Miracle-dz/UniFAR-FTUs huggingface dataset:
School's Out: Experimental Evidence on Limiting Learning Loss Using “Low-Tech” in a Pandemic

The COVID-19 pandemic closed schools at one point for over 1.6 billion children, with potentially long-term consequences. This paper provides some of the first experimental evidence on strategies to minimize the fallout of the pandemic on learning. We evaluate two low-technology interventions to substitute schooling during this period: SMS text messages and direct phone calls. We conduct a rapid trial in Botswana to inform real-time policy responses, collecting data in multiple waves. We find that phone calls and SMS messages result in cost-effective learning gains of 0.12 standard deviations. We cross-randomize targeted instruction, customizing instruction to a child’s learning level using data collected during the trial. We find evidence that targeted instruction can be more effective than non-targeted instruction, especially for SMS messages which have no effect on their own if they are not targeted. Learning gains are robust to a variety of tests, such as randomized problems of the same proficiency and measures of effort on the test. Parents update their beliefs about their child’s learning in tandem with progress and they feel greater self-efficacy to support their child’s learning. The “low-tech” interventions tested have immediate policy relevance and could have long-run implications for the role of technology and parents as substitutes or complements to the traditional education system.
Performance Analysis of Opportunistic Fog Based Radio Access Networks

The advent of the Internet-of-Things (IoT) has led to a rapid growth in data generation. The amount of computation resources required to process the massive data generated by IoT devices, along with the new intelligent use and applications of IoT data such as smart city that can be computation intensive and delay sensitive, have caused an increase in demand for locally available resources at network edge for computation offloading. To address this issue, we have proposed the concept of opportunistic fog radio access network (OF-RAN), which extends the computation capacity of existing fog-RAN (F-RAN) by establishing virtual fog access points (v-FAP) opportunistically using resourceful user devices that participate as service nodes. In this article, we develop an analytical model to evaluate the offloading performance of three RAN architectures: the traditional cloud radio access network (C-RAN), the existing F-RAN, and the proposed OF-RAN. The performances are analyzed in terms of their energy consumption, completion delay, and failure rate, under the effect of varying scenarios.
A Survey of Technologies for Unmanned Merchant Ships

Unmanned merchant ships are ships that carry goods and engage in commercial activities without manual operations on board. In contrast to traditional merchant ships, unmanned merchant ships have great advantages in economy, society, and safety. However, we observe that commercial applications of unmanned ships are still at an exploratory stage. We believe that unmanned merchant ships will be widely adopted and popular in the near future. There is a need to have a good survey of current technologies, foundation, and obstacles of implementation of future unmanned merchant ships. Such a survey is beneﬁcial to ship builders, researchers, owners, and students. Therefore, in this paper, we present a comprehensive survey of ship structure and technology, navigation, automation, algorithms, communication, Internet of Things (IOT), etc. We introduce traditional ships and analyze the role of crew and navigation operations on board. We summarize classiﬁcations, beneﬁts, and core technologies of unmanned merchant ships. We review architecture, communication standards, security, essential technologies of IOT for unmanned merchant ships. Moreover, we introduce intelligent awareness, data fusion, applications, and E-navigation for unmanned merchant ships. We also present several future research directions at the end.
Robot Variable Impedance Skill Transfer and Learning Framework Based on a Simplified Human Arm Impedance Model

A variable impedance skill transfer framework is introduced and experimentally evaluated in this paper. This framework mainly includes the following: 1. a real-time estimation algorithm of arm endpoint stiffness based on the human arm endpoint impedance model, and 2. the tele-impedance demonstration (tele-demonstration) method and Gaussian mixture model (GMM)-based learning from demonstration (LfD) method, which are designed according to the impedance model and the variable impedance skill transfer process. The modelling of the human arm endpoint impedance is inspired by human operation habits. The model can modify the task stiffness in three Cartesian directions based on the arm configuration and muscle co-contraction effect. The model parameters and their values are identified by performing a perturbation-based arm endpoint impedance identification experiment. The realtime impedance model is used for tele-demonstration and obtaining multimodality learning data, including human arm position and impedance information. These multimodality learning data are used for the variable impedance skill learning and generalization, and an electrical switch skill is learned and generalized by a compliant robotic arm in the experiments to execute a variable impedance interaction task in a dynamic uncertain environment. INDEX TERMS variable impedance skill transfer, human impedance modelling, surface electromyographic (sEMG) signal, learning from demonstration
A Zero Placement Algorithm for Synthesis of Flat Top Beam Pattern With Low Sidelobe Level

Flat top beam pattern synthesis is increasingly important for beamformer in high mobility scenarios due to the rapid change of Direction of Arrival (DOA). A Zero Placement for Flat Top (ZPFT) beam pattern synthesis algorithm is presented in this article. It works in Z domain directly and breaks down the total response into two portions. The first portion satisfies the beamwidth requirement with low Sidelobe Level (SLL) which is realized through algorithms like Dolph-Chebyshev algorithm. The second portion is then used to create a broadening effect. The location of the broadening zeros are derived using principles result from the broadening effect analysis of two quadratic functions. Compared to conventional Finite Impulse Response (FIR) method or iterative methods, the proposed method identifies the zeros of the array factor directly and computes the weight without iteration. Since it works in the spatial angle domain directly, the steering of the mainlobe beam could be implemented through a simple angle shift. Numerical simulation confirms the effectiveness of the algorithm. ZPFT can achieve 22dB lower SLL while maintaining the same main beam performance as compared with FIR method for an Uniform Linear Array (ULA) with 7 elements. It can achieve the same optimal performance as the iteration based global optimisation techniques like Semi-Definite Relaxation (SDR) with about 380 times less computing time in an Intel Core i7 Windows platform. ZPFT can steer the main beam easily in real time. All these make it an ideal candidate for high mobility applications where the DOA changes rapidly.
SELCOM: Selective Compression Scheme for Lightweight Nodes in Blockchain System

With the development of blockchain technology, participants need to have huge storage volumes to deal with the growing blockchain ledger size over time. This requirement leads to the conditional participation and veriﬁcation of participants, thus weakening the decentralization of a blockchain system. Several compression schemes have been proposed to mitigate this storage problem by compressing a blockchain ledger based on redundancy, modular functions, and hash functions. However, these schemes have the limitation of accumulating the compression results to validate the retained blocks. The accumulation gradually reduces the storage volume for the blockchain ledger within the storage volume of nodes with limited resources, thus reducing the veriﬁcation capability of the nodes. In this paper, a selective compression scheme using a checkpoint-chain is proposed to prevent the accumulation of compression results. The checkpoint-chain is a second blockchain that stores the checkpoints compressing existing blocks through a block Merkle tree. An update process is also proposed to prevent the accumulation of checkpoints by combining them. As numerous blocks can be veriﬁed with only a few updated checkpoints, blockchain nodes with limited resources can reduce the storage volume for the blockchain ledger and achieve high veriﬁcation capabilities. Finally, compared with the existing compression schemes, the proposed scheme can achieve an average reduction in the storage overhead and an average increase in the veriﬁcation capability of 76.02% and 13.90%, respectively. Moreover, the corresponding performance improvements are 86.14% and 15.44% when the update process is performed, respectively.
Making Connected Cars Untraceable via DSRC Radios

This paper shows potential of using DSRC radios for vehicle tracking protection. We focus on trafﬁc data collection as our target application where vehicles send their locations to a server while driving. Such vehicles are easily trackable—revealing location history—as the application often requires frequent and accurate location updates. This paper presents PathCloak, a method that enables vehicles to report their locations while preventing the server from properly tracking the vehicles. PathCloak leverages vehicles’ two network interfaces: in-car Internet (for accessing the server) and car-to-car DSRC (for creating path confusion). PathCloak-enabled vehicles exchange their kinematic information via DSRC radios, and use it to generate plausible path segments for each other, making their paths indistinguishable each other from the server’s viewpoint. We demonstrate its feasibility via ﬁeld experiments on real roads using our DSRC testbeds. Our evaluation shows that PathCloak offers strong privacy (tracking success ratio < 1%), while maintaining high utility for various trafﬁc statistics.
A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna

In this paper, we propose a deep neural network (DNN)- for the radiation pattern synthesis of an antenna. The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtained from the outputs of the trained DNN, which are amplitude and phase of the antenna elements. However, it is difficult to determine the amplitude and phase of each antenna element from the desired pattern in an environment where inter-element coupling exists. For this purpose, 6,859 radiation pattern samples for a 4 × 1 array patch antenna were generated by changing the phases of the antenna elements, and those patterns were leveraged to train the proposed DNN with low complexity. The radiation patterns of the ideal square and triangular array shapes, which are practically infeasible to implement, were used as inputs to the DNN. I t was confirmed that the radiation pattern generated from the output signals of the DNN was very similar to the input radiation pattern.
ReFPN-FCOS: One-Stage Object Detection for Feature Learning and Accurate Localization

One-stage object detectors are simple and efficient; however, they cannot extract sufficient object features due to simplistic structures. At the same time, the classification score cannot reflect the actual positioning of the candidate box. Therefore, it is not accurate to use classification score only as the candidate box position score in non-maximum suppression (NMS) stage. These two shortcomings degrade the detection accuracy. In this paper, a novel feature pyramid architecture named refined feature pyramid network (ReFPN) is introduced to obtain better object features. ReFPN designs a refined module which is parallel with feature pyramid network (FPN) to extract the semantic features of objects, and then the extraction of features are used to optimize the features of FPN by summation. In addition, we design the refined center-ness (RCenter-ness) branch that predicts the position score of each point on the feature map to improve the localization accuracy. The predicted position score is multiplied by the classification score to obtain the final position score that has a stronger correlation with localization accuracy. The final position score is inputted to the subsequent NMS, which improves localization accuracy. The proposed method in this paper is named ReFPN-FCOS. The sufficient experiments on COCO2017 datasets demonstrate the effectiveness of ReFPN-FCOS on improving classification accuracy and localization accuracy. The average precisions of this method achieve 1.1% and 1.3 % higher than those of FCOS, when using ResNet50 and ResNet101 as backbone respectively. Code download link: https://github.com/xjl-le/mmdete
A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and failing to accurately distinguish the item importance in the basket for detecting user intent. To tackle the above issues, we propose a hybrid model, i.e., Hybrid-Preference Neural Model (HPNM), where a user’s inherent preference is recognized by modeling the historical sequential baskets and the recent preference is identiﬁed by focusing on the current basket. In detail, we apply an attention mechanism for distinguishing the importance of items in a basket to generate an accurate basket representation. GRU is utilized for modeling the basket-level sequential information to obtain user’s long-term preference and the representation of the current session is regarded as user’s short-term preference. We evaluate the performance of our proposals against the state-of-the-art baselines in the ﬁeld of BSIR on two public datasets, i.e., TaFeng and Foursquare. The experimental results show that HPNM can achieve obvious improvements against the baselines in terms of HLU and Recall. In addition, we ﬁnd HPNM with an attention mechanism can lead to a larger improvement against the baseline for item recommendation in terms of HLU and Recall on testing baskets with relatively fewer items.
To Plan or Not to Plan? A Mixed-Methods Diary Study Examining When, How and Why Knowledge Work Planning is Inaccurate

Reliable and accurate planning is essential for modern knowledge workers. However, there is limited insight about when, how and why planning is inaccurate, and the circumstances in which those inaccuracies are troublesome. To investigate this, we asked 20 academics to keep a diary for a single work day. They estimated the duration of the tasks they wanted to achieve at the start of the day and noted down in detail the tasks they actually achieved during the day. Semi-structured interviews were conducted to complement this diary data. The diaries showed that some tasks, such as email and coding, were more susceptible to time underestimation bias while other tasks, such as writing and planning, were more susceptible to time overestimation bias in planning. Based on interviews, a typology of common reasons for delays in planned daily work is presented. It suggests that vague and optimistic planning leads to the observed discrepancy between planned and actual work. Finally, interviews suggested that participants adopted four planning strategies that vary in the frequency of planning, from minimal planning to daily, weekly and multi-level planning. We close by discussing ways support systems for accurate planning can be better designed for different use cases.
To "See" is to Stereotype

Machine-learned computer vision algorithms for tagging images are increasingly used by developers and researchers, having become popularized as easy-to-use "cognitive services. " Yet these tools struggle with gender recognition, particularly when processing images of women, people of color and non-binary individuals. Socio-technical researchers have cited data bias as a key problem; training datasets often over-represent images of people and contexts that convey social stereotypes. The social psychology literature explains that people learn social stereotypes, in part, by observing others in particular roles and contexts, and can inadvertently learn to associate gender with scenes, occupations and activities. Thus, we study the extent to which image tagging algorithms mimic this phenomenon. We design a controlled experiment, to examine the interdependence between algorithmic recognition of context and the depicted person's gender. In the spirit of auditing to understand machine behaviors, we create a highly controlled dataset of people images, imposed on gender-stereotyped backgrounds. Our methodology is reproducible and our code publicly available. Evaluating five proprietary algorithms, we find that in three, gender inference is hindered when a background is introduced. Of the two that "see" both backgrounds and gender, it is the one whose output is most consistent with human stereotyping processes that is superior in recognizing gender. We discuss the accuracy--fairness trade-off, as well as the importance of auditing black boxes in better understanding this double-edged sword.
Understanding and Predicting the Burst of Burnout via Social Media

Job burnout is a special type of work-related stress that is prevalent in our modern society, and constant burnout is extremely harmful for people's physical health and emotional wellbeing. Traditional studies for burnout mainly rely on surveys/questionnaires, which have revealed several interesting findings but are of high cost and very time consuming. With the prevalence of social networking applications, we aim to re-investigate the burnout phenomenon in a novel perspective. In this paper, we collected a dataset consisting of 1532 burnout Weibo users with their postings. Based on the previous literature, we propose a number of hypotheses about what might be the burst signal of the burnout from the perspective of language, time and interaction. Furthermore, extensive correlation analysis is conducted to investigate if these hypotheses are supported, which leads to a number of interesting findings. Finally, we develop machine learning models to predict the burst of burnout based on extracted features and achieve a relatively high accuracy, which reveals potential implications in early-stage intervention.
"An Ideal Human"

Driven by state-of-the-art AI technologies, human-AI collaboration has become an important area in computer-supported teamwork research. While human-AI collaboration has been investigated in various domains, more research is needed to explore human perceptions and expectations of AI teammates in human-AI teaming. To achieve an in-depth understanding of how people perceive AI teammates and what they expect from AI teammates in human-AI teaming, we conducted a survey with 213 participants and a follow-up interview with 20 participants. Considering the context-dependency of teamwork, we chose to study human-AI teaming in the context of multiplayer online games as a case study. This study shows that people have mixed feelings toward AI teammates but hold a positive attitude toward future collaboration with AI teammates in general. Our findings highlight people's expectations for AI teammates in a rapidly changing collaborative environment (e.g., instrumental skills for in-game tasks, shared understanding between humans and AI, communication capabilities, human-like behaviors and performance), as well as factors that impact people's willingness to team up with AI teammates (e.g., pre-existing attitudes toward AI, previous collaboration experience with humans). We contribute to CSCW by shedding light on how AI should be structured in human-AI teaming to support highly complex collaborative activities in CSCW environments.
Examining the process of legitimising corporate sustainability within the organisation: Towards a Generalised Legitimising Pathway

This empirical paper explores the process of legitimising sustainability within the organisation. Using interview data from 51 Heads of Sustainability, it finds that a conformance legitimising strategy is foundational to the process, a selection legitimising strategy is then added, often with multiple selection episodes used, and finally, manipulation is attempted in order to change the environment to accept sustainability. Building from this, a Generalised Legitimising Pathway is theorised providing the first legitimising framework to include both the temporal progression of legitimising and the overlap of strategies. Track Sustainable and Responsible Business
Divided We Stand

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and providers; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.
Gesprekken met honoursstudenten : over persoonlijke en professionele ontwikkeling

In deze derde inspiratiebundel zijn de verhalen van honoursstudenten over hun persoonlijke en professionele ontwikkeling bij elkaar gebracht. De inspiratiebundel beschrijft de ontstaansgeschiedenis en de belangrijkste kenmerken van praktijkgerichte honoursprogramma’s bij Hogeschool Rotterdam. Elf honoursstudenten en alumni komen aan het woord over hun ervaringen in de honoursprogramma’s en deze ervaringen worden vervolgens geanalyseerd aan de hand van de kenmerken.