Miracle-dz/UniFAR-FTUs huggingface dataset:
Partially occluded pedestrian classification using part-based classifiers and Restricted Boltzmann Machine model

One of the main challenges in pedestrian detection is occlusion. This paper presents a new method for pedestrian classification with partial occlusion handling. The proposed system involves a set of component-based classifiers trained on features derived from non-occluded dataset. The scores of all component classifiers are statistically modeled to estimate the final score of pedestrian. A generative stochastic neural network model namely Restricted Boltzmann Machine (RBM) is learned to estimate the posterior probability of pedestrian given its components scores. The training data used to train RBM model is artificially generated occluded data which simulate real occlusion conditions appeared in pedestrians. Experimental results on real-world dataset, with both partially occluded and non-occluded data shows the effectiveness of the proposed method.
Learning Hough Forest with Depth-Encoded Context for Object Detection

In this paper, we propose a novel extension to the Class-specific Hough Forest (CHF) framework for object detection and localization. Our approach utilizes depth information during training to build a more discriminative codebook which simultaneously encodes features from the object and the surrounding context. In particular, we augment the CHF with contextual image patches, and design a series of depth-aware uncertainty measures for the binary tests used in CHF training. The new splitting criteria integrates relative physical scales of image patches, 3D offset uncertainty of votes, and 3D-distance modulated voting confidence. We show that the extended CHF is capable of learning better context models and building high- quality codebooks in appearance. As the model relies on depth information only in training, our system can be applied to object localization in 2D images. We demonstrate the efficacy of our method by experiments on two challenging RGB-D object datasets, and we empirically show our method achieves significant improvement over the state of the art with a more robust Hough voting scheme.
Affective Wellbeing and the Teaching of Music in Ghanaian Basic Schools: A Reflection

Any healthy education aims to develop the individual holistically. To help achieve and assess this goal, educational objectives have been categorized under three domains: cognitive, psychomotor and affective. The arts (music particularly), have been identified as being best suited for the training of the affective domain. Using a number of instruments and triangulation methods, we examined this claim. We observed that music teaching in Ghanaian basic schools has, to a large extent, not fulfilled this mandate. We recommend the need to rethink the music program in two main ways: a) reviewing curricula materials and b) providing requisite training for music teachers.
Motor imagery: what does it offer for new digital musical instruments?

There have been more interest and research towards multisensory aspects of sound as well as vision and movement, especially in the last two decades. An emerging research field related with multi-sensory research is âmotor imageryâ, which could be defined as the mental representation of a movement without actual production of muscle activity necessary for its execution. Emphasizing its close relationship and potential future use in new digital musical instruments (DMI) practice and reviewing literature, this paper will introduce fundamental concepts about motor imagery (MI), various methods of measuring MI in different configurations and summarize some important findings about MI in various studies. Following, it will discuss how this research area is related to DMI practice and propose potential uses of MI in this field.
A class of frequency-domain adaptive approaches to blind multichannel identification

We extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain. A class of frequency-domain adaptive approaches, including the multichannel frequency-domain LMS (MCFLMS) and constrained/unconstrained normalized multichannel frequency-domain LMS (NMCFLMS) algorithms, are proposed. By utilizing the fast Fourier transform (FFT) and overlap-save techniques, the convolution and correlation operations that are computationally intensive when performed by the time-domain multichannel LMS (MCLMS) or multichannel Newton (MCN) methods are efficiently implemented in the frequency domain, and the MCFLMS is rigorously derived. In order to achieve independent and uniform convergence for each filter coefficient and, therefore, accelerate the overall convergence, the coefficient updates are properly normalized at each iteration, and the NMCFLMS algorithms are developed. Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases. It is remarkable that for a three-channel acoustic system with long impulse responses (256 taps in each channel) excited by a male speech signal, only the proposed NMCFLMS algorithm succeeds in determining a reasonably accurate channel estimate, which is good enough for applications such as time delay estimation.
Sumoylation is tumor-suppressive and confers proliferative quiescence to hematopoietic progenitors in Drosophila melanogaster larvae

Summary How cell-intrinsic regulation of the cell cycle and the extrinsic influence of the niche converge to provide proliferative quiescence, safeguard tissue integrity, and provide avenues to stop stem cells from giving rise to tumors is a major challenge in gene therapy and tissue engineering. We explore this question in sumoylation-deficient mutants of Drosophila. In wild type third instar larval lymph glands, a group of hematopoietic stem/progenitor cells acquires quiescence; a multicellular niche supports their undifferentiated state. However, how proliferative quiescence is instilled in this population is not understood. We show that Ubc9 protein is nuclear in this population. Loss of the SUMO-activating E1 enzyme, Aos1/Uba2, the conjugating E2 enzyme, Ubc9, or the E3 SUMO ligase, PIAS, results in a failure of progenitors to quiesce; progenitors become hyperplastic, misdifferentiate, and develop into microtumors that eventually detach from the dorsal vessel. Significantly, dysplasia and lethality of Ubc9 mutants are rescued when Ubc9wt is provided specifically in the progenitor populations, but not when it is provided in the niche or in the differentiated cortex. While normal progenitors express high levels of the Drosophila cyclin-dependent kinase inhibitor p21 homolog, Dacapo, the corresponding overgrown mutant population exhibits a marked reduction in Dacapo. Forced expression of either Dacapo or human p21 in progenitors shrinks this population. The selective expression of either protein in mutant progenitor cells, but not in other hematopoietic populations, limits overgrowth, blocks tumorogenesis, and restores organ integrity. We discuss an essential and complex role for sumoylation in preserving the hematopoietic progenitor states for stress response and in the context of normal development of the fly.
A Complexity Perspective

With the increase in the complexity of terrorism’s networks and activities, the advances in chemical and biological warfare, and the use of organized criminal activities, it is becoming apparent that dealing with this complexity is not possible with traditional problem-solving approaches. The artificial complexity area (Artificial Life, or ALife), complex systems and agentbased distillation (ABD) provide a new perspective to the problem and emphasize the importance of modeling the interaction between system components to tackle these issues. This chapter presents an introduction to Cellular Automota and ABD, and then reviews and critiques how these approaches specifically have been used to model aspects of bushfires, epidemics, biological warfare and terrorism. This chapter then extends upon previous works to present an overview of the possible use of artificial complexity models to the larger field of security and safety applications.
Reconceptualizing antisocial deviance in neurobehavioral terms

Abstract We propose that neuroscientific understanding of antisocial behavior can be advanced by focusing programmatic efforts on neurobehavioral trait constructs, that is, individual difference constructs with direct referents in neurobiology as well as behavior. As specific examples, we highlight inhibitory control and defensive reactivity as two such constructs with clear relevance for understanding antisocial behavior in the context of development. Variations in inhibitory control are theorized to reflect individual differences in the functioning of brain systems that operate to guide and inhibit behavior and regulate emotional response in the service of nonimmediate goals. Variations in defensive reactivity are posited to reflect individual differences in the sensitivity of the brain's aversive motivational (fear) system. We describe how these constructs have been conceptualized in the adult and child literatures and review work pertaining to traditional psychometric (rating and behaviorally based) assessment of these constructs and their known physiological correlates at differing ages as well as evidence linking these constructs to antisocial behavior problems in children and adults. We outline a psychoneurometric approach, which entails systematic development of neurobiological measures of target trait constructs through reference to psychological phenotypes, as a paradigm for linking clinical disorders to neurobiological systems. We provide a concrete illustration of this approach in the domain of externalizing proneness and discuss its broader implications for research on conduct disorder, antisocial personality, and psychopathy.
Penumbra: Doctoral support as drama: From the ‘lightside’ to the ‘darkside’. From front of house to trapdoors and recesses

Abstract Much international doctoral learning research focuses on personal, institutional and learning support provided by supervisors, managed relationships,‘nudging’ robust, conceptual, critical, creative work. Other work focuses on stresses experienced in supervisor-student relationships and doctoral journeys. Some considers formal and informal learning communities supporting students on research journeys, and roles played by families, friends and others, sometimes offering encouragement and sometimes added stress. However, little has been explored concerning often unofficial, largely unrecognised meaningful others in students’ ‘life-worlds’, variously supporting doctoral learning journeys in terms of research, writing and editing. Research, based in experience and interviews with doctoral students and supervisors from UK and international contexts, reveals support (‘the penumbra’), university sanctioned (‘lightside’), and less well recognised often unsanctioned (‘darkside’) on doctoral research and writing learning journey, instigating questions about doctoral student needs, and the range of support provided, both legitimate, well known, less legitimate. This work concentrates on the ‘darkside’.
A Survey on Network Security Monitoring Systems

Network monitoring is a difficult and demanding task that is a vital part of a network administrator's job. Network administrators are constantly striving to maintain smooth operation of their networks. If a network were to be down even for a small period of time, productivity within a company would decline, and in the case of public service departments the ability to provide essential services would be compromised. There are several approaches to network security monitoring. This paper provides the readers with a critical review of the prominent implementations of the current network monitoring approaches.
EPG Content Recommendation in Large Scale: A Case Study on Interactive TV Platform

Recommender systems in TV applications mostly focusing on the recommendation of video-on-demand (VOD) content, though the major part of users' content consumption is realized on linear channel programs, termed EPG content. In this case study we present how we tackled the EPG recommendation task, which exhibits several differences compared to the VOD scenario, including the lack of explicit user feedbacks, the magnitude of cold start problem, as well as data cleaning and feature selection necessary to be applied on raw consumption data. We provide both offline and online model validation. First we showcase the typical approach in machine learning by evaluating models against recall in an offline setting. Then, we investigate in depth the real-world results of the recommendation app using the pre-trained models, and analyze how personalized recommendation influence users watching behavior. The experimentation results are based on our recommender system deployed at a Canadian IPTV service provider using Microsoft Media room middleware.
Distributed Analytics over Web Archives

Evolving content of the Web is being accumulated over time into Web archival collections. This creates the need for time travel search to explore the dynamics of the content. Text analytics has also a key role in exploring interesting information in text collections. Moreover, frequent phrase mining, a special case of text analytics, is an important analytical task that is motivated by the need of knowledge on frequent phrases in various areas of computer science, such as information retrieval and machine translation etc. However, time travel search and frequent phrase mining have to be conducted on increasingly large-scale data. Distributed approaches such as MapReduce, which is mainly designed to work on vast amount of text, can be utilized in this case. We address two separate problems in this thesis. The first problem is that time travel inverted index, which enables searching on the time dimension, is proposed in centralized setting. In our work, we parallelize the construction of time travel inverted index using MapReduce, having a distributed index as an end product. The second problem is that finding frequent phrases through näıve counting, even in MapReduce, is a time consuming task, because data to be processed gets much larger in size, when phrases are considered. As our work, we present partitioned approximate phrase counting, a very fast way to retrieve most of the frequent phrases together with their counts out of a collection to enable interactive analysis of the content. Included in this, we propose and a novel technique, partitioned in-mapper combining, which enables us to aggregate data in memory correctly, even though the data to be aggregated is larger than the available memory. Evaluation of experiments on New York Times Annotated Corpus, which contains roughly 2 million documents, show that our approach works at least 2 times faster as compared to näıve approach. It finds more than 90% of frequent phrases with high precision. Moreover, it is able to find all highly frequent phrases exactly, along with their accurate counts. Furthermore, by a quick second pass on the data, we precisely provide most of the frequent phrases with their corresponding true counts, still being significantly faster than näıve approach. Figure 1: Tag cloud of phrases in New York Times Annotated Corpus (1987 2007)
Transcriptional Profiling of Lipopolysaccharide-Induced Acute Lung Injury

ABSTRACT Mortality associated with acute lung injury (ALI) induced by lipopolysaccharide (LPS) remains high in humans, warranting improved treatment and prevention strategies. ALI is characterized by the expression of proinflammatory mediators and extensive neutrophil influx into the lung, followed by severe lung damage. Understanding the pathogenesis of LPS-induced ALI is a prerequisite for designing better therapeutic strategies. In the present study, we used microarrays to gain a global view of the transcriptional responses of the lung to LPS in a mouse model of ALI that mimics ALI in humans. A total of 71 inflammation-associated genes were up-regulated in LPS-treated lungs, including a chemokine, LPS-induced CXC chemokine (LIX), whose role in the induction of ALI is unknown. Most of the inflammatory genes peaked at 2 h post-LPS treatment. Real-time reverse transcription-PCR confirmed the LPS-induced up-regulation of selected genes identified by microarray analysis, including LIX. The up-regulation of LIX, tumor necrosis factor alpha, and macrophage inflammatory protein 2 was confirmed at the protein level by enzyme-linked immunosorbent assays. To determine the role of LIX in the induction of ALI, we used both exogenous LIX and a LIX blocking antibody. Exogenous LIX alone elicited a neutrophil influx in the lungs, and the anti-LIX antibody attenuated the LPS-induced neutrophil accumulation in the lungs. Taken together, the results of our study demonstrate for the first time the temporal expression of inflammatory genes during LPS-induced ALI and suggest that early therapeutic intervention is crucial to attenuate lung damage. Moreover, we identified a role for LIX in the induction of ALI, and therefore LIX may serve as a novel therapeutic target for the minimization of ALI.
Performance-based empathy mediates the influence of working memory on social competence in schizophrenia.

Empathic deficits have been linked to poor functioning in schizophrenia, but this work is mostly limited to self-report data. This study examined whether performance-based empathy measures account for incremental variance in social competence and social attainment above and beyond self-reported empathy, neurocognition, and clinical symptoms. Given the importance of working memory in theoretical models of empathy and in the prediction of functioning in schizophrenia, we also examined whether empathy mediates the relationship between working memory and functioning. Sixty outpatients and 45 healthy controls were compared on performance-based measures of 3 key components of empathic responding, including facial affect perception, emotional empathy (affective responsiveness), and cognitive empathy (emotional perspective-taking). Participants also completed measures of self-reported empathy, neurocognition, clinical symptoms, and social competence and attainment. Patients demonstrated lower accuracy than controls across the 3 performance-based empathy measures. Among patients, these measures showed minimal relations to self-reported empathy but significantly correlated with working memory and other neurocognitive functions as well as symptom levels. Furthermore, cognitive empathy explained significant incremental variance in social competence (∆R (2) = . 07, P < . 05) and was found to mediate the relation between working memory and social competence. Performance-based measures of empathy were sensitive to functionally relevant disturbances in schizophrenia. Working memory deficits appear to have an important effect on these disruptions in empathy. Empathy is emerging as a promising new area for social cognitive research and for novel recovery-oriented treatment development.
Neural correlates of visual working memory load through unsupervised spatial filtering of EEG

Understanding the neural basis of working memory (WM) is critical to study of the higher level cognitive processes. The WM load and the confluence of related features can be used to characterize many cognitive processes and their limits. In particular, to study the neural correlates of visual working memory load, we recorded multichannel EEG during a visual WM task which varied in difficulty. A set of features extracted from the event-related potentials (ERPs) and event-related synchronization/desynchronization (ERS/ERD) were used to quantify changes in activation with increasing WM demands. To further our understanding of relationship between the stimuli and the underlying neural processes, Independent component analysis (ICA) was used on EEG data for spatial filtering. Common independent components across variation in task difficulties were then found by clustering the ICs and used to investigate changes in brain activity with increasing memory load. It was observed that temporal properties of ERPs obtained from the independent components are highly distinguishable as opposed to traditional region of interest (ROI) approach. This result suggests that more representative neural correlates could be obtained by using this technique.
How Does Sibling Relationship Affect Children’s Prosocial Behaviors and Best Friend Relationship Quality?

The aim of this study was to explore whether the sibling condition (having a sibling) and sibling relationship quality affect prosocial behaviors and best friend relationship quality, deepening the mediating role of prosocial behaviors on the relationship between sibling relationship quality and best friend relationship quality. A sample of 310 children (161 males and 149 females) aged 8 to 11 years (M = 9.50, SD = 0.66) completed a battery of questionnaires. Results revealed that the sibling condition did not affect prosocial behaviors and best friend relationship quality. However, the quality of sibling relationships positively affected prosocial behaviors, which in turn positively influenced best friend relationship quality, supporting the mediator hypothesis. Limitations, strengths, and further development of the present study are discussed.