Miracle-dz/UniFAR-FTUs huggingface dataset:
An Examination of Innovation Capabilities and Corporate Reputation in Banking Sector

The aim of this study was to investigate the effect of innovation capabilities on corporate reputation in banking sector. A quantitative method was used for collecting the data and fulfilling the stated objective. Specifically, the data were collected from 188 employees in banking sector in the United Arab Emirates. The analysis for the collected data was conducted using the partial least square-structural equation modelling (PLS-SEM) approach. The findings indicated that product innovation as well as service innovation have significant positive impacts on corporate reputation. The results also confirmed that process innovation has a positive effect on corporate reputation. Finally, the outcomes verified that marketing innovation has a significant impact on corporate reputation. The results confirm the significance of innovation capabilities in enabling business practitioners in the banking sector to improve their businesses reputations and thrive in today’s dynamic market environment.
Electricity Consumers’ Satisfaction During COVID-19 Lockdown in Some Selected States in Nigeria

This work studies the satisfaction experienced by electricity customers in Osun, Lagos, and Ogun States during the recent lockdown occasioned by the COVID-19 pandemic. The measures of the satisfaction are based on the hours of electricity supply during the period, easiness experienced for payment of prepaid bill and swiftness in faults correction of the distribution companies involved. A questionnaire was developed using Google Forms. The form was shared through various online platforms. A total of 274 electricity consumers responded to the questionnaire, but after sorting, 259 consumers across the three states were analyzed. From the responses, majority of respondents claim there was increased hours of supply during the period when compared to before the period. For prepaid customers who had reasons to pay electricity bill, majority claim it was easy paying while most of those who claimed it was not easy was because of their payment method choices, which is using designated points. Majority of those who have reasons for faults to be corrected by the distribution companies claim that there was not promptness in fault correction.
Huffmanela sp. (Nematoda: Trichosomoididae: Huffmanelinae) Encountered in the Whitecheek Shark (Carcharhinus Dussumieri) in The Arabian Gulf

Summary Veterinary inspection of fish caught at the western side of the Arabian Gulf off the shore of Saudi Arabia in 2017 revealed abnormal irregular zigzag black lines in the skins of 135 out of 1500 (prevalence = 9 %) whitecheek sharks (Carcharhinus dussumieri). These lesions have been noticed in the dorsal and ventral part of the head near the snout and around the eyes, as well as in the skin of the pectoral fins. The parasitological, pathological and ultramorphological aspects of the sampled fish were studied using a light microscope, as well as scanning and transmission electron microscopes. Trichosomoidid-type eggs identified as Huffmanela sp. were found in the superficial layer of the skin. The mature eggs containing larvae (n = 75) were ellipsoidal or spindle-shaped, smooth-shelled, amber-brown to dark brown or even black in colour. The width of the eggs ranged from 34 to 49μm with lengths ranging from 95 to 107μm. The polar plugs of the immature eggs were slightly protruding, whereas mature eggs had clearly protruding polar plugs. The width of the polar plugs ranged from 4.5 – 9. 5μm. The shells of the eggs were smooth and there was a thin layer seen near the polar areas. The infected skin exhibited desquamated epithelium, with inflammatory cell aggregations and melanophores in the debris. The underlying musculatures of the skin were free from any eggs, but showed mild pathological changes, including congestion of blood vessels, haemorrhages and oedema. The prevalence of infection showed seasonality, peaking significantly in the spring (18.18 %), with comparable prevalence rates in winter and summer (9.43 % and 8.57 %, respectively). However, infections dropped significantly during the autumn months (3.75 %). It is believed that this is the first study reporting the presence of Huffmanela sp. in whitecheek sharks at El-Jubail Province, Saudi Arabia, Arabian Gulf.
Microbiological and biochemical findings in relation to clinical periodontal status in active smokers, non-smokers and passive smokers

INTRODUCTION Cigarette users are more susceptible than non-smokers to periodontitis, a bacterial-induced, inflammation-driven, destructive disease of the supporting tissues of the teeth. We hypothesized that clinical periodontal findings and microbiological and/or inflammatory marker levels would be intermediate in those exposed to environmental tobacco smoke compared to active smokers and non-smokers. METHODS Sixty individuals were recruited from a University periodontal clinic and assigned as non-smokers, active smokers or passive-smokers according to their self reports. Clinical periodontal measurements, comprising plaque index, probing depth (PD), clinical attachment level (CAL) and bleeding on probing, were recorded at six sites per tooth. Cotinine levels were determined in whole saliva samples by EIA. Treponema denticola and Porphyromonas gingivalis infection was determined by PCR, while matrix metalloproteinase-8 (MMP-8) and interleukin-8 (IL-8) concentrations were determined by ELISA. RESULTS Study groups were subsequently reassigned in accordance with the cotinine data. The smoker group exhibited higher mean PD and CAL values compared to the non-smoker group (p<0.05). Passive-smokers exhibited PD and CAL values smaller than those of the active smokers and greater than those of the non-smokers, but the differences were not statistically significant. PD and CAL values correlated with cotinine concentrations (p<0.05). P. gingivalis infection was noted in most subjects, irrespective of smoking status. T. denticola infection was noted in 4/23 (17.4%) smokers, 0/16 (0%) environmentally-exposed recruits and 2/21 (9.5%) non-smokers. Salivary MMP-8 and IL-8 levels were lower in smokers compared to both non-smokers and passive-smokers but the differences were not significant (all p>0.05). CONCLUSIONS The present clinical periodontal findings provide further support for a negative, dose-related effect of tobacco exposure on periodontal health. The tendency for a more prevalent detection of T. denticola and for a suppressed inflammatory response observed in the smokers may partly explain the increased susceptibility to periodontal tissue destruction, but needs to be verified in larger scale studies.
Scan, Test, Execute: Adversarial Tactics in Amplification DDoS Attacks

Amplification attacks generate an enormous flood of unwanted traffic towards a victim and are generated with the help of open, unsecured services, to which an adversary sends spoofed service requests that trigger large answer volumes to a victim. However, the actual execution of the packet flood is only one of the activities necessary for a successful attack. Adversaries need, for example, to develop attack tools, select open services to abuse, test them, and adapt the attacks if necessary, each of which can be implemented in myriad ways. Thus, to understand the entire ecosystem and how adversaries work, we need to look at the entire chain of activities. This paper analyzes adversarial techniques, tactics, and procedures (TTPs) based on 549 honeypots deployed in 5 clouds that were rallied to participate in 13,479 attacks. Using a traffic shaping approach to prevent meaningful participation in DDoS activities while allowing short bursts of adversarial testing, we find that adversaries actively test for plausibility, packet loss, and amplification benefits of these servers, and show evidence of a 'memory' of previously exploited servers among attackers. In practice, we demonstrate that even for commonplace amplification attacks, adversaries exhibit differences in how they work.
Cert-RNN: Towards Certifying the Robustness of Recurrent Neural Networks

Certifiable robustness, the functionality of verifying whether the given region surrounding a data point admits any adversarial example, provides guaranteed security for neural networks deployed in adversarial environments. A plethora of work has been proposed to certify the robustness of feed-forward networks, e.g., FCNs and CNNs. Yet, most existing methods cannot be directly applied to recurrent neural networks (RNNs), due to their sequential inputs and unique operations. In this paper, we present Cert-RNN, a general framework for certifying the robustness of RNNs. Specifically, through detailed analysis for the intrinsic property of the unique function in different ranges, we exhaustively discuss different cases for the exact formula of bounding planes, based on which we design several precise and efficient abstract transformers for the unique calculations in RNNs. Cert-RNN significantly outperforms the state-of-the-art methods (e.g., POPQORN) in terms of (i) effectiveness -- it provides much tighter robustness bounds, and (ii) efficiency -- it scales to much more complex models. Through extensive evaluation, we validate Cert-RNN's superior performance across various network architectures (e.g., vanilla RNN and LSTM) and applications (e.g., image classification, sentiment analysis, toxic comment detection, and malicious URL detection). For instance, for the RNN-2-32 model on the MNIST sequence dataset, the robustness bound certified by Cert-RNN is on average 1.86 times larger than that by POPQORN. Besides certifying the robustness of given RNNs, Cert-RNN also enables a range of practical applications including evaluating the provable effectiveness for various defenses (i.e., the defense with a larger robustness region is considered to be more robust), improving the robustness of RNNs (i.e., incorporating Cert-RNN with verified robust training) and identifying sensitive words (i.e., the word with the smallest certified robustness bound is considered to be the most sensitive word in a sentence), which helps build more robust and interpretable deep learning systems. We will open-source Cert-RNN for facilitating the DNN security research.
Locating the Security Patches for Disclosed OSS Vulnerabilities with Vulnerability-Commit Correlation Ranking

Security patches play an important role in defending against the security threats brought by the increasing OSS vulnerabilities. However, the collection of security patches still remains a challenging problem. Existing works mainly adopt a matching-based design that uses auxiliary information in CVE/NVD to reduce the search scope of patch commits. However, our preliminary study shows that these approaches can only cover a small part of disclosed OSS vulnerabilities (about 12%-53%) even with manual assistance. To facilitate the collection of OSS security patches, this paper proposes a ranking-based approach, named PatchScout, which ranks the code commits in the OSS code repository based on their correlations to a given vulnerability. By exploiting the broad correlations between a vulnerability and code commits, patch commits are expected to be put to front positions in the ranked results. Compared with existing works, our approach could help to locate more security patches and meet a balance between the patch coverage and the manual efforts involved. We evaluate PatchScout with 685 OSS CVEs and the results show that it helps to locate 92.70% patches with acceptable manual workload. To further demonstrate the utility of PatchScout, we perform a study on 5 popular OSS projects and 225 CVEs to understand the patch deployment practice across branches, and we obtain many new findings.
Demo: Detecting Third-Party Library Problems with Combined Program Analysis

Third-party libraries ease the software development process and thus have become an integral part of modern software engineering. Unfortunately, they are not usually vetted by human developers and thus are often responsible for introducing bugs, vulnerabilities, or attacks to programs that will eventually reach end-users. In this demonstration, we present a combined static and dynamic program analysis for inferring and enforcing third-party library permissions in server-side JavaScript. This analysis is centered around a RWX permission system across library boundaries. We demonstrate that our tools can detect zero-day vulnerabilities injected into popular libraries and often missed by state-of-the-art tools such as snyk test and npm audit.
Let's Downgrade Let's Encrypt

Following the recent off-path attacks against PKI, Let's Encrypt deployed in 2020 domain validation from multiple vantage points to ensure security even against the stronger on-path MitM adversaries. The idea behind such distributed domain validation is that even if the adversary can hijack traffic of some vantage points, it will not be able to intercept traffic of all the vantage points to all the nameservers in a domain. In this work we show that two central design issues of the distributed domain validation of Let's Encrypt make it vulnerable to downgrade attacks: (1) the vantage points are selected from a small fixed set of vantage points, and (2) the way the vantage points select the nameservers in target domains can be manipulated by a remote adversary. We develop off-path methodologies, based on these observations, to launch downgrade attacks against Let's Encrypt. The downgrade attacks reduce the validation with 'multiple vantage points to multiple nameservers', to validation with 'multiple vantage points to a single attacker-selected nameserver'. Through experimental evaluations with Let's Encrypt and the 1M-Let's Encrypt-certified domains, we find that our off-path attacker can successfully launch downgrade attacks against more than 24.53% of the domains, rendering Let's Encrypt to use a single nameserver for validation with them. We then develop an automated off-path attack against the 'single-server'-domain validation for these 24.53% domains, to obtain fraudulent certificates for more than 107K domains, which constitute 10% of the 1M domains in our dataset. We also evaluate our attacks against other major CAs and compare the security and efforts needed to launch the attacks, to those needed to launch the attacks against Let's Encrypt. We provide recommendations for mitigations against our attacks.
Prime+Scope: Overcoming the Observer Effect for High-Precision Cache Contention Attacks

Modern processors expose software to information leakage through shared microarchitectural state. One of the most severe leakage channels is cache contention, exploited by attacks referred to as PRIME+PROBE, which can infer fine-grained memory access patterns while placing only limited assumptions on attacker capabilities. In this work, we strengthen the cache contention channel with a near-optimal time resolution. We propose PRIME+SCOPE, a cross-core cache contention attack that performs back-to-back cache contention measurements that access only a single cache line. It offers a time resolution of around 70 cycles (25ns), while maintaining the wide applicability of PRIME+PROBE. To enable such a rapid measurement, we rely on the deterministic nature of modern replacement policies and their (non-)interaction across cache levels. We provide a methodology to, essentially, prepare multiple cache levels simultaneously, and apply it to Intel processors with both inclusive and non-inclusive cache hierarchies. We characterize the resolution of PRIME+SCOPE, and confirm it with a cross-core covert channel (capacity up to 3.5 Mbps, no shared memory) and an improved attack on AES T-tables. Finally, we use the properties underlying PRIME+SCOPE to bootstrap the construction of the eviction sets needed for the attack. The resulting routine outperforms state-of-the-art techniques by two orders of magnitude. Ultimately, our work shows that interference through cache contention can provide richer temporal precision than state-of-the-art attacks that directly interact with monitored memory addresses.
CapSpeaker: Injecting Voices to Microphones via Capacitors

Voice assistants can be manipulated by various malicious voice commands, yet existing attacks require a nearby speaker to play the attack commands. In this paper, we show that even when no speakers are available, we can play malicious commands by utilizing the capacitors inside electronic devices, i.e., we convert capacitors into speakers and call it CapSpeaker. Essentially, capacitors can emit acoustic noises due to the inverse piezoelectric effect, i.e., varying the voltage across a capacitor can make it vibrate and thus emit acoustic noises. Forcing capacitors to play malicious voice commands is challenging because (1) the frequency responses of capacitors as speakers have poor performance in the range of audible voices, and (2) we have no direct control over the voltage across capacitors to manipulate their emitting sounds. To overcome the challenges, we use a PWM-based modulation scheme to embed the malicious audio onto a high-frequency carrier, e.g., above 20 kHz, and we create malware that can induce the right voltage across the capacitors such that CapSpeaker plays the chosen malicious commands. We conducted extensive experiments with 2 LED lamps (a modified one and a commercial one) and 5 victim devices (iPhone 4s, iPad mini 5, Huawei Nova 5i, etc.). Evaluation results demonstrate that CapSpeaker is feasible at a distance up to 10.5 cm, triggering a smartphone to receive voice commands, e.g., "open the door''.
A new set of design aids for groups of four cylindrical silos due to interstice and internal loadings

The computation of design forces for reinforced concrete groups of four cylindrical silos requires significant computational effort due to continuity in the walls between adjacent silos. This paper presents a simple but accurate procedure for the computation of design forces acting on groups of four cylindrical silos due to stored materials. In this conjunction, finite element analyses were performed by using eight‐node solid elements for various groups of four cylindrical silos under interstice and internal loadings. The design forces at the prescribed sections of interstice and external walls were computed. The silo wall thicknesses, intersection wall thicknesses and intersection wall lengths of these grouped silos were varied with an extensive parametric study to demonstrate their influences on resultant design forces. Design formulas were proposed and dimensionless design coefficients were derived from the comprehensive series of finite element analyses considering the effect of continuity in the walls. The results were expressed in simplified form, so that the prediction of bending moments, axial forces and shear forces became straightforward for design purposes. In addition, the rigidity of the interstice walls was varied by changing the modulus of elasticity and the redistribution of design forces was investigated. Copyright © 2007 John Wiley & Sons, Ltd.
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection

Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt uniform sampling to select grasp candidates. However, we discover that ignoring where to grasp greatly harms the speed and accuracy of current grasp pose detection methods. In this paper, we propose "graspness", a quality based on geometry cues that distinguishes graspable area in cluttered scenes. A look-ahead searching method is proposed for measuring the graspness and statistical results justify the rationality of our method. To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process. Extensive experiments verify the stability, generality and effectiveness of our graspness model, allowing it to be used as a plug-and-play module for different methods. A large improvement in accuracy is witnessed for various previous methods after equipping our graspness model. Moreover, we develop GSNet, an end-to-end network that incorporate our graspness model for early filtering of low quality predictions. Experiments on a large scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin (30 + AP) and achieves a high inference speed.
Improved Healthcare Access using Big Data Analytics through Hadoop and Spark

: One of the greatest concerns in both developed and developing countries like India, is access to the primary health care information and maintenance of EHR (Electronic Health Records) and EMR (Electronic Medical Records). Though, the people living in cities have better access to high end health services, the millions of people residing in rural parts of the country are facing serious problems in accessing healthcare. Also the healthcare organizations have become data rich but information poor. Now-a-days, large amounts of heterogeneous medical data have become very common in most of the healthcare organizations. Further, it has become mandatory for the healthcare organizations to use Big Data analytics such as non-linear multivariate predictive analytic models to understand the better diagnoses, origin of diseases, health care fraud detection, provide personal health care and deliver high quality care with low cost to the patients by combing the emerging technologies like Hadoop Framework, Map Reduce and Spark for better data analytics. This paper addresses the difficulty in accessing primary health care, EMR & EHR, big data and its use cases in healthcare sector, also discusses how spark overcomes Hadoop for better analytics.
An Analytical Study of The Determinants of Corporate Dividend Policy

Dividend is the actual reward given to the shareholder after a long wait of commitment of the funds in an organization. The policy which decides whether this reward should be given to the shareholder or not depends upon various factors. The main objective of the paper is to discuss the determinants of dividend policy covering the time-period of 1975 to 2017. The findings of the study reveals that profitability position of the concern has a positive impact on the flow of dividend followed by company size, leverage position, company size etc. and the factors like government Policy, competitors strategies, growth plans etc. have a negative effect on the flow of dividends.
Biological Activity and Composition of the Essential Oil of Dracocephalum Moldavica L. Grown in Iran

The biological activity and composition of the essential oil of the aerial flowering parts of D. moldavica have been investigated. The oil analysis was performed by GC and GC-MS. Thirteen compounds were identified, representing 99.8% of the oil. Oxygenated monoterpenes (96.3%) were found to be the principal group of compounds, of which neral, geranial, geranyl acetate and geraniol with 32.1, 21.6, 19.9 and 17.6% of the total oil were the main constituents, respectively. The in vitro antimicrobial activity of the essential oil and its main components, citral, geraniol and geranyl acetate, showed that all of the tested microorganisms were highly inhibited by the essential oil with inhibition zones ranged from 15 – 41 mm for bacteria and 29 – 30 mm for fungi. The most sensitive microorganisms were Bacillus subtilis, Staphylococcus aureus and S. epidermidis with the lowest MIC values of 0.9 mg/mL. Considering sensitivity screening, it is conceivable that the activity of the oil from D. moldavica could be attributed mainly to the presence of citral.