Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Site-Invariant Neural Decoding from Local Field Potentials

—The non-stationary nature of neural activity prevents Brain-Computer Interfaces (BCIs) from leveraging data sets collected at different recording sites, such as cortical depths. As a result, invasive BCIs are continuously retrained on up-to-date site-speciﬁc data which is inefﬁcient. In this paper we study the important intra-subject problem where neural activity signals collected at different cortical sites within a given subject are jointly used to train a decoder of motor intentions that generalizes well across the entire range of sites. We use directed graphical model with hidden latent variables that are censored via adversarial training that produces site-invariant low-dimensional representations of the neural activity. We evaluate the performance of our method on an experiment in which two macaque monkeys perform memory-guided visual saccades to one of eight target locations. The data sets are collected across range of cortical sites, from superﬁcial to deep cortical sites. The results demonstrate that the site-invariant latent representations result in improved neural decoding by a peak margin of ≈ 50% , relative to the baseline approach where the neural decoder is trained on site-dependent features. The ﬁndings reported in this paper are an important step towards the development of efﬁcient intra-subject BCIs that generalize well across range of cortical sites.
Forensic Analysis of Configuration-based Attacks

—Causality analysis is an effective technique for investigating and detecting cyber attacks. However, by focusing on auditing at the Operating System level, existing causal analysis techniques lack visibility into important application-level semantics, such as conﬁguration changes that control application runtime behavior. This leads to incorrect attack attribution and half-baked tracebacks. In this work, we propose Dossier, a specialized provenance tracker that enhances the visibility of the Linux auditing infrastructure. By providing additional hooks into the system, Dossier can generate a holistic view of the target application’s event history and causal chains, particularly those pertaining to conﬁguration changes that are among the most common attack vectors observed in the real world. The extra vantage points in Dossier enable forensic investigators to bridge the semantic gap and correctly piece together attack fragments. Dossier leverages the versatility of information ﬂow tracking and system call introspection to track all conﬁguration changes, including both dynamic modiﬁcations that directly update conﬁguration-related program variables and revisions to conﬁguration ﬁles on disk with negligible runtime overhead (less than 7%). Evaluation on realistic workloads and real-world attack scenarios shows that Dossier can effectively reason about conﬁguration-based attacks and accurately reconstruct the whole attack stories.
Time dependent reliability analysis for a critical reactor safety system based on fault tree approach

Loss of coolant in the operation of any nuclear power plant will eventually become the primary source of hazard in the sequence of events leading to reactor core uncovery. Subsequent failure in removing the nuclear decay heat and preventing the core uncovery will further lead to loss of coolant accident (LOCA). In conjunction to this safety concern, it is crucial that the reliability of the plant emergency core cooling system is systematically and critically analysed. This article presents a case study on the time dependent reliability analysis for a safety injection system (SIS) of an advanced pressurized water reactor, based on the failure mode and effect analysis (FMEA) and fault tree (FT) analysis approach. The identified generic data for component reliability are carefully reviewed and used in this study. Based on the base case model, sensitivity and importance measure analysis for basic events are performed and the outcomes gained are presented and discussed. From the analysis, it is shown that the safety injection pumps of the SIS contribute significantly to the reliability of the system. In the short (at 0.5 hour) and long (7.0 hours and 72.0 hours) run, safety injection pumps are critical and influence the reliability of the SIS the most. The SIS’s FT logic model that has been developed and calculated shows the usability of the FMEA and FT approach that are implemented in analysing SIS time dependent reliability.
Abnormal Detection Model of Energy Consumption Data in Beneficiation and Metallurgy Enterprises based on Transfer Learning

The energy consumption data of beneficiation and metallurgy enterprises has the characteristics of multi-dimensional and timing. An energy consumption data anomaly detection model based on transfer learning is proposed to help enterprises monitor the abnormal energy use. The combination of transfer learning and DTW algorithm is used to eliminate the adverse impact of timing on the model, and the ensemble learning is used to realize data detection. Finally, an efficient and accurate energy consumption data anomaly detection model is formed. The detection accuracy of the model is 91.6%, which can effectively meet the requirements of enterprise energy management.
Efficient Self-attention with Relative Position Encoding for Electric Power Load Forecasting

To effectively mine historical data information and improve the accuracy of short-term load prediction, this paper aims at the characteristics of time series and nonlinear power load. Deep learning for load forecasting has received a lot of attention in recent years, and it has become popular in the analysis of electricity load forecasting. Long short-term memory (LSTM) and gated recurrent unit (GRU) are specifically designed for time-series data. However, due to the gradient disappearing and exploding problem, recurrent neural networks (RNNs) cannot capture long-term dependence. The Transformer, a self-attention-based sequence model, has produced impressive results in a variety of generating tasks that demand long-range coherence. This shows that self-attention could be useful in power load forecasting modeling. In this paper, to effectively and efficiently model the large-scale load forecasting, we further design the transform encoder with relative position encoding, which consists of four main components: single-layer neural network, relative positional encoding module, encoder module, and feed-forward network. Experimental results on real-world datasets demonstrate that our method outperforms the GRU, LSTM, and original Transformer encoder.
Analysis of the Li-ion battery industry in light of the global transition to electric passenger light duty vehicles until 2050

The decarbonization of the transport sector requires a rapid expansion of global battery production and an adequate supply with raw materials currently produced in small volumes. We investigate whether battery production can be a bottleneck in the expansion of electric vehicles and specify the investment in capital and skills required to manage the transition. This may require a battery production rate in the range of 4–12 TWh/year, which entails the use of 19–50 Mt/year of materials. Strengthening the battery value chain requires a global effort in many sectors of the economy that will need to grow according to the battery demand, to avoid bottlenecks along the supply chains. Significant investment for the establishment of production facilities (150–300 billion USD in the next 30 years) and the employment of a large global workforce (400k–1 million) with specific knowledge and skillset are essential. However, the employment and investment required are uncertain given the relatively early development stage of the sector, the continuous advancements in the technology and the wide range of possible future demand. Finally, the deployment of novel battery technologies that are still in the development stage could reduce the demand for critical raw materials and require the partial or total redesign of production and recycling facilities affecting the investment needed for each factory.
The Lenticular Lens

Time and again, researchers are presented with problems for which they postulate and test hypotheses in order to provide us with robust explanations for research questions successfully investigated. Oftentimes, solid explanations for complex problems require exploring a multitude of data-sources rather than the use of a unique (authoritative) data-source. This is the case, for example, in domains such as e-(science/commerce), tourism, digital humanities, etc. In the Digital Humanities, in particular, answering a research question often requires a researcher to combine several datasets that each contain specific and single scoped information on the entities it describes/contains. However, the use of multiple sources comes with a problem of its own: data integration. In the Semantic Web, entity matching is a well-known technique for which there exists a considerable body of work on generic matching algorithms (Hassanzadeh et al., 2009,? ; Mirani and Radke, 2014; Ngomo and Auer, 2011; Volz et al., 2009a) dedicated for the task of integrating data. Only, these are scattered and solely generate links between a pair of datasets. This triggers the need for tailoring and/or combining algorithms which is not an unusual reality for resource linking. Once executed, this computationally expensive task of contextual data integration should record the processes that lead to the discovery of the links. Only, developers of matching algorithms do not concern themselves with such metadata acquisition, leaving data providers to resort to time-consuming and error-prone manual labor for describing their data. When available, the metadata is often provided using VoID
Beefy Drivers for Flaky Hardware

The current implementation of device drivers assumes and relies on the correctness of the underlying hardware. Any unresponsive hardware can cause the application and / or the system to crash or hang. However, current technology trends indicate that devices are becoming more susceptible to hardware failures, both permanent and transient in nature. Hence, the current implementation of drivers is poorly suited to run on increasingly unreliable device hardware. This project intends to solve this problem by building reliable device drivers that can tolerate hardware failures and recover as necessary. This project intends to statically analyze device drivers and identify driver code that assumes correct hardware behaviour as a basis for forward progress of the driver. These drivers will then be hardened by modifying the code to alleviate this reliance. Time permitting, we also intend to build a fault injection framework to test the robustness of our methodology. Based on our results, we also plan to discuss possible recovery strategies that can be performed in cases where the driver cannot continue to perform normally due to corruption of the driver’s state.
SHAKEDOWN ANALYSIS OF 3D FRAMES UNDER MULTIPLE LOAD COMBINATIONS USING MIXED FIBER BEAM ELEMENTS

Abstract. This work presents an efficient shakedown analysis of 3D frames under multiple load combinations. Mixed fiber beam FEs are employed for an accurate discretization. The stress admissible domain is defined at fiber level as a function of the load factor using the maximum and minimum effect due to all loads. An incremental-iterative method is used at structural level. It evaluates a fictitious path made of a sequence of safe states with a load factor converging to the safety limit. Each point is obtained by finding kinematic variables corresponding to self-equilibrated stresses satisfying Melan’s condition for the current load factor to be safe. An iterative FE state determination provides stress DOFs corresponding to assigned kinematic DOFs and load factor. Important features of the method are: i) a direct application of the Newton method, ii) a computational cost unaffected by dimension and complexity of the load domain, iii) an accurate safety factor using a small number of fibers and iv) an efficient solution for large buildings.
Sharenting and Children's Privacy in the United States: Parenting Style, Practices, and Perspectives on Sharing Young Children's Photos on Social Media

Parents posting photos and other information about children on social media is increasingly common and a recent source of controversy. We investigated characteristics that predict parental sharing behavior by collecting information from 493 parents of young children in the United States on self-reported demographics, social media activity, parenting styles, children's social media engagement, and parental sharing attitudes and behaviors. Our findings indicate that most social media active parents share photos of their children online and feel comfortable doing so without their child's permission. The strongest predictor of parental sharing frequency was general social media posting frequency, suggesting that participants do not strongly differentiate between "regular" photo-sharing activities and parental sharing. Predictors of parental sharing frequency include greater social media engagement, larger social networks with norms encouraging parental sharing, more permissive and confident parenting styles, and greater social media engagement by their children. Contrasting previous research that often highlights benefits of parental sharing, our findings point to a number of risky online behaviors associated with parental sharing not previously uncovered. Implications for children's privacy and early social media exposure are discussed, including future directions for influencing parental sharing attitudes and behaviors.
DIFFERENTIAL EXPRESSION OF PROTEIN IN MUSCLE OF RATS TREATED WITH FLUORIDE AND EXERCISE

Chronic ingestion of fluoride (F) has been reported to produce insulin resistance. Conversely, exercising (E) increases the peripheral response to insulin, enhancing the effect of the hormone on target tissues. Previous work has shown that E can reverse insulin resistance caused by ingesting F. Therefore, the objective of this work was: to study the pattern of protein expression in rats that reversed the insulin resistance induced by F when performing E. Healthy female Sprague-Dawley rats (50 days) were divided into 3 groups (n = 6 per group): Control: drinking water F 0 ppm, F: drinking water F 15ppm and FE: drinking water F 15ppm + E 30 min per day on a treadmill. The treatments were applied for 30 days, then the animals were euthanized and the gastrocnemius muscles were obtained for proteomic analysis. Proteins related to carbohydrate metabolism are less expressed in group F than in the control. In contrast, the decrease in carbohydrate metabolism proteins was not observed in rats treated with F and E. Conclusion: physical exercise reverts the resistance to insulin induced by fluoride counteracting fluoride effects on proteins related to carbohydrate metabolism.
DPaSE: Distributed Password-Authenticated Symmetric-Key Encryption, or How to Get Many Keys from One Password

Cloud storage is becoming increasingly popular among end users that outsource their personal data to services such as Dropbox or Google Drive. For security, uploaded data should ideally be encrypted under a key that is controlled and only known by the user. Current solutions that support user-centric encryption either require the user to manage strong cryptographic keys, or derive keys from weak passwords. While the former has massive usability issues and requires secure storage by the user, the latter approach is more convenient but offers only little security since encrypted data is susceptible to offline attacks. The recent concept of password-authenticated secret-sharing (PASS) enables users to securely derive strong keys from weak passwords by leveraging a distributed server setup, and has been considered a promising step towards secure and usable encryption. However, using PASS for encryption is not as suitable as originally thought: it only considers the (re)construction of a single, static key -- whereas practical encryption will require the management of many, object-specific keys. Using a dedicated PASS instance for every key makes the solution vulnerable against online attacks, inherently leaks access patterns to the servers and poses the risk of permanent data loss when an incorrect password is used at encryption. We therefore propose a new protocol that directly targets the problem of boostrapping encryption from a single password: distributed password-authenticated symmetric encryption DPaSE. DPaSE offers strong security and usability, such as protecting the user's password against online and offline attacks, and ensuring message privacy and ciphertext integrity as long as at least one server is honest. We formally define the desired security properties in the UC framework and propose a provably secure instantiation. The core of our protocol is a new type of Oblivious Pseudorandom Function (OPRF) that allows to extend a previous partially-blind query with a follow-up request and will be used to blindly carry over passwords across evaluations and avoid online attacks. Our (proof-of-concept) implementation of DPaSE uses 10 exponentiations at the user, 4 exponentiations and 2 pairings at each server, and has a server throughput of 76 account creations and 37 (user authentication followed by) encryptions per second, when run between a user and 2-10 servers.
Universally Composable Σ-protocols in the Global Random-Oracle Model

Numerous cryptographic applications require efficient noninteractive zero-knowledge proofs of knowledge (NIZK PoK) as a building block. Typically they rely on the Fiat-Shamir heuristic to do so, as security in the random-oracle model is considered good enough in practice. However, there is a troubling disconnect between the stand-alone security of such a protocol and its security as part of a larger, more complex system where several protocols may be running at the same time. Provable security in the universal composition (UC) model of Canetti is the best guarantee that nothing will go wrong when a system is part of a larger whole. In this paper, we show how to achieve efficient UCsecure NIZK PoK in the global random-oracle model of Canetti, Jain,
An Indoor Fusion Fingerprint Localization Based on Channel State Information and Images

With the intelligence of society, location-based service (LBS) plays an increasingly prominent role in daily life. In this paper, an indoor fusion fingerprint localization based on channel state information (CSI) and image is proposed to solve the limitation of single sensor in positioning. To reduce the dimension of image data, Shared Convolutional-Neural-Network based Add Fusion Network(SC-AFN) is proposed to process multi-directional images. In SC-AFN, initial features of images from different directions are extracted by shared Convolutional-Neural-Network (CNN), and then the features are fused by Add strategy and trained for vector representation of images. On this basis, the measure index based fusion representation model (MI-FRM) is proposed to fuse CSI features and SC-AFN image features. MI-FRM introduces the measurement index of fingerprint database into the fusion method. The parameters of MI-FRM are optimized by maximizing the discrimination of fusion fingerprint database to improve the matching accuracy for positioning. Experiments show that the fusion localization based on MI-FRM achieves better positioning performance, with the average error 0.62m in office scene.
Role of Ca on the corrosion resistance of Mg-9Al and Mg-9Al-0.5Mn alloys

The effect of Ca additions on the microstructure and corrosion resistance of Mg-9Al and Mg-9Al0.5Mn alloys is investigated. Model alloys were used instead of a commercial AZ91 alloy in order to avoid masking effects from other alloying elements. Grain size and secondary phases were characterised with special interest in their Volta potential. Corrosion performance was evaluated by hydrogen evolution and electrochemical methods. A synergetic effect was found when Ca and Mn were added in small amounts (0.5 wt.%), reaching corrosion rates up to two orders of magnitude lower than the model Mg-9Al alloy. Such improvement was associated with Ca decreasing the Volta potential of the β-phase and with Ca and Mn decreasing the galvanic activity of Fe-rich intermetallics. Precipitation of Al2Ca decreased the amount of β-phase and had a negative impact on the corrosion resistance due to its cathodic behaviour.
Flourogenic Chemo-Sensor-Based Sensing of Heavy Metal ion by Using Gold Coated Lower Rim Amino Functionalized Calix [4] arene

In recent years, there has been increasing ecological and global public health concern associated with heavy metal ions even at lower concentration. These heavy metals are non-biodegradable and remain in the ecological system and food chain system at high levels of pollution. The chemical recognition of heavy-metal ions is the base for the mentioned techniques to enable a proper sensing through specific substances/species called receptors of the Fluorogenic chemo-sensor. So Fluorogenic chemo-sensor sensing technique having high reliability and sensitivity for analysis of target non biodegradable heavy metal ions detection by utilizing Gold Nanoparticles (AuNPs) surface coated with calix [4] arene amine derivative. Signaling event of chemo-sensor after binding with analyte of interest was characterized by H NMR, Fourier-transform infrared spectroscopy (FTIR), Photo luminescence (PL) and Transmission electron microscope (TEM) analysis. Calix [4] arene amine derivative capped gold nanoparticle (C [4] A3@AuNPs) in Tetrahydrofuran (THF) dispersible at low pH with specific size and zeta potential of 50±2 MeV make these nanoparticle has potential for various application. Calix [4] arene amine derivative capped gold nanoparticle (C [4] A3@AuNPs) were found to be fluorometric sensor for Zn metal ion among these metal ions i.e. Li, Zn, Ni, Ba, Cr, Which can detected at very low concentration level of 1 μM in facile way of fluorescence quenching via colorimetric sensor relies real time quantitative detection of Znmetal ion with detection limit of 1.5×10 M.
Identifying phrasal connectives in Italian using quantitative methods

In recent decades, the analysis of phraseology has made use of the exploration of large corpora as a source of quantitative information about language. This paper intends to present the main lines of work in progress based on this empirical approach to linguistic analysis. In particular, we focus our attention on some problems relating to the morpho-syntactic annotation of corpora. The CORIS/CODIS corpus of contemporary written Italian, developed at CILTA – University of Bologna (Rossini Favretti 2000; Rossini Favretti, Tamburini, De Santis in press), is a synchronic 100million-word corpus and is being lemmatised and annotated with part-of-speech (POS) tags, in order to increase the quantity of information and improve data retrieval procedures (Tamburini 2000). The aim of POS tagging is to assign each lexical unit to the appropriate word class. Usually the set of tags is pre-established by the linguist, who uses his/her competence to identify the different word classes. The very first experiments we made revealed how the traditional part-of-speech distinctions in Italian (generally based on morphological and semantic criteria) are often inadequate to represent the syntactic features of words in context. It is worth noting that the uncertainties in categorisation contained in Italian grammars and dictionaries reflect a growing difficulty as they move from fundamental linguistic classes, such as nouns and verbs, to more complex classes, such as adverbs, pronouns, prepositions and conjunctions. This latter class, that groups together elements traditionally used to express connections between sentences, appears inadequate when describing cohesive relations in Italian. This phenomenon actually seems to involve other elements traditionally