Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Suicidal Risk Assessment based on Clinician-Patient Verbal and Nonverbal Behavior Master Thesis

Suicide is the third leading cause of death in the United States of America for ages 13 to 18 and in some countries youth suicide is even the second leading cause of death for ages 10-24 according to Centers for Disease Control and Prevention and WHO. Hence, there is a need for a valid, rapid and accurate way to identify suicidal risk; it is especially important to be able to predict potential repeat occurrences of attempted suicides. In this thesis the conversation dynamics, verbal information and acoustic features of 30 suicidal and 30 nonsuicidal patients of ages 13 to 18 interviewed at the Cincinnati Children's Hospital Medical Center Emergency Department and the single clinician were statistically evaluated. Beside the identification of non-suicidal vs. suicidal patients, data of 17 suicidal non-repeaters and 13 repeaters was investigated. A hierarchical classification was used to confirm the discriminative faculty of the features to characterize the psychological states of the adolescents. Ensemble classifiers and SVM classifiers were used and tested with data from entire interviews and with data from the first five open-ended ubiquitous questions and answers. I was able to show that the analysis of verbal and nonverbal interactions between patient and clinician can identify the suicidal risk of adolescents. The investigation of the first five questions of the interview yielded similar promising results in comparison to the information observed in the entire interview. The backchannel of the clinician which lasted less than 700ms was useful to identify the suicidal risk including the patients' features. The hierarchical SVM-ensemble classification yielded an accuracy of 78.3% using data from entire interviews. Thus, the suicidal risk assessment of non-suicidal adolescents, suicidal non-repeaters and suicidal repeaters using clinician's and patient's verbal and nonverbal behaviors yielded promising results.
Killed by Proxy: Analyzing Client-end TLS Interce

To filter SSL/TLS-protected traffic, some antivirus and parental-control applications interpose a TLS proxy in the middle of the host’s communications. We set out to analyze such proxies as there are known problems in other (more matured) TLS processing engines, such as browsers and common TLS libraries. Compared to regular proxies, client-end TLS proxies impose several unique constraints, and must be analyzed for additional attack vectors; e.g., proxies may trust their own root certificates for externally-delivered content and rely on a custom trusted CA store (bypassing OS/browser stores). Covering existing and new attack vectors, we design an integrated framework to analyze such client-end TLS proxies. Using the framework, we perform a thorough analysis of eight antivirus and four parentalcontrol applications for Windows that act as TLS proxies, along with two additional products that only import a root certificate. Our systematic analysis uncovered that several of these tools severely affect TLS security on their host machines. In particular, we found that four products are vulnerable to full server impersonation under an active man-in-the-middle (MITM) attack out-of-the-box, and two more if TLS filtering is enabled. Several of these tools also mislead browsers into believing that a TLS connection is more secure than it actually is, by e.g., artificially upgrading a server’s TLS version at the client. Our work is intended to highlight new risks introduced by TLS interception tools, which are possibly used by millions of users.
KAAS : KEGG Automatic Annotation Server

The number of complete and draft genomes has rapidly increased in recent years, and it has become increasingly important to identify the functional properties and biological roles of genes in these genomes. We have been developing KEGG Orthology (KO) to classify gene functions. In KO, we annotate genes in complete genomes based on best-hit information using Smith-Waterman scores, as well as by manual curation [1]. KO entries are assigned unique identifiers, called K numbers, and they are directly linked to the KEGG PATHWAY. Here, we report on a rapid method to automatically annotate gene functions and reconstruct KEGG pathway maps. We have been using this method effectively to automatically annotate draft genomes and EST clusters. This method is now made available as a web-based server called KAAS.
‘misspelled’ visual words in unsupervised range data classification: the effect of noise on classification performance

Recent work in the domain of classification of point clouds has shown that topic models can be suitable tools for inferring class groupings in an unsupervised manner. However, point clouds are frequently subject to non-negligible amounts of sensor noise. In this paper, we analyze the effect on classification accuracy of noise added to both an artificial data set and data collected from a Light Detection and Ranging (LiDAR) scanner, and show that topic models are less robust to ‘misspelled’ words than the more näive k-means classifier. Furthermore, standard spin images prove to be a more robust feature under noise than their derivative, ‘angular’ spin images. We additionally show that only a small subset of local features are required in order to give comparable classification accuracy to a full feature set.
A New Error Concealment Algorithm for H.264/AVC

With the development of communications and network technologies, video streaming service is becoming increasingly important. However, in the transmission of high compressed video, bit-stream is easily damaged by channel errors which will lead to the decline of the video sequence. Error concealment is an effective approach to reduce the influence in error-prone network and notably improve the video quality. This paper presents a novel error concealment algorithm for temporal video error concealment with H.264/AVC standard. This method exploits both spatial and temporal information in the reference and current frame. To increase the accuracy, the macro block (MB) is divided into 16 4x4 blocks. The reference frame is classified by k-means clustering algorithm. The lost blocks are recovered by the neigh boring blocks which belong to the same class of the lost block. The experimental result shows that our method can achieve a better performance than the existing methods.
Performance comparison of a rotating coordinator and a leader based consensus algorithm

Protocols that solve agreement problems are essential building blocks for fault tolerant distributed systems. While many protocols have been published, little has been done to analyze their performance, especially the performance of their fault tolerance mechanisms. In this paper, we compare two well-known asynchronous consensus algorithms. In both algorithms, a leader process tries to impose a decision, and another leader retries if the leader fails doing so. The algorithms elect leaders differently: the Chandra-Toueg algorithm has a rotating leader, whereas processes in the Paxos algorithm elect leaders directly. We investigate the performance implications of this difference. In the system under study, processes send atomic broadcasts to each other. Consensus is used to decide the delivery order of messages. We evaluate the steady state latency in (1) runs with neither crashes nor suspicions, (2) runs with crashes and (3) runs with no crashes in which correct processes are wrongly suspected to have crashed, as well as the transient latency after (4) one crash and (5) multiple correlated crashes. The results show that the Paxos algorithm tolerates frequent wrong suspicions (3) and correlated crashes (5) better, while the performance is comparable in all other scenarios.
Classsification of Cellular Automata

Glossary Cellular Automaton For our purposes, a (one-dimensional) cellular automaton (CA) is given by a local map ρ : Σ → Σ where Σ is the underlying alphabet of the automaton and w is its width. As a data structure, suitable as input to a decision algorithm, a CA can thus be specified by a simple lookup table. We abuse notation and write ρ(x) for the result of applying the global map of the CA to configuration x ∈ ΣZ. Wolfram Classes Wolfram proposed a heuristic classification of cellular automata based on observations of typical behaviors. The classification comprises four classes: evolution leads to trivial configurations, to periodic configurations, evolution is chaotic, evolution leads to complicated, persistent structures. Undecidability
A programmable baseband chain for a GSM/DECT fully integrated CMOS RF receiver

A programmable/reconfigurable baseband chain with filter and amplifier sections that can be shared by two different wireless standards, GSM and DECT, is presented in this paper. This results in a simplified, low power and cost-effective design solution. Filters and amplifiers of the baseband chain were implemented using a fully balanced differential difference amplifier (FBDDA). A new approach is used to implement an analog to digital converter using the FBDDA in order to enhance the design regularity. The noise and linearity performance of individual stages was investigated, and simulations of the overall system showed that it complies with both standards under consideration. Using a suitable algorithm implemented by the digital signal processor (DSP) of the receiver, the chain could be configured to operate efficiently in different environments.
Detecting controversy on the web

A useful feature to facilitate critical literacy would alert users when they are reading a controversial web page. This requires solving a binary classification problem: does a given web page discuss a controversial topic? We explore the feasibility of solving the problem by treating it as supervised k-nearest-neighbor classification. Our approach (1) maps a webpage to a set of neighboring Wikipedia articles which were labeled on a controversiality metric; (2) coalesces those labels into an estimate of the webpage's controversiality; and finally (3) converts the estimate to a binary value using a threshold. We demonstrate the applicability of our approach by validating it on a set of webpages drawn from seed queries. We show absolute gains of 22% in F_0. 5 on our test set over a sentiment-based approach, highlighting that detecting controversy is more complex than simply detecting opinions.
Aspects regarding the plasticity of the steel subjected to non-conventional treatment

Plasticity is an important property to determine the capability of the steel to support plastic deformations, to obtain important pieces for industry. It is important to know the optimal cooling regime for this material because the steel have been rolling at high temperature or, the steel have been heated at high temperature after lamination process. It was considered a steel grade with a low carbon content, which can be find often in machine industry and in metallurgical operations. Three different mediums for directing of the cooling process have been considered and the properties of the steel have been modified. This study tried to determine a way to improve the properties of this kind of steel to obtain a higher durability corresponding to the exploitation conditions.
Subjective assessment of the quality-oriented adaptive scheme

The Quality-Oriented Adaptation Scheme (QOAS) supports the distribution of high quality multimedia services to a large number of simultaneous customers via given broadband IP infrastructure. This paper presents subjective testing results that augment previously reported objective performance assessment. Clips representing different classes of multimedia sequences in terms of motion content and types were selected and streamed using a QOAS-based prototype system. Congested delivery network conditions were emulated and the effects of the consequent QOAS-driven adaptations were subjectively assessed by end-users. The test subjects have also graded their perceived quality when using a nonadaptive streaming approach. The QOAS-related results were much higher than those obtained for a nonadaptive approach, being above the "good" perceptual level for all multimedia clips and in all tested delivery conditions.
Information Retrieval in Collaborative Engineering Projects - A Vector Space Model Approach

This work introduces a conceptual framework and its current implementation to support the classification and discovery of knowledge sources, where every knowledge source is represented through a vector (named Semantic Vector SV). The novelty of this work addresses the enrichment of such knowledge representations, using the classical vector space model concept extended with ontological support, which means to use ontological concepts and their relations to enrich each SV. Our approach takes into account three different but complementary processes using the following inputs: (1) the statistical relevance of keywords, (2) the ontological concepts, and (3) the ontological relations. SVs are compared against each other, in order to obtain their similarity index, and better support end users with a search/retrieval of knowledge sources capabilities. This paper presents the technical architecture (and respective implementation) supporting the conceptual framework, emphasizing the SV creation process. Moreover, it provides some examples detailing the indexation process of knowledge sources, results achieved so far and future goals pursued here are also presented.
Use of optical and radar data in synergy for mapping intertidal flats and coastal salt-marshes (Arcachon lagoon, France)

This contribution explores the potential of high-resolution satellite SAR imagery (TerraSAR-X, ALOS-PALSAR) for mapping coastal habitats in complement of optical data (SPOT-5). It addresses X- and L-band SAR signatures over intertidal flats and coastal salt-marshes by investigating the mean backscattering coefficient σ0 over the major environmental units and its variation associated with physical parameters (soil roughness, soil moisture, tidal inundation) and instrumental configurations. The major findings outline a great potential of TerraSAR-X data to detect oyster beds and discriminate Zostera noltii seabed from salt-marsh vegetation species. Based on these results, a multi-sensor multi-temporal mapping strategy was tested running simple supervised classification algorithms. These tests show that mapping performance is greatly enhanced when running Mahalanobis classifier on a 6-band concatenated image. Further work is needed to account with green macro-algae deposits and microphytobenthos in order to demonstrate the full capabilities of spaceborne sensors to provide an exhaustive mapping of intertidal environments.
Implicit Coordination in Crowded Multi-Agent Navigation

In crowded multi-agent navigation environments, the motion of the agents is significantly constrained by the motion of the nearby agents. This makes planning paths very difficult and leads to inefficient global motion. To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. With our approach, agents take into account the velocities and goals of their neighbors and optimize their motion accordingly and in real-time. We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents.
Modelling and verification of authentication using enhanced net semantics of SPL (Security Protocol Language)

This paper proposes an enhanced translation of Security Protocol Language (SPL) in high-level Petri nets in order to allow to prove automatically, using model-checking techniques, the authentication property of Needham-Schroeder-Lowe (NSL) protocol. The proposed approach generates finite nets and goes this way beyond the limitation which was imposed by the previous semantics due to the treatment of the replication operator. In order to reach this goal, we modify the way attacks are modelled. Due to fact that the presented approach focuses on the treatment of the protocol environment, it may be successfully reused for automated verification of properties of other security protocols
Endothelial Determinants of Dendritic Cell Adhesion and Migration: New Implications for Vascular Diseases

Objective—Atherosclerosis is a chronic disease triggered by endothelial injury and sustained by inflammation. Dendritic cells (DCs) are critical for the cell-mediated arm of an immune response and are known to influence inflammatory immunity. A fundamental aspect of DC function is their capacity to adhere and migrate through vascular endothelial cells (ECs). We investigated the role of endothelial activation and dysregulation of the NO pathway on DC adhesion and migration. Methods and Results—We discovered that DC adhesion and migration are modulated by changes in endothelial function. DC adhesion and transmigration were markedly increased after exposing ECs to hypoxia, oxidized low density lipoprotein, or tumor necrosis factor-&agr;. Specifically, inhibition of endothelial NO synthase increased DC binding and transmigration. l-Arginine or 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibition partially decreased DC-EC interaction. Conclusions—The results of this study suggest that the adhesion and migration of DCs are increased by stimuli known to accelerate atherogenesis. Vice versa, augmentation of endothelial NO synthase activity prevents DC adhesion. These findings may provide insight into the inflammatory processes occurring in atherosclerosis. Because DCs control immunity, regulating DC-EC interaction may be relevant to inflammation and atherogenesis.
SenSpeed: Sensing driving conditions to estimate vehicle speed in urban environments

Acquiring instant vehicle speed is desirable and a corner stone to many important vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In particular, we estimate the vehicle speed by integrating the accelerometer's readings over time and find the acceleration errors can lead to large deviations between the estimated speed and the real one. Further analysis shows that the changes of acceleration errors are very small over time which can be corrected at some points, called reference points, where the true vehicle speed is known. Recognizing this observation, we propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments including making turns, stopping and passing through uneven road surfaces, to derive reference points and further eliminates the speed estimation deviations caused by acceleration errors. Extensive experiments demonstrate that SenSpeed is accurate and robust in real driving environments. On average, the real-time speed estimation error on local road is 1.32mph, and the offline speed estimation error is as low as 0.75mph. Whereas the average error of GPS is 3.1mph and 2.8mph respectively.
Genome Sequence of Cluster W Mycobacteriophage Taptic

ABSTRACT The Taptic genome is the first to be annotated from the W cluster of mycobacteriophages infecting Mycobacterium smegmatis mc2155. All 92 predicted open reading frames (ORFs) and a single tRNA specifying glycine (tRNA-gly) are transcribed rightward. Many functionally uncharacterized ORFs appear to be W cluster specific, as nucleotide similarity is shared only with other W cluster genomes.
Smart * : An Open Data Set and Tools for Enabling Research in Sustainable Homes

The goal of the Smart* project is to optimize home energy consumption. As part of the project, we have designed and deployed a “live” system that continuously gathers a wide variety of environmental and operational data in three real homes. In contrast to prior work, our focus has been on sensing depth, i.e., collecting as much data as possible from each home, rather than breadth, i.e., collecting data from as many homes as possible. Our data captures many important aspects of the home environment, including average household electricity usage every second, as well as usage at every circuit and nearly every plug load, electricity generation data from on-site solar panels and wind turbines, outdoor weather data, temperature and humidity data in indoor rooms, and, finally, data for a range of important binary events, e.g., at wall switches, the HVAC system, doors, and from motion sensors. We also have electricity usage data every minute from 400 anonymous homes. This data corpus has served as the foundation for much of our recent research. In this paper, we describe our data sets as well as basic software tools we have developed to facilitate their collection. We are releasing both the data and tools publicly to the research community to foster future research on designing sustainable homes.
Suppression of MKK5 reduces ozone-induced signal transmission to both MPK3 and MPK6 and confers increased ozone sensitivity in Arabidopsis thaliana

In Arabidopsis thaliana, ozone-induced signaling has been shown to involve the mitogen-activated protein kinases (MAPKs) MPK3 and MPK6. To identify a possible ozone-induced mitogen-activated protein kinase kinase (MAPKK) involved in the activation of these specific MAPKs, we employed RNA interference-(RNAi)-based suppression of MKK5, a known cognate MAPKK to both MPK3 and MPK6. When exposed to ozone, activation of both MPK3 and MPK6 was markedly reduced in the MKK5-suppressed plants compared to WT. Additionally, the MKK5-suppressed plants were found to be highly sensitive to ozone as determined by visible leaf damage concomitant with elevated levels of leaf-localised H2O2. Taken together, our data suggest MKK5 functions both in ozone-induced activation of MPK3 and MPK6 and in integrating ROS homeostasis during ozone stress.
Hippocampal atrophy and T2-weighted signal changes in familial mesial temporal lobe epilepsy

Objective: To correlate the clinical phenotype with hippocampal volumes (HcVs) and signal changes in patients with familial mesial temporal lobe epilepsy (FMTLE). Methods: FMTLE was defined when at least two first-degree relatives in a family had a clinical-EEG diagnosis of MTLE. Hippocampal formation measurements were performed using 1- to 3-mm coronal T1-weighted MRIs. The presence of hyperintense T2 signal was evaluated by visual analysis. For statistical analyses, analysis of variance, χ2 test, and regression analysis were used. Results: A total of 142 patients from 45 unrelated families were studied: 113 individuals with MTLE (80 with good seizure control) and 29 family members with other seizure types. There were 99 patients (69.7%) with hippocampal atrophy (HA). Sixty-seven of the 99 patients with HA also had a hyperintense T2 signal. Hyperintense T2 signal was associated with more severe HA (p = 0.04). Patients with refractory FMTLE had more frequent HA (p = 0.03) and hyperintense T2 signal (p = 0.004) and more severe atrophy (p < 0.0001). Duration of epilepsy correlated with HcV asymmetry index (r2 = 0.12, p = 0.00008) and with the more atrophic hippocampi but not with contralateral hippocampi. Conclusion: In familial mesial temporal lobe epilepsy, seizure severity is variable in affected individuals. Hippocampal atrophy was present in 70% of these patients and 69% of these had an associated hyperintense T2 signal. Although hippocampal atrophy associated with abnormal T2 signal was more frequent and more severe in patients with poor seizure control, it was also frequent in affected individuals across families. These observations suggest that one or more genes resulting in familial mesial temporal lobe epilepsy predisposes both to the clinical features of mesial temporal lobe epilepsy and to the development of hippocampal sclerosis.