtained over existing algorithms when targeting five LUTs. Area and runtime results comparing IMap to the other mappers when targeting four and six LUTs are also presented
Recurrent Temporal Sparse Autoencoder for attention-based action recognition

Visual context is fundamental to understand human actions in videos. However, to efficiently employ temporal context information presents an enormous challenge to this area. Two main problems are long-standing: (1) video frames are redundant while discriminative information is sparse; (2) large amount of interference information is mixed in frame sequences. These factors results in redundant computation and recognition failures. In this paper, we propose a learnable temporal attention mechanism to automatically select important time points from action sequences. We design an unsupervised Recurrent Temporal Sparse Autoencoder (RTSAE) network, which learns to extract sparse key-frames to sharpen discriminative yet to retain descriptive capability, as well to shield interfere information. By applying this technique to a recent proposed action recognition model Adaptive Recurrent-convolutional Hybrid network (ARCH), we significantly improve its performance in both speed and accuracy. Experiments demonstrate that, with the help of the RTSAE, ARCH outperforms most state-of-the-art methods on UCF101 and HMDB51 datasets.
Heuristics and cognitive biases in entrepreneurs: a review of the research

The aim of this research is to highlight the main characteristics of empirical studies into the heuristics and cognitive biases of entrepreneurs, and to determine what could and should now be done. To review past research, we used ProQuest and the 31 databases to which it gave us access. Only 25 empirical papers were identified, mostly published since 2006. Among other things, the findings from these papers show that overconfidence and optimism, two biases often confused, are those that have received by far the most attention from researchers. Factors explaining or explained by them were highlighted. The only other biases to have received attention, albeit limited, were the law of small numbers, the illusion of control, the planning fallacy, escalation of commitment, the status quo bias, and the hindsight bias. Research on heuristics was rare, but the findings suggest that they tend to vary according to the context (stage in the entrepreneurial process, activity sector, country, etc.). As for the methodologies, most were structured around a synchronic design. A broad range of samples and data collection techniques were used, but analysis was mainly statistical. The findings suggest that the focus in future should be on which heuristics and biases are most used or present in a given context (and why), how they are formed and configured, and how to deal effectively with them. More in-depth studies would also be useful.
Wounded Healing: Forming a Storytelling Community in Hip-Hop Lit

Background/Context Over the past 5 years, there has been a growing body of scholarship that examines the intersections of hip-hop culture and classroom pedagogy. Although recent scholarship has persuasively demonstrated the classroom potential of hip-hop texts for promoting student engagement, scaffolding sanctioned forms of knowledge, and nurturing critical consciousness and activism, little work has been done to unpack the complex relations of power that emerge in such classrooms. In particular, we know very little about the ways in which students and teachers are (re)positioned within classrooms that engage in hip-hop-centered pedagogy. This article contributes to the current literature in hip-hop based education, culturally relevant pedagogy, and critical pedagogy by examining the some of the issues and tensions that emerge when teachers engage in hip-hop-centered classroom pedagogy. Purpose/Objective/Research Question/Focus of Study This study details how the articulation of personal narratives within Hip-Hop Lit, a hip-hop-centered high school English literature course, produced a practice of “wounded healing,” in which people bearing the scars of suffering shared their stories in ways that provided a form of release and relief for themselves and others. This article highlights the complex relationships that students forged with the course's hip-hop texts, many of which resonated with their own lived experiences. I then illustrate how these relationships enabled the classroom discussions and interactions from which the practices of wounded healing emerged. Finally, I highlight some of the dilemmas and tensions that emerged as my coteacher and I attempted to privilege the stories and experiences of our students and ourselves within the classroom. Setting Data for this study were collected at “Howard High School,” a small comprehensive urban high school in the northeastern United States. Research Design Data for this 1-year ethnography were collected using field notes, formal and informal interviews, document analysis, and video data. Conclusions/Recommendations This article shows how Hip-Hop Lit operated as a space in which members offered and responded to various types of individual and group narratives through the practice of “wounded healing. ” Through this practice, students were able to recognize the commonality of their experiences, challenge various ideologies, and produce new knowledge. In doing this, the members of the class forged a cohesive community replete with multiple roles and relations of power. This article points to the need for critically interrogating the ostensible virtues of hip-hop-based education, as well as critical and culturally relevant pedagogies more broadly. This article also underscores the need for more ethnographic research that unpacks the complexities, contours, and contradictions of curricula, and pedagogy that responds to the lived experiences of students.
ANALYSIS OF DEMINING PROJECT PROPOSALS

This paper is an analysis of project proposals for mine clearance of mine-affected areas. Specifically we are studying the relationship between proposals and their evaluation scores influencing their funding possibilities. First we investigate the possibility of building a reliable predictive model for automatic evaluation score prediction from the attributes of the proposal, and second we analyze individual attributes and their trends with respect to the evaluation score of the proposals.
The Genome of S-PM2, a “Photosynthetic” T4-Type Bacteriophage That Infects Marine Synechococcus Strains

ABSTRACT Bacteriophage S-PM2 infects several strains of the abundant and ecologically important marine cyanobacterium Synechococcus. A large lytic phage with an isometric icosahedral head, S-PM2 has a contractile tail and by this criterion is classified as a myovirus (1). The linear, circularly permuted, 196,280-bp double-stranded DNA genome of S-PM2 contains 37.8% G+C residues. It encodes 239 open reading frames (ORFs) and 25 tRNAs. Of these ORFs, 19 appear to encode proteins associated with the cell envelope, including a putative S-layer-associated protein. Twenty additional S-PM2 ORFs have homologues in the genomes of their cyanobacterial hosts. There is a group I self-splicing intron within the gene encoding the D1 protein. A total of 40 ORFs, organized into discrete clusters, encode homologues of T4 proteins involved in virion morphogenesis, nucleotide metabolism, gene regulation, and DNA replication and repair. The S-PM2 genome encodes a few surprisingly large (e.g., 3,779 amino acids) ORFs of unknown function. Our analysis of the S-PM2 genome suggests that many of the unknown S-PM2 functions may be involved in the adaptation of the metabolism of the host cell to the requirements of phage infection. This hypothesis originates from the identification of multiple phage-mediated modifications of the host's photosynthetic apparatus that appear to be essential for maintaining energy production during the lytic cycle.
Active co-analysis of a set of shapes

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the user-given set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in refining the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set.
Cognitive Effect Indicators: The Impact of Student and Teacher Styles on Course Grades

This study was descriptive, correlative and explanatory. It summarized the problem solving and learning styles of students enrolled in the spring 2008 Virginia Polytechnic Institute and State University Agricultural Technology program, identified relationships between problem solving and learning styles, and used problem solving and learning styles to explain students’ end of course grades. Ninety-three students and six faculty members elected to participate in the study. There were differences between degree options in terms of orientation to change. Second year students were likely to be internal processors. There were not significant differences among the population in ways of deciding. However, Agricultural Technology teachers were more task oriented problem solvers, while their students were people oriented. Teachers were more field independent than the students. There were no relationships between problem solving and learning style. There was a high degree of association between student ways of deciding and manner of processing. Student and teacher problem solving and learning styles were used to explain 11% of the variance in students’ end of course grades for the six teachers in the study. Student orientation to change, student manner of processing, teacher manner of processing and teacher ways of deciding scores produced a model that significantly explained end of course grades. Suggestions for further research included identifying other career areas with stylistic trends and further identifying the impact cognitive effect has on student behavior.