Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving the Torpedo Scheduling Problem

The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.
The Optimization Potential of Volunteer Computing for Compute or Data Intensive Applications

—The poor scalability of Volunteer Computing (VC) hinders the application of it because a tremendous number of volunteers are needed in order to achieve the same performance as that of a traditional HPC. This paper explores optimization potential to improve the scalability of VC from three points of view. First, the heterogeneity of volunteers’ compute-capacity has been chosen from the whole spectrum of impact factors to study optimization potential. Second, a DTH (Distributed Hash Table) based supporting platform and MapReduce are fused together as the discussion context. Third, transformed versions of work stealing have been proposed to optimize VC for both computeand data-intensive applications. On this basis, the proposed optimization strategies are evaluated by three steps. First, a proof-of-concept prototype is implemented to support the representation and testing of the proposed optimization strategies. Second, virtual tasks are composed to apply certain computeor data-intensity on the running MapReduce. Third, the competence of VC, running the original equity strategy and the optimization strategies, is tested against the virtual tasks. The evaluation of results has confirmed that the impaired performance has been improved about 24.5% for computeintensive applications and about 19.5% for data-intensive applications.
iADAATPA Project: Pangeanic use cases

The iADAATPA 1 project coded as N ◦ 2016-EU-IA-0132 that ended in February 2019 is made for building of customized, domain-speciﬁc engines for public admin-istrations from EU Member States. The consortium of the project decided to use neural machine translation at the beginning of the project. This represented a challenge for all involved, and the positive aspect is that all public administrations engaged in the iADAATPA project were able to try, test and use state-of-the-art neural technology with a high level of satisfaction.
Improvement of Simultaneous 5-Axis Controlled Machining Accuracy by CL-Data Modification

As the motion accuracy of 5-axis machining centers directly influences the geometrical shape accuracy of the machined workpieces, accuracy enhancement of the 5-axis machining centers is strongly needed. To improve the shape accuracy during the machining by a 5-axis machine tool, a method that modifies the CL-data based on the motion trajectory errors normal to the machined surface at each command point has been proposed. In this study, the proposed method is applied to simultaneous 5-axis controlled machining to improve motion accuracy. A normal vector calculation method for the simultaneous 5-axis controlled motion is newly proposed, and the compensation method is applied to turbine blade machining by 5-axis controlled motion. Measurement tests of the cutting motion for blade shape machining by a ball-end mill were carried out with a different control mode of NC. The CL-data for the machining tool path was also modified based on the calculated trajectory of the tool center point. Experimental results reveal that the feed speed and machining accuracy significantly depend on the control mode of NC, and that the shape accuracy can be improved by applying the proposed compensation method without any decrease in motion speed.
ETHDKG: Distributed Key Generation with Ethereum Smart Contracts

—Distributed key generation (DKG) is a fundamental building block for a variety of cryptographic schemes and protocols, such as threshold cryptography, multi-party coin tossing schemes, public randomness beacons and consensus protocols. More recently, the surge in interest for blockchain technologies, and in particular the quest for developing scalable protocol designs, has renewed and strengthened the need for efﬁcient and practical DKG schemes. Surprisingly, given the broad range of applications and available body of research, fully functional and readily available DKG protocol implementations still remain limited. This paper hereby aims to close this gap by tailoring Gennaro et al.’s [1] well known protocol design towards being ef-ﬁciently implementable within public cryptocurrency ecosystems such as Ethereum. Our theoretical improvements are supported by an open source, fully functional, well documented DKG implementation 1 that can employ any Ethereum Virtual Machine (EVM) compatible smart contract platform as a communication layer. We evaluate the efﬁciency of our protocol and demonstrate its practicability through the deployment and successful execution of our DKG contract in the Ethereum Ropsten testnet. Given the current Ethereum block gas limit, all steps required for the key generation process, even in demanding scenarios tested with up to 256 nodes, can be veriﬁed at the smart contract level.
ρ-VAE: Autoregressive parametrization of the VAE encoder

We make a minimal, but very effective alteration to the VAE model. This is about a drop-in replacement for the (sample-dependent) approximate posterior to change it from the standard white Gaussian with diagonal covariance to the first-order autoregressive Gaussian. We argue that this is a more reasonable choice to adopt for natural signals like images, as it does not force the existing correlation in the data to disappear in the posterior. Moreover, it allows more freedom for the approximate posterior to match the true posterior. This allows for the repararametrization trick, as well as the KL-divergence term to still have closed-form expressions, obviating the need for its sample-based estimation. Although providing more freedom to adapt to correlated distributions, our parametrization has even less number of parameters than the diagonal covariance, as it requires only two scalars, $\rho$ and $s$, to characterize correlation and scaling, respectively. As validated by the experiments, our proposition noticeably and consistently improves the quality of image generation in a plug-and-play manner, needing no further parameter tuning, and across all setups. The code to reproduce our experiments is available at \url{this https URL}.
Inhibition of ER-to-Golgi transport by coxsackievirus 3 A protein : Biological significance, underlying mechanism and structure-function relationship of 3A

The main objective of the studies described in this thesis was to obtain more insight into the functional and structural aspects of the enterovirus 3A protein. Many viruses modify cellular processes for their own benefit. The enterovirus 3A protein inhibits endoplasmic reticulum (ER)-to-Golgi transport, a function previously suggested to be important for viral suppression of immune responses. We showed that a virus carrying a 3A protein defective in inhibiting ER-to-Golgi transport is indeed less virulent in mice, and we unraveled the mechanism by which 3A inhibits this trafficking step. Evidence is provided that 3A inhibits the activation of the GTPase ADP-ribosylation factor 1 (Arf1), which regulates the recruitment of the COP-I coat complex to membranes. 3A specifically inhibits the function of GBF1, a guanine nucleotide exchange factor for Arf1, by interacting with its N terminus. Furthermore, the extensive structure-function analysis that is described in this thesis has given new insight into the structure of the 3A homodimer and in the importance of dimerization for viral RNA replication and the 3A-mediated inhibition of protein transport.
MAGMATic: A Multi-domain Academic Gold Standard with Manual Annotation of Terminology for Machine Translation Evaluation

This paper presents MAGMATic (Multi-domain Academic Gold Standard with Manual Annotation of Terminology), a novel Italian–English benchmark which allows MT evaluation focused on terminology translation. The data set comprises 2,056 parallel sentences extracted from institutional academic texts, namely course unit and degree program descriptions. This text type is particularly interesting since it contains terminology from multiple domains, e.g. education and different academic disciplines described in the texts. All terms in the English target side of the data set were manually identiﬁed and annotated with a domain label, for a total of 7,517 annotated terms. Due to their peculiar features, institutional academic texts represent an interesting test bed for MT. As a further contribution of this paper, we investigate the feasibility of exploiting MT for the translation of this type of documents. To this aim, we evaluate two state-of-the-art Neural MT systems on MAG-MATic, focusing on their ability to translate domain-speciﬁc terminology.
Design of CoTAS: Automated Computational Thinking Assessment System

Computational thinking (CT) is widely accepted as a fundamental practice for equip-ping students to formulate and solve problems in the digital era. Many countries are adopting mandatory curricula for computer science (CS) lessons and CT education at high school. However, assessing how well students perform in CT activities is hard. Teachers face many challenges in the assessment process, because there is a limited number of resources for assessment and a lack of online access to resources. Therefore, the goal of this paper is to support teachers by developing an eﬀec-tive automated Computational Thinking Assessment System (CoTAS) for instructing and evaluating CT skills of high school students in Python course. CoTAS facilitates the assessment of students’ CT skills. Supported by CoTAS, teachers will be able to determine students’ CT skill levels and shape their learning by continuously observing students’ individual levels of development during the learning process. Teachers can access diﬀerent resources to evaluate CT concepts, practices and perspectives. CoTAS can provide automatic feed-back, so teachers can guide students directly when misconceptions arise. Moreover, Co-Copyright
Critical Factors Influencing Mobile Marketing Technology Adoption by Micro Businesses in Nigeria: A Preliminary Study

: The paper examines critical factors influencing mobile marketing technology adoption by micro businesses using a qualitative method. Data for this study were adopted through interviews (unstructured and semi-structured) in two different stages with twenty micro-businesses drawn purposefully and underpinned by Technology, Organisation and Environment (TOE) framework. This assisted in unveiling 4 critical factors that shape micro –businesses’ adoption of mobile marketing devices in Amuwo- Odofin LGA of Lagos State, Nigeria. These critical factors include: ease of use, customer accessibility, firm’s sales promotion, and intense competition. These factors are classified based on the TOE framework. This study is vital to academics and practitioners because it provides further insight into mobile marketing technology adoption framework; and identified factors that could shape the decisions of micro- businesses in a bid to cope with technology adoption continually and reduce the enormous time spent in search of information during the adoption process.
Assessing the Impact of QUIC on Network Fairness

—As user applications require always more throughput and less latency, new transport protocols such as Quick UDP Internet Connections (QUIC) are proposed. QUIC is currently deployed by Google and is targeted to replace both Transmission Control Protocol (TCP) and Transport Layer Security (TLS) for HyperText Transfer Protocol (HTTP) transport. The flexibility of QUIC, which is implemented in user-space, can however, notably influence the fairness in the sharing of network resources. In this work, we are interested in evaluating the behavior of TCP and QUIC when they coexist together in the network with the same Congestion Control Algorithm (CCA), namely CUBIC. We concretely quantify session level fairness between QUIC and TCP on a platform that injects real traffic over emulated network conditions. Results show that a configurable parameter in QUIC stacks: the number of TCP connections emulated by a single QUIC connection has a strong impact on fairness.
Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning

Modeling anaphora resolution is critical for proper pronoun translation in neural machine translation. Recently it has been addressed by context-aware models with varying success. In this work, we pro-pose a carefully designed training curriculum that facilitates better anaphora resolution in context-aware NMT. As a baseline, we train context-aware models as was done in previous work. We leverage oracle information speciﬁc to anaphora resolution during training. Following the intuition behind curriculum learning, we are able to train context-aware models which are improved with respect to coreference resolution, even though both the baseline and the improved system have access to exactly the same information at test time. We test our approach using two pronoun-speciﬁc evaluation metrics for MT.
Enhancing Transformer for End-to-end Speech-to-Text Translation

Neural end-to-end architectures have been recently proposed for spoken language translation (SLT), following the state-of-the-art results obtained in machine translation (MT) and speech recognition (ASR). Motivated by this contiguity, we propose an SLT adaptation of Transformer (the state-of-the-art architecture in MT), which exploits the integration of ASR solutions to cope with long input sequences featuring low information density. Long audio representations hinder the training of large models due to Transformer’s quadratic memory complexity. Moreover, for the sake of translation quality, handling such sequences requires capturing both short-and long-range dependencies between bi-dimensional features. Focusing on Trans-former’s encoder, our adaptation is based on: i) downsampling the input with convolutional neural networks, which enables model training on non cutting-edge GPUs, ii) modeling the bidimensional nature of the audio spectrogram with 2D components, and iii) adding a distance penalty to the attention, which is able to bias it towards short-range dependencies. Our experiments show that our SLT-adapted Transformer outperforms the RNN-based baseline both in translation quality and training time, setting the state-of-the-art performance on six language directions.
Do translator trainees trust machine translation? An experiment on post-editing and revision

Despite the importance of trust in any work environment, this concept has rarely been investigated for MT. The present contribution aims at ﬁlling this gap by presenting a post-editing experiment carried out with translator trainees. An institutional academic text was translated from Italian into English. All participants worked on the same target text. Half of them were told that the text was a human translation needing revision, while the other half was told that it was an MT output to be post-edited. Temporal and technical effort were measured based on words per second and HTER. Results were complemented with a manual analysis of a subset of the observations.
Post-Quantum Variants of ISO/IEC Standards: Compact Chosen Ciphertext Secure Key Encapsulation Mechanism from Isogeny

ISO/IEC standardizes several chosen ciphertext-secure key encapsulation mechanism (KEM) schemes in ISO/IEC 18033-2. However, all ISO/IEC KEM schemes are not quantum resilient. In this paper, we introduce new isogeny-based KEM schemes (i.e., CSIDH-ECIES-KEM and CSIDH-PSEC-KEM) by modifying Diffie-Hellman-based KEM schemes in ISO/IEC standards. The main advantage of our schemes are compactness. The key size and the ciphertext overhead of our schemes are about five times smaller than these of SIKE-KEM which is submitted to NIST's post-quantum cryptosystems standardization.
Morphological Neural Pre- and Post-Processing for Slavic Languages

While developing NMT systems for our customers involving Slavic languages, we encountered certain issues that do not affect Latin or Germanic languages. The most striking of these is the morphological complexity inherent in a remarkable number of unique synthetic forms. For each language combination, the aim is always to find the best balance between the size of the vocabulary, the quality of the translation and the performance of the MT model (both training time and translation time). When working with Slavic idioms, the variety of cases and genders makes this challenge even more difficult and engaging. For Slavic source languages, our solution is to add an extra pre-processing step before the actual translation, in which the inflected word is reduced to its components; naturally, in the opposite direction this requires a symmetrical post-processing technique. Tests have proven high-quality results for Slavic languages, either source or target, confirming this as an effective approach.
Nonproliferative and Proliferative Lesions of the Rat and Mouse Hematolymphoid System

The INHAND Project (International Harmonization of Nomenclature and Diagnostic Criteria for Lesions in Rats and Mice) is a joint initiative of the Societies of Toxicologic Pathology from Europe (ESTP), Great Britain (BSTP), Japan (JSTP), and North America (STP) to develop an internationally accepted nomenclature for proliferative and nonproliferative changes in rats and mice. The purpose of this publication is to provide a standardized nomenclature for classifying changes observed in the hematolymphoid organs, including the bone marrow, thymus, spleen, lymph nodes, mucosa-associated lymphoid tissues, and other lymphoid tissues (serosa-associated lymphoid clusters and tertiary lymphoid structures) with color photomicrographs illustrating examples of the lesions. Sources of material included histopathology databases from government, academia, and industrial laboratories throughout the world. Content includes spontaneous lesions as well as lesions induced by exposure to test materials. The nomenclature for these organs is divided into 3 terminologies: descriptive, conventional, and enhanced. Three terms are listed for each diagnosis. The rationale for this approach and guidance for its application to toxicologic pathology are described in detail below.
SKELETAL MUSCLE VASCULAR AND METABOLIC CONTROL: IMPACTS OF EXOGENOUS VS. ENDOGENOUS NITRIC OXIDE SYNTHESIS

The purpose of this dissertation is to expand our knowledge on the physiological effects of the ubiquitous signaling molecule nitric oxide (NO). Focus is given to the impacts of the nitrate (NO3) nitrite (NO2) NO pathway on skeletal muscle vascular and metabolic function during exercise. The NO3-NO2-NO pathway has garnered tremendous research interest due to its ability to upregulate NO bioavailability independently of NO synthase (NOS) function and thus impact the metabolic responses to exercise. Chapter 2 demonstrates that NO3 supplementation via beetroot juice (BR) augments the skeletal muscle vascular responses to exercise. Five days of BR supplementation resulted in a significantly higher skeletal muscle blood flow (BF) and vascular conductance (VC) during exercise when compared to control. The increases in BF and VC were preferentially directed to muscles and muscle portions comprised predominantly of fast twitch fibers. Furthermore, exercising blood [lactate] was reduced, suggesting improved metabolic control. In chapter 3, BR resulted in a slower fall in the microvascular PO2 (PO2mv, the soul driving force for blood myocyte O2 flux) during the crucial rest-contraction transition thereby preserving the pressure head needed to move O2 from the capillary into the myocyte. Chapter 4 examines the effects of BR on fast vs. slow twitch muscles in which BR raised the PO2mv during the steady state of muscle contractions in fast but not slow twitch muscles, likely due to the lower PO2mv at rest and throughout muscle contractions within these tissues. Chapter 5 investigates the effects of direct arterial NO2 infusion on skeletal muscle BF and VC during exercise in rats with NOS blockade via NG-nitro-L arginine methyl ester. NO2 infusion restored MAP and VC to levels observed in healthy control animals (with intact NOS function) highlighting the potential for a NO2 based therapy to positively impact vascular function in those with compromised NOS function such that is evident in many prevalent diseases. These results provide crucial mechanistic insight into the improved exercise tolerance observed in humans following NO3 supplementation whilst also challenging our current understanding of NO’s role in physiology and pathophysiology. SKELETAL MUSCLE VASCULAR AND METABOLIC CONTROL: IMPACTS OF EXOGENOUS VS. ENDOGENOUS NITRIC OXIDE SYNTHESIS
Constructing Knowledge in the Workplace: Potential and Pitfalls

Workplace learning is commonly used as a means of securing skilful vocational knowledge. This approach to learning, which is usually characterised by guided access to authentic vocational activities, now finds support within the literature of the anthropological and sociological orientations to the study of thinking, acting and learning. It is proposed in this literature that access to a rich source of knowledge is provided by constructing knowledge in the circumstances of its utilisation. Yet imposing questions remain unanswered about both the potential and limits of informal learning processes, such as workplace learning. These questions remain central to the evaluation of learning arrangements which aim to develop skilful vocational knowledge. This paper draws on the findings of three recent studies of workplace learning conducted in Queensland, Australia. These studies are used to examine notions of guided learning situated in the workplace, through undertaking authentic vocational activities. To understand further the efficacy and limits of these arrangements, and in order to develop theoretical principles of workplace learning, the findings of these studies are examined from cognitive and socio-cultural perspectives. The paper concludes by identifying some strengths and weaknesses associated with workplace learning.