Ad-hoc Nature Research Articles

Unplanned managerial work: crucial support for knowledge creation in manufacturing SMEs

Knowledge capability and innovation performance are essential drivers of organizational success, with managerial work playing a crucial role in fostering these factors. This paper examines planned and unplanned managerial work from a knowledge creation perspective, analyzing 21 short interviews and 21 days of shadowing seven leaders, alongside interactive analyses with management teams in four manufacturing SMEs. A four-step analysis of 2125 activities revealed four categories of managerial work, with unplanned activities accounting for 52% of work time, highlighting the complexity and ad-hoc nature of managerial work. Internally initiated unplanned managerial work was crucial for tacit knowledge conversion. If policymakers want to support lifelong learning initiatives and strengthen innovation capabilities in manufacturing SMEs, they need to encourage the unplanned aspect of managerial work, as it is crucial for knowledge creation, fostering the evolutionary aspect of organizational learning and enhancing innovation capabilities.

“The Citizen” as a Ghost Subject in Co-Producing Smart Sustainable Cities: An Intersectional Approach

The importance and benefits of engaging citizens as co-producers of urban transformation have been increasingly recognised. However, the mere implementation of citizen co-production does not guarantee more legitimate or inclusive policy decisions and outcomes, especially when power inequalities that shape local decision-making remain unaddressed. This article examines the transformative potential of citizen co-production in smart sustainable city initiatives using two successive citizen panels in Trondheim, Norway, as cases. The study aimed to understand the role of citizen co-production in these panels, and the notion of “the citizen” within their frameworks. Three challenges with co-production were identified. Firstly, the ad-hoc nature of citizen engagement emphasised individual participation rather than facilitating collective spaces from which political agency could emerge. Secondly, citizens’ viewpoints were perceived as uninformed preferences that could be transformed through professional guidance. This, coupled with the closed nature of the initiatives, raises questions about the transformative potential of the processes, particularly in challenging the underlying premises of citizen co-production shaped by a neoliberal discourse of smart sustainable cities. The article concludes with a call to analyse citizen co-production spaces through an intersectional lens that attends to relational understandings of power dynamics and identities. This analysis should not only consider who participates, but also how “the citizen” as a subject is conceptualised and mobilised, how citizens’ interests and knowledge are taken into account, and the political significance of their involvement.

Scheduling Molds Manufacturing Processes through Process Mining

The molds industry, being a highly competitive sector, is constantly driven by organizations looking to optimize their business processes to gain a competitive edge. To access advanced management tools, there has been a strong push for integrating Industry 4.0 technologies, which in turn has resulted in a significant increase in the amount of manufacturing process data, enabling more accurate and real-time analysis of processes. However, challenges persist due to the unpredictable and ad-hoc nature of this industry’s business processes. As a result, effective resource management has become a crucial aspect that organizations are actively addressing. In this study, our objective is to leverage process mining algorithms and complement them with a genetic algorithm to generate efficient scheduling solutions to produce mold parts on factory floors. By analyzing existing data and identifying the workflows through process mining, we seek to gain a more realistic perspective on the mold manufacturing business process. Through this analysis, our aim is to uncover meaningful workflows and sequences for producing mold parts, ensuring that critical activities are appropriately incorporated while minimizing unnecessary repetitions. By identifying these purposeful sequences, we can provide the factory floor with schedules that optimize resource usage and enhance the overall efficiency of mold manufacturing processes.

Blockchain-Based Secure Distributed File Storage for Community Cloud Computing

Cloud storage is the most used option for storing and sharing files. However, cloud storage has issues like lack of transparency, privacy, security, and trust. On the other hand, local file storage is always faster and more secure than public cloud storage. Besides, a community cloud is more economical and scalable than private storage. However, due to the ad-hoc nature of community users, developing such a community cloud storage has always been a challenge due to data privacy, security, and availability concerns. Recent developments in blockchain technology and computer security are promising to resolve most of these challenges. Thus, in this research, we are presenting blockchain-based, distributed file storage for community cloud computing. This research, which focuses on local communities, allows them to securely share the excessive storage with the locals to earn something while gaining access to faster, more reliable large-scale local storage. By replacing the central authority of third-party storage vendors and migrating to distributed file storage, local communities will achieve greater security, reliability, and control over their data while reducing the risks of data failures and interruptions. Due to symmetric encryption, data privacy would be 100% assured even though they are stored in other peoples' computers. Moreover, this system uses Reed Solomon. Using the Reed Solomon algorithm, the system can retrieve files even when users holding 33.33% of the files are not present. Besides, it allows local communities to make effective use of their idle and unallocated computer resources transparently.

The Need to Standardize the Reanalysis of Genomic Sequencing Results: Findings from Interviews with Underserved Families in Genomic Research.

The reanalysis of genomic sequencing results has the potential to provide results that are of considerable medical and personal importance to recipients. Employing interviews with forty-seven predominantly medically underserved families and ethnographic observations we argue that there is pressing need to standardize the approach taken to reanalysis. Our findings highlight that study participants were unclear as to the likelihood of reanalysis happening, the process of initiating reanalysis, and whether they would receive revised results. Their reflections mirror the lack a specific focus upon reanalysis within consent and results sessions as observed in clinical settings. Mechanisms need to be put into place that standardize the approach to reanalysis in research and in clinical contexts. This would enable clinicians and genetic counsellors to communicate clearly with research participants with respect to potential for reanalysis of results and the process of reanalysis. We argue that that the role of reanalysis is too important to be referred to in an ad-hoc manner. Furthermore, the ad-hoc nature of the current process may increase health inequities given the likelihood that only those families who have the means to press for reanalysis are likely to receive it.

Parliamentary Dress

Parliamentary Dress

Deep Q learning based secure routing approach for OppIoT networks

Opportunistic IoT (OppIoT) networks are a branch of IoT where the human and machines collaborate to form a network for sharing data. The broad spectrum of devices and ad-hoc nature of connections, further alleviate the problem of network and data security. Traditional approaches like trust based approaches or cryptographic approaches fail to preemptively secure these networks. Machine learning (ML) approaches, mainly deep reinforcement learning (DRL) methods can prove to be very effective in ensuring the security of the network as they are profoundly capable of solving complex and dynamic problems. Deep Q-learning (DQL) incorporates deep neural network in the Q learning process for dealing with high-dimensional data. This paper proposes a routing approach for OppIoT, DQNSec, based on DQL for securing the network against attacks viz. sinkhole, hello flood and distributed denial of service attack. The actor–critic approach of DQL is utilized and OppIoT is modeled as a Markov decision process (MDP). Extensive simulations prove the efficiency of DQNSec in comparison to other ML based routing protocols, viz. RFCSec, RLProph, CAML and MLProph.

A Systemic Security and Privacy Review: Attacks and Prevention Mechanisms over IOT Layers

In this contemporary era internet of things are used in every realm of life. Recent software’s (e.g., vehicle networking, smart grid, and wearable) are established in result of its use: furthermore, as development, consolidation, and revolution of varied ancient areas (e.g., medical and automotive). The number of devices connected in conjunction with the ad-hoc nature of the system any exacerbates the case. Therefore, security and privacy has emerged as a big challenge for the IoT. This paper provides an outline of IoT security attacks on Three-Layer Architecture: Three-layer such as application layer, network layer, perception layer/physical layer and attacks that are associated with these layers will be discussed. Moreover, this paper will provide some possible solution mechanisms for such attacks. The aim is to produce a radical survey associated with the privacy and security challenges of the IoT. This paper addresses these challenges from the attitude of technologies and design used. The objective of this paper is to rendering possible solution for various attacks on different layers of IoT architecture. It also presents comparison based on reviewing multiple solutions and defines the best one solution for a specific attack on particular layer.

A privacy-preserving mutual authentication scheme for group communication in VANET

A Vehicular Ad-Hoc Network (VANET) represents a collection of vehicles that communicate among themselves and the road infrastructure for exchange of information through a wireless network. It provides services like road safety, infotainment, optimized traffic flow, location-based services and other on-demand services. However, due to its intrinsic ad-hoc nature, there are several challenges in the deployment of VANETs. One such challenging issue is ensuring security and privacy of the exchanged messages. Therefore, it is important that the vehicles are properly authenticated and necessary security associations are properly established. Most of the recent works in this area sends plain text messages while communicating, that results in compromise of message confidentiality. Moreover, in majority of these works, an active communication with a trusted authority is required during authentication that results in increased communication latency. This paper puts forward a privacy preserving mutual authentication scheme for group communication in VANET that does not require an active communication with a trusted authority. Furthermore, the proposed scheme use pseudonym for identity privacy and the messages exchanged in the scheme are encrypted before transmission. Formal and informal security analysis shows that the proposed scheme is robust against various security attacks. Through a performance analysis, the scheme is found to be efficient.

On obtaining sparse semantic solutions for inverse problems, control, and neural network training

Modern-day techniques for designing neural network architectures are highly reliant on trial and error, heuristics, and so-called best practices, without much rigorous justification. After choosing a network architecture, an energy function (or loss) is minimized, choosing from a wide variety of optimization and regularization methods. Given the ad-hoc nature of network architecture design, it would be useful if the optimization led to a sparse solution so that one could ascertain the importance or unimportance of various parts of the network architecture. Of course, historically, sparsity has always been a useful notion for inverse problems where researchers often prefer the L1 norm over L2. Similarly for control, one often includes the control variables in the objective function in order to minimize their efforts. Motivated by the design and training of neural networks, we propose a novel column space search approach that emphasizes the data over the model, as well as a novel iterative Levenberg-Marquardt algorithm that smoothly converges to a regularized SVD as opposed to the abrupt truncation inherent to PCA. In the case of our iterative Levenberg-Marquardt algorithm, it suffices to consider only the linearized subproblem in order to verify our claims. However, the claims we make about our novel column space search approach require examining the impact of the solution method for the linearized subproblem on the fully nonlinear original problem; thus, we consider a complex real-world inverse problem (determining facial expressions from RGB images).

Stock Price Prediction Using Datamining With Novel Computing Approach

Foreseeing financial exchange is consistently a difficult work. As an ever increasing number of individuals are associated with exchanging , its consistently an idealistic conduct to predict about the future price. By means of predicting the future stock price, experts and analysts in the stock field can buy or sell accordingly. In order to predict the stock value, financial specialists utilized crucial examination, specialized investigation and innovative investigation. Different strategies, for example, utilizing stock pointers additionally end up being a value a few. Yet, even though there are many techniques available the result of stock prediction is always a random one because of it adhoc nature. The experts in this field can also track the various information channel in order to increase their predicting performance. The previous work consolidates more on the result rather than the methods used. In this paper, we proposed a novel technique which uses the technological methods with that of the technical views and the changing news information’s about the stock. We implemented fuzzy rational algorithm with that of the hybrid neural network in conjunction with the k nearest matching points. The optimization techniques used here are the combination of the soft computing methods.

Energy efficient target set selection and buffer management for D2D mobile data offloading

Data offloading offers a significant solution to the problem of explosive rise in mobile data traffic. A naive approach would be to utilize the infrastructure (cellular tower, WiFi, femtocell) or other mobile devices to offload data. However, increasing the number of a cellular towers, WiFi, or femtocells is costlier deal for data delivery. Recently, device-to-device (D2D) paradigm of data communication has emerged out as one of the most promising solutions to deal with cost effective cellular traffic offloading. D2D communication provides a direct communication link between closely located mobile users. Another significant feature of D2D is its content centric nature, which makes it useful in data offloading. In this paper, we have addressed the issue of data offloading in mobile devices and proposed a hybrid model of D2D communication with ad-hoc nature. The paper also considers the issues like memory constraints of the devices, pruning of replicated messages and energy efficiency to increase the lifetime of the battery. Considering all the constraints and trade off, we have modeled our problem into optimal target selection problem and distributed community detection problem, both of which are NP-hard. We propose a clustering algorithm to optimize the cooperative mobile nodes. The proposed algorithm uses the betweenness centrality and k-means for optimizing target set section. Our proposed algorithm requires less time in terms of computational complexity with limited space. We compare it with the community-based approach in terms of load transferred for varying target set sizes for validation. The simulation results of the suggested algorithm may reduce the energy requirements up to 16.7% and is able to accommodate 80% more traffic as compared to the community-based algorithm.

Ministerial Advisers in Slovakia: Profiles and Career Paths, 2010 – 2020

Abstract This study examines the transparency of the regulatory framework under which ministerial advisors exist within the politicized context of a Central and Eastern European perspective. We compare profiles and career paths of ministerial advisers under five different types of coalition governments and examine if variance across government types can be explained by type of party – established vs. new parties. Empirically, the article draws on a cohort of 162 ministerial advisers in Slovakia across five governments from 2010 to 2020. We arrive at multiple findings. Firstly, we suggest the limitation in the availability and reporting of data is an important finding as it highlights accountability gaps and lack of government transparency irrespective of the party in power. Secondly, within the low regulatory environment, ministers appoint multiple types of staff including both formal “visible” ministerial advisers and “invisible” ministerial agents that, if one could accurately measure, would likely demonstrate that the ministerial advisory system is more inflated than we currently present. The ad-hoc nature of the advisory system also creates fluctuations in the size of the ministerial adviser cohort across governments and across different ministries. This would also help to explain the next finding, which is that, contrary to the experience in many countries, the overall size of the advisor population does not grow, probably because executive politicians have other avenues of appointing advisory agents. Fourthly, the advisers have a fairly equal distribution of prior employment from both the public sector and the private sector, but we do see some evidence of more established political parties preferring to recruit from the public sector and newer parties preferring to recruit from the private sector. Lastly, the appointment process appears to be highly controlled by individual ministers, suggesting personal ties are essential (link between ministerial and advisor education) and party-political criteria are a low consideration. The research is conducted using a biographical approach in which freedom of information requests and open source data is scrapped and then triangulated via a dozen interviews with current and former advisers. It argues that regulation is weak, lacking public scrutiny, which provides loopholes for employing ministerial agents in informal ways that could create, at worst, the opportunity for corrupt behavior, or at least, lead to poor practices in good governance. Therefore, future research should focus on both the formal “visible” and informal “invisible” ways that ministers recruit their advisory agents, how their agents function, and whether existing regulatory measures create a transparent and accountable governance framework.

Controlling 2D Artificial Data Mixtures Overlap

Clustering methods are used for identifying groups of similar objects considered as homogenous set. Unfortunately, analytic performance evaluation of clustering methods is a difficult task because of their ad-hoc nature. In this paper, we propose a new test case generator of artificial data for 2 dimensional Gaussian mixtures. The proposed generator has two interesting advantages: the first one is its ability to produce simulated mixture for any number of components, while the second one resides in the fact that it formally quantifies the overlap rate which allows us to add some complexity to the data. Clustering algorithms and validity indices behavior is also analyzed by changing the overlap rate between clusters.

India’s federalism puzzle, counter-terrorism challenge and NCTC debate

ABSTRACT Terrorism has been considered an unprecedented threat to national security and regional stability, and one which requires an equally complex response. The lack of effective institutional capacity to prevent and to manage the consequences of terrorist attacks has compounded India’s security challenges. This article shows that while some progress has been made since the 2008 Mumbai attacks in enhancing institutional capacity, no serious attempt seems to have been made to overhaul its counter-terrorism capacity. The prime example of this neglect has been the National Counter Terrorism Centre (NCTC) which is still on the backburner due to political bickering. This article holds that the prevailing mistrust in centre-state relations, differing political ideologies, and the ad-hoc nature of the crisis response mechanism are primarily responsible for this delay. It further recommends several measures to overcome these obstacles.

Incorporating security in opportunistic routing and traffic management in opportunistic sensor network

Nowadays, wireless sensors or opportunistic sensor networks (OSN) and its technologies are mainly used for bridging the spaces between the physical world and the virtual electronics. Due to several potentials like limited storage, unreliable communication, higher latency in communication and unattended operations of networks and sensor node powers, OSN faces many problems on routing and traffic management. Security on OSN involves routing and data aggregation deployed in OSN involves collaborations among nodes on the network due to ad-hoc nature. To solve these problems, our proposed method involves the novel traffic management scheme and opportunistic secure routing on sensor networks. This method involves mainly three algorithms such as range-based clustering (RBC) for clustering the sensor nodes, minimum waiting time routing (MWTR) algorithm for routing the data packets and in order to provide secure communication while coalition attack and replica attacks present on the network, we propose light weight key generation mechanism (LWKG). Thus, our proposed system provides less computational complexity on the networks and we provide effective results on experimental evaluation based on average packet reception ratio, detection ratio of replicas, connectivity and coalition attack resistance.

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as the network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G network can provide an excellent platform for autonomous vehicles because SDN offers open programmability and flexibility for new services incorporation. This separation of control and data planes enables centralized and efficient management of resources in a very optimized and secure manner by having a global overview of the whole network. The SDN also provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. In addition, it promises the overall improved performance. In this paper, we propose a flow-based policy framework on the basis of two tiers virtualization for vehicular networks using SDNs. The vehicle to vehicle (V2V) communication is quite possible with wireless virtualization where different radio resources are allocated to V2V communications based on the flow classification, i.e., safety-related flow or non-safety flows, and the controller is responsible for managing the overall vehicular environment and V2X communications. The motivation behind this study is to implement a machine learning-enabled architecture to cater the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently. This paper also presents a proof of concept for leveraging machine learning-enabled resource classification and management through experimental evaluation of special-purpose testbed established in custom mininet setup. Furthermore, the results have been evaluated using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). While concluding the paper, it is shown that the LSTM has outperformed the rest of classification techniques with promising results.

Feedback Recommendation System Based on Structured Feedback Acquisition

Users’ feedback are increasingly becoming a vital factor during the software evolution process. Users’ feedback are widely considered to be an indication of how satisfied are the system’s users. It was found that large percentage of feedback represents problems reported by users who face problems in using the system. Users’ feedback are usually communicated in an ad-hoc manner with no defined structure. The ad-hoc nature of the feedback makes it difficult to study and analyse feedback. Also, it usually happens that similar problems are reported by different users, so engineers spend wasted time and effort in duplicate issues. In this paper we propose a recommendation system which recommends to the user reported problems that are similar to his from prevoius feedback threads to reuse existing solutions. We based our work on a structured feedback system to ensure we get better results. Structured feedback ensures minimum level of useful and meanigful information that helps enhance the analysis results. Initial evaluation was conducted and the results are promising.

De-biasing role induced bias using Bayesian networks

AbstractThe merits of using subjective probability theory as a normative standard for evidence evaluation by legal fact-finders have been hotly debated for decades. Critics argue that formal mathematical models only lead to an apparent precision that obfuscates the ad-hoc nature of the many assumptions that underlie the model. Proponents of using subjective probability theory as normative standard for legal decision makers, specifically proponents of using Bayesian networks as decision aids in complex evaluations of evidence, must show that formal models have tangible benefits over the more natural, holistic assessment of evidence by explanatory coherence. This article demonstrates that the assessment of evidence using a Bayesian network parametrized with values obtained from the decision makers reduces role-induced bias, a bias that has been largely resistant to de-biasing attempts so far.

Malleability and ownership of proxy signatures: Towards a stronger definition and its limitations

Proxy signature is a cryptographic primitive that allows an entity to delegate singing rights to another entity. Noticing the ad-hoc nature of security analysis prevalent in the existing literature, Boldyreva, Palacio and Warinschi proposed a formal security model for proxy signature. We revisit their proposed security definition in the context of the most natural construction of proxy signature – delegation-by-certificate. Our analysis indicates certain limitations of their definition that arise due to malleability of proxy signature as well as signature ownership in the context of standard signature. We propose a stronger definition of proxy signature to address these issues. However, we observe that the natural reductionist security argument of the delegation-by certificate proxy signature construction under this definition seems to require a rather unnatural security property for a standard signature.