Miracle-dz/UniFAR-FTUs huggingface dataset:
On estimation in networked control systems with random delays and partial observation losses

The paper considers state estimation in networked control systems (NCS) where observations from multiple sensors are subject to random delays and packet losses. We derive the minimum error covariance estimator and the optimum estimator with constant gains as a low-complexity solution. Generalizations to account for the effects of measurements quantization and limited transmission bandwidth are investigated for a stable system. Assuming a simple scalar system, we show how the proposed framework can be exploited for the design of NCS. In particular we investigate i) cross-layer optimization of quantization processes and network resource allocation and ii) comparison between single-hop and multi-hop communication protocols. We show that simple BPSK and single-hop communication protocols provide close to optimum performance in applications dealing with state estimation of a stable system.
The Reconstruction of Facial Expressions in Embodied Systems New Approaches to an Old Problem

studied Zoology, Physics and Anthropology at the University of Munich. He received his PhD in Biology in 1982 at the University of Munich and the Research Institute for Human Ethology, Max-Planck-Society. His PhD thesis deals with self organization in groups of preschool children. In 1991 he became the Scientific Director of the Ludwig-Boltzmann-Institute for Urban Ethology in Vienna an d was appointed Professor by the University of Vienna. In 2002 he received the Zdenek-Klein Award for his integrative scientifc work and 2003 he received the first prize in the International competition Vienna-Cooperate for outstanding scientific and technological innovation. Currently he is working on communication research and non-verbal behavior simulation in embodied agents at the Center for Interdisciplinary Studies (ZiF) in Bielefeld/Germany as a resident fellow.
Cloud-Edge-End Collaboration Personalized Semi-supervised Federated Learning for Visual Localization

Deep learning-based visual localization methods use convolutional neural networks to directly regress the position of a target. However, previous studies only consider localization in a single scene and neglect personalized localization in multiple scenes. Furthermore, changes in the scene result in a reduced accuracy due to the model’s lack of adaptability. Moreover, traditional centralized training methods pose data privacy concerns. In this paper, we propose a personalized semi-supervised federated learning framework with cloud-edge-end collaboration, called FedVL. The hierarchical architecture extends single-scene localization to multiple scenes, while the federated learning mechanism ensures data privacy. In this framework, we apply personalized federated learning to achieve scene-specific model and employ semi-supervised federated learning to allow the localization model to adapt to scene changes. Experiments conducted on indoor and outdoor datasets demonstrate the effectiveness of this approach.
Inferring state-based behavior models

Dynamic analysis helps to extract important information about software systems useful in testing, debugging and maintenance activities. Popular dynamic analysis techniques synthesize either information on the values of the variables or information on relations between invocation of methods. Thus, these approaches do not capture the important relations that exist between data values and invocation sequences.In this paper, we introduce a technique, called GK-tail, for generating models that represent the interplay between program variables and method invocations. GK-tail extends the k-tail algorithm for extracting finite state automata from execution traces, to the case of finite state automata with parameters.The paper presents the technique and the results of some preliminary experiments that indicate the potentialities of the proposed approach.
FGCache: Accelerating Aggregation Queries for OLAP Applications via Caching

OLAP applications often perform aggregation queries, such as sum, avg, count, max, and min, to process a large dataset for getting a small result, and the key challenge for optimizing aggregation queries is to reduce the data volume needed to be read from disks. To address this problem, this paper presents an efficient caching component called FGCache (Fine-Grained Cache) for aggregation queries. FGCache is a query-driven caching policy, which aims to maintain the results of aggregation queries in the memory according to the predicates of user queries. The query results are divided into sets with fine-grained querying intervals, which are maintained via an in-memory tree index. We experimentally evaluate the performance of FGCache on various datasets and settings. The results suggest the efficiency and effectiveness of our proposal.
Social network patterns among the elderly in relation to their perceived life history in an Eriksonian perspective

Abstract Past research has shown that social network patterns, the remembered past, and sense of coherence (SOC) are all important factors for well-being in old age. In this study, these interrelationships were examined, with special attention given to the gender differences, concerning structures and functions of the social network. Fifty-eight elderly individuals reported life-history evaluations according to the psychosocial conflicts, proposed by Erik H. Erikson. They also reported social network patterns and SOC according to standardized questionnaires. Factor analyses revealed that there are gender differences concerning the patterns of social network. For women, social anchorage was an important factor, while for men, social influence was more important. Structures and functions of the social network correlated with both positive and negative life history evaluations, but the correlations differed between the sexes. When SOC was controlled the correlations changed, but to a small degree. Stepwise d.
LZ-207, a Newly Synthesized Flavonoid, Induces Apoptosis and Suppresses Inflammation-Related Colon Cancer by Inhibiting the NF-κB Signaling Pathway

Flavonoids and flavonoid derivatives, which have significant biological and pharmacological activities, including antitumor and anti-inflammatory activities, have been widely used in human healthcare. To design a more effective flavonoid antitumor agent, we altered the flavonoid backbone with substitutions of piperazine and methoxy groups to synthesize a novel flavonoid derivative, LZ-207. The anticancer effect of LZ-207 against HCT116 colon cancer cells and the underlying mechanism of this effect were explored in this study. Specifically, LZ-207 exhibited inhibitory effects on growth and viability in several human colon cancer cell lines and induced apoptosis in HCT116 cells both in vitro and in vivo. LZ-207 treatment also suppressed the nuclear translocation of NF-κB and the phosphorylation of IκB and IKKα/β in a dose-dependent manner in both HCT116 cells and human acute monocytic leukemia THP-1 cells. Moreover, LZ-207 also reduced the secretion of the pro-inflammatory cytokine interleukin-6 (IL-6) in LPS-induced THP-1 cells, and this effect was confirmed at the transcriptional level. Furthermore, LZ-207 significantly inhibited HCT116 cell proliferation that was elicited by LPS-induced THP-1 cells in a co-culture system. These findings elucidated some potential molecular mechanisms for preventing inflammation-driven colon cancer using the newly synthesized flavonoid LZ-207 and suggested the possibility of further developing novel therapeutic agents derived from flavonoids.
CDCP: A Framework for Accelerating Dynamic Channel Pruning Methods

Dynamic channel pruning is a technique aimed at reducing the theoretical computational complexity and inference latency of convolutional neural networks. Dynamic channel pruning methods introduce complex additional modules for dynamically selecting channels for images. Due to the additional modules, dynamic channel pruning methods never achieve optimal acceleration effect in real world. To address this problem, we propose Consecutive Dynamic Channel Pruning (CDCP), a novel dynamic channel pruning framework unified for almost all dynamic pruning methods designed for continuous image processing. The core idea of CDCP stems from our observation that adjusting the network for all frames in semantically continuous scenes is unnecessary since adjacent frames often share similar network structures in dynamic channel pruning. CDCP introduces a simple binary classifier to determine whether the network structure needs to be adjusted for a new frame. Our method can also be used for semantically non-continuous image processing tasks with a slightly lower probability of model reuse. We validate the effectiveness of CDCP on three dynamic channel pruning methods and better acceleration effects are achieved when applied them with CDCP to the semantically continuous Waymo dataset, the nuScenes dataset, and the semantically discontinuous COCO dataset.
Mining Crosscutting Concerns through Random Walks

Inspired by our past manual aspect mining experiences, this paper describes a probabilistic random walk model to approximate the process of discovering crosscutting concerns (CCs) in the absence of the domain knowledge about the investigated application. The random walks are performed on the concept graphs extracted from the program sources to calculate metrics of “utilization” and “aggregation” for each of the program elements. We rank all the program elements based on these metrics and use a threshold to produce a set of candidates that represent crosscutting concerns. We implemented the algorithm as the Prism CC miner (PCM) and evaluated PCM on Java applications ranging from a small-scale drawing application to a medium-sized middleware application and to a large-scale enterprise application server. Our quantification shows that PCM is able to produce comparable results (95 percent accuracy for the top 125 candidates) with respect to the manual mining effort. PCM is also significantly more effective as compared to the conventional approach.
Too Much of a Good Thing? Visitor Congestion Management Issues for Popular World Heritage Tourist Attractions

World Heritage Sites (WHS) are often popular and primary attractions for a destination for many reasons, in particular the economic benefits they bring in terms of increased visitation and extension of stay. However, without active management to reduce negative impacts, such as congestion, both tourists and the host community can become caught up in a downward spiral of poor visitor experiences and degradation of World Heritage values. Accordingly, this paper examines the nature of congestion and its impact on visitor experience at two popular WHS attractions in Macao. It also provides some insights into Chinese outbound tourist behaviour and visitation patterns. These observations should be of interest to planners and site managers of popular World Heritage attractions elsewhere in the world as China begins to adopt mass international leisure travel.
Expressing Emotions as Evidence in Osteoporosis Narratives: Effects on Message Processing and Intentions

This study examined the use of different narratives expressing positive or negative emotions, and varying the narrator's perspective on the arousal of discrete emotions, dominant cognitions, perceived evidence quality, and perceived message effectiveness related to osteoporosis behavioral intentions. Formative research led to the creation of narratives and selection of positive and negative emotions expressed. A 4 × 3 between-subjects posttest design revealed positive relationships between judgments of evidence quality and perceptions of message effectiveness for predicting behavioral intentions toward osteoporosis prevention. In addition, arousing the emotions of fear and/or hope cued processing of osteoporosis narratives. The theoretical implications of these findings for the use of narratives as evidence and the application of findings for osteoporosis health messages are discussed.
Understanding Perceptions of Benefit Following Adversity: How Do Distinct Assessments of Growth Relate to Coping and Adjustment to Stressful Events?

Research on stress-related growth typically relies on individuals' retrospective perceptions of growth, not indications of change in personal attributes over time (i.e., measured growth). This longitudinal study sought to: (1) examine the correspondence of perceived and measured growth, (2) elucidate the relations of these forms of growth, coping strategies, and change in adjustment in undergraduates (N = 162), and (3) examine whether current measurement approaches for self-perceived growth adequately capture the theoretical construct of psychological growth. Participants were undergraduates assessed at study entry and at 6 weeks. Perceptions of stress-related growth were not correlated with measured growth. In a path model, measured growth predicted increased positive mood and decreased distress. Perceived growth predicted increased positive mood, mediated by approach-oriented coping, as well as increased distress, partially mediated by coping through denial. These findings have implications for the meas.
Optimizing CNNs Throughput on Bandwidth-Constrained Distributed Multi-FPGA Architectures

Existing multi-FPGA architectures often leverage high-speed interconnect technologies to achieve higher performance by exploiting ample communication bandwidth. In this paper, we propose an effective mapping approach for accelerating CNNs on bandwidth-constrained distributed multi-FPGA architectures. We formulate the system-level mapping problem and then introduce a method based on Genetic Algorithm (GA) and Mixed-Integer Nonlinear Programming (MINLP) to attain optimal solutions.
STeP-1: A Set of Fundamental Tools for Persian Text Processing

Many NLP applications need fundamental tools to convert the input text into appropriate form or format and extract the primary linguistic knowledge of words and sentences. These tools perform segmentation of text into sentences, words and phrases, checking and correcting the spellings, doing lexical and morphological analysis, POS tagging and so on. Persian is among languages with complex preprocessing tasks. Having different writing prescriptions, spacings between or within words, character codings and spellings are some of the difficulties and challenges in converting various texts into a standard one. The lack of fundamental text processing tools such as morphological analyser (especially for derivational morphology) and POS tagger is another problem in Persian text processing. This paper introduces a set of fundamental tools for Persian text processing in STeP-1 package. STeP-1 (Standard Text Preparation for Persian language) performs a combination of tokenization, spell checking, morphological analysis and POS tagging. It also turns all Persian texts with different prescribed forms of writing to a series of tokens in the standard style introduced by Academy of Persian Language and Literature (APLL). Experimental results show high performance.
Relevance-Based Sequential Evidence Processing in Bayesian Networks

levance reasoning in Bayesian networks can be la~ed to improve efficiency of belief updating ",dgorithaL~ by identit~ing and pruning those parts of a network that are irrelevant ibr the computation. Relevance reaqon- ing is based on the graphical property of d-separation and other simple and efficient techniques, the compu- tational complexity of which is usuaUy negligible when compared to the complt~xity of belief updating in gen- eral. This paper describes a belief updating technique based on relevance reasoning that is applicable in practiced systems ia which observations are interleaved with be- lief updating. Our technique invalidates the posterior beliefs of those nodes that (h~pentl probabilisdcalb" oa the new evidence ~md focuses the subsequent belief up- dating on the inv~didated beliefs rather than on all bc- liet~. Vexy often observations inwflidate only a small fraction of the beliefs and our scheme can then lead to substaatial savings in computation. We report rcsuks of empirical tests that demomstratc practical signiii- canre of our approach.
Replication-Competent Infectious Hepatitis B Virus Vectors Carrying Substantially Sized Transgenes by Redesigned Viral Polymerase Translation

Viral vectors are engineered virus variants able to deliver nonviral genetic information into cells, usually by the same routes as the parental viruses. For several virus families, replication-competent vectors carrying reporter genes have become invaluable tools for easy and quantitative monitoring of replication and infection, and thus also for identifying antivirals and virus susceptible cells. For hepatitis B virus (HBV), a small enveloped DNA virus causing B-type hepatitis, such vectors are not available because insertions into its tiny 3.2 kb genome almost inevitably affect essential replication elements. HBV replicates by reverse transcription of the pregenomic (pg) RNA which is also required as bicistronic mRNA for the capsid (core) protein and the reverse transcriptase (Pol); their open reading frames (ORFs) overlap by some 150 basepairs. Translation of the downstream Pol ORF does not involve a conventional internal ribosome entry site (IRES). We reasoned that duplicating the overlap region and providing artificial IRES control for translation of both Pol and an in-between inserted transgene might yield a functional tricistronic pgRNA, without interfering with envelope protein expression. As IRESs we used a 22 nucleotide element termed Rbm3 IRES to minimize genome size increase. Model plasmids confirmed its activity even in tricistronic arrangements. Analogous plasmids for complete HBV genomes carrying 399 bp and 720 bp transgenes for blasticidin resistance (BsdR) and humanized Renilla green fluorescent protein (hrGFP) produced core and envelope proteins like wild-type HBV; while the hrGFP vector replicated poorly, the BsdR vector generated around 40% as much replicative DNA as wild-type HBV. Both vectors, however, formed enveloped virions which were infectious for HBV-susceptible HepaRG cells. Because numerous reporter and effector genes with sizes of around 500 bp or less are available, the new HBV vectors should become highly useful tools to better understand, and combat, this important pathogen.
How Simple Complex Words Can Be: Morphological Processing and Word Representations

In four lexical decision experiments we studied the effect of morphological complexity on word recognition. Some potentially relevant linguistic aspects of derived nouns were varied: the location of the affix (prefix vs. suffix); the genuineness of the affix (real vs. pseudo); the orthographic legality of pseudo-stems; semantic compositionality; the nature of the stem (free vs. bound); the origin of the complex word (Latinate vs. Germanic); the currency of the stem (current vs. moribund). Furthermore, in the first two experiments, we systematically varied the proportion of complex and simple words to see whether strategies influence morphological effects on recognition times. Consistent with Taft's notion of affix stripping, pseudoprefixed words show longer mean decision times and higher error rates than truly prefixed words. Further, the prefixed and nonprefixed (but not pseudoprefixed) words are processed equally rapidly, indicating that a decompositional process is efficient. No differences were found, however, for suffixed, pseudosuffixed, and nonsuffixed words. There was no effect of the proportion of simple and complex words. There are some indications that the etymological origin of words may affect recognition times, but no other linguistic aspects of derivations do so. The results of the four experiments are interpreted as supporting a left-to-right process for word recognition in which morphemes are extracted automatically. During this process information encoded by morphological structure becomes available for other processes.
Interview with Laura Mulvey

This conversation between Laura Mulvey and Roberta Sassatelli offers a historical reconstruction of Mulvey’s work, from her famous essay ‘Visual Pleasure and Narrative Cinema’ to her most recent reflections on male gaze, film technology and visual culture. The conversation initially deals with the socio-cultural context in which the ‘Visual Pleasure. ’ essay was produced by outlining a number of possible theoretical parallelisms with other scholars, from Foucault to Barthes to Goffman. Then, on the basis of Mulvey’s latest book, Death 24× a Second, and of a variety of contemporary examples, the emphasis is on the relative shift in Mulvey’s work from gender to time and visual technology. Finally, the conversation focuses on the concept of ‘gendered scopic regime’ and the potential re-articulation of the male gaze through the technological re-direction and control of the visual.
Mental task motor imagery classifications for noninvasive brain computer interface

A Brain computer interface (BCI) has introduced new scope and created a new period for developers and researchers giving alternative communication channels for paralysed peoples. Motor imagery refers to where EEG signals that being obtained while the subject is imagining or performing a motor response. This work is to examine this area from Machine Learning and exploit the Emotiv System as a cost-effective, noninvasive and also a portable EEG measurement device. The experiment was carried out based on Emotiv control panel focusing on cognitive commands such as (forward, backward, left and right). The data were preprocessed to remove the artifact as well as the noise by using EEGlab toolbox. Wavelet transforms namely Daubechies and symlets were used for feature extraction. The Multilayer perception (MLP), Simple logistic and Bagging were utilized to classify the mental tasks motor imagery. The performance of classifications was tested and satisfactory results were obtained with the accuracy rate 80.4% using the Simple logistic classifier.
Intelligent Node Deployment in Smart Spaces to Optimize Sensor Signal and Data Acquisition

The rapid growth of the Internet of Things (IoT) has led to the proliferation of interconnected devices and the generation of vast amounts of data. One of the critical factors in optimizing IoT data acquisition networks is the intelligent deployment of nodes to ensure adequate coverage. Therefore, throughout this paper, we propose an innovative approach to deploy sensor nodes as the primary data source of IoT in Smart Spaces. The proposed framework combines Poisson point processing, spatial partitioning by Voronoi diagrams and the modified Artificial Bee Colony (ABC) algorithm for position optimization. Voronoi’s tessellation of Euclidean space is constructed using the radical lines of the circular planes created by the intersections of the spheres. Initially, sensor nodes, which represent the centre of each sphere, are positioned erratically using the Poisson point process (PPP). Then ABC algorithm is utilized to intelligently explore the environment, detect, and cover the grey areas considering the overlapped coverage with other sensors. Comparison with the Voronoi-based algorithm for sensor network architecture optimization proves the effectiveness of the proposed ABC algorithm.