Concern, Conception, and Consequence: Re-thinking the Paradigm of Higher Education in Dangerous Times

Discussion of the role of universities in relation to broad issues of sustainability has been current for some decades, although predominantly at the margins of debate and policy. Yet a recent rapid rise of concern—catalyzed by mounting evidence of climate crisis, biodiversity loss, pandemic disease and further systemic issues -is focusing renewed attention on the adequacy of the response of higher education to unprecedented times of urgency, uncertainty and threat. Whilst it is now widely acknowledged that the fate of the planet and of humanity hangs in the balance, there still remains an astonishing disconnect between pressing signs of global change, and the relatively closed world of higher education. A trend toward greening universities' operations is positive, but fails to engage or galvanize the cultural and value shift toward a holistic and ecological zeitgeist that is now necessary to generate widespread institutional systemic change. This paper delves into deep causal factors that have historically impeded the ability of universities to respond fully and effectively to present and probable future realities, pointing to the foundations of Western thought such as reductionism, objectivism, dualism, individualism, anthropocentrism, rationalism, instrumentalism and technocentrism that shape mainstream education policy and practice, overlain and reinforced in more recent times by neo-liberal conceptions of the purpose of universities in a modern economy. It is argued that these elements of our culturally shared worldview constrain our ability to perceive and respond deeply, fully and wisely to the global predicament, but also maintain destructive patterns of development. Whilst there is increasing acceptance that education must “transform” in order to—in turn—be transformative in effect, there is less clarity about the guiding assumptions and ideas that inform mainstream policy and practice, and about the philosophic value bases that can facilitate transformative educational thinking, policy and practice. A framework of three broad and complementary components of paradigm—Concern, Conception, and Consequence—is employed to outline the shape of the systemic paradigmatic shift that universities need to urgently navigate in order to maximize their ability to respond fully to contemporary socio-economic and ecological conditions and trajectories.

A globális, az európai uniós vírusválság határpolitikai hatásai.

Összefoglaló. Az elmúlt mintegy másfél év alatt a COVID–19 vírus valójában több struktúrában megrengette a világot, az Európai Uniót és egyes országokat is. A világ államai rövid idő alatt bezárkóztak, az Európai Unió 30 napra lezárta külső határait, az egyes tagállamok pedig az uniós belső határokat is lezárták. Veszélybe került a schengeni rendszer. A Kárpát-medence államai az elsők között reagáltak a határok lezárásával. Az egyéni döntések kritikája erőteljesen megjelent az Európai Bizottság részéről. A globális, az európai és a szomszédállami folyamatok összefüggtek. A határok lezárása feltehetően hozzájárult a vírus terjedésének a korlátozásához. (Ausztrália példája ezt erősíti.) A határzárak a nemzetközi tranzitforgalomban, a határ menti területeken élők és az ingázók között okozták a legnagyobb bizonytalanságot, több esetben zűrzavart. Summary. According to the first ‘official announcement’ in December of 2019 the Covid-19 virus is reported to have emerged in China. The global spread of the virus was extremely fast. On 11 March 2020, the WHO declared Covid-19 to be a global pandemic. As of 31 March 2020 about 91% of the world population lived in countries with border and travel restrictions (border-closed world). The brief analysis reviews the main processes affecting EU and Member States borders, with a special regard to Hungary and its neighbours in the Carpathian Basin. On 17 March 2020, the EU closed its external borders for 30 days.to non-EU citizens. In parallel, a number of Member States decided to close their borders to both Schengen Zone members and third countries. As a response to border closures, the EU Commission and some states organized the repatriation of about 600,000 EU citizens. On 4 March, virus was officially reported to have been detected in Hungary. On 11 March the Hungarian Government declared a national state of emergency. On 15 March the first coronavirus-related death was announced. On 16 March the Government ordered the complete closure of Hungarian borders. After a border ‘traffic chaos’ along the Austrian-Hungarian border, the Hungarian Government – with collaborations with Romania – opened humanitarian corridors for foreign citizens. The possibilities of border crossings of citizens of seven neighbours of Hungary were formed not just by Hungary. In 2020 because of different changes (modifications, opening and closing) we could form at least three categories: open borders, partly open borders, closed borders. In the neighbouring countries (Austria, Slovakia, Ukraine, Romania, Serbia, Croatia, Slovenia) the progression of the epidemic followed the same pattern. Over the past year and a half the virus crisis has actually shaken many structures of the globalized world, the European Union and many countries in the Carpathian Basin. The virus crises has disrupted intensive connections between Hungary and neighbouring countries. Neither Hungary nor its neighbours were able to insulate themselves from the epidemic waves. The border restrictions primarily affected the movements of persons. Because of ‘permanent uncertainty’ commuters were the losers of the crisis.

Thirty years of Epistemic Specifications

The language of epistemic specifications and epistemic logic programs extends disjunctive logic programs under the stable model semantics with modal constructs called subjective literals. Using subjective literals, it is possible to check whether a regular literal is true in every or some stable models of the program, those models, in this context also called belief sets, being collected in a set called world view. This allows for representing, within the language, whether some proposition should be understood accordingly to the open or the closed world assumption. Several attempts for capturing the intuitions underlying the language by means of a formal semantics were given, resulting in a multitude of proposals that makes it difficult to understand the current state of the art. In this article, we provide an overview of the inception of the field and the knowledge representation and reasoning tasks it is suitable for. We also provide a detailed analysis of properties of proposed semantics, and an outlook of challenges to be tackled by future research in the area.

PF : Website Fingerprinting Attack Using Probabilistic Topic Model

Website fingerprinting (WFP) attack enables identifying the websites a user is browsing even under the protection of privacy-enhancing technologies (PETs). Previous studies demonstrate that most machine-learning attacks need multiple types of features as input, thus inducing tremendous feature engineering work. However, we show the other alternative. That is, we present Probabilistic Fingerprinting (PF), a new website fingerprinting attack that merely leverages one type of features. They are produced by using a mathematical model PWFP that combines a probabilistic topic model with WFP for the first time, due to a finding that a plain text and the sequence file generated from a traffic instance are essentially the same. Experimental results show that the proposed new features are more distinguishing than the existing features. In a closed-world setting, PF attains a better accuracy performance (99.79% at most) than prior attacks on various datasets gathered in the scenarios of Shadowsocks, SSH, and TLS, respectively. Besides, even when the number of training instances drops to as few as 4, PF still reaches an accuracy of above 90%. In the more realistic open-world setting, PF attains a high true positive rate (TPR) and Bayes detection rate (BDR), and a low false positive rate (FPR) in all evaluations, which outperforms the other attacks. These results highlight that it is meaningful and possible to explore new features to improve the accuracy of WFP attacks.

LightSEEN: Real-Time Unknown Traffic Discovery via Lightweight Siamese Networks

With the increase in the proportion of encrypted network traffic, encrypted traffic identification (ETI) is becoming a critical research topic for network management and security. At present, ETI under closed world assumption has been adequately studied. However, when the models are applied to the realistic environment, they will face unknown traffic identification challenges and model efficiency requirements. Considering these problems, in this paper, we propose a lightweight unknown traffic discovery model LightSEEN for open-world traffic classification and model update under practical conditions. The overall structure of LightSEEN is based on the Siamese network, which takes three simplified packet feature vectors as input on one side, uses the multihead attention mechanism to parallelly capture the interactions among packets, and adopts techniques including 1D-CNN and ResNet to promote the extraction of deep-level flow features and the convergence speed of the network. The effectiveness and efficiency of the proposed model are evaluated on two public data sets. The results show that the effectiveness of LightSEEN is overall at the same level as the state-of-the-art method and LightSEEN has even better true detection rate, but the parameter used in LightSEEN is 0.51 % of the baseline and its average training time is 37.9 % of the baseline.

Few-Shot Website Fingerprinting Attack with Data Augmentation

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).

Open for business in a closed world? Managing MNE nonmarket strategy in times of populism and geopolitical uncertainty

Purpose This paper aims to consider how multinational enterprises are increasingly augmenting their international strategies with insights on, and approaches to, external stakeholders and nonmarket dynamics. The rise of populism and increased geopolitical uncertainty have accelerated these efforts, particularly for business leaders anticipating and engaging external agents, events, and issues that challenge the strategic objectives of their enterprises. Design/methodology/approach This paper aims to explain why the increased preponderance of populism and geopolitical uncertainty are simultaneously posing an existential threat to the post-Cold War global economy predicated on free trade and (relatively) open borders, and consequently, challenging the structures and strategies of international business. Findings We provide an overview of the four papers in our special issue, and consider how each advances insights on how multinational enterprises effectively navigate the nonmarket uncertainties of the contemporary global economy. We then advance four important areas for international business research around multinational nonmarket strategies: (i) resilience and legitimacy; (ii), diversification; (iii), market and nonmarket strategy integration; and (iv), institutional arbitrage. Research limitations/implications We anticipate that nonmarket strategy scholars can build on these themes to assess how nonmarket strategies can better enable multinational enterprises to survive and thrive in an age of heightened global risk and uncertainty. Originality/value This paper and the related special issue provide new theoretical insights by bringing attention to the relatively under-researched realm of multinational enterprise nonmarket strategy, particularly in populist contexts and during periods of geopolitical uncertainty. We identify four promising domains – resilience and legitimacy, diversification, the integration of market and nonmarket strategy, and institutional arbitrage – for international business scholars investigating nonmarket strategy to consider. We hope that our paper, as well as other papers in this special issue, provide further momentum to this growing area of research.

Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases

Combining the closed-world reasoning of answer set programming (ASP) with the open-world reasoning of ontologies broadens the space of applications of reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in some cases without increasing the complexity of reasoning tasks. However, in many cases, solver development is lagging behind. As the result, the only known method of solving disjunctive hybrid MKNF knowledge bases is based on guess-and-verify, as formulated by Motik and Rosati in their original work. A main obstacle is understanding how constraint propagation may be performed by a solver, which, in the context of ASP, centers around the computation of \textit{unfounded atoms}, the atoms that are false given a partial interpretation. In this work, we build towards improving solvers for hybrid MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded sets for these knowledge bases, identify lower complexity bounds, and demonstrate how we might integrate these developments into a DPLL-based solver. We discuss challenges introduced by ontologies that are not present in the development of solvers for disjunctive logic programs, which warrant some deviations from traditional definitions of unfounded sets. We compare our work with prior definitions of unfounded sets.

Closed- and Open-world Reasoning in DL-Lite for Cloud Infrastructure Security

Infrastructure in the cloud is deployed through configuration files, which specify the resources to be created, their settings, and their connectivity. We aim to model infrastructure before deployment and reason about it so that potential vulnerabilities can be discovered and security best practices enforced. Description logics are a good match for such modeling efforts and allow for a succinct and natural description of cloud infrastructure. Their open-world assumption allows capturing the distributed nature of the cloud, where a newly deployed infrastructure could connect to pre-existing resources not necessarily owned by the same user. However, parts of the infrastructure that are fully known need closed-world reasoning, calling for the usage of expressive formalisms, which increase the computational complexity of reasoning. Here, we suggest an extension of DL-LiteF that is tailored for capturing such cloud infrastructure. Our logic allows combining a core part that is completely defined (closed-world) and interacts with a partially known environment (open-world). We show that this extension preserves the first-order rewritability of DL-LiteF for knowledge-base satisfiability and conjunctive query answering. Security properties combine universal and existential reasoning about infrastructure. Thus, we also consider the problem of conjunctive query satisfiability and show that it can be solved in logarithmic space in data complexity.