dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants would answer the question according to the expectations they infer from the experimenter, rather than according to spontaneous attributions toward the robots (e. g. ,  [26, 88, 92, 93, 96] ). We recommend the use of implicit measurement methods (mainly behavioral, such as non-verbal paradigms or social dilemmas), which would provide a more accurate measure of this phenomenon (e. g. ,  [16, 40, 65, 217, 228] ). A wide variety of robots have been used in the literature, and the diversity of their appearances makes it difficult to generalize results from one robot to another. Some studies do not specify the type of robot used (e. g. ,  [76, 80, 170, 196] ) or use a robot created by the research team itself (e. g. ,  [60, 70, 71, 173, 194, 225] ). The beginning of standardization is allowed by the Anthropomorphic roBOT (ABOT) database, which references an anthropomorphic score for 251 robots based on the voting of 1000 participants.
Furthermore, the duration of interaction with the robots varies considerably across studies: the most common duration is 30 min per session (15-30 min on average, and few studies exceed 60 min) but the longest lasts over 120 min  [229]. Overall, interactions with the robot are spread over a short period of time (often a single interaction, the duration of which is not always specified) and few studies are conducted over the long term  [229]. We recommend referring to the ABOT database to systematically specify the humanlike score of the robots used. The variability of the robot used should also be reduced, so that the results obtained can be generalized. As we have seen in this review, a moderately human-like appearance is preferable, due to the uncanny valley phenomenon. We also recommend privileging in vivo interactions with robots that are physically embodied-and over the long term-in order to approach, as much as possible, the real conditions of human-robot interactions.
A low profile Notwithstanding aid suspensions' fit with the notion of sanctions, they are often disregarded in mainstream scholarship. Numerous authors acknowledge aid cut-offs as a sanctions subtype  (Brooks, 2002). However, this recognition is not universal. Many economists exclusively study trade sanctions, e. g. leading authors  Kaempfer and Lowenberg (2007)  consider measures restricting trade and capital flows, alongside those banning the travel of elite members or freezing their assets, but disregard aid sanctions. The scarce presence of aid sanctions in sanctions databases mirrors this situation: Neither of the two databases that record aid sanctions contains information on their contents  (Jeong, 2019). 3  Because of the preponderant role of databases as tools of study in sanctions scholarship  (Peksen, 2019) , the low visibility of aid cuts in these compilationsor their outright absenceimplies that they are scarcely explored  (Portela & Charron, 2023).
The low profile of aid sanctions in sanctions scholarship is compounded by a discrepancy in disciplinary research foci, which prevents their integration. Most IR authors studying sanctions are preoccupied with effectiveness. By contrast, development scholars looking at aid conditionality and sanctions focus on consistency and selectivity  (Crawford & Kacarska, 2019; Del Biondo, 2011; Laakso et al. , 2007; Saltnes, 2018)  or sanctions onset  (Molenaers et al. 2015) , which in turn receives scarce attention from IR researchers  (Charron & Portela, 2015). While IR authors do not always consider aid sanctions in their research, development scholars focus solely on this type  (Crawford & Kacarska, 2019; Nielsen, 2013). These routinely source their own data, build tailored datasets, and typically analyse the behaviour of individual donors  (Crawford, 1997; Del Biondo, 2011; 2015; Fisher, 2015; Swedlund, 2017; Zimelis, 2011).
While the impact of foreign aid on the survival of non-democratic leaders has been investigated  (Licht, 2010) , the question as to how aid suspensions affect different autocratic types remains unexplored. Testing  Stokke's (1995)  propositions, Crawford concluded that 'military-backed governments, often characterised by a ruthless suppression of internal dissent, are generally more able to resist external pressure', confirming that 'external intervention is more successful when it combines with internal pressure from an active political opposition  ' (1997, p. 83 ). Yet, other autocracies are absent from his study. 3 Only data by  Morgan, Bapat, & Krustev (2009)  treat the 'termination of foreign aid' as a fully-fledged sanctions category. Aid sanctions feature in the popular sanctions database produced Gary Hufbauer and his collaborators (2007); however, aid cuts are integrated in the broader category of financial sanctions.
Dataset, hypotheses and methodology Autocratic survival relies on both repression and the distribution of rents among loyal elites, both of which are costly. Sanctions can reduce the availability of funds to the government, and thus diminish the ability of autocracies to fund both repression and rents (Bueno de  Mesquita et al. , 2003; Kaempfer et al. , 2004). Escribà-Folch and Wright suggest that personalist regimes and monarchies are more likely to be destabilised because they are more sensitive to the loss of external sources of revenue to fund patronage. Single-party and military regimes are, by contrast, more resilient because they can increase tax revenue to step up co-optation and repression while the economic disruption caused by sanctions is most likely to carry political costs for personalist regimes  (2010).
Dataset We test our hypotheses against an original dataset tailored to the purpose of our inquiry. Our dataset encompasses sanctions imposed by the principal senders and donors, including the US, the UN, Canada, the EU and other international organisations (I. O. s). We considered entries only when they could be verified against a minimum of two different sources. Albeit this choice restricts our aid sanctions universe, it ensures that all our episodes are correctly identified, i. e. that we exclude aid interruptions motivated by other reasons such as technical unviability or change in donor priorities. 5 The timeframe spans from 1990 to 2018, comprising 309 episodes in total. 6 Data on aid sanctions are sourced from  Hazelzet (2001) ,  Crawford (2001 ), Del Biondo (2015)  and  Molenaers et al.
(2016). These data are updated until 2018 with official documents and media sources. Data on standard economic sanctions from  Hufbauer et al. (2007)  are complemented with data from UN Targeted Sanctions Consortium (Graduate Institute, 2018) for UN sanctions, and  Borzyskowski and Portela (2018)  for sanctions by regional entities. Key innovations of our database are therefore the integration of aid sanctions and a comprehensive coverage of EU measures.
Dataset design and unit of analysis Various reasons advised against the use of conventional databases in our inquiry. Data by  Molenaers et al. (2016)  focus on aid sanctions but lacks information on effectiveness. The HSE  (Hufbauer et al. , 2007)  and TIES databases  (Morgan et al. , 2014)  stop in 2000 and 2005, respectively. The most comprehensive database, the Global Sanctions Database  (Felbermayr et al. , 2020)  excludes aid sanctions, as does the UN Targeted Sanctions Consortium database  (Graduate Institute, 2018). By contrast, our original data improves conventional compilations in several respects. Firstly, aid sanctions feature as a stand-alone sanction category, contrary to standard datasets that either subsume them into larger categories or mention them only when wielded in conjunction with other economic measures, as in  Hufbauer et al.
(2007) , making it difficult to observe independent effects. In a departure from the conventional focus on great powers, the dataset is unique in that it features regional organisations as senders, reflecting their increasingly prominent role in regional security  (Hellquist, 2022). Voluntary sanctions enacted by multinational entities, a category of sanctions routinely ignored  (Doxey, 2009) , are included too. The dataset encompasses the rich practice of EU aid suspensions, which has a modest presence in standard databases, often studied separately because they are adopted outside the EU foreign policy framework  (Koch, 2015; Portela, 2010). In contrast to  Morgan et al. (2014) , our data exclude threats 7 as well as measures of commercial defence. The unit of analysis is the episode, in tune with recent scholarship  (Biersteker et al.
, 2016). Every sanctions event features information on the nature of the restrictions wielded, recording whether measures have a bearing on elites or on the economy as a whole. Each episode features the set of measures imposed by a single sender against a target for the same violation. A new episode is created if the goals of the sanctions regime change  (Biersteker et al. , 2016). Two sanctions regimes imposed against the same target for different reasons are recorded as two different episodes. An episode terminates when sanctions are lifted, or when the goal pursued by the sender changes substantially. Where two senders impose sanctions simultaneously against the same target, 8 our dataset features two episodes rather than one, which allows us to separate measures by various senders that are dissimilar in nature and scope.
The definition of the episode as a unit of analysis allows us to disaggregate different measures by multiple senders that are conflated elsewhere. 9 This makes it possible to consider separately the outcome of individual sanctions regimes imposed on a target by different senders in pursuance of separate objectives, even if they occur simultaneously. This is particularly relevant to targets suffering multiple crises in close succession. Lastly, because our dataset encompasses foreign policy sanctions in addition to aid sanctions, it allows for the comparative analysis of aid sanctions with the entire sanctions universe to fit the purpose of this paper. Under 'foreign policy sanctions', we subsume all sanctions categories other than the interruption of development aid, i. e. trade and financial restrictions alongside a small number of diplomatic sanctions and travel bans.
5 Sadly, this excludes entries by  Molenaers et al. (2016)  that could not be confirmed independently. 6 Episodes initiated before 1990 that remained active after that year are included. 7 Including threats would disproportionately increase the number of economic sanctions as opposed to aid sanctions, which are less well documented. We forgo their inclusion to avoid exacerbating the imbalance. 8 The dataset only features targets endowed with state or quasi-state authority, excluding terrorists groups. 9 The definition of episode is less fine-grained than that applied by  Biersteker et al. (2016)  to avoid generating externalities  (Early, 2016). 10 The value 0 is allocated also to cases in which progress was negative, i. e. where measures contributed to an aggravation of the wrongdoing at hand  (Peksen, 2019).
Regime typification To code regime type, data originate from the 'Autocratic Regimes Dataset' by  Geddes et al. (2014). The use of data compiled under the direction of the author of the tripartition at the heart of our investigation  (Geddes, 1999)  ensures consistency between her characterisation of autocracies and our coding. Following her categories, we code autocratic regimes as single-party, personalistic, military or monarchies. Reflecting the rarity of pure autocratic types  (Schmidt, 2003) , a regime may fall into two categories simultaneously; e. g. , military and personalistic traits can combine. Such amalgams receive more than one code in our dataset. Where data were missing from  Geddes et al. (2014)  notably for minuscule countries -, we resort to data from  Cheibub et al. (2010).
does not allow us to detect transitions in which an autocratic regime replaces another, this phenomenon applies to very few events. The same Polity IV indicator captures cases of anarchyoften civil war -, which are coded as such in a separate variable. 14 Table  3  identifies the episodes of our database with the type of regime according to the classification by  Geddes et al. (2014). Most episodes feature some form of personalist government (110 cases), in exclusivity or in conjunction with another type. Geddes et al. (2014)  lack data for 39 of our episodes. Table  4  classifies episodes according to regime type categories by  Cheibub et al. (2010). Most targets are civilian regimes, closely followed by military regimes. Data from  Cheibub et al. (2010)  do not cover 48 of our episodes. Finally, Table  5  classifies our episodes according to Polity IV indicators of  Democracy and Autocracy (2018).
Autocracies dominate our dataset (68. 94 per cent). 41 target countries present an 'interregnum' or anarchy situation, due to civil war or state failure, and do not take either positive or negative values. 15  Polity IV is also the source employed for classifying regimes as democratic, hybrid or autocracies. According to Polity IV's own definition, regimes taking the value + 5 and above are democracies, those between -5 and + 5 are considered hybrid regimes, and those taking the value -5 or below are considered full autocracies. Frequencies are displayed on Table  6. To analyse the interrelation between sanctions effectiveness and the macroeconomic stance of the target, we include economic information on the target country, as shown on Table  7. 16.
Combined, these circumstances highlight a key difference in the operation of sanctions: the prosperity of a target cannot shield it from sanctions effects by any other means than the continuation of commercial exchange. While the receipt of economic assistance helps shielding targets under foreign policy sanctions against their impact, aid withdrawal has comparatively more severe effects. Lastly, we control for I. O. as senders, a determinant of sanctions effectiveness highlighted by  Bapat et al. (2013) , and one that our database is particularly apt to test due to the abundance of this sender type. As expected, I. O. senders are associated with a higher rate of effectiveness than individual countries. However, this effect subsides when we consider economic controls. Again, this can be indicative of a difference in the operation of aid sanctions and other sanctions.
Conclusion Our analysis explored whether aid suspensions display comparable effects on different autocratic types as foreign policy sanctions, testing them on an original dataset featuring aid sanctions obscured elsewhere. Our findings bring fresh insights to the sanctions and aid conditionality debate. Aid suspensions behave largely like any other sanctions in terms of their effectiveness on different autocratic types. This calls for this subtype's full integration in mainstream sanctions research, corroborating suggestions that 'scholars of International Relations should take interest in aid sanctions because they offer new evidence to adjudicate between competing explanations of state behaviour'  (Nielsen, 2013, p. 57). At the same time, we find that aid sanctions' specific characteristics result in differences in their impact and effectiveness vis-à-vis foreign policy sanctions, implying that more research is required to delve into how different subtypes of sanctions may behave differently.
However, the implementation of this promising research agenda is contingent upon improved compilations of data on which new research questions can be tested. Extant databases lack important information on the contents of aid sanctions  (Jeong, 2019) , and recent efforts tend to disregard aid sanctions. Our endeavour is but a first step towards remedying the unsatisfactory documentation of a widespread international coercive practice. Disclaimer: The views expressed in this article are those of the authors and should not be attributed to the Banque de France, the Banco de España or the Eurosystem.
Acknowledgements We are indebted to the participants in the workshop "Foreign aid withdrawals and suspensions: Why, when, and are they effective?" held virtually in May 2021 for their input, and in particular to  Cleo O'Brien-Udry ,  Haley Swedlund  and  Nic Cheeseman  for their kind support. Special thanks go to  Hillary Corwin ,  Samuel Brazys ,  Mario Larch ,  Dane Rowlands  and two anonymous reviewers for valuable comments on earlier versions of this manuscript. We are also grateful for the feed-back received at the  Scientific Workshop "Sanctions: Theory, Quantitative Evidence, and Policy Implications" held at LeBow College of Business, Drexel University, Philadelphia , PA, in April 2019 and the research seminar held at  Banco de España -Eurosystem in Madrid, Spain , in December 2019.
| Privacy preserving solutions Current solutions for biometric deployment do operate with protected (i. e. encrypted) biometric databases, but not sufficiently protected to guarantee privacy preservation even in case of data loss and the used encryption scheme being broken. This is highly problematic, as we have seen many attacks against biometric systems being facilitated by compromised biometric template databases (e. g. inversion attacks & PA to name two prominent examples). In order to achieve trust in public perception, privacy preserving technologies should be implemented in the early stage of the design of a biometric based system. This is reflected in the need to design privacy compliant biometric systems architectures and to design privacy enhancing techniques for the protection of biometric templates.
When crossing borders, the signature of the certification authority is checked. Biometric information could in itself be useful for binding the certificate of the right person when presenting the certificate at the borders. Yet another option is a traveller's mobile application with this private data about health which the officer at the border can read only when confirming the request on the mobile phone. Governments identify accredited laboratories and provide them with multi-factor authentication to access government platform and generate Digital Seal for signing health certificates. By doing so government set-up trusted ecosystem within country and could use ICAO Visible Digital Seal (VDS) standard for international recognition of the health certificates. International Civil Aviation Organisation VDS is an internationally recognised standard of a 2D bar code for sealing health certificates for travel-related purposes.
The VDS is signed using a Country Signing Certification Authority Public Key Infrastructure (PKI), which is already used for signing ePassports. A dedicated PKI can also be developed for health purposes. Therefore, the secure exchange of public keys can be done using a Public Key Directory either operated by EU or by WHO. Based on privacy by design approach, the health data are not required to be stored in any central database. The traveller is the only holder of his medical results and can select which data will be presented to the verifier for the verification. For the travel within Schengen area, a traveller could display ICAO VDS and verifier should access only to minimum data such as name, surname, passport number, vaccination/PCR/ immunity result (e. g. green = ok, red = not ok).
| Number plate recognition In the Netherlands, a pilot, then known as @Migo-Boras, was set up around 2010  [55] , patrolling the borders with Belgium and Germany in an area of 20 km by mobile and fixed cameras on highways, checking car licence plates against multiple police databases aiming to stop illegal immigrants and criminals. The project, which came after a similar initiative in Denmark (which was in the meantime stopped) was criticised as it was considered as re-establishing border control and leading to surveillance. Thereafter, the project was somehow modified, renamed as 'Mobile Surveillance Security (MTV)' (MTV) and continued in 2011 for then only about 6 h a day, and maximum 90 h per month. Research in close collaboration with the border police ('Marechaussee') indicated that there was a shift in use from migration control to combating crime and that the impact of the technology on the decision taken during MTV checks of the border police overall remained limited since 'the information they receive is often not specific enough and they see little added value in the intelligent camera system'  [56].
| Demographic fairness An essential consideration in all deployments of biometric systems is that, in as far as possible, operational performance in terms of accuracy is not biased towards a particular population subgroup, be that ethnicity, disability, age rage or other characteristic. It is vital therefore that developers and implementors ensure that systems are proven on a representative population with respect to the final deployment environment, including with a juvenile population if operationally appropriate. In order to achieve fairness, it is important that representative training datasets become available, which is currently a blocking issue for both Member States and eu-LISA. 1 Likewise, consideration need be given to acceptability of a proposed solution across the widest possible population.
3D face recognition for re-active forensic investigations (post-terror incident) � 4. 1 Thermal face recognition for re-active forensic investigations in poor illumination (post-terror incident) � 4. 2 Iris and periocular recognition for re-active forensic investigations under a masked face scenario (post-terror incident) � 4. 3 Soft biometric recognition for re-active forensic investigations (post-terror incident) � 4. 7 Self-registration for travellers as pro-active prevention of un-controlled migration and pandemic spread (voluntarily participation) � 4. 8 Privacy preserving solutions for pseudonymous solutions for infrastructure (i. e. database) implementations � 6. Smartphone tracking of suspects in a post-terror incident investigation � 7. 1. Number plate recognition as re-active forensic investigations (post-terror incident).
� 4. 1 41 3D face recognition for re-active forensic investigations (post-terror incident) � 4. 1 Thermal face recognition for re-active forensic investigations in poor illumination (post-terror incident) � 4. 2 Iris and periocular recognition for re-active forensic investigations under a masked face scenario (post-terror incident) � 4. 3 Soft biometric recognition for re-active forensic investigations (post-terror incident) � 4. 7 Self-registration for travellers as pro-active prevention of un-controlled migration and pandemic spread (voluntarily participation) � 4. 8 Privacy preserving solutions for pseudonymous solutions for infrastructure (i. e. database) implementations � 6. Smartphone tracking of suspects in a post-terror incident investigation � 7. 1. Number plate recognition as re-active forensic investigations (post-terror incident).
Vectors are massively used in many domains. Several techniques have been proposed for comparing two vectors, but they only perform the comparison according to the exact values of the vector components. Additionally, existing techniques used for comparing two vectors having different dimensions are limited by many factors. Furthermore, the problem of comparing two finite sets of vectors has not yet been specifically addressed. This paper attempts to overcome all these limitations by proposing a new technique based on hidden Markov models which enhances existing techniques by giving them the ability to compare two finite sets of vectors, each containing vectors having different dimensions, while precising the set of targeted properties on which the comparison should be performed. Classification experiments conducted on three online available custom datasets demonstrated that when the suitable set of targeted properties is selected, the proposed approach outperforms existing techniques with accuracy gains reaching +82.