Miracle-dz/UniFAR-FTUs huggingface dataset:
Self balancing robot using complementary filter: Implementation and analysis of complementary filter on SBR

The Self balancing robot is based on the inverted pendulum concept, wherein an inverted pendulum is positioned on a cart and the cart is allowed to move on the horizontal axis so as to keep the pendulum in the upright position. This is a classic case of an unstable system. The angle measurement with the help of a fusion of gyroscope and accelerometer requires filtering mechanism as both provide erroneous angle results. Kalman filter is one such filter, but the design and implementation of such a filter is lengthy, tiresome and difficult to implement on smaller 8-bit micro controllers. Thus, this paper intends to design and implement a Self balancing robot with the help of a complementary filter and its analysis using different filter coefficients using PID algorithm as the control strategy. The robot is powered with a lithium-polymer battery to drive the motors.
Practices in the pig value chain in Uganda; implications to African swine fever transmission

Uganda has the largest pig industry in eastern Africa, with majority of farmers still smallholders. African swine fever (ASF) is arguably one of the major constraints in the region, where it is considered endemic. Although the sylvatic cycle exists here, the pig-pig cycle of transmission is more important. The pig value chain is one critical area where there is need for study of the role it plays in the spread of ASF. In this study, different practices and their association with the transmission of ASF within and between farms were investigated on 101 pig farms in Uganda. Practices by 30 traders in pigs and pig products (slaughter slab, pork butchery and roasted pork restaurant operators) were investigated in the districts of Kabarole, Mityana, Moyo, Mukono, Soroti, and Tororo. Key informant interviews with the District Veterinary Officers (DVOs) about pig trade, pig slaughter infrastructure, ASF status and its management in the districts were conducted as well between May-November 2011. Questionnaires were administered to both pig farmers and the traders. Twenty six percent of the farmers reported having had the disease on the farm in the last one year. Although none of the risk factors was statistically significant for ASF outbreaks, results demonstrated presence of potential risk factors for ASF on the farms and the rest of the pig value chain.
Analisis Perilaku Masyarakat Untuk Ber-Ziswaf Secara Digital Melalui Layanan M-Banking Syariah Menggunakan Modifikasi Utaut

Tujuan penelitian untuk menganalisis dan memprediksi determinasi niat berperilaku masyarakat untuk ber-ZISWAF secara digital dengan adopsi layanan m-banking syariah. Model yang digunakan adalah Unified Theory of Acceptance and Use to Thechnology (UTAUT) dengan menambahkan konstruk habit dan trust. Pengumpulan data dilakukan secara online melului google form. Analisis dengan PLS-SEM menggunakan SmartPLS 3.3. 3 dengan jumlah sampel 146 respoden. Hasil penelitian menunjukan bahwa niat ber-ZISWAF secara digital melalui m-banking syariah dipengaruhi oleh performance expectancy dan habit secara signifikan mempengaruhi behavior intention. Namun variabel effort expectancy, facilitating condition dan social influence tidak signifikan pada behavior intention. Sedangkan untuk adopsi dipengaruhi habit dan trust secara signifikan mempengaruhi adopsi penggunaan (usage adoption) masyarakat pada layanan m-banking untuk ber-ZISWAF secara digital.
Multihop cognitive radio networks: to route or not to route

Routing is a fundamental issue to consider when dealing with multihop cognitive radio networks. We investigate in this work, the potential routing approaches that can be employed in such adaptive wireless networks. We argue that in multihop cognitive radio environments no general routing solution can be proposed, but cognitive environments can be classified into three separate categories, each requiring specific routing solutions. Basically, this classification is imposed by the activity of the users on the licensed bands that cognitive radios try to access. First, over a relatively static primary band, where primary nodes idleness largely exceeds cognitive users communication durations, static mesh routing solutions can be reused, whereas second, over dynamically available spectrum bands new specific routing solutions have to be proposed, we give some guidelines and insights about designing such solutions. Third, if cognitive radios try to access over highly active and rarely available primary bands, opportunistic forwarding without preestablished routing is to be explored.
RSS Feeds Filtering from multiple sources using automated techniques of Natural Language Processing

The Internet's rapid growth has resulted in a surge of information, posing challenges for users trying to stay updated. The World Wide Web, with its diverse websites updated at varying intervals, necessitates efficient categorization of news articles. As the number of online news sources continues to rise, categorization becomes crucial for users to find information easily. Given the overwhelming volume of digital media information, categorizing news using algorithms and assigning multiple tags based on their category becomes essential. In the modern digital era, users face difficulties filtering and accessing relevant content tailored to their preferences due to the abundance of information from various sources. This study employs Natural Language Processing (NLP) methods for automated content filtering of RSS feeds. The developed system extracts feeds via RSS, generates headings and summaries, and employs NLP for categorization. This research has broad implications, offering opportunities for improved information management, personalized recommendations, and informed decision-making across diverse domains.
An online kernel change detection algorithm

A number of abrupt change detection methods have been proposed in the past, among which are efficient model-based techniques such as the Generalized Likelihood Ratio (GLR) test. We consider the case where no accurate nor tractable model can be found, using a model-free approach, called Kernel change detection (KCD). KCD compares two sets of descriptors extracted online from the signal at each time instant: The immediate past set and the immediate future set. Based on the soft margin single-class Support Vector Machine (SVM), we build a dissimilarity measure in feature space between those sets, without estimating densities as an intermediary step. This dissimilarity measure is shown to be asymptotically equivalent to the Fisher ratio in the Gaussian case. Implementation issues are addressed; in particular, the dissimilarity measure can be computed online in input space. Simulation results on both synthetic signals and real music signals show the efficiency of KCD.
SINR Balancing Techniques in Coordinated Multi-Cell Downlink Systems

In this paper, we study coordinated multi-cell downlink systems where multiple base stations jointly design a transmission strategy by sharing channel state information. Particularly, we tackle the signal-to-interference-plus-noise ratio (SINR) balancing problem to maximize the worst-user rate. We consider single-input single-output (SISO) interference channels (IFC) where all nodes are equipped with a single antenna, and there is one active user in each cell. First, achievable rate regions with symmetric complex (SC) and asymmetric complex (AC) signaling techniques are investigated. Then, we present the optimal and near-optimal SINR balancing algorithms with the SC signaling for two and three user SISO IFC. Due to residual interference, the worst-user rate of the SC signaling is saturated at high signal-to-noise-ratio region. To alleviate this problem, we also propose efficient balancing schemes based on the AC signaling for both two and three-user cases. Simulation results confirm effectiveness of the proposed SINR balancing algorithms and show that a substantial gain of the AC signaling is achieved over the SC signaling in terms of the maximum worst-user rate.
Platelet Parameters and (1, 3)-β-D-Glucan as a Diagnostic and Prognostic Marker of Invasive Fungal Disease in Preterm Infants

The diagnosis of neonatal invasive fungal disease (IFD) is difficult and often delayed. The platelet parameters and (1, 3)-β-D-Glucan (BG) may be useful for diagnosing IFD, but their diagnostic performance are not well characterized in neonates. We studied 63 preterm infants with IFD, 160 preterm infants without sepsis (preterm control), and 41 preterm infants with bacterial sepsis. Platelet parameters at the first day of onset of IFD and at the fourteenth day after antifungal treatment were evaluated. Serum BG was measured. Platelet count (PC), plateletcrit (PCT), and platelet distribution width (PDW) values were significantly lower, and mean platelet volume (MPV) values significantly higher in the IFD versus preterm control infants. PC and PCT values were much lower in infants with IFD versus bacterial sepsis, and there were significant differences in BG value between the two groups. After 14 days of antifungal treatment, significant elevations in PC, PCT, PDW and reductions in MPV levels in IFD group were observed. Receiver operating characteristic (ROC) curves showed that PC and PCT were strong predictors of IFD. The PC and PCT cut-offs for predicting IFD were 119.5 (sensitivity 78%, specificity 95%) and 0.21 (sensitivity 83%, specificity 85%), respectively. There were significant differences in PC and PCT levels between deceased and survived patients. The PC and PCT cut-offs for predicting deceased IFD were 39 (sensitivity 62%, specificity 86%) and 0.04 (sensitivity 50%, specificity 95%), respectively. The sensitivity in diagnosing IFD by a BG cutoff of ≥10pg/ml was 68.3% and specificity was 75.6%. PC and PCT levels in the BG ≥400 pg/ml group were significantly lower compared to the BG<400 pg/ml group. Platelet parameters and BG could be useful biomarkers for the diagnosis and prognosis of neonatal IFD.
Identifying changes in the support networks of end-of-life carers using social network analysis

End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital.
Contextmapping: experiences from practice

In recent years, various methods and techniques have emerged for mapping the contexts of people's interaction with products. Designers and researchers use these techniques to gain deeper insight into the needs and dreams of prospective users of new products. As most of these techniques are still under development, there is a lack of practical knowledge about how such studies can be conducted. In this paper we share our insights, based on several projects from research and many years of industrial practice, of conducting user studies with generative techniques. The appendix contains a single case illustrating the application of these techniques in detail.
Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation

MOTIVATION MicroRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. RESULTS We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules.
Facilitation of visuomotor associative learning by the basal ganglia.

The notion that the basal ganglia were involved in motor control developed in the late 19 and early 20 centuries. Samuel Kinnier Wilson is given credit for coining the term ‘‘extrapyramidal’’ to describe a motor system distinct from the pyramidal system in his 1912 treatise describing hepatolenticular degeneration. The recognition that other neurological disorders with obvious motor disturbances such as Parkinson disease, Huntington disease, and hemiballismus involve the basal ganglia further evidenced their role in motor control. In the second half of the 20 century, however, studies using targeted lesions and pharmacological manipulations in animals began demonstrating that the basal ganglia were also involved in learning and memory. In particular, recent electrophysiological studies in behaving animals have demonstrated that the basal ganglia are involved in visuomotor associative learning, a type of instrumental conditioning in which, given a particular environmental situation or visual stimulus, the animal must choose a response. Because the ‘‘correct’’ response is typically indicated by some type of reward, the animal must learn to make arbitrary associations between a stimulus and its corresponding ‘‘correct’’ response. The pioneering work of Wolfram Schultz and colleagues provided a mechanism for reward sensitivity by demonstrating that the firing rate of dopaminergic neurons in the substantia nigra pars compacta encodes a reward signal (proportional to the difference between expected and received reward). Because the dorsal striatum (composed of the caudate nucleus and putamen) is the principal target of substantia nigra pars compacta neurons and the main input stage of the basal ganglia, considerable effort has focused on studying learningrelated changes in this region. The dorsal striatum also receives projections from virtually all areas of cortex, providing it with rich contextual cues about the environment. This unique convergence of information puts the dorsal striatum in an opportune position to integrate information about environmental stimuli and reward, as is necessary for associative learning. In support of this framework, recent studies have illustrated dynamic modulation of striatal activity during reinforcement-driven associative learning. Although prior work has concentrated on the role of the striatum in learning, relatively little is known regarding the role of the output nuclei. Output structures of the basal ganglia, the globus pallidus internus (GPi) and substantia nigra pars reticulata, tonically inhibit the thalamic motor nuclei and superior colliculus, respectively. The GPi is involved in skeletomotor control and is the focus of this study. Because the GPi and substantia nigra pars reticulata are the only structures that significantly project out of the basal ganglia, their activity should encode this associative processing and relay it to thalamocortical circuits to influence motor behavior. We therefore recorded the activity of GPi neurons in monkeys trained to perform a visuomotor associative learning task.
Impact of rubber plantation age on erosive potential studied with USLE model

Rubber (Hevea brasiliensis) plantations have a lifespan in the range of 25–40 years. We aimed at assessing soil losses in rubber plantations of different ages (4, 12, 18, 25 and 36 years old) and relating erosion risk to surface cover and fine root density by applying the Universal Soil Loss Equation (USLE) model. We measured runoff and sediment yields in rubber plantations in Xishuangbanna, Southwest China during 1 year. Fine root density, surface cover and understory plant cover were measured monthly. Annual soil loss in different plantations varied from 0.33 to 2.80 t ha−1. Combined annual cover, management and support practice factor CP from the USLE model varied with the growth phase of rubber in the range of 0.006–0.03. Monthly CP factor was related exponentially with the surface cover. Inhibited undergrowth and high decomposition both contributed to decreasing surface cover in mid-age rubber resulting in highest erosion risk phase.
SparGNN: Efficient Joint Feature-Model Sparsity Exploitation in Graph Neural Network Acceleration

With the rapid explosion in both graph scale and model size, accelerating graph neural networks (GNNs) at scale encounters significant pressure on computation and memory footprint. Exploiting data sparsity with pruning, which exhibits remarkable effect in deep neural networks (DNNs), while still lags behind in GNN acceleration. This is because costly pruning overhead upon large graphs and inefficient hardware support will eclipse the benefit of GNN sparsification. To this end, this paper proposes SparGNN, an algorithm and accelerator co-design that can efficiently exploit data sparsity in both features and models to speedup GNN acceleration while reserving its accuracy. In algorithm, to reduce the overhead of iterative pruning, we distill a sparsified subgraph to substitute the original input graph for pruning, which can low-costly excavate the potential data sparsity in both features and models without accuracy compromise. In hardware, to improve data locality of the sparsified feature-weight multiplication, we design compressed row-/column-wise product dataflow for efficient feature updating. We then propose lightweight hardware changes to make our design applicable to conventional GNN accelerators. The experimental results show that compared to the state-of-the-art GNN accelerators, SparGNN reduces $1.5 \sim 4.3 \times$ computation and gains an average of 1.8 6.8 $\times$ speedup with $1.4 \sim 9.2 \times$ energy efficiency improvement.
A Hybrid Framework Bridging Locality Analysis and Cache-Aware Scheduling for CMPs

Industry is rapidly moving towards the adoption of Chip Multi-Processors (CMPs). The sharing of memory hierarchy becomes deeper and heterogeneous. Without a good understanding of the sharing, most current systems schedule processes in a contention-oblivious way, causing systems severely underutilized with sub-optimal throughput and cache thrashing. In this report, we propose a three-stage framework to analyze shared-cache locality. It is based on inclusive locality model and unifies online and offline adaptive analysis. Unlike previous methods, inclusive locality model addresses all factors of cache contention at the same time. The goal is to produce a comprehensive understanding of the relations between program characteristics and run-time behavior in shared-cache systems, meanwhile developing a scalable adaptive contention-aware scheduling system. The preliminary experiments demonstrate the potential benefits of contention-aware scheduling on CMPs, and the promise of accurate runtime locality measurement, a critical component of the locality analysis framework.
DHIFS: A Dynamic and Hybrid Index Method with Low Memory Overhead and Efficient File Access

In the embedded Flash file system which used scarce memory to manage large-capacity Flash devices, the trade-off between memory overhead and performance of file management is worth studying. However, existing Flash file systems fall short in optimizing memory overhead and performance for three reasons. First, using Flash memory to maintain mapping structure will cause write amplification problems due to file updates and directory cascade updates. Secondly, using memory to maintain tree structures of files can meet the performance requirements but the memory overhead is excessive. Thirdly, using list structures of files can meet the memory overhead but the query performance is poor. To solve these problems, this paper proposes an adaptive file index structure called DHIFS which uses dynamic and hybrid index methods to balance the memory overhead and query performance. According to the relationship between file size and update frequency, the files are divided into three categories, and each category uses a different index structure. Then we implement and compare DHIFS with classic Flash file systems YAFFS2 and ELOFS in terms of memory overhead, file write, read, update, system unmount and mount. The experimental results show that we can get excellent results in these aspects when there are more large files. Compared with YAFFS2, the memory overhead was reduced by 53.39% and the time of mount, unmount, read, and write was reduced by 95.28%, 72.35%, 5.72%, and 6.62% respectively. In the same test platform and the same dataset test environment, DHIFS performs better than ELOFS in file read, write, update, and file system unmount.
Analytical Framework for Streaming over TCP

Multimedia streaming applications are traditionally delivered over UDP. Recent measurements show that more and more multimedia streaming data are over TCP as web-based TV, P2P streaming, video sharing websites are getting increasingly popular. To improve the Quality of Experience (QoE) for users and to cope with variability in TCP throughput, streaming applications typically implement buffers. Yet, for improving the QoE and the streaming quality, e.g., playback continuity and timeliness, it is critical to dimension buffers and the initial buffering delay appropriately. In this paper, we first develop a model for TCP streaming systems and an analytical framework to assess the QoE. Our emphasis is on buffer occupancy, which depends on the TCP arriving rate and the playout rate (the coding rate). We observe that TCP window “bounds”, namely congestion window sizes immediately before a triple duplicate or timeout event, allow to distinguish the minimum and maximum buffer occupancy for TCP streaming systems. As confirmed by experiments, the proposed analytical framework allows to estimate the frequency of buffer overflow or underflow events if buffer sizes and the initial buffering delays are known parameters, or conversely, to dimension the buffer and delay appropriately. We further extend our model and analysis for P2P multicast streaming systems. Simulations and experiments in real networks validate our proposed analytical framework in terms of underflow/overflow probabilities and delay.