Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Machine Learning Algorithms for Predicting Traffic Accident Severity

Traffic accidents are among the most critical issues facing the world as they cause many deaths, injuries, and fatalities as well as economic losses every year. Accurate models to predict the traffic accident severity is a critical task for transportation systems. This investigation effort establishes models to select a set of influential factors and to build up a model for classifying the severity of injuries. These models are formulated by various machine learning techniques. Supervised machine learning algorithms, such as AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Random Forests (RF) are implemented on traffic accident data. SMOTE algorithm is used to handle data imbalance. The findings of this study indicate that the RF model can be a promising tool for predicting the injury severity of traffic accidents. RF algorithm has shown better performance with 75.5% accuracy than LR with 74.5%, NB with 73.1%, and AdaBoost with 74.5% accuracy.
Expression of miR-122 mediated by adenoviral vector induces apoptosis and cell cycle arrest of cancer cells.

Background: microRNA-122 (miR-122) plays an important role in both of hepatic physiology and pathology. Downregulation of miR-122 was reported in human primary hepatocellular carcinoma (HCC) and restoration of miR-122 could suppress the growth of cancer cells. In this study, we presented a novel strategy for cancer therapy based on gene transfer of miR-122 by adenoviral vector. Methods: We generated a recombinant adenoviral vector expressing miR-122 (Ad-miR122). The miR-122 expression was measured by quantitative Real-Time PCR (qRT-PCR). Cell survival rate was determined by MTT assay. Results: Our data showed that Ad-miR122 could express functional miR-122 in tumor cells at a high level. Infection of tumor cells with Ad-miR122 resulted in inhibition of growth of cancer cells originating from liver (HepG2, Hep3B, Huh7 and PLC/PRF/5), lung (NCI-H460) and uterine cervix (HeLa). This antitumor activity was related to the induction of apoptosis and/or cell cycle arrest in cancer cells. Infection with Ad-miR122 resulted in decreased expression of Bcl-W and CCNG1 in cancer cells. Conclusion: The antitumor activity of Ad-miR122 was probably due to the induction of apoptosis and/or cell cycle arrest in cancer cells through inhibiting Bcl-W and/or CCNG1 expression. We concluded that expression of therapeutic microRNA, such as miR-122, via adenoviral vector is a promising strategy for cancer treatment.
The Broadcast Storm Problem in FANETs and the Dynamic Neighborhood-Based Algorithm as a Countermeasure

Flying ad hoc networks (FANETs) are a new paradigm that can overcome mission constraints of single unmanned aerial vehicles (UAVs). FANETs are composed of several UAVs that cooperate to accomplish some critical mission (i.e., hazardous area monitoring). In order to maintain UAVs coordination, all UAVs must continuously retransmit or relay messages through the wireless channel to assure that every UAV knows the FANET condition. However, when this message exchange is done arbitrarily, it may cause the broadcast storm problem, leading the wireless medium to an inoperable state. Despite some efforts reported in the literature for providing general broadcast storm problem mitigation techniques, the challenge of aggregating new information/knowledge to receivers, instead of just spreading the information in the network, has received less attention. In this paper, we not only prove that broadcast storm problem causes network contention as the number of UAVs increases but also propose the innovative dynamic neighborhood-based algorithm for the broadcast storm problem (DNA-BSP), which was developed and validated based on the outdoor experiments and computer simulations. It can mitigate the broadcast storm problem, which is a real challenge in FANETs, reducing message redundancy in more than 98%, and making message delivery more than 95% faster than in flooding scenario, outperforming classical broadcast storm mitigation techniques when applied in FANETs. Our detailed results can also guide future researches and provide useful insights for engineers planning and optimizing mission-critical mobile ad hoc network with the support of UAVs.
Improving Flash Memory Performance and Reliability for Smartphones With I/O Deduplication

Flash-based storage subsystem is the key component that affects the system performance, reliability, and cost efficiency of Android-based smartphones. In this paper, we first introduce a trace collection tool specifically designed to capture the I/O requests with important content features in Android-based smartphones, which are critically important but rarely available in content-aware designs and optimizations, such as JProbe and Netlink. Based on the analysis of the traces collected from 15 popular mobile applications, we find that 20%–40% of the I/O requests on the I/O critical path of the storage stack are redundant and this data redundancy is minimally shared among different applications. Based on this key observation, we propose a content-aware optimization, called APP-Dedupe, that applies data deduplication on the I/O critical path to improve both performance and efficiency by reducing write amplification and improving GC efficiency of the flash storage on Android smartphones. The evaluation results show that APP-Dedupe reduces the GC overhead by an average of 41.5%, reduces the response times by up to 15.4% and reduces the amount of write data by an average of 45.2%.
Topological Re-Organisation of the Brain Connectivity During Olfactory Adaptation - an EEG Functional Connectome Study

Perception of olfactory stimuli involves complex brain processing which can be directly associated with cognition and emotion. Neural structures embedded deep within the brain and several cortical entities collaborate for the processing of this unique sensory modality. In this study, we investigate the dynamic changes in the neural responses associated with prolonged and repeated exposure to pleasant odor stimuli. Graph metrics computed from EEG functional connectivity like clustering coefficient (p = 0.0008), characteristic path length (p = 0.014) and local efficiency (p = 0.0005) were seen to undergo statistically significant changes, indicating inhibition in the global and local information processing that can be attributed to olfactory adaptation. Moreover, dominant but diminishing activity was observed in the left cerebral hemisphere, signifying recruitment of various neuronal ensembles associated with complex cognitive processes for perception of pleasant odor stimuli.
Towards an Inpainting Framework for Visual Cultural Heritage

Cultural heritage takes an important part in defining the identity and the history of a civilization or a nation. Valuing and preserving this heritage is thus a top priority for governments and heritage institutions. Through this paper, we present an image completion (inpainting) approach adapted for the curation and the completion of damaged artwork. Our approach uses a set of machine learning techniques such as Generative Adversarial Networks which are among the most powerful generative models that can be trained to generate realistic data samples. As we are focusing mostly on visual cultural heritage, the pipeline of our framework has many optimizations such as the use of clustering to optimize the training of the generative part to ensure a better performance across a variety of cultural data categories. The experimental results of our framework are promising and were validated on a dataset of paintings.
The Acceptance of using Enterprise Resource Planning (ERP) System in Higher Education: A Case Study of Jordanian Universities

Enterprise resource planning (ERP) systems become very significant in managing business processes. However, higher education institution (HEI) should think more about implementing ERP systems to manage their processes and produce high quality output. Certainly, many research on ERP adoption have revealed that the organizations often faced many barriers and the failure rate is very high. In addition, different research projects conclude that, oftentimes, universities do not gain the expected benefits from adoption the ERP system. The HEI in Jordan should understand the factors that affect the acceptance of ERP in order to gain the expect benefits of ERP. Hence, it is significant to determine the factors affect the adoption of ERP in HEI in Jordan, that is, whether the universities is ready for implementing it. Therefore, the main purpose of this study is to determine the factors that affect the acceptance of using ERP by Jordanian universities, by evaluating through the questionnaire survey, in order find the most appropriate and prepared universities that willing to adopt ERP system in Jordan.
Finite Element Analysis of Anterior Lumbar Interbody Fusion: Threaded Cylindrical Cage and Pedicle Screw Fixation

Study Design. Pedicle screw fixation (PSF) has been used for the setting of a lumbar interbody fusion. Objective. To investigate analytically the effects of additional PSF to the anterior lumbar interbody fusion (ALIF) with the cylindrical cages (Ray TFC) in the immediate postoperative state. Summary of Background Data. An addition of PSF to the stand-alone ALIF increases segmental stiffness. Methods. A finite element model of ALIF (L4,–L5) was constructed. Finite element analyses were performed to investigate relative motion and bone stress at the bone-cage interface. The results were then compared with those of the stand-alone ALIF. Bone stress surrounding pedicle screws was also predicted. Results. An addition of PSF to the stand-alone ALIF led to higher segmental stiffness, smaller relative motion, smaller bone deformation, and lower bone stress level at the cage-bone interface. The bone stress level at the interface reduced substantially during extension. The bone stress level at the bone-screw tip interface was much lower than that of the bone-cage interface. The articulating facet had no significant load-bearing capacity because of stiff PSF. Little gap opening at the bone-cage interface was found during a compressive preload over 400 N; an excessive extension without preload caused considerable gap opening. Slip less than 0.15 mm occurred during all the loading modes except axial rotation; an excessive axial rotation caused to slip over 0.15 mm. Conclusion. Geometric constraints caused by PSF will reduce substantially the bone stress level and the relative motion, and therefore be more likely to allow bone ingrowth at the bone-cage interface, compared with the stand-alone ALIF. The use of pedicle screw stabilization would have significant beneficial effects on the rate of interbody fusion, regardless of whether ALIF or PLIF was used.
A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector

In the telecom sector, a huge volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining the existing ones. Business analysts and customer relationship management (CRM) analyzers need to know the reasons for churn customers, as well as, behavior patterns from the existing churn customers’ data. This paper proposes a churn prediction model that uses classification, as well as, clustering techniques to identify the churn customers and provides the factors behind the churning of customers in the telecom sector. Feature selection is performed by using information gain and correlation attribute ranking filter. The proposed model first classifies churn customers data using classification algorithms, in which the Random Forest (RF) algorithm performed well with 88.63% correctly classified instances. Creating effective retention policies is an essential task of the CRM to prevent churners. After classification, the proposed model segments the churning customer’s data by categorizing the churn customers in groups using cosine similarity to provide group-based retention offers. This paper also identified churn factors that are essential in determining the root causes of churn. By knowing the significant churn factors from customers’ data, CRM can improve productivity, recommend relevant promotions to the group of likely churn customers based on similar behavior patterns, and excessively improve marketing campaigns of the company. The proposed churn prediction model is evaluated using metrics, such as accuracy, precision, recall, f-measure, and receiving operating characteristics (ROC) area. The results reveal that our proposed churn prediction model produced better churn classification using the RF algorithm and customer profiling using k-means clustering. Furthermore, it also provides factors behind the churning of churn customers through the rules generated by using the attribute-selected classifier algorithm.
Manifold Regularization Graph Structure Auto-Encoder to Detect Loop Closure for Visual SLAM

Loop closure detection plays a vital role in the visual simultaneous localization and mapping (SLAM) systems. In order to overcome the shortcomings of the artificial design algorithm to extract insufficient features, this paper proposes a graph-regularization stacked denoising auto-encoder (G-SDAE) network that achieves high detection accuracy and improve reliability. This method is based on the SDAE and the manifold learning graph regularization structure. The G-SDAE preserves the local abstract geometry structure between features through spatial mapping in manifold learning, and the G-SDAE network can automatically extract abstract features, avoiding relying on empirical design algorithms to extract low-quality visual features. Compared with the bag-of-words (BoW) method, the OpenFABMAP algorithm, and the traditional SDAE method, extensive experiments show that the proposed algorithm achieves superior performances and provides a feasible solution for the loop closure detection part of the visual SLAM.
Double Pedicle Screw Instrumentation in the Osteoporotic Spine: A Biomechanical Feasibility Study

Study Design A biomechanical feasibility test. Objective To assess the overall feasibility, safety, and mechanical effectiveness of an intrapedicular double-screw construct in the thoracolumbar spine. Summary of Background Data The bony purchase of the pedicle screw fixation is often not strong enough in elderly patients with osteoporosis. Our hypothesis was that the elliptical cross-section of the pedicle would allow the insertion of 2 smaller diameter pedicle screws resulting in a bony purchase superior to the standard single-screw technique. Methods Thirty-six double-screw constructs (5 mm diameter AOUSS and 5 mm Schanz screw) and 36 standard single pedicle screws (6 mm diameter AOUSS screw) were placed. Screw pullout, multiaxial flexibility, and axial failure load testing was performed. Results Visual inspection, palpation, and radiograph confirmed that there were no pedicle breaches. In the double-screw group, all but 2 constructs had ideal direction. Pullout strength of the double-screw construct was no different than that of the single-screw construct. However, stiffness increased considerably in all testing modes. Axial load to failure, adjusted for bone mineral density, and dimensional variation, also increased. All differences were statistically significant except for axial rotation that was only marginally significant. Conclusions The double-screw construct appears feasible and safe in the thoracolumbar spine. In this study, the new technique demonstrates a mechanical advantage over the standard single-screw technique. Further in vitro cadaveric safety studies with better adapted instrumentation are needed before the technique can be widely recommended.
Information Mining From Criminal Judgments of Lahore High Court

Since the last few years, computers have become a prominent part of the court of law. Courts generate an enormous amount of unstructured text on a daily basis. Extraction of the desired information from this unstructured legal text is one of the major issues. So, there is a need to develop an intelligent system that can automatically find useful and critical information from the available text. Such a system will help judges and lawyers in their judgments and case preparations, common people in understanding law, and finding appropriate lawyer for their legal issues. Therefore, in this research, Punjab University Legal Mining System (PULMS) is developed using three different supervised machine learning algorithms; conditional random field (CRF), maximum entropy (MaxEnt), and trigram N tag (TNT). To train the system, 304 criminal miscellaneous judgments of the Lahore High Court (LHC) of Pakistan are manually tagged for nine named entities (NE). After training, among three machine learning algorithms, the system achieved significant precision, recall, and f-measure using CRF which are 0.97, 0.87, and 0.89, respectively.
Fluid–structure interaction simulation of an avian flight model

SUMMARY A three-dimensional numerical avian model was developed to investigate the unsteady and turbulent aerodynamic performance of flapping wings for varying wingbeat frequencies and flow velocities (up to 12 Hz and 9 m s–1), corresponding to a reduced frequency range of k=0.22 to k=1.0 and a Reynolds number range of Re=16,000 to Re=50,000. The wings of the bird-inspired model consist of an elastic membrane. Simplifying the complicated locomotion kinematics to a sinusoidal wing rotation about two axes, the main features of dynamic avian flight were approximated. Numerical simulation techniques of fluid–structure interaction (FSI) providing a fully resolved flow field were applied to calculate the aerodynamic performance of the flapping elastic wings with the Reynolds averaged Navier–Stokes (RANS) approach. The results were used to characterize and describe the macroscopic flow configurations in terms of starting, stopping, trailing and bound vortices. For high reduced frequencies up to k=0.67 it was shown that the wake does not consist of individual vortex rings known as the discrete vortex ring gait. Rather, the wake is dominated by a chain of elliptical vortex rings on each wing. The structures are interlocked at the starting and stopping vortices, which are shed in pairs at the reversal points of the wingbeat cycle. For decreasing reduced frequency, the results indicate a transition to a continuous vortex gait. The upstroke becomes more aerodynamically active, leading to a consistent circulation of the bound vortex on the wing and a continuous spanwise shedding of small scale vortices. The formation of the vortices shed spanwise in pairs at the reversal points is reduced and the wake is dominated by the tip and root vortices, which form long drawn-out vortex structures.
Rhythmical Index of Ictal High Frequency Oscillations in Stereo-Electroencephalograph from Epileptic Patients

High-frequency oscillations (HFOs) are considered reliable markers of epileptic tissues. Digital filter is widely used to extract the HFOs, but it also introduces fake oscillations because of Gibbs effects. In this paper we propose a pipeline for HFOs extraction using ensemble empirical mode decomposition (EEMD) method. Stereo-Electroencephalograph (SEEG) is decomposed into intrinsic mode functions (IMFs). Rhythmical index (RI) of detected HFOs is calculated, which corresponds to the normalized angular acceleration of oscillation. The ictal SEEG is processed with nine seizures recorded from three patients, who underwent surgical intervention and got seizure free. Results show that proposed pipeline can extract HFOs from ictal SEEG automatically. RI values of HFOs in resected area are higher than non-resected area. The proposed pipeline with RI measure may serve as an effective way for extracting pathological HFOs.
gem5-FIM: a flexible and scalable multicore soft error assessment framework to early reliability design space explorations

Increasing chip power densities allied to the continuous technology shrink are making emerging multiprocessor embedded systems more vulnerable to radiation-induced transient faults (i.e., soft errors). Due the high cost and design time inherent to board-based fault injection approaches, more appropriate and efficient simulation-based fault injection frameworks become crucial to guarantee the adequate design exploration support at the early design phases. Virtual platforms emerge as a solution to early reliability explorations as it offers a flexible modeling environment, acceptable simulation speed, and different accuracy levels. This work introduces a fast and flexible fault injector framework (gem5-FIM) developed using a state-of-the-art cycleaccurate virtual platform (i.e., gem5 simulator), which accelerates the analysis of complex and large-scale systems composed of commercial processors under different classes of fault campaigns. Further, the gem5-FIM supports multicore ARM processor models running sophisticated software stacks comprising a Linux OS and parallelization libraries (e.g., MPI, OpenMP). Authors also propose different techniques to boost up the fault injection process.
Sparse Wave Packets Discriminate Motor Tasks in EEG–based BCIs

We propose a novel non–linear source separation technique for single–channel, multi–trial Electroencephalogram (EEG). First, a generative model is posited as the generating process behind bandpassed traces. In particular, the inputs are conceived as the state variable of a switching mechanism that samples temporal snippets from two distributions corresponding to a background component and a phasic event or wave packet counterpart. In order to non–linearly separate the sources, we propose a neurophysiologically principled, non– linear mapping to a space of ℓ2–norms via the Embedding Transform. In this way, the estimated phasic event component— an ideal time series where neuromodulations are emphasized— is isolated for further processing. The algorithm is tested on the Brain–Computer Interface (BCI) Competition 4 dataset 2a. The results not only surpass classic power–based measures, but also highlight the discriminative nature of scale–specific wave packets in motor imagery tasks. The inherent switching mechanism that generates the traces suggests a transient, temporally sparse feature of the neuromodulations that can be further exploited in applications where compression is advantageous.
Popularity-Based Content Cache Management for in-Network Caching

In-network caching is a technique that stores contents in caches of routers, and thus reduces the loads for content servers and networks. To further enhance the performance of in-network caching, this paper proposes a popularity-based content cache management method. The proposed method divides the cached contents into some groups based on their popularity and manages information on content placement of each group in a distributed manner with a distributed hash table mechanism. The proposed method enables users to download contents stored in near routers with low overheads. This paper shows that the proposed method enhances the cache utilization efficiently through simulation experiments.