Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Networks for Unsupervised Object Co-localization

This paper introduces a novel approach for unsupervised object co-localization using Generative Adversarial Networks (GANs). GAN is a powerful tool that can implicitly learn unknown data distributions in an unsupervised manner. From the observation that GAN discriminator is highly influenced by pixels where objects appear, we analyze the internal layers of discriminator and visualize the activated pixels. Our important finding is that high image diversity of GAN, which is a main goal in GAN research, is ironically disadvantageous for object localization, because such discriminators focus not only on the target object, but also on the various objects, such as background objects. Based on extensive evaluations and experimental studies, we show the image diversity and localization performance have a negative correlation. In addition, our approach achieves meaningful accuracy for unsupervised object co-localization using publicly available benchmark datasets, even comparable to state-of-the-art weakly-supervised approach.
Arduino based temperature and humidity control for condensation on wettability engineered surfaces

Condensation is a ubiquitous phenomenon which depends on several factors, ranging from the degree of subcooling to the relative humidity of the condensing environment. Characterizing condensation in experimental setups, therefore, requires a proper control strategy of operating parameters. Although industrial vapor chambers are available for carrying out condensation experiments under precisely controlled environments, these setups are prohibitively expensive. Here we have developed a low-cost, prototype vapor chamber that is equipped with advanced control systems. Environmental control aspects of a Styrofoam- made condensation chamber are developed in-house for testing condensation on wettability engineered surfaces. Peltier-heat sink arrangement is used to cool the condenser surface under study and the desired relative humidity is maintained within the chamber by means of spraying deionized water with a nebuliser. In order to automate the process, an Arduino Duemilanove board is amalgamated with the setup. Temperature is controlled by an ON/OFF trigger-driven mechanical relay connected to the Arduino environment, which in turn generates an opportunely amplified signal to control the supply voltage of the Peltier element. A K-type thermocouple is interfaced to the Arduino board with the help of MAX31855K thermocouple amplifier for measuring the plate temperature. For humidity and chamber-temperature monitoring, an SHT35D sensor is used. The relative humidity of the chamber is maintained by a mechanical relay-driven spray arrangement. The time-domain plots of humidity and plate temperature response indicates that the temperature fluctuations are within 0.25°C and RH fluctuations are within 0.5% about the set-point. Transient response of the temperature and RH data are monitored by the Serial Monitor of Arduino software, which indicates that the set values of temperature and RH are obtained approximately within 0 to 1000 seconds.
A derivation of moment evolution equations for linear open quantum systems

Given a linear open quantum system which is described by a Lindblad master equation, we detail the calculation of the moment evolution equations from this master equation. We stress that the moment evolution equations are well-known, but their explicit derivation from the master equation cannot be found in the literature to the best of our knowledge, and so we provide this derivation for the interested reader.
Reliable channel allocation for mission oriented low-power wireless networks

Multichannel communication is widely adopted to improve the reliability of low power wireless networks (LPWNs). For mission oriented LPWNs, data transmissions are often required to be delivered before a given deadline, thus making deadline-driven channel allocation an essential task. The existing works on multichannel allocation often fail to establish channel schedules that can meet the deadline requirement, because they often assume the transmission can success within one transmission slot. Besides, the impact of allocation orders for different paths and channels is also overlooked. In this paper, we propose a Retransmission enabled Deadline-driven Channel Allocation scheme for mission oriented LPWNs (ReDCA). Compared to the existing works, 1) we propose a novel prioritized channel allocation scheme for different paths and channels, which jointly considers deadline requirement, link quality and network topology; 2) we enable retransmissions within the same transmission cycle and assign additional channel/slot pairs to lossy links for possible retransmissions. We conduct both simulation and testbed experiments. The results show that ReDCA can significantly improve the packet delivery ratio before deadline without incurring extra energy consumption compared to the existing works.
HTARF: A Hybrid Tourist Attraction Recommendation Framework for Trip Scheduling

As the users' demand for efficient and personalized services grows continuously, online tourism services are becoming more and more popular. However, many tourism products can only recommend existing tourist packages according to users' profile, which can hardly meet their personalized requirements. In this paper, we propose a novel attraction recommendation method, which takes advantages of quantities of information mined from attraction descriptions and tourist packages. The approach is modified on the basis of content matching and association rule mining and only requests a query from a user to recommend. In addition, the method is applied to our Smart Tourism Services Platform (STSP). Using attractions list offered by our method, a trip plan module of STSP will provide a personalized travel schedule. We examine our approach on a real-world dataset, and an online evaluation result shows that our approach is more effective and performs much better than baselines.
Agitated behaviors among elderly people with dementia living in their home in Taiwan

Background/aims Limited research has been conducted on agitated behavior in Taiwan and dementia among community-dwelling elderly. Therefore, this study focused on community elderly with dementia and a factor analysis of an inventory of their agitated behaviors was conducted. Patients and methods Participants (N=221) completed the Chinese Cohen-Mansfield Agitation Inventory, community form. Item analysis and exploratory factor analysis assessed reliability, validity, and the underlying factor structure. Results Five factors were extracted and accounted for 44.53% of the total variance. This study classified agitated behaviors into 5 main subtypes: physically agitated behaviors, destructive behaviors, verbally agitated behaviors, handling things behavior, and aggressive behaviors. Conclusion The results indicate that differences in the agitated behavior of elderly with dementia exist with respect to cultural background and setting. This novel research and its findings serve as a reference for assessing the agitated behaviors of elderly with dementia living in their homes. Applications may exist for other countries with Chinese/Taiwanese populations.
Energy-Efficient Monolithic Three-Dimensional On-Chip Memory Architectures

Memory bandwidth is one of the major performance bottlenecks for chip multiprocessors (CMPs), which continue to integrate an increasing number of cores with the help of Moore's Law. The growing disparity between the CPU clock rate and off-chip memory access speed is known as the Memory Wall. This problem has been actively studied in the past two decades. It is addressed by placing memory closer to the processor, such as stacking the memory directly on top of a CMP, thereby significantly reducing the interconnect latency between them. However, previous 3D-stacked memory architectures use through-silicon via (TSV)-based three-dimensional (3-D) integration, which bonds multiple dies with TSVs that have diameters in the 1-5 $\mu \text{m}$ range. Unlike TSV-based 3-D integration, monolithic 3-D integration builds device tiers sequentially on a single substrate. Different tiers are connected using monolithic inter-tier vias (MIVs), which have a diameter (around 50 $\text{nm}$) that is the same as that of a local via. Main memory typically consists of DRAM, which is volatile and thus requires periodic refresh to maintain the stored data. This increases both the energy consumption and access latency. However, various nonvolatile RAMs (NVRAMs) have emerged as possible universal memory technologies, which promise low power, fast read access, high density, and nonvolatility. In this paper, we present an efficient memory interface for monolithic 3D-stacked RAM (both DRAM and NVRAMs such as resistive RAM and nanotube RAM). It takes advantage of the tremendous bandwidth made available by MIVs to implement an on-chip memory bus in order to hide the latency of large data transfers. We propose a multientry row-based write buffer to increase the buffer hit rate and reduce the number of memory core accesses. We decouple read and write accesses using extra interconnects available through MIVs to increase memory throughput. We also present an adaptive power-down policy to maintain balance between energy efficiency and performance. Simulation results show that the proposed architecture can achieve both high performance and energy efficiency, and is thus attractive for low-power/high-performance computing.
Fair sub-carrier allocation in OFDMA and cognitive radio based IEEE 802.22 WRAN

In both orthogonal frequency division multiple access (OFDMA) and cognitive radio based IEEE 802.22 Wireless Regional Area Networks (WRAN), the problem of assigning the subcarriers to the users in a fairly manner is challenge. This paper investigates this issue and proposes a solution in the form of dynamic programming based sub-optimal algorithm. In this approach, the sub-carriers are initially allocated considering the equal power distribution. Then, a fair allocation of sub-carriers is achieved in such a way that comparatively equal data rate is obtained for all competing users and no user is completely starved; and the minimum desired sum rate is achieved in case a low channel gain user has been assigned the sub-carrier. Upon completion of the sub-channels allocation to all secondary users (SUs), the power allocation for each user is performed by means of the water-filling algorithm. Through simulations, our proposed scheme is shown to outperform a chosen benchmark scheme in terms of throughput and fairness, chosen as performance metrics.
Interpersonal Perceptions of Adverse Peer Experiences in First-Grade Students

Aim: The aim of this study was to identify which adverse peer experiences better predict perceived negative peer relationships among elementary school first graders according to sex. The peer experiences examined were peer rejection, peer victimization, and mutual antipathy; the interpersonal perceptions studied were perceived peer victimization, dyadic meta-perception of peer disliking, and loneliness. Methods: The participants were 809 children (Mage = 6.4 years, SD = 0.32; ngirls = 412, 50.9%) enrolled in 35 first-grade classes from 15 schools in 4 Spanish regions: Valencia, n = 276, 34.1%; Balearic Islands, n = 140, 17.3%; Andalusia, n = 199, 24.6%; Castile-Leon, n = 194, 24%. We calculated sex differences in peer experiences and interpersonal perceptions by means of one-way ANOVA for means differences and Fisher’s r-to-z transformation for correlations differences. We used a multilevel regression analysis (nesting variables: class and region) to determine whether the associations between each peer experiences and each perception were unique. Results: Each adverse peer relationship predicted each interpersonal perception differentially. Peer victimization was a good predictor of the three interpersonal perceptions, and the only predictor of perceived peer victimization. Peer rejection predicted loneliness, whereas mutual antipathies predicted dyadic meta-perception of peer disliking, although more so among girls. A significant effect at region level was found but not at class level. Conclusion: Our findings suggest that research should take into account the different levels of the social peer system when analyzing peer experiences within the classroom context. The study contributes to sensitize teachers about the greater responsiveness of 6-year-old girls to adverse peer experiences, and it could be useful for designing interventions that would help children oppose rejection and empower active bystanders to fight against peer mistreatment.
Cognitive chimera states in human brain networks

Researchers describe human brain organization using a framework that measures patterns of synchrony across cognitive systems. The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.
Blockchain and Internet of Things data provider for smart applications

This paper describes the use of blockchain technology as a data provider in Internet of Things (IoT) applications. Blockchain is a novel technology, which has gained a lot of attention in the last years, mainly due to its use as a backbone for cryptocurrencies. The main purpose of blockchain technology is to provide anonymous transactions between participants, over a peer-to-peer network, using a decentralized distributed ledger. The goal of this novel approach is to eliminate any 3rd party validation and replace the trust of a central authority for transaction validation with cryptographic proof. While most applications of the blockchain revolve around cryptocurrencies, the blockchain can be used in many other fields, such as finance, distributed data storage, health and medicine, automation, etc. By creating an open, decentralized network, the blockchain can be used to develop decentralized applications and enable data access and sharing on a much higher level than the common implementations of client-server architectures which are in use today. In this paper, we will present a proof of concept method for field devices to store and share data using a distributed ledger built on the IOTA tangle, as well as provide means of access to the data which can be used in IoT and decentralized applications.
Blur length estimation in linear motion blurred images using evolutionary algorithms

Motion blur is a common blur type that created due to motion of camera or some objects in scene. The blur kernel, i.e. the function that simulate the motion blur process, depends on the length of blur. Therefore, the estimation of blur length is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e. image deblurring. In this paper, a method is proposed for estimation of the motion blur length using the evolutionary methods. To do this, we take the advantage of the relation between a blur metric, called NIDCT, and the blur length. Then this relation is learned via the evolutionary algorithms. The learned relation can be used to estimate the motion blur length in a blurred image. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.
Workload distribution in heterogeneous SMT assembly lines

Kulicke & Soffa applies Surface Mount Technology to manufacture printed circuit boards (PCBs). They use two different types of machines to outfit these PCBs with components. We study the problem of distributing the workload among these different types of machines in order to minimize overall cycle time. Our approach consists of a simulated annealing algorithm, accompanied by a model of the machines used to estimate the cycle time of a particular solution. Various variants of simulated annealing and different ways to incorporate all operations of the machines in the model are discussed. The best variant of our simulated annealing algorithm yields an average improvement of 7.99% compared to the current system used to balance the workload. Taking the best result encountered in our experiments, we obtain an average improvement of 9.99%. We suggest improvements for future work that we believe to further improve the performance of our algorithms.
Improving energy consumption of NoC based architectures through approximate communication

This paper presents an approximate communication technique aimed at improving the energy efficiency of Network-on-Chip (NoC) based architectures. The basic idea is a selective adjustment, at run-time, of the voltage swing of the links of the NoC to obtain a trade-off between the communication energy consumption and the accuracy of the running application. The proposed technique is applied to a case study, namely, a JPEG encoder resulting in an energy saving up to 70% with a negligible impact on the quality of the compressed image.
TEMPO-Functionalized Nanoporous Au Nanocomposite for the Electrochemical Detection of H2O2

A novel nanocomposite of nanoporous gold nanoparticles (np-AuNPs) functionalized with 2,2,6,6-tetramethyl-1-piperidinyloxy radical (TEMPO) was prepared; assembled carboxyl groups on gold nanoporous nanoparticles surface were combined with TEMPO by the “bridge” of carboxylate-zirconium-carboxylate chemistry. SEM images and UV-Vis spectroscopies of np-AuNPs indicated that a safe, sustainable, and simplified one-step dealloying synthesis approach is successful. The TEMPO-np-AuNPs exhibited a good performance for the electrochemical detection of H2O2 due to its higher number of electrochemical activity sites and surface area of 7.49 m2g−1 for load bigger amount of TEMPO radicals. The TEMPO-functionalized np-AuNPs have a broad pH range and shorter response time for H2O2 catalysis verified by the response of amperometric signal under different pH and time interval. A wide linear range with a detection limit of 7.8 × 10−7 M and a higher sensitivity of 110.403 μA mM−1cm−2 were obtained for detecting H2O2 at optimal conditions.
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning

Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to large datasets. We adopt a deep learning approach to address these limitations, enhancing existing methods with the flexibility and scalability of deep neural networks while retaining the benefits of joint modeling. Using data from the Alzheimer's Disease Neuroimaging Institute, we show improvements in performance and scalability compared to traditional methods.
Authenticated Data Structures for Privacy-Preserving Monero Light Clients

Monero, a leading privacy-oriented cryptocurrency, supports a client/server operating mode that allows lightweight clients to avoid storing the entire blockchain, instead relying on a remote node to provide necessary information about the blockchain. However, a weakness of Monero's current blockchain data structure is that lightweight clients cannot authenticate the responses returned from a remote node. In this paper, we show that malicious responses from a remote node can lead to reduced privacy for the client. We discuss several lightweight mitigations that reduce the attack's effectiveness. To fully eliminate this class of attack, we also show how to augment Monero's blockchain data structure with an additional index that clients can use to authenticate responses from remote nodes. Our proposed solution could be implemented as a hard fork, or alternatively through a "Refereed Delegation" approach without needing any fork. We developed a prototype implementation to demonstrate the feasibility of our proposal.
Research of optimal operation method based on cross and piecewise PCA for industrial boilers

Industrial boiler is a complex dynamic system. Time-varying and multi-mode characteristics could be induced by load variation, which renders the system even more complicated. Traditional method of principal component analysis (PCA) for optimal operation is prone to be problematic because of the improper multi-model descriptions based on single linear model. This will lead to severe false or omitted alarms. In this paper, selecting a parameter in industrial processes and the size of parameter values as the modal identification value. And using the method of piecewise to monitoring and real-time monitoring by cross and repeated. Accuracy of optimal operation was effectively improved, compared to the traditional and conventional piecewise PCA. Finally, this cross and piecewise method PCA for optimal operation was tested with actual industrial hot water boiler, and proved that this method is superior to the traditional and conventional piecewise PCA methods.
A new FastSLAM algorithm based on the unscented particle filter

With the development of the artificial intelligence technology, mobile robots have been widely applied to various fields, such as human-computer interaction and self-cruise. Simultaneous localization and mapping is a key to realize the intelligent navigation. Based on the previous research fruits, this paper listed a construction method using lidar, and introduced the SLAM algorithm of the unscented particle filter to make the constructed maps more precise. In this method, the lidar is first used to scan the indoor environment, and then the collected data is adopted to fit the indoor map. Finally, the accurate information is shown in the experimental section.
A Real-time RGB-D tracker based on KCF

RGB-D tracking has attracted attentions of researchers in computer vision community, there is still a need to boost processing rate. We propose a RGB-D tracker based on color-only KCF which is able to handle the scale changes and occlusions. The proposed tracker fuses the response maps instead of the features to boost both the accuracy and processing rate. Meanwhile, depth data is utilized to form an indicator which is able to detect occlusions and reappearance of target object effectively. The experimental results evaluated on the public Princeton dataset demonstrate that the proposed tracker achieves state-of-the-art performance in accuracy while runs at speed exceeding 47 frames per second.
Research on location intelligent detection based on RFID technology

Location awareness plays an increasingly important role in the research of wireless sensor networks and Internet of Things. Existing methods based on ranging and non-ranging all have their own advantages and disadvantages. Aiming at the problem of location-aware method, combined with RFID technology, taking into account that under specific environment, some factors easily affect the multi-purpose positioning algorithm adversely, this paper presents a RSSI algorithm for referencing the adjacent beacon nodes. And the simulation results show that this method is effective in amending the environmental factors.