Miracle-dz/UniFAR-FTUs huggingface dataset:
A Human-Centric Approach For Binary Code Decompilation

Many security techniques have been developed both in academia and industry to analyze source code, including methods to discover bugs, apply taint tracking, or find vulnerabilities. These source-based techniques leverage the wealth of high-level abstractions available in the source code to achieve good precision and efficiency. Unfortunately, these methods cannot be applied directly on binary code which lacks such abstractions. In security, there are many scenarios where analysts only have access to the compiled version of a program. When compiled, all high-level abstractions, such as variables, types, and functions, are removed from the final version of the program that security analysts have access to. This dissertation investigates novel methods to recover abstractions from binary code. First, a novel pattern-independent control flow structuring algorithm is presented to recover highlevel control-flow abstractions from binary code. Unlike existing structural analysis algorithms which produce unstructured code with many goto statements, our algorithm produces fullystructured goto-free decompiled code. We implemented this algorithm in a decompiler called Dream. Second, we develop three categories of code optimizations in order to simplify the decompiled code and increase readability. These categories are expression simplification, controlflow simplification and semantics-aware naming. We have implemented our usability extensions on top of Dream and call this extended version Dream++. We conducted the first user study to evaluate the quality of decompilers for malware analysis. We have chosen malware since it represents one of the most challenging cases for binary code analysis. The study included six reverse engineering tasks of real malware samples that we obtained from independent malware experts. We evaluated three decompilers: the leading industry decompiler Hex-Rays and both versions of our decompiler Dream and Dream++. The results of our study show that our improved decompiler Dream++ produced significantly more understandable code that outperforms both Hex-Rays and Dream. Using Dream++ participants solved 3×more tasks than when using Hex-Rays and 2×more tasks than when using Dream. Moreover, participants rated Dream++ significantly higher than the competition.
Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular time-sampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.
Natural Autoantibodies: An Undersugn Hero of the Immune System and Autoimmune Disorders—A Point of View

Natural autoantibodies/natural autoimmunity forms a network that serves to protect the organism from outer and inner danger but may also contribute to autoimmune disease. This vital component of the immune system deserves, we think, much greater attention than it currently attracts. A better understanding of how natural autoimmunity contributes in the function of the immune system is needed and how the expansion of this process leads to autoimmune disorders is an intriguing question which we will address in this article. Since the early days of immunology, pioneering work by Landsteiner and his group on haptens showed that immunization is a process that can raise highly specific antibodies. In the same era, Ehrlich put forward the dogma of “horror autotoxicus,” proclaiming the inability of the immune system to recognize self-constituents. Later on, in the 1950s, Burnet builds upon these seminal observations and formulated the clonal selection theory, which states that “cell clones produce highly specific antibodies for environmental constituents while the immune system is educated during ontogeny not to recognize self-constituents. ” Nevertheless, other studies, contradicting the “dogma,” were mostly ignored. These studies showed that circulating antibodies in normal human sera could recognize various substances, including self-antigens, albeit with a low avidity (1, 2). In the following years, additional experiments reinforced the notion of a persistent immune system reactivity against self, e.g., in normal mice (3, 4). Many of these studies were neglected because the scientific community had adopted the “dogma” of clonal selection theory. Despite this opposition, “heretical” studies kept coming and demonstrated that autoreactive B-cells and polyreactive antibodies are abundant and actively participate in physiological immunity (5). In mice, innate-like B1 cells form a pool of long-lived population, which are positively selected for self reactivity and produce the majority of natural IgM antibodies. These antibodies protect against infection and are implicated in the gut microbiota homeostasis (6).
Collective Privacy Management in Social Media

If one wants to study privacy from an intercultural perspective, one must first validate whether there are any cultural variations in the concept of “privacy” itself. This study systematically examines cultural differences in collective privacy management strategies, and highlights methodological precautions that must be taken in quantitative intercultural privacy research. Using survey data of 498 Facebook users from the US, Singapore, and South Korea, we test the validity and cultural invariance of the measurement model and predictive model associated with collective privacy management. The results show that the measurement model is only partially culturally invariant, indicating that social media users in different countries interpret the same instruments in different ways. Also, cross-national comparisons of the structural model show that causal pathways from collective privacy management strategies to privacy-related outcomes vary significantly across countries. The findings suggest significant cultural variations in privacy management practices, both with regard to the conceptualization of its theoretical constructs, and with respect to causal pathways.
When deictic gestures in a robot can harm child-robot collaboration

This paper describes research aimed at supporting children's reading practices using a robot designed to interact with children as their reading companion. We use a learning by teaching scenario in which the robot has a similar or lower reading level compared to children, and needs help and extra practice to develop its reading skills. The interaction is structured with robot reading to the child and sometimes making mistakes as the robot is considered to be in the learning phase. Child corrects the robot by giving it instant feedbacks. To understand what kind of behavior can be more constructive to the interaction especially in helping the child, we evaluated the effect of a deictic gesture, namely pointing on the child's ability to find reading mistakes made by the robot. We designed three types of mistakes corresponding to different levels of reading mastery. We tested our system in a within-subject experiment with 16 children. We split children into a high and low reading proficiency even-though they were all beginners. For the high reading proficiency group, we observed that pointing gestures were beneficial for recognizing some types of mistakes that the robot made. For the earlier stage group of readers pointing were helping to find mistakes that were raised upon a mismatch between text and illustrations. However, surprisingly, for this same group of children, the deictic gestures were disturbing in recognizing mismatches between text and meaning.
Approaches in Immunotherapy, Regenerative Medicine, and Bioengineering for Type 1 Diabetes

Recent advances on using immune and stem cells as two-pronged approaches for type 1 diabetes mellitus (T1DM) treatment show promise for advancement into clinical practice. As T1DM is thought to arise from autoimmune attack destroying pancreatic β-cells, increasing treatments that use biologics and cells to manipulate the immune system are achieving better results in pre-clinical and clinical studies. Increasingly, focus has shifted from small molecule drugs that suppress the immune system nonspecifically to more complex biologics that show enhanced efficacy due to their selectivity for specific types of immune cells. Approaches that seek to inhibit only autoreactive effector T cells or enhance the suppressive regulatory T cell subset are showing remarkable promise. These modern immune interventions are also enabling the transplantation of pancreatic islets or β-like cells derived from stem cells. While complete immune tolerance and body acceptance of grafted islets and cells is still challenging, bioengineering approaches that shield the implanted cells are also advancing. Integrating immunotherapy, stem cell-mediated β-cell or islet production and bioengineering to interface with the patient is expected to lead to a durable cure or pave the way for a clinical solution for T1DM.
Do Parent Mental Illness and Family Living Arrangement Moderate the Effects of the Aussie Optimism Program on Depression and Anxiety in Children?

Parent mental illness and family living arrangement are associated with depression and anxiety in children, and may influence the effects of programs that aim to prevent these disorders. This study investigated whether these family context factors moderated the intervention effects of the enhanced Aussie Optimism Positive Thinking Skills program on depression and anxiety in primary school children. The intervention was a universal, cognitive-behavioral program, with a one hour session each week for 10 weeks, delivered by trained teachers. The participants were 502 children from 13 private schools, aged 9–11, with 347 in the intervention group and 155 in the control group. There were 267 females and 235 males. Data from 502 parents was also included. A cluster randomized controlled trial design was used, including eight intervention schools and five control schools. Depression and anxiety were assessed at pre-test, post-test, and 6-months follow-up. Information on parent mental illness and family living arrangement was collected through a parent questionnaire. The data was analyzed using covariance analysis with Generalized Linear Mixed Methods. At baseline, depressive and anxiety symptoms did not differ significantly based on parent mental illness. Symptoms of depression at baseline were significantly higher for children from a higher-risk family living arrangement, but anxiety symptoms were not. Parent mental illness and family living arrangement did not moderate the effects of the program on depression and anxiety at post-test or 6-months follow-up. Parent mental illness moderated the intervention effects on negative self-esteem, an aspect of depression, at post-test, with improvements seen only for children who did not have a parent with a mental illness. The findings indicate an association between family living arrangement and depressive symptoms in children. The findings suggest that the program is effective for children regardless of parent mental illness or family living arrangement, although parent mental illness has the capacity to influence the program's outcomes.
Modal-adversarial Semantic Learning Network for Extendable Cross-modal Retrieval

Cross-modal retrieval, e.g., using an image query to search related text and vice-versa, has become a highlighted research topic, to provide flexible retrieval experience across multi-modal data. Existing approaches usually consider the so-called non-extendable cross-modal retrieval task. In this task, they learn a common latent subspace from a source set containing labeled instances of image-text pairs and then generate common representation for the instances in a target set to perform cross-modal matching. However, these method may not generalize well when the instances of the target set contains unseen classes since the instances of both the source and target set are assumed to share the same range of classes in the non-extensive cross-modal retrieval task. In this paper, we consider a more practical issue of extendable cross-modal retrieval task where instances in source and target set have disjoint classes. We propose a novel framework, termed Modal-adversarial Semantic Learning Network (MASLN), to tackle the limitation of existing methods on this practical task. Specifically, the proposed MASLN consists two subnetworks of cross-modal reconstruction and modal-adversarial semantic learning. The former minimizes the cross-modal distribution discrepancy by reconstructing each modality data mutually, with the guidelines of class embeddings as side information in the reconstruction procedure. The latter generates semantic representation to be indiscriminative for modalities, while to distinguish the modalities from the common representation via an adversarial learning mechanism. The two subnetworks are jointly trained to enhance the cross-modal semantic consistency in the learned common subspace and the knowledge transfer to instances in the target set. Comprehensive experiment on three widely-used multi-modal datasets show its effectiveness and robustness on both non-extendable and extendable cross-modal retrieval task.
Metal-free C60/CNTs/g-C3N4 ternary heterostructures: synthesis and enhanced visible-light-driven photocatalytic performance

A metal-free C60/CNTs/g-C3N4 nanoheterostructure with excellent visible-light photocatalysis for rhodamine B (Rh B) degradation has been reported. Via a convenient low-temperature solution-phase method, g-C3N4 nanosheets can serve as substrate for dispersion of C60/CNTs. The loading of C60/CNTs onto g-C3N4 nanosheets surfaces significantly enhanced visible-light-driven photocatalytic activity of g-C3N4 catalyst, for oxidation of organic pollutant (Rh B, 100%). Excellent photocatalytic properties of C60/CNTs/g-C3N4 can be predominantly attributed to the intimate interfacial contact among constructing compounds, increased specific surface area and enhanced light adsorption efficiency resulted from C60/CNTs carbon materials. Particularly, the synergistic heterostructure interaction remarkably hinders the electron–hole pairs recombination, giving rise to significantly enhanced photocatalytic performance of C60/CNTs/g-C3N4 in comparison with other counterparts.
Factors influencing intention to use mobile technologies forlearning among technical universities students

Mobile technologies offer more than just calling. There are number of innovative features that are crucial and impactful to educational purpose. Despite the importance of understanding factors influencing mobile technologies for learning, this area has received limited research. Thus, the aim of this study is to investigate factors influencing intention to use m-learning among students at Malaysian technical universities. The research framework for this research has been adopted from unified theory of acceptance and use of technology (UTAUT) to achieve research objectives. A total of 400 set of data was collected from four Malaysia technical universities using purposeful structured random sampling and self-administrated survey questionnaire. AMOS was performed to test whether the model of its good fit to test hypotheses formulated. The results revealed that performance expectancy and self-management of learning are significant related to behavioral intention to use m-learning while effort expectancy, social influence and perceived playfulness do not indicate significant relationship with intention to use mobile technologies for leaming among students at Malaysia technical universities. The findings of this study added new understanding as regard to factors influencing students' intention to use m-learning in higher educatioli
A Late Cretaceous mammal from Brazil and the first radioisotopic age for the Bauru Group

In the last three decades, records of tribosphenidan mammals from India, continental Africa, Madagascar and South America have challenged the notion of a strictly Laurasian distribution of the group during the Cretaceous. Here, we describe a lower premolar from the Late Cretaceous Adamantina Formation, São Paulo State, Brazil. It differs from all known fossil mammals, except for a putative eutherian from the same geologic unity and Deccanolestes hislopi, from the Maastrichtian of India. The incompleteness of the material precludes narrowing down its taxonomic attribution further than Tribosphenida, but it is larger than most coeval mammals and shows a thin layer of parallel crystallite enamel. The new taxon helps filling two major gaps in the fossil record: the paucity of Mesozoic mammals in more northern parts of South America and of tribosphenidans in the Cretaceous of that continent. In addition, high-precision U-Pb geochronology provided a post-Turonian maximal age (≤87.8 Ma) for the type stratum, which is overlain by the dinosaur-bearing Marília Formation, constraining the age of the Adamantina Formation at the site to late Coniacian–late Maastrichtian. This represents the first radioisotopic age for the Bauru Group, a key stratigraphic unit for the study of Cretaceous tetrapods in Gondwana.
Robust Lexical Features for Improved Neural Network Named-Entity Recognition

Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute — offline — a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.
Entropy-Based Subword Mining with an Application to Word Embeddings

Recent literature has shown a wide variety of benefits to mapping traditional one-hot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have long-tail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropy-based subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.
Context Tree for Adaptive Session-based Recommendation

There has been growing interests in recent years from both practical and research perspectives for session-based recommendation tasks as long-term user profiles do not often exist in many real-life recommendation applications. In this case, recommendations for user's immediate next actions need to be generated based on patterns in anonymous short sessions. An often overlooked aspect is that new items with limited observations arrive continuously in many domains (e.g. news and discussion forums). Therefore, recommendations need to be adaptive to such frequent changes. In this paper, we benchmark a new nonparametric method called context tree (CT) against various state-of-the-art methods on extensive datasets for session-based recommendation task. Apart from the standard static evaluation protocol adopted by previous literatures, we include an adaptive configuration to mimic the situation when new items with limited observations arrives continuously. Our results show that CT outperforms two best-performing approaches (recurrent neural network; heuristic-based nearest neighbor) in majority of the tested configurations and datasets. We analyze reasons for this and demonstrate that it is because of the better adaptation to changes in the domain, as well as the remarkable capability to learn static sequential patterns. Moreover, our running time analysis illustrates the efficiency of using CT as other nonparametric methods.
Treatment restrictions in patients with severe stroke are associated with an increased risk of death

Introduction Treatment restrictions in the first 2 days after intracerebral haemorrhage have been independently associated with an increased risk of early death. It is unknown whether these restrictions also affect mortality if these are installed several days after stroke onset. Patients and methods Sixty patients with severe functional dependence at day 4 after ischaemic stroke or intracerebral haemorrhage were included in this prospective two-centre cohort study. The presence of treatment restrictions was assessed at the day of inclusion. Information about mortality, functional outcome (modified Rankin scale) score and quality of life (visual analogue scale) was recorded 6 months after stroke onset. Poor outcome was defined as modified Rankin scale >3. Satisfactory quality of life was defined as visual analogue scale ≥ 60. Results At 6 months, 30 patients had died, 19 survivors had a poor functional outcome and 9 patients had a poor quality of life. Treatment restrictions were independently associated with mortality at 6 months (adjusted relative risk, 1.30; 95% confidence interval, 1.06–1.59; p = 0.01), but not with functional outcome. Discussion Our findings were observed in 60 selected patients with severe stroke. Conclusion The instalment of treatment restrictions by itself may increase the risk of death after stroke, even if the first 4 days have passed. In future stroke studies, this potential confounder should be taken into account. Quality of life was satisfactory in the majority of the survivors, despite considerable disability.
Synthesis and photocatalytic activity of mesoporous g-C3N4/MoS2 hybrid catalysts

The key to solving environmental and energy issues through photocatalytic technology requires highly efficient, stable and eco-friendly photocatalysts. Graphitic carbon nitride (g-C3N4) is one of the most promising candidates except for its limited photoactivity. In this work, a facile and scalable one-step method is developed to fabricate an efficient heterostructural g-C3N4 photocatalyst in situ coupled with MoS2. The strong coupling effect between the MoS2 nanosheets and g-C3N4 scaffold, numerous mesopores and enlarged specific surface area helped form an effective heterojunction. As such, the photocatalytic activity of the g-C3N4/MoS2 is more than three times higher than that of the pure g-C3N4 in the degradation of RhB under visible light irradiation. Improvement of g-C3N4/MoS2 photocatalytic performance is mainly ascribed to the effective suppression of the recombination of charge carriers.