Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of rare semileptonic decays with non-universal Z′ effect

We analyze different decay observables of semileptonic decays , such as the branching ratio, forward-backward asymmetry, polarization fraction, and lepton polarization asymmetry in the non-universal model. We further study the dependence of the branching fraction to the new model parameters and find that the values of different decay parameters increase in the model, which indicates a possible approach for the search of new physics as well as for the unknown phenomena of the charm B meson.
Finding interest groups from Twitter lists

Twitter lists enable users of the social network to organize people they follow into groups of interest (e.g. politicians or journalists they like, favorite artists or athletes, authoritative figures in a given field, and so on). For the analyst, lists are a means of access to the structure of interactions between Twitter users and can be used to identify main actors of a field of interest. In this work, we introduce a methodology for constructing an edge-attributed multilayer network of Twitter users based on their membership to Twitter lists. We propose and validate a new approach that identifies local communities of users and their common interests from the constructed graph. We provide evidences that our method performs in a better way than global community detection approaches, and faster with as good results as competitive local methods.
Towards the improvement of natural language requirements descriptions: the C&L tool

A natural language-based requirements specification tends to be full of requirements that are ambiguous, unnecessarily complicated, missing, wrong, duplicated or conflicting. Poor quality requirements often compromise the subsequent software construction activities (e.g. planning, design, coding or testing). A strategy for requirements quality evaluation should enable a faster requirements analysis, highlight defect indicators and incorporate also fix recommendations to help practitioners to effectively improve their requirements. In this paper we brief describe a Natural Language Processing and Petri-Net strategy for automated analysis of scenario-driven requirements named C&L prototype tool. The C&L evaluates structural (Static analysis) aspects of scenarios and behavioral aspects (Dynamic analysis) of equivalent Petri-Nets. The feasibility of the C&L is evaluated in four projects described as use cases, which indicates promising results (the overall precision was 93.5% and the recalls were perfect).
Evaluating techniques for method-exact energy measurements: towards a framework for platform-independent code-level energy measurements

Reducing the energy consumption of executing programs decreases the operational cost and the environmental impact. For some well-known tasks, domain-specific guidelines for energy-efficient code exist. However, we want to enable profiling the energy consumption of code and selectively optimize hot spots, previously unknown. On our way to providing method-exact information about energy consumption in different hardware components, we currently focus on measuring the CPU's energy-consumption due to running a program itself. We identify three main challenges: measuring energy consumption at a fine granularity, constructing repeatable measurements, and mapping measurements to code. By actually building and using a platform-independent measurement setup, we show that it is feasible to measure the energy consumption at the granularity of method executions. The measurement time series must be aligned with execution events such as relevant method executions. We demonstrate how to evaluate alignment techniques by the examples of linear scaling and Dynamic Time Warping for mapping the measurements to code. While both are not yet sufficient to reliably map measurements to fine-grained code locations, our framework demonstrates the general feasibility of our goal and enables further research in this direction.
HeadPrint: detecting anomalous communications through header-based application fingerprinting

Passive application fingerprinting is a technique to detect anomalous outgoing connections. By monitoring the network traffic, a security monitor passively learns the network characteristics of the applications installed on each machine, and uses them to detect the presence of new applications (e.g., malware infection). In this work, we propose HeadPrint, a novel passive fingerprinting approach that relies only on two orthogonal network header characteristics to distinguish applications, namely the order of the headers and their associated values. Our approach automatically identifies the set of characterizing headers, without relying on a predetermined set of header features. We implement HeadPrint, evaluate it in a real-world environment and we compare it with the state-of-the-art solution for passive application fingerprinting. We demonstrate our approach to be, on average, 20% more accurate and 30% more resilient to application updates than the state-of-the-art. Finally, we evaluate our approach in the setting of anomaly detection, and we show that HeadPrint is capable of detecting the presence of malicious communication, while generating significantly fewer false alarms than existing solutions.
Authenticated and auditable data sharing via smart contract

Our main use case features multiple companies that iteratively optimize on the architectural properties of aircraft components in a decentralized manner. In each optimization step of the so-called multi-disciplinary optimization (MDO) process, sensitive data is exchanged between organizations, and we require auditability and traceability of actions taken to assure compliance with signed legal agreements. In this paper, we present a distributed protocol that coordinates authenticated and auditable exchanges of files, leveraging a smart contract. The entire life cycle of a file exchange, including file registration, access request and key distribution, is recorded and traceable via the smart contract. Moreover, when one party raises a dispute, the smart contract can be used to identify the dishonest party without compromising the file's confidentiality. The proposed protocol provides a simple, novel, yet efficient approach to exchange files with support for data access auditability between companies involved in a private consortium with no incentive to share files outside of the protocol. We implemented the protocol in Solidity, deployed it on a private Ethereum blockchain, and validated it within the use case of a decentralized workflow.
IntPred: flexible, fast, and accurate object detection for autonomous driving systems

Deep Neural-Network (DNN) based Object Detection is one of the most important and time-consuming stages of Autonomous Driving software in cars. In non-critical domains, the performance and energy requirements of object detection can be reduced at the cost of accuracy in the detected objects. This is not the case in a critical domain like automotive, for which a delicate balance between performance/energy overheads and accuracy of object detection must be found. We propose IntPred to achieve such a balance by leveraging on the fact that, with high frame rates, objects do not move significantly across frames. IntPred tailors object interpolation for the case of object detection in autonomous driving frameworks, in line with approaches devised for other domains, thus heavily reducing the performance requirements of full-fledged DNN-based object prediction. IntPred results in comparable accuracy to the original object detection, while saving more than 70% of the computations. The latter allows using lower-performance and cheaper platforms resulting in saving energy and reducing heat dissipation: for instance, in an NVIDIA Jetson TX2 platform, specific for autonomous driving systems, our technique increases the frame processing rate by 4.6x. IntPred also allows consolidating additional applications onto the same platform.
Transforming Smart Vehicles and Smart Homes into Private Diagnostic Spaces

The aging societies require disruptive technologies and digitization of health is one of these. Similar to the controller area network bus of (smart) cars we have developed a bus-based system for smart homes. We consider both, vehicles and homes as private spaces, which, in contrast to smart wearables or smart clothes, provide sufficient power supply, computer, and storage hardware. Today's homes and cars are already equipped with a variety of sensors that deliver data relevant with respect to health. The daily delay between opening of bedroom and bathroom doors, or the time between opening the car's door and starting its engine indicates mobility. We further empower eHealth if private spaces are equipped with medical sensors (Step I of the required transforms). However, unobtrusive continuous monitoring of vital signs and biosignals is no yet explored clinically, and data to train artificial intelligence is missing. We propose steering wheel integrated electrocardiography (ECG) recording in smart vehicles and capacitive ECG recording in the chair and bed of the smart home for stroke prevention due to early detection of latent atrial fibrillation. Furthermore, the processing unit needs data warehousing and analytics (Step II). The communication interface needs semantic operability and secure channels, which we propose to establish using the international standard accident number (ISAN) (Step III). Finally, the combination with a medical application such as stroke prevention (Step IV) turns smart environments into private diagnostic spaces.
VTKEL: a resource for visual-textual-knowledge entity linking

To understand the content of a document containing both text and pictures, an artificial agent needs to jointly recognize the entities shown in the pictures and mentioned in the text, and to link them to its background knowledge. This is a complex task, that we call Visual-Textual-Knowledge Entity Linking (VTKEL), which aims at linking visual and textual entity mentions to the corresponding entity (or a newly created one) of the agent knowledge base. Solving the VTKEL task opens a wide range of opportunities for improving semantic visual interpretation. For instance, given the effectiveness and robustness of state-of-the-art NLP technologies in entity linking, by automatically linking visual and textual mentions of the same entities with the ontology, we can obtain a huge amount of automatically annotated images with detailed categories. In this paper, we propose the VTKEL dataset, consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The VTKEL dataset can be used for training and evaluating algorithms for visual-textual-knowledge entity linking.
Appearance features for online multiple camera multiple target tracking

Multiple object tracking methods in the state-of-the-art are challenged by appearance variation, environment changes and long-term occlusions. Exploiting multiple calibrated and frame synchronized cameras holds the promise of alleviating these problems, in particular, the one pertaining to occlusion. The practical realization of this idea faces the problem that the appearance of the same target can change through different cameras. Thus, particular care should be taken in order to enhance the computation of appearance distances between targets in multiple cameras. In this paper, we tackle the problem of multiple object multiple camera tracking by adopting a Markov Decision Process framework. We concentrate on the effect of the affinity function by discussing different possible implementations and validating their performance, in terms of the MOT metric and the ID measure, on the PETS 2009 and EPFL datasets. Our experimental result shows a significant improvement of multiple cameras approaches with a sufficiently large overlapping zone compared to single camera ones.
HAL-RD: cross-correlating heterogeneous alerts and logs using resource dependencies

Many organizations today use a variety of security and monitoring tools at various levels of defense. These tools often generate heterogeneous alerts and logs when an attack occurs. Because of the large volume and dispersion of these alerts and logs, the manual cross-correlation of them is a time-consuming and labor-intensive task. The main challenge is that heterogeneous alerts and logs generated as a result of an attack stage do not necessarily have common features, or there are no explicit relationships between them that can be used for cross-correlation. In this paper, we overcome this deficiency by presenting HAL-RD, a novel technique that uses resource dependencies to cross-correlate heterogeneous alerts and logs. In this technique, we track logs for backward and forward dependencies between resources. This information is then used to construct an attack state graph, which is a directed graph whose nodes represent attack states and whose directed edges represent the chronological ordering between them. Each attack state integrates information found in multiple heterogeneous alerts, logs, and OS-level operations, which relate to one stage in a multi-stage attack. In certain circumstances, the attack state graph is incrementally updated. By doing this, when an attacker continues his/her multi-stage attack after a delay, all of his/her activities are identified. The evaluation results demonstrate the effectiveness of HAL-RD for cross-correlating heterogeneous alerts and logs.
Write-protection enforcement: hypervisor-backed kernel hardening

A rootkit is a piece of code that aims to manipulate the computer behaviour without being detected. Rootkits are mainly used to disable kernel self-protection, hide malware presence, provide a covert communication channel between malware and their Command and Control server (C&C), secretly maintain a breach viable. Depending on how rootkits are injected into the target, they can either reside in user or kernel space. Kernel space rootkits are the hardest to detect and prevent because they usually take control of the kernel once compromised. In this paper, we present WpE, Write-protection Enforcement: a hypervisor-backed kernel hardening system. WpE aims to preserve the kernel self-protection active in presence of attacks. WpE is comprised of two software components, one running in hypervisor mode and one running in kernel mode. At OS instantiation, the kernel component will arrange the kernel critical data in a way that facilitate labeling such data as either writable, rarely writable, or read only. The hypervisor component will stop a compromised kernel from altering memory or registers that were flagged for read only protection. In the current stage of implementation, we managed to successfully integrate WpE with KVM and Linux kernel. Evaluation showed that WpE adds acceptable overhead.
Explaining reasoning algorithms with persuasiveness: a case study for a behavioural change system

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behaviour. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: i) the natural language generation of messages that explain the reasoner inconsistency; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users' behaviours.
Role of Brain Dopamine in Centrally Evoked Angiotensin II Responses in Conscious Rats

Because intracerebroventricular (i.c.v.) injection of angiotensin II enhances the activity of brain dopaminergic neurons, the possible role of the brain dopamine neuronal system as a mediator of the pressor, dipsogenic, and neurohormonal effects of centrally administered angiotensin II was examined in this study. In conscious male Sprague-Dawley rats, injections of angiotensin II (125 ng i.c.v.) caused a significant increase in mean arterial pressure (19 ± 1 mm Hg), which was not changed by i.c. v. pretreatment with a subpressor dose of either dopamine (16 ± 1 mm Hg) or the dopamine antagonist metoclopramide (21 ± 1 mm Hg). However, angiotensin II-induced drinking (11.3 ± 0.4 ml/30 min) was partially inhibited by i.c. v. pretreatment with metoclopramide (7.4 ±1.0 ml/30 min, p < 0.01) but not affected by i.c. v. dopamine pretreatment. Intraventricular injections of angiotensin II caused a sixfold increase in plasma arginine vasopressin (10.9 ±1.3 pg/ml vs 1.7 ± 0.7 pg/ml in control rats, p < 0.01), which was enhanced by metoclopramide (17.6 ± 2.3 pg/ml, p < 0.01). Dopamine pretreatment had no significant effect on this increase in plasma arginine vasopressin (7.1 ±1.1 pg/ml) produced by angiotensin II. Angiotensin II-induced suppression of plasma renin activity from 14.7 ± 1.2 to 6.8 ± 1.6 ng/ml/hr was not affected by prior i.c. v. administration of either dopamine or metoclopramide. Central injection of dopamine or metoclopramide alone had no effect on plasma arginine vasopressin or plasma renin activity levels, mean arterial pressure, or water intake. Our results suggest that central dopaminergic neurons interact with centrally administered angiotensin II in the regulation of hydromineral balance and neurohypophysial release of arginine vasopressin but not in the regulation of blood pressure or plasma renin activity.
Canadian Guidelines on Cannabis Use Disorder Among Older Adults

Background Cannabis Use Disorder (CUD) is an emerging and diverse challenge among older adults. Methods The Canadian Coalition for Seniors’ Mental Health, with financial support from Health Canada, has produced evidence-based guidelines on the prevention, identification, assessment, and treatment of this form of substance use disorder. Conclusions Older adults may develop CUD in the setting of recreational and even medical use. Clinicians should remain vigilant for the detection of CUD, and they should be aware of strategies for prevention and managing its emergence and consequences The full version of these guidelines can be accessed at www.ccsmh. ca.
Current Developments in the Immunology of Psoriasis

Psoriasis is a frequent inflammatory skin disease. Fundamental research on the pathogenesis of psoriasis has substantially increased our understanding of skin immunology, which has helped to introduce innovative and highly effective therapies. Psoriasis is a largely T lymphocyte-mediated disease in which activation of innate immune cells and pathogenic T cells result in skin inflammation and hyperproliferation of keratinocytes. B cells have thus far largely been neglected regarding their role for the pathogenesis of psoriasis. However, recent data shed light on their role in inflammatory skin diseases. Interestingly, interleukin (IL)-10-producing regulatory B cells have been assumed to ameliorate psoriasis. In this review, we will discuss the development of disease, pathogenicity, and current developments in therapeutic options. We describe different roles of T cells, B cells, and cytokines for the immunopathology and disease course of psoriasis.
Heart Failure: Prevalence, Symptoms, Treatments

Heart failure (HF) is a growing epidemic that affects about six million people in the United States and more than 23 million worldwide [1,2]. The prevalence of HF is greatest among people aged 65 years or older and has been increasing with the growth of this demographic population [3-6]. Around 50% and 90% of patients with HF die within 5 and 10 years of diagnosis, respectively [1]. Total medical costs attributable to diagnosis, treatment, and prevention of HF in the developed countries are approximately 1% 2% of the total health care expenditure [7-9]. In the United States, total costs for HF care are more than $39 billion annually [2].
Reinforcing the energy efficiency of cyber-physical systems via direct and split cache consolidation on MLC STT-RAM

Energy efficiency has become one of the primary considerations in the designs of cyber-physical systems (CPS). However, CPS with static RAM (SRAM)-based processors suffers from the high leakage power issue of SRAM, thus limiting the energy efficiency of CPS. Recently, Spin-Transfer Torque RAM (STT-RAM) has emerged and been widely regarded as a great alternative as the on-chip memory within processors, owing to STT-RAM's high density and near-zero leakage power characteristics. In addition, recent advances in Magnetic Tunneling Junction (MTJ) technology also realize the multi-level cell (MLC) STT-RAM to further enhance the memory density. Nevertheless, the write disturbance issue greatly limits the energy efficiency of MLC STT-RAM. Even though studies have been proposed to alleviate this issue, most of the previous disturbance reduction strategies could induce additional management overhead by utilizing counters or cause frequent swap operations when the write disturbance happens. Such observations motivate us to propose a simple and effective solution to unify the direct and split mapping cache designs for improving the energy efficiency of MLC STT-RAM. The proposed design is evaluated through a series of experiments on an emulator with encouraging results.
Spatial bloom filter in named data networking: a memory efficient solution

Among the possible future Internet architectures, Information Centric Networking (ICN) is the most promising one and researchers working on the Named Data Networking (NDN) project are putting efforts towards its deployment in a real scenario. To properly handle content names, the different components of an NDN network need efficient and scalable data structures. In this paper, we propose a new data structure to support the NDN forwarding procedure by replacing the current Forwarding Information Base (FIB): the Spatial Bloom Filter (SBF), a probabilistic data structure that guarantees fast lookup and efficient memory consumption. Through a set of simulations run to compare the performance of FIB and SBF, we found that the latter uses less than 5 KB of data to handle 106 queried interests, with a (negligible) probability 10-4 of false positive events. Conversely, the FIB requires up to 2.5 GB of data in disadvantageous cases, e.g. when interests are composed of a considerable number of components.
Comparing identifiers and comments in engineered and non-engineered code: a large-scale empirical study

Identifiers and comments are important sources of information to understand software. Approximately 70% of the code of a system consists of identifiers [1]. Identifier length and quality, as well as naming conventions overall, influence the difficulty and time required to complete maintenance tasks like code reading or debugging [2--4]. After the code itself, comments are the main source of documentation for software maintainers [5], and experiments show that commented code is easier to understand when compared to uncommented code [6]. Therefore these elements are of paramount importance for software development in general.