Miracle-dz/UniFAR-FTUs huggingface dataset:
How IoT Re-using Threatens Your Sensitive Data: Exploring the User-Data Disposal in Used IoT Devices

With the rapid technology evolution of the Internet of Things (IoT) and increasing user needs, IoT device re-using becomes more and more common nowadays. For instance, more than 300,000 used IoT devices are selling on Craigslist. During IoT re-using, sensitive data such as credentials and biometrics residing in these devices may face the risk of leakage if a user fails properly dispose of the data. Thus, a critical security concern is raised: do (or can) users properly dispose of the sensitive data in used IoT? To the best of our knowledge, it is still an unexplored problem that desires a systematic study.In this paper, we perform the first in-depth investigation on the user-data disposal of used IoT devices. Our investigation integrates multiple research methods to explore the status quo and the root causes of the user-data leakages with used IoT devices. First, we conduct a user study to investigate the user awareness and understanding of data disposal. Then, we conduct a large-scale analysis on 4,749 IoT firmware images to investigate user-data collection. Finally, we conduct a comprehensive empirical evaluation on 33 IoT devices to investigate the effectiveness of existing data disposal methods.Through the systematical investigation, we discover that IoT devices collect more sensitive data than users expect. Specifically, we detect 121,984 sensitive data collections in the tested firmware. Moreover, users usually do not or even cannot properly dispose of the sensitive data. Worse, due to the inherent characteristics of storage chips, 13.2% of the investigated firmware perform "shallow" deletion, which may allow adversaries to obtain sensitive data after data disposal. Given the large-scale IoT re-using, such leakage would cause a broad impact. We have reported our findings to world-leading companies. We hope our findings raise awareness of the failures of user-data disposal with IoT devices and promote the protection of users’ sensitive data in IoT devices.
Optimal localization patterns in bacterial protein synthesis

In $\textit{Escherichia coli}$ bacterium, the molecular compounds involved in protein synthesis, messenger RNAs (mRNAs) and ribosomes, show marked intracellular localization patterns. Yet a quantitative understanding of the physical principles which would allow one to control protein synthesis by designing, bioengineering, and optimizing these localization patterns is still lacking. In this study, we consider a scenario where a synthetic modification of mRNA reaction-diffusion properties allows for controlling the localization and stoichiometry of mRNAs and polysomes$\mathrm{-}$complexes of multiple ribosomes bound to mRNAs. Our analysis demonstrates that protein synthesis can be controlled, e.g., optimally enhanced or inhibited, by leveraging mRNA spatial localization and stoichiometry only, without resorting to alterations of mRNA expression levels. We identify the physical mechanisms that control the protein-synthesis rate, highlighting the importance of colocalization between mRNAs and freely diffusing ribosomes, and the interplay between polysome stoichiometry and excluded-volume effects due to the DNA nucleoid. The genome-wide, quantitative predictions of our work may allow for a direct verification and implementation in cell-biology experiments, where localization patterns and protein-synthesis rates may be monitored by fluorescence microscopy in single cells and populations.
Packing non-equal hyperspheres into a hypersphere of minimal radius

The problem of packing different hyperspheres into a hypersphere of minimal radius is considered. All hypersphere radii are supposed to be variable. Solving the problem is reduced to solving a sequence of mathematical programming problems. A special way of construction of starting pointsis suggested. A smooth transition from one local minimum point to another providing a decrease of the objective value is realized using the jump algorithm is fulfilled. Then, solution results are improved due to reduction of the solution space dimension by step-by-step fixing radii of hyperspheres and rearrangements of hypersphere pairs. Non-linear mathematical programming problems are solved with the IPOPT (Interior Point Optimizer) solver and the concept of active inequalities. A number of numerical results are given.
Turbulent mixing near the ground for the nested grid model

The physical principles that have been used to develop mixed-layer models for a surface layer of variable depth "h", are applied instead directly to the bottom layers of-the Nested Grid Model (NGM). This eliminates the need to predict h explicitly and greatly simplifies the application of these ideas. The principles are reviewed in section 2. Generation of turbulence by shear at the top of the mixed region is ignored in this first formulation. In section 3 the principles are expressed as integrals with respect to the sigma coordinate of the NGM, and the NGM history variables of potential temperature and specific humidity are related to the "buoyancy". Section 4 spells out the details of the numerical method as applied to the discrete sigma layers of the model. The numerical method determines how many layers (K) participate in the mixing for each column. It then produces a uniform (larger) value of buoyancy in layers 1 through (K-i), and determines the appropriate decrease in the buoyancy of the capping layer K. Section 5 shows how the changes in buoyancy derived in section 4 can be re-converted to changes in the NGM variables of potential temperature and specific humidity. An entrainment factor xm that describes the mixing between layers K-1 and K is used to effect this conversion. Section 6 describes how the mixing between layers K and K-1 could be modified when saturation occurs in layer K-1. Tests with the Nested Grid Model in winter of 1985-6 were made in which only mechanical stirring was effective over land, because the model did not contain sensible heat flux over land, nor radiation. The stirring increased the horizontal averaged temperature in the bottom layer of the model over land by about 1 deg/day. Changes as large as 10 deg/day were observed in regions of strong wind and stable air. The Appendix contains test results from computations with a single column using fixed values of surface heat flux and mechanical stirring. The functional dependence of the mixed layer depth with time agrees with well-known results from models that are formulated with an explicit mixed layer depth "h".
IIR Filter-Based Spiking Neural Network

Spiking Neural Networks (SNNs) are closely related to the dynamics of the human brain and use spatiotemporal encoding of information to generate spikes. Implementing various neuronal models in hardware is a popular field of research aiming to mimic biological behavior. The leaky integrate-and-fire model of the neuron is generally chosen for hardware implementation owing to its simplicity and accuracy in modeling the neuron. This paper proposes an infinite impulse response (IIR) filter-based neuron model and describes a backpropagation-based training algorithm for an SNN built using the proposed neurons. The trained network is implemented on an Ultra96-V2 FPGA to validate the design and demonstrate the power and resource efficiency. The implemented design achieves an accuracy of 98.91% on the MNIST dataset and classifies images at 13,021 frames-per-second (FPS) with a 200 MHz clock while consuming < 700 mW of power. The proposed design achieves similar energy efficiency as previous works and $\approx 7.5\times$ higher resource efficiency than previous publications.
RL-based Distributed Parametric Resource Allocation Scheme for Multi-Hop IAB Networks

As the communication demand increases and base stations (BSs) are deployed more densely, the traditional fiber backhaul will increase the cost of network construction and limit the scalability of the network. Especially for mountainous areas and suburban rural areas, using wireless backhaul links instead of the traditional fiber backhaul is an attractive scheme. Compared with traditional integrated access and backhaul (IAB) networks, this paper studies resource allocation in two-hop IAB networks based on layer division multiplexing (LDM) and multi-connection (MC). IAB-donor obtains data from the core network (CN) and transmits it to the corresponding UE groups through the two-hop IAB network. Since the resource allocation problem is intractable by conventional algorithms, a distributed and parameterized framework is proposed. Simulation results show that the proposed two-hop IAB resource allocation algorithm based on LDM and MC can achieve higher capacity than traditional IAB networks.
Architectural Exploration of Neuromorphic Compression based Neural Sensing for Next-Gen Wireless implantable-BMI

This work explores the architectural trade-offs and implications of a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless iBMI. We use quantitative metrics such as root-mean-square error and correlation coefficient between the original and recovered signal to assess the effect of neuromorphic compression on spike shape, and spike detection accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks. We demonstrate that a data compression ratio of $> 50$ can be achieved by selective transmission of event pulses generated in different modes for large electrode arrays with a correlation coefficient of $\approx 0.9$ and a spike detection accuracy of over 90%.
A Use Case of Iterative Logarithmic Floating-Point Multipliers: Accelerating Histogram Stretching on Programmable SoC

Programmable system-on-chip (SoC) platforms often have a hard-core processor and programmable logic for heterogeneous computing. A custom hardware design implemented with programmable logic accelerates specific operations in an application. This paper applies an area-efficient iterative logarithmic floating-point multiplier to histogram stretching on a programmable SoC platform. Specifically, the floating-point multiplier is implemented with programmable logic, and the hard-core processor on the same platform determines the accuracy and latency of the multiplier by changing the number of iterations used for error correction at runtime. To deploy many multiplier cores working in parallel and have the iterative architecture process streaming data, a higher-level pipelined architecture is proposed. The number of multipliers in this architecture is configurable by changing the maximum number of iterations. For $352\times 288$ resolution videos, with 250 frames per video, the proposed histogram stretching design reduces the multiplication time by 78% and the total execution time by 13% compared to a design performing exact floating-point multiplications on a hard-core processor.
An FPGA-Friendly Algorithm for QR Code Detection

QR code is widely used in different applications, and its detection is currently being done by software. However, hardware detection using FPGAs offers real-time processing ability, which makes it attractive for time-critical applications, such as high-precision robotics and augmented reality. In light of this, an FPGA algorithm for QR code detection is proposed in this paper. It operates with a maximum latency of 12.2 ms to detect a QR code when the input image resolution is $640\times 480$, which offers a 85.3% performance boost over the best state-of-the-art software detector according to benchmarks. To the best of the authors' knowledge, this is the first work that explores the use of FPGA in QR code detection.
Could you clean up the Internet with a Pit of Tar? Investigating tarpit feasibility on Internet worms

Botnets often spread through massive Internet-wide scanning, identifying and infecting vulnerable Internet-facing devices to grow their network. Taking down these networks is often hard for law enforcement, and some people have proposed tarpits as a defensive method because it does not require seizing infrastructure or rely on device owners to make sure their devices are well-configured and protected. These tarpits are network services that aim to keep a malware-infected device busy and slow down or eradicate the malicious behavior.This paper identifies a network-based tarpit vulnerability in stateless-scanning malware and develops a tarpitting exploit. We apply this technique against malware based on the Mirai scanning routine to identify whether tarpitting at scale is effective in containing the spread of self-propagating malware. We demonstrate that we can effectively trap thousands of devices even in a single tarpit and that this significantly slows down botnet spreading across the Internet and provide a framework to simulate malware spreading under various network conditions to apriori evaluate the effect of tarpits on a particular malware. We show that the self-propagating malware could be contained with the help of a few thousand tarpits without any measurable adverse impact on compromised routers or Internet Service Providers, and we release our tarpitting solution as an open platform to the community to realize this.
Design and Implementation of Optimized Low Power Pre-Charge Sense Amplifier

In recent time Researchers and VLSI designers are very concerned with low energy consumption because of the exponential improvement in nanoscale technologies. In this proposed research, a stack-based low power reduction technique has been applied in charge sense amplifier utilising the Cadence tool in standard gpdk 45nm technology documentation. Power dissipation of specific sense amplifier topology is the main topic of observations. According to the outcomes of the simulation, the modified charge sense amplifier consumes 123.5094 pW of power. In addition, the proposed application of the stack-based technology in charge sense amplifier results in a reduction of power dissipation by 10-50% when the power supply voltage is 1.8V.
DBREACH: Stealing from Databases Using Compression Side Channels

We introduce new compression side-channel attacks against database storage engines that simultaneously support compression of database pages and encryption at rest. Given only limited, indirect access to an encrypted and compressed database table, our attacks extract arbitrary plaintext with high accuracy. We demonstrate accurate and performant attacks on the InnoDB storage engine variants found in MariaDB and MySQL as well as the WiredTiger storage engine for MongoDB.Our attacks overcome obstacles unique to the database setting that render previous techniques developed to attack TLS ineffective. Unlike the web setting, where the exact length of a compressed and encrypted message can be observed, we make use of only approximate ciphertext size information gleaned from file sizes on disk. We amplify this noisy signal and combine it with new attack heuristics tailored to the database setting to extract secret plaintext. Our attacks can detect whether a random string appears in a table with > 90% accuracy and extract 10-character random strings from encrypted tables with > 95% success.
Mobile Programming with Kuika: A Course Design

Programming education has been included in the curriculum of various university departments of for many years. It is possible to say that programming teaching has a valuable effect especially in Engineering education. With the development of mobile technologies in recent years, programming processes have begun to focus on web and mobile environments. For this reason, the importance of mobile programming teaching in universities has also increased. There are many programming languages and frameworks used to develop mobile programs. One of these platforms, Kuika, is an environment that is developed with the latest software technologies for the purpose of developing mobile applications, and offers rapid application development and new software capabilities with a model-based platform. In this study, it is aimed to design a 14-week course for mobile programming teaching in engineering education. In the designed course content, it is aimed to teach Kuika as a mobile programming environment. An effective course design facilitates the learning process of people and increases the effectiveness and quality of education. For this reason, instructional design, which is a systematic method based on learning and teaching theories that includes analysis of learners, objectives and content, methodology and finally evaluation, is important for content development studies. In this study, a course design for mobile programming education with Kuika was developed. During the course design process, the ADDIE instructional design model was used. This model includes the stages of analysis, design, development, implementation and evaluation.
A 1S1R Model with the Monte Carlo Function for Subthreshold Sensing Operation

3-D cross-point Phase change memory (PCM) is one of the most promising next-generation nonvolatile memory for storage-class memories. , and the subthreshold sensing strategy can effectively improve its limited endurance. For the first time, we propose a one-selector-one-resistor (1S1R) model with Monte Carlo (MC) function. Based on this model, we analyze the device/array/circuit parameters setting requirements for subthreshold sensing. The bit line voltage and the array should be less than 2.96V and 2k, respectively. The sensing time of the 512b array should be longer than 220ns. A large selected cell current $(\mathbf{I}_{\mathbf{c}\mathbf{e}\mathbf{ll}})$ difference is always accompanied by a large leakage current $(\mathbf{I}_\mathbf{leak})$ difference, which severely limits array size. The bias voltage $(\mathbf{V}_{\mathbf{b}\mathbf{i}\mathbf{a}\mathbf{s}})$ adjustment scheme could increase the array size, but also the power consumption. The leakage current compensation scheme should adjust the compensation current dynamically as the variation of $\mathbf{I}_{\text{cell}}$ is large.
Development of a Custom Wrist Wearable for Use in Nursing Homes

The wearable technology market is consistently growing. Many devices are being developed for commercial applications and individual use incorporating sensors that measure vital information such as heart rate, blood oxygen, and temperature. These general market application devices lack optimization for some target markets such as nursing home patients. For this application, optimized hardware and software are required to extract meaningful data to improve outcomes on a wide scale. Patient-maintained devices are not a practical option; thus, the task of charging and setup is placed on Certified Nursing Assistants (CNAs). These wearables are wireless sensors without user displays and controls. Typical sensing devices consist of Inertial Measurement Units (IMUs), temperature sensors, and photoplethysmography (PPG) sensors for heart rate (HR) and SpO2 with wireless communication and a rechargeable battery. The key objectives for specific use in nursing homes are optimal hardware design for measurement and power efficiency and firmware for the measurement scenarios present in the nursing home. An overview of HR, SpO2, IMU, and temperature measurements will be presented with unique considerations for the target population. The battery life of this wrist wearable was estimated to last 79 days.
Design of a Robot for carrying out research on hybrid robot's mobility: case of a mecanum wheel-legged robot

The advancement of technology favors the creation of intelligent machines capable of executing complex tasks. Automated machines that can be teleoperated or autonomous open up a vast pool of possibilities, among which substituting humans in some instances where their safety is endangered, which is one of the significant advantages. Having the ability to substitute humans sets some requirements like object manipulation, mobility, and intelligence. Various mechanisms are employed to provide mobility to the machines considering the mobility aspect. The most common mobile robots are quadruped robots because of their advantages. Over the years, many ideas have been proposed for quadruped robot designs to address specific problems. An improvement of quadruped robots is hybrid wheel-legged robots, on which researches are still ongoing-this paper attempt to address the robots' mobility problem with a mecanum wheel-legged robot design proposal. The paper discusses the design concerning three aspects: mechanical, electronic, and software. The targeted goal is to provide a robot platform that can be used to conduct research on wheel-legged robot mobility.
Stepped Frequency Bluetooth Radar for Wireless Sensing Applications

The use of off-the-shelf wireless hardware for the purpose of ambient sensing is usually referred to as wireless sensing. Typical applications include indoor localization or human behaviour recognition. However, due to several limitations of commodity hardware, precise ranging as with radar systems has not been described so far. The support for Direction Finding in recent versions of the Bluetooth Core Specification now enables fine-grained measurements of wireless signals as it has not been the case with off-the-shelf hardware before. Combining a phase calibration technique with a custom Bluetooth 5.1 sensor device, we present a fully-functional stepped frequency radar system that can be built solely from commercially-available chipsets. This system is capable of resolving both the direction and the path length of a reflected signal. The proposed radar approach was evaluated by estimating the distance to a reflector placed under a certain angle in an experimental setup. Using 80 frequency steps and a total bandwidth of 80 MHz, it was found that the reflection path with a length of up to more than 12m could be estimated with an average accuracy of just 28 cm.
Cardiac Disease Prediction using Artificial Intelligence

Machine Learning and big data analytics are providing promising solutions in the fields of healthcare and patient care. These technologies help in the prediction of disease by accurate interpretation of medical data. This early prediction can help in the detection of early signs of diseases, as well as in the planning of proper treatment. Machine learning approaches can be used to predict chronic diseases by developing classification models based on training data. This work outlines a method for predicting cardiac disease, specifically coronary heart disease, using machine learning. The work will examine the prediction accuracy of a number of algorithms such as Naïve Bayes, Decision Trees, Support Vector Machines, and K-Nearest Neighbors. Two training databases are examined: “Framingham Heart Study” and “Cleveland Heart Disease”. It will be shown that Decision Tree trained on the Cleveland Heart Disease dataset produced the highest accuracy. One of the advantages of the proposed model is its ability to work on real-time datasets.
HN-mPBFT: A Healthy Node based Modified Secure and Fast Consensus Mechanisms for Internet of Vehicles

The Internet of Vehicles (IoV) enables vehicles and Road-Side-Unit to communicate and exchange data, promising to improve transportation safety, efficiency, and convenience. However, to realize the full potential of IoV, secure and fast traffic accident information (TAI) communication among IoV nodes are essential. The Practical Byzantine Fault Tolerance (PBFT)-based blockchain for IoV secures TAI; it is prone to high message overhead and transaction latency, which causes TAI transmission between IoV nodes to be delayed. This paper proposes a new consensus algorithm, named HN-mPBFT (Healthy Node based Modified PBFT), specifically designed for IoV. The HN-mPBFT consensus method reduces message overhead and transaction delay by reducing PBFT consensus phases while achieving consensus among healthy nodes. According to security analysis, HN-mPBFT is secure from Sybil and DDoS attacks. Performance analysis shows that HN-mPBFT reduces transaction latency by 50%.