Miracle-dz/UniFAR-FTUs huggingface dataset:
ICT in Higher Education: An Exploration of Practices in Malaysian Universities

Information and communication technologies (ICTs) have been provided by higher education authorities to ensure that teachers use ICT to enhance their teaching, learning, and research outcomes. However, ICT has been found to be underutilized, especially by teachers. To investigate this issue, the technology adoption and gratification (TAG) model was validated and used to examine Malaysian university teachers’ adoption and gratification of ICT for teaching and research purposes and then used to investigate the moderating effect of universities in different regions. The 397 teachers from the west and east Malaysian universities were surveyed using a stratified random sampling technique. The research data were analyzed using the three-stage structural equation modeling. There were focused use factors: 1) academic purpose; 2) research purpose; and 3) teaching purpose. The TAG model was useful in explaining differences in ICT uses. West Malaysian teachers’ perceived usefulness and ease of use mediated the relationships between computer self-efficacy, gratification, and intention to use ICT, respectively. However, for east Malaysian teachers, only perceived ease of use did not mediate the relationship between computer self-efficacy and gratification. Finally, west and east Malaysian teachers’ intentions to use ICT mediated the relationships between actual use, perceived usefulness, and ease of use. This paper confirms the utility of the TAG model for comparing teachers’ adoption and gratification of ICT. In addition, this paper provides a basis for the targeted professional development of teachers in the east and west Malaysian universities which were selected based on the known differences.
Python Source Code De-anonymization Using Nested Bigrams

An important issue in cybersecurity is the insertion or modification of code by individuals other than the original authors of the code. This motivates research on authorship attribution of unknown source code. We have addressed the deficiencies of previously used feature extraction methods and propose a novel approach: Nested Bigrams. Such features are easy to extract and carry substantial information about the interconnections between the nodes of the abstract syntax tree. We also show that for large number of authors, a Strongly Regularized Feed-forward Neural Network outperforms the Random Forest Classifier used in many code stylometric studies. A new ranking system for reducing the number of features is also proposed, and experiments show that this approach can reduce the feature set to 98 nested bigrams while maintaining a classification accuracy above 90 percent.
Common Criterion of Privacy Metrics and Parameters Analysis Based on Error Probability for Randomized Response

Privacy security issues under the classic randomized response (RR) model proposed by Warner and its extended <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-RR model are studied. First, in order to provide references for the accuracy of the private distribution estimation problem under RR mechanism, the lower bounds of the differential privacy parameter and the number of participates are deduced for an accuracy objective of <inline-formula> <tex-math notation="LaTeX">$({\alpha }, {\delta })$ </tex-math></inline-formula>–<italic>accurate</italic> in statistics. Second, when the prior distribution is nonuniform, the data utility has a ceiling effect in the high privacy region by taking the prior into account. In this case, the average distortion, which is defined as the expected Hamming distance between the input and output data, is no longer feasible to measure the data utility. Motivated by this, the error probability is proposed as a measure of data utility for unifying different privacy metrics, where the error probability is defined to be the expected Hamming distance between the input and reconstructed data based on maximum <italic>a posteriori</italic> estimation. Third, under a unified privacy-preserving framework using RR mechanism based on error probability criterion, the relationship among differential privacy, identifiability privacy, and mutual information privacy is established. Given a maximum allowable error probability <inline-formula> <tex-math notation="LaTeX">$P_{E}^{\mathrm {max}}$ </tex-math></inline-formula>, the optimal privacy parameters of these three privacy notions are derived with the full consideration of the prior distribution. Then, a Bayes-based utility function, which corresponds to the converse of the Bayes risk, is constructed to measure the degree of privacy leakage. Given a maximum allowable correct probability <inline-formula> <tex-math notation="LaTeX">$P_{C}^{\mathrm {max}}$ </tex-math></inline-formula>, the accuracy objective of the statistical estimate is considered to derive the range of local differential privacy parameter from the perspective of security. Fourth, all the research results above are further extended to <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-RR mechanism too. Finally, the correctness and effectiveness are further verified by simulation experiments. The results reveal that the error probability can be applied to any prior distribution case, while the average distortion criterion is only a special case with uniform distribution. Therefore, the error probability proposed in the paper is more reasonable to be used as a common criterion to measure the data utility for the RR model so as to unify different privacy metrics.
Experimental Power and Performance Evaluation of CAESAR Hardware Finalists

In 2013 the Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR) was started. It aims at determining a portfolio of ciphers for authenticated encryption that has advantages over AES-GCM in terms of performance, security, and ease of implementation. This competition, for the first time, provides a standardized hardware API, which allows a fair comparison of hardware implementations. In this work, we extend a opensource evaluation platform using the CAESAR-API in a Xilinx Zynq-7000 System on Chip (SoC) with ARM processors and an AXI interface to support on-chip power measurement and dynamic frequency scaling. We then show the applicability of our measurement setup, giving results for the CAESAR finalists and finally pointing out some bugs in the latest official reference implementations.
Adaptive Distributed Consensus Control of One-Sided Lipschitz Nonlinear Multiagents

This paper addresses the leaderless consensus control for a network of multiagent systems with the one-sided Lipschitz and quadratic inner-boundedness nonlinear dynamics. A distributed dynamic consensus protocol as a function of relative state feedback of the neighboring agents with edge-based adaptive coupling weights is designed for an undirected communication graph to achieve asymptotic consensus among the nonlinear agents. Further, an extension of the proposed consensus protocol in the presence of external disturbance is devised to ensure the ${L_{2}}$ stability of the consensus error. This paper is less conservative and more general owing to its application to the systems with large Lipschitz constants and to a broad class of the nonlinear systems. In comparison to the previous consensus control schemes, the proposed approach can be employed to design consensus protocols for the one-sided Lipschitz systems with unknown communication topology. This feature enables the fully distributed consensus control as the communication graph information is not required for the consensus protocol design. Simulation results for a network of six mobile agents are provided to demonstrate the effectiveness of proposed consensus schemes in the absence or presence of an external disturbance.
Late Fusion of Multiple Convolutional Layers for Pedestrian Detection

We propose a system design for pedestrian detection by leveraging the power of multiple convolutional layers explicitly. We quantify the effect of different convolutional layers on the detection of pedestrians of varying scales and occlusion level. We show that earlier convolutional layers are better at handling small-scale and partially occluded pedestrians. We take cue from these conclusions and propose a pedestrian detection system design based on Faster-RCNN which leverages multiple convolutional layers by late fusion. In our design, we introduce height-awareness in the loss function to make the network emphasize on pedestrian heights which are misclassified during the training process. The proposed system design achieves a log-average miss-rate of 9.25% on the caltech-reasonable dataset. This is within 1.5% of the current state-of-art approach, while being a more compact system.
I-Am: Implicitly Authenticate Me—Person Authentication on Mobile Devices Through Ear Shape and Arm Gesture

Today, identity verification is required in many common activities, and it is arguably true that most people would like to be authenticated in the easiest and most transparent way, without having to remember a personal identification number. To this regard, this paper presents a multibiometric system based on the observation that the instinctive gesture of responding to a phone call can be used to capture two different biometrics, namely ear and arm gesture, which are complementary due to their, respectively, physical and behavioral nature. We conducted a comprehensive set of experiments aimed at assessing the contribution of each of the two biometrics as well as the advantage in their fusion to the system’s overall performance. Experiments also provide objective measurement of both saliency and correlation of data captured by each sensor involved (accelerometer, gyroscope, and camera) according to various features extraction, features matching, and data-fusion techniques. The reports provide evidences about the potential of the proposed system and method for user authentication “in-the-wild,” whilst its eventual usage for person identification is also investigated. All of the experiments have been carried out on a specifically built, publicly available ear–arm database, including multibiometric captures of more than 100 subjects performed during different sessions, that represents an additional contribution of this paper.
Effect of Propolis Preparations on Transepithelial Electrical Potential, Resistance, and Ion Transport in In Vitro Study

Background Propolis and its ethanol extract show positive germicidal, bacteriostatic, and anti-inflammatory antioxidants and regenerative properties after use on the surface of the skin. Propolis is in common use in production of cosmetics and in folk medicine. The influence of this resinous mixture on ion channels, channels located in skin cells membranes and skin electrical resistance, was not explained. Objective The main aim of the study was the evaluation of electrophysiological skin parameters during mechanical and chemical-mechanical stimulation after use of ethanol extract of propolis and propolis ointment in comparison with iso-osmotic Ringer solution. Methods Skin fragments were taken from white New Zealand rabbits and distributed into three experimental groups which were incubated in ethanol extract of propolis (EEP), propolis ointment, and Ringer solution. Then they were placed in a Ussing chamber to measure electrophysiological parameters values. Results In this study the influence of EEP on changes in value of electrical potential during block of chloride ions transport at the same time was observed. Ethanol propolis extract dissolved in water increases the transepidermal sodium ions transport in contrast to propolis ointment. Conclusion The way of preparation cosmetics, which contain propolis, has effects on transepidermal ions transport in the rabbit's skin. The value of skin electrical resistance is changing with penetration depth of active propolis substances contained in cosmetics.
Path-Length Compensation in Multiloop Retrodirective Cross-Eye Jamming

Multiloop retrodirective cross-eye jammers offer the possibility of both simplifying system implementation and improving system performance. However, the signals for each jammer loop propagate along different paths, leading to potentially detrimental effects on system performance. Static and dynamic compensation for path-length differences are introduced and analyzed. Of the two, static compensation is simpler but is only effective for limited engagement geometries. Dynamic compensation is more general but requires an accurate estimate of the engagement geometry.
MPT-GRE: A Novel Multipath Communication Technology for the Cloud

The Cognitive Infocommunications became a hot research area during the past years and it is becoming more and more prevalent. For this reason, telemedicine and the financial services industry are becoming more and more widespread, relying on cloud services. The underlying network infrastructures for the cloud are provided by data centers, which needs to be able to satisfy the new requirements set forth by these systems. The currently deployed data center networks usually have multipath data transfer connections to provide inter-server physical redundancy. Using traditional TCP, they cannot utilize the advantages offered by path aggregation, e.g. improved throughput performance. However, during data center operations both throughput-oriented longer dataflows and delay-sensitive applications can occur. In the case of the latter, even the most representative multipath solution, Multipath TCP (MPTCP), performs poorly, because of its operational architecture. In this paper we present a new L3 multipath communication technology that is capable of efficient data transfer both over TCP and UDP in a dual-path Gigabit Ethernet environment. The MPT network layer multipath communication library – which was proposed to be a possible new basis for the future Cognitive Infocommunications – is capable to use multiple communication channels to create an UDP tunnel which uses GRE tunnel protocol, improving the throughput performance of the cloud-based data centers.
Estimating Subseasonal Variability and Trends in Global Atmosphere Using Reanalysis Data

A new measure of subseasonal variability is introduced that provides a scale‐dependent estimation of vertically and meridionally integrated atmospheric variability in terms of the normal modes of linearized primitive equations. Applied to the ERA‐Interim data, the new measure shows that subseasonal variability decreases for larger zonal wave numbers. Most of variability is due to balanced (Rossby mode) dynamics but the portion associated with the inertio‐gravity (IG) modes increases as the scale reduces. Time series of globally integrated variability anomalies in ERA‐Interim show an increase in variability after year 2000. In recent years the anomalies have been about 2% above the 1981–2010 average. The relative increase in variability projecting on the IG modes is larger and more persistent than for the Rossby modes. Although the IG part is a small component of the subseasonal variability, it is an important effect likely reflecting the observed increase in the tropical precipitation variability.
Performance Evaluation of QUIC with BBR in Satellite Internet

Quick UDP Internet connection(QUIC) protocol and bottleneck bandwidth and round-trip propagation time (BBR) based congestion control algorithm are two major contributions out of Google’s persistent attempts to make the Internet faster. QUIC is an user level reliable protocol running on top of UDP to enable evolution of transport mechanisms, while BBR is designed for better TCP congestion control. Although in two different directions, they also converge with BBR support in QUIC. Many researches have proved the performance of QUIC and BBR in different network scenarios, but the evaluation in satellite Internet with long propagation delays and relatively high error rates is still missing.In this paper, we have a preliminary evaluation of the performance of QUIC with BBR via GEO satellite Internet access on dedicated network emulation testbeds. The results and our analysis confirms that for satellite Internet QUIC with the new BBR congestion control has improvements compared with the classic CUBIC.
Contactless Multiple Finger Segments based Identity Verification using Information Fusion from Higher Order Spectral Invariants

A methodology for identity verification from contactless finger images using ridge orientation profiles and bispectral invariant features is extended to use fusion at data, feature levels and combination of both levels using multiple finger segments. Performance is evaluated on 1341 images (selected 24 Megapixel video frames obtained with the finger to camera distance between 12 and 20cms, reduced to finger regions of about 1250 x 3000 pixels) from 41 individuals of different ethnicities. Features are extracted from profiles along key lines between landmarks that facilitate segmentation. The methodology is designed by means of the segmentation procedure, the invariant features and fusion techniques to be robust to geometric and photometric transformations and partial occlusion. Feature fusion, data fusion and a combination of the two are tested. The methodology yields around 5% EER with a combination of data and feature fusion providing the best performance. It can be applied to soft, on-the-move biometric systems.
Patients’ opinions on enhanced recovery after surgery perioperative care principles: a questionnaire study

Introduction The enhanced recovery after surgery (ERAS) protocol relies on patients’ compliance and their awareness of its principles. Patients’ views on ERAS implementation have never been evaluated in Poland before. Aim To analyse patients’ opinions about this protocol. Material and methods One hundred forty consecutive patients undergoing elective laparoscopic procedures in which ERAS was implemented were asked to join the study. Out of them, 120 fulfilled the trial criteria and were surveyed once before and twice after surgery. A 22-question survey about the patient’s perception of ERAS principles was presented upon admission and on the day of discharge. A telephone follow-up was performed 2 weeks after discharge. Results Patients reported the need for being counselled by the anaesthetist/surgeon as the most important element of the protocol. Items such as being free of pain, being free of gastrointestinal symptoms, and being free of catheter were rated more highly preoperatively than on the day of discharge, whereas telephone check-up call, being able to drink and eat early after surgery, avoiding bowel preparation, avoiding preoperative fasting and reducing postoperative intravenous fluids were rated more highly on the day of discharge. Pain was still present in over half of patients two weeks after discharge. Only 4.76% of patients admitted in the telephone survey that they were afraid of early discharge. Shortening of the length of hospital stay thanks to ERAS was widely appreciated among all patients who did not find it necessary to spend a longer time in the hospital. Conclusions Polish patients approve the ERAS protocol as modern perioperative care. Patients emphasize the need for preoperative counselling and painless recovery.
The nature of positive emotions via online language learning

Online courses make learning possible for wider audiences. Working or learning on a gamified interface enables deeper immersion into the task as well as better engagement to the task. How these learning environments affect the conscious use of our positive emotions needs further analysis, therefore, the aim of this study is to explore the connection between gamification and savoring, through two studies. Study 1 contained a 10-minute language practice, where participants learned a new language from either a wordlist, or via the online program Duolingo, and registered their emotions during the practice. Results showed a significantly larger number of positive emotions while completing the ten minutes on Duolingo. In Study 2 participants were asked to learn a new language of their choosing for ten consecutive days, while they completed a daily diary about their emotions and motivation to complete the task. Participants also completed tests about levels of intrinsic motivation for studying languages, self-efficacy and learning style. Results showed significant correlations between daily attitude towards the program and daily motivation to learn the chosen language. There was no difference between users with different preference in learning style regarding daily motivation and attitudes.
Region Role Detection in Autism Spectrum Disorder using Graph Theoretical Approaches

Connectome analysis has received increased attention in the field of neurological research. Graph theoretical measures are extensively applied to understand the intricate structure of the brain. In this work, resting state functional connectome of Autism Spectrum Disorder and Typically Developing brain are investigated to reveal the influential regions in the brain. Centrality measures are involved in the detection of global region role identification and they are compared against functional cartography. Then, the modular region role is determined from both individual functional connectome and group averaged connectome of both Autism Spectrum Disorder and Typically Developing subjects. The modular roles are compared using supervised association rule mining. The major alterations are identified mostly in visual and frontal regions of Autism Spectrum Disorder functional connectome.
Practice patterns and outcomes with the use of regorafenib in metastatic colorectal cancer: Results from the Regorafenib in Metastatic colorectal cancer - An Indian exploratory analysis study

Abstract Background: Regorafenib is considered a standard of care as third-line therapy in metastatic colorectal cancers (mCRCs). Materials and Methods: The study was based on a computerized clinical data form sent to oncologists across the country for entry of anonymized patient data. The data entry form was conceived and generated by the coordinating center's (Tata Memorial Hospital) gastrointestinal medical oncologists and disseminated through personal contacts at academic conferences as well as through E-mail to various oncologists across India. Results: A total of 19 physicians contributed data resulting in 80 patients receiving regorafenib who were available for the evaluation of practice patterns. The median age was 55 years (range: 24–75). Majority had received oxaliplatin-based (97.5%), irinotecan-based (87.5%), and targeted therapy (65%), previously. Patients were primarily started on reduced doses of regorafenib upfront (160 mg – 28.8%, 120 mg – 58.8%, and 80 mg – 12.5%). The median duration of treatment (treatment duration) with regorafenib was 3.1 months (range: 0.5–18), while the median progression free survival was 3.48 months (range: 2.6–4.3). Forty-five percent of patients required dose modifications due to toxicities, and the most common were (all grades) hand-foot syndrome (68.8%), fatigue (46.3%), mucositis (37.6%), and diarrhea (31.3%). Conclusions: Majority of physicians in this collaborative study from India used a lower dose of regorafenib at the outset in patients with mCRC. Despite a lower dose, there was a significant requirement for dose reduction. Duration of treatment with regorafenib as an efficacy end point in this study is similar to available data from other regions as it is the side effect profile.