Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved dv-hop localization algorithm based on centroid multilateration

Localization has been a fundamental service for Wireless Sensor Network (WSN) based applications. Among all localization technologies, the Distance Vector-Hop (DV-Hop) algorithm is one of the most popular algorithm because it is easy to implement. It simply utilizes the hop counts as connectivity measurements, but this often brings big errors. In this paper, we propose an improved DV-Hop scheme in which a centroid multilateration method is presented to solve the inconsistent problem of multilateration in the original DV-Hop localization scheme. Our algorithm can effectively improve the accuracy while maintaining low computational complexity. Experiments are conducted on two different network topologies and the simulation results show that our scheme out-performs related algorithms in accuracy and communicational cost.
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efﬁcient inference routines. However, in order to guarantee exact inference, they require speciﬁc structural constraints, which complicate learning SPNs from data. Thereby, most SPN structure learners proposed so far are tedious to tune, do not scale easily, and are not easily integrated with deep learning frameworks. In this paper, we follow a simple “deep learning” approach, by generating unspecial-ized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization. Somewhat surprisingly, our models often perform on par with state-of-the-art SPN structure learners and deep neural networks on a diverse range of generative and discriminative scenarios. At the same time, our models yield well-calibrated uncertainties, and stand out among most deep generative and discriminative models in being robust to missing features and being able to detect anomalies.
Queec: QoE-aware edge computing for complex IoT event processing under dynamic workloads

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) which are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this paper, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relative computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes and the cloud. We conduct extensive evaluations and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state of art under dynamic workloads, while incurring acceptable overhead.
An Adaptive Transmission Power Control Algorithm for Wearable Healthcare Systems Based on Variations in the Body Conditions

In wearable healthcare systems, sensor devices can be deployed in places around the human body such as the stomach, back, arms, and legs. The sensors use tiny batteries, which have limited resources, and old sensor batteries must be replaced with new batteries. It is difficult to deploy sensor devices directly into the human body. Therefore, instead of replacing sensor batteries, increasing the lifetime of sensor devices is more efficient. A transmission power control (TPC) algorithm is a representative technique to increase the lifetime of sensor devices. Sensor devices using a TPC algorithm control their transmission power level (TPL) to reduce battery energy consumption. The TPC algorithm operates on a closed-loop mechanism that consists of two parts, such as sensor and sink devices. Most previous research considered only the sink part of devices in the closed-loop. If we consider both the sensor and sink parts of a closed-loop mechanism, sensor devices reduce energy consumption more than previous systems that only consider the sensor part. In this paper, we propose a new approach to consider both the sensor and sink as part of a closed-loop mechanism for efficient energy management of sensor devices. Our proposed approach judges the current channel condition based on the values of various body sensors. If the current channel is not optimal, sensor devices maintain their current TPL without communication to save the sensor’s batteries. Otherwise, they find an optimal TPL. To compare performance with other TPC algorithms, we implemented a TPC algorithm and embedded it into sensor devices. Our experimental results show that our new algorithm is better than other TPC algorithms, such as linear, binary, hybrid, and ATPC.
A syntactic treatment of adjectival non-intersectivity in English

A SYNTACTIC TREATMENT OF ADJECTIVAL NON-INTERSECTIVITY IN ENGLISH by Alexander Funk ! Adviser: Marcel den Dikken Natural language has long been observed to be rife with apparently ‘non-intersective’ modification constructions (false teeth, huge flea, heavy smoker, etc.), whose apparent noncompositionality poses difficulties for formally-articulated theories of language. Bolinger’s (1967) demonstration of the extent and significance of the issue ushered in several lines of investigation, first in semantics (most notably Kamp 1975, Siegel 1976, Partee 2009), but more recently in syntax as well, with the insights of Larson (1998) and Bouchard (2002) informing approaches to the nominal domain such as that in Cinque (2010). However, ‘semantics-only’ accounts of non-intersectivity phenomena have limited explanatory capacity given the clear sensitivity of syntax to intersectivity, and recent syntactic proposals leave many questions about non-intersectivity largely unresolved, particularly the implications of non-intersective adnominal modification (NIAM) for the internal structure of the Determiner Phrase (DP), and for fundamental conceptions of category and predication. This thesis articulates the alignment of NIAM intepretation patterns based on an enriched typology of both adjectives and nouns. In particular, it exploits non-lexicalist approaches to syntax—the ‘late insertion’ approach of Distributed Morphology (Halle & Marantz 1993, 1994) —as well as non-directional predication structures (den Dikken 2006) to develop an
Hybrid Recommendation Method Based on Tag and Collaborative Filtering

Aiming at the problem of sparse user ratings facing tradition recommendation system,this paper proposes a hybrid recommendation method based on Tag and collaborative filtering(TAG-CF) to provide a solution to this problem.The neighbors set for the target item can be gained based on tagging information.It uses item-based collaborative filtering to generate the predictive ratings.By filing these predictive ratings into the sparse user-item rating matrix,it constructs a full pseudo ratings matrix.It computes the predictions based on the pseudo ratings matrix by using user-based collaborative filtering.Experimental results show that the proposed method performs significantly better than the traditional CF method.
On the Relationship Between Satisfiability and Markov Decision Processes

Stochastic satisﬁability (SSAT) and decision-theoretic planning in ﬁnite horizon partially observable Markov decision processes (POMDPs) are both PSPACE-Complete. We describe constructive reductions between SSAT and ﬂat POMDPs that open the door to comparisons and future cross-fertilization between the solution techniques of those problems. We also propose a new SSAT solver called Prime that incorporates recent advances from the SAT and #SAT literature. Using our reduction from POMDP to SSAT, we demonstrate the competitiveness of Prime on ﬁnite horizon POMDP problems.
Correlated Learning for Aggregation Systems

Aggregation systems (e.g., Uber, Lyft, Food-Panda, Deliveroo) have been increasingly used to improve efﬁciency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as proﬁt, number of requests, delay etc. Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacriﬁced. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence of a self interested central entity. Since there are large number of learning agents that are homogenous, we represent the problem as an Anonymous Multi-Agent Reinforcement Learning (AyMARL) problem. By using the self interested centralized entity as a correlation entity, we provide a novel learning mechanism that helps individual agents to maximize their individual revenue. Our Correlated Learning (CL) algorithm is able to outperform existing mechanisms on a generic simulator for aggregation systems and multiple other benchmark Multi-Agent Reinforcement Learning (MARL) problems.
A hybrid multiuser detection approach based on tabu search algorithm and multistage detector

In this study, a. hybrid approach that employs a tabu search (TS) algorithm and a multistage detector (MSD) for the multiuser detection problem in code-division multiple-access communication system is proposed. Using this approach, the TS is used as the first stage of the MSD to provide a good initial point for successive stages of the MSD. Such a hybridization of the TS with the MSD reduces its computational complexity by providing faster convergence. In addition, a better initial data estimate supplied by the TS improves the performance of the MSD. Simulation results for the synchronous and asynchronous cases are provided to show that the approach is promising.
An Examination of Teachers' Integration of Web 2.0 Technologies in Secondary Classrooms: A Phenomenological Study.

Web 2.0 tools may be able to close the digital gap between teachers and students if teachers can integrate the tools and change their pedagogy. The TPACK framework has outlined the elements needed to effect change, and research on Web 2.0 tools shows its potential as a change agent, but little research has looked at how the two interrelate. Using a rigorous phenomenological research methodology, the “lived experiences” of seven teachers successfully adapting pedagogy with Web 2.0 tools were examined giving an in-depth qualitative analysis of how and why teachers integrate Web 2.0 to change pedagogy. The research validated the use of TPACK as a framework as well as the use of phenomenological research methodology in researching about educational technology.
Computer Science: Abstraction to Implementation

ion could be shown as in Figure 4, where the numbers at the left are the indices of the boxes. We use an index value of –1 to represent the null pointer. The empty boxes are currently unused. With this representation, we also need to keep track of the first element, which in this case is the one at index 0. Note that there can be many representations for a single abstraction; this is a common phenomenon. Figure 4: A representation, using indices, for a linked list of four elements At the representation level, in order to insert a new element, we simply obtain a new box for it and adjust the structure accordingly. For example, since the box at index 1 is unused, we can use it to hold the new element. Suppose the element is e anl it is to be inserted after b. The new representation picture would be as shown in Figure 5 Figure 5: A representation for the modified linked list, of five elements c d b 0 1 2 3 4 5 a 3
Hands-on Tutorial for Piecewise Linear Mixed-effects Models Using SAS® PROC MIXED

Clinical trials and public health studies are focused on studying changes associated with interventions, events, or critical periods in human development. Evaluating the effect of critical or high-risk events/periods in longitudinal studies of growth may provide clues to the long-term effects of life events and efficacies of preventive/therapeutic interventions. Conventional linear longitudinal models typically involve a single growth profile to represent linear changes in an outcome variable across time, which sometimes does not fit the empirical data. The piecewise linear mixed-effects models allow different linear functions of time corresponding to the pre- and post-critical time point trends. This step-by-step, hands-on tutorial first introduces the reader to how to perform piecewise linear mixed effects models by using SAS PROC MIXED in the context of a clinical trial with 2 intervention arms and a predictive covariate-of-interest. Second, it illustrates how to obtain the slopes and corresponding p-values of intervention and control groups during pre- and post-critical periods, conditional on different values of the predictive covariate. Third, it explains how to make meaningful comparisons and present results in a scientific manuscript. Illustrative SAS commands are provided to fit piecewise linear mixed-effects models and to generate the summary tables, assisting in the interpretation of the results.
Tracking and visualizing dimension space coverage for exploratory data analysis

In this dissertation, I investigate interactive visual history for collaborative exploratory data analysis (EDA). In particular, I examine use of analysis history for improving the awareness of the dimension space coverage 1 2 3 to better support data exploration. Commonly, interactive history tools facilitate data analysis by capturing and representing information about the analysis process. These tools can support a wide range of use-cases from simple undo and redo to complete reconstructions of the visualization pipeline. In the context of exploratory collaborative Visual Analytics (VA), history tools are commonly used for reviewing and reusing past states/actions and do not efficiently support other use-cases such as understanding the past analysis from the angle of dimension space coverage. However, such knowledge is essential for exploratory analysis which requires constant formulation of new questions about data. To carry out exploration, an analyst needs to understand “what has been done” versus “what is remaining” to explore. Lack of such insight can result in premature fixation on certain questions, compromising the coverage of the data set and breadth of exploration [80]. In addition, exploration of large data sets sometimes requires collaboration between a group of analysts who might be in different time/location settings. In this case, in addition to personal analysis history, each team member needs to understand what aspects of the problem his or her collaborators have explored. Such scenarios are common in domains such as science and business [34] where analysts explore large multi-dimensional data sets in search of relationships, patterns and trends. Currently, analysts typically rely on memory and/or externalization to keep track of investigated versus uninvestigated aspects of the problem. Although analysis history 4 mechanisms have the potential to assist analyst(s) with this problem, most common visual representations of history are geared towards reviewing & reusing the visualization pipeline or visualization states. I started this research with an observational user study to gain a better understanding of analysts’ history needs in the context of collaborative exploratory VA. This study 1In this dissertation, a dimension refers to a column in a tabular dataset where dimension’s name is the column’s header name. For instance, a financial dataset may include dimensions such as Sales, Profit and Inventory Cost. 2I define and use dimension space to refer to the set of all dimensions in a tabular dataset. 3I define dimension space coverage as the set of investigated data dimensions, either individually (e.g. a histogram showing distribution of Sales values) or collectively (e.g. a bar chart showing averages of Sales and Profit for different States). 4In the context of visual data analysis, history is usually comprised of recorded information about the analysis states/processes and/or outcomes. For more detailed description see Chapter 2.
Subjective evaluation of single microphone noise reduction with different time constants

Abstract Objective: Previous studies on single microphone noise reduction (NR) in hearing aids (HAs) have shown that some NR algorithms provide beneficial effects in terms of listener preference. To improve HA user satisfaction, we are interested in characteristics that determine preferences for NR, and in the inter-individual variability. The aim of this study was to test if dynamic properties of NR influence listener preference. Design: The gain reduction at speech offsets of a NR algorithm was slowed down by applying temporal exponential smoothing. At speech onsets the gain recovery was left unchanged. Test signals consisted of speech in continuous and modulated speech-shaped background noise, processed with three time constants: 0, 100, and 200 ms. Study sample: 16 Normal hearing (NH) and 16 hearing impaired (HI) subjects participated in a paired-comparison listening test. Results: NH subjects as a group had a significant preference for NR with time constants of 100, or 200 ms (slower acting NR). HI listeners as a group preferred NR over no NR, but had no clear preference for fast or slow NR. Patterns of preference differed between individual listeners. Conclusions: NR dynamics had an impact on individual listener preference and should be considered when optimising HAs.
High-throughput peptide-MHC complex generation and kinetic screenings of TCRs with peptide-receptive HLA-A*02:01 molecules

Functionally empty and peptide-receptive soluble HLA-A*02:01 enables high-throughput analysis of TCR binding to peptide-MHCs. See the related Research Article by Saini et al. in this issue. Empty MHC reagents improve T cell detection Recombinant MHC molecules displaying single peptides in their peptide binding cleft are valuable reagents for identifying T cells that bind specific peptide-MHC complexes. Two studies in this week’s issue show that disulfide-stabilized (DS) MHC class I molecules offer a more efficient path to preparing large libraries of MHC-peptide reagents. Saini et al. developed DS versions of three MHC class I molecules and used a library of DS HLA-A2–peptide multimers to rapidly screen T cells infiltrating human melanoma tumors for neoantigen reactivity. Moritz et al. prepared libraries of DS HLA-A2–peptide complexes to screen an affinity-matured TCR for cross-reactivity with self-peptide–MHC complexes. Empty MHC class I molecules that are stable and easily loaded with peptide will facilitate the wider use of MHC-peptide reagents for T cell detection. See related Research Article by Saini et al. in this issue. Major histocompatibility complex (MHC) class I molecules present short peptide ligands on the cell surface for interrogation by cytotoxic CD8+ T cells. MHC class I complexes presenting tumor-associated peptides such as neoantigens represent key targets of cancer immunotherapy approaches currently in development, making them important for efficacy and safety screenings. Without peptide ligand, MHC class I complexes are unstable and decay quickly, making the production of soluble monomers for analytical purposes labor intensive. We have developed a disulfide-stabilized HLA-A*02:01 molecule that is stable without peptide but can form peptide-MHC complexes (pMHCs) with ligands of choice in a one-step loading procedure. We illustrate the similarity between the engineered mutant and the wild-type molecule with respect to affinity of wild-type or affinity-matured T cell receptors (TCRs) and present a crystal structure corroborating the binding kinetics measurements. In addition, we demonstrate a high-throughput binding kinetics measurement platform to analyze the binding characteristics of bispecific TCR (bsTCR) molecules against diverse pMHC libraries produced with the disulfide-stabilized HLA-A*02:01 molecule. We show that bsTCR affinities for pMHCs are indicative of in vitro function and generate a bsTCR binding motif to identify potential off-target interactions in the human proteome. These findings showcase the potential of the platform and the engineered HLA-A*02:01 molecule in the emerging field of pMHC-targeting biologics.
Bridging the Knowledge Gap for the Impact of Non-Thermal Processing on Proteins and Amino Acids

Proteins represent one of the major food components that contribute to a wide range of biophysical functions and dictate the nutritional, sensorial, and shelf-life of food products. Different non-thermal processing technologies (e.g., irradiation, ultrasound, cold plasma, pulsed electric field, and high-pressure treatments) can affect the structure of proteins, and thus their solubility as well as their functional properties. The exposure of hydrophobic groups, unfolding followed by aggregation at high non-thermal treatment intensities, and the formation of new bonds have been reported to promote the modification of structural and functional properties of proteins. Several studies reported the reduction of allergenicity of some proteins after the application of non-thermal treatments. The composition and concentration of free amino acids could be changed after non-thermal processing, depending on the processing time and intensity. The present review discusses the effects of different non-thermal treatments on protein properties in detail, and highlights the opportunities and disadvantages of these technologies in relation to protein functionality.
Prospective Study of Attachment as a Predictor of Binge Eating, Emotional Eating and Weight Loss Two Years after Bariatric Surgery

Bariatric surgery remains the most effective treatment for severe obesity, though post-surgical outcomes are variable with respect to long-term weight loss and eating-related psychopathology. Attachment style is an important variable affecting eating psychopathology among individuals with obesity. To date, studies examining eating psychopathology and attachment style in bariatric surgery populations have been limited to pre-surgery samples and cross-sectional study design. The current prospective study sought to determine whether attachment insecurity is associated with binge eating, emotional eating, and weight loss outcomes at 2-years post-surgery. Patients (n = 108) completed questionnaires on attachment style (ECR-16), binge eating (BES), emotional eating (EES), depression (PHQ-9), and anxiety (GAD-7). Multivariate linear regression analyses were conducted to examine the association between attachment insecurity and 2-years post-surgery disordered eating and percent total weight loss. Female gender was found to be a significant predictor of binge eating (p = 0.007) and emotional eating (p = 0.023) at 2-years post-surgery. Avoidant attachment (p = 0.009) was also found to be a significant predictor of binge eating at 2-years post-surgery. To our knowledge, this study is the first to explore attachment style as a predictor of long-term post-operative eating pathology and weight outcomes in bariatric surgery patients.