Miracle-dz/UniFAR-FTUs huggingface dataset:
Acupuncture for the Treatment of Taxane-Induced Peripheral Neuropathy in Breast Cancer Patients: A Pilot Trial

Objectives Some chemotherapy drugs can damage the nerves and cause peripheral neuropathy which is accompanied by severe neuropathic pain or gait impairment. The purpose of this study was to assess the feasibility and the safety of acupuncture for the treatment of peripheral neuropathy following chemotherapy in Korean breast cancer patients. Design This study was a prospective single-arm observational study using before and after measurements in breast cancer patients presenting with taxane-induced peripheral neuropathy. Settings/Location This study was performed at East-West Medical Center at Daegu Catholic University Hospital, Daegu, South Korea. Interventions Acupuncture was administered 3 times a week for 4 consecutive weeks, for 25 ± 5 minutes at each session. Outcome Measures The primary outcome measure was severity of CIPN using the Neuropathic Pain Symptom Inventory (NPSI) assessed by a self-administered questionnaire and Nerve Conduction Study (NCS) of extremities. The secondary outcome measure was quality of life (QoL) assessed by a self-administered questionnaire using the 36-Item Short From Health Survey (SF-36). Results Acupuncture significantly reduced the severity of CIPN assessed by NPSI score. Four weeks after the last treatment, the symptoms were not aggravated. According to NCS, 42.9% of participants showed improvement of sensory neuropathy. At the end of the treatment, SF-36 scores were significantly increased for variables including physical functioning, role limitations due to physical health problems, social functioning, and general health perceptions compared to those of baseline measurement. Conclusions Acupuncture improved symptoms of CIPN and QoL in Korean women suffering from peripheral neuropathy after chemotherapy using taxane for breast cancer. The effects of acupuncture lasted for at least 1 month after the treatment.
Multi-labeled Relation Extraction with Attentive Capsule Network

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.
An Analysis of the Energy Consumption of LPWA-based IoT Devices

The unique challenges posed by the breadth of Internet of Things applications have resulted in the development of a number of different Low Power Wide Area wireless solutions. These technologies enable scalable long range networks on cheap low power devices, facilitating the development of a ubiquitous Internet of Things. The energy efficiency of these wireless technologies have a significant impact on battery lifetime. In this paper we propose an approach to energy efficiency calculations suited to this new paradigm by focusing on daily throughput. We present a set of deployment cases, develop energy models to represent each of the technologies studied, and use these models to provide a thorough comparison in terms of predicted device lifetime for a range of daily throughputs. This quantitative analysis of network device efficiency vs. daily throughput enables identification of the changeover point between optimal solutions. Our contributions are the integration of different energy models that have not been previously compared into a common framework, and the identification of the energy-efficiency crossover points between these models. This enables the selection of the most efficient wireless solution for specific Internet of Things applications, which is a key factor in optimising device lifetime.
Accurate and Communication-Efficient Detection of Widespread Events

Monitoring agents are deployed in distributed network environments to collect data and report events to a central coordinator. A widespread event is defined as an event that is observed by all monitoring agents simultaneously. Identifying widespread events can assist in detecting problems, such as cyberattacks, network malfunctions, and abnormal usages. However, detecting widespread events is challenging and a significant communication overheadcan be incurred, because every monitoring agent must report any observed event to a central coordinator. In this paper, we study the problem of detecting widespread events accurately. Our proposed schemes can significantly reduce communication overhead and achieve perfect detection accuracy, which was not achieved by any previous studies. The new schemes can also identify generalized widespread events, defined as events observed by more than apredefined number of monitoring agents.
Performance of Small Cell Networks Under Multislope Bounded Pathloss Model: From Sparse to Ultradense Deployment

Network densification is proved to be the most efficient method to expand network capacity over the past few decades. Nevertheless, can network densification always boost network performance? To shed light on this, we explore the pros and cons of network densification in <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-dimension downlink small cell networks by analyzing transmission success probability (SP) and network spatial throughput (ST). Remarkably, different from long-distance propagation in sparse networks, short-distance propagation of distinctive features dominates as well under dense deployment of small cells. On this account, we apply a multislope bounded pathloss model (MBPM) to characterize signal attenuation within long and short propagation distance. Under MBPM, it is surprisingly found that network densification would enhance SP and ST only when small cell base station density <inline-formula><tex-math notation="LaTeX">$\lambda \left(\mathrm{SBS}/\mathrm{m}^{k}\right)$ </tex-math></inline-formula> is smaller than some critical densities. Beyond the critical densities, both SP and ST are proved to exponentially diminish with <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> and even approach zero when <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> is sufficiently large. In other words, ultradense deployment of small cells indeed deteriorates network performance. Besides, the numerically derived critical densities are helpful for the practical deployment of small cell networks.
A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

Abstract Purpose Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN. Methods Data of 100 patients (50 in supine and 50 in prone) with rectal cancer were collected for this study. We designed three sets of experiments for comparison: (a) segmentation using the model trained with data from the same orientation; (b) segmentation using the model trained with data from the opposite orientation; (c) segmentation using the model trained with data from both orientations. We performed fivefold cross‐validation. The performance was evaluated on segmentation of the clinical target volume (CTV), bladder, and femurs with Dice similarity coefficient (DSC) and Hausdorff distance (HD). Results Compared with models trained on cases positioned in the same orientation, the models trained with cases positioned in the opposite orientation performed significantly worse (P < 0.05) on CTV and bladder segmentation, but had comparable accuracy for femurs (P > 0.05). The average DSC values were 0.74 vs 0.84, 0.85 vs 0.88, and 0.91 vs 0.91 for CTV, bladder, and femurs, respectively. The corresponding HD values (mm) were 16.6 vs 14.6, 8.4 vs 8.1, and 6.3 vs 6.3, respectively. The models trained with data from both orientations have comparable accuracy (P > 0.05), with average DSC of 0.84, 0.88, and 0.91 and HD of 14.4, 8.1, and 6.3, respectively. Conclusions Orientation affects the accuracy for CTV and bladder, but has negligible effect on the femurs. The model trained from data combining both orientations performs as well as a model trained with data from the same orientation for all the organs. These observations can offer guidance on the choice of training data for accurate segmentation.
Signal Multiobjective Optimization for Urban Traffic Network

This paper proposes a multiobjective optimization method for signal control design at intersections in urban traffic network. The cell transmission model is employed for macroscopic simulation of the traffic. Additional rules are introduced to model different route choices from origins to destinations. Vehicle turning, merging, and diverging behaviors at intersections are considered. A multiobjective optimization problem (MOP) is formulated considering four measures in network traffic performance, i.e., maximizing system throughputs, minimizing traveling delays, enhancing traffic safety, and avoiding spillovers. The design parameters for an intersection include turning signal type, cycle time, signal offset, and green time in each phase. The resulting high-dimensional MOP is solved with the genetic algorithm (GA). An algorithm is proposed to assist the user to select and implement the optimal designs from the Pareto optimal solution set. A case study in a grid network of nine intersections is carried out to test the optimization algorithm. It is observed that the proposed method is able to achieve the optimal network performance with different traffic demands. The convergence and coefficient selection of GA are discussed. The guidelines for network signal design and operation from the current studies are presented.
Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations: excitatory pyramidal cells, inhibitory interneurons, astrocytes, and microglia. We investigated various pathways leading to hyperexcitability, and our results suggest an important role for astrocytes and microglia in the generation of SEDs of various durations. Analysis of the resultant SEDs revealed two underlying duration distributions with differing properties. Particularly, short and long SEDs are associated with deterministic and random underlying processes, respectively. The mesoscale of this model makes it well-suited for (a) the elucidation of glia-related hypotheses in hyperexcitable conditions, (b) use as a testing platform for neuromodulation purposes, and (c) a hardware implementation for closed-loop neuromodulation.
The Key Success Factors in E-Marketplace Implementation: A Systematic Literature Review

Globally, internet users and mobile phone are increasing significantly all the time. One of the impacts from the growth of it, there is the revolution in a traditional market. Nowadays, people tend to buy something through an online channel or electronic marketplace (e-marketplace). This study aims to obtain literature of key success factors of e-marketplace implementation. It uses Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines to articles published in 2005–2018. A total of 231 articles obtain from considerable databases for instance: IEEE Explore, ACM Digital Library, Science Direct and etc. Furthermore, it validates and examines in order to obtain 37 articles which can be used as the main study. As the result, it finds 11 key success factors for e-marketplace as follows: trust, technical, platform, platform owner, product, service operation, seller, marketing and sales, payment channel, buyer, and environmental.
A Deep Ensemble Framework for Fake News Detection and Classification

Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. The rate of such information is increasing rapidly due to the availability of enormous web information sources including social media feeds, news blogs, online newspapers etc. In this paper, we develop various deep learning models for detecting fake news and classifying them into the pre-defined fine-grained categories. At first, we develop models based on Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations obtained from these two models are fed into a Multi-layer Perceptron Model (MLP) for the final classification. Our experiments on a benchmark dataset show promising results with an overall accuracy of 44.87\%, which outperforms the current state of the art.
An Efficient Booth Multiplier Using Probabilistic Approach

In VLSI Design, low power reduction is achieved by mainly reducing the power. At present, low power designs are predominant in VLSI due to many reasons. The main focus is to reduce the heat in the device. From the basic mathematical equations of power, reduction of power can be done by either decreasing clock, decreasing voltage or decreasing load. The option of reducing power can be done by reducing voltage as clock should be maintained for faster systems. Power reduction can be done at various levels like architecture, logic and transistor. Reduction of power and area can be done by sacrificing one factor to achieve the other. In this work, a booth multiplier is designed based on probabilistic approach. In the truncation part of partial products a probabilistic estimation bias circuit is introduced. Ripple Carry Adder (RCA) was replaced withCarry Look Ahead (CLA) adder in the implementation. Simulations were carried out using Synopsys Design Compiler for saed 90nm technology. 9.7% area reduction and 3.9%power reduction was reported for $\mathbf{L}=8$ and $\mathbf{L}=10$ when compared with existing work.
Return to Invested Capital and the Performance of Mergers and Acquisitions

We evaluate the efficiency of capital deployment for acquiring firms before M&As, and link this ex ante measure to firms’ post-acquisition performance. We construct the efficiency measure as the residual from regressions of firms’ return on assets, net of cost of capital, on invested capital and other firm characteristics for each industry and year. A higher residual thus indicates that a firm generates higher net returns on investment than its industry peers in a given year. Acquirers with higher residuals have higher announcement returns and better long-run operating and stock performance than acquirers with lower residuals. The hedge portfolios based on the measure also generate significant abnormal returns.
Factors Influencing Continuance Usage of Mobile Wallets in Indonesia

This study identifies the factors that influence the continuance usage of mobile wallets and examines continuance usage of mobile wallets that have been registered with Bank Indonesia and issued by non-banks. The respondents were the Indonesian population that have used one of the mobile wallets, such as DOKU, Dompetku, Telkomsel Wallet (T-Cash), UNIK, GO-PAY, XL Tunal, or TrueMoney. The data were then analyzed with a structural equation modeling (SEM). The results of the data processing show that the factors influencing continuance usage of mobile wallets are trust, flow, and satisfaction. Furthermore, quality factors consist of system quality, service quality, and information quality, which affect trust and flow directly. Satisfaction is directly influenced by trust and indirectly by the previously mentioned quality factors. Thus, mobile wallet service providers must continue to improve the system quality, service quality, and information quality of their m-wallet applications. The trust factor also influences flow and satisfaction; thus, directly improving the quality can improve the user experience and user satisfaction.
Medical Spending for the 2001 Anthrax Letter Attacks

ABSTRACT Introduction This paper assesses the total medical costs associated with the US anthrax letter attacks of 2001. This information can be used to inform policies, which may help mitigate the potential economic impacts of similar bioterrorist attacks. Methods Journal publications and news reports were reviewed to establish the number of people who were exposed, were potentially exposed, received prophylactics, and became ill. Where available, cost data from the anthrax letter attacks were used. Where data were unavailable, high, low, and best cost estimates were developed from the broader literature to create a cost model and establish economic impacts. Results Medical spending totaled approximately $177 million. Conclusions The largest expenditures stemmed from self-initiated prophylaxis (worried well): people who sought prophylactic treatment without any indication that they had been exposed to anthrax letters. This highlights an area of focus for mitigating the economic impacts of future disasters. (Disaster Med Public Health Preparedness. 2019;13:539-546)
Training Aspiring Entrepreneurs to Pitch Experienced Investors: Evidence from a Field Experiment in the United States

Accredited investors finance more than 75,000 U.S. startups annually. We explain how training aspiring entrepreneurs to pitch their new business ideas to these investors affects their odds of continued funding discussions. We model accredited investors’ decision to continue investigation as a real option whose value is a function of their experience and the information contained in the entrepreneurs’ pitches. We derive four hypotheses from the model, which we test through a field experiment that randomly assigns pitch training at four elevator pitch competitions. The data support all four hypotheses and are inconsistent with alternative explanations. The online appendix is available at https://doi.org/10.1287/mnsc.2017. 2882. This paper was accepted by Ashish Arora, entrepreneurship and innovation.
Competitive vs. Complementary Effects in Online Social Networks and News Consumption: A Natural Experiment

Using hourly traffic and readership data from a major news website, and taking advantage of a global Facebook outage, we study the relationship between social networks and online news consumption. More specifically, we test if online social networks compete with content providers or instead play a complementary role by promoting and attracting traffic to external websites. During the outage, consistent with a promotional effect, we observe a significant decrease in traffic and unique visitors to the news website lasting beyond the outage hours. We further find that direct referrals from Facebook links grossly underestimated the actual impact of Facebook in generating traffic. Instead, during the outage, we observe a more significant reduction in visitors arriving at the news website from search engines or directly typing the website URL or using bookmarks. Additionally, readership of articles and types of pages viewed also changed during the outage. Although we observe a drop in news consumption during th.
Insight Into Insiders and IT

Insider threats are one of today’s most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. In this work, we propose structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research while using an existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include incidents and datasets, analysis of incidents, simulations, and defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents that is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers’ efforts in the domain of insider threat because it provides (1) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, (2) an overview on publicly available datasets that can be used to test new detection solutions against other works, (3) references of existing case studies and frameworks modeling insiders’ behaviors for the purpose of reviewing defense solutions or extending their coverage, and (4) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
Reasoning over RDF Knowledge Bases using Deep Learning

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular, the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.