Miracle-dz/UniFAR-FTUs huggingface dataset:
Digitized thought records: a practitioner-focused review of cognitive restructuring apps

Abstract Mental health (MH) apps can be used as adjunctive tools in traditional face-to-face therapy to help implement components of evidence-based treatments. However, practitioners interested in using MH apps face a variety of challenges, including knowing which apps would be appropriate to use. Although some resources are available to help practitioners identify apps, granular analyses of how faithfully specific clinical skills are represented in apps are lacking. This study aimed to conduct a review and analysis of MH apps containing a core component of cognitive behaviour therapy (CBT) – cognitive restructuring (CR). A keyword search for apps providing CR functionality on the Apple App and Android Google Play stores yielded 246 apps after removal of duplicates, which was further reduced to 15 apps following verification of a CR component and application of other inclusionary/exclusionary criteria. Apps were coded based on their inclusion of core elements of CR, and general app features including app content, interoperability/data sharing, professional involvement, ethics, and data safeguards. They were also rated on user experience as assessed by the Mobile App Rating Scale (MARS). Whereas a majority of the CR apps include most core CR elements, they vary considerably with respect to more granular sub-elements of CR (e.g. rating the intensity of emotions), other general app features, and user experience (average MARS = 3.53, range from 2.30 to 4.58). Specific apps that fared best with respect to CR fidelity and user experience dimensions are highlighted, and implications of findings for clinicians, researchers and app developers are discussed. Key learning aims (1) To identify no-cost mobile health apps that practitioners can adopt to facilitate cognitive restructuring. (2) To review how well the core elements of cognitive restructuring are represented in these apps. (3) To characterize these apps with respect to their user experience and additional features. (4) To provide examples of high-quality apps that represent cognitive restructuring with fidelity and facilitate its clinical implementation.
Nouvelles Architectures Hybrides : Logique / Mémoires Non-Volatiles et technologies associées.

Les nouvelles approches de technologies memoires permettront une integration dite back-end, ou les cellules elementaires de stockage seront fabriquees lors des dernieres etapes de realisation a grande echelle du circuit. Ces approches innovantes sont souvent basees sur l'utilisation de materiaux actifs presentant deux etats de resistance distincts. Le passage d'un etat a l'autre est controle en courant ou en tension donnant lieu a une caracteristique I-V hysteretique. Nos memoires resistives sont composees d'argent en metal electrochimiquement actif et de sulfure amorphe agissant comme electrolyte. Leur fonctionnement repose sur la formation reversible et la dissolution d'un filament conducteur. Le potentiel d'application de ces nouveaux dispositifs n'est pas limite aux memoires ultra-haute densite mais aussi aux circuits embarques. En empilant ces memoires dans la troisieme dimension au niveau des interconnections des circuits logiques CMOS, de nouvelles architectures hybrides et innovantes deviennent possibles. Il serait alors envisageable d'exploiter un fonctionnement a basse energie, a haute vitesse d'ecriture/lecture et de haute performance telles que l'endurance et la retention. Dans cette these, en se concentrant sur les aspects de la technologie de memoire en vue de developper de nouvelles architectures, l'introduction d'une fonctionnalite non-volatile au niveau logique est demontree par trois circuits hybrides: commutateurs de routage non volatiles dans un Field Programmable Gate Arrays, un 6T-SRAM non volatile, et les neurones stochastiques pour un reseau neuronal. Pour ameliorer les solutions existantes, les limitations de la performances des dispositifs memoires sont identifies et resolus avec des nouveaux empilements ou en fournissant des defauts de circuits tolerants.
Development of a Scale to Measure Psychosocial Risks and Protective Factors in Adolescents

Background: Resilience among adolescents can best be understood by examining the risk and protective factors in the lives of at-risk adolescents. A standardized tool to identify adolescent psychosocial risk and protective factors may help better understand the strengths, vulnerabilities, and the potential for resilience among adolescents. Hence, the present study aims to identify and measure the risk and protective factors in the lives of adolescents. Methods: A sequential exploratory mixed-methods design was used to develop a tool that measures risk and protective factors among adolescents. The qualitative study explored psychosocial risk and protective factors among high-risk adolescents using 4 major domains: individual, family, school, and community. To develop the tool, prisoners (N = 12), caregivers of the prisoners’ children (N = 12), teachers of the prisoners’ children (N = 12), and prison authorities (N = 12) were interviewed about their experiences with prisoners’ children. The interview data were analyzed, and items reflecting the risk and protective factors in the lives of the adolescent children of incarcerated parents were generated. The item pool consisting of 239 items was administered to a sample of 312 rural adolescent students. To validate the risk factors scale and protective factors scale, self-report scales measuring behavior problems, adjustment, well-being, and positivity were used. Results: The new tool developed to measure the risk and protective factors for adolescent psychosocial development was found to have adequate test-retest reliability, content validity, convergent validity, and divergent validity. Conclusion: In this study, a scale to identify psychosocial risk and protective factors was developed and standardized. The tool will be helpful to psychologists and counselors working with adolescents to understand the risks and protective factors present in their lives and design interventions to nurture resilience in them.
Characterization and evaluation of anti-Salmonella enteritidis activity of indigenous probiotic lactobacilli in mice

Abstract Lactobacilli (n = 24), isolated from human infants and yogurt, showed variable in vitro activity against Salmonella enteritidis (8.0 ± 1.0 to 16.6 ± 0.5 mm) and other gut pathogens (9.0 ± 1.0 to 15.3 ± 0.5 mm), as determined by a well diffusion assay. The isolates were identified as Limosilactobacillus fermentum (FY1, FY3, FY4, IL2, and IL5), Lactobacillus delbrueckii (FY6 and FY7), Lactobacillus sp. (IL7), and Lactobacillus gasseri (IL12). All isolates showed variable in vitro tolerance to acidic pH for 3 h and visible growth at pH 4 and in the presence of 0.3% ox-bile. The antibiotic susceptibility profile of Lactobacillus isolates indicated resistance against vancomycin, ciprofloxacin, streptomycin, and lincomycin. Isolates had variable auto-aggregation and showed variable capabilities to co-aggregate with S. enteritidis. Based on all tested parameters, L. fermentum IL2, L. fermentum IL5, and L. gasseri IL12 were selected for co-culture experiments, followed by in vivo evaluation in Balb/c mice. All the selected isolates resulted in a 100% reduction in S. enteritidis in broth. Lactobacillus isolates efficiently colonized mouse guts and inhibited S. enteritidis colonization. Overall, there was ≥99.06% and ≤4.32 Mean log10 reduction in Salmonella counts in mice feces within 7 days. The study, thus, provided characterized lactobacilli that could be considered as potential ingredients for probiotic formulations intended to prevent S. enteritidis infection in humans.
Analysis of Remotely Piloted Aircraft Payload for Oil Spill Detection

Abstract Operational monitoring of large sea aquatorium areas with the aim of detecting and controlling oil pollution is now carried out using various technological systems, such as satellite remote sensing, sea-going vessels, various aircraft and remotely piloted aircraft (RPA). Currently, the use of RPA for the fulfilment of monitoring tasks in the aquatorium is being intensively developed and can eliminate problems of remote sensing performed by satellites and piloted aircraft, such as short presence in the monitoring area, very long delay of information (up to 48 hours) and low quality of imagery. This paper presents mathematical modelling of RPA multi-sensor pay-loads for oil spill detection, monitoring and control. Information obtained from payload sensors is critical for increasing effectiveness of detection and monitoring of oil spills. Nowadays, many types of sensors are used for oil spill detection and monitoring. The most common sensors for detection of oil pollution are optical, multispectral, hyperspectral, thermal and laser fluorometers. Some oil pollution detection sensors have limitations, such as false alarm, only daytime operation, weather restrictions. Airborne remote sensors cannot provide all information required for detection of and response to oil spills, and water quality monitoring in the spill area. A model for selecting sensors for multi sensor payload that will make it possible to optimize the application of RPA for oil spill detection was developed. The RPA payload can be increased/reduced to the greatest possible extent with the help of different types of equipment at various parameters. The mathematical model of the integrated payload considers detection capability of sensors, weather conditions, sensor characteristics, and false alarm rate. The optimal multi-sensor payload will optimize the application of RPA for oil spill detection and monitoring.
On Secure and Side-Channel Resistant Hardware Implementations of Post-Quantum Cryptography

Currently, many post-quantum cryptography schemes have been implemented on various hardware platforms in order to provide efficient solutions in cybersecurity services. As researchers and hardware developers focus primarily on designs providing small latency and requiring fewer hardware resources, their implementations could seldom omit protection techniques against various physical attacks. This paper studies potential attacks on the cryptography implementations that run on Field-Programmable Gate Array (FPGA) platforms. We mainly analyze how Post-Quantum Cryptography (PQC) implementations could be vulnerable on various platforms. Further, we aim at the FPGA-based implementations of National Institute of Standards and Technology (NIST)’s PQC competition finalists. Our study should present to developers the current overview of attacks and countermeasures that can be implemented on specific PQC schemes on FPGA platforms. Moreover, we present novel implementation of one universal countermeasure component and reveal additional resources that are needed.
Secure Services for Standard RISC-V Architectures

In systems security Trusted Execution Environments have been developed as a mean to offer additional security to existing complex system designs. In the past multiple vulnerabilities have affected TEE implementations like ARM TrustZone and Intel SGX, which is why the research community has been looking to identify and solve existing design flaws. Another branch of computer science looks at RISC-V, a modern processor architecture that allows everyone to use and extend it. In this work, we analyze the current possibilities of the RISC-V architecture to provide TEE-related functionality while avoiding potential pitfalls and vulnerabilities early on in the design process. By looking at the current problems in established TEE frameworks, we implemented and tested actual services used by user applications and operating systems that implement common TEE features on a recent version of the standard RISC-V ISA. We found that the current technology can be used to implement file storage and cryptographic key management services without modifications to the standard. Unfortunately, our results show that RISC-V offers no solution to secure I/O communication with peripherals on a system, and therefore also no safe way to interact with the user in case of an OS compromise. We discuss potential solutions to this remaining problem.
UNCOVER: Development of an efficient steganalysis framework for uncovering hidden data in digital media

This paper presents the general goals of Horizon 2020 project UNCOVER, whose overall purpose is to close the gap between academic work and operational needs in the fields of data-hiding. While digital data-hiding is a relatively new area of research, our motivation in this project has been rooted in the growing gap between the academic community and the operational needs of a ”real-life” scenario of object inspection in order to UNCOVER the presence of data secretly hidden. As well as an oversight into the structure of UNCOVER, our paper presents an empirical study on the impact of specifically training a detection method for a given data-hiding scheme, the so-called Stego-Source Mismatch, as an example of unexplored issues that raises important and mostly ignored consequences within the operational context the UNCOVER project targets.
Rumor and clickbait detection by combining information divergence measures and deep learning techniques

In this article we address the challenge of detecting the generation and spreading of misleading information in the specific scenario of clickbait. Our contribution consists of a methodology that combines a deep neural network and an information divergence measure to overcome the limitations of deep learning techniques in this scenario. This analysis is conducted by considering a clickbait challenge dataset. We realise that the construction of the dataset used to study this kind of problems dramatically affects the performance of the model and, thus, its selection. Since clickbait is a result of the inconsistency between headlines and content, we integrate a divergence measure as a layer of a deep learning model. The resulting model overcomes the limitations of conventional machine learning and deep learning models in clickbait detection.
Enabling useful provenance in scripting languages with a human-in-the-loop

Most data scientists must build substantial data pipelines using scripting languages like Python and R. These pipelines are hard to get correct due to the large volume of data they process (thus the long execution time), and the fact that they are tested mainly by inspection of output data quality. It is therefore crucial for developers to reason about data through each step in the pipeline, starting from the raw input; this information is akin to data provenance in a relational setting. Past efforts for capturing data provenance for scripting languages have required substantial manual modifications to the scripts, or else yield information that is too inflexible for many debugging tasks. We instead propose a "human-in-the-loop" provenance generation model with three key improvements: (1) allowing humans to express the desired provenance through a provenance schema, (2) enabling one-time execution capture of scripts to produce traces that are later combined with different provenance schemata to yield useful provenance for different tasks, (3) providing a modular rule-based recommendation component to help design provenance schemata through a user interaction interface. We describe the concepts, the user experience with our system, explain the system components, and present preliminary experiment results.
NF-B by hydroxylases

Hypoxia is a prominent feature of chronically inflamed tissues. Oxygen-sensing hydroxylases control transcriptional adaptation to hypoxia through the regulation of Hypoxia-Inducible Factor (HIF) and nuclear factor " B (NF- " B), both of which can regulate the inflammatory response. Furthermore, pharmacologic hydroxylase inhibitors reduce inflammation in multiple animal models. However, the underlying mechanism(s) linking hydroxylase activity to inflammatory signaling remain unclear. IL-1 ! , a major pro-inflammatory cytokine which regulates NF- " B is associated with multiple inflammatory pathologies. We demonstrate that a combination of PHD1 and FIH hydroxylase isoforms regulates IL-1 ! –induced NF- " B at the level of (or downstream of) the TRAF6 complex. Multiple proteins of the distal IL-1 ! -signaling pathway are subject to hydroxylation and form complexes with either PHD1 or FIH. Thus, we hypothesize that hydroxylases regulate IL-1 ! signaling and subsequent inflammatory gene expression. Furthermore, hydroxylase inhibition represents a new approach to the inhibition of IL-1 ! -dependent inflammatory signaling. 12 MS/MS-spectra
Heterogeneous Catalysis for Carbon Dioxide Mediated Hydrogen Storage Technology Based on Formic Acid

In the context of global carbon dioxide increasing drastically, renewable energy is crucial maintain the economic growth of the world. Hydrogen has attracted considerable attention as a clean fuel, but the large‐scale storage and controllable release of H2 is still urgently needed, yet largely not yet accomplished. Through the reactions of CO2 hydrogenation to formic acid (FA) and FA dehydrogenation to hydrogen, a “carbon neutral” sustainable hydrogen storage system can be constructed. With advantages of excellent recyclability and facile separation for heterogeneous catalysis, developing efficient heterogeneous catalysts for hydrogen storage based on FA is the primary focus of this review, mainly involving metal catalysts from nanoscale to single‐atom scale. Firstly, the effects of metal size on the catalytic activities are highlighted in detail. Additionally, special attention is paid to the relevant structure–activity relationships of various supported catalysts and the mechanistic insights, which can provide a theoretical foundation for rational catalyst design. This review brings new and systemic insights into innovative and efficient heterogeneous catalysts design and applications for the ultimate FA‐based sustainable hydrogen storage system with a closed carbon loop.
Transmission Line Fault Classification Using Modified Fuzzy Q Learning

The authors propose an adaptive, self-learning fault classifier based on modified fuzzy Q learning (MFQL) for transmission lines. Proposed MFQL fault classifier is able to achieve very high classification accuracy with relatively small number of samples. The authors’ is a first attempt at designing a fault identifier using reinforcement learning for fault segregation in transmission lines. The authors’ identifier does not assume prior knowledge of transmission line model or target fault information. Raw voltage and current data (supply and load side) is processed using empirical mode decomposition to generate 13 intrinsic mode functions (IMFs’). Classifier employs the J48 algorithm to further prune these 13 IMF's to eight most relevant input variables, which serve as inputs to the MFQL fault classifier. The authors compare performance of the proposed MFQL classifier to other contemporary AI-based classifiers, e.g. neural networks and support vector machines. Simulation results and performance comparison against other AI-based classifiers elucidates that the proposed MFQL-based identifier achieves a significantly higher performance level and could serve as an important tool for transmission line fault diagnosis.
HADOOP- An Open Source Framework for Big Data

In this paper we will discuss about an open source framework for storing and processing a huge amount of data, known as HADOOP (High Availability Distributed Object Oriented Platform). Originally HADOOP is written in Java Language. HADOOP work on the concept of Write Once Read as many as times as you want but don’t change the content of the file (Stream Line Access Pattern). HADOOP consist a cluster containing heterogeneous computing devices with commodity hardware. A HADOOP cluster consist two things: HDFS (Hadoop Distributed File System) and MapReduce. HDFS used for data storage and MapReduce used for data process. HDFS is suitable for storing data from Tear byte to Petabyte on a cluster and it run on a commodity hardware.
Advances In Computational Complexity Theory

Approximate counting with uniform constant-depth circuits by M. Ajtai On strong separations from $AC^0$ by E. Allender and V. Gore Parallel matching complexity of Ramsey's theorem by J. Beck On algorithms for simple stochastic games by A. Condon Locally random reductions in interactive complexity theory by J. Feigenbaum An application of game-theoretic techniques to cryptography by M. J. Fischer and R. N. Wright Composition of the universal relation by J. Ha stad and A. Wigderson Practical perfect cryptographic security by U. M. Maurer Fair games against an all-powerful adversary by R. Ostrovsky, R. Venkatesan, and M. Yung Factoring integers and computing discrete logarithms via diophantine approximation by C. P. Schnorr A new lower bound theorem for read-only-once branching programs and its applications by J. Simon and M. Szegedy On the E -isomorphism problem by J. Wang.
A 22-30 GHz Single-Ended-To-Differential LNA Using Double Defected Ground Marchand Balun

In this paper, a 2-stage Ka-band single-ended-to differential LNA design is presented. Common source configuration with inductive degeneration is used for input stage to optimize noise Figure (NF) and input matching. The output stage with capacitive cross-coupling neutralization technique help to improve gain and reverse isolation for increasing stability. For the 1-st time, a double defected ground Marchand balun is implemented to convert single-ended signal to differential signal. The proposed LNA uses 2S-nm CMOS technology from TSMC and shows a typical NF below 3.9 dB, a 22.5 dB gain peak, a small amplitude and phase imbalance in the bandwidth from 22 to 30 GHz. The LNA has the die dimension of 764 |1mx50511m including test pads and consumes 25 mW from a supply voltage of 0.9 V.
An Automated Technology for IoT based Rail-Track Inspection to Locate Surface Flaws by Robotics and Neural Networks

To maintain national security, railway transportation needs regular examinations and quick servicing. Conventional manual screenings are not always time-consuming and costly, but they also depend on individual skill and effectiveness at the time of survey to discover defects accurately. It is possible to automate Internet of Things (IoT)-based solutions using technology and automation, for example, machinery installed on train lines or other difficult-to-reach areas and handled through control rooms. A revolutionary automatic model based on robots and visually inspecting is suggested in this research. While inspecting, the technology enables for on-site image analysis, cloud services for photographs of broken railway tracks, and robot localization within a 3-6-inch range. The suggested method relates a cutting-edge Machine Learning technique to pictures received from the rails in order to identify them as regular or dangerous. Such places are then identified, and a specialized operator with a small number of sites to examine can do a more thorough examination.