Miracle-dz/UniFAR-FTUs huggingface dataset:
A Recommendation System of Sightseeing Places based on User's Behavior of Taking and Editing Photos

With the advancement of digital camera functions that are installed in smartphones, the behavior of taking photos and sharing them with others has become a daily experience for many people. Meanwhile, for travelers, it is important to select sightseeing places and plan an appropriate route to them. There are many studies on recommendation systems for sightseeing places and their routes. However, in these conventional studies, there is a problem that the information of a place that is reluctantly visited by a traveler, such as on a business trip, becomes noise and, as a result, it might cause a recommendation system to suggest a sightseeing place that does not match the interests of the traveler. In addition, in general, it is difficult to form a good recommendation unless a large-scale data set of sightseeing places is collected and each location is actively evaluated by many users. In this study, we propose a method for extracting users’ preferences based on the behavior of taking and editing photos of sightseeing places. The feature of our method is that it calculates the degrees of interest in photos that are actually taken by a user by counting the numbers of pictures and editing processes such as color adjustments and clipping, which are regarded as a part of the users’ behavior of taking and editing photos of sightseeing places. This is done for the purpose of providing recommendations of sightseeing places. By associating the result of the content analysis of the sightseeing place photos with the degrees of a user’s interest in the photos, it would be possible to extract the user’s preferences from the sightseeing place photos. The feasibility of the proposed method is evaluated by several experiments using a prototype of our system.
Design of Multi-Staged Power Electronic Interface for Stand-alone Application Powered by Photovoltaic Module

This paper provides design details of a multi-staged power electronic interface (PEI) which is used to process the low output voltage of photovoltaic module and deliver power to stand-alone load. The PEI comprises of an active-clamp current-fed full-bridge isolated converter and a single-phase voltage source inverter. Active-clamp current-fed full-bridge isolated converter is used to step-up the low voltage of PV module to a desired DC-link voltage. Perturb and Observe maximum power point tracking is used to control the DC-DC converter and fixed-frequency sliding mode control technique is used to control the single-phase inverter because the said control technique reduces the chattering effect of classical sliding mode controller and provides good steady state as well as dynamic voltage regulation during sudden change of load profile.
Super-pixel Semantic Segmentation Method for Urban Complex Scenes

Semantic segmentation in complex scenes is one of the challenges currently facing. In this paper, we aim to improve the semantic segmentation effect in complex urban scenes. By applying the pyramid pooling model, the multi-scale semantic information is aggregated, and more global information is combined to enhance the semantic segmentation effect. To further enhance the semantic segmentation effect, we introduce the super-pixel segmentation algorithm and propose a semantics super-pixel annotation method combining superpixel information. Our approach shows good results on the cityscapes dataset.
On an Improved State Parametrization for Soft Robots With Piecewise Constant Curvature and Its Use in Model Based Control

Piecewise constant curvature models have proven to be an useful tool for describing kinematics and dynamics of soft robots. However, in their three dimensional formulation they suffer from many issues limiting their range of applicability - as discontinuities and singularities - mainly concerning the straight configuration of the robot. In this work we analyze these flaws, and we show that they are not due to the piecewise constant curvature assumption itself, but that instead they are a byproduct of the commonly employed direction/angle of bending parametrization of the state. We therefore consider an alternative state representation which solves all the discussed issues, and we derive a model based controller based on it. Examples in simulation are provided to support and describe the theoretical results. When using the novel parametrization, the system is able to perform more complex tasks, with a strongly reduced computational burden, and without incurring in spikes and discontinuous behaviors.
Automatic Epileptic Seizure Detection via Attention-Based CNN-BiRNN

Epileptic seizure detection with multi-channel electroencephalography (EEG) signals is a commonly used method, but it is tedious and error-prone to manually detect seizures through EEG signals. In this work, we propose an end-to-end deep neural network called attention-based CNN-BiRNN for automatic seizure detection. Attention-based CNN-BiRNN mainly consists of three parts: the multi-scale convolution model, the attention model, and the multi-stream bidirectional recurrent model. Original signals are firstly sent to the multi-scale convolution model to extract multi-scale features. Then the attention model exploits the differences among channels for seizure detection. Afterwards, the robust temporal features are obtained by the multi-stream bidirectional recurrent model, and are further fed into a fully connected layer for classification. Moreover, a channel dropout method is proposed, for the model training stage, to obtain inconspicuous characteristics from all the channels of a certain EEG signal. The results on the dataset of CHB-MIT demonstrate that our approach outperforms state-of-the-art approaches in terms of both sensitivity and specificity. Furthermore, with the channel dropout method, our approach is shown to have a powerful ability of handling EEG signals with missing channels and different channels.
SCENARIO: Discovery of Similar Aspects for Gene Similarity Explanation from Gene Information Network

Gene similarity analysis not only provides information on understanding the biological roles and functions of a gene, but also reveals the relationships among different genes. In this paper, we identify the novel idea of mining similar aspects from gene information network, i.e., given a pair of genes, we want to know, in which aspects (meta paths) the two genes are mostly similar from the perspective of gene information network? We define a similarity metric based on the set of meta paths connecting the query genes in the gene information network, and use the rank of the similarity of a gene pair in a meta path set to measure the similarity significance in the aspect. A minimal set of meta paths where the query gene pair is ranked the best is a similar aspect. Computing the similar aspects of a query gene pair is far from trivial. In this paper, we propose a novel heuristic based-mining method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from seven public gene-related databases, verified that our proposed method is effective, efficient, and useful.
On Stochastic Link and Energy Scheduling for Energy Harvesting Bidirectional Communications

In this paper, a solar-powered bidirectional communication system is studied for a pair of energy harvesting (EH) nodes that intend to communicate with each other over wireless fading channels. The conventional time-division duplex (TDD) transmission is revisited by proposing a stochastic resource scheduling scheme to minimize an average rate outage probability based on a Markov decision process (MDP) design framework. Different from the conventional TDD transmission, the proposed scheme can adjust the link direction and energy expenditure for data transmissions between the two EH nodes, in response to the dynamics of the solar EH, channel fading and battery storage conditions. A downstairs threshold structure is theoretically proved under a special optimal on-off policy, in which two-dimensional thresholds pinpoint the interplay between the transmission actions and the available energy in the batteries of the two nodes. Also the optimal on-off policy at asymptotically high signal-to-noise power ratios (SNRs) is revealed. The outage performance of the proposed stochastic resource scheduling scheme is validated by extensive computer simulations, and it shows that the proposed optimal MDP policy can achieve significant performance gains over the combinations of other compared schemes, including round-robin and battery state-oriented link scheduling schemes, and greedy and conservative energy scheduling schemes.
Learning Systems Engineering Domain Ontologies from Text Documents

Ontologies are playing an increasingly important role in knowledge management, and their functions have been appreciated and exploited by a broad range of communities, including systems engineering researchers and practitioners. Encompassing domain-related vocabularies, concepts, concept hierarchy, along with the properties and relationships, domain ontologies are becoming a promising medium for knowledge sharing and exchange. With the emergence of the semantic web and big data, learning domain ontologies from text is becoming a cutting-edge technique as it is an automatic process of deriving ontological knowledge. Specifically, a set of representative concepts and semantic relations can be rapidly derived from unstructured text documents in a hierarchical structure to model a domain. In this paper, we aim at exploiting the ontology learning approach to extract a domain ontology from systems engineering handbooks. An approach is proposed for learning terms, concepts, taxonomic and non-taxonomic relations. By incorporating both linguistic-based and statistical-based natural language processing techniques, we realized an automatic detection of complex domain terms and conceptualized the systems engineering body of knowledge in a semantic fashion. To evaluate the proposed approach, a case study is conducted, wherein the hybrid approach is applied with template-driven and machine learning algorithms. The result shows that the proposed approach has a robust performance in decreasing ontology development costs. This paper contributes to a good starting point for learning systems engineering ontologies to enhance knowledge acquisition and management.
Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI’s potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.
A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

Recently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications.
Individual Similarity Guided Transfer Modeling for EEG-based Emotion Recognition

Intelligent recognition of electroencephalogram (EEG) signals has been an important means to recognize emotions. Traditional user-independent method, which treats each individual's EEG data as independent and identically distributed (i.i.d.) samples and ignores destruction on i.i. d. condition caused by individual differences, usually has lower generalization performance. Although user-dependent method could alleviate abovementioned problem, it faces difficulty in collection of sufficient training EEG data for each individual. In order to construct user-dependent model merely based on a small amount of training EEG data, we incorporate transfer learning framework and propose a individual similarity guided transfer modeling method for EEG-based emotion recognition. We first measure the similarities between individuals using maximum mean discrepancy (MMD), then utilize pre-existing EEG data of similar individuals to assist construction of user-dependent model for the target individual using an instance-based transfer learning algorithm named TrAdaBoost. We compared this method with traditional user-independent and user-dependent methods on DEAP dataset. Experimental results showed that our method could transfer useful knowledge from other individuals for user-dependent emotion recognition, which achieved classification accuracies of 66.1% and 66.7% on arousal and valence dimentions, respectively.
An Improved Segmentation Algorithm for Detecting Defects on Fruit Surface

Images are the best tool for the information to be visualize and analyze it further. Thus, for this purpose and to extract information and features, image segmentation has been used. The popularity of image segmentation has achieved a remark in the few years. Its application has been increasing day by day. It is a great field of interest for the researchers. It is used in medical, agricultural, engineering, security, industrial and many more fields. Even for the layman it is a boon. Image segmentation refers to the procedure of dividing an image into segments which further process for finding the desired results. Based on the characteristic and properties of an image, an outline is formed for segmentation. In this paper the focus is on finding the defects of apple such as fungal growth, bruising, scab and disease which is harmful for the humans. Different methods have been used for finding the defects by image segmentation such as Gabor Method, Clustering, Edge Detection, Otsu Method and Watershed Method. We have compared different methods and find the best result.
Facial Emotion Recognition using Convolutional Neural Networks

Facial emotion recognition is one of the promptly developing branches within the machine learning domain. In this paper, we are presenting our model based on Convolutional Neural Networks, which is trained on Cohn-Kanade and RAVDESS datasets. The proposed model gets satisfactory results in detecting macro facial emotions on the aforementioned datasets.
Characterizing Mutually Exclusive Driver Mutations in Pan-Cancer

Efforts on cancer genomic study have revealed that mutations in cancer-driving oncogenes tend to mutually exclusive. To understand this tumorigenic mechanism and common cancer features, large-scale genomic study and molecular analyses across many cancer types are essential. We compiled somatic mutation and transcriptome profiles for a pan-cancer dataset that contains tumor tissue samples from 32 cancer types for over 10,000 patients. The genomic and molecular analysis was applied to four pan-cancer subgroups including pan-gastrointestinal, pan-gynecological, pan-kidney and pan-squamous, as well as the entire patient group. We identified seven genes, TP53, PIK3CA, PTEN, PIK3R1, LRP1B, CDH1, and TRRAP exhibited mutually exclusive mutation patterns across all cancer types (adjusted $\mathrm{p} < 0.05$). The mutations in these genes occurred in 55.9% of all cancer patients and over 70% of patients with pan-gastrointestinal, pan-gynecological and pan-squamous. Excepting LRP1B, the other six genes are included in the known cancer pathways. Notably, TP53, PIK3CA, and PIK3R1 are involved in over 20 cancer-related pathways. The co-expression network analysis suggested that LRP1B was associated with multiple genes that showed significant enrichment in Wnt signal pathway (adjusted $\mathrm{p}=0.0023$) and ERBB signal pathway (adjusted $\mathrm{p}=0.0097$). We further constructed a regulatory network to infer novel regulations of LRP1B in cancer. In addition, based on the mutations in the seven genes, we divided the patients into different groups for survival analysis. We found that the survival rate of the patient group with TP53 mutations was significantly shorter than in other groups (log rank $\mathrm{p}= 5.88\mathrm{e}-10$) in pan-gynecological. Our analysis systematically revealed the mutually exclusive mutation patterns of seven cancer genes across 32 cancer types. It helps us to better understanding the relations among different cancer types and pan-cancer subgroups, providing new insights into common mechanisms underlying diverse cancer types.
Enhanced Package Fault Isolation Method Using Time Domain Reflectometry (TDR) Incorporation with Mathematics

Time domain reflectometry (TDR) has gained attention in electrical verification for electronic components over the years. TDR works by sending a series of short pulses through a transmission line under investigation, and measures the reflections that result from a signal travelling through a transmission environment. [1], [2] It is often being used for short or open failures defect localization, to isolate whether the short/ open failure is in the die region or package region. With the enhancement of process technology and increase of layers in the package/ silicon die, and due to limitation of reference medium, the results produced by using TDR can only be used as a rough guide, i.e. based on the TDR results, we can only narrow the open/ short failure to be in the die or package, we could not tell which layer of package the short/open is. This paper demonstrates some successful cases with the incorporation of mathematics together with the use of TDR machine. Open failure can be accurately localized down to specific substrate layer via mathematics calculation. Physical failure analysis further confirmed the accuracy of this method by showing the defect at that specific layer in those cases.
Environmental Impacts of Utility-Scale Battery Storage in California

Battery storage is an emerging solution to increase renewable penetration to the grid by using surplus daytime solar generation to meet evening peak electricity demand, thereby reducing solar curtailment and the need for ramping of natural gas marginal generation. Based on life cycle environmental impact assessment, utility-scale Li-ion battery storage has significantly lower impacts than natural gas power in four out of six environmental impact categories assessed (climate change, fine particulate matter, photochemical ozone formation, and terrestrial acidification). Implementing utility-scale battery storage through 2030 can reduce CO2e emissions from California’s electricity sector by 8 percent (15.5 million tonnes CO2e on a life cycle basis) compared to exclusively using natural gas power to back up solar. Therefore, utility-scale battery storage has the potential to reduce the climate change and air pollution impact of California’s electricity sector, while increasing solar electricity grid penetration through improved grid flexibility.
WisDriver:A WiFi and Smartphone Sensing System for Safely Driving

There has been an increasing interest to monitor driver’s behavior, vehicles and driving data aim to identify risky driving maneuvers and prevent accidents. Current monitor methods, however, most data obtained from the vehicle sensors are sent to the control units through buses such as: CAN, MOST, LIN and FlexRay, but cannot recognize the dangerous actions of the drivers accurately. Past proposals for addressing this limitation rely on leveraging more sensors attached to drivers and hence significantly increase the cost and affect the driver’s comfort. This paper introduces the first fine-grained and device-free driver’s behavior monitoring system that is accurate to 15 dangerous driving actions. Unlike past work, which only depends on sensors, our system exploits fusion data combining smartphone and WiFi signals to accurately identify risky driving operations. We present the design and implementation of WisDriver and discuss the evaluation by experiment from the 20-drivers field trial. Our evaluation results show that WisDriver can achieve high accuracy (92%) in detecting 15 different dangerous driving behaviors. It will have a great significance for assisting the driver and improving traffic safety.
Simulation and Analysis of a Full-Active Electro-Hydrostatic Powered Ankle Prosthesis

This paper presents the design and control architecture of a novel full-active powered ankle prosthesis which uses integrated force-controllable electro-hydrostatic actuator (EHA) to provide both initiative compliance and sufficient positive power output at terminal stance to assist walking in whole gait cycle. A 100W brushless DC motor driving a 0.45 cc/rev bi-directional gear pump operates as the power kernel. Based on finite-state machine (FSM), a hierarchical controller was designed to ensure the control system performance while different control strategies were implemented on each individual gait phase. Three independent force sensing resistor (FSR) mounted under sole, two pressure transducers and a displacement sensor used as ankle rotation sensor provide feedback signal for both state detection and low-level impedance control. A simulation model of the ankle prosthesis system was established with the help of Matlab/Simulink to validate its feasibility. Using pre-sampled biomechanics profile as input variable and matched group, the conceptual ankle prosthesis turns out to be able to restore the dynamic interaction response of a wholesome ankle-foot to a great extent.