Miracle-dz/UniFAR-FTUs huggingface dataset:
On the dirty paper channel with fast fading dirt

Costa's “writing on dirty paper” result establishes that full state pre-cancellation can be attained in the Gel'fand-Pinsker problem with additive state and additive white Gaussian noise. This result holds under the assumptions that full channel knowledge is available at both the transmitter and the receiver. In this work we consider the scenario in which the state is multiplied by an ergodic fading process which is not known at the encoder. We study both the case in which the receiver has knowledge of the fading and the case in which it does not: for both models we derive inner and outer bounds to capacity and determine the distance between the two bounds when possible. For the channel without fading knowledge at either the transmitter or the receiver, the gap between inner and outer bounds is finite for a class of fading distributions which includes a number of canonical fading models. In the capacity approaching strategy for this class, the transmitter performs Costa's pre-coding against the mean value of the fading times the state while the receiver treats the remaining signal as noise. For the case in which only the receiver has knowledge of the fading, we determine a finite gap between inner and outer bounds for two classes of discrete fading distribution. The first class of distributions is the one in which there exists a probability mass larger than one half while the second class is the one in which the fading is uniformly distributed over values that are exponentially spaced apart. Unfortunately, the capacity in the case of a continuous fading distribution remains very hard to characterize.
Device Identification using Network Traces in a Smart Home IoT Network

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques.
A Greedy Algorithm-Based Self-Training Pipeline for Expansion of Dental Caries Dataset

Dental caries, the most prevalent oral disease, poses a significant healthcare challenge. Deep Neural Network (DNN)-based object detection techniques offer promising solutions to improve the efficiency of dental caries diagnosis. It is widely acknowledged that the performance of DNN models heavily relies on the availability of sufficient and accurately labeled data. The collection and annotation of dental X-ray images encounter obstacles due to privacy concerns and the requirement for specialized expertise. Consequently, the limited access to labeled dental image datasets restricts the potential of DNNs in supporting oral and dental healthcare. Self-Training (ST) is a semi-supervised machine leaning approach that addresses this problem to a large extent. It repeats the procedures of training a model on the labeled dataset, and then applying it to generate pseudo labels on the unlabeled dataset, and further using the combined data with the original and pseudo labels to train new models. However, the latent errors of the pseudo labels can arise and even be amplified throughout the ST pipeline, which leads to a significant performance decline for DNN models. In this paper, we propose a Greedy algorithm-based Self-Training (Greedy-ST) pipeline to address this problem. At each iteration, the Greedy-ST selects an optimal confidence threshold to generate predictions as pseudo labels, and uses static fine-tuning (SFT) and dynamic fine-tuning (DFT) to refine them. Experimental results demonstrate that by utilizing the pseudo labels generated by the Greedy-ST pipeline, the selected baseline model achieves improved performance compared to using the pseudo labels generated by the vanilla ST approach.
Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization

This paper proposes an interactive particle-swarm metaheuristic for multiobjective optimization (MOO) that seeks to encapsulate the positive aspects of the widely used approaches, namely, Pareto dominance and interactive decision making in its solution mechanism. Pareto dominance is adopted as the criterion to evaluate the particles found along the search process. Nondominated particles are stored in an external repository which updates continuously through the adaptive-grid mechanism proposed. The approach is further strengthened by the incorporation of a self-adaptive mutation operator. A decision maker (DM) is provided with the knowledge of an approximate Pareto optimal front, and his/her preference articulations are used to derive a utility function intended to calculate the utility of the existing and upcoming solutions. The incubation of particle-swarm mechanism for the MOO by incorporating an adaptive-grid mechanism, a self-adaptive mutation operator, and a novel decision-making strategy makes it a novel and efficient approach. Simulation results on various test functions indicate that the proposed metaheuristic identifies not only the best preferred solution with a greater accuracy but also presents a uniformly diverse high utility Pareto front without putting excessive cognitive load on the DM. The practical relevance of the proposed strategy is very high in the cases that involve the simultaneous use of decision making and availability of highly favored alternatives.
’ s repository of research publications and other research outputs Experiments with discourse-level choices and readability Conference Item

This paper reports on pilot experiments that are being used, together with corpus analysis, in the development of a Natural Language Generation (NLG) system, GIRL (Generator for Individual Reading Levels). GIRL generates reports for individuals after a literacy assessment. We tested GIRL’s output on adult learner readers and good readers. Our aim was to find out if choices the system makes at the discourse-level have an impact on readability. Our preliminary results indicate that such choices do indeed appear to be important for learner readers. These will be investigated further in future larger-scale experiments. Ultimately we intend to use the results to develop a mechanism that makes discourse-level choices that are appropriate for individuals’ reading skills.
A lower-bound on the number of rankings required in recommender systems using collaborativ filtering

We consider the situation where users rank items from a given set, and each user ranks only a (small) subset of all items. We assume that users can be classified into C classes, and users in a given class c have the same ranking for all items. For this situation we are interested in the following question. As a function of the number of users N in a given class c and the numbers of items IN to be ranked, how many rankings mN per user are needed in order to be able to correctly identify all user in class c This question is of interest because correctly identifying all users in a class allows to accurately predict the ranking of an item by a given user that the user has not ranked, but that was ranked by another user in the same class. This is exactly the goal recommender systems using collaborative filtering. Therefore, being able to answer the above questions allows us to characterize how much data (i.e. how many rankings per user) is required by a recommender system using collaborative filtering to accurately predict user-item ranking pairs. We study the above question using a random graph model. Even though the resulting random graph is not a Erdos-Renyi graph, this allows us to use for our analysis similar techniques that have been developed for the analysis of Erdos-Renyi graphs.
The use of wastewater analysis in forensic intelligence: drug consumption comparison between Sydney and different European cities

Abstract Wastewater analysis offers objective and complementary information to illicit drug agencies by monitoring patterns of illicit drug consumption. In this study, wastewater samples from three different wastewater treatment plants in Sydney, Australia were collected in March 2016. Ten targeted drugs were analysed and temporal and geographical analyses were performed to obtain a better understanding of the type and amount of illicit drugs consumed in Sydney in comparison with similar studies conducted around Australia and in Europe. Among the targeted drugs, methamphetamine was consumed the most, followed by cocaine and 3,4-methylenedioxymethamphetamine (MDMA). Weekly patterns were observed where a peak during the weekend was present. The geographical analysis showed differences between the regions targeted. This observation may be related to socio-demographic aspects. The comparison of our study to other data sources from Australia showed a high consumption of methamphetamine in Sydney and Western Australia. The comparison between Sydney and different European cities revealed a difference in consumption, which is in line with traditional market indicators. The information obtained through wastewater analysis provides complementary information regarding illicit drug consumption, the size, and the evolution of the illicit drug market. This, ultimately, will assist authorities in making informed decisions.
Transcription‐induced chromatin association of RNA surveillance factors mediates facultative heterochromatin formation in fission yeast

Facultative heterochromatin is reversibly established and disrupted during differentiation, but its regulation remains mechanistically unclear. Here, we show that two meiotic gene loci in fission yeast, mei4 and ssm4, comprise facultative heterochromatin that is regulated in a developmental stage‐dependent manner. This heterochromatin coordinates expression levels by associating with a chromodomain protein Chp1 and an antisilencing factor Epe1. It has been recently shown that an RNA surveillance machinery for eliminating meiotic gene transcripts, which involves a cis‐element called the determinant of selective removal (DSR) and transacting factors, Mmi1 and Red1, also participates in heterochromatin formation at the meiotic genes, but the molecular mechanism underlying the process is largely unknown. By dissecting the mei4 gene, we identified a region that promotes DSR‐dependent methylation of histone H3 lysine 9 (H3K9). Integration of this mei4 region together with DSR into an unrelated gene results in ectopic H3K9 methylation. Moreover, our results suggest that transcription of these elements induces chromatin association of Mmi1, which, in turn, recruits Red1 interacting with Clr4/Suv39h H3K9 methyltransferase. Mmi1 remains associated in cells lacking Red1, suggesting that the recruitment of Red1 follows the chromatin association of Mmi1. Overall, we provide detailed insights into the facultative heterochromatin regulation in fission yeast.
Pengaruh Model Penerimaan Teknologi terhadap Loyalitas Penggunaan Mobile Banking oleh Nasabah Bank Syariah di Kota Bandung

This research was conducted to determine the effect of Islamic bank customer loyalty which is influenced by the technology acceptance model which is proxied by perceived ease of use and perceived usefulness. Data obtained from customers of Islamic banks in the city of Bandung as many as 168 respondents using a questionnaire. Dayang data analysis used was SEM-PLS. the results of the study show that perceived ease of use has a significant positive effect on customer loyalty. In addition, perceived usefulness has a significant positive effect on customer loyalty. The results of this study are expected to trigger Islamic banks to further improve mobile banking facilities so that customers can continue to use Islamic bank mobile banking technology.
Psychological Problems and Coping Patterns among Parents of Children with Neuromuscular Diseases

Background: Neuromuscular diseases are chronic and have a large impact on patients and their families. The study aimed to: Assess psychological problems and coping patterns among parents of children's with neuromuscular diseases in pediatric intensive care unit. Research design: A descriptive research design was used to conduct the study. Setting: Study was conducted at pediatric intensive care unit affiliated to Benha university hospital, Qalyubia, Egypt. Sample: A total of 80 parents of children with neuromuscular diseases from the above mentioned setting. Tools: Interview Questionnaire including 3 tools: Tool (I): Demographic and clinical characteristics data for parents and children. Tool (II) Beck depression inventory scale and Taylor manfist anxiety scale. Tool (III): Parental coping strategy Results: Findings of the present study showed that, 58.8% of the parent of children with neuromuscular disease had moderate depression.30. 0% of the parents of children with neuromuscular disease had moderate level of anxiety. 78.75% of the parents of children with neuromuscular disease had effective coping. Conclusion: Parents of children with neuromuscular diseases prone to moderate level of anxiety and depression and had effective coping strategies . parents of children with neuromuscular diseases needed different types of support that to continual care of their child. Recommendation: Psychosocial counseling program can be designed and administered to parents of child with neuromuscular disease to improve their psychological problems such as (anxiety, and depression) Further research is needed to shed light on the gaps in the support chain and the potential to improve the current care, to identify evidence-based family interventions that aim to support individual family members’ well-being, integrity, and family functioning through the course of the disease and at important transitions
Plagiarism: Taxonomy, Tools and Detection Techniques

To detect plagiarism of any form, it is essential to have broad knowledge of its possible forms and classes, and existence of various tools and systems for its detection. Based on impact or severity of damages, plagiarism may occur in an article or in any production in a number of ways. This survey presents a taxonomy of various plagiarism forms and include discussion on each of these forms. Over the years, a good number tools and techniques have been introduced to detect plagiarism. This paper highlights few promising methods for plagiarism detection based on machine learning techniques. We analyse the pros and cons of these methods and finally we highlight a list of issues and research challenges related to this evolving research problem.
BNN-Flip: Enhancing the Fault Tolerance and Security of Compute-in-Memory Enabled Binary Neural Network Accelerators

Compute-in-memory based binary neural networks or CiM-BNNs offer high energy/area efficiency for the design of edge deep neural network (DNN) accelerators, with only a mild accuracy reduction. However, for successful deployment, the design of CiM-BNNs must consider challenges such as memory faults and data security that plague existing DNN accelerators. In this work, we aim to mitigate both these problems simultaneously by proposing BNN-Flip, a training-free weight transformation algorithm that not only enhances the fault tolerance of CiM-BNNs but also protects them from weight theft attacks. BNN-Flip inverts the rows and columns of the BNN weight matrix in a way that reduces the impact of memory faults on the CiM-BNN’s inference accuracy, while preserving the correctness of the CiM operation. Concurrently, our technique encodes the CiM-BNN weights, securing them from weight theft. Our experiments on various CiM-BNNs show that BNN-Flip achieves an inference accuracy increase of up to 10.55% over the baseline (i.e. CiM-BNNs not employing BNN-Flip) in the presence of memory faults. Additionally, we show that the encoded weights generated by BNN-Flip furnish extremely low (near ‘random guess’) inference accuracy for the adversary attempting weight theft. The benefits of BNN-Flip come with an energy overhead of < 3%.
Tribological Properties of the Fast Ceramic Conversion Treated Ti-6Al-2Sn-4Zr-2Mo Alloy with a Pre-Deposited Gold Layer

Ceramic conversion treatment (CCT) is an effective way to modify the surface of titanium alloys. However, this process normally needs more than a 100-h treatment at 600–700 °C to form a hard and wear-resistant titanium oxide layer. In this paper, we pre-deposited a thin gold layer on the surface of Ti-6Al-2Sn-4Zr-2Mo (Ti6242) samples before CCT to investigate if Au can speed up the treatment. Treatments at 640/670/700 °C were carried out for 10 or 120 h. After CCT, the surface roughness, surface morphology, microstructure, elemental composition, and phase constituents were characterized. Surface hardness and the nano-hardness depth distribution were measured. Finally, reciprocating sliding tribological tests were carried out to study the friction and wear of the surface layers. Thin gold layers accelerated the CCT significantly with a much thicker oxide layer. The friction of the untreated Ti6242 alloy against the WC ball was unsteady and high, but it was much lower and stable for the CCTed samples pre-deposited with Au because of the formation of titanium oxides and lubrication effect of the gold particles. The wear resistance of the CCTed Ti6242 alloy samples with gold was reinforced significantly. By pre-depositing a thin gold layer on the surface of Ti6242, the treatment time can be cut significantly, and CCT becomes more efficient.
Comparison of macroinvertebrate assemblages across a gradient of flow permanence in an agricultural watershed

In the south‐eastern United States and globally, increasing human water demand coupled with climate change is diminishing stream flows and increasing stream intermittency in many watersheds. We characterized benthic invertebrate assemblages across a stream flow gradient ranging from intermittent to perennial following a multiyear drought by examining the functional traits that can influence assemblage response to drying. We sampled 13 reaches within the Lower Flint River Basin in south‐western Georgia, from September to December 2013. Reaches included perennial, near‐perennial (ceased flowing but maintained a wetted channel during drought), intermittent‐dry (seasonally dry), and intermittent‐frequent (frequently dry). Distinct assemblages were documented across this gradient. Reaches that dried during the drought had a lower richness of aquatic insects, especially Ephemeroptera, Plecoptera, and Trichoptera taxa, partly due to inadequate time for life cycle completion and lack of adaptations to avoid drying. Intermittent reaches also included abundant and unique noninsect taxa such as Gammarus spp. and Isopoda. Projected trends towards increased water demand and drought severity and frequency in the south‐eastern United States will magnify shifts towards dominance by drought‐tolerant taxa as greater portions of stream networks become intermittent.
Neural control of lengthening contractions

ABSTRACT A number of studies over the last few decades have established that the control strategy employed by the nervous system during lengthening (eccentric) differs from those used during shortening (concentric) and isometric contractions. The purpose of this review is to summarize current knowledge on the neural control of lengthening contractions. After a brief discussion of methodological issues that can confound the comparison between lengthening and shortening actions, the review provides evidence that untrained individuals are usually unable to fully activate their muscles during a maximal lengthening contraction and that motor unit activity during submaximal lengthening actions differs from that during shortening actions. Contrary to common knowledge, however, more recent studies have found that the recruitment order of motor units is similar during submaximal shortening and lengthening contractions, but that discharge rate is systematically lower during lengthening actions. Subsequently, the review examines the mechanisms responsible for the specific control of maximal and submaximal lengthening contractions as reported by recent studies on the modulation of cortical and spinal excitability. As similar modulation has been observed regardless of contraction intensity, it appears that spinal and corticospinal excitability are reduced during lengthening compared with shortening and isometric contractions. Nonetheless, the modulation observed during lengthening contractions is mainly attributable to inhibition at the spinal level. Summary: The neural control of lengthening contraction differs from that of shortening or isometric contractions; current knowledge on the specific control of lengthening contractions is summarized.
Opening Pandora's box

Will reading habit influence your life? Many say yes. Reading opening pandoras box is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.
3D Reconstruction of Single Indoor Image Based on Relational Network

To address the problems of object scale discrepancies and insufficient detail precision in reconstructing indoor scenes using only one image in 3D, a method for 3D reconstruction that increases the proportion of geometric features is proposed. The method for 3D reconstruction is based on scene understanding and uses the spatial layout estimation module to obtain the layout information of the scene. The method adopts the relational network to allocate the relationships between objects and increases the weight of geometric features to deal with the problem of mismatches in target scales in the reconstruction based on a single image. The reconstructed surface of the object is calculated using the sign distance function, and the surface details of the resulting reconstruction are optimized by combining them with a Marching Cubes algorithm. The method has been shown to be capable of obtaining a reasonable-scale reconstructed object model and the detailed message of the model, as evidenced by the experimental results.
Reward Shaping in Reinforcement Learning of Multi-Objective Safety Critical Systems

Reinforcement learning (RL) depends critically on how well the reward function formulates the aim of the application. In safety-critical systems, several safety properties should be met during the RL process in addition to achieving the primary target. In this paper, we utilize the reward shaping technique to guide the RL algorithm in a way that satisfies the target and safety properties and fulfills other properties, such as efficiency, as much as possible. Reward shaping is a technique to enhance the performance of RL by adding more information to the reward function. Reward shaping functions utilize the system’s main target for the base reward signal and add some other reward signals based on other system properties. Suppose each property has its own reward function. In this case, the reward is a vector rather than a scalar value, and the original reinforcement learning approach converts to multi-objective reinforcement learning (MORL). In MORL, one scalarized objective can be defined by fusing several objectives. This paper proposes an aggregation method based on prioritized aggregation operators for scalarizing multiple reward functions. We illustrate our method with two case studies: cart-pole with an obstacle and lunar-lander. The simulation results show that our method guides the RL algorithm to converge to optimal policies faster than existing approaches.