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We present through this essay some socio-cultural aspects of the use of psychoactive substances, namely: what can be a definition of the term drug; examples of uses in the history and formation of Brazil, and its social reverberations; which powers and disciplines operate in the subjectivation and objectification of people; and how we classify and name people who have chosen to use drugs.

Background: Decompressive craniectomy (DC) may reduce mortality but might increase the number of survivors in a vegetative state. In this study, we assessed the long-term functional outcome of patients undergoing DC in a middle-income country.

A comprehensive overview of antenna evolution for mobile devices is initially presented. A detailed list of desired specifications for future mobile devices is illustrated with adequate examples with reference to the form factor of modern commercial smartphones. The desired specifications are specifically listed for 4G LTE (Long term Evolution) and mmWave 5G antennas and its coexistence. These desired specifications are tailored for modern commercial ultra-thin smartphones. Feasibilities of up-scaling of 4G antennas and down-scaling of 5G antennas are presented with the necessary design justifications. Co-designing 4G LTE and mmWave 5G antennas with optimal characteristics is the appropriate method to achieve backward compatibility. Design examples for the co-design of 4G-LTE and 5G antennas are also presented which meet the desired specifications and are compliant with modern mobile devices. Some design considerations are also introduced for future 4G–5G co-designed antennas integrated inside a smartphone.

Testing is becoming increasingly important in product development. This is especially true with new products. Testing may refer to testing of individual materials, individual machine elements and assemblies, on testing the compatibility of materials with individual components or testing their durability. This is especially challenging in the case of testing the interaction of different materials e.g. new type of hydraulic fluid.

For thousands of years, people have been living close to, or actually in, nature spending most of their times with plants and animals—that is, in real life. It is assessed that Csango people living in the Gyimes region of Romania are still spending between 200 and 210 days in a year in nature (Babai and Molnar 2013, personal communication). However, today, as a result of radical changes in lifestyle related chiefly to increased industrialization and urbanization, most people are living lives more separated from nature and students no longer spend their childhood in natural settings. This trend has such significant multiple effects on the human psyche and behaviour, that Richard Louv (2005) introduced the term ‘Nature Deficit Disorder’ (NDD), as a way to describe the human cost of alienation from nature, whose consequences include a diminished use of the senses, attention difficulties and higher rates of physical and emotional illnesses.

The planning of hydrothermal power systems determines the best possible generation of thermal alongside hydro units to attain the optimum fuel cost of thermal power plants in a short term (1 day) or long term (1 week) in view of several electric system limits and also hydraulic constraints. Owing to complicating variables and decomposable framework of the problem, a dependable approach based upon Benders decomposition algorithm to handle the complex short-term hydrothermal generation planning issue is presented in this chapter. The efficiency of the suggested procedure is validated on a well-known multi-reservoir cascaded hydrothermal system containing four hydro and one thermal units. The approach presented here addresses constraints such as load production balance, unit generation restrictions, reservoir flow balance, reservoir physical appearance constraints, reservoir connection, and water transport delay between the connected reservoirs. Moreover, the cost function of the thermal units is inclusive of the valve point effect. The results are conducted by GAMS solvers and the strengths as well as weaknesses of the proposed method are weighted up with those in this chapter. The obtained simulation results evidently illustrate that the suggested Benders decomposition algorithm can provide great convergence behavior and considerably better solution than other methods associated with the total operation cost and execution time.

This chapter considers the history of the verb hack and the related noun hacker. The chapter offers a ‘social etymology’—an account of how the term has changed over time and how such changes re-reorient the word. The chapter considers the various ways in which the term hacker has been used in a range of different historical sources and contemporary films and television shows and how the current use of the term strategically draws together particular historical meanings to position hackers as a threat to both individual wellbeing and possibly capitalism itself.

Cosmetic or functional concerns, especially for multiple operated noses, are always a challenge even for an experienced surgeon. Some authors distinguish the term “revision” rhinoplasty which is done by the same surgeon with “secondary” rhinoplasty which refers to more extensive surgeries. Regardless of the term used, this chapter aimed to mention the major and minor problems in multiple operated noses and define some surgical or nonsurgical solutions for such cases. Finally, the surgeon should recognize the objective problems, correlating them with subjective complaints, to suggest the possibilities of surgical intervention.

We present a new method to find conflicting instances of quantified formulas in the context of SMT solving. Our method splits the search for such instances in two parts. In the first part, E-matching is used to find candidate instances of the quantified formulas. In principle, any existing incremental E-matching technique can be used. The incrementality avoids duplicating work for each small change of the E-graph. Together with the candidate instance, E-matching also provides an existing node in the E-graph corresponding to each term in this instance. In the second part, these nodes are used to evaluate the candidate instance, i.e., without creating new terms. The evaluation can be done in constant time per instance. Our method detects conflicting instances and unit-propagating instances (clauses that propagate new literals). This makes our method suitable for a tight integration with the DPLL(\(\mathcal {T}\)) framework, very much in the style of an additional theory solver.

This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as a verification technique. The term property-directed reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover small counterexamples fast, but also to generalize them by pumping towards faulty flows hinting at the underlying error in the RNN. We also show that our method can be efficiently used for adversarial robustness certification of RNNs.

Interrelated computing device’s system such as IoT, RFID, or edge device’s systems are pervasively equipped for today’s information application and service systems, protecting them from unauthorized access i.e. safety is critical, because a breach from the device may cause cascading effects resulting to data lost or even crash of the whole information system. However, to determine a protection system’s safety is proven to be undecidable unless the system has limited management capabilities. And even with such limitation, it is too expensive to perform a safety test in term of computation time when a device has more than hundreds of subjects which is not uncommon for interrelated computing devices. Nevertheless, the required exponential computing time for safety test can be significantly reduced to its square root if computed by quantum algorithm. In this paper we demonstrate an application of quantum search algorithm to reduce the computation time for safety test for limited (i.e. mono operational) protection systems which are based on attribute-based access control model. The improvement of the performance allows the safety test for interrelated computing device’s system to be much less expensive to compute.

The search for a new conception of monetary policy must begin with admitting that monetary theory that derives policy recommendations from traditional variables such as money supply, the current inflation rate, potential output, or even nominal GDP has reached its limit.

Open trade policies are needed to ensure economic growth for all countries. This requires an understanding of the interaction between trade growth dynamics, trade costs, reforms and other factors. Recent literature has decomposed total exports growth into the sum of changes in demand and changes in trade costs arising out of ‘explicit beyond the border barriers’, ‘implicit beyond the border barriers’ and ‘behind the border barriers’. Reforms promote trade growth by reducing ‘implicit beyond the border barriers’. However, this method decomposes trade growth for a specific country. This idea is extended to analysing world trade flows using a new trade decomposition framework based on productivity analysis. Trade growth is decomposed into input, technological, efficiency effects and random effects. The first three are similar to output growth, while the fourth, a new term, captures the impact of random shocks. Hypotheses are formulated on trade growth patterns and on impact of reforms in influencing trade growth components. Model results are confirmatory and recommend its use as a supporting tool for ongoing researches in trade and development. Few of these are the New Structural Economics and World Bank’s Umbrella Facility for Trade Trust Fund (UF).

YouTube is one of the giants in social media arena. The Google owned website provides its users the facility to upload and view videos on its platform. YouTube’s ever increasing popularity and influence on the society makes it an active area of research. The spread of information through certain videos is so rapid that it can be easily compared to an outbreak of an infection and hence the term ‘viral videos’ aptly describes them. The spread of infection in the masses has been mathematically explained by scientists through epidemic modelling. Drawing parallels with an epidemic breakout, in the current proposal we have tried to capture view-count growth patterns. To better understand the viral content, a two-stage modelling framework inculcating Susceptibility (awareness) and Infection (viewing) has been proposed. The models have been validated on various YouTube datasets with good results.

Concepts contribute to maintaining knowledge and are constantly changing. Several conceptual analysis methods are used to clarify concepts, however, they are better applied for mature concepts. The purpose of this article is to describe a mixed conceptual analysis method developed to analyze the term “gender violence”, comparing it with Rodgers’ conceptual analysis method. The developed method started from five substitute terms of “gender violence”, and used Knowledge Discovery in Databases to discover patterns between the terms. The terms highlighted in the patterns were validated through content analysis. As a result, twice as many attributes were identified, enabling understanding concepts which are part of gender violence concepts, such as sexual violence, psychological violence and physical violence.

Data Lake is a term denoting a repository storing heterogeneous data, both structured and unstructured, resulting in a flexible organization that allows Data Lake users to reorganize and integrate dynamically the information they need according to the required query or analysis. The success of its implementation depends on many factors, notably the distributed storage, the kind of media deployed, the data access protocols and the network used. However, flaws in the design might become evident only in a later phase of the system development, causing significant delays in complex projects. This article presents an application of queuing networks modeling technique to detect significant issues, such as bottlenecks and performance degradation, for different workload scenarios.

The term “game mechanic” is often used when discussing games, but are we all talking about the same thing? While game studies and related fields have produced several notable definitions, there is currently no accepted standard for the term within the broader community. Through a systematic literature review spanning six academic venues and several prominent books, we identify and analyze 49 explicit definitions for the concept of “game mechanics”. Though some of the definitions are similar, they are all fundamentally distinct. Our work demonstrates the importance of providing or citing a definition when discussing game mechanics, and we provide a wide range of options to choose from.

Change seems to be an inherent property of organizations and the enterprises they undertake. During such changes, coordination among the different actors involved is key, in particular when there is a need to consider the longer term impact of change.

Natural language processing (NLP) is a problem which attracts lots of attention from researchers. This study analyzes and compares a different method to classify text sentences or paragraphs in Vietnamese into different categories. The work utilizes a sequence of techniques for data-preprocessing, customize learning model and methods before using Term Frequency-Inverse Document Frequency (TF-IDF) for model training. This classification model could contribute positively to many Vietnamese text-analyzing based businesses, such as social network, e-commerce, or data mining in general. This problem’s challenge relies on two main aspects: the Vietnamese language itself and current NLP researches for the Vietnamese language. The paper utilizes the pros of many different classification methods to provide better accuracy in text classification.

CS Peirce introduced the concept of abduction into our epistemic lexicon. It is a view of problem solving that emphasizes ecological contexts, preparatory or predictive predilection knotted to learning and inquiry. Abduction is essentially tied more broadly to pragmatism. One view of the brain reflects the fact that predictive predilections knotted to abduction or hypothesis testing dominates the landscape of diverse forms of problem solving. Abduction is biologically constrained and contextual, not a monolithic term and runs the range of neural capability.

Multimodal optimization is a relatively young term for the aim of finding several solutions of a complex objective function simultaneously. This has been attempted under the denomination ‘niching’ since the 1970s, transferring ideas from biological evolution in a very loose fashion. In this chapter we more formally define it, and then highlight its most important perspectives: how do we measure what is good? On what problems do we measure it? Which type of algorithms may be effectively employed for multimodal optimization? How do they relate to each other? Competitions at two major evolutionary computation conferences have driven algorithm development in recent years. We therefore report, in a concise fashion, what we have learned from competition results and give an outlook on interesting future developments.

International audience; The performance evaluation of Cable-Driven Parallel Robots in terms of accuracy, repeatability and long term capacities is a key for their industrial deployment. This paper presents and analyses corresponding experimental results for the PACE prototype, a fully-constrained, redundantly restrained, eight cables and six degrees of freedom cabledriven parallel robot. First, a long-term running test is carried out to evaluate the deviation of the moving-platform pose along time of use. A second experiment consists into a full design of experiments to evaluate the effect of some factors specific to this type of robot on its absolute accuracy and repeatability. Experimental results are presented and discussed.

This chapter discusses the origins and development of a widely used term in esotericism: that of the “adept.” The terminology originated in medieval Arabic philosophy, mainly in Al Farabi, Averroes, and Avicenna, who used the term intellectus adeptus (“acquired intellect”) to denote a mind perfected by the Active Intellect. The concept was innovatively used by Theophrastus von Hohenheim, called Paracelsus (1493-1541), who employed the term Philosophia Adepta to refer to an ancient and secret form of knowledge and to Uberirrdisch (super-earthly) philosophy. The Paracelsian followers Petrus Severinus (1540-1602) and Oswald Croll (1563-1608) picked up on the term, using it in a more specific alchemical context. This chapter shows that the “adept” term did not have a “stable,” ahistorical meaning but emerged through several stages of innovation.

This case study tells the story of time zone arbitraging of shares in a mutual fund. This form of trading is short term, usually within 24 or 48 hours, and takes advantage of price discrepancies that arise from a situation where primary markets for the underlying securities of a mutual fund are closed at the times the fund is traded. To evaluate the conduct of time zone arbitraging in mutual funds, we use two ethics tools: (1) the fiduciary principle and (2) the concepts of commutative and distributive justice.

In some previous works, two of the authors introduced a strategy to develop high-order well-balanced numerical methods for 1d systems of balance laws. There, a strategy which allows us to modify any standard reconstruction operator in order to be well-balanced was also described. This strategy involves a nonlinear problem at every cell, at every time step, that consists in finding the stationary solution whose average is the given cell value. Our goal is to present a general efficient implementation that can be applied to any system of balance laws by interpreting these nonlinear problems as control problems that are rewritten in functional form. Newton’s and descent methods are applied and compared. Applications to the Burgers’ equation with a nonlinear source term and to the 1d shallow water model are finally shown.

Advances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

This chapter opens the book with a detailed, in-depth review of the literature that considers the evolution and many interpretations of the term ‘Minority Entrepreneurship’. Its purpose is to take a reader on a journey of this evolution in a comprehensive fashion. It guides a reader through the world of various minorities highlighted in the handbook, including the unemployed, gay, ex-offenders, diverse indigenous people and disabled communities. While an answer to the question who qualify as minority has proven to be time-, context- and case-dependent, what all minorities share in common appears to be their unfavourable position within power networks. Understanding different behaviours and unique challenges of these disparate communities can guide policymakers to become increasingly relevant.

Within integrated steelworks, several sub-processes produce off-gases, which are suitable for reuse as energy sources for other internal processes as well as for the production of energy. An adequate and optimal distribution of these gases among their users allows valorizing at best their energy content by minimizing the need to both burn them through torches due to storage issues and to acquire natural gas to satisfy the internal energetic demand. To this purpose, the volume and energetic value of produced gases as well as the demands from internal users must be known in advance, in order to implement model-predictive control strategies aimed at satisfying the demands on the short-medium term based on the production scheduling. Such forecasting knowledge also enhances the capability to react to the variability of the process scheduling as well as to other unforeseen events.

Text classification plays a vital role in many real-life applications. There are different methods for text classification primarily Naive Bayes classifier, support vector machine, etc. A good text classifier must efficiently classify large set of unstructured documents with optimal accuracy. Many techniques have been proposed for text classification. In this paper, we propose an integrated approach for text classification which works in two phases. In initial preprocess phase, we select the frequent terms and adjust the term weight by use of information gain and support vector machines. Second phase consists of applying Naive Bayes classifier to the document vector. The experiment has been performed on the open research dataset of Forum of Information Retrieval (FIRE). In association rule, the correlation between data items is obtained with no requirement of external knowledge, whereas in classification, attention is given to small set of rules with the help of external knowledge. The proposed work uses FP-growth algorithm with absolute pruning for obtaining frequent text sets, and then, Naive Bayes classifier model is used for training and constructing a model for classification. Our experimental result shows increase in efficiency while comparing with other traditional text classification methods.

Despite appearances, Newton’s dynamics is not simple at all, nor very intuitive. The three fundamental principles, for example, took centuries before they were recognised as true, as our collective experience tells us that bodies don’t move unless a force is applied to them. Galileo Galilei first realised that friction is responsible for this, a fact that is not straightforward in the absence of Newton’s first Law. Many conceptual difficulties in classical mechanics, such as those connected to Newton’s third Law or to the usage of non-inertial reference frames, can be at least partly ascribed to the confusion between the term force and the term interaction. Forces are the result of interactions, and interactions are only effective among at least two entities. In this respect, what we call fictitious or apparent forces can be considered, in fact, to be as real as gravity or the elastic force. On the other hand, we can measure them, thus they exist. The confusion comes from the fact that, in the past, forces and interactions were, in fact, used as if they were synonyms, as any interaction gave rise to a force, represented as a vector in Newtonian mechanics. Today, we know that this is not always the case. The weak interaction, for example, is responsible for radioactive decays. The interaction causes the change of state of a system composed of an atom of a given species into another in which the state is composed of an atom of a different species, together with one or more particles (a photon, an \(\alpha \)-particle or an electron and a neutrino). There is no place to which we can attach a vector in this case, hence there is no force, in the Newtonian sense. It would be better for us to say that interactions are responsible for the change of state of a system and the effects of interactions often can be, but are not always, represented by forces. In this chapter, we perform experiments in which we explore the physics in non-inertial frames, to dissipate doubts and to allow you to familiarise with them. This chapter introduces topics that are often only marginally covered in physics books. We perform experiments in a rotating reference frame and, interestingly enough, in a free-falling system. It is an interesting experiment, that today we can perform, thanks to the availability of tools like smartphones, but, in the past, it was considered an experiment that could only be conducted mentally: a gedankenexperiment. We learn the principles of operation of gyroscopes and study the Euler acceleration in a rotating bucket.

This paper includes the discussion on the modelling of mechatronic systems and introduces the term related to mechatronic systems. Following a brief introduction the next chapter analyzes some challenges and threats regarding modelling of such systems.

Despite their impressive performance, deep neural networks (DNNs) are widely known to be vulnerable to adversarial attacks, which makes it challenging for them to be deployed in security-sensitive applications, such as autonomous driving. Image-dependent perturbations can fool a network for one specific image, while universal adversarial perturbations are capable of fooling a network for samples from all classes without selection. We introduce a double targeted universal adversarial perturbations (DT-UAPs) to bridge the gap between the instance-discriminative image-dependent perturbations and the generic universal perturbations. This universal perturbation attacks one targeted source class to sink class, while having a limited adversarial effect on other non-targeted source classes, for avoiding raising suspicions. Targeting the source and sink class simultaneously, we term it double targeted attack (DTA). This provides an attacker with the freedom to perform precise attacks on a DNN model while raising little suspicion. We show the effectiveness of the proposed DTA algorithm on a wide range of datasets and also demonstrate its potential as a physical attack (Code: https://github.com/phibenz/double-targeted-uap.pytorch).

In the current European context, the term Easy Language (previously easy-to-read language) refers to a language form that has been modified to be easier to read and understand for people with various linguistic challenges. This empirical pilot study combined Minimal Finnish and Easy Finnish, investigating whether Minimal Finnish could provide core vocabulary for Easy Finnish. Easy Finnish experts adapted a Standard Finnish text on discrimination, using both conventional Easy Finnish guidelines and the 300-word lexicon of Minimal Finnish, then presented the texts to test readers. The study found that Minimal Finnish is beneficial for explaining abstract concepts, and the semantic primes and molecules may be considered a list of ‘safe’ words. However, it can also give rise to more complex grammar or lengthen the text.

In finite-control-set model predictive control (FCS-MPC) for PWM rectifiers, the control performance is degraded seriously when the filter inductance is mismatched. To deal with this problem, this paper proposes a simple FCS-MPC strategy for PWM rectifiers to achieve high control performance regardless of the parameter mismatch. In the proposed FCS-MPC, the predictive model is modified by adding a correction term to compensate for the uncertainty, and high prediction accuracy is maintained irrespective of the parameter mismatch. The proposed FCS-MPC shows highly improved control performance compared to conventional FCS-MPC, and its effectiveness is experimentally verified.

Designers’ attention is drawn to the necessary constructive calculations not only for the strength of multi-storey, multi-layered, and multi-loaded walls, but also for deformations. The article shows peculiar features of deformation-strength properties of mortars in horizontal masonry joints under forceful compression, compared with those in standard mortar samples in standard tests. It is necessary to jointly consider the conditions of equilibrium and deformations, since the work conditions in the masonry of stone and mortar of different types are polar opposite. The stone is vertically compressed by the load and transversely stretched by the mortar that is usually more subjected to deformation, as a result of which its strength in the masonry is significantly lower than the standard compressive strength. The mortar, whose transverse deformations are constrained by less deformable stones, on the contrary, is unevenly compressed both in the vertical and transverse directions, due to which its strength, unlike deformations, increases significantly.

Credit card fraud detection over the years remains a major concern for all and this has made the field to receive huge attention from researchers. One way of addressing this concern is for financial sectors and government agencies to reliably detect fraud in any transaction so as to prevent financial losses incurred by these sectors and card owners. Exiting fraud detection system is plagued with misclassification and high false-positive rate. To prevent misclassification, this research therefore aims to deploy Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) for classifying financial transaction as fraudulent or not. The system extracted desired features from two different dataset gotten from kaggle repository using Principal Component Analysis and preprocessed using Arbitrary Assignment Method and Min–Max scalar algorithm for Normalization. The work was implemented using python programming language. The relevant features selected were then fed into the LSTM-RNN for classification. The results obtained were compared with past work and our fraud model recorded high classification accuracy as well as reduced false alarm rate. It has 99.58% Prediction Accuracy, 99.6% Precision, and Recall of 80%. The system will enable financial institutions and government agencies involved in financial transaction to detect the occurrence of fraud and be able to proffer the necessary solution as appropriate.

Each entity builds a specific model of functioning, which is related to the selection and configuration of resources, so that the assumed goal can be achieved in given market conditions. This goal in the short term amounts to achieving the planned profit, while in the long term it means competitive advantage and durability of being. This objective is accomplished by shaping the level of used and necessary resources, such as material and personal resources. The article assumes that for transport companies the basic resources that affect the level of profit are means of transport and employment. The thesis of the article is that it is possible to build a model describing the determinants of the use of resources to generate a certain profit by transport companies. The test method of Engle–Granger cointegration was used to build the model. The study was based on data of the Transpolonia entity.

For large image retrieval applications, collecting images from the Internet is a relatively low-cost way to obtain labeled training data. However, images from the Internet are often falsely annotated, which may lead to unsatisfying performances of trained models. To alleviate the impact of label noise, our work adopts non-deterministic features for training deep convolutional neural networks. Suppose that non-deterministic features of images obey multi-dimensional Gaussian distribution, and a feature is represented by its mean and variance. During the training, when images are mapped to non-deterministic features, the model will tend to assign large variance to mislabeled samples, suggesting low confidence levels, instead of causing negative optimization by directly changing estimation of mean value. During the test, mean value of non-deterministic features are used for image retrieval. This method increases redundancy by adding variance term of features, so the model has stronger tolerance to label noise. Experimental results and analysis show that on datasets with label noise, using non-deterministic features leads to better image retrieval results than using deterministic features.

The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computationally very expensive, many optimizers need days or even weeks to find suitable architectures. However, this search time can be significantly reduced if knowledge from previous searches on different tasks is reused. In this work, we propose a generally applicable framework that introduces only minor changes to existing optimizers to leverage this feature. As an example, we select an existing optimizer and demonstrate the complexity of the integration of the framework as well as its impact. In experiments on CIFAR-10 and CIFAR-100, we observe a reduction in the search time from 200 to only 6 GPU days, a speed up by a factor of 33. In addition, we observe new records of 1.99 and 14.06 for NAS optimizers on the CIFAR benchmarks, respectively. In a separate study, we analyze the impact of the amount of source and target data. Empirically, we demonstrate that the proposed framework generally gives better results and, in the worst case, is just as good as the unmodified optimizer.

Whether in the near or far term, people change as a result of an event or crisis. Sometimes this change is unavoidable. If someone is injured, the change is obvious. However, some people may want to look past the event or crisis after it is contained or under control and simply revert to an earlier, more convenient way of functioning. There are many ways to approach this chapter’s theme, but let’s highlight one thing: the tendency to rush to a solution as a means for demonstrating action is not a good idea when faced with a troublesome event or emerging crisis. Managing a troublesome event has its own challenges, but if the event morphs into a crisis, then different effort, thinking, resources and actions are required.

Aspect category sentiment classification aims at predicting the sentiment polarity of the given aspect category. Since the aspect category may not occur in the sentence, it is hard for the model to directly find the appropriate sentiment words for the aspect category and disregard unrelated ones. To address it, previous works have explored leveraging implicitly the information of the aspect term in the sentence and demonstrated the effectiveness of such information. Inspired by this conclusion, we propose a two-stage strategy named Locate-Combine(LC) to utilize the aspect term in a more straightforward way, which first locates the aspect term and then takes it as the bridge to find the related sentiment words. Specifically, in the “Locate” stage, we locate the aspect term corresponding to the given aspect category in the sentence, which can crystallize the target and further enable our model to focus on the target-related words. In the “Combine” stage, we first apply the graph convolutional network (GCN) over the dependency tree of the sentence to combine the information of the aspect term and related sentiment words and then take the output representation corresponding to the located aspect term to predict the sentiment polarity. The experimental results on the public datasets show that the proposed two-stage strategy is effective, which achieves state-of-the-art performance. Furthermore, our model can output explainable intermediate results for model analysis. (Code can be found at https://github.com/SCIR-MSA-Team/LC-ACSA)

Contrasting elected leaders, those voted into position as representatives of others, with other paths may be novel for many of our readers. There are important differences for Representatives (We use the “Representative” label in the generic sense of the word, as it applies to many titles of elected leaders) largely emanating from their status as the “entity” who embodies the power of the position, allowing opportunity to leverage their vision and resources to connect with constituents according to the needs and interests of all parties. This comes with potential challenges that are in some ways akin to celebrity status, including public visibility, demanding expectations, and busy schedules. Other challenges vary by the particular administrative system, such as election cycle, term limits, and demands attached to party hierarchy.

Frequent communications among massive terminal devices are ubiquitous in forthcoming 5G Internet of Thing (IoT) networks. It strengthens links of massive machine-type-communication (MMTC), pushes forward the process of Internet of everything. However, due to continual interactions among different devices and the broadcast characteristic of wireless channels, it also brings new security challenges. Recently, physical layer security launches a new solution to guarantee information theoretic security. To enhance the physical layer security performance of massive intelligent devices, especially in D2D communications, the game theory and machine learning methods are introduced. In this paper, we first review physical layer security problems on D2D communications under different attack scenarios. Game theory is proposed to describe hierarchical and heterogeneous interactions among legitimate users and active attackers in 5G IoT networks, then some distributed machine learning methods are proposed to obtain equilibrium states among different agents. Moreover, numerical results are provided to verify availability and efficiency of proposed game-theoretic learning approaches. Finally, we discuss open issues and future research directions in term of anti-eavesdropping and anti-jamming problems in D2D communications when facing active attackers.

I criticize the current usage of the terms “enhancement” and “disenhancement” in the debate over the genetic modification of animals and propose an alternative definition of these terms based on how modifications affect animals’ welfare in particular contexts. The critique largely follows a similar criticism of the use of the term “enhancements” in the human bioethics literature. I first describe how the term “disenhancement” has been used in debates thus far, and argue that the present lack of a shared definition is problematic. I then consider some potential definitions of “disenhancement” that can be adapted from the human bioethics literature and argue that most of these uses are flawed for the purposes of using the term in current ethical debates. Finally, I elaborate on the welfarist conception of disenhancement and consider some potential objections, using examples from the literature to illustrate key points.

This chapter traces the origin of the term geopolitics as an imperial tool, how it went out of fashion and was then re-introduced. It considers how geography has been politicised and points out that geopolitics is a mainstay of political realism/power politics. The result is that it tends to sweep the human factor under the carpet, since it is concerned with the acquiring of resources, such as oil, which has included the drawing up of ‘business borders’ according to the interests of large powers, rather than natural borders. The Sykes-Picot agreement is used as an example.

The long term privacy of voting systems is of increasing concern as quantum computers come closer to reality. Everlasting privacy schemes offer the best way to manage these risks at present. While homomorphic tallying schemes with everlasting privacy are well developed, most national elections, using electronic voting, use mixnets. Currently the best candidate encryption scheme for making these kinds of elections everlastingly private is PPATC, but it has not been shown to work with any mixnet of comparable efficiency to the current ElGamal mixnets. In this work we give a paper proof, and a machine checked proof, that the variant of Wikstrom’s mixnet commonly in use is safe for use with the PPATC encryption scheme.

Future generations is the term used to refer to the next and subsequent generations of humans. Whatever context it is used in it acknowledges that those who do not yet exist matter today and that present generations who are now living have an obligation to think and act not only for themselves, but also for others including those who will follow them in the future.

We define and study a term calculus implementing higher-order node replication. It is used to specify two different (weak) evaluation strategies: call-by-name and fully lazy call-by-need, that are shown to be observationally equivalent by using type theoretical technical tools.