Miracle-dz/UniFAR-FTUs huggingface dataset:
Web Cache Deception Escalates!

Web Cache Deception (WCD) tricks a web cache into erroneously storing sensitive content, thereby making it widely accessible on the Internet. In a USENIX Security 2020 paper titled “Cached and Confused: Web Cache Deception in the Wild ”, researchers presented the first systematic exploration of the attack over 340 websites. This state-of-the-art approach for WCD detection injects markers into websites and checks for leaks into caches. However, this scheme has two fundamental limitations: 1) It cannot probe websites that do not present avenues for marker injection or reflection. 2) Marker setup is a burdensome process, making large-scale measurements infeasible. More generally, all previous literature on WCD focuses solely on personal information leaks on websites protected behind authentication gates, leaving important gaps in our understanding of the full ramifications of WCD. We expand our knowledge of WCD attacks, their spread, and implications. We propose a novel WCD detection methodology that forgoes testing prerequisites, and utilizes page identicality checks and cache header heuristics to test any website. We conduct a comparative experiment on 404 websites, and show that our scheme identifies over 100 vulnerabilities while “Cached and Confused” is capped at 18. Equipped with a technique unhindered by the limitations of the previous work, we conduct the largest WCD experiment to date on the Alexa Top 10K, and detect 1188 vulnerable websites. We present case studies showing that WCD has consequences well beyond personal information leaks, and that attacks targeting non-authenticated pages are highly damaging.
Communications of the IIMA Communications of the IIMA

The query used for Web searching is usually short and may not be able to reflect the intrinsic semantics of the user information need. The purpose of the paper is to take into account user information feedback, and to develop a semi-automatic query expansion approach to improve the effectiveness of Web searching. A search engine has been developed using the vector information retrieval model to validate the semi-automatic query expansion approach. The experiments show that this approach may improve the effectiveness of web searching.
Succinct Representation of Static Packet Classifiers

We develop algorithms for the compact representation of the 1- and 2-dimensional tries that are used for Internet packet classification. Our compact representations are experimentally compared with competing compact representations for 1- and multi-dimensional packet classifiers and found to simultaneously reduce the number of memory accesses required for a lookup as well as the memory required to store the classifier.
Improved Root-Mean-Square-Gain-Combining for SIMO Channels

systems undergo is multipath fading caused by scattering of the transmitted signal. However, we can treat multipath propagation as multiple channels between the transmitter and receiver to improve the signal-to-scattering-noise ratio. While using Single Input Multiple Output (SIMO) systems, the diversity receivers extract multiple signal branches or copies of the same signal received from different channels and apply gain combining schemes such as Root Mean Square Gain Combining (RMSGC). RMSGC asymptotically yields an identical performance to that of the theoretically optimal Maximum Ratio Combining (MRC) for values of mean Signal-to-Noise-Ratio (SNR) above a certain threshold value without the need for SNR estimation. This paper introduces an improvement of RMSGC using two different issues. We found that post-detection and de-noising the received signals improve the performance of RMSGC and lower the threshold SNR.
Chronos Ações: Ferramenta para Apoiar a Tomada de Decisão de Investidores da Bolsa de Valores [Chronos Acoes: Tool to Support Decision Making for Investor of the Stock Exchange]

Investors in the stock market employ various techniques of graphical analysis to assist in decision making about their investments. To facilitate the use of these techniques are developed tools able to process large volumes of data in order to present consolidated information to investors. However, most of these tools is complex to use for those who are beginners in this kind of market and therefore needs help to make more informed decisions to minimize the damage and maximize profits at the same time. In this context, this work presents the Chronos Acoes, a tool developed to support investors in making decision to purchasing or sale of assets on the BM and FBOVESPA, which is the main stock exchange of Brazil.
Computer immunology

Summary: This review describes a body of work on computational immune systems that behave analogously to the natural immune system. These artificial immune systems (AIS) simulate the behavior of the natural immune system and in some cases have been used to solve practical engineering problems such as computer security. AIS have several strengths that can complement wet lab immunology. It is easier to conduct simulation experiments and to vary experimental conditions, for example, to rule out hypotheses; it is easier to isolate a single mechanism to test hypotheses about how it functions; agent‐based models of the immune system can integrate data from several different experiments into a single in silico experimental system.
Labor Force Competitiveness in Croatia: Status and Problems

Prema najsire prihvacenoj definiciji, konkurentnost je sposobnost postizanja uspjeha na tržistima koji omogucava visoko produktivno1 gospodarstvo i poboljsanje životnog standarda cjelokupnog stanovnistva. Konkurentnost je posljedica utjecaja brojnih cimbenika – sastavnica ili odrednica konkurentnosti, koji međusobno utjecu jedni na druge, a neprisutnost nekih može sprijeciti (ograniciti) pozitivne utjecaje prisutnih na konkurentnost. Prihvacanje koncepta konkurentnosti kljucno je pitanje daljnjeg razvoja Hrvatske jer su brojna istraživanja pokazala snažnu vezu pokazatelja konkurentnosti i dinamike gospodarskog rasta, a to povratno utjece na privlacenje stranih ulaganja, ublažavanja siromastva i nejednakosti, politicke stabilnosti i drugo. U ostvarivanju konkurentnosti gospodarstva posebno se istice važnost konkurentnost radne snage2. Najvažniji cimbenici konkurentnosti radne snage su obrazovna struktura, kompatibilnost (podudarnost) ponude i potražnje za radom u pogledu znanja, strucnosti i sposobnosti te troskovi rada. U clanku nakon uvodnih razmatranja u drugom dijelu dajemo teorijski okvir odrednica konkurentnosti radne snage. U trecem dijelu izlažemo bitna obilježja stanja u Hrvatskoj: obrazovnu strukturu, sustav obrazovanja i postojeca znanja, institucionalno okruženje, troskove radne snage, te pitanja konkurentnosti i prikljucivanja Hrvatske EU. Rad zavrsava zakljuckom.
Design and Control of an Anthropomorphic Robotic Finger with Multi-point Tactile Sensation

The Problem: The human hand is the principal organ for the reception of and reaction to tactile stimuli, a perception that guides our repertoire of manual functions. However, the integration of an analogous sensing suite into a robotic platform poses a major technological challenge. Our goal is the design and control of a prototype for anthropomorphic manipulation research based on high resolution artificial taction. The problem is constrained to the active detection of multi-point pressures applied to the surface of a robot finger.
A NOVEL APPROACH TO HEART ATTACK PREDICTION IMPROVEMENT VIA EXTREME LEARNING MACHINES CLASSIFIER INTEGRATED WITH DATA RESAMPLING STRATEGY

: The heart attack is a disorder that is frequently seen in low-income countries and causes the death of many people. Cardiologists benefit from electrocardiography (ECG) tests to determine this condition. Supervised classification algorithms are frequently used and provide very successful results in computer-aided diagnostic systems. In this study, a new approach to predict a heart attack is proposed for classification via extreme learning machines (ELM) integrated with the resampling strategy. This study aims to reveal a new diagnostic system that will increase the success of current studies. The study has three basic steps. In order to determine the features that will ensure the system’s optimized operation, firstly, the ReliefF feature selection method was applied to the data set, and then, the system was modeled by different classifiers via resampling. Besides, the as-proposed approach was applied to the breast cancer data to test the accuracy of the current system. The as-obtained results from both Statlog (heart disease) and the breast cancer data were seemed to be more successful than the studies in the literature. Thus, the as-proposed system reveals a successful and effective approach that can be applied in clinical data sets.
Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalised medicine. As the evolutionary course of a patient's disease progresses, its medical follow-up generates more and more information that should be processed immediately in order to review and update its prognosis and treatment. Our objective in this thesis focuses on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo methods for static parameter joint models in order to update the posterior distribution of the parameters, hyperparameters, and random effects with the intention of reducing computation time in each update of the inferential process. Our proposal is very general and can be easily applied to most popular joint models approaches. We illustrate our research with two different studies: (i) a joint model for longitudinal data with informative dropout simulated through an own novel mechanism, and (ii) a joint model with competing risk events for a real problem about patients receiving mechanical ventilation in intensive care units.
Voltage-Drop Aware Analytical Placement by Global Power Spreading for Mixed-Size Circuit Designs

Excessive supply voltage drops in a circuit may lead to significant circuit performance degradation and even malfunction. To handle this problem, existing power delivery aware placement algorithms model voltage drops as an optimization objective. We observe that directly minimizing the voltage drops in the objective function might not resolve voltage-drop violations effectively and might cause problems in power-integrity convergence. To remedy this deficiency, in this paper, we propose new techniques to incorporate device power spreading forces into a mixed-size analytical placement framework. Unlike the state-of-the-art previous work that handles the worst voltage-drop spots one by one, our approach simultaneously and globally spreads all the blocks with voltage-drop violations to desired locations directly to minimize the violations. To apply the power force, we model macro current density and power rails for our placement framework to derive desired macro/cell locations. To further improve the solution quality, we propose an efficient mathematical transformation to adjust the power force direction and magnitude. Experimental results show that our approach can substantially improve the voltage drops, wirelength, and runtime over the previous work.
Structure and evolution of super-Earth to super-Jupiter exoplanets - I. Heavy element enrichment in the interior

Aims. We examine the uncertainties in current planetary models and quantify their impact on the planet cooling histories and massradius relationships. Methods. These uncertainties include (i) the differences between the various equations of state used to characterize the heavy material thermodynamical properties, (ii) the distribution of heavy elements within planetary interiors, (iii) their chemical composition, and (iv) their thermal contribution to the planet evolution. Our models, which include a gaseous H/He envelope, are compared with models of solid, gasless Earth-like planets in order to examine the impact of a gaseous envelope on the cooling and the resulting radius. Results. We find that, for a fraction of heavy material larger than 20% of the planet mass, the distribution of the heavy elements in the planet’s interior substantially affects the evolution and thus the radius at a given age. For planets with large core mass fractions (>50%), such as the Neptune-mass transiting planet GJ 436b, the contribution of the gravitational and thermal energy from the core to the planet cooling history is not negligible, yielding a ∼10% effect on the radius after 1 Gyr. We show that the present mass and radius determinations of the massive planet Hat-P-2b require at least 200 M⊕ of heavy material in the interior, at the edge of what is currently predicted by the core-accretion model for planet formation. As an alternative avenue for massive planet formation, we suggest that this planet, and similarly HD 17156b, may have formed from collisions between one or several other massive planets. This would explain these planets unusually high density and high eccentricity. We show that if planets as massive as ∼25 MJ can form, as predicted by improved core-accretion models, deuterium is able to burn in the H/He layers above the core, even for core masses as high as ∼100 M⊕. Such a result highlights the confusion provided by a definition of a planet based on the deuterium-burning limit. Conclusions. We provide extensive grids of planetary evolution models from 10 M⊕ to 10 MJup, with various fractions of heavy elements. These models provide a reference for analyzing the transit discoveries expected from the CoRoT and Kepler missions and for inferring the internal composition of these objects.
PAID I ESTIMATING PHONEME CLASS CONDITIONAL PROBABILITIES FROM RAW SPEECH SIGNAL USING CONVOLUTIONAL NEURAL NETWORKS

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.
Principles of Robot Autonomy I Problem Set 1 Due Tuesday

While not required, another useful tool is Conda, which is a package and environment management system that can be run on Windows, macOS, and Linux. With conda you can create local “environments” that utilize specific package versions without affecting other packages! For example, say you use Python 3.8 as default for all of your personal projects but need Python 2.7 for AA274A. Using conda, an environment can be created that uses Python 2.7 and within that environment you can download versions of any additional packages you need that depend on Python 2.7 without affecting your default setup.
How Children with Autism Spectrum Disorder, Developmental Language Disorder, and Typical Language Learn to Produce Global and Local Semantic Features

A local processing bias, often considered a cognitive style unique to autism spectrum disorder (ASD), may influence the types of semantic features acquired by children with ASD and could contribute to weaknesses in word learning. Children with developmental language disorder (DLD) also struggle to learn semantic aspects of words, but this cognitive style has not been ascribed to children with DLD. The purpose of this study was to explore whether global–local processing differences influence the type of semantic features children with ASD, DLD, and their neurotypical peers learn to produce when learning new words. Novel word definitions produced by 36 school-aged children (12 with ASD, 12 with DLD, and 12 with typical language) who participated in an extended word-learning paradigm were used to extract newly learned semantic features. These semantic features were then coded for global and local attributes and analyzed to detect whether there were differences between groups. Results indicated that the children with ASD and DLD produced more global, rather than local, semantic features in their definitions than the children with typical language. An over-reliance on global, rather than local, features in children with ASD and DLD may reflect deficits in depth of word knowledge.
Pulmonary osteoclast-like cells in silica induced pulmonary fibrosis

The pathophysiology of silicosis is poorly understood, limiting development of therapies for those who have been exposed to the respirable particle. We explored the mechanisms of silica-induced pulmonary fibrosis in a mouse model using multiple modalities including wholelung single-nucleus RNA sequencing. These analyses revealed that in addition to pulmonary inflammation and fibrosis, intratracheal silica challenge induced osteoclast-like differentiation of alveolar macrophages and recruited monocytes, driven by induction of the osteoclastogenic cytokine, receptor activator of nuclear factor-κB ligand (RANKL) in pulmonary lymphocytes and alveolar type II cells. Furthermore, anti-RANKL monoclonal antibody treatment suppressed silica-induced osteoclast-like differentiation in the lung and attenuated silica-induced pulmonary fibrosis. We conclude that silica induces osteoclast-like differentiation of distinct recruited and tissue resident monocyte populations, leading to progressive lung injury, likely due to sustained elaboration of bone resorbing proteases and hydrochloric acid. Interrupting osteoclast-like differentiation may therefore constitute a promising avenue for moderating lung damage in silicosis. One Sentence Summary Silica induces the alveolar epithelium to reprogram recruited and resident pulmonary myeloid cells to become osteoclasts that contribute to pulmonary fibrosis.
Electronic and Paper-Based Teaching Portfolios: Student Perceptions and Recommendations

This paper documents a yearlong pilot study, conducted by teacher education faculty, to evaluate methods for creating, sustaining, and assessing teaching portfolios. Selected issues with paper-based and software-based programs are compared. Key design, instructional, and procedural problems that arose during this study are discussed as well. The findings of this study support the need for a flexible electronic portfolio system in which students play a key role in the design, development, and content of their portfolios as well as the need for a clearly articulated academic purpose and requirements for the teaching portfolio with implications for students in all disciplines.
Textual and Content-Based Search in Repositories of Web Application Models

Model-Driven Engineering relies on collections of models, which are the primary artefacts for software development. To enable knowledge sharing and reuse, models need to be managed within repositories, where they can be retrieved upon users queries. This paper examines two different techniques for indexing and searching model repositories, with a focus on Web development projects encoded in a Domain Speciﬁc Language. Keyword-based and content-based search (also known as query-by-example) are contrasted, with respect to the architecture of the system, the processing of models and queries, and the way in which meta-model knowledge can be exploited to improve search. A thorough experimental evaluation is conducted to examine what parameter conﬁgurations lead to better accuracy and to offer an insight in what queries are addressed best by each system.
Dartmouth Digital Commons Dartmouth Digital Commons

We construct a bump-in-the-wire (BITW) solution that retroﬁts security into time-critical communications over bandwidth-limited serial links between devices in Supervisory Control And Data Acquisition (SCADA) systems. Previous BITW solutions fail to provide the necessary security within timing constraints; the previous solution that does provide the necessary security is not BITW. At a comparable hardware cost, our BITW solution provides suﬃcient security, and yet incurs minimal end-to-end communication latency. A microcontroller prototype of our solution is under development.
Microsoft Word - social_sensemaking_chi_final.docx

Telemonitoring healthcare solutions often struggle to provide the hoped for efficiency improvements in managing chronic illness because of the difficulty interpreting sensor data remotely. Computer-Mediated Social Sensemaking (CMSS) is an approach to solving this problem that leverages the patient's social network to supply the missing contextual detail so that remote doctors can make more accurate decisions. However, implementing CMSS solutions is difficult because users need to know who can see which information, and whether private and confidential information is adequately protected. In this paper, we wish to explore how socio-material design solutions might offer ways of making properties of a CMSS solution tangible to participants so that they can control and understand the implications of their participation.
Controllable Video Generation through Global and Local Motion Dynamics – Supplementary Material –

In the main paper we present GLASS, a method for Global and Local Action-driven Sequence Synthesis. GLASS, trained on unlabeled video sequences, allows to animate an input image at test time. The method builds a global and local action representation that is used to generate transitions of the segmented foreground sequences. Moreover, we introduced a novel dataset (W-Sprites) with a predefined action space for analysis. This supplementary material provides details and visual examples that could not be included in the main paper due to the space limitations. In section A we describe the implementation details, such as network architecture and training parameters. Section B provides details on the dataset generation protocol. In Section C we include more visual examples of the evaluation of our method. Further details, the code and example videos are available at https://araachie.github. io/glass/ .