Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing Games That Capture and Engage Users

Developing games that can capture and engage users is not an easy process. The game industry, a multi-billion industry, is composed of many companies delivering a variety of games and adopting many different business and software development models. One trend that started to appear within the industry in recent years is the use of data to guide the design process and provide insights on different aspects of production, including software development, usability, monetization, and marketing, to mention a few. In this technical briefing, I will concentrate on the role of data in the game software development process and how data can be used to derive design and development insights. I will use case studies of collaborative work we have accomplished with different game companies as examples to demonstrate the utility of the approach.
Rate-distortion optimization for arbitrarily-shaped object coding

The trade-off between rate and distortion suggests two approaches for the rate-distortion optimization: (1) reduce the rate at a smaller increase in distortion (pushing the rate-distortion curve to the left) and (2) reduce the distortion at a smaller increase in rate (pushing the rate-distortion curve downward). In general, only the first approach is applicable for ordinary fully-defined blocks of square size, which is the case in all existing image/video coding standards other than MPEG-4. The authors show that for arbitrarily-shaped video objects, the inner object blocks can be optimized in the same way; while the boundary blocks (of arbitrary shapes), if padded properly, can be optimized using not only the first approach but also the second approach as well. To circumvent the high computational complexity, fully embedded pattern tables are designed so that the most computationally expensive part can be replaced by relatively simple table searching operations. Extensive simulation have been done and the results are found to support the conclusion drawn.
Traffic Monitoring using an Object Detection Framework with Limited Dataset

Vehicle detection and tracking is one of the key components of the smart traffic concept. Modern city planning and development is not achievable without proper knowledge of existing traffic flows within the city. Surveillance video is an undervalued source of traffic information, which can be discovered by variety of information technology tools and solutions, including machine learning techniques. A solution for real-time vehicle traffic monitoring, tracking and counting is proposed in Jelgava city, Latvia. It uses object detection model for locating vehicles on the image from outdoor surveillance camera. Detected vehicles are passed to tracking module, which is responsible for building vehicle trajectory and its counting. This research compares two different model training approaches (uniform and diverse data sets) used for vehicle detection in variety of weather and day-time conditions. The system demonstrates good accuracy of given test cases (about 92% accuracy in average). In addition, results are compared to non-machine learning vehicle tracking approach, where notable vehicle detection accuracy increase is demonstrated on congested traffic. This research is fulfilled within the RETRACT (Enabling resilient urban transportation systems in smart cities) project.
Statistical Analysis and Anonymity of TOR's Path Selection

Tor [1] is a network based on the onion routing infrastructure [2] and provides many advantages, including tracking avoidance, research, wider access and, unfortunately, illegal activities. To achieve this, the client will connect to a TOR circuit consisting of nodes chosen under certain restrictions. The purpose of this paper is to draw attention of the narrow range of available and constraints obedient nodes. This is of interest because it impacts the anonymity and the privacy of users and their internet traffic.
Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can't enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting

End-to-end neural models for goal-oriented conversational systems have become an increasingly active area of research, though results in real-world settings are few. We present real-world results for two issue types in the customer service domain. We train models on historical chat transcripts and test on live contacts using a human-in-the-loop research platform. Additionally, we incorporate customer profile features to assess their impact on model performance. We experiment with two approaches for response generation: (1) sequence-to-sequence generation and (2) template ranking. To test our models, a customer service agent handles live contacts and at each turn we present the top four model responses and allow the agent to select (and optionally edit) one of the suggestions or to type their own. We present results for turn acceptance rate, response coverage, and edit rate based on approximately 600 contacts, as well as qualitative analysis on patterns of turn rejection and edit behavior. Top-4 turn acceptance rate across all models ranges from 63%-80%. Our results suggest that these models are promising for an agent-support application.
PHYCOBILIPROTEIN COMPONENTS AND CHARACTERISTICS OF THE PHYCOBILISOME FROM A THERMOPHILIC CYANOBACTERIUM MYXOSARCINA CONCINNA 1

A phycocyanin (PC) and three allophycocyanin (AP) components (designated PC, AP1, AP2, and AP3) were prepared from Myxosarcina concinna Printz phycobilisomes by the native gradient PAGE performed in a neutral buffer system combined with the ion exchange column chromatography on DEAE‐DE52 cellulose. PC contained one β subunit () and two α ones ( and ), and it carried two rod linkers ( and ) and one rod‐core linker (). AP1 and AP3 were characterized as peripheral core APs, whereas AP2 was an inner‐core one. AP2 and AP3 were demonstrated to function as the terminal emitters. Each of the three APs contained two β subunits ( and ), two α subunits ( and ) and an inner‐core linker (). AP2 and AP3 had another subunit of the allophycocyanin B (AP‐B) type () belonging to the β subunit group, and AP1 and AP3 carried their individual specific core linkers ( and ), respectively. No AP component was shown to associate with the core‐membrane linker LCM. The functions of the linker polypeptides in the phycobilisome (PBS) construction are discussed.
Faster Algorithms for Binary Matrix Factorization

We give faster approximation algorithms for wellstudied variants of Binary Matrix Factorization (BMF), where we are given a binarym×nmatrix A and would like to find binary rank-k matrices U, V to minimize the Frobenius norm ofU ·V −A. In the first setting, U · V denotes multiplication over Z, and we give a constantfactor approximation algorithm that runs in 2 2 log poly(mn) time, improving upon the previous min(2 k , 2)poly(mn) time. Our techniques generalize to minimizing ‖U · V − A‖p for p ≥ 1, in 2O(k log poly(mn) time. For p = 1, this has a graph-theoretic consequence, namely, a 2 2)poly(mn)-time algorithm to approximate a graph as a union of disjoint bicliques. In the second setting, U · V is over GF(2), and we give a bicriteria constant-factor approximation algorithm that runs in 2 3)poly(mn) time to find binary rankO(k logm) matrices U , V whose cost is as good as the best rank-k approximation, improving upon min(2 k mn,min(m,n) O(1) poly(mn)) time.
Designing a system with persuasive communication to improve diet compliance for elderly users

This paper introduces a system with persuasive communication to improve the diet compliance and adherence of participants of the PROMISS-project. For this persuasive communication different strategies and ways of personalisation are used, in accordance with existing literature. Furthermore, as the target group is elderly users, the design is tailored to their specific needs. A first prototype was created, based on seven functional requirements. During a pilot study, the prototype is evaluated with seven participants. Based on lessons learned during this pilot, as well as new requirements from the PROMISS dietitians, a refined design of the system was implemented. This refined design is briefly evaluated with four participants of the pilot during individual interviews. The changes made to the prototype were evaluated positively.
Towards Balancing Energy Savings and Performance for Volunteer Computing through Virtualized Approach

Computational grids consist of distributed environments where partner institutions offer hosts along with computational resources that can be used by all members of the grid. When an application needs to run in such environment, it allocates a portion of hosts necessary for its executions. Traditionally, the workload imposed on computational grids has a characteristic of being bag-of-tasks (BoT). It means that multiple replicas are submitted to different hosts, and when a response is processed, such replicas are either ignored or released. On resource allocation, as the grid is distributed among different participants, only idle resources can be leased by a new application. However, due to the behavior of BoTs, many allocated resources do not use their resources in their entirety. Another important fact is that only fully idle hosts can be added to the grid pool, and used only at these times. From the above, this paper proposes an approach that uses underutilized resource slice of grid hosts through virtualization, adjusting the use of grid applications to the leftover resources from daily hosts usage. It allows grid applications to run, even when hosts are in use, as long as there is an idle slice of resources, and their use does not interfere with the host’s current running. To evaluate this approach, we performed an empirical evaluation of a virtualized server running applications concurrently with a virtualized grid application. The results have showed that our scheme could accelerate the performance of grid applications without impacting on higher energy consumption.
Evaluation of Embedded Camera Systems for Autonomous Wheelchairs

Autonomously driving Power Wheelchairs (PWCs) are valuable tools to enhance the life quality of their users. In order to enable truly autonomous PWCs, camera systems are essential. Image processing enables the development of applications for both autonomous driving and obstacle avoidance. This paper explores the challenges that arise when selecting a suitable embedded camera system for these applications. Our analysis is based on a comparison of two well-known camera principles, Stereo-Cameras (STCs) and Time-of-Flight (ToF) cameras, using the standard deviation of the ground plane at various lighting conditions as a key quality measure. In addition, we also consider other metrics related to both the image processing task and the embedded system constraints. We believe that this assessment is valuable when choosing between using STC or ToF cameras for PWCs.
Towards Reliable Data Collection and Annotation to Extract Pulmonary Digital Biomarkers Using Mobile Sensors

Proliferation of sensors embedded in smartphones and smartwatches helps capture rich dataset for machine learning algorithms to extract meaningful digital bio-markers on consumer devices for monitoring disease progression and treatment response. However, development and validation of machine learning algorithms depend on gathering high fidelity sensor data and reliable ground-truth. We conduct a study, called mLungStudy, with 131 subjects with varying pulmonary conditions to collect mobile sensor data including audio, accelerometer, gyroscope using a smartphone and a smartwatch, in order to extract pulmonary biomarkers such as breathing, coughs, spirometry, and breathlessness. Our study shows that commonly used breathing ground-truth data from chestband may not always be reliable as a gold-standard. Our analysis shows that breathlessness biomarkers such as pause time and pause frequency from 2.15 minutes of audio can be as reliable as those extracted from 5 minutes' worth of speech data. This finding can be useful for future studies to trade-off between the reliability of breathlessness data and patient comfort in generating continuous speech data. Furthermore, we use crowdsourcing techniques to annotate pulmonary sound events for developing signal processing and machine learning algorithms. In this paper, we highlight several practical challenges to collect and annotate physiological data and acoustic symptoms from chronic pulmonary patients and ways to improve data quality. We show that the waveform visualization of the audio signal improves annotation quality which leads to a 6.59% increase in cough classification accuracy and a 6% increase in spirometry event classification accuracy. Findings from this study inform future studies focusing on developing explainable machine learning models to extract pulmonary digital bio-markers using mobile sensors.
Long-term exposure to road , railway , and aircraft noise levels and their association with incidence of obesity and obesity parameters

The contribution of different transportation noise sources to obesity and its subphenotypes remains understudied. We evaluated the associations of long-term exposure to road, railway and aircraft noise with measures of general, central obesity and incidence of overweight and obesity in an adult Swiss cohort using cross-sectional and longitudinal designs. We assessed 4678 SAPALDIA cohort participants visited in 2001 and 2010/2011. We measured body mass index (BMI, kilograms/metre), waist circumference (WC, centimetres), and Kyle body Fat Index (BF, %) and derived incidence by severity: overweight only (iOW, BMI: 25-29.9) or obesity (iOB, BMI: ≥ 30). We assigned annual average aircraft, railway, and road traffic noise levels at the most exposed dwelling façade (Ldensource, dB) using Swiss noise models for 2001 and 2011. Associations were evaluated with multivariable linear and multinomial regression models. We observed positive associations between Ldenroad and BMI, WC, %BF and iOB. Ldenrail and Ldenair were related to iOW. Associations were independent of the other noise sources and air pollution. Long-term exposure to road traffic noise may be more obesogenic than railway or aircraft noise.
Forest Stand Species Mapping Using the Sentinel-2 Time Series

Accurate information regarding forest tree species composition is useful for a wide range of applications, both for forest management and scientific research. Remote sensing is an efficient tool for collecting spatially explicit information on forest attributes. With the launch of the Sentinel-2 mission, new opportunities have arisen for mapping tree species owing to its spatial, spectral, and temporal resolution. The short revisit cycle (five days) is crucial in vegetation mapping because of the reflectance changes caused by phenological phases. In our study, we evaluated the utility of the Sentinel-2 time series for mapping tree species in the complex, mixed forests of the Polish Carpathian Mountains. We mapped the following nine tree species: common beech, silver birch, common hornbeam, silver fir, sycamore maple, European larch, grey alder, Scots pine, and Norway spruce. We used the Sentinel-2 time series from 2018, with 18 images included in the study. Different combinations of Sentinel-2 imagery were selected based on mean decrease accuracy (MDA) and mean decrease Gini (MDG) measures, in addition to temporal phonological pattern analysis. Tree species discrimination was performed using the Random Forest classification algorithm. Our results showed that the use of the Sentinel-2 time series instead of single date imagery significantly improved forest tree species mapping, by approximately 5–10% of overall accuracy. In particular, combining images from spring and autumn resulted in better species discrimination.
The properties of reticular thalamic neuron GABAA IPSCs of absence epilepsy rats lead to enhanced network excitability

Both human investigations and studies in animal models have suggested that abnormalities in GABAA receptor function have a potential role in the pathophysiology of absence seizures. Recently we showed that, prior to seizure onset, GABAA IPSCs in thalamic reticular (NRT) neurons of genetic absence epilepsy rats from Strasbourg (GAERS) had a 25% larger amplitude, a 40% faster decay and a 45% smaller paired‐pulse depression than those of nonepileptic control (NEC) rats. By means of a novel mathematical description, the properties of both GAERS and NEC GABAergic synapses can be mimicked. These model synapses were then used in an NRT network model in order to investigate their potential impact on the neuronal firing patterns. Compared to NEC, GAERS NRT neurons show an overall increase in excitability and a higher frequency and regularity of firing in response to periodic input signals. Moreover, in response to randomly distributed stimuli, the GAERS but not the NEC model produces resonance between 7 and 9 Hz, the frequency range of spike–wave discharges in GAERS. The implications of these results for the epileptogenesis of absence seizures are discussed.
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI

We consider the problem of multi-agent reinforcement learning (MARL) in video game AI, where the agents are located in a spatial grid-world environment and the number of agents varies both within and across episodes. The challenge is to flexibly control an arbitrary number of agents while achieving effective collaboration. Existing MARL methods usually suffer from the trade-off between these two considerations. To address the issue, we propose a novel architecture that learns a spatial joint representation of all the agents and outputs grid-wise actions. Each agent will be controlled independently by taking the action from the grid it occupies. By viewing the state information as a grid feature map, we employ a convolutional encoder-decoder as the policy network. This architecture naturally promotes agent communication because of the large receptive field provided by the stacked convolutional layers. Moreover, the spatially shared convolutional parameters enable fast parallel exploration that the experiences discovered by one agent can be immediately transferred to others. The proposed method can be conveniently integrated with general reinforcement learning algorithms, e.g., PPO and Q-learning. We demonstrate the effectiveness of the proposed method in extensive challenging multi-agent tasks in StarCraft II.
Extended 3-Party ACCE and Application to LoRaWAN 1.1

LoRaWAN is an IoT protocol deployed worldwide. Whereas the first version 1.0 has been shown to be weak against several types of attacks, the new version 1.1 has been recently released, and aims, in particular, at providing corrections to the previous release. It introduces also a third entity, turning the original 2-party protocol into a 3-party protocol. In this paper, we provide the first security analysis of LoRaWAN 1.1 in its 3-party setting using a provable approach, and show that it suffers from several flaws. Based on the 3(S)ACCE model of Bhargavan et al., we then propose an extended framework that we use to analyse the security of LoRaWAN-like 3-party protocols, and describe a generic 3-party protocol provably secure in this extended model. We use this provable security approach to propose a slightly modified version of LoRaWAN 1.1. We show how to concretely instantiate this alternative, and formally prove its security in our extended model.
Extraction of Message Sequence Charts from Software Use-Case Descriptions

Software Requirement Specification documents provide natural language descriptions of the core functional requirements as a set of use-cases. Essentially, each use-case contains a set of actors and sequences of steps describing the interactions among them. Goals of use-case reviews and analyses include their correctness, completeness, detection of ambiguities, prototyping, verification, test case generation and traceability. Message Sequence Chart (MSC) have been proposed as a expressive, rigorous yet intuitive visual representation of use-cases. In this paper, we describe a linguistic knowledge-based approach to extract MSCs from use-cases. Compared to existing techniques, we extract richer constructs of the MSC notation such as timers, conditions and alt-boxes. We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques. We also discuss the benefits and limitations of the extracted MSCs to meet the above goals.
Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language

Grapheme to phoneme (G2P) conversion is an integral part in various text and speech processing systems, such as: Text to Speech system, Speech Recognition system, etc. The existing methodologies for G2P conversion in Bangla language are mostly rule-based. However, data-driven approaches have proved their superiority over rule-based approaches for large-scale G2P conversion in other languages, such as: English, German, etc. As the performance of data-driven approaches for G2P conversion depend largely on pronunciation lexicon on which the system is trained, in this paper, we investigate on developing an improved training lexicon by identifying and categorizing the critical cases in Bangla language and include those critical cases in training lexicon for developing a robust G2P conversion system in Bangla language. Additionally, we have incorporated nasal vowels in our proposed phoneme list. Our methodology outperforms other state-of-the-art approaches for G2P conversion in Bangla language.
Activity sense organs: energy-efficient activity sensing with adaptive duty cycle control

Recently, smartphones with built-in sensors are exploited to detect human activities. Since sensing human activities may exhaust energy of a smartphone, this work brings up the concept of "activity sense organs" to the uprising smartphone sensing paradigm. As the human sense organs are the most important sensors to perceive real-world information, this work identifies the most important sensors to perceive a particular human activity based on the "quality of the features" contributed by sensors. To optimize the energy usage of activity sensing, we exploit the importance of sensors to profile the duty-cycles of sensors for activity detection. Specifically, a smartphone will operate in a duty-cycled sensing mode, where sensors in the activity sense organs will collect data at the higher sampling rate than those sensors not in the activity sense organs. We design a knowledge lifecycle to find out the activity sense organs, profile the sensing modes of activity detection, and adjust the sensing mode for real-time activity detection. The experimental results indicate that the activity sense organs provide sufficient information for activity detection.