Miracle-dz/UniFAR-FTUs huggingface dataset:
The International Workshop on Computer Architecture for Machine Perception and Sensing a Fully-pipelined Parallel Architecture for Kalman Tracking Filter

The Kalmanfilter is a set ofmathematical equations that provides an efficient computational (recursive) mean to estimate the state of a process, in a way that minimizes the mean of the squared error. This filter is very powerful in several aspects. it provides estimations ofpast, present, and future states, and it can do so when the precise nature of the modeled system is unknown, and even with the presence of measurement and process noise. Moreover, Kalman filter for linear estimate is the most complex and precise algorithm used for target tracking. However, using Kalman filter algorithms in software for multi-target tracking (MTT) radar system would result in a very long computational time which may not be suitable for today's warfare constraints, or real-time processing. Consequently, a hardware alternative has to be developed which may result in big area overhead which is not suitable for today's area constraints such as sensor nodes in a sensor network. In this paper, we break the arrays into their scalar forms, and develop fully-pipelined hardware architecture for the radar tracking Kalman filter, with time division multiplex blocks to decrease the silicon area. The proposed architecture contains 6 multipliers, 2 dividers, 9 adders, 5 subtractors, one control unit, and some registers and multiplexers for pipeline and control. Simulation results show that the loss in accuracy between the exact track and the estimated is found to be only 4.9/
Adaptive sensor networks for consensus based distributed estimation

In this paper consensus based algorithms for distributed estimation in sensor networks are discussed and a new algorithm with decentralized adaptation is proposed for solving the problem where the state of a monitored process is observed only by a relatively small percentage of the sensors at each iteration of the algorithm. The given analysis shows that adaptation of the gains in the consensus scheme is of crucial importance for getting simple yet efficient estimation algorithms. It is also shown that the exchange of an additional binary information between the nodes on whether or not a node has received the observation, along with the information on state estimates, is sufficient to obtain a robust and efficient tool for practice. Selected examples illustrate performance of the proposed algorithm in terms of the mean square estimation error and the disagreement between the nodes.
Querying Communities in Relational Databases

Keyword search on relational databases provides users with insights that they can not easily observe using the traditional RDBMS techniques. Here, an l-keyword query is specified by a set of l keywords, {k1, k2, · · · , kl}. It finds how the tuples that contain the keywords are connected in a relational database via the possible foreign key references. Conceptually, it is to find some structural information in a database graph, where nodes are tuples and edges are foreign key references. The existing work studied how to find connected trees for an l-keyword query. However, a tree may only show partial information about how those tuples that contain the keywords are connected. In this paper, we focus on finding communities for an l-keyword query. A community is an induced subgraph that contains all the l-keywords within a given distance. We propose new efficient algorithms to find all/top-k communities which consume small memory, for an l-keyword query. For top kl-keyword queries, our algorithm allows users to interactively enlarge k at run time. We conducted extensive performance studies using two large real datasets to confirm the efficiency of our algorithms.
Observations of water-flea Daphnia magna and avian fecalia in rock pools: is traditional natural history reporting still relevant for science?

ABSTRACT Unexpected observation of freshwater invertebrate Daphnia magna in temporary rock pools on a small island off the Norwegian Atlantic coast confirms colonising ability, mediated through avian dispersal. Robust diapausal eggs of D. magna pass the gut of migrating geese and subsequently hatch in environments where such waterfowl forage and rest along migrating routes. The incubation experiment demonstrates that decomposing fecalia of geese constitute sufficient feed to support developing populations of D. magna in experimental oligotrophic conditions. The findings also show that D. magna is highly adapted for colonisation of temporary pools north of the Arctic Polar Circle, with excellent tolerance to low temperatures. Discussion part I relates the findings to published evidence on avian transport of invertebrate propagules, such as correlations between migration patterns of waterfowl and geographical patterns of aquatic invertebrate diversity. Discussion part II reflects on academic cultures, epistemological aspects of natural history reporting, the presence of a whale cadaver as an ecological indicator and the relevance of simple observation as a starting point for further discourse.
FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN -BASED KNOWLEDGE -DISCOVERY PROCESS

Nowadays the electric utilities have to handle problems with the non-technical losses caused by frauds and thefts committed by some of their consumers. In order to minimize this, some methodologies have been created to perform the detection of consumers that might be fraudsters. In this context, the use of classification techniques can improve the hit rate of the fraud detection and increase the financial income. This paper proposes the use of the knowledge-discovery in databases process based on artificial neural networks applied to the classifying process of consumers to be inspected. An experiment performed in a Brazilian electric power distribution company indicated an improvement of over 50% of the proposed approach if compared to the previous methods used by that company.
WSN09-4: Key Distribution in Mobile Heterogeneous Sensor Networks

Key predistribution is a popular technique for key distribution in sensor networks. The existing predistribution schemes are designed for nodes with no or limited mobility. The existing schemes make use of only one key pool to establish links between the stationary and the mobile nodes. This restricts the mobility of nodes to one specific network. If the same key pool is used in multiple networks, the compromise of keys in one network would lead to compromise of keys in all the networks. We present two different solutions to this problem. The first approach uses a separate disjoint key pool to establish links between the stationary and mobile nodes of the network. In the second approach we take a large key pool and segment it into smaller key pools. Each of these segments acts as the key pool for different stationary sensor networks. The mobile nodes get keys from the aggregate of all these segments. The aggregate key pool can have some segments which can be used for future deployments. We compare the two schemes and analyze their performance. It is assumed that the stationary nodes of the sensor networks are securely connected.
Spread influence algorithm of news website based on PageRank

In order to measure the mainstream news sites spread influence of mass media, an efficient algorithm has been proposed in this paper. By introduction PageRank algorithm and dynamics of human behavior, the algorithm with website attention factor archive the goal of quantitative analysis of mainstream news sites spread influence. For illustration, an example close to the actual situation is utilized to show the effect of the news sites rank algorithm in solving problem with website attention factor. Experimental results show that the algorithm based on PageRank algorithm effectively reflecting the spread influence of the news site.
First Manic Episode in a Young Woman with Schizophrenia after Initiation of Oral Aripiprazole and Aripiprazole Long-Acting Injection

changed to oral aripiprazole to check her tolerance to aripiprazole before initiating ALAI. Oral aripiprazole was escalated to 20 mg at 1 week after admission. However, 5 days after initiating oral aripiprazole, she suddenly exhibited mood swing, irritability, and aggressive behaviors, such as throwing objects at other patients, which seemed to indicate hypomania. Thus, escitalopram was discontinued and ALAI was added to the 20-mg oral aripiprazole on hospital day (HD) 7 in an attempt to remove these symptoms based on the theory of high-dosage aripiprazole acting as a D2 receptor antagonist. However, her psychopathology including psychotic features remained, although her disorganized speech and behavior improved slightly. In addition, her symptoms continued to worsen. Blonaserin at 4 mg was added to oral aripipra-Case A woman in her twenties with schizophrenia developed immediate-onset mania after taking oral aripiprazole and re-ceiving aripiprazole long-acting injection (ALAI). The dosage of aripiprazole was rapidly increased due to inadequate stimulating effect of low-dosage aripiprazole, but her manic symptomatology worsened. Clinicians should therefore carefully monitor for the induction of mania by oral aripiprazole and ALAI. Her manic symptomatology improved after adding 20 mg of blonanserin, 3 mg of risperidone, and 300 mg of quetiapine.
HiCUP: pipeline for mapping and processing Hi-C data

HiCUP is a pipeline for processing sequence data generated by Hi-C and Capture Hi-C (CHi-C) experiments, which are techniques used to investigate three-dimensional genomic organisation. The pipeline maps data to a specified reference genome and removes artefacts that would otherwise hinder subsequent analysis. HiCUP also produces an easy-to-interpret yet detailed quality control (QC) report that assists in refining experimental protocols for future studies. The software is freely available and has already been used for processing Hi-C and CHi-C data in several recently published peer-reviewed studies.
Analyzing performance and dynamic behavior of embedded Java software with calling-context cross-profiling

Prevailing approaches to analyze embedded software performance either require the deployment of the software on the embedded target, which can be tedious and may be impossible in an early development phase, or rely on simulation, which can be extremely slow. We promote cross-profiling as an alternative approach, which is particularly well suited for embedded Java processors. The embedded software is profiled in any standard Java Virtual Machine in a host environment, but the generated cross-profile estimates the execution time on the target. We implemented our approach in the customizable cross-profiler CProf, which generates calling-context cross-profiles. Each calling-context stores dynamic metrics, such as the estimated CPU cycle consumption on the target. We visualize the generated calling-context cross-profiles as ring charts, where callee methods are represented in segments surrounding the caller's segment. As the size of each segment corresponds to the relative CPU consumption of the corresponding calling-context, the visualization eases the location of performance bottlenecks in embedded Java software, revealing hot methods, as well as their callers and callees, at one glance.
SERVICE PRIORITIZATION IN NODE-DISJOINT ROUTING FOR AD HOC NETWORKS BASED ON QoS

Ad hoc network (MANET) is a collection of mobile nodes that can communicate with each other without using any fixed infrastructure. To support multimedia applications such as video and voice MANETs require an efficient routing protocol and quality of service (QoS) mechanism. Node-Disjoint Multipath Routing Protocol (NDMR) is a practical protocol in MANETs: it reduces routing overhead dramatically and achieves multiple node-disjoint routing paths. QoS support in MANETs is an important issue as besteffort routing is not efficient for supporting multimedia applications. This paper presents a novel adaptation of NDMR, QoS enabled NDMR, which introduces agent-based SLA management. This enhancement allows for the intelligent selection of node-disjoint routes based on network conditions, thus fulfilling the QoS requirements of Service Level Agreements (SLAs). Keywords— MANET, Node-Disjoint, Multipath, Management Protocols
Left ventricular torsion and strain patterns in heart failure with normal ejection fraction are similar to age-related changes.

AIMS We used speckle tracking echocardiography (STE) to make a comparison between the effects of ageing and of heart failure with normal ejection fraction (HfnEF) on left ventricular (LV) torsion and strain patterns. METHODS AND RESULTS Forty patients with HfnEF, 27 young controls and 26 older controls, were prospectively recruited. All subjects underwent clinical examination, 12-lead electrocardiogram, pulmonary function test, echocardiogram, and metabolic exercise test. LV torsion increases with advancing age (older controls vs. young controls, 2.2 +/- 0.9 vs. 1.4 +/- 0.8 degrees /cm; P = 0.03). Circumferential strain was enhanced in patients with HfnEF (-24.7 +/- 4.7 vs. -20.0 +/- 4.9%; P = 0.003). Rotational deformation delay (time difference between peak basal rotation and peak apical rotation), global circumferential strain, E-velocity deceleration time, and LV end-diastolic volume index were independent predictors of LV torsion. LV torsion and body mass index were independent predictors of LV untwist rate. CONCLUSION Ageing is associated with increased LV torsion secondary to reduced rotational deformation delay and increased peak basal rotation. LV torsion and strain patterns in patients with HfnEF are similar to age-related changes apart from circumferential strain, which is enhanced in patients with HfnEF.
On the benefits of formalization: Panel evidence from Vietnam

This paper examines the relationship between formalization and firm-level outcomes in Vietnam using a unique panel dataset. Results show that switching firms differ from informal nonswitching firms, confirming heterogeneity. We also find that becoming formal leads to increased profits, value added, and revenue. The performance of switching firms is higher than that of informal ones, but remains lower compared to non-switching formal firms. Our results suggest that the benefits of formalization materialize in the short term and persist over time. These benefits run through access to improved equipment, larger customer base, advertising, and business association membership, but not access to credit.
Analysis of Random Telegraph Noise in 45-nm CMOS Using On-Chip Characterization System

An on-chip variability characterization system implemented in a 45-nm CMOS process is used for direct time-domain measurements of random telegraph noise (RTN) in small-area devices. A procedure for automated extraction of RTN parameters from large volumes of measured data is developed. Statistics for number of traps, NT, and single-trap amplitudes, ΔV<sub>th</sub>, are studied across device polarity, bias, and gate area. A Poisson distribution is used to model NT and a log-normal distribution is used to model ΔV<sub>th</sub>. The scaling of the two statistics across gate dimensions is discussed; the expected value of NT is shown to scale with (L - ΔL)<sup>-1</sup>, whereas the expected value of AV<sub>th</sub> is shown to scale with W<sup>-1</sup>(L - ΔL)<sup>-0.5</sup>. The two statistics are combined in a compact RTN probabilistic model representing the statistics of the overall ΔV<sub>th</sub> fluctuations because of RTN. This model is demonstrated to give accurate predictions of the tails of the measured RTN distributions at the 95th percentile level, which scale with W<sup>-1</sup>(L - ΔL)<sup>-1.5</sup>. A comparison between nMOS and pMOS devices shows that pMOS devices exhibit both a higher average number of traps and a larger average single-trap ΔV<sub>th</sub> amplitude, leading to a comparatively larger overall impact of RTN.
An EEG Study on Working Memory and Cognition

The primary objective of the brain is to collect information from the body via nervous system and then either process those information or store them. These stored information are basically known as memory. Over the years Electroencephalography (EEG) signals have been used quite efficiently to evaluate the cognitive state of brain. In this paper, we aim to study the cognitive functioning of the brain when a user memorizes and then recognizes an object from a list of similar ones. For this purpose, we have employed wavelet transform to extract the relevant features pertaining to the two mental activities and support vector machine to distinguish between the two states. The classification accuracy, thus obtained, is above 79% for all subjects. It is also inferred from this study that during memorization the signals from the frontal and temporal lobes are dominant and during recognition, signals from the frontal and parietal lobes are dominant.
Logo Recognition with an Incremental Learning method and Consensus for enabling Blockchain Implementations

A large amount of non-processed visual information is uploaded in social networks every day. Different features can be analyzed from the images such as objects, scenes, sentiments, people’s mood, color, etc. In this paper, we propose a novel method to detect, locate and classify logos in images, based on consensus. First, we present a basic logo recognition method. Second, an incremental learning algorithm is proposed to detect logos of any class by just using a synthetic image template, without the need of annotating a training set. Then, a crowdsourced solution (collaborative network) is generated within a VisualAD platform to carry out the consensus between several executions of the incremental learning method. The predictions will be the result of individual predictions from several users that improve the recognition. Finally, the principles enabling its Blockchain implementation are set and considerations on their extension to visual identity
Bioremediation of textile dye effluent by Shewanella Putrefaciens

The aim of the present study was to use a bacterial isolate for decolorization and reduction of pollution from textile effluent. Shewanella putrefaciens was isolated from raw textile effluent and used to decolorize the effluent, in unoptimized and optimized conditions. In unoptimized condition the decolorization was 27.95% whereas in optimized condition it reached 63.15% (Chrysophenine optimized value) and 89.4% (Red 3BN optimized value). It was essential to substitute the raw effluent with co-factors to enhance decolorization process. It was also important to provide suitable culture conditions to improve the decolorization efficiency, in the absence of which the bacterial isolate could not decolorize to the extent required. The physicochemical characterization of the effluent was carried out before and after decolorization process to assess the change in the parameters and thereby the pollution load on the environment. Sulphate (79.4%) and Phosphate (78.6%) showed highest reduction whereas Total suspended solids, BOD and COD showed 55%, 41.1% and 30.4% reduction, respectively. This study recommends the application of shewanella putrefaciens, as a potent bacterial strain in decolorization of textile dyes and effluents under suitable nutritional and environmental conditions.
Quantum Software Reusability

The design of efficient quantum circuits is an important issue in quantum computing. It is in general a formidable task to find a highly optimized quantum circuit for a given unitary matrix. We propose a quantum circuit design method that has the following unique feature: It allows to construct efficient quantum circuits in a systematic way by reusing and combining a set of highly optimized quantum circuits. Specifically, the method realizes a quantum circuit for a given unitary matrix by implementing a linear combination of representing matrices of a group, which have known fast quantum circuits. We motivate and illustrate this method by deriving extremely efficient quantum circuits for the discrete Hartley transform and for the fractional Fourier transforms. The sound mathematical basis of this design method allows to give meaningful and natural interpretations of the resulting circuits. We demonstrate this aspect by giving a natural interpretation of known teleportation circuits.
Comparative Evaluation of the Antibacterial Effects of Honey with Standard Antibiotic on Bacterial Isolates from Wound Infection

Aim: The aim of this study was to compare the antibacterial activities of the Honey against Ciprofloxacin on four bacterial isolates from a wound. Study Design: It is a cross sectional comparative and observational study. Place and Duration of Study: The study was conducted in Usmanu Danfodiyo University Teaching Hospital (UDUTH), Sokoto State, Nigeria between July 2017 and October 2017. Methodology: One hundred and one (101) bacterial wound isolates were collected and identified using the standard microbiological methods of Gram staining and biochemical test. The activity patterns of the Honey concentrations and the standard antibiotic were determined using Kirby-Bauer disc diffusion and Punched Holes techniques. Similarly, minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of the Honey were determined using Macrobroth dilution technique. Results: Out of 101 isolates collected and identified, 33(32.7%) were Staphylococcus aureus, 29(28.7%) Pseudomonas aeruginosa, 21(20.8%) Escherichia coli and 18(17.8%) Proteus mirabilis. Antibacterial activity of honey was observed at 100% and 50% concentrations for S. aureus (10.7±0.13 mm and 8.4±0.16 mm), P. aeruginosa, (11.0±0.45 mm and 7.6±0.26 mm) and E. coli, (11.1±0.61 mm and 7.5±0.55 mm) respectively. Comparison of the inhibitory zone diameters showed that Ciprofloxacin (30.65±0.37 mm) had higher antibacterial activity than the raw honey (10.45±0.51 mm). The Minimum inhibitory concentration (MICs) of crude honey on S. aureus was 5%, P. aeruginosa 50%, E. coli 20%, and P. mirabilis 100%, while the minimum bactericidal concentration (MBC) of crude honey on S. aureus was 50%, P. aeruginosa 100%, E. coli 100%, and P. mirabilis was resistant. Conclusion: The result obtained from this study established that honey possessed antibacterial activity at 50% and 100% concentrations against S. aureus, P. aeruginosa and E. coli, which indicates that development of inhibition zones, depends on the concentration of the honey used as well as the nature of the tested pathogen. The findings also revealed that ciprofloxacin has higher antimicrobial activity than the type of honey used in this study.