dataesr/scientific-paragraphs-categorization huggingface dataset:
500 ms target presentation. An inter-trial-interval of 1 to 2 s was used. Data from three consecutive months of training (one session each day) were collected and confirmed to yield nearly identical results, so one of these sessions was chosen arbitrarily for presentation below. This session consisted of 1500 trials over 2 hours. At all times, the animal was handled in accord with EU guidelines and those of the ICM Animal Care and Use Committee. Analog X and Y position signals conveying behavioral output con-sisted of an optical eye-tracking system (Eyelink 2k, SR Research Ltd. , Mississauga, Ontario, Canada) running at 1000 Hz. For a more straightforward demonstration, a schematic diagram, the code (timing script) and the conditions file for a simpler task (a standard prosaccade task) is shown in Figure ??.
Existing alternatives One millisecond is a relatively course (small ?) unit of measure by electronic standards. However such temporal precision on a non-hard-real-time system, running on most popular operating systems (Windows, Mac, Unix), has no guarantees, because the predictability of software events is limited by the design of the operating system (OS). Specifically, even those processes designated as having a real-time priority can be pre-empted by both kernel-level events and by interrupt requests, as well as by other processes with equally high-priority  (Ramamritham et al. , 1998). While using systems with multiple processors may provide some benefit, they do not alter the fundamental problem. System developers have no way to control the hardware outside of the capabilities provided by the running OS.
Matlab & PsychoToolBox Matlab is a possible platform for such projects, nevertheless, there are notable limitations. Windows or Mac cannot support hard real-time operation. Therefore, while sub-millisecond jitter is acceptable in many, if not most, psychophysical settings, there are nonetheless many potential applications for which the software described here would not be suitable. In particular, experiments that must provide feedback within a very small temporal window (for example, to influence an on-going synaptic event) would find 2-5 ms jitter simply too variable. Furthermore, likewise, delivering feedback that requires a great deal of processing could potentially incur unacceptably long delays. There is a potential risk of blind period at the beginning of each behavioral tracking episode.
Hardware Prioritizing portability, we interfaced high quality hardware that was already commonplace in the world of visual cognition study. Here is a list of the hardware used for our system : • Display : CRT Screen • Data Acquisition : National Instruments DAQ NI 6220 • Graphic Card : NVidia • CPU : Intel Xeon E5405 2. 2. 3. Software & Solutions Rexeno uses several freely available libraries, compatible with mainstream and high quality hardware, in order to obtain the best performances possible. Here are the main software ressources that we used in order to create our program : • Xenomai 2. 6. 0 : One of the most interesting options in Real-Time Operating Systems. Enables us to control software behabiour down to the µsec. Also provides real-time drivers for Analog/Digital interactions with AD cards.
Definitions As can be seen (in Figure  1 ), the interaction with the environment can be summerized with the following outputs : • Display : What appears on the subject's screen. • Backup : What was recorded by the machine • Digital Triggers : Sent by the NIDAQ card. Rexeno's goal is to bring Hard Real-Time capability to these outputs. Definition : A program is said to respect hard real time constraints when it's time of execution is deterministic. The current system uses Xenomai's Analogy Drivers for the interaction with NIDAQ card, so we know than these programs are real time compatible  [? ]. The interaction with the screens use NVidia proprietary drivers that do not o↵er real time guarentee (which is normal, this was not what they were designed for). Because of these drivers, it is not possible today to guarentee hard real time constraints while displaying something on a screen, what our tests will have to do is define under what conditions, jitters are still acceptable.
Considering the Newton laws on each cell, it enables to generate easily a dynamic model of the cell positions (output) function of the voltages on electrodes (input). This dynamic model of our system is required to design the future closed-loop control law. The predicted dielectrophoretic forces are compared to a numerical simulation based on finite element model using COMSOL software. The model presented in this paper enables to compute the dielectrophoretic force applied to a cell by an electrode array in a few tenths of milliseconds. This model could be consequently used in future works for closed-loop control of dielectrophoretic devices.
Thus, the boundary condition on the lateral walls can be seen as a symmetry condition. The electric potential in the system is virtually expanded to the whole space by repetitive symmetry along the lateral borders forming a periodic pattern (see Figure  3 ). These assumptions will be discussed at the end of this paper by comparing the analytical model obtained using these assumptions to a numerical model implemented in COMSOL software, using the previously cited exact boundary conditions. Assuming that there are N electrodes regularly spaced in the channel of width L, as in classical setup  [20, 21] , the electrode width and the space between each electrode is λ = L 2N-1. The boundary conditions can be analytically expressed as follows: 1. the electric potential on the n th electrode is u n , ∀ 0 ≤ n ≤ N -1, y ∈ [2nλ; (2n + 1)λ] φ(y, z = 0) = u n ; (8) 2.
Comparison between the Analytical Model and a Numerical Simulation The analytical formulation proposed in this paper is compared to a numerical simulation performed with COMSOL multiphysics software. It is a common practice to compare the value of the dielectrophoretic force obtained by analytical models with the ones obtained using a numerical software. To name only a few,  [22]  compares the analytical expression of the force to a numerical simulation,  [23, 24]  compare the analytical solution to a finite element simulation performed using COMSOL software and  [26]  compares the analytical expression of the force to a numerical simulation performed with CFD-ACE software. In addition, a good agreement between the numerical simulation and the experimental results is shown in  [24]. We thus made the choice, in this article, to take as a reference the numerical simulation.
Computing Time of the Fourier Series Beside the precision of the force estimation, a second critical point for real-time control is the computing time. The size of A [P×N] being (2P + 1) × N, this matrix does not need a lot of space in memory and can be pre-computed to improve the calculation in real-time application. Table  3  summarizes the average time to compute the dielectrophoretic force applied to one point (y,z) by our method using Matlab. The mean value is 0. 215 ms. This is 5000 times faster than the computing time using COMSOL FEM solver (>1 s).
The Existing Leakage Profile Classification Cash et al. [6, Section 2. 1] remark that a large number of papers follow the method of Curtmola et al. [9]  as they resort to leakage profiles for the security analysis of SE schemes. Cash et al. [6]  generalize this approach by suggesting a classification of most existing SE schemes based on their leakage profile. To this end they define four profiles named from L1, that reveals the least information, to L4 that reveals the most information. We remind the definitions of the L1 to L3 profiles, and omit L4 as it reveals too much information to be useful for our study. The L1 leakage profile or query-revealed occurrence pattern regroups SE schemes whereby the CSP sees the access pattern of each search query (the occurrence pattern of a keyword denotes the indices where this keyword occurs in the dataset).
The authors show that the count attack outperforms the similar "IKK attack" presented by Islam et al. [15]. Cash et al. also present a simple attack against the L3 profile requiring that the CSP knows the plaintext of a small number of documents. Since L3 reveals the order of keywords and their occurrence patterns, the CSP is able for each keyword in a known document to see where this keyword occurs in other documents. Active attacks, for instance where the adversary can plant arbitrary documents in the system, were also presented by Cash et al. [6, Section 5. 2]  and by Zhang et al. [32] , but we only consider passive attacks in this paper. {(i, l) : W i [l] = w 1 },. , {(i, l) : W i [l] = w n } 2. A recent paper by Grubbs et al. [12]  present many ways of attacking a software framework named Mylar  [24]  whose building blocks include a SE protocol by Popa and Zeldovich  [25].
Leakage-Abuse Attacks against MUSE Recent papers on leakage-abuse attacks  [6, 12, 15, 21, 22, 32]  show that the threat caused by prior knowledge is becoming a matter of concern in single-user SE. This threat is even more serious in the multi-user setting where the dataset is owned and accessed by a number of users and users do not trust other users except the ones they explicitly authorize to search their data. In this section we show that a large number of MUSE schemes share a common design flaw which, combined with the specificity of the multi-user setting, creates a very serious vulnerability that has been neglected so far.
Finally Grubbs et al. [12]  do not provide evaluation results of the attacks they present but these results should not exceed the efficiency reached by the other existing attacks. As we show in Section 4, the attack we present outperforms all of these attacks by a large margin. As a consequence, for SE schemes that are vulnerable to the attack presented in this paper, there is no more interest to study attacks based on access pattern leakage: these attacks are overridden due to the efficiency and simplicity of our new attack. Unfortunately, a large number of MUSE schemes  [3, 4, 11, 14, 24, 25, 30, 31, 33]  are in this situation since they do use the iterative testing structure and the adversary can easily obtain the required prior knowledge in the multi-user setting. In the next section we discuss the impact of this new attack on an existing MUSE scheme named MK (Multi-Key searchable encryption) as well as on an existing software framework named Mylar.
We now show how this weakness of the MK scheme allows to very efficiently break the privacy of the Mylar software framework that is based on MK. Mylar  [24] , described as "a platform for building web applications which protects data confidentiality against attackers with full access to servers", uses the MK scheme to enable shared encrypted cloud-hosted data. It received a large attention both from the academia  [13, [18] [19] [20] 28]  and the industry  1. A large number of security issues of Mylar have been very recently presented by Grubbs et al. [12]  but very few of them are related to the underlying MK scheme; one could then hope that a better implementation of the framework could result in a quite secure solution, without having to change the cryptographic protocol used for MUSE operations.
This would be a serious mistake, as we show that exploiting the weakness of the MK scheme suffices to break privacy in Mylar. We only give the general idea and the practical results of our experiments here, but the source code of our implementation and the instructions necessary to reproduce the attack are publicly available  [2]. Our proofof-concept implementation targets EncChat, a "secure" chat application that has been built using Mylar. In the EncChat application, each message is considered as a document and an index is created out of the words contained in the message. This index is encrypted through the MK scheme and uploaded to the CSP. A user receives an authorization for each chat room it is given access to, and can search over all these chat rooms by sending a single encrypted query thanks to the use of the MK scheme.
Difference between L KWAP and L1 First we confirm in the framework of Cash et al. [6]  our claim that L KWAP should not be considered as equivalent to L1. The attack presented in Figure  1  is an (partial) index-recovery attack and is effective with a very small amount of prior knowledge; it would then be difficult to compare it with the count attack of Cash et al. [6]  which is a query-recovery attack and requires a large amount of prior knowledge (at least 80% of the dataset corpus must be known by the CSP). We then design a query-recovery attack against L KWAP that we think makes comparison between L KWAP and L1 easier. Our attack is trivial: The CSP has some prior knowledge in the form of tuples (w, (i, l)), each such tuple meaning that the hash C i [l] represents the keyword w. In our setting an index is either entirely revealed to the CSP (all of its hashes are in the prior knowledge) or not revealed at all.
As we already said, this kind of prior knowledge is immediate in a MUSE system based on iterative testing when some users collude with the CSP. The query recovery process is as follow: If a query matches one of the known hashes, the query is recovered; otherwise it is not. We evaluated this attack experimentally, following a methodology that is as close as possible to the one of Cash et al. [6] : the dataset used is a subset of 30,000 mails from the ENRON email dataset  [1] , indices are built by taking all words in the corpus, removing stop words, applying the porter stemming algorithm  [26] , then selecting the 500 most common words obtained this way as the index vocabulary. The source code necessary to reproduce all experiments is publicly accessible  [2]. Experimental results show a very good success rate of the attack (red discs in Figure  6 ): for instance when the amount of revealed indices represent only 1% of all indices, the CSP is still able to recover about 65% of the queries.
However comparing these results with the one of the count attack would not be fair, the prior knowledge of the count attack being very different from the one of our attack. We then design an attack against a L1 profile that uses the same prior knowledge as the one we designed against a L KWAP profile. The attack against a L1 profile consists in keeping track of the keywords that have unique occurrence pattern among the revealed indices: For instance, the CSP can remember that only the keyword "dog" appears in W 3 and W 5 but not in W 8. Queries targeting such keywords are recovered; other queries are not recovered. The performance of this attack (blue squares in Figure  6 ) is much lower than the corresponding attack against L KWAP , especially when only a small fraction of the dataset is revealed to the CSP.
This is highly beneficial, as experts are not forced to come one bad compromise, but the model is able to understand emerging disagreements. Moreover, local views allow one to validate data instead of results. This means that all parts of the model per se are understandable and validatable by using common sense or by validating small subparts against ground truth. No large, holistic ground truth datasets are required to validate algorithms, as the emerging problem and associated algorithms to solve these problems are inherently defined by the model itself. The following sections introduce views on OIA from three different perspectives, each defining one dependency model as a probabilistic graphical model of random variables and respective dependencies. Remark 3. 1 (Impact) The abstract term "impact" is used in this work in the sense of "not operating as fully intended.
Designing BPMN models is handled manually by an expert from a company or by an external business consultant having a precise expertise business analyses. Business analysis is performed on a pure business perspective and stops at a "resource" level. For example, a business analyst may identify a web-service, but will not describe the technical dependencies of the webservice on a database or a data center. This is a reasonable approach, as the latter perspective comes from a very different expertise and would require very broad-range experts. Further, an identification of a web-service as a business relevant object is causally more precise in the terms of an operation perspective: A failing database might cause a web-service to not operate as intended and therefore might lead to an unaccomplishment of a business-process.
Still, the direct cause for the business-process being unaccomplishable is a rogue web-service and not the database. An "IT" expert might identify a web-service to be irrelevant, as the crucial point of failure or the point of interest lies in the availability of data from a database. Nevertheless, the latter dependencies must be covered and are discussed in the upcoming subsection. The main intention of using multiple models here is to reflect all views exactly and make the global model able to understand the discrepancies instead of enforcing a bad compromise.
Resource dependency model (operation view) Critical resources identified in a mission dependency model are almost certainly dependent on further resources. For example, a web-server is likely dependent on a database-server. In effect, it is completely natural that multiple experts will disagree on the identification of critical devices. For example, a data analyst will solely be interested in data from the database, whereas the accountant will require data insights provided through the webserver. Essentially, no difference on the importance exists between two devices. No matter which one will be identified by a mission dependency model, the vast dependence between both must be covered, which is performed by the resource dependency model discussed here. Formally, a resource dependency model is a probabilistic graphical model, where every resource represents a random variable, and a dependency is modeled as a conditional probability fragment as shown in Fig.
Network dependency analysis As mentioned earlier, a resource dependency model must be kept up-to-date over time and must automatically adapt to concept drifts, i. e. , to changes in the network. Therefore, a resource dependency model is continuously learned from network traffic analyses inside an infrastructure. In more detail, a resource dependency model is learned from maximum likelihood estimates based on statistical analyses in network traffic meta data in a similar fashion to learning classical probabilistic graphical models. In our use case, a resource dependency model consists of a medium-sized ICT environment, in which some ICT devices also represent gateways to an industry SCADA system. Further, it can be assumed that every device drives one purpose. This allows for a simple heuristic on exchanged information amounts, initially proposed by us in  [10]  and  [11]  to obtain a plausible resource dependency network, explained at the following simple example: A workstation X consuming different query results from multiple databases distribute gained and processed information from such queries to other devices.
The percentage of received traffic T Y i ,X from every database Y i towards the total received traffic gives a good guideline for the conditional dependency between them as p( + x| + y i ) = T Y i ,X i T Y i ,X. This measure is seen as a maximum likelihood estimate for the conditional probability and directly reflects classical learning approaches for learning probabilistic graphical models from data, e. g. , learning parameters of Bayesian networks  [28, pp. 806-808]  from (in)complete data using "counts. " Periodically capturing and analyzing conversation statistics from traffic metadata is a feasible process and, e. g. , directly provided by Wireshark  [29]. Therefore, this heuristic is trivially to implement and has low and constant memory requirements with low computational load over time.
These control commands will be similarly encoded and share similar entropy. In effect, each RTU will be highly dependent on each SCADA server as almost all received traffic will originate from those. Additionally, each RTU will acknowledge received commands, i. e. , each SCADA server will be slightly dependent on each RTU. Moreover, SCADA servers frequently synchronize with each other, leading to larger information transfers, and will frequently communicate with human machine interfaces (HMIs). Therefore, SCADA servers will be highly dependent on each other, moderately dependent on HMIs, and slightly on RTUs, as one expects. Similar assumptions can be made for ICT architectures, where distributed databases synchronize, data is received from and transferred to webservers, and analyzed in large computational clusters.
Use case demonstration All presented models, approaches, and optimizations are implemented in the cyber-defense system PANOPTESEC, whose use case partner gives us the opportunity to evaluate and study the application of the proposed multidimensional evaluation and optimization of response plans in an infrastructure environment of an energy distribution organization (EDO). The environment consists of a distributed network of remote terminal units (RTU) in energy stations of medium voltage (MV = 20,000 V) and high voltage (HV = 150,000 V). RTUs acquire data from electrical equipments (e. g. , PLC, sensors), and send data to a supervisor terminal unit (STU) of the headquarter. The RTU network utilizes Supervisory Control and Data Acquisition (SCADA) protocols and is composed of over 13,000 energy stations, 6000 of which are controlled by the STU.
Threat scenarios Use case providers have identified five threat scenarios that could lead to severe consequences on the target system. Table  2  summarizes the information associated to all possible threats, PEPs, and attack vectors of each identified threats. Threat AS02 has been detailedly analyzed. AS02 corresponds to a compromise of a specific target through vulnerability exploitation, which will cause data corruption or leakage of a database in the ICT domain. For such a threat, there exists a specific attack vector, as shown in Table  2.
Operational impact assessment To perform a response operational impact assessment, a resource dependency model is needed. As described in Section 3, a manual assessment is said to be infeasible, and a solution based on a heuristic of exchanged traffic information was proposed in Section 4. 2. 1. Network traffic has been analyzed in a completely replicated backup environment of the use case partner involving all SCADA and ICT communications over multiple months. Metadata of all traffic, i. e. , header information such as IPs and MACaddresses, are recorded constantly over 50 min intervals and are analyzed postponed by the introduced NDA module. All obtained information is synchronized and crosschecked against a central network inventory s. t. one is able to distinguish communicating-and communicationestablishing devices.
This study evaluates the potential of using aerosol optical depth (τ a ) measurements to characterise the microphysical and optical properties of atmospheric aerosols. With this aim, we used the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties) code for numerical testing of six different aerosol models with different aerosol loads. The direct numerical simulations (selfconsistency tests) indicate that the GRASP-AOD retrieval provides modal aerosol optical depths (fine and coarse) to within 0. 01 of the input values. The retrieval of the finemode radius, width and volume concentration are stable and precise if the real part of the refractive index is known. The coarse-mode properties are less accurate, but they are significantly improved when additional a priori information is available.