Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainability Key Performance Indicators in Islamic Banking Sectors of Malaysia and Pakistan: A Scale Based Evidence

The marvelous development in the Islamic banking necessitates sustainable practices in the emerging industry. This study intends to ascertain the most followed or usual sustainability key performance indicators (KPIs) in the Islamic banking industry of Malaysia and Pakistan. For developing the sustainability KPIs index, the study follows the systematic steps as highlighted by prior such studies. The index composed of 109 items in 7 main sections of sustainability reporting adapted to collect data through content analysis for 21 Islamic banks from both the countries for the year 2016. Despite signiﬁcant heterogeneity among the Islamic banks of the two countries, it is found that they show good compliance with the sustainability indicators. Though the Islamic banking industry of Pakistan is small than that of Malaysia, but still it is good in reporting sustainable practices except for environment and product responsibility, where the latter has an edge over the former. The study contributes a theoretical deliverable i.e. sustainability KPIs index for the Islamic banking industry through testing it with data from 21 Islamic banks from Malaysia and Pakistan. The ﬁndings could be signiﬁcant for practitioners, regulators, policymakers, and especially the Islamic banking industry of both countries.
The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations

Predicting future behavior of other traffic participants is an essential task that needs to be solved by automated vehicles and human drivers alike to achieve safe and situation-aware driving. Modern approaches to vehicles trajectory prediction typically rely on data-driven models like neural networks, in particular LSTMs (Long Short-Term Memorys), achieving promising results. However, the question of optimal composition of the underlying training data has received less attention. In this paper, we expand on previous work on vehicle trajectory prediction based on neural network models employing distributed representations to encode automotive scenes in a semantic vector substrate. We analyze the influence of variations in the training data on the performance of our prediction models. Thereby, we show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set and thereby, that the composition of training data in vehicle trajectory prediction is crucial for successful training. We conduct our analysis on challenging real-world driving data.
Trait depressivity prediction with EEG signals via LSBoost

Purpose: This study aims to identify EEG biomarkers that predict the level of depressive personality (where extreme scores indicate disorder), as opposed to the presence or absence of a depressive state or a depression diagnosis.Methods: Fourier features were extracted from 2-second epochs of resting state EEG and used by LSBoost to maximise the correlation with depressive trait tendencies (PID-5 depressivity index). Results: Our method accounted for 25.75% of the variance in PID-5 scores, albeit in females only. The recording channel C3 and frequencies in the gamma band were the most important contributors to the prediction. The findings are consistent with previous psychological studies and suggest that our method is a feasible strategy for developing quantitative EEG biomarkers for trait depressivity in a neuropsychologically interpretable form. We have also shown that there might be different markers for depressivity between males and females.
Physiological hyperarousal: construct validity of a central aspect of the tripartite model of depression and anxiety

Physiological hyperarousal (PH) is an understudied component of the tripartite model of depression and anxiety. This study contributes to the literature on PH, the tripartite model, and anxiety and its disorders, using data from psychotherapy outpatients (n = 2,448), air force cadets (n = 1,335), and undergraduates (n = 284). Psychometrics and exploratory and confirmatory factor analyses showed that PH is a reliable, cohesive, discriminable, and valid construct. Compared with subjective anxiety, PH was more associated to panic versus mood disordered status, and to panic versus generalized anxiety disordered status. As hypothesized, an aspect of anxiety sensitivity (i.e., fear of body sensations) was particularly related to subjective anxiety in the presence of PH. Results support the PH construct as replicable, valid, and clinically important and support the utility of the tripartite and related models for understanding the relation of depressive and anxious syndromes.
WiBorder

Recent research has shown great potential of exploiting Channel State Information (CSI) retrieved from commodity Wi-Fi devices for contactless human sensing in smart homes. Despite much work on Wi-Fi based indoor localization and motion/intrusion detection, no prior solution is capable of detecting a person entering a room with a precise sensing boundary, making room-based services infeasible in the real world. In this paper, we present WiBorder, an innovative technique for accurate determination of Wi-Fi sensing boundary. The key idea is to harness antenna diversity to effectively eliminate random phase shifts while amplifying through-wall amplitude attenuation. By designing a novel sensing metric and correlating it with human's through-wall discrimination, WiBorder is able to precisely determine Wi-Fi sensing boundaries by leveraging walls in our daily environments. To demonstrate the effectiveness of WiBorder, we have developed an intrusion detection system and an area detection system. Extensive results in real-life scenarios show that our intrusion detection system achieves a high detection rate of 99.4% and a low false alarm rate of 0.68%, and the area detection system's accuracy can be as high as 97.03%. To the best of our knowledge, WiBorder is the first work that enables precise sensing boundary determination via through-wall discrimination, which can immediately benefit other Wi-Fi based applications.
A New Species of the Family Theraphosidae,with Taxonomic Study on the Species Selenocosmia Hainana (Arachnida:Araneae)

In the present paper a new species belonging to the family Theraphosidae from Hainan Province, China, is reported under the name of Chilobrachys jingzhao sp. nov. Besides, the species Selenocosmia hainana Liang et al., 1999 is transferred to the genus Ornithoctonus, Ornithoctoninae. These species are described and illustrated in details including the new discovery of male of the species Ornithoctonus hainana (Liang et al., 1999). Specimens are deposited in the College of Life Sciences, Hebei University, China. All measurements given are in millimeters. Measurements of palp and legs are shown as: total (femur, patella, tibia, metatarsus, tarsus).
Concurrent Blind Decision Feedback Equalizer

—Decision Feedback Equalizers (DFEs) play an important role in digital communication systems. They reduce the effect of intersymbol interference, holding a favorable compromise between computational complexity and efﬁcient behavior. When blind adaptation of DFEs is required, algorithms based on the constant modulus criterion may converge to so-called degenerative solutions. Among different algorithms which avoid these undesirable solutions, the one proposed by Szczecinski and Gei is the most efﬁcient. However, in many practical situations, as for example time-variant channels, its good behavior is not always assured. In this paper, it is proposed an improvement of that algorithm based on a concurrent blind deconvolution method. At the cost of a moderate increase in computational complexity, the resulting algorithm presents faster convergence and lower symbol error rate than the previous one. Illustra-tive simulations considering M -QAM signaling and high deﬁnition television (HDTV) channels are presented.
Flooded Paddy Ecosystem Harbors Methanol Oxidizing-Plant Growth Promoting Bacteria Belonging to Order Enterobacterales

Methanol concentration in environment plays an important role in atmospheric chemistry as low methanol emission is associated with low ozone concentration in the atmosphere (Galbally and Kirstine, 2002; Wennberg et al., 1998; Warneke et al., 1999). Approximately, 100 Tg y −1 of methanol emission is contributed by the plants (Galbally & Kirstine, 2002). Methanol synthesis in plant is associated with stabilization of pectin in plant cell walls (Fall and Benson, 1996).
Research on the Impact of Micro-Blog Marketing on Consumer Perceived Quality

Abstract. Micro-blog has set off an "Internet revolution" with its convenience, digitalization and strong interaction since 2010, which has changed the way of enterprise information dissemination. Nowadays, the user group of Micro-blog is more and more extensive, and more and more enterprises begin to carry out micro-blog marketing. How to make good use of this platform to promote sales is an important topic worthy of in-depth exploration. Based on the in-depth study of relevant literature at home and abroad, this paper takes empirical quantitative analysis as the research method to extract the influencing factors of consumer perceived quality under Micro-blog marketing, namely brand image, innovation of marketing methods, and participation of network community members. The results show that these three factors can positively affect the consumer perceived quality. Additionally, This paper puts forward some suggestions on micro-blog marketing for enterprises, such as shaping a good brand image, enhancing the innovation of micro-blog marketing methods, attracting the attention of potential consumers, improving the participation of members of the network community, and emphasizing the dissemination of product information among members of the network community, striving to play a useful practical reference value and guiding role for enterprises to implement micro-blog marketing strategy.
Relational Disjoint-Set Forests

We give a simple relation-algebraic semantics of read and write operations on associative arrays. The array operations seamlessly integrate with assignments in the Hoare-logic library. Using relation algebras and Kleene algebras we verify the correctness of an array-based implementation of disjoint-set forests with a naive union operation and a ﬁnd operation with path compression.
Concurrent response-selection processes in dual-task performance: Evidence for adaptive executive control of task scheduling.

Abstract : Four experiments with the psychological refractory period (PRP) procedure are reported that investigate how people perform multiple tasks concurrently. In each experiment, a primary task was paired with a secondary task that had two levels of response-selection difficulty. Experiments 1 and 2 varied response-selection difficulty by manipulating the number of alternative stimulus-response (S-R) pairs in the secondary task. In both experiments, the effect of this factor on secondary-task reaction times (RTs) decreased reliably as the stimulus onset asynchrony (SOA) decreased. Experiments 3 and 4 varied response-selection difficulty by manipulating S-R compatibility for the secondary task. Again, the effect of this factor on secondary-task RTs decreased reliably as SOA decreased, regardless of whether or not the primary and secondary tasks involved the same response modality. Taken together, these results raise doubts about the existence of an immutable structural central bottleneck in response selection. Rather, it appears that response-selection processes for two concurrent tasks may temporally overlap. This outcome is consistent with dual-task performance models (Meyer & Kieras, 1997a, 1997b, 1997c Meyer et al., 1995) under which people have adaptive executive control of their task-scheduling strategies.
Complex Evolutionary Patterns of tRNAUAALeu Group I Introns in Cyanobacterial Radiation

ABSTRACT Based on the findings that plastids and cyanobacteria have similar group I introns inserted into tRNAUAALeu genes, these introns have been suggested to be immobile and of ancient origin. In contrast, recent evidence suggests lateral transfer of cyanobacterial group I introns located in tRNAUAALeu genes. In light of these new findings, we have readdressed the evolution and lateral transfer of tRNAUAALeu group I introns in cyanobacteral radiation. We determined the presence of introns in 38 different strains, representing the major cyanobacterial lineages, and characterized the introns in 22 of the strains. Notably, two of these strains have two tRNAUAALeu genes, with each of these genes interrupted by introns, while three of the strains have both interrupted and uninterrupted genes. Two evolutionary distinct clusters of tRNA genes, with the genes interrupted by introns belonging to two distinct intron clusters, were identified. We also compared 16S rDNA and intron evolution for both closely and distantly related strains. The distribution of the introns in the clustered groups, as defined from 16S rDNA analysis, indicates relatively recent gain and/or loss of the introns in some of these lineages. The comparative analysis also suggests differences in the phylogenetic trees for 16S rDNA and the tRNAUAALeu group I introns. Taken together, our results show that the evolution of the intron is considerably more complex than previous studies found to be the case. We discuss, based on our results, evolutionary models involving lateral intron transfer and models involving differential loss of the intron.
The C-Terminal Sequence of the λ Holin Constitutes a Cytoplasmic Regulatory Domain

ABSTRACT The C-terminal domains of holins are highly hydrophilic and contain clusters of consecutive basic and acidic residues, with the overall net charge predicted to be positive. The C-terminal domain of λ S was found to be cytoplasmic, as defined by protease accessibility in spheroplasts and inverted membrane vesicles. C-terminal nonsense mutations were constructed in S and found to be lysis proficient, as long as at least one basic residue is retained at the C terminus. In general, the normal intrinsic scheduling of S function is deranged, resulting in early lysis. However, the capacity of each truncated lytic allele for inhibition by the S107 inhibitor product ofS is retained. The K97am allele, when incorporated into the phage context, confers a plaque-forming defect because its early lysis significantly reduces the burst size. Finally, a C-terminal frameshift mutation was isolated as a suppressor of the even more severe early lysis defect of the mutant SA52G, which causes lysis at or before the time when the first phage particle is assembled in the cell. This mutation scrambles the C-terminal sequence ofS, resulting in a predicted net charge increase of +4, and retards lysis by about 30 min, thus permitting a viable quantity of progeny to accumulate. Thus, the C-terminal domain is not involved in the formation of the lethal membrane lesion nor in the “dual-start” regulation conserved in lambdoid holins. Instead, the C-terminal sequence defines a cytoplasmic regulatory domain which affects the timing of lysis. Comparison of the C-terminal sequences of within holin families suggests that these domains have little or no structure but act as reservoirs of charged residues that interact with the membrane to effect proper lysis timing.
Deep-learning-based beamforming for rejecting interferences

Antenna arrays have been widely used for space and space–time processing to estimate the desired signals in the presence of narrowband and wideband interferences. Estimating the optimal weight vector is a challenging problem in space and space–time processing due to its high computational complexity. The problem escalates when the number of antennas and taps of filters for time-domain processing increases. In this study, a convolutional neural network is employed to estimate nearly-optimal weight vectors even when the number of available snapshots of the received signal is as low as 400 and a signal-to-noise ratio as low as −5 dB. Unlike the conventional processors, the authors proposed method requires no prior knowledge about the direction of arrival of the desired signal. In addition, due to its parallel architecture, the computational complexity of the neural processors is reasonable using a single graphical processing unit on a PC to run the algorithms. Through simulations of the uniform linear array in the presence of narrowband and wideband interferences, they demonstrate that the output signal-to-interference plus noise ratio (SINR) is very close (by <0.5 dB) to the max SINR obtained by the optimal weight vector of antenna arrays.
SmileAuth

User authentication is crucial for security and privacy protection on smartphones. While a variety of authentication schemes are available on smartphones, security flaws have been continuously discovered. Fingerprint films can deceive fingerprint sensors and anti-surveillance prosthetic masks can spoof face recognition. In this paper, we propose a novel user authentication system SmileAuth that leverages the unique features of people's dental edge biometrics for reliable and convenient user authentication. SmileAuth extracts a series of dental edge features by slightly moving the smartphone to capture a few images from different camera angles. These unique features are determined by the tooth size, shape, position and surface abrasion. SmileAuth is robust against image spoofing, video-based attack, physically forced attack and denture attack. We implemented the prototype of SmileAuth on Android smartphones and comprehensively evaluated its performance by recruiting more than 300 volunteers. Experimental results show that SmileAuth can achieve an overall 99.74% precision, 98.69% F-score, 2.31% FNR and 0.25% FPR in diverse scenarios. Additional experiments with two pairs of twins demonstrate that dental edge biometrics are unique enough to effectively distinguish twins.
Ongoing margin migration of Ice Stream B, Antarctica

Abstract The rate of margin migration of an ice stream can be determined using repeat measurements of the surface velocity profile within the marginal shear zone. The method relies on the assumption that the velocity profile is a characteristic feature of the margin, and that this profile is shifted laterally as the margin migrates. Application of the method to Ice Stream B, Antarctica, indicates that the southern margin is moving outward into the inland ice at a rate of at least 9.7 ± 1.1 m a−1.
5G Small Cell Backhaul: A Solution Based on GSM-Aided Hybrid Beamforming

— In the proposed 5G architecture where cell densification is expected to be used for network capacity enhancement, the deployment of millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) in urban microcells located outdoor is expected to be used for high channel capacity small cell wireless traffic backhauling as the use of copper and optic-fibre cable becomes infeasible owing to the high cost and issues with right of way. The high cost of radio frequency (RF) chain and its prohibitive power consumption are big drawbacks for mmWave massive MIMO transceiver implementation and the complexity of using optimal detection algorithm as a result of inter-channel interference (ICI) as the base station antenna approaches large numbers. Spatial modulation (SM) and Generalized Spatial Modulation (GSM) are new novel techniques proposed as a low-complexity, low cost and low-power-consumption MIMO candidate with the ability to further reduce the RF chain for mmWave massive MIMO hybrid beamforming systems. In this work, we present the principles of generalized spatial modulation aided hybrid beamforming (GSMA-HBF) and its use for cost-effective, high energy efficient mmWave massive MIMO transceiver for small cell wireless backhaul in a 5G ultra-dense network
Development of Hyperspectral ImagingTechnique for Salmonella Enteritidis and Typhimurium on Agar Plates

Abstract. Salmonella is a common cause of foodborne disease resulting from the consumption of contaminated food products. Although a direct plating method is widely used for presumptive positive screening of pathogenic Salmonella colonies on agar plates, it is labor-intensive, time-consuming, and also prone to human errors. This article reports the development of a hyperspectral imaging technique for automated screening of the two common serotypes of Salmonella, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), grown on agar plates and for differentiating them from background microflora often found in poultry carcass rinses. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora on brilliant green sulfa (BGS) and/or xylose lysine tergitol 4 (XLT4) agar plates. Five different machine-learning algorithms including Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) in addition to a multivariate data analysis method, the principal component analysis (PCA), were compared to determine the best classification algorithm in Salmonella detection and classification. When trained on the data from pure cultures of Salmonella and known background microflora, the classification accuracy of each classification algorithm in detecting Salmonella on BGS agar was about 98% on average, although it was difficult to differentiate between SE and ST. The classification accuracy in detecting Salmonella colonies on XLT4 agar was about 88% on average while the detection accuracy for ST colonies were over 99%. The validation of the classification algorithms with independent test samples of chicken carcass rinses spiked with SE and ST showed that the best performance was achieved by QDA with the prediction accuracy of about 99% (Kappa coefficient=0.97).
Generalizing Curricula for Reinforcement Learning

Curriculum learning for reinforcement learning (RL) is an active area of research that seeks to speed up training of RL agents on a target task by ﬁrst training them through a series of progressively more challenging source tasks. Each task in this sequence builds upon skills learned in previous tasks to gradually develop the repertoire needed to solve the ﬁnal task. Over the past few years, many automated methods to develop curricula have been developed. However, they all have one key limitation: the curriculum must be regenerated from scratch for each new agent or task encountered. In many cases, this generation process can be very expensive. However, there is structure that can be exploited between tasks and agents, such that knowledge gained developing a curriculum for one task can be reused to speed up creating a curriculum for a new task. In this paper, we present a method to generalize a curriculum learned for one set of tasks to a novel set of unseen tasks.