Miracle-dz/UniFAR-FTUs huggingface dataset:
BiTouch: enabling secure touch-to-access device pairing based on human body's electrical response

We present a secure device pairing approach, called BiTouch, using the human body as a conductor to generate and transmit user-specific pairing keys for advancing touch-to-access policy. BiTouch is designed based on the observation that the human body responds uniquely to electrical signals flowing through it. Built-in microphones on devices are essentially used to capture ambient sound as entropy and convert it into an electrical signal, which is subsequently transmitted by the body for device pairing. We implement BiTouch using off-the-shelf microphones and evaluate it with 31 participants. The results demonstrate that BiTouch ensures the user's legitimacy and key transmission reliability, and achieves a pairing success rate of 97.74% and an equal error rate of 2.28%.
Experience: pushing indoor localization from laboratory to the wild

While GPS-based outdoor localization has become a norm, very few indoor localization systems have been deployed and used. In this paper, we share our 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system. We address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild. The system is currently used in 1469 shopping malls, 393 office buildings and 35 hospitals across 35 cities to provide location service to millions of users on a daily basis. We hope the shared experience can benefit the design of real-world indoor localization systems and the practical problems identified can change the focus of indoor localization research. We released our dataset that contains fingerprints collected from 1469 shopping malls and one office building.
Leveraging public buses to relay UAVs for on-demand applications

Unmanned Aerial Vehicles (UAVs) are widely employed in smart city. However, the limited battery capacity is one of the UAV's most critical obstacles to monitoring mission. Recently, leveraging public buses to relay UAVs has been shown to be a promising solution to this critical issue. Existing works on this solution focused on pre-determined scenarios, namely the mission time and location of the UAVs are determined in advance. While in on-demand mission such as emergency response, mission time and location of the UAV is on-demand and stochastic. How the UAV riding on a bus perform in such stochastic missions remains open. In this paper, driven by the bus mobility data, a sampling-based algorithm is designed to navigate UAVs to land on buses in on-demand applications. A simple greedy algorithm is proposed to determine the appropriate buses to relay UAVs, so that the scheduling performance of the UAVs is optimized. Comprehensive evaluation using a large-scale bus trajectory data is conducted.
Mask does not matter: anti-spoofing face authentication using mmWave without on-site registration

Face authentication (FA) schemes are universally adopted. However, current FA systems are mainly camera-based and hence susceptible to face occlusion (e.g., facial masks) and vulnerable to spoofing attacks (e.g., 3D-printed masks). This paper exploits the penetrability, material sensitivity, and fine-grained sensing capability of millimeter wave (mmWave) to build an anti-spoofing FA system, named mmFace. It scans the human face by moving a commodity off-the-shelf (COTS) mmWave radar along a specific trajectory. The mmWave signals bounced off the human face carry the facial biometric features and structure features, which allows mmFace to achieve reliable liveness detection and FA. Due to the penetrability of mmWave, mmFace can still work well even if users wear masks. We explore a distance-resistant facial structure feature to suppress the impact of unstable face-to-device distance. To avoid inconvenient on-site registration, we also propose a novel virtual registration approach based on the core idea of cross-modal transformation from photos to mmWave signals. We implement mmFace with various antenna configurations and prototype two typical modes of mmFace. Extensive experiments show that mmFace can realize accurate FA as well as reliable liveness detection.
Towards automatic troubleshooting for user-level performance degradation in cellular services

Troubleshooting cellular service issues at the per-UE (User Equipment) level is an essential task for cellular providers. However, diagnosing service issues at per-UE level is costly because it requires advanced expertise and in-depth inspection of massive network log data. This paper presents NeTExp, a generic and comprehensive data-driven approach to automatically troubleshoot cellular service issues reported by customers. NeTExp determines whether the root cause of a user-reported service issue is from the network side or the device side through deep neural networks, which extract complex spatial-temporal feature profiles from massive network log data. The system is trained and validated using an extensive period of network and customer care data from a major cellular service provider in United States. We also present a case study on an external event that caused cellular service issues in 2020 to demonstrate the effectiveness of NeTExp on detecting network issues and identifying network-issue-related root causes at per-UE level.
A-mash: providing single-app illusion for multi-app use through user-centric UI mashup

Mobile apps offer a variety of features that greatly enhance user experience. However, users still often find it difficult to use mobile apps in the way they want. For example, it is not easy to use multiple apps simultaneously on a small screen of a smartphone. In this paper, we present A-Mash, a mobile platform that aims to simplify the way of interacting with multiple apps concurrently to the level of using a single app only. A key feature of A-Mash is that users can mash up the UIs of different existing mobile apps on a single screen according to their preferences. To this end, A-Mash 1) extracts UIs from unmodified existing apps (dynamic UI extraction) and 2) embeds extracted UIs from different apps into a single wrapper app (cross-process UI embedding), while 3) making all these processes hidden from the users (transparent execution environment). To the best of our knowledge, A-Mash is the first work to enable UIs of different unmodified legacy apps to seamlessly integrate and synchronize on a single screen, providing an illusion as if they were developed as a single app. A-Mash offers great potential for a number of useful usage scenarios. For instance, a user can mashup UIs of different IoT administration apps to create an all-in-one IoT device controller or one can mashup today's headlines from different news and magazine apps to craft one's own news headline collection. In addition, A-Mash can be extended to an AR space, in which users can map UI elements of different mobile apps to physical objects inside their AR scenes. Our evaluation of the A-Mash prototype implemented in Android OS demonstrates that A-Mash successfully supports the mashup of various existing mobile apps with little or no performance bottleneck. We also conducted in-depth user studies to assess the effectiveness of the A-Mash in real-world use cases.
Demonstrating hitonavi-μ: a novel wearable LiDAR for human activity recognition

In this paper, we demonstrate a brand new design of a wearable device that enables privacy-preserving human activity recognition based on light-weight compact-size LiDAR. The device scans and represents the surrounding environment in 3D point clouds form. The system further processes this representation to define discriminative features that facilitate recognizing human activity on edge. These features are extracted using Spatio-temporal probabilistic clustering and fisher vector representations and then used to train a classification model for activity recognition purposes. Implementation and evaluation of the proposed system confirm its efficient ability to identify human activities.
An Anomaly Detection Framework for Internal and External Interaction of Power Grid Information Network based on the Attack-chain Knowledge Graph

With the gradual opening of the interaction method between the internal and external network, how to effectively detect the attack for the internal network through the external network becomes more and more important. However, traditional security protection measures cannot well detect unknown attacks and multi-step attacks, which leads to a constant threat. This paper proposes a network security knowledge graph model based on an extended attack-chain, combined with a multi-layer anomaly detection system to detect the threat lurked in the network. Finally, the application of the multi-layer anomaly detection framework in the security protection for internal and external boundary of state grid information network is prospected.
Improved algorithm of indoor visual odometer based on point and line feature

When the traditional Visual Odometry calculation method based on image feature points is used for indoor positioning and mapping, there will be holes in the depth map obtained by the depth camera due to the highly reflective or light-transmitting area on the surface of the target object, resulting in low positioning accuracy. Moreover, when encountering a scene with a textureless area, there will be insufficient effective feature points, resulting in loss of tracking. To solve the above problems, an improved indoor visual odometry algorithm based on point and line features is proposed. On the one hand, this method uses a Curvature Driven Diffusion model based on edge-first filling to repair the depth map extracted by the depth camera. The repair results are compared with Joint Bilateral Filtering, Fast Marching Method and Curvature Driven Diffusion. Experiments show that this method has better filling effect, clear edges and more complete object structure information. On the other hand, in the feature extraction stage, the method of multi-feature fusion positioning is used, and the point and line features is extracted at the same time to complete indoor positioning and mapping. The algorithm in this paper is adopted in the Visual Odometry section of ORB-SLAM2, and the result obtained from 6 sets of TUM data set sequences with different structures and texture features is compared with the traditional ORB-SLAM2. The absolute trajectory error of ORB-SLAM2 using the algorithm in this paper is reduced by 8.99% on average. The experimental result shows that the improvement of the algorithm in this paper in two aspects effectively improves the positioning accuracy and robustness of the system.
To v∞ and beyond! The He i absorption variability across the 2014.6 periastron passage of η Carinae

We have monitored the massive binary star $\eta$ Carinae with the CTIO/SMARTS 1.5~m telescope and CHIRON spectrograph from the previous apastron passage of the system through the recent 2014.6 periastron passage. Our monitoring has resulted in a large, homogeneous data set with an unprecedented time-sampling, spectral resolving power, and signal-to-noise. This allowed us to investigate temporal variability previously unexplored in the system and discover a kinematic structure in the P Cygni absorption troughs of neutral helium wind lines. The features observed occurred prior to the periastron passage and are seen as we look through the trailing arm of the wind-wind collision shock cone. We show that the bulk of the variability is repeatable across the last five periastron passages, and that the absorption occurs in the inner 230 AU of the system. In addition, we found an additional, high-velocity absorption component super-imposed on the P Cygni absorption troughs that has been previously un-observed in these lines, but which bears resemblance to the observations of the He~I $\lambda$10830 A feature across previous cycles. Through a comparison of the current smoothed particle hydrodynamical simulations, we show that the observed variations are likely caused by instabilities in the wind-wind collision region in our line-of-sight, coupled with stochastic variability related to clumping in the winds.
Dengue virus downregulates TNFR1- and TLR3-stimulated NF-κB activation by targeting RIPK1

Dengue virus (DENV) infection is the most prevalent arthropod-borne virus disease and is endemic in more than 100 countries. Several DENV proteins have been shown to target crucial human host proteins to evade innate immune responses and establish a productive infection. Here we report that the DENV NS3 protein targets RIPK1 (Receptor Interacting Protein Kinase I), a central mediator of inflammation and cell death, and decreases intracellular RIPK1 levels during DENV infection. The interaction of NS3 with RIPK1 results in the inhibition of NF-κB activation in response to TNFR or TLR3 stimulation. Also, we observed that the effects of NS3 on RIPK1 were independent of NS3 protease activity. Our data demonstrate a novel mechanism by which DENV suppresses normal cellular functions to evade host innate immune responses
Media representations of crimes in close relationships: Qualitative analysis of narratives in a television broadcast

Recent years have seen increased media attention paid to crimes committed against women by partners or former partners has grown. Crime, especially violent crime, dominates the news and the mass media. In this sense, criminology” impacts publicly the collective representation of crime. The study was motivated by the interest to understand what representations are conveyed by television broadcasts and how closely related they are to criminological theories and literature on the subject. The objective of the proposed qualitative research is to examine the media representations proposed in thematic television broadcasts, as well as the narrative profiles relating to the victims, perpetrators of the crimes and their relationships, as described in the news and by a multiplicity of roles associated with them based on kinship, friendship or professional role.
U(1)
′ mediated decays of heavy sterile neutrinos in MiniBooNE

The MiniBooNE low-energy excess is a long-standing problem which has received further confirmation with a reanalysis using newly collected data, with the anomaly now at the 4. 8σ level. In this paper we propose a novel explanation which advocates a low-energy sector containing Z0 bosons with GeV-scale masses and sterile neutrinos with masses around 100–500 MeV. We show that this scenario provides excellent spectral agreement with the MiniBooNE low-energy excess in the form of Z0 -mediated neutral current production of heavy sterile states, a fraction of whose subsequent decay to eþe− pairs are misidentified as single electronlike electromagnetic showers. Our model inscribes itself in the broad class of models in which sterile neutrinos are charged under new interactions, allowing new couplings to hiddensector physics. Alongside the electronlike MiniBooNE signature this model also predicts a novel, lowbackground, signal in LArTPC detectors such as MicroBooNE consisting of two distinguishable electronlike electromagnetic showers originating from a single vertex.
A STELLAR MASS THRESHOLD FOR QUENCHING OF FIELD GALAXIES

We demonstrate that dwarf galaxies (107 < Mstellar < 109 M☉, −12 > Mr > −18) with no active star formation are extremely rare (<0.06%) in the field. Our sample is based on the NASA-Sloan Atlas which is a reanalysis of the Sloan Digital Sky Survey Data Release 8. We examine the relative number of quenched versus star-forming dwarf galaxies, defining quenched galaxies as having no Hα emission (EWHα < 2 Å) and a strong 4000 Å break. The fraction of quenched dwarf galaxies decreases rapidly with increasing distance from a massive host, leveling off for distances beyond 1.5 Mpc. We define galaxies beyond 1.5 Mpc of a massive host galaxy to be in the field. We demonstrate that there is a stellar mass threshold of Mstellar < 1.0 × 109 M☉ below which quenched galaxies do not exist in the field. Below this threshold, we find that none of the 2951 field dwarf galaxies are quenched; all field dwarf galaxies show evidence for recent star formation. Correcting for volume effects, this corresponds to a 1σ upper limit on the quenched fraction of 0.06%. In more dense environments, quenched galaxies account for 23% of the dwarf population over the same stellar mass range. The majority of quenched dwarf galaxies (often classified as dwarf elliptical galaxies) are within 2 virial radii of a massive galaxy, and only a few percent of quenched dwarf galaxies exist beyond 4 virial radii. Thus, for galaxies with stellar mass less than 1.0 × 109 M☉, ending star formation requires the presence of a more massive neighbor, providing a stringent constraint on models of star formation feedback.
Clinical characteristics and genotype-phenotype correlation analysis of familial Alzheimer’s disease patients with pathogenic/likely pathogenic amyloid protein precursor mutations

Alzheimer’s disease (AD) is a progressive neurodegenerative disease associated with aging, environmental, and genetic factors. Amyloid protein precursor (APP) is a known pathogenic gene for familial Alzheimer’s disease (FAD), and now more than 70 APP mutations have been reported, but the genotype-phenotype correlation remains unclear. In this study, we collected clinical data from patients carrying APP mutations defined as pathogenic/likely pathogenic according to the American college of medical genetics and genomics (ACMG) guidelines. Then, we reanalyzed the clinical characteristics and identified genotype-phenotype correlations in APP mutations. Our results indicated that the clinical phenotypes of APP mutations are generally consistent with typical AD despite the fact that they show more non-demented symptoms and neurological symptoms. We also performed genotype-phenotype analysis according to the difference in APP processing caused by the mutations, and we found that there were indeed differences in onset age, behavioral and psychological disorders of dementia (BPSD) and myoclonus.
Impact of skeletal muscle loss during conversion therapy on clinical outcomes in lavage cytology positive patients with gastric cancer

Background The relationship between sarcopenia and clinical outcomes during conversion therapy in patients with lavage cytology positive gastric cancer (GC-CY1) remains unclear. This study aimed to investigate the impact of sarcopenia and skeletal muscle loss on the efficacy of conversion therapy, tumour response and survival in GC-CY1 patients. Methods Retrospective analysis of data from a prospective trial of conversion therapy conducted between April 2018 and August 2019 in patients with GC-CY1 (NCT03718624). Skeletal muscle index (SMI) was measured at the level of the third lumbar (L3) vertebra and the sarcopenia was defined using published cut-off points in all patients. We defined ΔSMI (%)/50 days above 9.53% for men and ΔSMI (%)/50 days above 8.81% for women as significant muscle loss (SML) and analysed the changes in skeletal muscle during conversion therapy in relation to treatment efficacy, survival and tumour response. Results Of the 36 patients, 7 patients (19.44%) developed sarcopenia before conversion therapy, 6 (16.67%) developed new sarcopenia after conversion therapy, and 8 (22.22%) developed SML during treatment. Multivariate analysis showed that sarcopenia before treatment [Odds Ratio (OR) =8.923, 95%CI: 1.341-25.321, p=0.002] and SML during treatment (OR=7.803, 95%CI: 1.106-16.189, p=0.001) had a negative impact on the success rate of conversion therapy. Cox multifactorial analysis found that pre-treatment sarcopenia [overall survival (OS): Hazard Ratio (HR) =6.341, 95%CI: 1.269-18.943, p=0.001; progression-free survival (PFS): HR=8.212, 95%CI: 1.569-36.582, p=0.001], newly developed sarcopenia after conversion therapy (OS: HR=3.189, 95%CI: 1.023-9.811, p=0.012; PFS: HR=3.084, 95%CI: 1.042-14.236, p=0.013) and the presence of SML during treatment (OS: HR=10.234, 95%CI: 2.532-54.231, p=0.002; PFS: HR=9.562, 95%CI: 2.341-38.092, p=0.002) were independent risk factor for OS and PFS in GC-CY1 patients. Conclusion Pre-treatment sarcopenia and the presence of SML during treatment are strongly correlated with the immediate and long-term outcomes of GC-CY1 patients and can be used as imaging markers to predict the treatment efficacy and prognosis of patients in clinical practice.
Learning explainable templated graphical models

Templated graphical models (TGMs) encode model structure using rules that capture recurring relationships between multiple random variables. While the rules in TGMs are interpretable, it is not clear how they can be used to generate explanations for the individual predictions of the model. Further, learning these rules from data comes with high computational costs: it typically requires an expensive combinatorial search over the space of rules and repeated optimization over rule weights. In this work, we propose a new structure learning algorithm, Explainable Structured Model Search ( ESMS ) , that learns a templated graphical model and an explanation framework for its predictions. ESMS uses a novel search procedure to efﬁciently search the space of models and discover models that trade-off predictive accuracy and explainabil-ity. We introduce the notion of relational stability and prove that our proposed explanation framework is stable. Further, our proposed piecewise pseudolikelihood (PPLL) objective does not require re-optimizing the rule weights across models during each iteration of the search. In our empirical evaluation on three realworld datasets, we show that our proposed approach not only discovers models that are explainable, but also signiﬁcantly out-performs existing state-of-the-art structure learning approaches.
SOLAR MODELS WITH REVISED ABUNDANCE

We present new solar models in which we use the latest low abundances and further include the effects of rotation, magnetic fields, and extra-mixing processes. We assume that the extra-element mixing can be treated as a diffusion process, with the diffusion coefficient depending mainly on the solar internal configuration of rotation and magnetic fields. We find that such models can well reproduce the observed solar rotation profile in the radiative region. Furthermore, the proposed models can match the seismic constraints better than the standard solar models, also when these include the latest abundances, but neglect the effects of rotation and magnetic fields.
Who did it? Identifying the Most Likely Origins of Events

One probabilistic inference task concerns answering queries for conditional marginal distributions, where a set of events is given. In this paper, we investigate the problem of only knowing that events are observed, from a number of sensors or for individuals, but not which sensors or individuals exhibit those events speciﬁcally. This situation might occur in multi-agent settings, such as in nanosystems, where single agents can no longer be tracked. However, to be able to perform probabilistic inference, those events need to be mapped to random variables, speciﬁcally to those that are most likely to exhibit those events. For the mapping, we show how lifting allows for generating all diﬀerent possibilities to map those events, as we can do it over sets of indistinguishable random variables, leading to a set of queries. Given the mapping that leads to the most likely answer, we can construct evidence to perform probabilistic inference with. Finally, we compare solving the problem on the propositional level, which cannot be done in reasonable time, to our approach, which returns liftable evidence for tractable inference.