Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-Scale Characterization of the Soil Microbiome in Ancient Tea Plantations Using High-Throughput 16S rRNA and Internal Transcribed Spacer Amplicon Sequencing

There is a special interaction between the environment, soil microorganisms, and tea plants, which constitute the ecosystem of tea plantations. Influenced by environmental factors and human management, the changes in soil microbial community affected the growth, quality, and yield of tea plants. However, little is known about the composition and structure of soil bacterial and fungal communities in 100-year-old tea plantations and the mechanisms by which they are affected. In this regard, we characterized the microbiome of tea plantation soils by considering the bacterial and fungal communities in 448 soil samples from 101 ancient tea plantations in eight counties of Lincang city, which is one of the tea domestication centers in the world. 16S and Internal Transcribed Spacer (ITS) rRNA high-throughput amplicon sequencing techniques were applied in this study. The results showed that the abundance, diversity, and composition of the bacterial and fungal communities have different sensitivity with varying pH, altitude, and latitude. pH and altitude affect soil microbial communities, and bacterial communities are more sensitive than fungi in terms of abundance and diversity to pH. The highest α-diversity of bacterial communities is shown in the pH 4.50–5.00 and 2,200-m group, and fungi peaked in the pH 5.00–5.50 and 900-m group. Because of environmental and geographical factors, all microbes are similarly changing, and further correlations showed that the composition and structure of bacterial communities are more sensitive than fungal communities, which were affected by latitude and altitude. In conclusion, the interference of anthropogenic activities plays a more important role in governing fungal community selection than environmental or geographical factors, whereas for the bacterial community, it is more selective to environment adaptation than to adaptation to human activities.
Transport from production facility to release locations caused a decline in quality of sterile Queensland fruit fly received for SIT application

Fruit flies are among the most destructive horticultural pests. For Queensland fruit fly (Qfly), Bactrocera tryoni (Froggatt) (Diptera: Tephritidae), the sterile insect technique (SIT) is the main control method used in southeastern Australia. SIT involves the mass rearing of Qfly, sterilisation of pupae by irradiation, transport to release centres, and release of sterile Qfly to disperse into the environment. Mating of sterile and wild flies results in no offspring; thus, the wild population rapidly decreases. Previously, research has mainly focused on improvements within the production facility. However, transport from production facility to release sites is also an essential component in all SIT programs. Here, we examined historical records from the Victorian SIT program between 2002 and 2013 in three seasons (spring, summer, and autumn). We compared quality control (QC) data of irradiated fly pupae at the mass‐rearing facility in New South Wales (limited transport) to those of pupae transported to a release centre in Victoria (extensive transport). After extensive transport, pupal weight (−0.3 mg, or −3%) and flight ability (an overall 26% decline) were lower across all seasons, but particularly in spring. Adult emergence was lower across all seasons (an overall 23% decline), but particularly in summer. All three QC parameters were more variable after extensive transport. We discuss potential causes of the observed decreased pupal quality and avenues of future research to mitigate transport losses.
Making pointer analysis more precise by unleashing the power of selective context sensitivity

Traditional context-sensitive pointer analysis is hard to scale for large and complex Java programs. To address this issue, a series of selective context-sensitivity approaches have been proposed and exhibit promising results. In this work, we move one step further towards producing highly-precise pointer analyses for hard-to-analyze Java programs by presenting the Unity-Relay framework, which takes selective context sensitivity to the next level. Briefly, Unity-Relay is a one-two punch: given a set of different selective context-sensitivity approaches, say S = S1, . , Sn, Unity-Relay first provides a mechanism (called Unity)to combine and maximize the precision of all components of S. When Unity fails to scale, Unity-Relay offers a scheme (called Relay) to pass and accumulate the precision from one approach Si in S to the next, Si+1, leading to an analysis that is more precise than all approaches in S. As a proof-of-concept, we instantiate Unity-Relay into a tool called Baton and extensively evaluate it on a set of hard-to-analyze Java programs, using general precision metrics and popular clients. Compared with the state of the art, Baton achieves the best precision for all metrics and clients for all evaluated programs. The difference in precision is often dramatic — up to 71% of alias pairs reported by previously-best algorithms are found to be spurious and eliminated.
The Spheres of Player Motivation: Towards a New Typology for Player Motivation in Digital Games

Due to the subjective and complex nature of topics like fun and enjoyment, the motivations for players engaging with interactive games are particularly difficult to explore. However, understanding player motivation within games is indisputably valuable for those wishing to succeed in this highly competitive industry. While numerous models exist to quantify the motivations felt by players, the industry is ever evolving, and emerging sub industries have given rise to new reasons to play. In this work, though a meta-ethnography study clustering 284 motivation metaphors extracted from 30 included papers, the Spheres of Motivation is proposed; a new preliminary typology which synthesizes the last three decades of player motivation research.
Understanding Personalised Auditory-Visual Associations in Multi-Modal Interactions

Can we sharpen our auditory and visual senses and better understand the relationship between these modalities to benefit our interactions in human-computer interfaces? This research paper proposes a framework to understand auditory-visual associations and explore the impact of emotion, personality, age and gender in understanding information from both modalities. Studies into the areas of emotion and personality as well as their association with the auditory and visual senses have increased within the fields of psychology, neuroscience, affective computing and human-computer interaction (HCI). From a HCI perspective, advances in technologies and machine learning techniques provide a new way to understand people and to develop systems where computers work along side people to help develop efficient interactions and clearer perceptions of our environment. The proposed framework will be developed along side a personalised auditory-visual interface that can be used to provide intelligent interactions with users that can help them learn from efficient associations between their senses. This research can be used to create personalised auditory-visual-emotion-personality profiles that can be use in adaptive musical teaching platforms, as well as mental health and wellness applications for more personalised care programs.
Multimodal Fusion Strategies for Physiological-emotion Analysis

Physiological-emotion analysis is a novel aspect of automatic emotion analysis. It can support revealing a subject's emotional state, even if he/she consciously suppresses the emotional expression. In this paper, we present our solutions for the MuSe-Physio sub-challenge of Multimodal Sentiment Analysis (MuSe) 2021. The aim of this task is to predict the level of psycho-physiological arousal from combined audio-visual signals and the galvanic skin response (also known as Electrodermal Activity signals) of subjects under a highly stress-induced free speech scenario. In the scenarios, the speaker's emotion can be conveyed in different modalities including acoustic, visual, textual, and physiological signal modalities. Due to the complementarity of different modalities, the fusion of the multiple modalities has a large impact on emotion analysis. In this paper, we highlight two aspects of our solutions: 1) we explore various efficient low-level and high-level features from different modalities for this task, 2) we propose two effective multi-modal fusion strategies to make full use of the different modalities. Our solutions achieve the best CCC performance of 0.5728 on the challenge testing set, which significantly outperforms the baseline system with corresponding CCC of 0.4908. The experimental results show that our proposed various effective features and efficient fusion strategies have a strong generalization ability and can bring more robust performance.
Looking for Laughs: Gaze Interaction with Laughter Pragmatics and Coordination

Laughter and gaze have an important role in managing and coordi-nating social interactions. In the current work, using a multimodal corpus of dyadic taste-testing interactions, we explore whether laughs performing different pragmatic functions are accompanied by different gaze patterns towards the interlocutor, both from the point of view of the laughing participant and from her partner. We also investigate the role of gaze in laughter coordination between interactants. Our results show that laughs performing different pragmatic functions are related to different gaze patterns, both for the laugher and her partner, and that gaze is an important cue exploited by interactants when reciprocating laughter or laughing simultaneously. We discuss our data in relation to the literature about laughter and gaze functions in interaction, linking them to dialogic context. Our results stress the importance of laughter and gaze for modeling of multimodal meaning construction and coordination in interaction, and are therefore relevant for researchers designing human-like embodied conversational agents.
Multi-modal Fusion for Continuous Emotion Recognition by Using Auto-Encoders

Human stress detection is of great importance for monitoring mental health. The Multimodal Sentiment Analysis Challenge (MuSe) 2021 focuses on emotion, physiological-emotion, and stress recognition as well as sentiment classification by exploiting several modalities. In this paper, we present our solution for the Muse-Stress sub-challenge. The target of this sub-challenge is continuous prediction of arousal and valence for people under stressful conditions where text transcripts, audio and video recordings are provided. To this end, we utilize bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit networks (GRU) to explore high-level and low-level features from different modalities. We employ Concordance Correlation Coefficient (CCC) as a loss function and evaluation metric for our model. To improve the unimodal predictions, we add difficulty indicators of the data obtained by using Auto-Encoders. Finally, we perform late fusion on our unimodal predictions in addition to the difficulty indicators to obtain our final predictions. With this approach, we achieve CCC of 0.4278 and 0.5951 for arousal and valence respectively on the test set, our submission to MuSe 2021 ranks in the top three for arousal, fourth for valence, and in top three for combined results.
Technology as Infrastructure for Dehumanization:: Three Hundred Million People with the Same Face

Based on humanistic psychology, this paper reflects on how and why technology has led to increasing dehumanization within society. The literature on human autonomy, or individuals’ ability to exercise agency and control over their own lives, emphasizes that erosion of autonomy adversely impacts human behavior and health—including demotivating people, heightening their anxiety and apathy, undermining interpersonal relations, reducing self-efficacy and learning, and damaging mental and physical health in substantial ways. This paper describes concrete examples of how technology is accelerating loss of human autonomy, which often occurs during invasive surveillance and covert manipulation during user-technology interactions. These examples highlight abuses of multimodal-multisensor technologies, especially during education. An analysis is provided of the psychosocial context that encourages people to use technology in abusive ways, which directly fuels the growing schism between those who control and those who are disempowered victims. Five specific directions are outlined for how our community can design future technology using counter tactics, practices, and policies that strengthen rather than eroding autonomy and well-being.
Efficient compilation of algebraic effect handlers

The popularity of algebraic effect handlers as a programming language feature for user-defined computational effects is steadily growing. Yet, even though efficient runtime representations have already been studied, most handler-based programs are still much slower than hand-written code. This paper shows that the performance gap can be drastically narrowed (in some cases even closed) by means of type-and-effect directed optimising compilation. Our approach consists of source-to-source transformations in two phases of the compilation pipeline. Firstly, elementary rewrites, aided by judicious function specialisation, exploit the explicit type and effect information of the compiler’s core language to aggressively reduce handler applications. Secondly, after erasing the effect information further rewrites in the backend of the compiler emit tight code. This work comes with a practical implementation: an optimising compiler from Eff, an ML style language with algebraic effect handlers, to OCaml. Experimental evaluation with this implementation demonstrates that in a number of benchmarks, our approach eliminates much of the overhead of handlers, outperforms capability-passing style compilation and yields competitive performance compared to hand-written OCaml code as well Multicore OCaml’s dedicated runtime support.
Interaction Modalities for Notification Signals in Augmented Reality

The rise of ubiquitous computing has led to the rapid development of intelligent systems such as augmented reality (AR). AR is being employed for various usage including common tasks such as web browsing, navigating and controlling other internet of things (IoT) devices. In these types of system usage, similar to mobile phones, the presentation of notifications is inevitable. However, studies with regards to the effective delivery of notification signals within the AR environment is still lacking. Thus, the present study aims to investigate the effects of different output modalities (visual, auditory and multimodal) for presenting notifications during simple search and selection tasks within the AR system. Twenty individuals participated in a user study where they were asked to perform a simple search and selection task while using the AR device. Participants were asked to confirm the notifications that were presented at random in each trial. Results of the user study showed that multimodal notification signals (both visual and auditory) yielded a higher notification recognition rate and were rated as the most appropriate modality and most preferred among the conditions tested. Also, it was found that speech input modality was used more frequently in response to notifications, regardless of the output type. Several design implications were derived from the findings of the study.
Multi-modal Stress Recognition Using Temporal Convolution and Recurrent Network with Positional Embedding

Chronic stress causes cancer, cardiovascular disease, depression, and diabetes, therefore, it is profoundly harmful to physiologic and psychological health. Various works have examined ways to identify, prevent, and manage people's stress conditions by using deep learning techniques. The 2nd Multimodal Sentiment Analysis Challenge (MuSe 2021) provides a testing bed for recognizing human emotion in stressed dispositions. In this study, we present our proposal to the Muse-Stress sub-challenge of MuSe 2021. There are several modalities including frontal frame sequence, audio signals, and transcripts. Our model uses temporal convolution and recurrent network with positional embedding. As result, our model achieved a concordance correlation coefficient of 0.5095, which is the average of valence and arousal. Moreover, we ranked 3rd in this competition under the team name CNU_SCLab.
Approximating the Mental Lexicon from Clinical Interviews as a Support Tool for Depression Detection

Depression disorder is one of the major causes of disability in the world that can lead to tragic outcomes. In this paper, we propose a method for using an approximation to a mental lexicon to model the communication process of depressed and non-depressed participants in spontaneous North American English clinical interviews. Our approach, inspired by the Lexical Availability theory, identifies the most relevant vocabulary of the interviewed participant, and use it as features in a classification process. We performed an in-depth evaluation on the DAIC-WOZ [20] and the E-DAIC [11] clinical datasets. Obtained results indicate that our approach can compete against recent contextual embeddings when modeling and identifying depression. We show the generalization capabilities of our algorithm using outside data, reaching a macro F1 = 0.83 and F1 = 0.80 in the DAIC-WOZ and E-DAIC datasets respectively. An analysis of our method’s interpretability allows understanding how the classifier is making its decisions. During this process, we observed strong connections between our obtained results and previous research from the psychological field.
The semantics of shared memory in Intel CPU/FPGA systems

Heterogeneous CPU/FPGA devices, in which a CPU and an FPGA can execute together while sharing memory, are becoming popular in several computing sectors. In this paper, we study the shared-memory semantics of these devices, with a view to providing a firm foundation for reasoning about the programs that run on them. Our focus is on Intel platforms that combine an Intel FPGA with a multicore Xeon CPU. We describe the weak-memory behaviours that are allowed (and observable) on these devices when CPU threads and an FPGA thread access common memory locations in a fine-grained manner through multiple channels. Some of these behaviours are familiar from well-studied CPU and GPU concurrency; others are weaker still. We encode these behaviours in two formal memory models: one operational, one axiomatic. We develop executable implementations of both models, using the CBMC bounded model-checking tool for our operational model and the Alloy modelling language for our axiomatic model. Using these, we cross-check our models against each other via a translator that converts Alloy-generated executions into queries for the CBMC model. We also validate our models against actual hardware by translating 583 Alloy-generated executions into litmus tests that we run on CPU/FPGA devices; when doing this, we avoid the prohibitive cost of synthesising a hardware design per litmus test by creating our own 'litmus-test processor' in hardware. We expect that our models will be useful for low-level programmers, compiler writers, and designers of analysis tools. Indeed, as a demonstration of the utility of our work, we use our operational model to reason about a producer/consumer buffer implemented across the CPU and the FPGA. When the buffer uses insufficient synchronisation -- a situation that our model is able to detect -- we observe that its performance improves at the cost of occasional data corruption.
Prisoner of Words: Lessons Learnt from Mobile Gamification of Lab Memory Experiments

Gamifying experiments in-the-wild showed promising opportunities in supporting participants’ engagement, increasing sample size and diversity, and saving lab and personnel resources needed for lab experiments. However, transforming memory lab experiments to mobile games is challenging as some standard game design guidelines jeopardize the validity of the experimental design. Our work draws attention to this trade-off by providing design guidelines for critical game elements, namely scoring systems and input methods. We distil those guidelines from a case study where we replicated a lab memory experiment via a mobile game apparatus and produced congruent psychological results. Our work sheds the light on the special challenges in gamifying memory experiments and encourages psychologists and game designers to investigate human memory in natural settings.
Detection of Anomalous Behavior of Smartphone Devices using Changepoint Analysis and Machine Learning Techniques

Detecting anomalous behavior on smartphones is challenging since malware evolution. Other methodologies detect malicious behavior by analyzing static features of the application code or dynamic data samples obtained from hardware or software. Static analysis is prone to code’s obfuscation while dynamic needs that malicious activities to cease to be dormant in the shortest possible time while data samples are collected. Triggering and capturing malicious behavior in data samples in dynamic analysis is challenging since we need to generate an efficient combination of user’s inputs to trigger these malicious activities. We propose a general model which uses a data collector and analyzer to unveil malicious behavior by analyzing the device’s power consumption since this summarizes the changes in software. The data collector uses an automated tool to generate user inputs. The data analyzer uses changepoint analysis to extract features from power consumption and machine learning techniques to train these features. The data analyzer stage contains two methodologies that extract features using parametric and non-parametric changepoint. Our methodologies are efficient in data collection time than a manual method and the data analyzer provides higher accuracy compared to other techniques, reaching over 94% F1-measure for emulated and real malware.
Personality Prediction with Cross-Modality Feature Projection

In this paper, we propose an approach to predict customers’ personalities leveraging two modalities of customers’ data: (a) service usages logs of online services and (b) visual data collected in physical stores by surveillance cameras (e.g., gait and whereabouts). A number of companies provide services via online and offline nowadays, thus need to serve two different kinds of customers: “online customers,” who use services completely online and have only (a); and “offline customers,” who use only physical stores and have only (b). To improve personality prediction accuracy for these customers, our approach generates pseudo features of a non-existent modality from the other modality that the customers actually have (i.e., feature projection; e.g., generate pseudo visual data of the online customers from their real online service logs), and uses both pseudo and real features to predict their personalities. The evaluation using real-world data of a mobile carrier’s customers showed that our approach predicted personality more accurately than an ordinary unimodal approach for both online and offline customers. We also examined importance of the feature projection and compared two different projection methods.