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The Classic McEliece cryptosystem is one of the most trusted quantum-resistant cryptographic schemes. Deploying it in practical applications, however, is challenging due to the size of its public key. In this work, we bridge this gap. We present an implementation of Classic McEliece on an ARM Cortex-M4 processor, optimized to overcome memory constraints. To this end, we present an algorithm to retrieve the public key ad-hoc. This reduces memory and storage requirements and enables the generation of larger key pairs on the device. To further improve the implementation, we perform the public key operation by streaming the key to avoid storing it as a whole. This additionally reduces the risk of denial of service attacks. Finally, we use these results to implement and run TLS on the embedded device.

International audience; The aim of Traffic Engineering is to provide routing configurations in networks such that the used resources are minimized while maintaining a high level of quality of service (QoS). Among the optimization problems arising in this domain, we address in this paper the one related to setting weights in networks that are based on shortest path routing protocols (OSPF, IS-IS). Finding weights that induce efficient routing paths (e.g that minimize the maximum congested link) is a computationally hard problem. We propose to use Monte Carlo Search for the first time for this problem. More specifically we apply Nested Rollout Policy Adaptation (NRPA). We also extend NRPA with the force_exploration algorithm to improve the results. In comparison to other algorithms NRPA scales better with the size of the instance and can be easily extended to take into account additional constraints (cost utilization, delay,. . .) or linear/non-linear optimization criteria. For difficult instances the optimum is not known but a lower bound can be computed. NRPA gives results close to the lower bound on a standard dataset of telecommunication networks.

We present Pay To Transport (P2T), a protocol that lets customers buy an item remotely in an atomic, privacy preserving and trustless manner. P2T needs only basic features of a blockchain scripting language and does not need any tracking systems, arbitrator or deposit to preserve its security properties. For this reason the protocol can be implemented on any permissionless blockchain, regardless of its scripting language, without additional trust. Merchants’ and transporters’ addresses are public, but in P2T the parties never pay those addresses directly. Therefore P2T maintains the privacy of customers, merchant and transporters.

The Hamming Quasi-Cyclic (HQC) proposal is a promising candidate in the second round of the NIST Post-Quantum Cryptography Standardization project. It features small public key sizes, precise estimation of its decryption failure rates and contrary to most of the code-based systems, its security does not rely on hiding the structure of an error-correcting code. In this paper, we propose the first power side-channel attack on the Key Encapsulation Mechanism (KEM) version of HQC. Our attack utilizes a power side-channel to build an oracle that outputs whether the BCH decoder in HQC’s decryption algorithm corrects an error for a chosen ciphertext. Based on the decoding algorithm applied in HQC, it is shown how to design queries such that the output of the oracle allows to retrieve a large part of the secret key. The remaining part of the key can then be determined by an algorithm based on linear algebra. It is shown in experiments that less than 10000 measurements are sufficient to successfully mount the attack on the HQC reference implementation running on an ARM Cortex-M4 microcontroller.

We describe several additions to the ENIGMA system that guides clause selection in the E automated theorem prover. First, we significantly speed up its neural guidance by adding server-based GPU evaluation. The second addition is motivated by fast weight-based rejection filters that are currently used in systems like E and Prover9. Such systems can be made more intelligent by instead training fast versions of ENIGMA that implement more intelligent pre-filtering. This results in combinations of trainable fast and slow thinking that improves over both the fast-only and slow-only methods. The third addition is based on “judging the children by their parents”, i.e., possibly rejecting an inference before it produces a clause. This is motivated by standard evolutionary mechanisms, where there is always a cost to producing all possible offsprings in the current population. This saves time by not evaluating all clauses by more expensive methods and provides a complementary view of the generated clauses. The methods are evaluated on a large benchmark coming from the Mizar Mathematical Library, showing good improvements over the state of the art .

The first-order theory of rewriting is a decidable theory for linear variable-separated rewrite systems. The decision procedure is based on tree automata techniques and recently we completed a formalization in the Isabelle proof assistant. In this paper we present a certificate language that enables the output of software tools implementing the decision procedure to be formally verified. To show the feasibility of this approach, we present , a reincarnation of the decision tool with certifiable output, and the formally verified certifier .

Search-optimization problems are plentiful in scientific and engineering domains. MaxSAT and answer set programming with weak constraints (ASP-WC) are popular frameworks for modeling and solving search problems with optimization criteria. There is a solid understanding on how SAT relates to ASP. Yet, the question on how MaxSAT relates to ASP-WC is not trivial. The answer to this question provides us with the means for cross fertilization between distinct subareas of automated reasoning. In this paper, we propose a weighted abstract modular framework that allows us to (i) capture MaxSAT and ASP-WC and (ii) state the exact link between these distinct paradigms. These findings translate, for instance, into the immediate possibility of utilizing MaxSAT solvers for finding solutions to ASP-WC programs.

Power consumption is the major cost factor in data centers. It can be reduced by dynamically right-sizing the data center according to the currently arriving jobs. If there is a long period with low load, servers can be powered down to save energy. For identical machines, the problem has already been solved optimally by [25] and [1].In this paper, we study how a data-center with heterogeneous servers can dynamically be right-sized to minimize the energy consumption. There areddifferent server types with various operating and switching costs. We present a deterministic online algorithm that achieves a competitive ratio of 2das well as a randomized version that is 1.58d-competitive. Furthermore, we show that there is no deterministic online algorithm that attains a competitive ratio smaller than 2d. Hence our deterministic algorithm is optimal. In contrast to related problems like convex body chasing and convex function chasing [17, 30], we investigate the discrete setting where the number of active servers must be an integral, so we gain truly feasible solutions.

In engineering optimization with continuous variables, the use of Stochastic Global Optimization (SGO) algorithms is popular due to the easy availability of codes. All algorithms have a global and local search character, where the global behaviour tries to avoid getting trapped in local optima and the local behaviour intends to reach the lowest objective function values. As the algorithm parameter set includes a final convergence criterion, the algorithm might be running for a while around a reached minimum point. Our question deals with the local search behaviour after the algorithm reached the final stage. How fast do practical SGO algorithms actually converge to the minimum point? To investigate this question, we run implementations of well known SGO algorithms in a final local phase stage., This paper has been supported by The Spanish Ministry (RTI2018-095993-B-I00) in part financed by the European Regional Development Fund (ERDF) and by FCT Fundacao para a Ciencia e Tecnologia within the Project Scope: UIDB/00319/2020.

International audience; Evidential preference based on belief function theory has been proposed recently, simultaneously characterizing preference information with uncertainty and imprecision. However, traditional distances on belief functions do not adapt to some intrinsic properties of preference relations, especially when indifference relation is taken into comparison, therefore may cause inconsistent results in preference-based applications. In order to solve this issue, Unequal Singleton Pair (USP) distance has been proposed previously, with applications limited in preference aggregation. This paper explores forward the effectiveness of USP distance in preference clustering, especially confronting multiple conflicting sources. Moreover, a combination strategy for multiple conflicting sources of preference is proposed. The experiments on synthetic data show that USP distance can effectively improve the clustering results in Adjusted Rand Index (ARI).

Formal methods are becoming an indispensable part of the design process in software and hardware industry. It takes robust tools and proofs to make formal validation of large scale projects reliable. In this paper, we will describe the current status of formal verification at Centaur Technology. We will explain our challenges and our methodology—how various proofs and verification artifacts are interconnected and how we keep them consistent over the duration of a project. We also describe our main engine—a powerful symbolic simulator with rewriting capabilities that is integrated in a theorem prover and proven correct.

We introduce Bayesian strategies, a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks.Bayesian strategies are based on a new form of event structure, with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming.From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.

We introduce a new problem which we call the Pony Express problem. n robots with differing speeds are situated over some domain. A message is placed at some commonly known point. Robots can acquire the message either by visiting its initial position, or by encountering another robot that has already acquired it. The robots must collaborate to deliver the message to a given destination (We restrict our attention to message transmission rather than package delivery, which differs from message transmission in that packages cannot be replicated.). The objective is to deliver the message in minimum time. In this paper we study the Pony Express problem on the line where n robots are arbitrarily deployed along a finite segment. We are interested in both offline centralized and online distributed algorithms. In the online case, we assume the robots have limited knowledge of the initial configuration. In particular, the robots do not know the initial positions and speeds of the other robots nor even their own position and speed. They do, however, know the direction on the line in which to find the message and have the ability to compare speeds when they meet.

Following the immense recent advances in distributed networks, the explosive growth of the Internet, and our increased dependence on these infrastructures, guaranteeing the uninterrupted operation of communication networks has become a major objective in network algorithms. The modern instantiations of distributed networks, such as, the Bitcoin network and cloud computing, introduce in addition, new security challenges that deserve urgent attention in both theory and practice.

The brain is a highly reconfigurable machine capable of task-specific adaptations. The brain continually rewires itself for a more optimal configuration to solve problems. We propose a novel strategic synthesis algorithm for feedforward networks that draws directly from the brain’s behaviours when learning. The proposed approach analyses the network and ranks weights based on their magnitude. Unlike existing approaches that advocate random selection, we select highly performing nodes as starting points for new edges and exploit the Gaussian distribution over the weights to select corresponding endpoints. The strategy aims only to produce useful connections and result in a smaller residual network structure. The approach is complemented with pruning to further the compression. We demonstrate the techniques to deep feedforward networks. The residual sub-networks that are formed from the synthesis approaches in this work form common sub-networks with similarities up to ~90%. Using pruning as a complement to the strategic synthesis approach, we observe improvements in compression.

Contains fulltext : 230666.pdf (Publisher’s version ) (Open Access) Side-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling side-channel attacks has received a lot of attention in recent years due to the fact that this type of attack defines the worst-case security assumptions. The SCA community realized that the same approach is actually used in other domains in the form of supervised machine learning. Consequently, some researchers started experimenting with different machine learning techniques and evaluating their effectiveness in the SCA context. More recently, we are witnessing an increase in the use of deep learning techniques in the SCA community with strong first results in side-channel analyses, even in the presence of countermeasures. In this chapter, we consider the evolution of profiling attacks, and subsequently we discuss the impacts they have made in the data preprocessing, feature engineering, and classification phases. We also speculate on the future directions and the best-case consequences for the security of small devices.

Let \(N=pq\) be an RSA modulus with balanced prime factors. In 2018, Murru and Saettone presented a variant of the RSA cryptosystem based on a cubic Pell equation in which the public key (N, e) and the private key (N, d) satisfy \(ed\equiv 1\pmod {\left( p^2+p+1\right) \left( q^2+q+1\right) }\). They claimed that the classical small private attacks on RSA such as Wiener’s continued fraction attack do not apply to their scheme. In this paper, we show that, on the contrary, Wiener’s method as well as the small inverse problem technique of Boneh and Durfee can be applied to attack their scheme. More precisely, we show that the proposed variant of RSA can be broken if \(d

The Hybrid Public Key Encryption (HPKE) scheme is an emerging standard currently under consideration by the Crypto Forum Research Group (CFRG) of the IETF as a candidate for formal approval. Of the four modes of HPKE, we analyse the authenticated mode HPKE Auth in its single-shot encryption form as it contains what is, arguably, the most novel part of HPKE and has applications to other upcoming standards such as MLS. HPKE Auth 's intended application domain is captured by a new primitive which we call Authenticated Public Key Encryption (APKE). We provide syntax and security definitions for APKE schemes, as well as for the related Authenticated Key Encapsulation Mechanisms (AKEMs). We prove security of the AKEM scheme DH-AKEM underlying HPKE Auth based on the Gap Diffie-Hellman assumption and provide general AKEM/DEM composition theorems with which to argue about HPKE Auth 's security. To this end, we also formally analyse HPKE Auth 's key schedule and key derivation functions. To increase confidence in our results we use the automatic theorem proving tool CryptoVerif. All our bounds are quantitative and we discuss their practical implications for HPKE Auth. As an independent contribution we propose the new framework of nominal groups that allows us to capture abstract syntactical and security properties of practical elliptic curves, including the Curve25519 and Curve448 based groups (which do not constitute cyclic groups).

Kernel machines are a powerful class of methods for classification and regression. Making kernel machines fast and scalable to large data, however, is still a challenging problem due to the need of storing and operating on the Gram matrix. In this paper we propose a novel approach to sample condensation for kernel machines, preferably without impairing the classification performance. To our best knowledge, there is no previous work with the same goal reported in the literature. For this purpose we make use of the neural network interpretation of kernel machines. Explainable AI techniques, in particular the Layer-wise Relevance Propagation method, are used to measure the relevance (importance) of training samples. Given this relevance measure, a decremental strategy is proposed for sample condensation. Experimental results on three data sets show that our approach is able to achieve the goal of substantial reduction of the number of training samples.

Significant progress has been made in the South-African early childhood and Grade R spheres. However, South-Africa has a long way to go to meet the needs of majority of its children. Institutional capacity (IC) refers to the administrative and managerial aspects of an Early Childhood Development Centre (ECDC). Failure to build this capacity impacts the quality of services delivered to the most vulnerable children in our society. The purpose of this article is to extract knowledge from the institutional capacity knowledge area, as well as the enterprise engineering body-of-knowledge as a baseline for developing an enterprise capacity development approach (ECDA) for early childhood development centers. The ECDA should be useful to South-African ECD administrators, if they intend to develop enterprise capacity, leading to the improvement in quality of services delivered. ECDA is the main contribution of this article. As a second contribution, we validate its completeness when compared to existing IC literature, as well as its comprehensiveness in terms of eleven approach design principles. Finally, we provide a partial demonstration of ECDA’s heuristic at a real-world ECDC in South Africa.

International audience; Experimental data is often given as bit vectors, with vectors corresponding to observations, and coordinates to attributes, with a bit being true if the corresponding attribute was observed. Observations are usually grouped, e.g. into positive and negative samples. Among the essential tasks on such data, we have compression, the construction of classifiers for assigning new data, and information extraction. Our system, MCP, approaches these tasks by propositional logic. For each group of observations, MCP constructs a (usually small) conjunctive formula that is true for the observations of the group, and false for the others. Depending on the settings, the formula consists of Horn, dual-Horn, bijunctive or general clauses. To reduce its size, only relevant subsets of the attributes are considered. The formula is a (lossy) representation of the original data and generalizes the observations, as it is usually satisfied by more bit vectors than just the observations. It thus may serve as a classifier for new data. Moreover, (dual-)Horn clauses, when read as if-then rules, make dependencies between attributes explicit. They can be regarded as an explanation for classification decisions.

Edge computing brings the service and utilities of cloud computing closer to end users. At the same time, the devices forming edge clusters are limited in featured computing resources, e.g., memory and storage. Running multi-component applications on edge clusters hence requires suitably selecting the nodes where to deploy the software stacks forming an application, so that they get actually hosted on cluster nodes featuring enough computing resources. In this paper, we illustrate a solution allowing to automatically determine feasible application deployments in edge clusters, i.e., automatically associating the software stacks forming an application to the nodes in a cluster that feature enough memory and storage for running them. Our solution is based on an existing graph transformation-based algorithm, with the main aim of demonstrating its potentials when applied to edge-related problems. Finally, we also illustrate an open-source prototype implementation of our solution.

Recent progress in quantum computing has increased interest in the question of how well the existing proposals for post-quantum cryptosystems are suited to replace RSA and ECC. While some aspects of this question have already been researched in detail (e.g. the relative computational cost of pre- and post-quantum algorithms), very little is known about the RAM footprint of the proposals and what execution time they can reach when low memory consumption rather than speed is the main optimization goal. This question is particularly important in the context of the Internet of Things (IoT) since many IoT devices are extremely constrained and possess only a few kB of RAM. We aim to contribute to answering this question by exploring the software design space of the lattice-based key-encapsulation scheme ThreeBears on an 8-bit AVR microcontroller. More concretely, we provide new techniques for the optimization of the ring arithmetic of ThreeBears (which is, in essence, a 3120-bit modular multiplication) to achieve either high speed or low RAM footprint, and we analyze in detail the trade-offs between these two metrics. A low-memory implementation of BabyBear that is secure against Chosen Plaintext Attacks (CPA) needs just about 1.7 kB RAM, which is significantly below the RAM footprint of other lattice-based cryptosystems reported in the literature. Yet, the encapsulation time of this RAM-optimized BabyBear version is below 12.5 million cycles, which is less than the execution time of scalar multiplication on Curve25519. The decapsulation is more than four times faster and takes roughly 3.4 million cycles on an ATmega1284 microcontroller.

19th International Conference, ACNS 2021, Kamakura, Japan, June 21–24, 2021, Proceedings, Part II; International audience; The Secure Remote Password protocol is a password-based authenticated key-exchange between two parties. One advantage is to prevent offline dictionary attacks from an adversary eavesdropping the communication. We present how such an attack is feasible if the modular exponentiation at the heart of the protocol is vulnerable and leaks some data related to the password.In the case of a fixed exponent, adding randomness during the execution is a classical protection mechanism, and such a mechanism is present in Apple’s cryptographic library to randomize the exponent. Despite being intended to protect against complex side-channel attacks, we show that its usage makes the implementation vulnerable to simple side-channels such as power analysis.This leakage observed in the library is mild but is useful for the attack we propose on the Secure Remote Password protocol.

Post-quantum cryptography has known a Cambrian explosion in the last decade. What started as a very theoretical and mathematical area has now evolved into a sprawling research field, complete with side-channel resistant embedded implementations, large scale deployment tests and standardization efforts. This study systematizes the current state of knowledge on post-quantum cryptography. Compared to existing studies, we adopt a transversal point of view and center our study around three areas: (i) paradigms, (ii) implementation, (iii) deployment. Our point of view allows to cast almost all classical and post-quantum schemes into just a few paradigms. We highlight trends, common methodologies, and pitfalls to look for and recurrent challenges.

Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union’s General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data.

Formulating cryptographic definitions to protect against software piracy is an important research direction that has not received much attention. Since natural definitions using classical cryptography are impossible to achieve (as classical programs can always be copied), this directs us towards using techniques from quantum computing. The seminal work of Aaronson [CCC’09] introduced the notion of quantum copy-protection precisely to address the problem of software anti-piracy. However, despite being one of the most important problems in quantum cryptography, there are no provably secure solutions of quantum copy-protection known for any class of functions.

We describe a new protocol to achieve two party \(\varepsilon \)-fair exchange: at any point in the unfolding of the protocol the difference in the probabilities of the parties having acquired the desired term is bounded by a value \(\varepsilon \) that can be made as small as necessary. Our construction uses oblivious transfer and sidesteps previous impossibility results by using a timed-release encryption, that releases its contents only after some lower bounded time. We show that our protocol can be easily generalized to an \(\varepsilon \)-fair two-party protocol for all functionalities. To our knowledge, this is the first protocol to truly achieve \(\varepsilon \)-fairness for all functionalities. All previous constructions achieving some form of fairness for all functionalities (without relying on a trusted third party) had a strong limitation: the fairness guarantee only holds if the honest parties are at least as powerful as the corrupted parties and invest a similar amount of resources in the protocol, an assumption which is often not realistic. Our construction does not have this limitation: our protocol provides a clear upper bound on the running time of all parties, and partial fairness holds even if the corrupted parties have much more time or computational power than the honest parties. Interestingly, this shows that a minimal use of timed-release encryption suffices to circumvent an impossibility result of Katz and Gordon regarding \(\varepsilon \)-fair computation for all functionalities, without having to make the (unrealistic) assumption that the honest parties are as computationally powerful as the corrupted parties – this assumption was previously believed to be unavoidable in order to overcome this impossibility result. We present detailed security proofs of the new construction, which are non-trivial and form the core technical contribution of this work.

Identity-Based Cryptography (IBC) is a useful tool for the security of IoT devices, but securely deploying this cryptographic technique to the IoT systems is quite challenging. For instance, a leakage of the master secret key will result in the leakage of all IoT devices’ private keys. SM9 is the only approved IBC algorithm standard in China. It is critical to have mechanisms to protect the SM9 master secret keys. In this work, to reduce the risk of the master secret key leakage, we propose a (t, n)-threshold distributed private key generation scheme for SM9 with some techniques from multiparty computation. Our scheme is compatible with all the three SM9 sub-algorithms (i.e., the encryption, signature and key agreement). It is also provably secure and completely eliminates the single point of failures in SM9 that is concerned by the industry. The experimental analysis indicates that the proposed scheme is efficient, e.g., up to 1 million private key generation requests can be handled per day.

Because of billions of users, the social network is the best choice for person who has an urgent task which needs enough people to participant or that only a few people are able to solve. Inspired by incentive mechanisms for retrieving information from networked agents and motivating the participation of people in crowdsourcing or human tasking systems, we design a paid message forwarding scheme based on social network so that the task will be known to capable persons in a short time. In our scheme, each participant helps solving the task directly or forwards the message he has received. Both kinds of contribution will bring a reward. Additionally, we use Elliptic Curve Digital Signature Algorithm to make the real contribution of participants known to who will pay the reward. Our scheme is shown sybil-proof, incentive compatible, efficient and is proved secure with the assumption that Elliptic Curve Digital Signature Algorithm is safe.

Day by day, online content delivery services suppliers grow the volume of data on the internet. Music streaming services are one of those services that increase the number of users every day, as well as the number of songs in their catalog. To help the users to find songs that fit their interests, music recommender systems can be used to filter a large number of songs according to the preference of the user. However, the context in which the users listen to songs must be taken into account, which justifies the usage of context-aware recommender systems. The goal of this work is to use a Dual Recurrent Neural Network to acquire contextual information (represented by embeddings) for each song, given the sequence of songs that each user has listened to. We evaluated the embeddings by using four context-aware music recommender systems in two datasets. The results showed that the embeddings (i.e. the contextual information) obtained by our proposed method are able to improve context-aware music recommender systems.

Assume that \(m \ge 1\) autonomous mobile agents and \(0 \le b \le m\) single-agent transportation devices (called bikes) are initially placed at the left endpoint 0 of the unit interval [0, 1]. The agents are identical in capability and can move at speed one. The bikes cannot move on their own, but any agent riding bike i can move at speed \(v_i > 1\). An agent may ride at most one bike at a time. The agents can cooperate by sharing the bikes; an agent can ride a bike for a time, then drop it to be used by another agent, and possibly switch to a different bike.

Analysis of biological processes (signals) is always probabilistic and statistical analysis. Machine Learning stemmed from advanced probabilistic and statistical analysis is the exact tool what the medical workforces are looking for. Research and studies in the area of Machine Learning algorithms enhances its accuracy; its healthcare applications in medical diagnosis can improve the treatment procedure. Healthcare application being directly related to human health, dimensionality reduction is very critical; it sometime triggers to panic losses; therefore, the complete feature involvement in the model development is challenging to enhance the reliability of the Machine Learning algorithm and accuracy of the algorithm. Depending upon the diagnosis area, Regression analysis leading to dimensionality reduction and Principal Component Analysis (PCA) to remove less significant features can be applied. Primarily, in cancer diagnosis, only noise removal is acceptable, not the feature with small significance removal and minimal human intervention is acceptable which could lead to reduced accuracy of diagnosis, so the following treatment procedure.

In this work we study the quantum security of public key encryption schemes (PKE). Boneh and Zhandry (CRYPTO’13) initiated this research area for PKE and symmetric key encryption (SKE), albeit restricted to a classical indistinguishability phase. Gagliardoni et al. (CRYPTO’16) advanced the study of quantum security by giving, for SKE, the first definition with a quantum indistinguishability phase. For PKE, on the other hand, no notion of quantum security with a quantum indistinguishability phase exists.

Goblintis a static analysis framework for C programs specializing in data race analysis. It relies on thread-modular abstract interpretation where thread interferences are accounted for by means of flow-insensitive global invariants.

We introduce a new approach for constructing range proofs. Our approach is modular, and leads to highly competitive range proofs under standard assumption, using less communication and (much) less computation than the state of the art methods, without relying on a trusted setup. Our range proofs can be used as a drop-in replacement in a variety of protocols such as distributed ledgers, anonymous transaction systems, and many more, leading to significant reductions in communication and computation for these applications. At the heart of our result is a new method to transform any commitment over a finite field into a commitment scheme which allows to commit to and efficiently prove relations about bounded integers. Combining these new commitments with a classical approach for range proofs based on square decomposition, we obtain several new instantiations of a paradigm which was previously limited to RSA-based range proofs (with high communication and computation, and trusted setup). More specifically, we get: – Under the discrete logarithm assumption, we obtain the most compact and efficient range proof among all existing candidates (with or without trusted setup). Our proofs are 12% to 20% shorter than the state of the art Bulletproof (Bünz et al., IEEE S&P ’18) for standard choices of range size and security parameter, and are more efficient (both for the prover and the verifier) by more than an order of magnitude. – Under the LWE assumption, we obtain range proofs that improve over the state of the art in a batch setting when at least a few dozen range proofs are required. – Eventually, under standard class group assumptions, we obtain the first concretely efficient standard integer commitment scheme (without bounds on the size of the committed integer) which does not assume trusted setup.

Symbolic model checking is an important tool for finding bugs (or proving the absence of bugs) in modern system designs. Because of this, improving the ease of use, scalability, and performance of model checking tools and algorithms continues to be an important research direction. In service of this goal, we present , an open-source SMT-based model checker. is designed to be both a research platform for developing and improving model checking algorithms, as well as a performance-competitive tool that can be used for academic and industry verification applications. In addition to performance, prioritizes transparency (developed as an open-source project on GitHub), flexibility ( can be adapted to a variety of tasks by exploiting its general SMT-based interface), and extensibility (it is easy to add new algorithms and new back-end solvers). In this paper, we describe the design of the tool with a focus on the flexible and extensible architecture, cover its current capabilities, and demonstrate that is competitive with state-of-the-art tools.

Driven by the success of Internet of Things, the number of embedded systems is constantly increasing. Reducing power consumption and improving energy efficiency are among the key challenges for battery-powered embedded systems. Additionally, threats like climate change clearly illustrate the need for systems with low resource usages. Due to the impact of software applications on the system’s power consumption, it is important to optimize the software design even in early development phases. The important role of the software layer is often overlooked because energy consumption is commonly associated with the hardware layer. As a result, existing research mainly focuses on energy optimization at the hardware level, while only limited research has been published on energy optimization at the software design level. This work presents a novel approach to propose an energy-aware software design pattern framework description, which takes power consumption and time behavior into account. We evaluate the expressiveness of the framework by defining design patterns, which use elaborated power-saving strategies for various hardware components to reduce the overall energy consumption of an embedded system. Furthermore, we introduce a dimensionless numerical efficiency factor to make energy savings quantifiable and a comparison for design patterns applied in various use cases possible.

Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called SD-(1+1) EA introduced by Rajabi and Witt (GECCO 2020) adds stagnation detection to the classical (1+1) EA with standard bit mutation, which flips each bit independently with some mutation rate, and raises the mutation rate when the algorithm is likely to have encountered local optima.

Coronavirus pandemic has changed our lives and is likely to have a lasting impact on our economic development, i.e., industry and services. Most organisations must change their businesses and services to comply with the strategies and rules published by the governments of different countries for providing agility, sustainability, and resilience in the current situation. Non-compliance can result in an organisation paying a considerable sum of money in fines and litigation. In Collaborative Networks 4.0 (CN4.0), the importance of compliance is even more evident as its issue becomes more complicated when it involves collaborative processes due to its design principles for decentralized decision-making. The Collaborative Processes in CN 4.0 imply the collaborative business process and their relevancy in industry 4.0, i.e., the collaborative processes through Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES). In this paper, we adopt two motivating use cases, define some of the regulatory requirements that govern the execution of each process, and then evaluate each process with the current compliance checking approaches. Based on this, we identify the challenges of compliance checking of collaborative processes, formalized as requirements needed to support the compliance checking of collaborative processes at design and running time, respectively. This paper further explores how the FIWARE architecture supports the automated compliance checking solution of collaborative processes in industry 4.0.

The paper presents the initial stages of the development of an information technology of the structural synthesis of multi-stage technological schemes, which is based on a constraint satisfaction theory. In contrast to the well known approaches used in solution of structural synthesis problems, the methods and heuristics developed in the constraint satisfaction theory allow formal specifying and joint processing of heterogeneous constraints on combined elements under the target object formation. The technology is developed on the example of mineral processing. In so doing, use is made of the principle of stage-by-stage generation of the flowsheet, taking the efficiency criteria, both technological and economical, into account. The paper gives the characterization of the initial data representation and considers some examples of constraints induced by the specific nature of the subject domain itself.

Wireless mesh networks (WMNs) are one of the wireless network technologies that have received much attention in recent years, and as the name implies, routers can provide a stable network over a wide area by configuring the network like a mesh. In order to provide a lower cost and more stable network, various methods for optimizing the placement of mesh routers are being studied. In a previous work, we proposed a Coverage Construction Method (CCM) for this mesh router placement problem. In this paper, we propose a CCM based Hill Climbing (HC) for mesh router placement optimization problem.

A digital signature is essential in verifying the reliability of people and data over networks, such as through web server certificates, authentication, and blockchain technologies. In blockchain, multi-signature signature schemes have recently attracted attention for reducing the amount of data in transactions. While such schemes support only a single message, Interactive Aggregate Signatures (IAS), an extended Schnorr multi-signature scheme, supports some messages under the plain public key model. However, there are three problems with this scheme in certain use cases.

In this work a three-layer password protection approach is proposed by utilizing Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC) and Reversible Data Hiding (RDH). RDH is a sort of data concealing approach whereby the host image can be recuperated precisely. The proposed approach was implemented and evaluated by considering various images. Results have been presented by calculating Peak Signal to Noise Ratio (PSNR) and Rate. Obtained experimental results demonstrate the effectiveness of the proposed approach.

This article discusses the complexities of porting a performance blockchain library, encompassing core cryptographic operations, to the OpenCL framework. We present the solution we developed as a general guideline and we highlight the limitations of the OpenCL framework. Given the potential use case of multiple platforms and devices, the effective portability of the library for end users is presented. Finally, a comparison with a CUDA variant of the library is discussed, both in terms of code complexity, runtime and performance.

The paper presents methods of modeling the real estate market using trend functions reflecting changes in real estate prices over time. Real estate transaction prices that are used to create data-driven valuation models must be updated in line with the trend of their change. The primary purpose of the first part of the study was to examine the extent to which splines are suitable for the trend function compared to polynomials of the degree from 1 to 6. In turn, the second part was to compare the performance of prediction models built on the basis of updated data with various trend functions: splines and polynomials. The experiments were conducted using real data on purchase and sale transactions of residential premises concluded in one of the Polish cities. Four machine learning algorithms implemented in the Python environment were used to generate property valuation models. Statistical analysis of the results was carried out using non-parametric Friedman and Wilcoxon tests. The study showed the usefulness of applying splines to model trend functions.

Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach.

Social networks typically include a community structure, and the connections between nodes within the same community are very close; however, the connections between communities are sparse. In this study, we analyze the main challenges behind the problem and then resolve it using differential privacy. First, we choose the Louvain algorithm as a benchmark community detection algorithm for the algorithmic perturbation scheme. We introduce an exponential mechanism that uses modularity as a score. Secondly, by transforming each community into a hierarchical random graph model, and its edge connection probability is noisy by differential privacy mechanism to ensure the security of relevant information in the protected community.

The paper describes the implementation of non-hierarchical methods k-means and fuzzy c-means on nosily images from different medical modalities as computed tomography and magnetic resonance. Modern devices are created on the basis of advanced technology, both during the actual acquisition of the image and subsequently during its processing. The problem is caused by the unexpected disturbance of the image by parasitic noise, which may already occur in the electronics of the device or in dependence on the phenomena caused by the external environment. The testing was carried out on 3 datasets of medical images and the evaluation per individual images was determined based on the correlation factor and the mean quadratic error. The result is evaluation of non-hierarchical clustering techniques for the creation of mathematical models of tissue depending on the noise intensity.

The field of Automated Machine Learning (AutoML) has as its main goal to automate the process of creating complete Machine Learning (ML) pipelines to any dataset without requiring deep user expertise in ML. Several AutoML methods have been proposed so far, but there is not a single one that really stands out. Furthermore, there is a lack of studies on the characteristics of the fitness landscape of AutoML search spaces. Such analysis may help to understand the performance of different optimization methods for AutoML and how to improve them. This paper adapts classic fitness landscape analysis measures to the context of AutoML. This is a challenging task, as AutoML search spaces include discrete, continuous, categorical and conditional hyperparameters. We propose an ML pipeline representation, a neighborhood definition and a distance metric between pipelines, and use them in the evaluation of the fitness distance correlation (FDC) and the neutrality ratio for a given AutoML search space. Results of FDC are counter-intuitive and require a more in-depth analysis of a range of search spaces. Results of neutrality, in turn, show a strong positive correlation between the mean neutrality ratio and the fitness value.