Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a performance-grade bitumen specification in South Africa

STEPH J BREDENHANN (Pr Eng, Pr CPM) matriculated at Upington High School in 1972, graduated in 1977 from Stellenbosch University and completed his Master’s degree in 2000. He started his career at the Sishen Iron Ore Mine in the industrial engineering field, and later joined the Kimberley City Council before he moved to a consulting engineering firm, Entech Consultants, in Stellenbosch, where he was a director. He became a regional director of WSP Consulting Engineers, then joined Goba Consulting Engineers as technical director before he joined SANRAL as the Western Region Project Manager for Research. He specialises in pavement and materials engineering. He is a Fellow of the South African Institution of Civil Engineering, as well as of the Society for Asphalt Technology.
Adverse effects of benzodiazepine use in elderly people: a meta-analysis

Background. Benzodiazepines are commonly prescribed to elderly people for anxiety or insomnia despite adverse effects. This metaanalysis focused on the risk of adverse effects of benzodiazepine use in elderly people. Study selection. Two reviewers independently searched MEDLINE, EMBASE, and the Cochrane Database of Systemic Reviews for Englishlanguage publications from 1950 to June 2010, using the keywords ‘benzodiazepines’, ‘elderly’ and ‘adverse effects’. Retrieved studies were screened according to the inclusion criteria of: (1) mean age older than 65 years, (2) use of benzodiazepines for anxiety or insomnia, (3) randomised controlled trial design, and (4) adverse events in the treatment and placebo groups, including accidental injuries/falls, confusion, dizziness, somnolence, headache, depression, hypertension, and hangover. Seven studies met all the inclusion criteria and had the appropriate data for analysis. Three of the 7 studies contained data suitable as sub-studies and were entered as separate entities. Data extraction. Data were extracted and synthesised into pooled odd ratios, relative risk, and number needed to harm. Heterogeneity of studies was assessed by the Q-statistic and I 2 index. Data synthesis. Of a total of 894 participants, 411 took benzodiazepines and 483 took placebo. 96 (23%) and 46 (10%) of the participants reported adverse effects after taking benzodiazepines and placebo, respectively. The pooled odds ratio was 3.07 (p<0.0001) and relative risk was 2.45. The absolute risk reduction of taking placebo over benzodiazepines was 14%. The number needed to harm was 7. Heterogeneity of studies is deemed acceptable with a Q-statistic of 5.76 (p=0.76) and I 2 index of 0%. Conclusion. Use of benzodiazepines lead to various adverse effects in elderly people. These drugs should be prescribed with caution, and the patients should be monitored closely.
Inventory Process Cost Reduced With RFID, Simulation and Open Source Technologies

— One ofthe possibilities for companies to seek cost reductionis through the improvement of their processes. RFID (Radio Frequency Identification) can make this possible for its traceability of company assets, as well as the development of IoT projects, which are characterized by the generation of large amounts of unstructured data. RFID is widespread worldwide and research on the subject has grown in Brazil. However, the implantation of this technology, in Brazilian companies, faces its associated costs. Based on those arguments, this paper presents the development of a system based onRFID technology and open source tools (NodeJs, Python and MongoDB), as well as simulating RFID antennas in a virtual environment (Rifidi). Indeed, it is possible to reduce the costs on the development of the system, since it was not necessary to purchase any equipment or software licensing.
Transformation of Blockchain as Social Good

Blockchain is currently considered as a key technology that can significantly reduce the level of transaction costs in the economy, reduce the level of economic and financial risks, and increase the efficiency of economy and financial sector. In this regard, it is relevant to study the possible trajectories of blockchain transformation under conditions when the state has not yet developed a universal model for blockchain integration into the economic system. The blockchain research method as a good is represented by economic sociodynamics, which studies the typology of goods and their transitions under the influence of interests that society presents to a particular product or service. The models of dynamics of social groups of M. Olsen and R. Putnam are also used. The endowment of blockchain with the features of a social good by the state shall not be accompanied by the deprivation of blockchain of the properties of a private good that can satisfy private, not just public interests. This will create conditions for increasing the share of Putnam's groups and reducing the share of Olsen's groups, which will increase the level of trust in society and stimulate the improvement of macroeconomic indicators, such as economic growth and increase in the GDP investment share.
Chapter 1: Message Design for Instructional Designers - An Introduction

Instructional message design is the use of learning theories to effectively communicate information using technology. Theories involving gestalt, cognitive load, multimedia learning, media selection, media attributes, and general communication systems help us guide design. Our communication designs can be based on a wide variety of technologies or a combination of technologies. Technology in the form of tools and techniques includes the study and the use of typography, color, illustrations, photographs, modeled graphics, augmented reality, animation, video, video games, simulations, and virtual reality. This introduction serves as a brief overview of these theories, tools, and techniques while subsequent chapters will dive much deeper into practical applications in instructional design. Key Points: • Instructional message design is the application of theory and techniques to communicate information to learners. • Cognitive load and multimedia design theory can be used to help design our instructional message. • Visual communications can include static art (illustrations, diagrams, photographs) or dynamic art (animation, video, virtual reality, video games and simulations) with or without accompanying audio.
The Power of Composition: Abstracting a Multi-Device SDN Data Path Through a Single API

Software Defined Networking aims to separate network control and data plane by moving the control logic from network elements into a logically-centralized controller. Using a well-defined, unified control-channel protocol, such as OpenFlow, the controller is able to configure the forwarding behavior of data plane devices. Here, the OpenFlow protocol is translated to vendor- and device-specific instructions that, for instance, manipulate the flow table entries of a switch. In practice, SDN-enabled switches often feature different hardware capabilities and configurations with respect to the number of flow tables, their implementation, and which kind of data plane features they support. This leads to device heterogeneity within the SDN landscape, thereby obstructing the increased scalability and flexibility promised by the SDN paradigm. To overcome this challenge we propose TableVisor, a transparent proxy-layer for the SDN control channel that enables the flexible abstraction of heterogeneous data plane devices into a single emulated data plane switch. In this paper, we extend our previous work by introducing features to integrate modern P4 devices into an existing SDN environment and perform a detailed performance evaluation to quantify the overhead induced by our approach.
Context-Aware TDD Configuration and Resource Allocation for Mobile Edge Computing

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.
Clicker Interventions at University Lectures and the Feedback Gap

The article presents a mixed methods study on clicker interventions conducted in collaboration with four philosophy teachers at fourteen university lectures. The aim was to examine how feedback from the interventions were received and used by teachers and students. The data material comprises a quasi-experiment based on 6,772 student responses, student logs, a student survey and semi-structured interviews with the teachers. Findings show that students experience feedback that supports their self-monitoring and understanding of the content, and that the peer discussions enhanced student performance. The teachers also experienced an increased awareness of the students’ understanding of the topics. Yet, the findings indicate a gap between the reception and use of the feedback.
Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

The Internet of Things (IoT) increases the number of connected devices and supports the ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance the computational capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels, and imperfect estimation of channel state information (CSI). In this article, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter $\beta $ without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.
Impact of the modulus switching technique on some attacks against learning problems

The modulus switching technique has been used in some cryptographic applications as well as in cryptanalysis. For cryptanalysis against the learning with errors (LWE) problem and the learning with rounding (LWR) problem, it seems that one does not know whether the technique is really useful or not. This work supplies a complete view of the impact of this technique on the decoding attack, the dual attack and the primal attack against both LWE and LWR. For each attack, the authors give the optimal formula for the switching modulus. The formulas get involved the number of LWE/LWR samples, which differs from the known formula in the literature. They also attain the corresponding sufficient conditions saying when one should utilise the technique. Surprisingly, restricted to the LWE/LWR problem that the secret vector is much shorter than the error vector, they also show that performing the modulus switching before using the so-called rescaling technique in the dual attack and the primal attack make these attacks worse than only exploiting the rescaling technique as reported by Bai and Galbraith at the Australasian conference on information security and privacy (ACISP) 2014 conference. As an application, they theoretically assess the influence of the modulus switching on the LWE/LWR-based second round NIST PQC submissions.
Research on Transmission Lines Early Warning Technology Based on Deep Learning

At the present stage, high-voltage transmission lines are distributed in long distance chains, with large space span, complicated meteorological and geographical environment. The operating environment of the transmission lines is poor. Hence, manual patrol and maintenance are difficult. Various faults are very likely to affect the safe and stable operation of the power grid system. Therefore, this paper proposes a transmission lines early warning method based on Deep Learning. Through object detection technology of Deep Learning, intrusion objects in the monitoring screen are automatically identified, at same time, their positions and types are marked. The experimental results show that this method has high accuracy and is suitable for the current power grid monitoring system.
Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects

Recently research in opinions summarization focuses on rating expressions by aspects and/or sentiments they carry. To extract aspects of an expression, most studies require a predefined list of aspects or at least the number of aspects. Instead of extracting aspects, we rate expressions by aspect similarity recognition (ASR), which evaluates whether two expressions share at least one aspect. This subtask relaxes the limitation of predefining aspects and makes our opinions summarization applicable in domain adaptation. For the ASR subtask, we propose an attention-cell LSTM model, which integrates attention signals into the LSTM gates. According to the experimental results, the attention-cell LSTM works efficiently for learning latent aspects between two sentences in both settings of in-domain and cross-domain. In addition, the proposed extractive summarization method using ASR shows significant improvements over baselines on the Opinosis corpus.
User adoption of physician's replies in an online health community: An empirical study

Online health question‐and‐answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual‐process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.
Parasite Analyser using Artificial Neural Network

Crop destruction causes so much damage to the crops or agricultural products which causes reduction in the productivity. There are numerous important crops are at risk. Parasites are controlled environmentally by unfriendly pesticides. Once a pest has reached either an economic threshold, or intolerable level action should be taken. Pesticides are used as a control measure while other strategies will not bring the parasite population under the threshold. So, here we use early parasite analyser to detect the parasite in a plant and its location. We use artificial network method to analyse the parasite. ANN technique is used to detect the pest in the field. This method is used to resolve the problem of classification identification, authentication, diagnostics, optimization and approximation.
An Assessment of the Visual Features Extractions for the Audio-Visual Speech Recognition

Utilization of the visual data from the speakers mouth region has appeared to develop presentation of the Automatic Speech-Recognition ASR frameworks. This is the particularly valuable in nearness of the clamor, which uniform in the moderate structure seriously debases discourse acknowledgment execution of frameworks utilizing just sound data. Different arrangements of highlights separated from speakers mouth area have been utilized to improve the showing of an ASR framework. In such testing situations and have met various triumphs, and to the best of creators information, the impact of utilizing these methods on the acknowledgment execution based on the phonemes have not been examined at this point. This paper presents examination of phoneme acknowledgement execution utilising visual highlights removed from mouth area of-enthusiasm utilising discrete cosine transform and discrete wavelet transform. Therefore, new discrete cosine transform and discrete wavelet transform feature have likewise been extricated and contrasted and the recently utilized one. These highlights were utilized alongside sound highlights dependent on the Mel-Frequency Cepstral Coefficients MFCCs. This recent research will help in the choosing appropriate feature for various application as well as distinguish the restrictions of these techniques in the acknowledgment of the individual-phonemes.
Hydro-turbine monitoring: from self-learned equipment behavior to a single global deviation indicator

In large machines, the possible failures are numerous and complex events. Furthermore, monitoring a multitude of measurement points leads to an accumulation of a large amount of data, making the task of tracking the equipment even more difficult. Often, there might even be an emerging problem that can influence several indicators simultaneously without exceeding the alert level of any one of them in particular. For end-users, the proposed monitoring system acts like a black box with an alert that comes on when the equipment's behaviour changes, much like the "check engine" of a car. The behavior of the equipment is tracked by a continuously updated statistical model and it is thus possible to compare future measurements with model predictions. Both the model and measurement uncertainties are taken into account by the procedure, and the confidence level can be determined by the user and is therefore a fixed quantity. In addition, the approach combines all the individual exceedances of each indicator in a single global deviation. When an alert is raised, the individual exceedances can be used in order to determine the source of the alert. Two different examples are presented where failures occurred on hydraulic turbine-generator units: the loss of a hydraulic turbine runner cone and a breakage of a thrust bearing.
An Iteration Restoration Technique Of An Image Sequence

This paper presents a new iterative Image Restoration based on the least-Square method. Based on adaptive this filtration algorithm is focused on transformation the given restoration model from the spatial domain to the Fourier domain. This implementation of derived models needs less storage capacity and far less time in execution. Also in this paper present ANew three Step Search for motion estimation technique (NTSS). In this paper two goal have been achieved, once obtained a restored image without adopted restoration technique on it (B-frame), also achieved a good rate of compression ratio by adopted motion estimation technique.
RELIABILITY AND VALIDITY OF A SIMPLE ENDURANCE TEST FOR THE ELDERLY ; SHUTTLE STAMINA WALK TEST (SSTw)

To develop a simple test of endurance capacity in the elderly, we evaluated the reliability and validity of the shuttle stamina walk test (SSTw), in which the running in the shuttle stamina test (SST : 3-minute shuttle running along a 10-meter course) was changed to walking. We found that: 1) The walking distance in the SSTw was correlated with •¬o2max (r=0.827) , 2) the walking distance showed a correlation between the first test and a re-test (r=0.853), 3) the mean peak heart rate during the test was 86.3% of the estimated maximum heart rate with no difference according to age or sex, 4) subjective evaluation of the intensity of exercise was expressed as•g fairly light•hor•gsomewhat hard•hby 73.7% of the subjects, 5) the results of the SSTw reflected the subjects' self-evaluation of endurance capacity, and 6) the age-related percentage reduction of the walking distance in the SSTw was similar to that in •¬o2max in subjects aged 40 years and above. These results suggest that the SSTw, a simple test of endurance capacity that can be performed safely over a wide range of middle-aged to elderly people including those in late old age and those with a low fitness level, has sufficient reliability and validity. (Jpn. J. Phys. Fitness Sports Med. 1998, 47:401•`410) key words : the elderly, endurance test, shuttle stamina walk test
Social-Aware Incentive Mechanisms for D2D Resource Sharing in IIoT

The industrial Internet of Things (IIoT), as one of the indispensable paradigms of the future network, challenges existing computing network architectures by supporting computational-intensive applications. In the IIoT, resource-rich industrial devices may share idle computing resource to lightweight nodes through device-to-device (D2D) technology, whereas such resource sharing is under social and locality constraints. When industrial devices are carried by human or installed on manned machines, resource sharing more likely occurs among social-trustworthy and locality-adjacent devices. In this article, we propose two social-aware incentive mechanisms for D2D resource sharing in the IIoT, namely one-hop-based social-aware incentive mechanism (OSIM) and relay-based social-aware incentive mechanism (RSIM). In the OSIM, resource-constrained devices bid for offloading tasks using a Vickrey–Clarke–Groves auction, while the RSIM relaxes the locality constraint to two hops to achieve a higher resource utilization ratio. Extensive simulation results show that the performance of the proposed mechanisms can significantly improve the system efficiency while maintaining truthfulness.
Data Security in Cloud Computing Using Elliptic Curve Cryptography

Encryption helps in transmitting sensitive data over an insecure channel without any danger of data being lost or being manipulated by some unauthorized entity. Different Encryption schemes have been applied for Data security in a different environment. Many cryptosystems worked during different eras and evolved accordingly with time. This paper mainly focuses on asymmetric encryption which is also known as Public key encryption scheme or Holomorphic encryption. However, due to large key size asymmetric encryption is mostly used for Key exchange rather than data Encryption. Nowadays, Data security is the main issue in large data centers and Cloud computing. This paper uses Elliptic Curve Cryptography to encrypt data in the cloud environment because the size of the key used in Elliptic Curve Cryptography is very small. Due to the small key size of Elliptic Curve, computational power is reduced and this results into least energy consumption. This paper shows that elliptic curve cryptography is fast and more efficient for data protection in a cloud computing environment and reduces the computational power and also increases the efficiency.