With the rapid advancement of Industry 5.0, the impact of worker resource constraints on scheduling problems has become increasingly significant. The Distributed Heterogeneous Hybrid Flow-shop Scheduling Problem with Worker constraints (DHHFSP-W) has emerged as an important extension of traditional Flow-shop Scheduling Problem (FSP). To address this NP-hard problem, this paper aims to minimize Total Energy Consumption (TEC) and Worker Costs (WC) by proposing a Q-learning driven Multi-objective Hybrid Particle Swarm Optimization (QL-MOHPSO) algorithm based on four vectors encoding. The algorithm integrates a hybrid sampling strategy combining VEGA and PDDR-FF to divide particles into three subgroups and employs a Q-learning driven multi-group search strategy to effectively balance solution diversity and convergence. Furthermore, to enhance solution quality, eight local search strategies are designed for specific vectors. Experimental results demonstrate that QL-MOHPSO exhibits superior performance across small, medium, and larger-scale instances, significantly outperforming six benchmark algorithms. This study provides an efficient and reliable optimization approach for addressing complex scheduling problems.

Voice is envisioned to be a popular way for humans to interact with Internet-of-Things (IoT) devices. We propose a proximity-based user authentication method (called PIANO) for access control on such voice-powered IoT devices. PIANO leverages the built-in speaker, microphone, and Bluetooth that voice-powered IoT devices often already have. Specifically, we assume that a user carries a personal voice-powered device (e.g., smartphone, smartwatch, or smartglass), which serves as the user's identity. When another voice-powered IoT device of the user requires authentication, PIANO estimates the distance between the two devices by playing and detecting certain acoustic signals; PIANO grants access if the estimated distance is no larger than a user-selected threshold. We implemented a proof-of-concept prototype of PIANO. Through theoretical and empirical evaluations, we find that PIANO is secure, reliable, personalizable, and efficient.

l2,1-norm is an effective regularization to enforce a simple group sparsity for feature learning. To capture some subtle structures among feature groups, we propose a new regularization called exclusive group l2,1-norm. It enforces the sparsity at the intra-group level by using l2,1-norm, while encourages the selected features to distribute in different groups by using l2 norm at the inter-group level. The proposed exclusivegroup l2,1-norm is capable of eliminating the feature correlationsin the context of feature selection, if highly correlated features are collected in the same groups. To solve the generic exclusive group l2,1-norm regularized problems, we propose an efficient iterative re-weighting algorithm and provide a rigorous convergence analysis. Experiment results on real world datasets demonstrate the effectiveness of the proposed new regularization and algorithm.

Sharing photos through Online Social Networks is an increasingly popular fashion. However, it poses a seriousthreat to end users as private information in the photos maybe inappropriately shared with others without their consent. This paper proposes a design and implementation of a system using a dynamic privacy preserving partial image sharing technique (namely PUPPIES), which allows data owners to stipulate specific private regions (e.g., face, SSN number) in an image and correspondingly set different privacy policies for each user. As a generic technique and system, PUPPIES targets at threats about over-privileged and unauthorized sharing of photos at photo service provider (e.g., Flicker, Facebook, etc) side. To this end, PUPPIES leverages the image perturbation technique to "encrypt" the sensitive areas in the original images, and therefore it can naturally support popular image transformations (such as cropping, rotation) and is well compatible with most image processing libraries. The extensive experiments on 19,000 images demonstrate that PUPPIES is very effective for privacy protection and incurs only a small computational overhead. In addition, PUPPIES offers high flexibility for different privacy settings, and is very robust to different types of privacy attacks.

Web map services today, such as Google and Bing maps, have digitalized a great portion of the physical world into easily accessible location databases. After the industry invested huge efforts in gathering related information, a user now can search a physical location on the map and know what kind of place it is, known as reverse geo-coding. However, this functionality is mostly limited to public outdoor locations and to building level granularity. We believe that many services can benefit from knowing the semantic meanings of fine-grained locations including indoor places. For example, the phone can mute and delay incoming calls when a user enters a meeting room. Cameras can be disabled in bathrooms to protect users' privacy. In this paper, we present PinPlace, an on-device service that can automatically associate semantic meanings with outdoor and indoor locations using the activity, transit, and time related features.

Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.

People around the world are more connected today than ever before. By making phone calls, sending text messages and participating in online chats, mobile users are frequently interacting with their social connections through multiple communication channels. This trend is expected to continue with the emergence of immensely popular communication apps on mobile devices. Intuitively, these interactions on users' mobile phones can reveal valuable information regarding their social relationship with their phone contacts. Understanding such relationship can help provide new services and improve users' mobile experience. In this paper, we explore the opportunity to deeply understand these social relationship through mining mobile communication data. By building an on-device mining framework called Commsense, we show that automatically learning and understanding such relationship can efficiently support useful applications such as categorizing mobile contacts, identifying their relative importance, and automatically managing mobile contacts with very little human interference.

This paper develops techniques using which humans can be visually recognized. While face recognition would be one approach to this problem, we believe that it may not be always possible to see a person?s face. Our technique is complementary to face recognition, and exploits the intuition that human motion patterns and clothing colors can together encode several bits of information. Treating this information as a "temporary fingerprint", it may be feasible to recognize an individual with reasonable consistency, while allowing her to turn off the fingerprint at will.

No abstract available.

Mobile phones are becoming the convergent platform for personal sensing, computing, and communication. This paper attempts to exploit this convergence toward the problem of automatic image tagging. We envision TagSense, a mobile phone-based collaborative system that senses the people, activity, and context in a picture, and merges them carefully to create tags on-the-fly. The main challenge pertains to discriminating phone users that are in the picture from those that are not. We deploy a prototype of TagSense on eight Android phones, and demonstrate its effectiveness through 200 pictures, taken in various social settings. While research in face recognition continues to improve image tagging, TagSense is an attempt to embrace additional dimensions of sensing toward this end goal. Performance comparison with Apple iPhoto and Google Picasa shows that such an out-of-band approach is valuable, especially with increasing device density and greater sophistication in sensing and learning algorithms.

This paper describes a system for automatically rating content - mainly movies and videos - at multiple granularities. Our key observation is that the rich set of sensors available on today's smartphones and tablets could be used to capture a wide spectrum of user reactions while users are watching movies on these devices. Examples range from acoustic signatures of laughter to detect which scenes were funny, to the stillness of the tablet indicating intense drama. Moreover, unlike in most conventional systems, these ratings need not result in just one numeric score, but could be expanded to capture the user's experience. We combine these ideas into an Android based prototype called Pulse, and test it with 11 users each of whom watched 4 to 6 movies on Samsung tablets. Encouraging results show consistent correlation between the user's actual ratings and those generated by the system. With more rigorous testing and optimization, Pulse could be a candidate for real-world adoption.

The proliferation of pictures and videos in the Internet is imposing heavy demands on mobile data networks. Though emerging wireless technologies will provide more bandwidth, the increase in demand will easily consume the additional capacity. To alleviate this problem, we explore the possibility of serving user requests from other mobile devices located geographically close to the user. For instance, when Alice reaches areas with high device density - Data Spots - the cellular operator learns Alice's content request, and guides her device to nearby devices that have the requested content. Importantly, communication between the nearby devices can be mediated by servers, avoiding many of the known problems of pure ad hoc communication. This paper argues this viability through systematic prototyping, measurements, and measurement-driven analysis.

A growing number of mobile apps are exploiting smartphone sensors to infer user behavior, activity, or context. Inference requires training using labeled ground truth data. Obtaining labeled data for new apps is a "chicken-egg" problem. Without a reasonable amount of labeled data, apps cannot provide any service. But until an app provides useful service it is not worth installing and has no opportunity to collect user data. This paper aims to address this problem. Our intuition is that even though users are different, they exhibit similar patterns on certain sensing dimensions. For instance, different users may walk and drive at different speeds, but certain speeds will indicate driving for all users. These common patterns could be used as "seeds" to model new users through semi-supervised learning. We prototype a technique to automatically extract the commonalities to seed personalized inference models for new users. We evaluate the proposed technique through example apps and real world data.

Many context aware applications can benefit from using high-level sensing results with semantic meanings (e.g, busy/idle). This paper proposes a platform design that provides high-level "virtual sensor" abstractions and enables new virtual sensors to be bootstrapped from existing ones.

Mobile phones are becoming the convergent platform for personal sensing, computing, and communication. This paper attempts to exploit this convergence towards the problem of automatic image tagging. We envision TagSense, a mobile phone based collaborative system that senses the people, activity, and context in a picture, and merges them carefully to create tags on-the-fly. The main challenge pertains to discriminating phone users that are in the picture from those that are not. We deploy a prototype of TagSense on 8 Android phones, and demonstrate its effectiveness through 200 pictures, taken in various social settings. While research in face recognition continues to improve image tagging, TagSense is an attempt to embrace additional dimensions of sensing towards this end goal. Performance comparison with Apple iPhoto and Google Picasa shows that such an out-of-band approach is valuable, especially with increasing device density and greater sophistication in sensing/learning algorithms.

Smartphones are becoming the convergent platform for personal sensing, computing, and communication. Our work attempts to exploit this convergence towards the problem of automatic image tagging. We envision TagSense, a smartphone based collaborative system that senses the people/activity/context in a picture, and merges them carefully to create tags on-the-fly. The main challenge pertains to discriminating phone users that are in the picture, from those that are not. Our demonstration system consists of 8 Android phones and a laptop. Phones -- with the TagSense application running -- will be randomly distributed to participants. Once a picture is taken by a participant with the phone, tags generated by TagSense will be shown on the phone screen.

The nature of human mobility demands that mobile devices become agile to diverse operating environments. Coping with such diversity requires the device to assess its environment, and trigger appropriate responses to each of them. While existing communication subsystems rely on in-band wireless signals for context-assessment and response, we explore a lateral approach of using out-of-band sensor information. We propose a relatively novel framework that synthesizes in-band and out-of-band information, facilitating more informed communication decisions. We believe that further research in this direction could enable a new kind of device agility, deficient in today's communication systems. Since such a framework is located at the boundaries of mobile sensing and wireless communication, we call it sensor assisted wireless communication.

Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occasions, but such maps may not be always handy. In a small scale poll, 80% of users responded that the ideal solution would be "to have an escort walk me to the desired person".

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as replacement of motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially "interesting" events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are "stitched" into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of "event" has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.