Miracle-dz/UniFAR-FTUs huggingface dataset:
Study and Characterization of the Main Tools for Human Activity Recognition using Accelerometer Sensors

Human Activity Recognition (RAH) aims to classify the activities performed by a user collecting data from heterogeneous sensors. The RAH allows the monitoring of user actions, offering services in the area of medical care, in the accompaniment of the elderly, health monitoring, fitness tracking, home and work automation, among others. The RAH can be seen as an Information System composed by three steps: data collection and preprocessing, feature extraction and classification. Despite the abundance of works proposed for this subject, an important issue to be addressed is how to choose the tools and methods to be used in each step of the RAH. This choice is a difficult process, because it involves comparing the results obtained by other works, most of which use private datasets, extract different sets of features, and use different classification algorithms. This paper aims to characterize and compare the main tools, methods and databases for the RAH task. In addition, it aims to provide guidance and guidelines for future research in the area. Experiments were performed in order to identify the main attributes to be used in the classification. It can observed the attributes mean, standard deviation, and variance produce the best models to the classification task.
Store and Visualize EER in Neo4j

NoSQL databases have become very popular in the last few years. Graph databases, as a major NoSQL database type, are used for many problems. In relational databases, conceptual modeling is very important, for which Enhanced Entity-Relationship (EER) model is widely used. Since an EER schema can be considered as a special graph, the idea is given: try to realize it in a graph database. I used Neo4j database management system as a graph database. This paper introduces the Cypher statements and formatting in Neo4j to store and visualize EER.
Continuous User Authentication by Contactless Wireless Sensing

This paper presents BodyPIN, which is a continuous user authentication system by contactless wireless sensing using commodity Wi-Fi. BodyPIN can track the current user’s legal identity throughout a computer system’s execution. In case the authentication fails, the consequent accesses will be denied to protect the system. The recent rich wireless-based user identification designs cannot be applied to BodyPIN directly, because they identify a user’s various activities, rather than the user herself. The enforced to be performed activities can thus interrupt the user’s operations on the system, highly inconvenient and not user-friendly. In this paper, we leverage the bio-electromagnetics domain human model for quantifying the impact of human body on the bypassing Wi-Fi signals and deriving the component that indicates a user’s identity. Then, we extract suitable Wi-Fi signal features to fully represent such an identity component, based on which we fulfill the continuous user authentication design. We implement a BodyPIN prototype by commodity Wi-Fi NICs without any extra or dedicated wireless hardware. We show that BodyPIN achieves promising authentication performances, which is also lightweight and robust under various practical settings.
Agile Formal Engineering Method for Software Productivity and Reliability

In spite of the extensive efforts made by researchers and practitioners over the last fifty years, software engineering is still facing tremendous challenges. Several well-recognized software development paradigms have been studied and applied, but considerable difficulties and problems with software productivity and quality are still serious. In this paper, we present an Agile Formal Engineering Method called Agile-SOFL for achieving high software productivity and reliability, which is the result of our recent five-year KAKENHI research project involving four universities and one national research institute in Japan. We describe the overall idea of the method and discuss every activity involved in the method with examples to ensure the readability of our discussions.
Postmortem transport in fossil and modern shelled cephalopods

The chambered shells of cephalopod mollusks, such as modern Nautilus and fossil ammonoids, have the potential to float after death, which could result in significant postmortem transport of shells away from living habitats. Such transport would call into question these clades’ documented biogeographic distributions and therefore the many (paleo)biological interpretations based on them. It is therefore imperative to better constrain the likelihood and extent of postmortem transport in modern and fossil cephalopods. Here, I combine the results of classic experiments on postmortem buoyancy with datasets on cephalopod shell form to determine that only those shells with relatively high inflation are likely to float for a significant interval after death and therefore potentially experience postmortem transport. Most ammonoid cephalopods have shell forms making postmortem transport unlikely. Data on shell forms and geographic ranges of early Late Cretaceous cephalopod genera demonstrate that even genera with shell forms conducive to postmortem buoyancy do not, in fact, show artificially inflated biogeographic ranges relative to genera with non-buoyant morphologies. Finally, georeferenced locality data for living nautilid specimens and dead drift shells indicate that most species have relatively small geographic ranges and experience limited drift. Nautilus pompilius is the exception, with a broad Indo-Pacific range and drift shells found far from known living populations. Given the similarity of N. pompilius to other nautilids in its morphology and ecology, it seems unlikely that this species would have a significantly different postmortem fate than its close relatives. Rather, it is suggested that drift shells along the east African coast may indicate the existence of modern (or recently extirpated) living populations of nautilus in the western Indian Ocean, which has implications for the conservation of these cephalopods.
A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans

Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3–10 mm), the proposed system can determine the nodule type with 94.68% accuracy.
There's a Hole in that Bucket!: A Large-scale Analysis of Misconfigured S3 Buckets

Cloud storage services are an efficient solution for a variety of use cases, allowing even non-skilled users to benefit from fast, reliable and easy-to-use storage. However, using public cloud services for storage comes with security and privacy concerns. In fact, managing access control at scale is often particularly hard, as the size and complexity rapidly increases, especially when the role of access policies is underestimated, resulting in dangerous misconfigurations. In this paper, we investigate the usage of Amazon S3, one of the most popular cloud storage services, focusing on automatically analyzing and discovering misconfigurations that affect security and privacy. We developed a tool that automatically performs security checks of S3 buckets, without storing nor exposing any sensitive data. This tool is intended for developers, end-users, enterprises, and any other organization that makes extensive use of S3 buckets. We validate our tool by performing the first comprehensive, large-scale analysis of 240,461 buckets, obtaining insights on the most common mistakes in access control policies. The most concerning one is certainly the (unwanted) exposure of storage buckets: These can easily leak sensitive data, such as private keys, credentials and database dumps, or allow attackers to tamper with their resources. To raise awareness on the risks and help users to secure their storage services, we show how attackers could exploit unsecured S3 buckets to deface or deliver malicious content through websites that relies on S3 buckets. In fact, we identify 191 vulnerable websites. Finally, we propose a browser extension that prevents loading resources hosted in unsecured buckets, intended either for end-users, as a mitigation against vulnerable websites, and for developers and software testers, as a way to check for misconfigurations.
Česká adaptace dotazníku CORE-OM: vybrané psychometrické charakteristiky

Cile. Cilem teto studie bylo adaptovat zahranicni dotaznik CORE-OM do ceskeho prostředi a ověřit jeho psychometricke vlastnosti. Soubor a metoda. Testovou baterii zahrnujici CORE-OM, RSES a SCL-90 vyplnilo 375 pacientů hospitalizovaných na ctyřech klinických pracovistich (klinický vzorek). Prvni dva nastroje vyplnilo take 233 respondentů z běžne populace (neklinický vzorek). Analýza. Struktura dotazniku byla zjisťovana pomoci exploracni faktorove analýzy, souběžna validita prostřednictvim korelace s nastroji SCL-90 a RSES a vnitřni konzistence pomoci Cronbachova koeficientu alfa. Rozdil mezi klinickým a neklinickým výběrem byl testovan Mann-Whitneyovým testem a citlivost ke změně pomoci Wilcoxonova znamenkoveho testu. Výsledky. Faktorova struktura dotazniku byla tvořena dvěma faktory, faktorova řeseni pro klinický a neklinický výběr se vsak lisila. Pro klinický výběr dosahovala vnitřni konzistence celkoveho skoru hodnoty alfa = 0,933, korelace celkoveho skoru s GSI SCL-90 nabývala hodnoty rs = 0,80 a s RSES rs = -0,51. Dotaznik dobře rozlisoval mezi klinickým a neklinickým výběrem. U celkoveho skoru dosahoval hranicni skor hodnoty c = 1,349 a index spolehlive změny RCI = 0,445. Citlivost k terapeuticke změně byla adekvatni (r ~ -0,35 až -0,50 pro různa klinicka pracovistě). Limity studie. Data byla ziskana přiležitostným výběrem a nezahrnovala klienty ambulantnich pracovisť ani respondenty s psychotickým onemocněnim. Testovani citlivosti ke změně bylo založeno pouze na sebeposuzovacich metodach.
Coating rice with mulberry leaves rich in deoxynojirimycin ameliorates hyperglycemia and dyslipidemia in C57BL/KsJ db/db mice

BACKGROUND/OBJECTIVES Mulberry leaf (ML) has been shown to have an inhibitory effect on α-glucosidase, and suppresses postprandial hyperglycemia, which may be related to its deoxynojirimycin (DNJ) content. This study was conducted to investigate the hypoglycemic and dyslipidemic effects of rice coated with ML rich in DNJ in a type 2 diabetes mouse model. MATERIALS/METHODS The mice were divided into four groups (n = 8 each): non-diabetic normal control (NC); diabetic control (DM-C), fed with 10% polished rice powder (DM-R); and fed with 10% polished rice powder coated with DNJ-rich ML (DM-DNJR). RESULTS Supplementation with DNJR for six weeks decreased levels of fasting blood glucose, plasma insulin, triglyceride, total cholesterol, and blood glycosylated hemoglobin; conversely, levels of glucagon-like peptide-1 and high-density lipoprotein-cholesterol showed an increase in the same treatment. In addition, weights of mesenteric, epididymal, and total adipose tissues decreased with DNJR supplementation, when compared with diabetic control db/db mice, while maltase, lactase, and sucrase activity in the small intestine were inhibited. The anti-diabetic effects were marginally greater in the DM-DNJR group than in the DM-R group. CONCLUSIONS These results suggest that rice coated with ML rich in DNJ can reduce hyperglycemia and hyperlipidemia in db/db mice, and may prove useful for individuals with diabetes.
Beware of Your Screen: Anonymous Fingerprinting of Device Screens for Off-line Payment Protection

QR-code mobile payment becomes increasingly popular, being offered by major banks (e.g., ICBC) and payment service providers (e.g., PayPal). Unlike mobile payment solutions provided by hardware vendors (e.g., Apple Pay and Samsung Pay), QR code payment schemes do not rely on any hardware support and can therefore be easily deployed. However, the security guarantee of the new scheme is less clear: in the absence of hardware protection, users' digital wallet can be vulnerable to an OS-level adversary, who could steal her secret for generating payment tokens. We find that the physical features of a phone's screen can enhance the security protection of this QR-code payment, serving as a second-factor authentication. Due to manufacturing imperfections, the luminance levels of the pixels on the screen vary across the screen's display area, which can be used to uniquely characterize the screen. This physical fingerprint cannot be stolen even when the OS is fully compromised, since the adversary cannot observe the physical features of the screen. However, screen fingerprinting could also undermine the mobile payer's privacy, as less trusted merchants could use it to track customers and infer their purchase history and preferences. In this paper, we propose a new authentication solution that anonymously fingerprints mobile screens. The approach, called AnonPrint obfuscates a screen, which hides its fingerprint from the merchants. In the meantime, the payment provider, who shares a secret with the payer, is able to reconstruct the mask and authenticate the payer through her obfuscated fingerprint.
Learning 3D Human Dynamics From Video

From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video can be found at https://akanazawa.github. io/human_dynamics/.
Plasma Orexin-A Levels Do Not Undergo Circadian Rhythm in Young Healthy Male Subjects

Orexin-A (OXA) has been originally isolated from a precursor peptide prepro-orexin from the lateral hypothalamus. The orexin system has been attributed to important functions in sleep, arousal and regulation of energy homeostasis. In addition to its high levels in cerebrospinal fluid, OXA is present in blood. However, reported peptide concentrations in plasma vary significantly depending on the method used. Therefore, a specific and sensitive OXA radioimmunoassay (RIA) with solid phase extraction method was developed to determine whether plasma OXA concentrations is affected by acute feeding and/or wake and sleep in young healthy males. Blood samples were collected for 24 h from nine healthy males (aged 20–24 years; BMI 20.7–26.5) every 2 h starting at 11 a.m. Food was served at 12 p.m, 5:30 p.m, 8 p.m and 8 a.m and the sleep time was between 10 p.m and 7 a.m. Plasma samples were analyzed in addition for cortisol and melatonin levels. Blood pressure was monitored through the experimental period. OXA antibody was raised in rabbits. OXA antiserum had only minor cross-reactivity with prepro-orexin precursor (<0.001%), amino-terminal peptide (<0.001%), carboxy-terminal peptide (0.001%), and orexin-B (0.3%) with high sensitivity (0.15 pg/tube). Plasma OXA levels varied between 0.5 and 16 pg/ml in seven subjects and were undetectable (below 0.5 pg/ml) in two subjects. The OXA concentrations did not correlate to feeding nor wake/sleep, whereas cortisol, melatonin and mean arterial blood pressure presented a clear circadian rhythm in each subject. In conclusion, OXA is present in blood in low amounts and its levels do not follow autonomic nor neuroendocrine circadian rhythms. Thereby, studies examining regulatory mechanisms and influences of OXA from blood samples should interpret results very cautiously.
Deep Attention-Guided Hashing

With the rapid growth of multimedia data (e.g., image, audio, and video) on the Web, the learning-based hashing techniques, such as deep supervised hashing, have proven to be very efficient for large-scale multimedia search. The recent successes seen in the learning-based hashing methods are largely due to the success of the deep learning-based hashing methods. However, there are some limitations to the previous learning-based hashing methods (e.g., the learned hash codes containing repetitive and highly correlated information). In this paper, we propose a novel learning-based hashing method, named deep attention-guided hashing (DAgH). DAgH is implemented using two stream frameworks. The core idea is to use the guided hash codes which are generated by the hashing network of the first stream framework (called the first hashing network) to guide the training of the hashing network of the second stream framework (called the second hashing network). Specifically, in the first network, it leverages an attention network and hashing network to generate the attention-guided hash codes from the original images. The loss function we propose contains two components: the semantic loss and the attention loss. The attention loss is used to punish the attention network to obtain the salient region from pairs of images; in the second network, these attention-guided hash codes are used to guide the training of the second hashing network (i.e., these codes are treated as supervised labels to train the second network). By doing this, DAgH can make full use of the most critical information contained in images to guide the second hashing network in order to learn efficient hash codes in a true end-to-end fashion. Results from our experiments demonstrate that DAgH can generate high-quality hash codes and it outperforms the current state-of-the-art methods on three benchmark datasets: CIFAR-10, NUS-WIDE, and ImageNet.
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation

In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several hospitals might wish to share patient medical records for enhanced diagnostics and disease prediction, but may not be able to share data in the clear because of privacy concerns. In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations. Recent works on the problem have mainly focused on passively secure protocols, whose security holds against passive (`semi-honest') parties but may completely break down in the presence of active (`malicious') parties who can deviate from the protocol. Secure neural networks based classification algorithms can be seen as an instantiation of an arithmetic computation over integers. We showcase the efficiency of our protocol by applying it to real-world instances of arithmetized neural network computations, including a network trained to perform collaborative disease prediction.
Noise Matters: Using Sensor and Process Noise Fingerprint to Detect Stealthy Cyber Attacks and Authenticate sensors in CPS

A novel scheme is proposed to authenticate sensors and detect data integrity attacks in a Cyber Physical System (CPS). The proposed technique uses the hardware characteristics of a sensor and physics of a process to create unique patterns (herein termed as fingerprints) for each sensor. The sensor fingerprint is a function of sensor and process noise embedded in sensor measurements. Uniqueness in the noise appears due to manufacturing imperfections of a sensor and due to unique features of a physical process. To create a sensor's fingerprint a system-model based approach is used. A noise-based fingerprint is created during the normal operation of the system. It is shown that under data injection attacks on sensors, noise pattern deviations from the fingerprinted pattern enable the proposed scheme to detect attacks. Experiments are performed on a dataset from a real-world water treatment (SWaT) facility. A class of stealthy attacks is designed against the proposed scheme and extensive security analysis is carried out. Results show that a range of sensors can be uniquely identified with an accuracy as high as 98%. Extensive sensor identification experiments are carried out on a set of sensors in SWaT testbed. The proposed scheme is tested on a variety of attack scenarios from the reference literature which are detected with high accuracy
Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination

Haze limits the visibility of outdoor images, due to the existence of fog, smoke and dust in the atmosphere. Image dehazing methods try to recover haze-free image by removing the effect of haze from a given input image. In this paper, we present an end to end system, which takes a hazy image as its input and returns a dehazed image. The proposed method learns the mapping between a hazy image and its corresponding transmittance map and the environmental illumination, by using a multi-scale Convolutional Neural Network. Although most of the time haze appears grayish in color, its color may vary depending on the color of the environmental illumination. Very few of the existing image dehazing methods have laid stress on its accurate estimation. But the color of the dehazed image and the estimated transmittance depends on the environmental illumination. Our proposed method exploits the relationship between the transmittance values and the environmental illumination as per the haze imaging model and estimates both of them. Qualitative and quantitative evaluations show, the estimates are accurate enough.