Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly-supervised US breast tumor characterization and localization with a box convolution network

In US breast tumor diagnosis, machine learning approaches for the malignancy classification and the mass localization have been attracting many researchers to improve the diagnostic sensitivity and specificity while reducing the image interpretation time. Recently, fully-supervised deep learning methods showed their promising results in those tasks. However, the full supervision for the localization requires human efforts and time to annotate ground truth regions. In this paper, we present a weakly-supervised deep network which can localize breast masses in US images from only diagnostic labels (i.e., malignant and benign). Specifically, we exploit a flexible convolution method, which learns the size and offset of the convolution kernel, in the classification network to detect more relevant regions of breast masses against their various size and shape. Experimental results show that the proposed network outperform conventional CNN models, such as VGG-16 and VGG-16 with dilated convolution. The proposed model achieved 89.03% in the binary classification accuracy. To evaluate the localization performance with weakly-supervised manners, we also compared class activation maps for each instance with manual masks of breast mass in terms of the Dice similarity coefficient and localization recall. The experimental results also demonstrate that the deep network with the adjustable convolution layers can clinically relevant features of breast mass and its surrounding area for both benign and malignant cases.
What is best for spoken language understanding: small but task-dependant embeddings or huge but out-of-domain embeddings?

Word embeddings are shown to be a great asset for several Natural Language and Speech Processing tasks. While they are already evaluated on various NLP tasks, their evaluation on spoken or natural language understanding (SLU) is less studied. The goal of this study is two-fold: firstly, it focuses on semantic evaluation of common word embeddings approaches for SLU task; secondly, it investigates the use of two different data sets to train the embeddings: small and task-dependent corpus or huge and out-of-domain corpus. Experiments are carried out on 5 benchmark corpora (ATIS, SNIPS, SNIPS70, M2M, MEDIA), on which a relevance ranking was proposed in the literature. Interestingly, the performance of the embeddings is independent of the difficulty of the corpora. Moreover, the embeddings trained on huge and out-of-domain corpus yields to better results than the ones trained on small and task-dependent corpus.
MINIMIZING E2E DELAY IN V2X OVER CELLULAR NETWORKS: REVIEW AND CHALLENGES

V2X (Vehicle-to-Everything) evolution over cellular networks has been an excelling topic with the advances of high throughput and low latency LTE networks, and the introduction of 5G networks. According to recent researches, obtaining acceptable End-to-End (E2E) delay has been a challenging design process since data travels through several steps from the originating source to the data center and vice versa. V2X latency comprises mainly of three levels: source processing, cellular network, and data center processing. Delay reduction can be achieved on the three levels. However, many conventional solutions have not reached the required and acceptable range of latency to enable V2X communication over cellular networks. In this paper, a general review of challenges to make V2X feasible on cellular network has been discussed, and the proposed solutions in the literature has been introduced. As a conclusion, a various types of aiding tools to design and test V2X tools are given, so that a right path should be taken to consider challenges and improving design metrics.
Quantum chemical exercise linking computational chemistry to general chemistry topics

Abstract Students in a second semester general chemistry course used quantum chemical calculations to investigate and reinforce general chemistry concepts. Students explored the isomers of hypochlorous acid, made predictions of miscibility via dipole moments calculated from ab-initio means, experimentally validated/disqualified their miscibility predictions, and used molecular models to visualize intermolecular attraction forces between various compounds. Student responses in pre-/post-exercise assessments show evidence of student learning. Responses in pre-/post-exercise surveys showed an increase in student understanding of basic concepts and of the importance of quantum mechanics in common general chemistry topics.
The Routing Algorithm for Wireless Sensor Networks with Random Mobile Nodes

Sensor Networks (WSNs) can be defined as a self-configured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location or base-station where the data can be observed and analyzed. Typically a wireless sensor network contains hundreds of thousands of sensor nodes. The sensor nodes can communicate among themselves using radio signals. A wireless sensor node is equipped with sensing and computing devices, radio transceivers and power components. The individual nodes in a wireless sensor network (WSN) are inherently resource constrained: they have limited processing speed, storage capacity, communication bandwidth and limited-battery power. At present time, most of the research on WSNs has concentrated on the design of energy-and computationally efficient algorithms and protocols In order to extend the network life-time, in this paper we are looking into a routing protocol, especially LEACH and LEACH-related protocol. LEACH protocol is a representative routing protocol and improves overall network energy efficiency by allowing all nodes to be selected to the cluster head evenly once in a periodic manner. In LEACH, in case of movement of sensor nodes, there is a problem that the data transmission success rate decreases. In order to overcome LEACH’s nodes movements, LEACH-Mobile protocol
Directional and Explainable Serendipity Recommendation

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users’ trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users’ long-term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short-term demands with the capsule network at first. Then, we propose the serendipity vector to combine long-term preferences with short-term demands and generate directionally serendipitous recommendations with it. Finally, a back-routing scheme is exploited to offer explanations. Extensive experiments on real-world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity-based methods.
An improved Performance of Segmentation Evaluation Based on Feature Extraction using Kinect Sensors

Kinect sensor suggestions new viewpoints for the advance and application of inexpensive, portable and easy-to-use indication less motion capture skill. The goal of this work is to estimate accuracy of the Kinect cameras for full body motion investigation. This study developed an application that of using multiple depth and RGB Kinect sensors for that reasonable system that prepared with multi-depth of sensing was used in this work. Additional application confirmed the Kinect camera validity the evaluated of postural control and different images of biomedical for segmentation skin lesions. In this work, multi-depth assessment and segmentation are conjointly addressed using RGB input image under Median filter with post-processing. Compared with our algorithm outputs an organized-to-use highly suitable for creating 3D Kinect sensors with pre and post-processing steps. The multi-depth extracted image features have higher measurement and accuracy. The results are dealing out the depth and RGB picture with segmentation evaluation depend on feature extraction technique to enhance accuracy.
Ascribing patients a passive role: Conversation analysis of practice nurses’ and patients’ goal setting and action planning talk

Within primary care, nurses are expected to set goals and make action plans with patients. However, little information is available on how this is done. The aim of this study is to gain insight into how nurses set goals and action plans with patients. Data were collected from three practice nurses working in elderly care and five patients in the Netherlands. Practice nurse is the term for the home care registered nurse in the Netherlands. Five home visits for which goal setting and action planning were the main agreed-upon objectives, were video-recorded and transcribed. We found that explicit goal setting was not observable. However, in 16 episodes, action plans were discussed. Action planning was always initiated by the nurse and was oriented toward offering professional help or assistance in order to solve the patient's problems. In response, patients tended to resist the proposed solutions, with the result that action planning was abandoned. By the nurse proposing professional help, patients were positioned as passive rather than active agents. Our results contribute to the understanding of how nurses ’ communicative behavior influences the extent to which patients feel encouraged to actively participate in goal setting/action planning.
Hierarchical energy management scheme for residential communities under grid outage event

The ever-increasing energy demand and extreme weather days lead to more frequent power outage events. This paper proposes a hierarchical energy management scheme for residential communities, aiming to facilitate energy sharing among houses and minimize the impact on the grid outage on the whole community. In the proposed model, the complexity of scheduling residential energy resources of multiple houses is decomposed into a bi-level structure, in which the Home Energy Management System (HEMS) of each house iteratively interacts with the Community Energy Management System (CEMS). In the upper layer, the CEMS receives the information of the planned outage; it then solves a social warfare optimization model to determine: (1) charging/discharging power of a community battery energy storage system, and (2) load re-shaping instructions of each house. In the lower layer, the HEMS of each house performs appliance-level autonomous scheduling to try to satisfy the load re-shaping instructions received from the CEMS. The autonomous scheduling result of individual HEMSs are sent back to the CEMS, and the latter updates the upper-layer result based on the received result. This process continues until the convergence criteria is achieved. Extensive simulations are conducted to validate the proposed solution
Categorising a United States based group insurance carrier’s broker network: A graph theoretic approach

ed example of the group insurance carrier’s distribution model using a broker. In this example a group insurance carrier employs two sales representatives R​1​ and R​2​ . R​1​ sells insurance to employers E​1​ and E​2​ through broker B​1​. R​2​ sells insurance to E​4​ through B​2​. At the macro level considering all business relationships together, the insurance carrier needs to understand how many connections they have established and where the connections are concentrated. At a micro level, relationship management, the insurance carrier needs to understand the drivers behind individual relationships, which encompasses thousands of connections. For example, the participating group insurance carrier in this work employs hundreds of sales representatives who work with tens of thousands of brokers to sell insurance to hundreds of thousands of employers throughout the United States. Both network structure and relationship management have two components. First, for the network structure, companies need to understand their
FABTOTS- An early years community -based obesity prevention programme: A pilot study

FABTOTS, Fit Active Bodies babies and toddlers, was a community-based intervention programme targeting parents of children up to the age of three. It consisted of 2 hour weekly sessions over a six week period aimed at building parental confidence and fostering positive parenting through nutrition, physical activity and lifestyle choices. The programme was delivered by staff at the 18 Children Centres and one teenage pregnancy centre in Sandwell, one of most deprived and ethnically diverse towns in the UK. This paper outlines the development of FABTOTS and an evaluation of the short term impact of the pilot programme on the 173 parents who took part in the first year. Mixed methods of data collection were used; group discussions with the participants took place at the end of each session and facilitators’ observations were noted for process evaluation. Participants completed a questionnaire about their experience of FABTOTS and behavioural changes. After six months, 25 participants took part in a follow up telephone interview to identify the sustainability of the reported behavioural changes. Findings suggested that the programme was feasible and sustainable in the short term but highlighted a number of issues: recruitment, acceptability, retention, duration and content of the programme. Challenges in developing rigorous acceptable tools for data collection in an area with predominantly low literacy rates were noted. Evaluation of its long term sustainability is highly recommended.
A Lightweight Mutual Authentication for Smart Grid Neighborhood Area Network Communications Based on Physically Unclonable Function

Smart grid as the future of power system improves efficiency, flexibility, and reliability by employing the information and communication technologies. However, these new technologies cause some challenges in security issues in the smart grid. In addition to the active and passive attacks in the communication link, such as tampering and eavesdropping, the physical attacks can also be performed on smart meters. In the absence of hardware protection, physical attacks could easily disrupt the major performance of the system. On the other hand, the security schemes must be designed as lightweight as possible according to the constrained resource of the smart meters. To solve the mentioned problems simultaneously, this article proposes a secure and lightweight protocol based on physically unclonable function for communication between the neighborhood gateways and smart meters. The security analysis shows that the proposed scheme not only resists against the all possible cyber and physical attacks, but also provides confidentiality, integrity, and mutual authentication. Moreover, the comparative performance evaluation demonstrates that the proposed scheme is efficient in terms of usage of storage space, communication overhead, and computational cost.
Principal-Agent Problems and Experimentation

Chapter 1 studies contracts which provide incentives for information acquisition that is costly for an agent and unobservable to a principal. We show that each Pareto optimal contract pays a fraction of output, a state-dependent transfer, and a decisiondependent transfer. The fraction of output is increasing in the set of experiments available to the agent, the state-dependent transfer indexes contract payments to differences in output between states, and the decision-dependent transfer punishes decisions that are likely to make liability limits bind. Chapter 2 studies the effect of capacity constraints capping maximum agent effort in principal-agent problems. We show that the form of optimal contracts after introducing a capacity constraint can be obtained from the form of the original optimal contracts by replacing output with a fraction of output and we show that this fraction is increasing in the agent’s capacity. One implication of this result is that if before introducing a capacity constraint all optimal contracts result in the principal receiving a debt, then optimal contracts after introducing a capacity constraint result in the principal receiving a debt and a fraction of equity. Chapter 3 studies the relationship between core and competitive equilibira in economies that consist of a continuum of agents and some large agents. We construct a class of these economies in which the core and competitive allocations do not coincide.
Spectral Estimation of Stationary Time Series: Recent Developments

Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a (cid:12)nite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the de(cid:12)nition of power spectral density functions, and reviews some important and new techniques in nonparametric and parametric spectral estimation. We also consider the problem in the context of multivariate time series.
Halophytophthora elongata, a new marine species from Taiwan

Halophytophthora elongata, a new marine species, was isolated from fallen, yellow and submerged leaves of mangrove (Avicinnia marina) at Shih-tsao, Tainan County, Taiwan. It is distinguished from other species of Halophytophthora primarily by (a) the variable but often exceptionally long sporangia, (b) the production of a solid dehiscence plug which is ejected and becomes evanescent and (c) the formation of a persistent, tubular vesicle through which zoospores are released.
Advances in mesothelioma imaging and implications for surgical management

In malignant pleural mesothelioma (MPM), surgery may be offered for diagnostic or palliative purposes, or in selected patients as part of a multi-modality radical treatment strategy. Imaging results are routinely used to select patients for surgery in these settings. However, the initially subtle morphology and subsequent rind-like growth pattern of the primary tumour combined with complex pleural space geometry and unfamiliar nodal drainage to present major challenges in this regard. In this article, we summarize recent imaging developments that allow clinicians to make better diagnostic, staging and perioperative decisions in MPM. We also discuss the challenges involved in selecting patients for palliative MPM surgery, particularly those with non-expansile (or trapped) lung.
A Modified Artificial Bee Colony Based Test Suite Generation Strategy for Uniform T-Way Testing

Today, t-way testing has been widely known with the ability to reduce test suite size compared to exhaustive testing. At the same time, it has been proven by many researchers to provide maximum bug detection capability. Thus, various t-way strategies were developed since the past three decades. The paper proposed a new test generation strategy, named Modified Artificial Bee Colony T-Way Test Suite Generation (MABCTS). It supports uniform strength t-way testing. Experimentation results are compared with present strategies and produced comparable results. Since t-way testing is considered an NP-hard problem, there are no strategies that can be demanded to produce the best results.
Design of a wideband low-power continuous-time sigma-delta (ΣΔ) analog-to-digital converter (ADC) in 90nm CMOS technology

The growing trend in VLSI systems is to shift more signal processing functionality from analog to digital domain to reduce manufacturing cost and improve reliability. It has resulted in the demand for wideband high-resolution analog-to-digital converters (ADCs). There are many different techniques for doing analog-to-digital conversions. Gversampling ADC based on sigma-delta (EA) modulation is receiving a lot of attention due to its significantly relaxed matching requirements on analog components. Moreover, it does not need a steep toll-off anti-aliasing filter. A E A ADC can be implemented either as a discrete time system or a continuous time one. Nowadays growing interest is focused on the continuous-time EA ADC for its use in the wideband and low-power applications, such as medical imaging, portable ultrasound systems, wireless receivers, and test equipments. A continuous-time E A ADC offers some important advantages over its discrete-time counterpart, including higher sampling frequency, intrinsic anti-alias filtering, much relaxed sampling network requirements, and low-voltage implementation. Especially it has the potential in achieving low power consumption. This dissertation presents a novel fifth-order continuous-time EA ADC which is implemented in a 90nm CMOS technology with single 1.0-V power supply. To speed up design process, an improved direct design method is proposed and used to design the loop filter transfer function. To maximize the in-band gain provided by the loop filter, thus maximizing in-band noise suppression, the excess loop delay must be kept minimum. In this
Design and Implementation of Smart E-Health System Based on Cloud Computing to Monitor the Vital Signs in Real-Time and Measurements Validation

Wearable devices used to monitor patients are classified as part of mobile health, one of the branches of e-health. They are widely used to monitor the vital signs of patients outside of the health institutions environment. The aim of this paper is to design a device that can be worn at low cost and of small size to provide comfort to the patient. The accuracy of this device should be high compared to the benchmark. Also, this study takes into account real-time remote monitoring based on the wireless sensor network and cloud computing where cloud computing is integrated with the Internet of Things to solve the problem of the flow of the huge amount of data. The Wearable Remote Vital Signs Monitoring System (WRVSMS) was manufactured by a printed circuit board, where the ESP01, MAX30100, NTC, OLED, and Li-ion battery were used. The WRVSMS was connected to the cloud server via the HTTP protocol where the data was stored and analysed. The WRVSMS works on basis the combining data of vital signs through which the stakeholders to whom the alert is sent will be assigned based on the patient‘s case where the alert will be sent, which is short message to the stakeholders that are important in rescuing the patient when the patient‘s vital signs are outside threshold. The results showed that the device is 99.37% accurate and statistical analysis was performed to test the error.