A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems

Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information. As a result, claims of prior CRS work do not generalise to real-world settings where conversations take unexpected turns, or where conversational and intent understanding is not perfect. To tackle this challenge, the research community has started to examine holistic CRS, which are trained using conversational data collected from real-world scenarios. Despite their emergence, such holistic approaches are under-explored. We present a comprehensive survey of holistic CRS methods by summarizing the literature in a structured manner. Our survey recognises holistic CRS approaches as having three components: 1) a backbone language model, the optional use of 2) external knowledge, and/or 3) external guidance. We also give a detailed analysis of CRS datasets and evaluation methods in real application scenarios. We offer our insight as to the current challenges of holistic CRS and possible future trends.Comment: Accepted by 5th KaRS Workshop @ ACM RecSys 2023, 8 page

Experiences with GreenGPS – Fuel-Efficient Navigation using Participatory Sensing

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5% over the fastest, 11.2% over the shortest, and 8.4% over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.This research was sponsored in part by IBM Research and NSF Grants CNS 10-59294, CNS 10-40380 and CNS 13-45266.Ope

SmartRoad: A Crowd-Sourced Traffic Regulator Detection and Identification System

In this paper we present SmartRoad, a crowd-sourced sensing system that detects and identifies traffic regulators, traffic lights and stop signs in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from invehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification, SmartRoad addresses various challenges in participatory sensing scenarios, including data unreliability/sparsity, energy constraints, and the general lack of ground truth information. SmartRoad automatically adapts to different application requirements by intelligently choosing the most appropriate information representation and transmission schemes; it also dynamically evolves its core detection and identification engines to effectively take advantage of any external ground truth information or opportunity. With these two characteristics, SmartRoad consistently delivers outstanding performance for its road sensing tasks. We implement SmartRoad on a vehicular smartphone testbed, and deploy on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively and efficiently carry out its detection and identification tasks without consuming excessive communication energy/bandwidth or requiring too much ground truth information.unpublishe

SmartRoad: A Mobile Phone Based Crowd-Sourced Road Sensing System

In this paper we describe SmartRoad, a road sensing system that generates and collects mobile sensory data from vehicle-resident mobile phones, in enabling and supporting crowd-sourced road sensing applications and services, as an alternative to expensive road surveys conducted traditionally. We implement the SmartRoad prototype system, and deploy it on 35 volunteer users’ vehicles for 2 months, collecting about 4,000 miles of driving data.unpublishednot peer reviewe

Rapid monitoring the water extraction process of Radix Paeoniae Alba using near infrared spectroscopy

Near infrared (NIR) spectroscopy has been developed into one of the most important process analytical techniques (PAT) in a wide field of applications. The feasibility of NIR spectroscopy with partial least square regression (PLSR) to monitor the concentration of paeoniflorin, albiflorin, gallic acid, and benzoyl paeoniflorin during the water extraction process of Radix Paeoniae Alba was demonstrated and verified in this work. NIR spectra were collected in transmission mode and pretreated with smoothing and/or derivative, and then quantitative models were built up using PLSR. Interval partial least squares (iPLS) method was used for the selection of spectral variables. Determination coefficients (Rcal2 and Rpred2), root mean squares error of prediction (RMSEP), root mean squares error of calibration (RMSEC), and residual predictive deviation (RPD) were applied to verify the performance of the models, and the corresponding values were 0.9873 and 0.9855, 0.0487mg/mL, 0.0545mg/mL and 8.4 for paeoniflorin; 0.9879, 0.9888, 0.0303mg/mL, 0.0321mg/mL and 9.1 for albiflorin; 0.9696, 0.9644, 0.0140mg/mL, 0.0145mg/mL and 5.1 for gallic acid; 0.9794, 0.9781, 0.00169mg/mL, 0.00171mg/mL and 6.9 for benzoyl paeoniflorin, respectively. The results turned out that this approach was very efficient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba

MINERVA: Information-Centric Programming for Social Sensing

unpublishe

Mechanism of Citri Reticulatae Pericarpium as an Anticancer Agent from the Perspective of Flavonoids: A Review

Citri Reticulatae Pericarpium (CRP), also known as “chenpi”, is the most common qi-regulating drug in traditional Chinese medicine. It is often used to treat cough and indigestion, but in recent years, it has been found to have multi-faceted anti-cancer effects. This article reviews the pharmacology of CRP and the mechanism of the action of flavonoids, the key components of CRP, against cancers including breast cancer, lung cancer, prostate cancer, hepatic carcinoma, gastric cancer, colorectal cancer, esophageal cancer, cervical cancer, bladder cancer and other cancers with a high diagnosis rate. Finally, the specific roles of CRP in important phenotypes such as cell proliferation, apoptosis, autophagy and migration–invasion in cancer were analyzed, and the possible prospects and deficiencies of CRP as an anticancer agent were evaluated

Coinfection of Clonorchis sinensis and hepatitis B virus: clinical liver indices and interaction in hepatic cell models

Abstract Background In China, people infected with hepatitis B virus (HBV) are commonly found in areas with a high prevalence of Clonorchis sinensis, a trematode worm. Published studies have reported that the progression of hepatitis B is affected by coinfection C. sinensis. Methods Clinical data from a total of 72 patients with C. sinensis and HBV (as sole infection or with coinfections) and 29 healthy individuals were analysed. We also incubated the hepatic stellate cell line LX-2 with total proteins from C. sinensis adult worms (CsTPs) and HBV-positive sera. In addition, the human hepatoblastoma cell line HepG2.2.15 was treated with the antiviral drug entecavir (ETV), CsTPs and the anti-C. sinensis drug praziquantel (PZQ). Results Our clinical data indicated that the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TB) and hyaluronic acid (HA) were significantly higher in patients with coinfection than in those infected with HBV only. In cell models, compared with the model in which LX-2 cells were incubated with HBV-positive sera (HBV group), transcripts of alpha-smooth muscle actin and types I and III collagen were significantly elevated in the models of LX-2 cells treated with CsTPs and HBV-positive sera (CsTP+HBV group), while the messenger RNA levels of tumour necrosis factor-α, interleukin (IL)-1β and IL-6 in the CsTP+HBV group were clearly lower. The HBV surface antigen and hepatitis B e-antigen levels were higher in the HepG2.2.15 cells treated with ETV and CsTPs than in those in the ETV group and in the cells administered a mixture of ETV, CsTPs and PZQ. Conclusions These results confirmed that C. sinensis and HBV coinfection could aggravate the progression of liver fibrosis. CsTPs might promote chronic inflammation of the liver in individuals with HBV infection, resulting in the development of hepatic fibrosis. Graphic abstrac

Reliable Social Sensing with Physical Dependencies: Analytic Bounds and Performance Evaluation

Correctness guarantees are at the core of cyber-physical computing research. While prior research addressed correctness of timing behavior and correctness of program logic, this paper tackles the emerging topic of assessing correctness of input data. This topic is motivated by the desire to crowd-source sensing tasks, an act we henceforth call social sensing, in applications with humans in the loop. A key challenge in social sensing is that the reliability of sources is generally unknown, which makes it difficult to assess the correctness of collected observations. To address this challenge, we adopt a cyber-physical approach, where assessment of correctness of individual observations is aided by knowledge of physical dependencies between sources and observed variables to compensate for the lack of information on source reliability. We cast the problem as one of Maximum Likelihood Estimation (MLE). The goal is to jointly estimate both (i) the latent physical state of the observed environment, and (ii) the inferred reliability of individual sources such that they are maximally consistent with both provenance information (who claimed what) and physical dependencies. We also derive new analytic bounds that allow the social sensing applications to accurately quantify the estimation error of source reliability for given confidence levels. We evaluate the framework through both a real-world social sensing application and extensive simulation studies. The results demonstrate significant performance gains in estimation accuracy of the new algorithms and verify the correctness of the analytic bounds we derived.unpublishedis peer reviewedU of I OnlyStill in submission for revie