Miracle-dz/UniFAR-FTUs huggingface dataset:
Networking Support for Bidirectional Cross-Technology Communication

Recent research on physical layer cross technology communication (PHY-CTC) brings a timely answer for escalated wireless coexistence and open spectrum movement. PHY-CTC achieves direct communication among heterogeneous wireless technologies (e.g.,WiFi, Bluetooth, and ZigBee) in physical layer and thus brings communication support for coexistence service such as spectrum management and IoT device control. To put PHY-CTC into service, however, there still exists a gap due to its transmission failure and asymmetric link (i.e., one-way PHY-CTC) issues. In this paper, we propose NetCTC – the first networking support design for PHY-CTC to establish feedbacks (e.g., ACKs) and thus meet the upper layer networking requirements in heterogeneous unicast, multicast and broadcast. The core design of NetCTC is a real-time interaction mechanism which achieves reliable, transmission efficient and concurrent interactive communication among heterogeneous devices. We implement and evaluate NetCTC on commodity devices and the USRP-N210 platform. Our extensive evaluation demonstrates that NetCTC achieves reliable bidirectional cross technology communication under a full range of wireless configurations including stationary, mobile and duty-cycled settings.
Mixed-Signal Physically Unclonable Function With CMOS Capacitive Cells

An electronic physically unclonable function usually includes an on-chip error-correcting code unit, which is vulnerable to security attacks and adds area, power, and data processing time overheads. This paper proposes a mixed-signal physically unclonable function circuit for authentication purposes, which we call the enhanced capacitive physically unclonable function. It divides the input challenge word over multiple computational groups to decrease processing time, increase security, and eliminate the need for error-correcting code units. Most of the challenge bits control capacitive networks grouped into several capacitive cells, while some are analogized through two digital-to-analog converters. One digital-to-analog converter controls the discharge loads of the capacitive cells; the other controls the reference voltage of comparator units. Each comparator controls a counter that digitizes the discharge time into a response chunk. Most of these counters operate at high frequencies for more precise time-to-digital conversion and are overflown to act as roulettes to promote unpredictability. One counter is not overflown to generate a reference response chunk to support error handling. The design allows for more intrinsic variations throughout the fabrication process, leading to unique response chunks. It applies an expanding challenge-response pair approach, generating a 128-bit response word for a 64-bit challenge word. The capacitive nature of the design supports various security features. Simulating the circuit using 45 nm complementary metal-oxide semiconductor technology resulted in an average power of 921.67 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, a layout area of 22,470 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}^{2}$ </tex-math></inline-formula>, and an average data processing time of 118 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>.
Smartphone Modulated Colorimetric Reader with Color Subtraction

Color analysis has been essential for the interpretation of optical readouts, e.g. colorimetry, fluorescence, spectroscopy, and scanometry. However, existing colorimetric readers can hardly eliminate the color interference of colored solutions, e.g., interpreting pH test strips to assess the pH value of red wine. This paper introduces a smartphone modulated colorimetric reader that is compatible with most smartphone models and a novel color subtraction algorithm that eliminates color interferences due to colored solutions. Experiments were conducted to validate the effectiveness of the developed reader and algorithm on evaluating pH test strips produced from transparent and colored solutions using multiple smartphone models. Applicability of the developed reader was demonstrated through its interpretation of pH test strips measuring pH values of colored and non-transparent food samples including red wine and milk.
Optimal maintenance and use of uncertainty in visual working memory

Unlike in perceptual tasks, it is unclear whether humans near-optimally use uncertainty information in their visual working memory (VWM) decisions. Some circumstantial evidence is available: people can explicitly report their uncertainty after a delay and can near-optimally integrate knowledge of uncertainty with working memories. However, it is unclear whether people can do the conjunction: accurately store uncertainty information in VWM and use it in a subsequent decision. To investigate this, we collected data in two orientation change detection tasks. One task did not require the maintenance of uncertainty information and the other did. We factorially evaluate Bayesian observer models with different assumptions about the memory noise generating process, the observer’s assumption of this process, and the observer’s decision rule. For both experiments, the model that best fits human data assumes that memory precision varies as a function of stimulus reliability and other internal fluctuations, observers know their memory uncertainty on an individual-item basis, and observers optimally integrate information across items when making their decision. These results provide evidence that participants are able to maintain uncertainty information across a delay, and use it optimally in subsequent decisions.
Identification of previous hamstring muscle injury by isokinetic concentric and eccentric torque measurement in elite soccer player

Twenty-eight elite soccer players (23 years ± 3.3; 74 kg ± 7.5; 178 cm ± 6.5) were tested to find out whether isokinetic parameters could identify sportsmen who had previously suffered from hamstring muscle injury. Eleven players with 15 previous moderate or major hamstring muscle injuries in the preceding 2 years were compared with 17 controls. Strength was measured using a Cybex Norm isokinetic dynamometer on a concentric and eccentric contraction at the angular speed of 60°/s. An ipsilateral concentric Hamstring-to-Quadriceps ratio lower than 0.6 and an eccentric hamstring asymmetry of more than 10% did not enable identification of soccer players with previous hamstring muscle injury. On the other hand, concentric hamstring torque asymmetry of more than 10% identified the uninjured soccer players (probability of 90.1%). A dynamic control ratio (eccentric hamstring-to-concentric quadriceps) lower than 0.6 provided the best predictor of previous hamstring muscle injury (probability of 77.5%). In conclusion, concentric and eccentric isokinetic evaluation of hamstring muscle at the speed of 60°/s renders the search for previous hamstring muscle injury possible by revealing muscle torque asymmetry, which persists despite the resumption of competitive soccer.
Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization

The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the #MeToo and #TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected >10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers’ characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.
HideNoSeek: Camouflaging Malicious JavaScript in Benign ASTs

In the malware field, learning-based systems have become popular to detect new malicious variants. Nevertheless, attackers with specific and internal knowledge of a target system may be able to produce input samples which are misclassified. In practice, the assumption of strong attackers is not realistic as it implies access to insider information. We instead propose HideNoSeek, a novel and generic camouflage attack, which evades the entire class of detectors based on syntactic features, without needing any information about the system it is trying to evade. Our attack consists of changing the constructs of malicious JavaScript samples to reproduce a benign syntax. For this purpose, we automatically rewrite the Abstract Syntax Trees (ASTs) of malicious JavaScript inputs into existing benign ones. In particular, HideNoSeek uses malicious seeds and searches for isomorphic subgraphs between the seeds and traditional benign scripts. Specifically, it replaces benign sub-ASTs by their malicious equivalents (same syntactic structure) and adjusts the benign data dependencies--without changing the AST--so that the malicious semantics is kept. In practice, we leveraged 23 malicious seeds to generate 91,020 malicious scripts, which perfectly reproduce ASTs of Alexa top 10,000 web pages. Also, we can produce on average 14 different malicious samples with the same AST as each Alexa top 10. Overall, a standard trained classifier has 99.98% false negatives with HideNoSeek inputs, while a classifier trained on such samples has over 88.74% false positives, rendering the targeted static detectors unreliable.
The natural history of the cerebral blood flow regulation after acute ischaemic stroke

Acute stroke is known to lead to impairment of cerebral blood flow (CBF) regulation, but its natural history and techniques for its comprehensive assessment have not been previously reported. Noninvasive measurements of blood pressure (BP), end-tidal CO2 and CBF velocity (CBFv, using transcranial Doppler ultrasound) during active, passive and motor imagery paradigms were performed in healthy older controls (n=27) and in stroke patients (n=27). Two innovative analytical techniques were firstly used in stroke studies: subcomponent analysis and multivariate dynamic modeling. In controls, significant increase in CBFv during the paradigms with no significant difference in the response amplitude was found. A reproducibility study, not previously reported, was also performed. Following acute stroke, subcomponents analysis revealed a decrease of CBFv response to the passive paradigm and impairment of the myogenic pathways of CBF regulation. Multivariate dynamic modeling removed the influences of BP and PaCO2 showing that the reduced CBFv response to neural activation was directly related and better expressed by the contribution of the stimulation component, instead of the CBFv raw change. The contribution of motor imagery in the CBFv increase was lower compared to the other two paradigms. Impairment of cerebrovascular reactivity to CO2 was also detected by the model, without the need of performing specific tests for this purpose. The natural history of CBF regulation revealed a deterioration of control mechanisms in both the acute ( 72h) and subacute (2 weeks) phases, reaching the controls’ levels in the chronic phases (1 and 3 months). It has been demonstrated in this thesis that CBF regulation changes significantly over time after stroke (particularly in the first weeks after onset), having potential impact not only immediately post ictus but also during the subsequent rehabilitation phase.
Original Equipment Manufacturer (OEM) Site Selection of Traditional Medicine Companies in Indonesia using Analytic Hierarchy Process (AHP) Method

OEM Selection is a unique selection methods, it’s a combination of site selection and supplier selection. This study aims to determine the best location of the Original Equipment Manufacturer (OEM) in traditional medicine companies in Indonesia using Analytic Hierarchy Process (AHP) method. The results showed that the factors that have the highest weight in determining OEM locations are availability of raw material, transportation access and labor cost. The combination of these factors indicates that the best location for OEM development is Central Java province. This study is useful to provide insight to traditional medicine companies in Indonesia regarding the importance of determining the location of OEMs to build competitive strategies in the face of competition from competitors.
Economic Operation of Unit Commitment Using Multiverse Optimization Algorithm

Security Constraint Unit commitment (SCUC) is one of the challenging economic problem of the power utilities due to the ON and OFF status of the units. Indeed, in SCUC we should determine the status of the units for the day-ahead horizon. SCUC is a mixed-integer linear problem (MILP), which is hard to solve. Hence, in this paper, a new evolutionary algorithm, known as the multiverse optimization algorithm is developed to solve the problem.
Learner and User Experience Research

Humans’ responses to experiences are central to the learning process. User and learner experience design (UX and LX, respectively) focuses on designing learning in a human-centered manner so as to provide learners with experiences that intentionally propel them towards learning goals. The vision of this edited open access book is to create an introductory resource for a variety of audiences–from students who are beginning their program of study to faculty who teach in HCI, usability, and related programs– focusing specifically on the theory and practice of UX and LX in the field of learning design and technology (LDT).
Visual Features for Invariant Coding by Face Selective Neurons

Complex visual objects like faces are encoded through the primate ventral visual pathway in a network of cortical patches. Neurons across the nodes display specialized tuning to faces and increasing tolerance to image transformations. However, the exact features that neurons in different nodes use to attain selectivity and tolerance remain elusive. In this paper, we first quantified the representational content of neural populations in two fMRI-identified face patches to four attributes: viewpoint, identity, expression and mirror-symmetry. We found that neural population activity is driven by compartamentalized time-varying image attributes, and that multiple variables are represented in the anterior but not the posterior face patch. We then derived maps of feature selectivity by sampling images with Gaussian apertures that linked the evoked neuronal activity and the informative image features (IFs). This allowed us to evaluate the relationship between IFs and global stimulus tuning. We report that the set of discovered IFs explain the patterns of dissimilarity for the global viewpoint tuning. The alphabet of IFs also preserves local image preferences across changes in size and position. Crucially, the derived features are interpretable, and tend to cluster on consistent image regions, providing information about the global tuning that organize the neurons into functional groups.
Estimation of Multiple Atmospheric Pollutants Through Image Analysis

Multiple atmospheric pollutants, such as PM2.5, PM10, and NO2, degrades air quality in many parts of the world. Fine-grained air pollution data can help combat the problem, but conventional monitoring stations are too expensive to support high spatial resolution; image-based estimates have the potential to improve spatial coverage. We estimate pollutant concentrations from images using the position-and color-dependent properties of scattering and absorption. We are the first to use images to estimate pollutant concentrations in systems with multiple pollutants. We achieve this by considering the differences in scattering and absorption spectra between different pollutants. Our system improves the accuracy of PM2.5, PM10, and NO2 estimation by 22% for single-scene images in Beijing and Shanghai compared to the best existing image-based techniques.
EMOTION ANALYSIS OF TWEETS USING DEEP LEARNING TECHNIQUES

Microblogging is one of the most prominent forms of communication among internet users today. These services offer users a tool to share opinions on a plethora of issues and aspects of their lives, and hence, they are rich data sources for textual analysis, and offer vast potential to gain insight into the way people communicate. We examine the task of emotion analysis of twitter data and design a neural network architecture for the task of classifying each tweet into one of five emotion categories: joy, sadness, anger, laughter and fear. We also show a method to automatically collect and tag a corpus of data for emotion analysis. Our experiments showed that our models outperform current state of the art techniques for the proposed problem.
Robust data profiling and schema design for incomplete relational databases

After more than 40 years in use, relational database systems provide a mature and sophisticated technology for effective and efficient data management. For example, SQL is the number one skill that data scientists possess. Relational database systems model a given domain of interest. One of the core problems that still remain is the correct handling of incomplete information. For example, we still do not know how database schemata for data with missing values should be designed. Many approaches to this problem exist, with most depending on a specific interpretation of missing values. Already in classical applications such as accounting, but even more so in modern day applications such as data integration, a reasonable approach should not be dependent on a specific interpretation of missing values. In this thesis, a new approach is established for the design of incomplete relational databases. The new approach is robust under different interpretations of missing values, and only depends on the complete fragments of an incomplete database. The thesis introduces the classes of embedded uniqueness constraints and embedded functional dependencies that enable users to declare completeness and integrity requirements of a given application within a single framework. Embedded functional dependencies are sources of redundant data value occurrences, while embedded uniqueness constraints avoid redundant data value occurrences. This makes them particularly interesting for the design of databases, as data redundancy largely determines how efficiently queries and updates can be processed. We study several computational problems related to the combined class, including axiomatic and algorithmic solutions to their implication problem, structural and computational aspects of Armstrong relations, their discovery problem, and the development of normal forms and decompositions that subsume the classical cases of Boyce-Codd and Third normal forms, respectively, as special cases. Many of our solutions are implemented as prototypes, and theoretical investigations of the computational complexity are complemented by empirical investigations in terms of performance studies on benchmark datasets. As a side result, we also develop a new algorithm that discovers all classical functional dependencies that hold on a given incomplete relation and show that it outperforms stateof-the-art for efficiency, row-, and column-scalability. Overall, the thesis offers a completeness-tailored approach to the design of relational databases.
One-Factor Cancellable Palmprint Recognition Scheme Based on OIOM and Minimum Signature Hash

The traditional Cancellable biometrics scheme needs another key or token to generate the revocable template, which usually suffers from the token-stolen problem. To solve this problem, a one-factor cancellable palmprint biometric recognition scheme based on Orthogonal Index of Maximum (OIOM) hash and Minimum Signature Hash (MSH) is proposed to generate pseudonymous identifier. Firstly, to improve the efficiency and effectiveness, a parallel structure is designed to obtain Orthogonal Gaussian random projection(GRP)matrices, which is employed to generate the OIOM hash code. Secondly, a random binary string is XOR the binary OIOM hash code to construct the helper data and it is stored in the database. Meanwhile, this string is hashed by MSH to get the final pseudonymous identifier. Lastly, during matching stage, based on helper data and a palmprint images, another pseudonymous identifier is generated to recognition. This implies that, just one factor, a palmprint of user, is needed in the matching stage, and therefore the privacy of user is preserved. To evaluate the proposed scheme, PolyU database and touchless TJU database are used in experiments. The noninvertible, renewability, unlinkability and several security attacks of the proposed scheme are analyzed. The experiment results and analysis show that the proposed scheme has strong security on the basis of maintaining palmprint recognition performance.