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The fourth edition retains many of the features of the first three editions: 1. Knowledge about judgment and decision making has been scattered among a number of different fields. Philosophers, psychologists, educators, economists, decision scientists, and computer scientists have different approaches to the theory. The approach in this book represents my own effort to draw together some of the key ideas from these different disciplines. Much of what I present is not original or new. If it were either of these, I would not be so confident that it is basically correct. 2. I retain the idea that all goal-directed thinking and decision making can be described in terms of what I call the search-inference framework : Thinking can be described as inferences made from possibilities, evidence, and goals that are discovered through searching. 3. I also argue that one main problem with our thinking and decision making is that much of it suffers from a lack of active open-mindedness : We ignore possibilities, evidence, and goals that we ought to consider, and we make inferences in ways that protect our favored ideas. In the course of this book, I apply these ideas to the major concepts and theories in the study of thinking. I begin, in Part I, with general considerations: the nature of rationality; methods for studying thinking; and logic.

Supply chain resilience assessment and strengthening has become a strategic topic in supply chain management. Extant literature on the subject of supply chain resilience assessment mainly focuses on evaluating the operational performance (e.g., inventory, capacity, lead time) while overlooking aspects of the financial performance. This article presents an event-based Bayesian approach as an effective tool for modeling the causal relationships among variables at different time intervals to evaluate resilience across operational and financial criteria. The model focuses on two key elements of resilience—reliability (the ability of the system to sustain an original state) and recoverability (the ability of a system to recover following a disruption), which allows us to investigate how different operational and financial factors affect the network reliability, recoverability, and resilience. This article contributes to the literature in three ways—first, we incorporate a financial perspective to the quantitative study of supply chain resilience; second, we evaluate both the financial and operational aspects from a longitudinal perspective; third, we show that the financial performance of individual supply chain entities has a strong influence on the overall supply chain resilience. From a practical standpoint, the article presents a model that can help managers identify the weakest nodes in their supply chain, considering operational and financial aspects over time, allowing them to design and operate more resilient supply networks.

Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao, Ching and Wan, 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in its implementation, is nevertheless unresolved in data-rich environment when many possible controls are at the researcher's disposal. We propose the forward selection method to choose control units, and establish validity of the post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings., Comment: accepted for publication at the Journal of Econometrics

Truck congestion at chemical sites is a persistent problem that is difficult to solve, even using a truck appointment system. This study presents an alternative solution to improve the flexibility of chemical sites by creating a drop-swap terminal adjacent to the site location. The terminal serves as an intermediate depot where the trucks can drop empty containers and swap them with preloaded containers without entering the site. This study aims to evaluate the performance of such a solution in mitigating truck congestion at chemical sites. The problem is modeled as a nonstationary semi-open queueing network with time-varying arrivals. We propose a combination of a fluid-flow approximation and a decomposition-aggregation method to estimate the time-dependent performance of the system. A chemical site in the Netherlands is presented as a case study. Several scenarios are tested and evaluated. Numerical results show that a drop-swap terminal can effectively reduce truck idling time and increase logistics efficiency at chemical sites. We also found that swapping containers on chassis is a cheaper and greener option to operate the terminal. However, the investment needed to support the operation should not be overlooked to sustain the benefits. The study concluded with several key messages for site operators who wish to maximize the benefit from a drop-swap terminal.

WiFi-based passive gesture tracking plays a crucial role in promoting human-computer interface, due to its pervasive availability and cost-effectiveness. Prior works focus on tracking gestures on 2D sensing plane by aggregating multiple WiFi links (typically 2), with Uniform Linear Arrays (ULAs). However, gestures actually contain 3D spatial information instead of just 2D, thus interpreting the 3D traces as 2D may lead to enormous tracking errors. This paper aims at exploring the possibility of passively tracking 3D hand traces likewise leveraging two WiFi links with standard 3-element ULAs, and presents a generic 3D centimeter-level passive gesture tracking system, called CentiTrack-3D. To this end, we make two key observations: (1) The ULA-resolved angle contains the integrated information of the azimuth and elevation in 3D space, despite its inability to estimate azimuth and elevation separately. (2) The radial motion deviating from the sensing plane also leads to length variations of paths. Motivated by the observations, a 3D tracking model named Chaos is elaborately designed to deduce the hand 3D coordinates, so as to track the traces. Extensive experiments yield that CentiTrack-3D achieves an overall median tracking granularity of 2.5 cm in 3D space in case of diverse users and environment conditions.

Next generation Radio Access Networks (RANs) consider virtualized architectures in which base station functions are distributed in different logical nodes, connected through fronthaul (FH) links. To reduce the FH deployment costs and the required FH capacity, operators may install a single FH link shared among multiple cells and exploit key enabling techniques, such as modulation compression, to reduce FH data. In shared FH capacity scenarios, it is essential to provide efficient methods to control and optimize the FH resources' utilization with limited impact on the air interface performance. In this paper, a multi-cell multi-user scenario with a shared FH link across multiple cells is considered. We focus on optimizing the resource allocation and modulation compression of each user, in a centralized and dynamic manner, aiming to maximize the air interface performance subject to a shared FH capacity constraint. The problem is formulated as a convex optimization problem, which allows deriving the optimal resource allocation and modulation compression per user. Then, we evaluate the proposed FH-aware scheduling methods against baseline holistic strategies over an end-to-end dynamic 5G NR system-level simulator based on ns-3. Under a tight available FH capacity, results show gains that vary from 16% to 567% in different percentile statistics of the user-perceived throughput.

Deep generative models have gained much attention given their ability to generate data for applications as varied as healthcare to financial technology to surveillance, and many more - the most popular models being generative adversarial networks and variational auto-encoders. Yet, as with all machine learning models, ever is the concern over security breaches and privacy leaks and deep generative models are no exception. These models have advanced so rapidly in recent years that work on their security is still in its infancy. In an attempt to audit the current and future threats against these models, and to provide a roadmap for defense preparations in the short term, we prepared this comprehensive and specialized survey on the security and privacy preservation of GANs and VAEs. Our focus is on the inner connection between attacks and model architectures and, more specifically, on five components of deep generative models: the training data, the latent code, the generators/decoders of GANs/ VAEs, the discriminators/encoders of GANs/ VAEs, and the generated data. For each model, component and attack, we review the current research progress and identify the key challenges. The paper concludes with a discussion of possible future attacks and research directions in the field., Comment: To be published in IEEE Transactions on Knowledge and Data Engineering

In recent years, mobile edge computing (MEC), as a key technology that facilitates the 5G mobile network, has raised a number of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an online approach for solving dynamic EUA problem in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated against a baseline approach and two state-of-the-art approaches on a widely-used real-world dataset.

Social innovation (SI) research tends to understand rural SI processes as collective initiatives of equally engaged, supportive, and like-minded people. However, recent research has indicated that SI projects often benefit from dedicated key players who find support in their communities but also must deal with resistance and barriers. Against this backdrop, this article explores the figure of the key player in SI research and exposes the patterns of rural SI processes with the empirical cases of two small communities in Germany that jointly created rural infrastructure systems, thus seeking emancipation from remote political and economic control and marginalization. Using the empirical cases, we tested and specified a heuristic framework of key players that combines analytical dimensions at the micro-, meso-, and macro-levels. According to our findings, the heuristic framework is appropriate for analyzing key players in rural SI processes. Notably, we show that SI processes benefit from assertive key players and strong communities capable of joint action. Both are crucial for dealing with internal opposition and overcoming external hurdles that appear when novel approaches irritate familiar solutions and routines.

As anaesthetists we rely on the monitoring that we attach to our patients to be accurate, responsive and reliable. The display screen we examine closely daily with its beeps and alarms represents the processing of a large volume of biological signals of disparate types into something easily recognizable that can rapidly alert us to changes in a patient's physiology. This article uses some common examples of physiological measurements that undergo the pathway of detection, transduction, processing, amplification and filtering before being displayed on a monitoring screen and stored. We explain key components of this pathway such as Fourier analysis, gain and analogue to digital conversion; and how confounding factors such as noise, interference and damping are dealt with. We also discuss some newer technologies that are finding their way into common anaesthetic practice such as depth of anaesthesia monitors and electronic anaesthesia records.

Extrusion-based (fused filament fabrication) three-dimensional (3D) printing of shape-memory polymers (SMPs) has the potential to rapidly produce highly customized smart-material parts. Yet, the ef...

Exciting recent advances in genome sequencing, coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Already today, one’s digitized DNA can be easily obtained from a sequencing lab and later used to conduct numerous tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and the often-proprietary nature of genomic tests, privacy is a natural and crucial issue. While genomic privacy received a great deal of attention within and outside the research community, genomic security has not been sufficiently studied. This is surprising since the usage of fake or altered genomes can have grave consequences, such as erroneous drug prescriptions and genetic test outcomes. Unfortunately, in the genomic domain, privacy and security (as often happens) are at odds with each other. In this article, we attempt to reconcile security with privacy in genomic testing by designing a novel technique for a secure and private genomic range query protocol between a genomic testing facility and an individual user. The proposed technique ensures authenticity and completeness of user-supplied genomic material while maintaining its privacy by releasing only the minimum thereof. To confirm its broad usability, we show how to apply the proposed technique to a previously proposed genomic private substring matching protocol. Experiments show that the proposed technique offers good performance and is quite practical. Furthermore, we generalize the genomic range query problem to sparse integer sets and discuss potential use cases.

Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). One of the approaches to encourage more systematic and efficient exploration relies on surprise as an intrinsic reward for the agent. We introduce a new definition of surprise and its RL implementation named variational assorted surprise exploration (VASE). VASE uses a Bayesian neural network as a model of the environment dynamics and is trained using variational inference, alternately updating the accuracy of the agent's model and policy. Our experiments show that in continuous control sparse reward environments, VASE outperforms other surprise-based exploration techniques.

In regions such as hawai'i, south australia, Tasmania, Texas, and Ireland, power systems are commonly experiencing instantaneous penetration levels of inverter-based power sources (IBPSs) such as wind, solar photovoltaics (PV), and battery storage in excess of 50n60% relative to system demands.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are c o-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

Recent advancements of Industrial IoT (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contains rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time is challenging. Recent advancements in AI allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this paper, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. Firstly, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real-time. Secondly, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real-time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

We propose a systematic algorithmic reverse-stress testing methodology to create "worst case" scenarios for regulatory stress tests by accounting for losses that arise from distressed portfolio liquidations. First, we derive the optimal bank response for any given shock. Then, we introduce an algorithm which systematically generates scenarios that exploit the key vulnerabilities in banks' portfolio holdings and thus maximize contagion despite banks' optimal response to the shock. We apply our methodology to data of the 2016 European Banking Authority (EBA) stress test, and design worst case scenarios for the portfolio holdings of European banks at the time. Using spectral clustering techniques, we group 10,000 worst-case scenarios into twelve geographically concentrated families. Our results show that even though there is a wide range of different scenarios within these 12 families, each cluster tends to affect the same banks. An "Anna Karenina" principle of stress testing emerges: Not all stressful scenarios are alike, but every stressful scenario stresses the same banks. These findings suggest that the precise specification of a scenario is not of primal importance as long as the most vulnerable banks are targeted and sufficiently stressed. Finally, our methodology can be used to uncover the weakest links in the financial system and thereby focus supervisory attention on these, thus building a bridge between macroprudential and microprudential stress tests., Mathematical Finance, 33 (2), ISSN:0960-1627, ISSN:1467-9965

In this article, the cooperative output regulation problem of heterogeneous linear multiagent systems under jointly connected digraphs is addressed. The event-triggered control protocols based on state feedback and output feedback are proposed, respectively. It is shown that the output tracking errors of the resulting closed-loop control systems converge to 0 exponentially via the proposed protocols. One of the key advantages of the proposed event-triggering mechanism is that the information transmissions induced by event triggerings and topology switchings are independent, and data transmissions among agents are thus reduced. Furthermore, an explicit minimum interevent time is provided for all the agents so that the Zeno-behavior is excluded strictly. Finally, three numerical examples are provided to verify the effectiveness of the proposed protocols.

Enhancing the energy efficiency of structures has been a staple of energy policies. The key goal is to slash electricity usage in order to minimize the footprint of houses. This goal is sought by putting restrictions on the design specifications with respect to the properties of the raw materials and components as well as the exploitation of sustainable sources of energy. These facts for the basis for zero-energy building (ZEB) being established. This novel technology has faced several obstacles impeding its commercialization and future advancement. This investigation therefore holistically explored and evaluated the state of zero energy building and factors impeding their commercialization. The review further proposed some suggestion in terms of technology that can be considered by the sector to augment existing technologies. Similarly, the investigation touched on the effect of occupant's character in zero energy structures. Policies in terms of government subsidies and tax rebates were recommended to encourage more investors into the sector. Finally, the perception of zero energy building being more expensive compared to the traditional structures can equally be curbed via efficient and effective public sensitization.

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity.

Artificial Intelligence of Things (AIoT), as a fusion of AI and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest, named D2MIF, for the federated learning empowered AIoT. In D2MIF, an isolation forest is constructed to compute the malicious score for each model uploaded by the corresponding participant, then the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public datasets Mnist and Fashion_Mnist. And the experiment results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning empowered AIoT.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

The dynamic property and increasing complexity are the key challenges for modeling financial technology (FinTech) related applications such as stock markets. Over the years, a lot of inflexible predictive strategies have been proposed for predicting stock price movements that failed to achieve satisfactory results especially when a market crash occurs. To cope with this challenge, we propose a prediction framework based on an adversarial training strategy using reinforcement learning for the said FinTech application. The framework uses a heterogeneous knowledge-base including stock prices, tweets, and global indicators. We propose a modified newton-divided difference polynomial (NDDP) for missing data imputation. The informative patterns representing the intrinsic characteristics of financial markets were extracted using long short-term memory networks (LSTM). The two adversarial networks are heterogeneous data fusion representing market crash (HDFM) Q-learning and confrontational Q-learning network. Both networks are trained in an adversarial fashion to increase the effectiveness of prediction even when the financial market is volatile. The experimental results show the importance of global indicators and the proposed adversarial learning network (ALN) for improving the predictive performance in comparison with the existing state-of-the-art works.

The purpose of the present study is to comprehensively evaluate the present status of the Internet of Things (IoT) in fintech ecology and ahead. Study finds that the concept of communication between the devices and financial technology are not new yet only a considerable number of studies are done on IoT in fintech. Study highlights that both the IoT & fintech show an upward trend in the marketspace. Then study identifies that the increase in the demand for blockchain, internet, mobile network, cloud storage and IoT devices among the organization are the key drivers of IoT in fintech. Study also shows that the issues related to sustainable energy, digital payments and security are the current area of applications and challenges for IoT in Fintech. Thereafter study concludes that the growth of neurotech will influence the next generation IoT in fintech as the catalytic agent. Neurotech enabled IoT (NIoT) in Fintech will enhance the 3Ms (Man, Machine & Memory) relationship for business process innovation.

Several African countries have recently adopted fairly democratic constitutions. An emerging trend in these constitutions is the establishment of constitutional courts or constitutional councils as key institutions for human rights protection. However, the adoption of fairly democratic constitutions sometimes is not complemented by the democratisation of politics, as autocratic governments remain in place after the adoption of a new constitution. When this happens, these countries' constitutional courts become key sites of human rights protection struggles, as many turn to public interest litigation in an effort to protect human rights. Using the Constitutional Court of Zimbabwe as a case study, the article investigates the behaviour of these courts and their reliability as human rights protection institutions, when they operate under autocratic political conditions. The article makes use of the systemic level factors theory as a conceptual framework for analysing the jurisprudence of this Court. It identifies trends in the manner in which this Court has handled high-level profile human rights cases that involve the interests of the ruling party or government. These trends are that in the majority of cases the Court has ignored the Constitution and delivered judgments that are meant to protect the government from certain political risks. In a few cases the Court appeared to be bold enough to enforce the Constitution, but a closer analysis of these cases reveals that the Court decided in that manner because the reconfigured political strategy of the ruling party and the internal factional contestations in the ruling party required or permitted the Court to make those decisions. The article concludes that the performance of the Constitutional Court of Zimbabwe thus far paints a gloomy picture as far as its reliability and utility as a guardian of human rights and democratic institutions under the current autocratic regime in Zimbabwe are concerned.

The use of the internet in this modern era is increased many fold. The communications between different peers take place in digital form. While sharing the messages between different recipients, the confidentiality of the messages is very important. For creating the high level of security while sharing the secret messages, the cryptographic algorithms are introduced along with steganography. Image Steganography is a methodology used to hide the messages inside of the cover image. Initially, the secret information is encrypted by using the RSA Algorithm. Then the encrypted secret information is hidden in the Least Significant Bit (LSB) of the different components of the color image in such a way that the original quality of the image to be minimized. The recipient of the message is able to retrieve the encrypted secret message from the LSB bit of stego_image and then the cipher text is converted into original plain text by using the RSA algorithm. The proposed algorithm verified and analysed its performance against the different combinations of key pairs.

In quantum cryptography, quantum key distribution protocols (QKDPs) make use of quantum mechanisms to circulate treatment secrets as well as public conversations to look for viewers and confirm the accuracy of a session key. Having said that, social conversations call for additional communication rounds in between a sender as well as the receiver and expense precious qubits. By comparison, timeless cryptography supplies handy techniques that make it possible for efficient key confirmation and user verification.

A key factor in big data analytics and artificial intelligence is the collection of user data from a large population. However, the collection of user data comes at the price of privacy risks, not only for users but also for businesses who are vulnerable to internal and external data breaches. To address privacy issues, local differential privacy (LDP) has been proposed to enable an untrusted collector to obtain accurate statistical estimation on sensitive user data (e.g., location, health, and financial data) without actually accessing the true records. As key-value data is an extremely popular NoSQL data model, there are a few works in the literature that study LDP-based statistical estimation on key-value data. However, these works have some major limitations, including supporting small key space only, fixed key collection range, difficulty in choosing an appropriate padding length, and high communication cost. In this paper, we propose a two-phase mechanism PrivKVM* as an optimized and highly-complete solution to LDP-based key-value data collection and statistics estimation. We verify its correctness and effectiveness through rigorous theoretical analysis and extensive experimental results.

A key component for preserving a WSN’s integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network’s harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR).

The new generation of edge computing supported industrial cyber-physical system (ICPS) promotes the deep integration of sensing and control. The unknown model is one of the key challenges to characterize their interactions. In most existing works, many efforts have been devoted to overcoming the challenge for the single aspect of sensing and control. However, the industrial revolution puts forward the higher requirements of the overall production performance. To solve this problem, we propose a novel framework for learning-based edge sensing and control co-design. Specifically, the model learning error is first analyzed to bound the actual control performance. Then, the bound is further linked to the sensing design through the bridge of relaxed assumptions of the nonzero initial state and unknown order. Besides, the

Recognizing the emotional state of a person within the image in real-world scenarios is a key problem in affective computing and has various promising applications. Local regions in the image, including different objects in the background scene and parts within the foreground body, usually have different contributions to emotion perception of the target person. This, however, has not been well exploited in most existing methods. In this paper, we propose to make relational region-level analysis to account for the different contributions of different regions to emotion recognition. For the background scene, we propose a Body-Object Attention (BOA) module to estimate the contributions of background objects to emotion recognition given the target foreground body. Within the foreground body, we propose a Body Part Attention (BPA) module to recalibrate the channel-wise body feature responses to attend on body parts that are more important. Moreover, we propose to model the emotion label dependency in real-world images, considering both the semantic meanings of these labels and their co-occurrence patterns. We evaluate the proposed method on the EMOTIC and CAER-S datasets, and experimental results show the superiority of our method compared with the state-of-the-art algorithms.

The Crime against the women in our country today is becoming a daily routine. There continues to be increased demand in the development of safety devices to ensure a secure and safe environment for the women. The key objectives are to develop a kind of “Virtual Friendly” machine to protect you from serious troubles. These suggested systems are made up of the GPS, the GSM transmitter, the Microcontrollers (LPC812), Voice Recognizer, a RF transceiver and Temperature Sensors. GPS Receiver receives satellite locations information’s I n the indirect form of the longitude and the latitude. It information is handled by the Microcontroller and this gathered information the sent to the main control point of POLICE. These special temperature sensors continuously sense the body temperature. In a case of a very low temperature indicating some emergency conditions, a kind of SMS alert would be automatically sent. The device can be triggered by the consumer on its own when self-defense is necessary. To ensure maximum performance, functionality such as warning tone, voice recognition is applied to the device. Using the SAVE ME and Support ME commands, for the voice recognizers this circuit is utilized to transmit voice commands. The device is designed as a small package and is easy to operate, so that a simple women can conveniently take these along everywhere.

Key semantics can come from everywhere on an image. Semantic alignment is a key part of few-shot learning but still remains challenging. In this paper, we design a Mixer-Based Semantic Spread (MBSS) algorithm that employs a {\it mixer} module to spread the key semantic on the whole image, so that one can directly compare the processed image pairs. We first adopt a convolutional neural network to extract features from both support and query images and separate each of them into multiple Local Descriptor-based Representations (LDRs). The LDRs are then fed into the {\it mixer} for semantic spread, where every LDR attracts complementary information from its peers. In this way, the objective semantic is made spread on the whole image in a data-driven manner. The overall pipeline is supervised by a voting-based loss, guaranteeing a good {\it mixer}. Visualization results validate the feasibility of our {\it mixer}. Comprehensive experiments on three benchmark datasets, miniImageNet, tieredImageNet, and CUB, show that our algorithm achieves the state-of-the-art performance in both $5$ -way $1$ -shot and $5$ -way $5$ -shot settings.

Computational resources are increasingly virtualized to enable computational tasks to be offloaded to remote facilities along the route between the source and destination. The principle that underlies traditional routing, i.e., that only networking resources need to be considered, may no longer be true in a virtualized environment. In this paper, we propose a framework for the efficient utilization of multi-resource infrastructures in which computational resources can be used via the network. Such a framework intrinsically calls for the joint consideration of networking and computational resources. In particular, we focus on unifying the controls in dynamic service chaining and multiple resource management, which are the key technologies in an integrated SDN/NFV architecture. We formulate a multi-path problem for choosing the resources to use in different services. The problem can be viewed as variational inequality using the Lagrange duality and saddle point theory. Based on this, we develop an extragradient-based algorithm that controls and splits the sending rate of each service. We prove that the algorithm converges to the optimal, minimizing the system cost while maximizing service utility. Simulations for diverse scenarios demonstrate that our algorithm achieves high QoS while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

The rapid progress of artificial intelligence expands its wide applicability in internet of things (IOT). Meanwhile, data insufficient and data source privacy are key supply chain challenges facing IOT especially in the healthcare industry. To address this problem in healthcare IOT, we propose a skin cancer detection model based on federated learning integrated with deep generation model. First, we employ dual generative adversarial networks to address the problem of insufficient data. In addition, to improve the quality of generated images, we synchronously optimize the sharpness of images, Frechet inception distance, image diversity, and loss using knee point-driven evolutionary algorithm (KnEA). Then, we protect patient information privacy by training federated skin cancer framework. Finally, we employ the ISIC 2018 dataset to test the performance of the proposed training model under different situations, including using identically distributed data, nonidentically distributed data, a sparse convolutional neural network, and a fully connected convolutional neural network. The experiment results demonstrate that the accuracy and area under the curve reach 91% and 88%, respectively. This model can help resolve problems of insufficient data in smart medicine of IOT and protect the privacy of user data while also providing an excellent detection rate.

Smart grid is a typical application of industrial cyber-physical systems (ICPS) in the electric power industry. Due to the exposure to different kinds of uncertainties and unpredictable faults, how to reliably assess the short-term voltage stability (SVS) of smart grids to prevent the occurrence of large-scale blackouts is still of primary concern. To tackle this challenging problem, this article develops a novel machine learning scheme to achieve accurate and interpretable online SVS assessment in two steps. First, it utilizes time-series shapelet transform to extract key dynamics and convert the postfault time series into flat features. Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information for SVS assessment. The GCN explores the spatial-temporal dynamics of power system via graph convolution and introduces a system layer to derive the final assessment result. Compared with conventional methods, this novel scheme makes full use of the spatial-temporal information in SVS dynamics, resulting in higher assessment accuracy and stronger adaptability. Besides, it is capable of discovering certain valuable underlying rules and patterns related to SVS. Test results on the IEEE 39-bus system and real-world Guangdong Power Grid in South China verify the effectiveness of the proposed scheme.

Various attempts are being carried out for automating the applications of house. Automation itself has many limitations with respect to cost and accuracy. In this context, virtual assistant plays a key role. In this work, we are planning a smart virtual assistant to illustrate the implementation of various voice commands and perform various tasks or services to an individual. The functions of these tasks or services are based on user input and mainly depends on the ability to access information from an enormous online sources (such as weather report, time and playing music for us etc). Raspberry Pi is used as the key hardware for implementing this project and its essential primary input considered is user’s voice. Whenever the information is fetched through the voice commands, it will produce the appropriate output. In-addition to this, some extra modules have been used for implementing voice to text conversions and controlling home appliances. This type of application will be more helpful for the persons who are partially or completely blind, by giving them access to Wikipedia, Calculator, Email and Music. From the results, we could attain the features like knowing the time and date, switching on and off of the lights, making a call to a registered mobile number and start/stop of fan. Finally, it can act as a main source of information and entertainment for visually impaired persons.

The scientific advancement in urbanised locations with the relation of the lower classes and discovery to Secularized culture is boosting the growth of food delivery applications and restaurant industries in India. The convenience of users is the primary objective aspect of such, while the second thing that these software's are also beneficial for the development of the market in eating and food supply, as the use of the online platform is a place where the restaurant owner is prepared to attract as many users as he can by allowing them to see the entire food menu in accordance with item name, price, photo, etc. Apps for food delivery have proven to be a big success in India as people are very well about all its operations and services. As one can order food for any type of occasion, it is one of the most comfortable and trusted options available. This helps to save time, resources, fuel and efforts for people, which is one of the key reasons why this industry is thriving and will tend to do so. Therefore, part of my initiative would be to review the theoretical background in various aspects of food sector apps, business models and various factors affecting adoption of food delivery apps that have never been addressed before.

In today’s technological world, one of the most challenging problems is information security. In the past ten years, many methods were introduced for securing the secret data transmission over the public network (Internet). One of the best choices to solve such issue is to combine AI and steganography methods. In the presented work, the text will be hidden inside an image depending on the ant colony optimization algorithm for generation of secret key for hiding. First an image is chosen as a carrier media of the secret text, then a set of rows are chosen from carrier image and used as an entry point for the ant colony optimization algorithm where the matrix resulting from the algorithm is used as a key to determine the hiding pixels of secret text bits in an image. The secret text will convert to ASCII code then convert to binary bits and hiding these bits on pixels of image depending on generation key using least significant bits techniques. The experimental results were the high-quality stego images as well as its minimal histogram changeability, also validating the efficiency of the suggested method, and it’s hard to determine pixels of hiding and complexity to determine the sequence of hiding bits.

The COVID-19 pandemic has exposed the obsolescence and vulnerability of many existing auditing practices. Whilst some progressive practices have been implemented (i.e., remote audits using rudimentary Information & Communication Technologies), a new paradigm is needed to not only account for the risk of repeated lockdowns, but also to align practices with the level of digitalization, automation, and use of artificial intelligence in the current business environment. In this paper, we argue that the adoption of new technologies requires a fundamental rethinking of how auditing services are delivered. We argue that new technological possibilities have implications for five other auditing elements that enable a shift from the old to the new paradigm of auditing, namely actors, processes, spaces, training and skills development, and services. We explain how non-financial audits conducted under the new paradigm are key enablers of a firm’s ability to participate and thrive in a competitive international marketplace.

Mobile Edge Computing (MEC) infrastructure raises security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the devices. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs, and broadcasts its public key to neighbouring peer users. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Theoretical security analysis shows that the proposed scheme can defend against the 51% attack and malicious entities in the blockchain network. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.

Key assignment and key maintenance in encrypted networks of resource-limited devices may be a challenging task, due to the permanent need of replacing out-of-service devices with new ones and to the consequent need of updating the key information. Recently, Aragona et al. proposed a new cryptographic scheme, ECTAKS, which provides a solution to this design problem by means of a Diffie-Hellman-like key establishment protocol based on elliptic curves and on a prime field. Even if the authors proved some results related to the security of the scheme, the latter still lacks a formal security analysis. In this paper, we address this issue by providing a security proof for ECTAKS in the setting of computational security, assuming that no adversary can solve the underlying discrete logarithm problems with non-negligible success probability.

The growth of transferring data via networks and the internet yield to safe them through communication. The image encryption process is a valuable strategy for protecting image information and it is used as an effective way of communicating several sensitive data with continuous use of images in all forms of visual media. In this paper, an efficient method of image encryption is proposed based on PRESENT-SPECK algorithms with a hybrid method for key generation called Henon and Arnold-cat map that is suggested for sequences. The proposed method petitioned the image data into the block for encryption. The total blocks joined and produced an encrypted image. The proposed method tested with various types, and the generated key also have passed various tests.

We are creating a garbage segregation and monitoring system in this paper, which is a critical issue in most Indian cities. Garbage bins are seen overflowing in certain regions due to poor monitoring and collection, which can lead to long-term difficulties. This garbage should be managed effectively to maintain the beauty of the neighbourhood as well as to maintain suitable hygienic conditions by establishing an automation system to sort out wet and dry trash, which helps to separate degradable and non-degradable garbage. The key microcontroller in this Optimized Garbage segregation and monitoring system is the NodeMCU. It detects the amount of waste in the bin using an ultrasonic sensor as a level detector. Different sensors, including as infrared, moisture, servo motor, and Global system for mobile (GSM), GPS, will provide real-time information and location of the bin to the authority via SMS as to whether the dustbin is totally filled or not to the truck driver. As a result, this system offers a mobile platform for effective waste management.

Its size and complexity, cloud externalization is considered for enhancing security in API. Nonetheless, cloud storage API has two key problems that allows user usability to be effectively functioning and that securely update cipher text in cloud through a new access policy for data owner. This paper recommends an effective secure access update algorithm based on an optimum cryptographic algorithm for API in cloud. Proposed method uses an optimal homomorphic Encryption (OHE) algorithm for secure storage. With Adaptive Salp Swarm Optimizations (ASSO), key values for the OHE algorithm are optimally selected. The proposed project also allows cloud server to efficiently update cipher text and monitor updates against cloud misbehaviors when a new access policy has been mentioned by data owner. It enables data proprietor and skilled users to effectively monitor legitimacy user data access and allow the user to verify that information provided by others is correctly recovered in plaintext. For that, the suggested method utilizes the attribute based access control model. Its analysis reveals how the policy reform program is formulated accurately, completely, safely and efficiently. The performance of the method proposed is measured by differentiating data and the cluster size due to its memory and execution times. Unlike the existing systems with a secure update in the access policies, this approach consumes minimum memory and time. The proposed process is introduced in cloud handset to Hadoop Map Reduction Framework.

Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by \emph{stragglers}---workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy, called \emph{semi-dynamic load balancing}, to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at \emph{iteration boundaries}, being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities---by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to $54\%$ .

Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value functions and policies. One efficient multigoal RL algorithm is the hindsight experience replay (HER). By treating a hindsight goal from failed experiences as the original goal, HER enables the agent to receive rewards frequently. However, a key assumption of HER is that the hindsight goals do not change the likelihood of the sampled transitions and trajectories used in training, which is not the fact according to our analysis. More specifically, we show that using hindsight goals changes such a likelihood and results in a biased learning objective for multigoal RL. We analyze the hindsight bias due to this use of hindsight goals and propose the bias-corrected HER (BHER), an efficient algorithm that corrects the hindsight bias in training. We further show that BHER outperforms several state-of-the-art multigoal RL approaches in challenging robotics tasks.

For emerging Industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to build smart industry. Specially, explosive time series data brings enormous challenges to the information mining and processing for modern industry. How to identify and detect the multi-dimensional industrial time series anomaly is an important issue. However, most existing studies fail to handle with large amounts of unlabeled data and get the undesirable results. In this paper, we propose a novel integrated deep generative model (DGM), which is built by generative adversarial networks based on bi-directional LSTM and attention mechanism (AMBi-GAN). The structure for generator and discriminator is the bi-directional long short-term memory with attention mechanism (AMBi-LSTM), which can capture time series dependence. Reconstruction loss and generation loss test the input of sample training space and random latent space. Experimental results show that the detection performance of our proposed AMBi-GAN has the potential to improve the detection accuracy of industrial multi-dimensional time series anomaly, towards IIoT in the era of AI.