Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonance Thinking in Cartesian Systemic Emergence

Cartesian Systemic Emergence (CSE) is concerned with strategic aspects relative to the conception of Symbiotic Recursive Pulsative Systems intended to solve real-world problems handling control and prevention in incomplete domains. This work is performed in order to prepare fundamentals for designing automated tools that help to perform this complex task. This paper presents the most important features of one particular way of thinking present in CSE. We call it ‘Resonance Thinking’. Resonance Thinking takes care of generating and handling experiments during CSE. We explain that Resonance Thinking causes the complexity of CSE to be analogous to Ackermann’s function computation complexity. The work presented is related to cognitive and computation models of human creative reasoning mechanisms. Our approach brings forward several challenging questions to Cognitive Science that will be given in the paper.
Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach

In today’s interconnected era, intrusion detection system (IDS) has the potential to be the frontier of defense against cyberattacks and plays an essential role in achieving security of networking resources and infrastructures. The performance of IDS depends highly on data features. Selecting the most informative features eliminating the redundant and irrelevant features from network traffic data for IDS is still an open research issue. The key impetus of this paper is to identify and benchmark the potential set of features that can characterize network traffic for intrusion detection. In this correspondence, an ensemble approach is proposed. As a first step, the approach applies four different feature evaluation measures, such as correlation, consistency, information, and distance, to select the more crucial features for intrusion detection. Second, it applies the subset combination strategy to merge the output of the four measures and achieve the potential feature set. Along with this, a new framework that adopts the data analytic lifecycle practices is explored to employ the proposed ensemble for building an effective IDS. The effectiveness of the proposed approach is demonstrated by conducting several experiments on four intrusion detection evaluation datasets, namely KDDCup’99, NSL-KDD, UNSW-NB15, and CICIDS2017. The obtained results prove that the proposed approach contributes more potential features compared to the state-of-the-art approaches, leading to achieve a promising performance gain in the detection rate of 3.2%, the false alarm rate of 38%, and the detection time of 12%. Furthermore, ROC and statistical significance are analyzed for the identified feature subset to strongly conform its acceptability as a future benchmark for building an effective IDS.
A Framework to Fuse Subject-Specific Musculoskeletal Models into Knee Finite Element Analyses

Anatomically-accurate finite element models of the knee can be generated using medical imaging data. However, the time to segment and generate an adequate mesh of the geometry limits the adoption of such models in a clinical setting. Finite element models also require loads and boundary conditions, which typically come from rigid body musculoskeletal models. The geometry of these models is typically ‘scaled’ to match some anthropometrical measurements, and hence, there is a ‘disconnect’ in the anatomy across these two model domains. The purpose of this work is to bridge this gap by developing an integrated workflow to generate subject-specific lower limb models comprised of femur, patella, and tibial surface meshes; cartilage thicknesses fields; and knee ligamentous attachment zones. Medical imaging and motion capture data from twenty-three participants were used to describe the lower extremity anatomical landmarks. The MR images of the knee joints for all subjects were manually segmented to extract bone-cartilage boundaries and ligamentous attachment sites. With the aid of lower limb statistical shape models, marker data and segmented surfaces were fused to create a consistent model that best-fit all the experimental data available. Whole-bone surface meshes were predicted from marker locations, while the portions of the meshes local to the knee were fitted to the segmented data. Subject-specific femur, tibia-fibula and patella surface meshes were locally fitted to their corresponding segmentations with mean errors of 1.32±0.30, 1.59±0.33 and 1.23±0.51 mm, respectively. Cartilage thickness fields for patellofemoral and tibiofemoral joints were calculated from the
Investigating the Determinant and Impact of Digital Library Engagement: A Conceptual Framework

: Facing an unprecedented explosion and growth of Information and Communication Technology (ICT) signal the overwhelming dependency on the digital library (DL). Over the years, libraries spent a great amount of investment to subscribe to various digital resources such as online databases, electronic journals, electronic books and other digital resources. However, previous studies reported that the use of DL is underutilized; hence undermining its original purposes and making it harder for the libraries to justify their investment on DL resources. It is reported that users prefer to use free online resources compared to the DL resources. The lack of DL usage is credited to the lack of DL engagement and the impact of contextual factors leading to DL engagement. Yet, not much is known about the contributing factors of DL engagement as well as its impacts. Therefore, the purpose of this paper is twofold: (1) to introduce the concept of Digital Library Engagement, and (2) to discuss at the conceptual level on the determinants and impacts of digital library engagement and development of a conceptual framework. In order to develop the conceptual framework, the study adopted a systematic literature review approach of over 100 papers. Grounded in previous literature on DL, the contributions of this paper are as follows: first, the paper provides insight on the new concept of digital library engagement; second, the paper proposes a con-Mohamad
Temporal Decision-making: Common Procedures and Contemporary Approaches

Various procedures have been developed to investigate interval timing and time-based choice behavior. These methods are typically first developed and validated in non-human animals and then adapted for human testing. Several researchers in particular have contributed to the adaptation of prominent procedures to humans and, thus, to our cross-species understanding of interval timing behavior (e.g., Allan & Gibbon 1991; Rakitin et al. 1998; Wearden 2002). Each of these procedures has advantages and disadvantages in terms of their sensitivity to measuring psychological variables. Thus, the choice of the most suitable procedure for a given research question entails a deep understanding of the procedure and its analysis. In this chapter, we describe three different interval timing procedures: the peak interval task, the switch task, and the differential reinforcement of low rates of responding (drl) task. Our aim is to provide a wide range of data acquisition and analysis tools to our readers. The peak interval task is a temporal differentiation task, the switch task is a real-time temporal discrimination task, and the drl task is a response inhibition task. The peak interval procedure is used most widely, whereas the switch and drl tasks are used relatively less often. However, together, these three tasks add complementary results about human timing ability. Furthermore, performance on the switch and drl tasks (and, maybe, the peak interval task) can be evaluated with respect to objective optimal functions to investigate the temporal risk assessment (for review see Balcı et al. 2011). We will describe each procedure separately. We will first provide background information regarding the use of each procedure, followed by the description of the analysis. Programming code written in Python to collect and analyze the data accompany this chapter. These applications include line by line descriptions and tutorials for readers who are interested in following each step of the experimental procedure through data processing. The Python code can be found
New Results on Non-Normalized Floating-Point Formats

Compulsory normalization of the represented numbers is a key requirement of the floating-point standard. This requirement contributes to fundamental characteristics of the standard, such as taking the most of the precision available, reproducibility and facilitation of comparison and other operations. However, it also imposes a high restriction in effectiveness of basic arithmetic operation implementation. In many embedded applications may be worth to sacrifice the benefits of normalization for gaining in implementation metrics. This paper analyzes and measures the effect of removing the normalization requirement in terms of precision and implementation savings for embedded applications. We propose several adder and multiplier architectures to deal with non-normalized floating-point numbers, and quantify the accuracy loss and the improvements in hardware implementation. Our experiments show that it is possible to reduce the area and power consumption up to 78 percent in ASIC and 50 percent in FPGA implementations with a reasonable accuracy loss.
Detecting SQL Injection Attacks based on Text Analysis

SQL injection attacks have been a major security threat to web applications for many years. Detection of SQL injection attacks has been a great challenge to researchers due to its diversity and complexity. In this paper, we present a novel approach to detect SQL injection attacks based on text analysis. We utilize query tokenization to express information of each SQL query, then we use Skip-gram model in Word2vec to generate word embedding expressing eigenvectors for each query, and finally we train an SVM classifier with eigenvectors to identify malicious queries. Experimental results confirm the effectiveness of our approach to all types of SQL injection attacks, especially tautological attacks, with good accuracy and negligible performance overhead. The approach does not require access to the source code, moreover, it can be easily implemented on other platforms with minimal changes.
Random Pattern Multiplexing for Random Access in IoT-Oriented Satellite Networks

In this paper, random pattern multiplexing (RPM) for random access (RA) is proposed to improve the throughput in Internet of Things-oriented satellite networks. The pattern is defined as the mapping scheme of packet onto a resource block (RB) that consists of multiple resource elements (REs). The patterns are randomly selected by active satellite machine-type-communication terminals (SMTs) from a set of available patterns, and are employed to differentiate signals of different SMTs sharing a RB. Message passing detection algorithm is employed to decode multiple superimposed packets, which enables multiple packet reception capability in the RA scheme. The proposed RPM can be combined with contention resolution diversity slotted ALOHA (CRDSA), which generates RPM–CRDSA protocol. In this protocol, the interference cancellation technique can be carried out across RBs to remove the received signals that are already decoded, which further improves the throughput. The performance of RPM–CRDSA is evaluated via mathematical analysis and computer simulation. The probability of preamble collision and an upper bound of throughput are derived, respectively. Simulation results show that the peak normalized throughput of RPM–CRDSA with $N = 4$ REs can reach 2.03 b/symbol, which largely outperforms traditional protocols.
Segment Routing Based Traffic Scheduling for the Software-Defined Airborne Backbone Network

With the ubiquitous use of networking technologies in the military domain, network-centric warfare, which aims at improving the system of systems (SoS) capability, is a no-way-back trend of military development. Recently, it has become increasingly important to construct an airborne backbone network (ABN) to maintain coverage and provide reach-back to military units because the terrestrial networks cannot be flexibly deployed and have poor survivability. Given the advantages of software-defined networking (SDN), a software-defined airborne backbone network architecture (SD-ABN) is proposed for the ABN to ensure network flexibility, openness, interoperability, and evolvability. To meet the challenges of traffic management in SD-ABN, segment routing (SR) is applied with some practical modifications for the SD-ABN. Moreover, a network traffic scheduling algorithm termed MRP-TS is designed based on SD-ABN to improve the transmission reliability and bandwidth utilization by balancing network traffic to multiple reliable transmission paths. Relevant theories have been proposed and proved to illustrate the correctness of the algorithm. The network simulation experiments have been conducted using the network simulator EXata 5.1, and the simulation results validate the effectiveness of our proposals.
PASS: Reducing Redundant Notifications between a Smartphone and a Smartwatch for Energy Saving

Smartwatches have gained significant popularity in recent years. One major use of smartwatches is notification checking as an extended display. Smartwatch use provides an opportunity for energy saving because it affords a reduction in the frequency of smartphone use. However, sometimes user input is limited in smartwatches due to its small screen size and users are forced to use their smartphones to fully read notifications. In such cases, the advantage of an extended display in the smartwatch diminishes because users need to access their smartphones, which causes extra smartphone battery consumption. We define phone-preferable notification that requires a user to take further actions, such as checking detailed content and replying to a message. Given that phone-preferable notifications are likely to be handled on smartphones, it is possible to defer notification delivery to smartwatches. In this paper, we develop a novel notification manager, called PASS that automatically defers phone-preferable notifications and piggybacks them on watch-preferable notifications. For model building and evaluation, we collect 15,659 notifications in-the-wild from 11 users for approximately 31 days. In addition, for approximately two months we gather self-reported data from five users regarding which devices were used to respond to notifications. The results show that PASS can save daily battery for smartwatches and daily battery for smartphones up to 43.5 and 0.9 percent without introducing any noticeable negative results on user experiences.
Black Box Analytics and Ethical Decision Making

Using an experiment with participants having management experience, we examine sales target setting decisions using an analytics-based forecasting system in a situation involving an ethical dilemma. Specifically, participants have private information that the forecast significantly underestimates likely sales, making the suggested target easily achievable. We explore the extent to which participants act unethically by not adjusting the sales target upwards. We employ a 2x2 between-subjects design, manipulating forecasting system transparency (opaque vs transparent) and accountability both as a measured continuous variable and with the use of a prompt either before (pre-prompt) or after (post-prompt) the adjustment decision. We find that participants make less ethical decisions when the system is opaque and more ethical decisions when they feel greater accountability. The effect of accountability is greatest when the system is opaque. We also examine reasons provided for less ethical decisions and find that the least ethical participants use more rationalizations than those whose decisions are not as unethical. Our results suggest that organizations should endeavor to make data analytics systems transparent to decision making users. However, when they cannot, they should ensure that decision makers feel accountable for their decisions; for example, with a prompt or decision aid.
Performance Analysis and Optimization of Caching Placement in Heterogeneous Wireless Networks

Caching at the network edge is regarded as a promising approach to handle content-dominated wireless traffic congestion problem and improve the quality of experience at mobile users (MUs). In this letter, we consider a heterogeneous wireless network (HetNet) where caches are not only deployed at base stations (BSs) but also at MUs. To study the caching optimization in such an HetNet, we first derive the analytical results of successful transmission probability (STP) using stochastic geometry. Then, we propose a subgradient descent algorithm (SGDA) to optimize the caching placement with an aim to maximize the STP. Simulations indicate that a significant performance gain can be obtained by our proposed solution when compared with existing caching placements.
A Round Earth Loss Model and Small-Scale Channel Properties for Open-Sea Radio Propagation

In this paper, a path-loss model for the open-sea environment is proposed, in which different propagation phenomena including effective reflection, shadowing, divergence, and diffraction, related to the sea surface and earth curvature in the open-sea environments, are taken into account. The channel model is parameterized and validated by experimental results from our measurement campaign at 2 GHz over a distance range of 45 km in calm, cold Norwegian ocean waters. Model and measurements show excellent agreement in terms of the root mean square error. By evaluating the channel model parameters like mean-square surface slope and standard deviation of surface height related to the sea surface roughness, it can be concluded that the effects of shadowing and scattering on the reflected rays will influence the fading amplitude within the distance range where the Line-Of-Sight (LOS) is tangential to the surface of the earth. It is also found that the diffraction loss starts to influence the path-loss results beyond the distance of 0.6 × the first Fresnel zone clearance. The amplitude probability density function (PDF) of fading is studied as well. By using the Akaike information criterion for model selection, it is found that the amplitude PDF can be modeled as Weibull distribution at short distances and very large distances. The two-wave with diffuse power distribution, Rician distribution, and Rayleigh distribution dominate at distances between 9 and 45 km. The correlation coefficient between the signal amplitudes at two antennas that are vertically separated by 3 m was studied, and found to be close to zero when the transmitter is below the horizon. This indicates the potential to employ multi-antenna techniques for maritime communication systems.
Developing Blue Ocean Strategy Theory Based on Spiral Dynamics System Spectrum Model in Education Management

History of Arts and Culture Science Management owned by an Islamic civilization in Pondok Pesantren Modern in Gontor, it is important to do research based on perspective of life view. This is the background of this research because every scientific activity in a society will shape their perspective on the world. This view of life is built on these scientific elements which then construct the Arts Science and Cultures of that society in the perspective of Islamic Educators. It is necessary for humans to have a good Science, Art and Culture one of them is in Islamic education. Also, due not yet actualized science paradigm science, Faith and Saleh deeds and the foundation, a strong foundation for every Muslim at the institution of education breath of Islam to merwuiudkan vision and mission together. The success of the Gontor PPM santri in the spectacular Extracurricular Law in educational management (Arts, Sciences and Islamic Arts) at the previous research stage, is a very strong foundation, able to integrate all the components of education management Science, Charity, and Faith can synergize with education management Arts and culture as an Exschool activity, with a continuous and uninterrupted balance like Spiral Dynamics.
A Survey of Point-of-Interest Recommendation in Location-Based Social Networks

With the rapid development of mobile devices, global position system (GPS) and Web 2.0 technologies, location-based social networks (LBSNs) have attracted millions of users to share rich information, such as experiences and tips. Point-of-Interest (POI) recommender system plays an important role in LBSNs since it can help users explore attractive locations as well as help social network service providers design location-aware advertisements for Point-of-Interest. In this paper, we present a brief survey over the task of Point-of-Interest recommendation in LBSNs and discuss some research directions for Point-of-Interest recommendation. We first describe the unique characteristics of Point-of-Interest recommendation, which distinguish Point-of-Interest recommendation approaches from traditional recommendation approaches. Then, according to what type of additional information are integrated with check-in data by POI recommendation algorithms, we classify POI recommendation algorithms into four categories: pure check-in data based POI recommendation approaches, geographical influence enhanced POI recommendation approaches, social influence enhanced POI recommendation approaches and temporal influence enhanced POI recommendation approaches. Finally, we discuss future research directions for Point-of-Interest recommendation.
Backpropagation artificial neural network versus empirical models for estimating daily global radiation in Sinaloa, Mexico

1Campo Experimental Valle de México-INIFAP. Carretera Los Reyes-Texcoco, Coatlinchán, km 13.5, C. P. 56230, A. P. 307 y 10, Texcoco, Estado de México, México. Tel: 01 800 088 2222, Ext. 85565. (rcervanteso@hotmail.com). 2Universidad Autónoma Chapingo, Departamento de Irrigación, Sección Meteorología agrícola, km 38.5, Carretera México-Texcoco, C. P. 56230, Estado de México, México. Tel: 01 (595) 95 21500. Ext. 5157. (mvazquezp@correo.chapingo.mx). 3Instituto Mexicano de Tecnología del Agua, Paseo Cuauhnáhuac 8532, Colonia Progreso C. P. 62550, Jiutepec, Morelos, México. Tel: 01 (777) 3293 600. Ext: 445. (wojeda@tlaloc.imta.mx). §Autor para correspondencia: rarteagar@taurus.chapingo. mx.
An 8 Bit 12.4 TOPS/W Phase-Domain MAC Circuit for Energy-Constrained Deep Learning Accelerators

A small-gate-count 8 bit bidirectional phase-domain MAC (PMAC) circuit is proposed to minimize both area and energy consumption of extremely energy-efficient deep neural network (DNN) accelerators, targeting the Internet-of-Things (IoT) edge devices operating with very strict power budgets (e.g., energy harvesting). PMAC consumes significantly less energy than standard fully digital MACs, due to its efficient analog accumulation nature based on gated-ring oscillator (GRO). The architectural analysis of energy-efficient accelerators is performed, and the energy budget analysis is disclosed. Furthermore, theoretical analysis of PMAC is conducted and comparisons with the conventional analog approaches are shown. By exploiting the DNN digital quantization noise, we further improve the PMAC energy efficiency by designing the internal gain. The bidirectional architecture proposed in this paper achieves an area comparable to those of digital MACs and up to a fivefold improvement in power efficiency. Moreover, the system design constraints are relaxed by eliminating the phase error originating in leakage currents. An asynchronous readout technique and a two-step digital-to-time converter (DTC) to enhance system throughput and compact implementation, respectively, are presented for the first time. We also present DNN hardware-software co-training procedures to show further scaling of the PMAC efficiency. Utilizing such techniques, the measured PMAC achieves peak efficiency of 12.4 TOPS/W in 28 nm CMOS.