Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evolution of DNS-based Email Authentication: Measuring Adoption and Finding Flaws

Email is still one of the most common ways of communication in our digital world, the underlying Simple Mail Transport Protocol (SMTP) is crucial for our information society. Back when SMTP was developed, security goals for the exchanged messages did not play a major role in the protocol design, resulting in many types of design limitations and vulnerabilities. Especially spear-phishing campaigns take advantage of the fact that it is easy to spoof the originating email address to appear more trustworthy. Furthermore, trusted brands can be abused in email spam or phishing campaigns. Thus, if no additional authentication mechanisms protect a given domain, attackers can misuse the domain. To enable proper authentication, various extensions for SMTP were developed in the past years. In this paper, we analyze the three most common methods for originating DNS domain email authentication in a large-scale, longitudinal measurement study. Among other findings, we confirm that Sender Policy Framework (SPF) still constitutes the most widely used method for email authentication in practice. In general, we find that higher-ranked domains use more authentication mechanisms, but sometimes configuration errors emerge, e.g., we found that amazon.co.jp had an invalid SPF record. A trend analysis shows a (statistically significant) growing number of domains using SPF. Furthermore, we show that the Domain-based Message Authentication, Reporting and Conformance (DMARC) distribution evolved significantly as well by increasing tenfold over the last five years. However, is still far from being perfect with a total adoption rate of about 11%. The US and UK governmental domains are an exception, given that both have a high adoption rate due to binding legal directives. Finally, we study DomainKeys Identified Mail (DKIM) adoption in detail and find a lower bound of almost 13% for DKIM usage in practice. In addition, we reveal various flaws, such as weak or shared duplicate keys. As a whole, we find that about 3% of the domains use all three mechanisms in combination.
Reflecting Stacked Continuations in a Fine-Grained Direct-Style Reduction Theory

The delimited-control operator shift0 has been formally shown to capture the operational semantics of deep handlers for algebraic effects. Its CPS translation generates λ-terms in which continuation composition is not expressed in terms of nested function calls, as is typical of other delimited-control operators, e.g. shift, but with function applications consuming a sequence of continuations one at a time, as if they formed a stack. We present a novel reduction theory for Moggi’s computational λ-calculus extended with shift0 and a control delimiter dollar, which models the capture of evaluation contexts in a fine-grained manner as an interaction between the let-expressions and the delimiter. We establish a connection between our reduction theory and the existing theories of shif0 and dollar. Moreover, we develop a CPS translation for our calculus along with a direct-style translation that together form a reflection, i.e. the translations preserve reductions and the direct-style translation is a right inverse of the CPS translation. This construction relies on the invariant that CPS root terms are in η-head-normal form. The results of this work could potentially be used for compiler optimisations and lead to a similar development for algebraic effects.
Exploring Learning Analytics on YouTube: a tool to support students’ interactions analysis

YouTube is a free online video-sharing platform that is often used by students for their learning activities. The interactions of the students when using the platform to shape new concepts, are worth to be investigated to better understand and to optimize the learning opportunities that take place in this platform. In this paper, we investigate which types of data are relevant to analyse the interactions of students with content on YouTube, and we introduce a new tool that emulates students’ interactions with the platform in order to provide data to be used in supporting Learning Analytics approaches. Our preliminary study inspects the tool effectiveness in data collection and analyses the effects of the YouTube recommendation system in students’ activities. We also identify methodologies based on statistical indexes and social network analysis that can be adopted to analyse students’ experiences. Results show how the YouTube recommendation system plays a critical role in affecting the student learning trajectory.
A multi-faceted approach to large scale financial forecasting

Accurate forecasting of a company's financial performance is critical to capital market management and analysis. Thus, building a framework that is able to produce highly reliable and robust forecasts of financial metrics provides a positive impact on market participants such as investors who can make better trading decisions and manage their portfolios more suitably. We developed a multi-faceted modeling approach which leveraged univariate and multivariate models to identify the best performing model setting. Through large scale experiments of financial time series, we demonstrate this framework can produce more accurate forecasts than those made by professional financial analysts.
The effect of time drift in source code authorship attribution: Time drifting in source code - stylochronometry

Stylochronometry deals with the influence of time in an author's style, specifically how it changes stylometric features. Analysis of time drift occurrence is important especially for a dataset creation process of other works in this area. In this paper, we performed experiments using the Google Code Jam dataset to show the influence of time drift in the area of source code authorship attribution. Our experiments revealed that there is significant time drift in stylometric features in one year difference, which is enlargening as the difference of time increases. Another interesting result is that when training our authorship attribution method on data from the future and testing on data from the past, the time drift is lower than in opposite direction. Also, we found the relation between the length of source code and the accuracy of our authorship attribution method.
SecretMatch: inventory matching from fully homomorphic encryption

Inventory matching is a process by which a broker or bank pairs buyers and sellers, without revealing their respective orders in a public exchange. Banks often undertake to match their clients, so that these clients can trade securities without incurring adverse price movements. If a bank finds matches between clients, it may execute them at reduced rates; if no matches are found, the clients must trade in a public market, which introduces costs for both parties. This problem is distinct from that solved by dark pools or public exchanges, which implement Continuous Double Auctions (CDAs). CDAs incorporate both price and volume. Inventory matching incorporates volume alone, and extracts price from an external source (such as a public market). As it is currently conducted, inventory matching requires that clients share their intentions to buy or sell certain securities---along with the sizes of their positions---with the bank. Clients worry that if this information were to "leak" in some way, other market participants could become aware of their intentions, and cause the price to move adversely against them before they trade. A solution to this problem promises to enable more clients to match their orders more efficiently---with reduced market impact---while also eliminating the risk of information leakage. We present a cryptographic approach to multi-client inventory matching, which preserves the privacy of clients. Our central tool is threshold fully homomorphic encryption; in particular, we introduce an efficient, fully-homomorphic integer library which combines GPU-level parallelism with insights from digital circuit design. Our solution is also post-quantum secure. We report on an implementation of our protocol, and describe its performance.
Designing Media Provenance Indicators to Combat Fake Media

With the growth of technology that produces misinformation, there is a growing need to help users identify emerging types of fake media such as edited images and manipulated videos. In this work, we conduct a mixed-methods investigation into how we can provide provenance indicators to assist users in detecting newer forms of fake media. Specifically, we interview users regarding their experiences with different misinformation modes (text, image, video) to inform the design and content of indicators for previously unexplored media, especially fake videos. We find that media provenance – the source of the information – is a key heuristic used to evaluate all forms of fake media, and a heuristic that can be addressed by emerging technology. Thus, we subsequently design and investigate the use of provenance indicators to help users identify fake videos. We conduct a participatory design study to develop and design provenance indicators and evaluate participant-designed indicators via both expert evaluations and quantitative surveys (n=1,456) with end-users. Our results provide concrete design guidelines for the emerging issue of fake media. Our findings also raise concerns regarding users’ tendency to overgeneralize indicators used to assist users in identifying misinformation, suggesting the need for further research on warning design in the ongoing fight against misinformation.
Développement de polymères à empreintes ioniques pour l'extraction sélective des lanthanides dans des échantillons environnementaux

L'analyse des lanthanides a l'etat de trace dans les matrices environnementales requiert souvent une etape de purification et de preconcentration. L'extraction sur phase solide (SPE) est la technique la plus utilisee dans le domaine du traitement de l'echantillon. Cette these a consiste a developper des polymeres a empreintes ioniques (IIP) pour l'extraction selective des lanthanides (Ln3+). Tout d'abord, des IIP ont ete prepares selon l'approche de piegeage en utilisant le 5,7-dichloroquinoline-8-ol, un ligand non vinylique. La perte de ce ligand, suppose piege, durant les etapes d'elimination de l'ion empreinte et de sedimentation succedant a la polymerisation a ete demontree en HPLC-UV. Cette perte non repetable a entraine un manque de repetabilite des profils SPE sur deux IIP prepares dans les memes conditions. Cela a permis de demontrer que cette approche de synthese pour des IIP n'etait pas adequate. Ensuite, des IIP ont ete synthetises par immobilisation chimique en utilisant l'acide methacrylique comme monomere vinylique. La repetabilite des etapes de synthese et d'extraction a ete demontree. Une selectivite pour l'ensemble des lanthanides a ete obtenue et des applications avec des echantillons reels ont ete faites avec succes. Enfin, des IIP obtenus par immobilisation chimique d'acide methacrylique et de 4-vinylpyridine comme monomeres ont ete synthetises avec comme ion empreinte soit un Ln3+ leger (Nd3+) soit un Ln3+ lourd (Er3+). Des selectivites identiques de ces IIP pour l'ensemble des Ln3+ ont ete obtenues dans les deux cas. Cependant, cette selectivite a pu etre modulee en changeant la nature et le pH de la solution de lavage utilisee dans le protocole SPE.
Nonlinear Model Predictive Control Using State Estimation for Stabilization of Vehicle Dynamics to Avoid the Secondary Accident

In recent years, various control problems of vehicle dynamics such as collision avoidance, rollover prevention, wheel slip control, driver assistance control have been studied. This study focuses on the stabilization problem of unstable vehicle dynamics caused by a collision accident. In this paper, we consider the model predictive control (MPC) problem for stabilization of vehicle nonlinear dynamics to avoid the second accident after the first collision accident. MPC is a well-established control method in which the current control input is obtained by solving a finite horizon open-loop optimal control problem using the current state of the system as the initial state. However, MPC method is inapplicable to systems whose all state variables are not exactly known. In general, it is usual that the state variables of systems are measured through output sensors. Thus, only limited parts of them can be used for designing control inputs. Therefore, automatic control systems must incorporate some type of state estimation. In order to apply the MPC method to the automatic control systems for nonlinear vehicle dynamics, we need to establish a state estimation method for nonlinear vehicle systems with limited measurable state variables. The objective of this study is to establish a control method for unstable vehicle dynamics caused by a collision accident by means of incorporating state estimation into model predictive control.
AttkFinder: Discovering Attack Vectors in PLC Programs using Information Flow Analysis

To protect an Industrial Control System (ICS), defenders need to identify potential attacks on the system and then design mechanisms to prevent them. Unfortunately, identifying potential attack conditions is a time-consuming and error-prone process. In this work, we propose and evaluate a set of tools to symbolically analyse the software of Programmable Logic Controllers (PLCs) guided by an information flow analysis that takes into account PLC network communication (compositions). Our tools systematically analyse malicious network packets that may force the PLC to send specific control commands to actuators. We evaluate our approach in a real-world system controlling the dosing of chemicals for water treatment. Our tools are able to find 75 attack tactics (56 were novel attacks), and we confirm that 96% of these tactics cause the intended effect in our testbed.
Brothers in arms: proBDNF/BDNF and sAPPα/Aβ-signaling and their common interplay with ADAM10, TrkB, p75NTR, sortilin, and sorLA in the progression of Alzheimer’s disease

Abstract Brain-derived neurotrophic factor (BDNF) is an important modulator for a variety of functions in the central nervous system (CNS). A wealth of evidence, such as reduced mRNA and protein level in the brain, cerebrospinal fluid (CSF), and blood samples of Alzheimer’s disease (AD) patients implicates a crucial role of BDNF in the progression of this disease. Especially, processing and subcellular localization of BDNF and its receptors TrkB and p75 are critical determinants for survival and death in neuronal cells. Similarly, the amyloid precursor protein (APP), a key player in Alzheimer’s disease, and its cleavage fragments sAPPα and Aβ are known for their respective roles in neuroprotection and neuronal death. Common features of APP- and BDNF-signaling indicate a causal relationship in their mode of action. However, the interconnections of APP- and BDNF-signaling are not well understood. Therefore, we here discuss dimerization properties, localization, processing by α- and γ-secretase, relevance of the common interaction partners TrkB, p75, sorLA, and sortilin as well as shared signaling pathways of BDNF and sAPPα.
An experimental study on the effect of emotion lines in comics

Abstract Both mainstream and art comics often use various flourishes surrounding characters’ heads. These so-called “pictorial runes” (also called “emanata”) help convey the emotional states of the characters. In this paper, using (manipulated) panels from Western and Indian comic albums as well as neutral emoticons and basic shapes in different colors, we focus on the following two issues: (a) whether runes increase the awareness in comics readers about the emotional state of the character; and (b) whether a correspondence can be found between the types of runes (twirls, spirals, droplets, and spikes) and specific emotions. Our results show that runes help communicate emotion. Although no one-to-one correspondence was found between the tested runes and specific emotions, it was found that droplets and spikes indicate generic emotions, spirals indicate negative emotions, and twirls indicate confusion and dizziness.
Synthesis, characterization, and antimicrobial properties of novel dual drug loaded electrospun mat for wound dressing applications

Wound healing properties of some herbs have been known for decades. Recently, electrospun mats have been used as a wound dressing material due to the high surface area of fiber and ease of incorporation of drug into the fiber matrix. In this aspect, the incorporation of herbal extracts in electrospun matrix could provide synergistic effect for wound healing. In the present work, extracts from Cissus quadrangularis (CQ) and Galinsoga parviflora Cav (GP) were loaded into the PVA solution in different proportions. These solutions were used to produce nanofibrous mat in electrospinning and the characteristics of the mat were analyzed. The morphology of the fiber was analyzed using scanning electron microscope (SEM), the presence of functional groups was identified using Fourier transform infrared spectroscopy (FTIR). The result of drug release shows that the GP extract loaded PVA nanofibrous mat has sustained drug release of 28% after 8 h of incubation compared to CQ loaded PVA nanofibrous mat. This trend follows as the concentration of GP increases in the mixture. The antimicrobial efficiency of the prepared mat was evaluated against both Gram-negative bacteria E. coli and Gram-positive bacteria S. aureus. The prepared nanofibrous mat has shown excellent antibacterial activity, cell viability, hemocompatibility, and sufficient tensile properties which indicates that it could be a promising biomaterial for wound dressing application.
Overcoming the Cold Start Problem of Customer Relationship Management Using a Probabilistic Machine Learning Approach

The success of customer relationship management programs ultimately depends on the firm's ability to identify and leverage differences across customers—a difficult task when firms attempt to manage new customers, for whom only the first purchase has been observed. The lack of repeated observations for these customers poses a structural challenge for firms to infer unobserved differences across them. This is what the authors call the “cold start” problem of customer relationship management, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. The authors propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it flexibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is flexible enough to capture a wide range of heterogeneity structures. The authors validate their approach in a retail context and empirically demonstrate the model's ability to identify high-value customers as well as those most sensitive to marketing actions right after their first purchase.
Staging of human papilloma virus related cancers of the oropharynx

For the past two decades an increasing number of oropharyngeal cancers have been found to be associated with the human papilloma virus (HPV). These tumors are a biologically distinct entity with better prognosis and excellent response to therapy. Therefore, a separate staging system has been introduced for HPV‐related oropharyngeal tumors in the latest edition of the American Joint Committee on Cancer (AJCC eighth Ed).
A Hierarchical Reinforcement Learning Model Explains Individual Differences in Attentional Set Shifting

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks such as CANTAB IED reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability, and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED and psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data, and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. This data showcase a new methodology to analyse data from the CANTAB IED task, and suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.
Ancient and modern geochemical signatures in the 13,500-year sedimentary record of Lake Cadagno

Although lake sediments are globally important organic carbon sinks and therefore important habitats for deep microbial life, the deep lacustrine biosphere has thus far been little studied compared to its marine counterpart. To investigate the impact of the underexplored deep lacustrine biosphere on the sediment geochemical environment and vice versa, we performed a comprehensive microbiological and geochemical characterization of a sedimentary sequence from Lake Cadagno covering its entire environmental history since formation following glacial retreat. We found that both geochemical gradients and microbial community shifts across the ∼13.5 kyr subsurface sedimentary record reflect redox changes in the lake, going from oxic to anoxic and sulfidic. Most microbial activity occurs within the top 40 cm of sediment, where millimolar sulfate concentrations diffusing in from the bottom water are completely consumed. In deeper sediment layers, organic carbon remineralization is much slower but microorganisms nonetheless subsist on fermentation, sulfur cycling, metal reduction, and methanogenesis. The most surprising finding was the presence of a deep, oxidizing groundwater source. This water source generates an inverse redox gradient at the bottom of the sedimentary sequence and could contribute to the remineralization of organic matter sequestered in the energy-limited deep subsurface.
DOES PISUM SATIVUM EXHIBIT A RESPONSE TO NEIGHBOUR AND POT VOLUME MANIPULATIONS?

Plant-plant competition is ubiquitous in nature. However, studying below ground 15 competition has been plagued with methodological problems, and arguments about the appropriate 16 controls. One experimental design that controls total nutrients and soil nutrient concentrations among 17 plants grown alone or with neighbours achieves this by intentionally confounding pot volume ( ) with 18 neighbour addition. Here, we examined 18 unique experiments from 7 different studies for a total of 19 254 replicate alone/2 and with neighbourpairs of Pisum sativum (L.) plants in a Bayesian 20 hierarchical meta-analysis. The posterior distributions show that it is unlikely that P. sativum root 21 growth is influenced by either pot volume or neighbours. Pods are slightly more likely to increase in the 22 neighbourtreatment compared to the alone/2 treatment, though it is impossible to know if this is 23 due to neighbour interactions or potting volume effects. Some researchers have dismissed experiments 24 that seek to control soil nutrients when plants are grown alone or with neighbours, however, we believe 25 this is still an active area of debate, and there is value in further study that seeks to understand how 26 plants assess and respond to cues in their environment. More data and not more rhetoric is needed to 27 resolve this problem. We suggest that further work should not use P. sativum anymore, and suggest 28 some promising model plant candidates based on results in the literature. 29 30