Miracle-dz/UniFAR-FTUs huggingface dataset:
Litedge: Towards Light-weight Edge Computing for Efficient Wireless Surveillance System

Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss. In this work, we present Litedge, a light-weight edge computing strategy to improve the QoS (i. e., the latency and accuracy) of wireless surveillance systems. Two main modules are designed on edge cameras: (i) the light-weight video compression module for frame filtering, mainly realized by model compression and convolutional acceleration; and (ii) the collaborative validation module for error compensation between the master-slave camera pair. We also implement an enhanced surveillance system prototype from real-time monitoring and pre-processing on edge cameras to the backend data analysis on a server. Experiments based on real-world collected videos show the efficiency of Litedge. It achieves 82% transmission latency reduction with a maximal 0.119s additional processing delay, compared with the full video transmission. Remarkably, 91.28% of redundant frames are successfully filtered out, greatly reducing the transmission burden. Litedge outperforms state-of-the-art light-weight AI models and video compression methods by balancing the QoS balance ratio between accuracy and latency.
Experimental Evaluation of a Data Driven Cooling Optimization Framework for HVAC Control in Commercial Buildings

Heating, ventilation, and air conditioning (HVAC) is known to dominate energy consumption in commercial buildings. Increasing electricity prices worldwide is putting pressure on facility managers to reduce the energy consumption and costs associated with operating their HVAC. In this paper, we evaluate the efficacy of a data driven energy cost optimization framework for reducing HVAC cooling energy consumption via experiments conducted in a large commercial building. The major contributions of the paper are as follows. First, we develop an integer linear program (ILP) based cooling optimization framework to minimize the electricity costs incurred for cooling a building, subject to satisfying the thermal comfort of the occupants. The ILP formulation relies on data readily available from a building management system (BMS), paving the way for widespread adoption of our solution. Second, we describe the system architecture of the framework, which has been hosted on the IBM cloud platform. We outline the motivation behind implementing the solution on the cloud and highlight its key components, including the ability to use secure RESTful APIs alongside the Project Haystack open source IoT initiative to autonomously communicate with a BMS situated anywhere in the world. Third, we have deployed our framework to control the HVAC of a large office building located in northern Australia. The experiments, commenced mid Nov 2018 (and currently ongoing), carried out in two sections of the building, spanning approximately 1500 m2 and housing 100 people, demonstrate that HVAC cooling energy consumption and costs can be reduced by 20%, amounting to substantial savings in annual electricity bills, without impacting the thermal comfort of the occupants. Our data driven solution is low-cost, scalable and uses sensor data commonly logged by all BMSs, providing an effective and practical mechanism for facility managers to reduce the energy consumption of their building HVAC today.
Using Synthetic Traces for Robust Energy System Sizing

Due to the inherent randomness of both solar power generation and residential electrical load, jointly sizing solar panel and storage capacity to meet a given quality-of-service (QoS) constraint is challenging. The challenge is greater when there is limited representative historical data. We therefore propose generating synthetic solar and load traces, corresponding to different realizations of the underlying stochastic processes. Specifically, we compare the effectiveness of three generative models: autoregressive moving-average (ARMA) models, Gaussian mixture models (GMMs), and generative adversarial networks (GANs) -- as well as two direct sampling methods -- for synthetic trace generation. These traces are then used for robust joint sizing by a technique described in recent work. Extensive experiments based on real data show that our approach finds robust sizing with only one year's worth of hourly trace data. Moreover, assuming that solar data are available, given a database of load traces, we demonstrate how to perform robust sizing with access to only twelve data points of load, one for each month of one year.
An Empirical Study of Mobile Network Behavior and Application Performance in the Wild

Monitoring mobile network performance is critical for optimizing the QoE of mobile apps. Until now, few studies have considered the actual network performance that mobile apps experience in a per-app or per-server granularity. In this paper, we analyze a two-year-long dataset collected by a crowdsourcing per-app measurement tool to gain new insights into mobile network behavior and application performance. We observe that only a small portion of WiFi networks can work in high-speed mode, and more than one-third of the observed ISPs still have not deployed 4G networks. For cellular networks, the DNS settings on smartphones can have a significant impact on mobile app network performance. Moreover, we notice that instant messaging (IM) and voice over IP (VoIP) services nowadays are not as performant as Web services, because the traffic using XMPP experiences longer latencies than HTTPS. We propose an automatic performance degradation detection and localization method for finding possible network problems in our huge, imbalanced and sparse dataset. Our evaluation and case studies show that our method is effective and the running time is acceptable.
Toward Reliable Localization by Unequal AoA Tracking

Emerging applications require the location information of clients to enable human-environment interactions or personalized services. With an increasing number of antennas equipped in today's wireless devices, recent research has shown possibility of sub-meter level localization based only on the angle of arrival (AoA) of WiFi sig- nals. While most existing work provides promising median accu- racy, their tail performance however is usually far worse. We ob- serve from measurements that the root cause is due to unequal AoA estimation reliability. In some critical areas, a small variation in the channel state information of signals could introduce an extremely large AoA estimation error. With this observation, we propose UAT (Unequal Angle Tracking), a confidence-aware AoA-based localiza- tion system. We show that unequal reliability of AoA measures can be mathematically quantified, allowing a system to weigh the de- cisions of different APs according to their confidence. Our testbed evaluation shows that UAT's confidence-aware design provides reli- able decimeter level localization for around 90% of locations. UAT is especially effective for risky areas and can reduce their localiza- tion errors by 27.5%, as compared to reliability-oblivious designs.
Are RFID Sensing Systems Ready for the Real World?

Passive Radio Frequency IDentification (RFID) tags are commonly used to provide Radio Frequency (RF) accessible unique identifiers for physical objects due to their low-cost, lack of battery, and small size. Besides this basic function, many novel RFID-based sensing applications have been proposed in the last decade, including localization, gesture sensing, and touch sensing, among others. Nevertheless, none of these systems are in widespread use today. We hypothesize that this is because the accuracy of these systems does not meet application requirements when there are even minor changes in the RF environment or in tag geometry, i.e., changes in a tag's orientation or flexing. This paper uses both theoretical analysis and real-world experiments to test this hypothesis. Our theoretical analysis shows that even a small phase or RSS noise level can result in significant estimation errors. Our extensive real-world experiments find that both the absolute and differential values of phase and RSS readings of an RFID tag's signal can vary as much as by π radians and 10 dB, respectively, due to small changes in the tag's orientation or flexing. Because of these large variations, RFID-based application systems relying on the signal phase or RSS cannot meet application requirements, confirming our hypothesis. In addition to this strong negative result, we also present some insights into designing robust RFID systems that are suitable for use in the real world.
Shaving Peaks by Augmenting the Dependency Graph

Demand Side Management (DSM) is an important building block for future energy systems, since it mitigates the non-dispatchable, fluctuating power generation of renewables. For centralized DSM to be implemented on a large scale, considerable amounts of electrical demands must be scheduled rapidly with high time resolution. To this end, we present the Scheduling With Augmented Graphs (SWAG) heuristic. SWAG uses simple, efficient graph operations on a job dependency graph to optimize schedules with a peak shaving objective. The graph-based approach makes it independent of the time resolution and incorporates job dependencies in a natural way. In a detailed evaluation of the algorithm, SWAG is compared to optimal solutions computed by a mixed-integer program. A comparison of SWAG to another state-of-the-art heuristic on a set of instances based on real-word consumption data demonstrates that SWAG outperforms this competitor, in particular on hard instances.
Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer’s benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).
Understanding I/O Performance of IPFS Storage: A Client's Perspective

IPFS has surged into popularity in recent years. It organizes user data as multiple objects where users can obtain the objects according to their Content IDentifiers (CIDs). As a storage system, it is of great importance to understand its data I/O performance. But existing work still lacks such a comprehensive study. In this work, we deploy an IPFS storage system with geographically-distributed storage nodes on Amazon EC2. We then conduct extensive experiments to evaluate the performance of data I/O operations from a client's perspective. We find that the access patterns of I/O operations (e.g., request size) severely affect the I/O performance, since IPFS typically uses multiple I/O strategies to perform different I/O requests. Moreover, for the read operations, IPFS requires to resolve remote nodes and downloading objects via the internet. Our experimental study reveals that both resolving and downloading operations can become bottlenecks. Our results can shed light to optimizing IPFS in avoiding high-latency I/O operations.
pRide: Private Ride Request for Online Ride Hailing Service with Secure Hardware Enclave

Promising unprecedented convenience, Online Ride Hailing (ORH) service such as Uber and Didi has gained increasing popularity. Different from traditional taxi service, this new on-demand transportation service allows users to request rides from the online service providers at the touch of their fingers. Despite such great convenience, existing ORH systems require the users to expose their locations when requesting rides – a severe privacy issue in the face of untrusted or compromised service providers. In this paper, we propose a private yet efficient ride request scheme, allowing the user to enjoy public ORH service without sacrificing privacy. Unlike previous works, we consider a more practical setting where the information about the drivers and road networks is public. This poses an open challenge to achieve strong security and high efficiency for the secure ORH service. Our main leverage in addressing this problem is hardware-enforced Trusted Execution Environment, in particular Intel SGX enclave. However, the use of secure enclave does not lead to an immediate solution due to the hardware's inherent resource constraint and security limitation. To tackle the limited enclave space, we first design an efficient ride-matching algorithm utilizing hub-based labeling technique, which avoids loading massive road network data into enclave during online processing. To defend against side-channel attacks, we take the next step to make the ride-matching algorithm data-oblivious, by augmenting it with oblivious label access and oblivious distance computation. The proposed solution provides high efficiency of real-time response and strong security guarantee of data-obliviousness. We implement a prototype system of the proposed scheme and thoroughly evaluate it from both theoretical and experimental aspects. The results show that the proposed scheme permits accurate and real-time ride-matching with provable security.
COMPARISON OF TWO METHODS OF MANUAL DIGITIZATION ON ACCURACY AND TIME OF COMPLETION

The purpose of this study was to compare the accuracy and time efficacy of two methods of manual digitization (frame by frame, FXF and points over frame, POF). Two groups (experienced, N=20 and inexperience, N= 20) digitized markers using the FXF and POF method. The markers were digitized automatically using software HUMAN and the coordinates from the automatic digitizing were the true values and the manually digitized values were the measured values. These coordinates were used to compute an average root mean square (RMS) value for each subject. An ANOVA (Group x Method) was used to analyze the difference between digitizing time and RMS values. The POF method was more accurate than the FXF method in all of the landmarks analyzed. The advantage of the POF method lies in the ability of the subjects to continuously track the same point throughout the trial.
Elix: Path-Selective Taint Analysis for Extracting Mobile App Links

App links, also known as mobile deep links, are URIs that point to specific pages in an app. App links are essential to many mobile experiences: Google and Bing use them to link search results directly to relevant pages in an app and apps use them for cross-app navigation. However, app links are hard to discover and, since they must be explicitly built into apps by developers, only exist for a small fraction of apps. To address these two problems, we propose Elix, an automated app link extractor. We define link extraction as a static information flow problem where a link, with its scheme and parameters, is synthesized by analyzing the data flow between subsequent pages in an app. As static analysis is prone to false positives, Elix adopts a novel, path-selective taint analysis that leverages symbolic execution to reason about path constraints and abandon infeasible paths. Elix can automatically and correctly discover links that are exposed by an app, and many others that are not explicitly exposed, thus increasing coverage of both link-enabled apps and link-enabled pages in an app. Elix also simplifies the scheme of extracted links by reducing complex types to a minimal set of primitive types. We have implemented Elix on Android and applied it to 1007 popular Android apps. Elix can extract 80-90% of an app's links, and above 80% of the extracted links are stable.
Impact of IT in the Public Sector Workplace

Digitalization of government and public sector has been not only a fad but a norm for decades. This paper extends the virtual state model developed by Jane Fountain by explicating the detail of impact using the frame developed and presented by Andersen and Danziger. This extended framework includes the outcome classifications in four areas of impacts - capabilities, interaction, orientation, and value/health, in addition to the original conception of organizational forms and institutionalization. Using this extended framework of virtual state institutionalization model, supporting cases are presented exemplifying the applicability of this framework for policy analysis. Stakeholder interviews have been conducted in South Korea and Denmark. Content of interviews along with publically available information and documents collected are analyzed and presented supporting the viability of the framework. In-depth analysis reveals different paths in institutionalizing IT indicating the importance of institutionalization. Despite high degree of overlap in impacts in two cases, some unique characteristic are identified. Also, our qualitative study also reveal two very different feedback processes from organizational form and institutional arrangements leading to shaping of the transformation of government and governance in general with further development of IT. Practical utility of this extended framework with outcome framing are discussed in detail with implications for policy making and execution.
NEIVA: Environment Identification based Video Bitrate Adaption in Cellular Networks

With the popularization of advanced cellular networks, mobile video occupies nearly three quarters of cellular network traffic. While previous adaptive bitrate (ABR) algorithms perform well under broadband network, their performance degrades in cellular networks due to throughput fluctuation. Through real world 4G/LTE network measurement, we find that throughput in cellular networks exhibits high fluctuation. It follows Markov behaviors with different states and different transition probability among states. We further find that the transition probability is stable along time but varies significantly under different environments. This inspires us to design ABR algorithms by improving throughput prediction in cellular networks. We propose NEIVA, a network environment identification based video bitrate adaption method in cellular networks. NEIVA trains a network environment identifier based on throughput data and trains a hidden Markov model (HMM) based throughput predictor for different environments. In online video bitrate selection, NEIVA utilizes the environment identifier to select the model for corresponding environment. Then NEIVA predicts future network performance by combining offline model and online throughput data. We implement NEIVA with MPC and evaluate it in real environment. The evaluation results show that with manually identifying environment, NEIVA improves 20%–25% bandwidth prediction accuracy and 11%–20% QoE improvement over the baseline predictors. With online environment identification, online NEIVA achieves 3.8% and 11.1% average QoE improvement over MPC and HMM, respectively.
Towards Precise Localization of E-Scooters Using Sidewalk Ramps (poster)

Electric scooters (e-scooters) are proliferating rapidly as an inexpensive mode of transportation. GPS equipped smartphones are used to guide riders from points A to B, but GPS is known to have hundreds of meters of error in areas where the line of sight to navigation satellites is fully or partially obscured. To address this problem, we present ScootLoc, which enables precise e-scooter localization, by leveraging physical characteristics of sidewalk ramps, to correct for GPS error. We show that e-scooters equipped with a gyroscope and an accelerometer are able to uniquely identify sidewalk ramps and match the ramp to its physical location, thereby augmenting noisy GPS based navigation systems. We implement ScootLoc and present a preliminary evaluation on a route containing ten ramps, achieving 97% classification accuracy.
Freedom: Fast Recovery Enhanced VR Delivery Over Mobile Networks

In this paper we design and implement Freedom, a mobile VR system that deliver high quality VR content on today's mobile devices using 4G/LTE cellular networks. Compared to existing state-of-the-art, Freedom does not rely on any video frame pre- rendering or viewpoint prediction. We send a latency-adaptive VAM frame that contains pixels around the FoV. This allows the clients to render locally at a high refresh rate of 60 Hz to accommodate and compensate for the user's head movements before the next server update arrives. We demonstrate that Freedom is the first system in the world that can support dynamic and live 8K resolution VR content, while adapting to the real-world latency variations experienced in cellular networks. Compared to streaming the whole 360° panoramic VR content, we show that Freedom achieves up to 80% bandwidth savings. Finally, we provide detailed end to end latency measurements of actual VR systems by running extensive experiments in a private LTE testbed using a Mobile Edge Cloud (MEC).
CASTLE over the Air: Distributed Scheduling for Cellular Data Transmissions

This paper presents a fully distributed scheduling framework called CASTLE (Client-side Adaptive Scheduler That minimizes Load and Energy), which jointly optimizes the spectral efficiency of cellular networks and battery consumption of smart devices. To do so, we focus on scenarios when many smart devices compete for cellular resources in the same base station: spreading out transmissions over time so that only a few devices transmit at once improves both spectral efficiency and battery consumption. To this end, we devise two novel features in CASTLE. First, we explicitly consider inter-cell interference for accurate cellular load estimation. Based on our observations, we exploit the RSRQ (Reference Signal Received Quality) and SINR as features in a machine learning algorithm to accurately estimate the cellular load. Second, we propose a fully distributed scheduling algorithm that coordinates transmissions between clients based on the locally estimated load level at each client. Our formulation for minimizing battery consumption at each device leads to an optimized backoff-based algorithm that fits practical environments. To evaluate these features, we prototype a complete LTE system testbed consisting of mobile devices, eNodeBs, EPC (Evolved Packet Core) and application servers. Our comprehensive experimental results show that CASTLE's load estimation is up to 91% accurate, and that CASTLE achieves higher spectral efficiency with less battery consumption, compared to existing centralized scheduling algorithms as well as a distributed CSMA-like protocol. Furthermore, we develop a light-weight SDK that can expedite the deployment of CASTLE into smart devices and evaluate it in a commercial LTE network.