Miracle-dz/UniFAR-FTUs huggingface dataset:
CRCount: Pointer Invalidation with Reference Counting to Mitigate Use-after-free in Legacy C/C++

Pointer invalidation has been a popular approach adopted in many recent studies to mitigate use-after-free errors. The approach can be divided largely into two different schemes: explicit invalidation and implicit invalidation. The former aims to eradicate the root cause of use-after-free errors by explicitly invalidating every dangling pointer. In contrast, the latter aims to prevent dangling pointers by freeing an object only if there is no pointer referring to it. A downside of the explicit scheme is that it is expensive, as it demands high-cost algorithms or a large amount of space to maintain up-to-date lists of pointer locations linking to each object. Implicit invalidation is more efficient in that even without any explicit effort, it can eliminate dangling pointers by leaving objects undeleted until all the links between the objects and their referring pointers vanish by themselves during program execution. However, such an argument only holds if the scheme knows exactly when each link is created and deleted. Reference counting is a traditional method to determine the existence of reference links between objects and pointers. Unfortunately, impeccable reference counting for legacy C/C++ code is very difficult and expensive to achieve in practice, mainly because of the type unsafe operations in the code. In this paper, we present a solution, called CRCount, to the use-after-free problem in legacy C/C++. For effective and efficient problem solving, CRCount is armed with the pointer footprinting technique that enables us to compute, with high accuracy, the reference count of every object referred to by the pointers in the legacy code. Our experiments demonstrate that CRCount mitigates the useafter-free errors with a lower performance-wise and space-wise overhead than the existing pointer invalidation solutions.
Hybrid mining: exploiting blockchain's computational power for distributed problem solving

In today's cryptocurrencies, Hashcash proof of work is the most commonly-adopted approach to mining. In Hashcash, when a miner decides to add a block to the chain, she has to solve the difficult computational puzzle of inverting a hash function. While Hashcash has been successfully adopted in both Bitcoin and Ethereum, it has attracted significant and harsh criticism due to its massive waste of electricity, its carbon footprint and environmental effects, and the inherent lack of usefulness in inverting a hash function. Various other mining protocols have been suggested, including proof of stake, in which a miner's chance of adding the next block is proportional to her current balance. However, such protocols lead to a higher entry cost for new miners who might not still have any stake in the cryptocurrency, and can in the worst case lead to an oligopoly, where the rich have complete control over mining. In this paper, we propose Hybrid Mining: a new mining protocol that combines solving real-world useful problems with Hashcash. Our protocol allows new miners to join the network by taking part in Hashcash mining without having to own an initial stake. It also allows nodes of the network to submit hard computational problems whose solutions are of interest in the real world, e.g. protein folding problems. Then, miners can choose to compete in solving these problems, in lieu of Hashcash, for adding a new block. Hence, Hybrid Mining incentivizes miners to solve useful problems, such as hard computational problems arising in biology, in a distributed manner. It also gives researchers in other areas an easy-to-use tool to outsource their hard computations to the blockchain network, which has enormous computational power, by paying a reward to the miner who solves the problem for them. Moreover, our protocol provides strong security guarantees and is at least as resilient to double spending as Bitcoin.
Detecting stress from mouse-gaze attraction

Mental stress can cause mental illnesses like anxiety and depression. It is often detected by experts, or via intrusive devices attached to human, both are not viable in daily life. Recent research demonstrated that non-intrusive stress detection is viable through the analysis of mouse and gaze dynamics as a user is using the computer. However, these works require prior knowledge of the user interface (UI) layout information to build up appropriate models. This greatly reduces their generalizability to real usage, where most tasks are carried out with dynamic UIs. This paper presents a UI-agnostic stress detection system, MoGa, to infer user stress level in tasks without being concerned on the actual UI. MoGa adopts an innovative mouse-gaze attraction model, which leverages the coordination between mouse and eye movement without considering the UI information. We evaluate the performance of MoGa with tasks on dynamic and fixed UI. Our experimental results demonstrate the effectiveness of the underlying mouse-gaze attraction model, which can achieve stress detection accuracy of 74.3% for dynamic UI tasks, beating the state-of-the-art approach by around 15%. It opens up a new avenue for cognitive-aware intelligent user interface and numerous advanced HCI studies.
aMV-LSTM: an attention-based model with multiple positional text matching

Deep models are getting a wide interest in recent NLP and IR state-of-the-art. Among the proposed models, position-based models and attention-based models take into account the word position in the text, in the former, and the importance of a word among other words in the latter. The positional information are some of the important features that help text representation learning. However, the importance of a given word among others in a given text, which is an important aspect in text matching, is not considered in positional features. In this paper, we propose a model that combines position-based representation learning approach with the attention-based weighting process. The latter learns an importance coefficient for each word of the input text. We propose an extension of a position-based model MV-LSTM with an attention layer, allowing a parameterizable architecture. We believe that when the model is aware of both word position and importance, the learned representations will get more relevant features for the matching process. Our model, namely aMV-LSTM, learns the attention based coefficients to weight words of the different input sentences, before computing their position-based representations. Experimental results, in question/answer matching and question pairs identification tasks, show that the proposed model outperforms the MV-LSTM baseline and several state-of-the-art models.
Predicting event attendance exploring social influence

The problem of predicting people's participation in real-world events has received considerable attention as it offers valuable insights for human behavior analysis and event-related advertisement. Today social networks (e.g. Twitter) widely reflect large popular events where people discuss their interest with friends. Event participants usually stimulate friends to join the event which propagates a social influence in the network. In this paper, we propose to model the social influence of friends on event attendance. We consider non-geotagged posts besides structures of social groups to infer users' attendance. To leverage the information on network topology we apply some of recent graph embedding techniques such as node2vec, HARP and Poincarè. We describe the approach followed to design the feature space and feed it to a neural network. The performance evaluation is conducted using two large music festivals datasets, namely the VFestival and Creamfields. The experimental results show that our classifier outperforms the state-of-the-art baseline with 89% accuracy observed for the VFestival dataset.
Mycobacterium tuberculosis heat-shock protein 16 . 3 activates macrophages probably through TLR 4 / MyD 88 pathway

Tuberculosis is a chronic disease caused by Mycobacterium tuberculosis (Mtb), and many previous studies indicated that Hsp16.3 may play an important role in long-term survival of Mtb within macrophages. However, the underlying mechanism of Hsp16.3 in protecting of Mtb is not clear. In this study, we constructed a prokaryotic expression vector, pET28a-Mtb Hsp16.3, which induced the expression of Mtb Hsp16.3 and purified the fusion protein through nickel-affinity chromatography. Subsequently, qPCR and EIA assays were used to assess the expression of TNF-α and IL-10 in RAW264.7 cells after treated with Mtb Hsp16.3 in vitro. Finally, we evaluated the impact of small interference RNA-mediated TLR4 silencing on Mtb Hsp16.3-induced macrophage activation, and further confirmed its molecular mechanism. We found that Mtb Hsp16.3 was successfully purified and could activate macrophages by enhancing the levels of TNF-α and IL-10 in vitro. In addition, knockdown of TLR4 via using siTLR4 could obviously reduce TNF-α and IL-10 expression and significantly increase the levels of IL-12 and IL-4. Moreover, Mtb Hsp16.3 could enhance the activation of MYD88-NF-κB signaling in RAW264.7 cells in a concentration dependent manner. Our data suggests that Mtb Hsp16.3 could activate macrophage through TLR4-NF-κB signaling pathway, providing a basis for further studies on the biological activity of Mtb Hsp16.3 and its role in the pathogenesis of TB.
Don't Trust The Locals: Investigating the Prevalence of Persistent Client-Side Cross-Site Scripting in the Wild

The Web has become highly interactive and an important driver for modern life, enabling information retrieval, social exchange, and online shopping. From the security perspective, Cross-Site Scripting (XSS) is one of the most nefarious attacks against Web clients. Research has long since focused on three categories of XSS: Reflected, Persistent, and DOMbased XSS. In this paper, we argue that our community must consider at least four important classes of XSS, and present the first systematic study of the threat of Persistent Client-Side XSS, caused by the insecure use of client-side storage. While the existence of this class has been acknowledged, especially by the non-academic community like OWASP, prior works have either only found such flaws as side effects of other analyses or focused on a limited set of applications to analyze. Therefore, the community lacks in-depth knowledge about the actual prevalence of Persistent Client-Side XSS in the wild. To close this research gap, we leverage taint tracking to identify suspicious flows from client-side persistent storage (Web Storage, cookies) to dangerous sinks (HTML, JavaScript, and script.src). We discuss two attacker models capable of injecting malicious payloads into storage, i.e., a Network Attacker capable of temporarily hijacking HTTP communication (e.g., in a public WiFi), and a Web Attacker who can leverage flows into storage or an existing reflected XSS flaw to persist their payload. With our taint-aware browser and these models in mind, we study the prevalence of Persistent Client-Side XSS in the Alexa Top 5,000 domains. We find that more than 8% of them have unfiltered data flows from persistent storage to a dangerous sink, which showcases the developers’ inherent trust in the integrity of storage content. Even worse, if we only consider sites that make use of data originating from storage, 21% of the sites are vulnerable. For those sites with vulnerable flows from storage to sink, we find that at least 70% are directly exploitable by our attacker models. Finally, investigating the vulnerable flows originating from storage allows us to categorize them into four disjoint categories and propose appropriate mitigations.
rORAM: Efficient Range ORAM with O(log2 N) Locality

Oblivious RAM protocols (ORAMs) allow a client to access data from an untrusted storage device without revealing to that device any information about their access pattern. Typically this is accomplished through random shuffling of the data such that the storage device cannot determine where individual blocks are located, resulting in a highly randomized access pattern. Storage devices however, are typically optimized for sequential access. A large number of random disk seeks during standard ORAM operation induce a substantial overhead. In this paper, we introduce rORAM, an ORAM specifically suited for accessing ranges of sequentially logical blocks while minimizing the number of random physical disk seeks. rORAM obtains significantly better asymptotic efficiency than prior designs (Asharov et al., ePrint 2017, Demertzis et al., CRYPTO 2018) reducing both the number of seeks and communication complexity by a multiplicative factor of O(logN). An rORAM prototype is 30-50x times faster than Path ORAM for similar range-query workloads on local HDDs, 30x faster for local SSDs, and 10x faster for network block devices. rORAM’s novel disk layout can also speed up standard ORAM constructions, e.g., resulting in a 2x faster Path ORAM variant. Importantly, experiments demonstrate suitability for real world applications – rORAM is up to 5x faster running a file server and up to 11x faster running a range-query intensive video server workloads compared to standard Path ORAM.
Performance Analysis of Spark using k-means

Big Data has long been the topic of fascination for Computer Science enthusiasts around the world, and has gained even more prominence in the recent times with the continuous explosion of data resulting from the likes of social media and the quest for tech giants to gain access to deeper analysis of their data. This paper discusses two of the comparison of Hadoop Map Reduce and the recently introduced Apache Spark – both of which provide a processing model for analyzing big data. Although both of these options are based on the concept of Big Data, their performance varies significantly based on the use case under implementation. This is what makes these two options worthy of analysis with respect to their variability and variety in the dynamic field of Big Data. In this paper we compare these two frameworks along with providing the performance analysis using a standard machine learning algorithm for clustering (K-Means).
Don't hesitate to share!: a novel IoT data protection scheme based on BGN cryptosystem

In cloud-based Internet of Things (IoT), sharing of data with third-party services and other users, inherently incurs potential risk and leads to unique security and privacy concerns. Existing cryptographic solutions ensure the security of IoT data, but due to their significant computational overhead, most of them are not suitable for resource-constrained IoT devices. To address these concerns, we propose a data protection system to store encrypted IoT data in a cloud while still allowing query processing over the encrypted data. More importantly, our proposed system features a novel encrypted data sharing scheme based on Boneh-Goh-Nissim (BGN) cryptosystem, with revocation capabilities and in-situ key update. We perform exhaustive experiments on real datasets, primarily to assess the feasibility of the proposed system on resource-constrained IoT devices. We next measure the computation overhead, storage overhead and throughput. The experimental results show that our system is not only feasible, but also provides a high level of security. Furthermore, the results show that our system is 34% more computationally faster, requires 25% less storage and 15% more throughput than the best performed system in the state-of-the-art.
NMDA Receptor Activation Produces Concurrent Generation of Nitric Oxide and Reactive Oxygen Species: Implications for Cell Death

Abstract: The ability of glutamate to stimulate generation of intracellular oxidant species was determined by microfluorescence in cerebellar granule cells loaded with the oxidant‐sensitive fluorescent dye 2,7‐dichlorofluorescin (DCF). Exposure of cells to glutamate (10 µM) produced a rapid generation of oxidants that was blocked ∼70% by MK‐801 (a noncompetitive NMDA‐receptor antagonist). To determine if nitric oxide (NO) or reactive oxygen species (ROS) contributed to the oxidation of DCF, cells were treated with compounds that altered their generation. NO production was inhibited with NG‐nitro‐l‐arginine methyl ester (l‐NAME) (nitric oxide synthase inhibitor) and reduced hemoglobin (NO scavenger). Alternatively, cells were incubated with superoxide dismutase (SOD) and catalase, which selectively metabolize O2−· andH2O2. Concurrent inhibition of O2−· and NO production nearly abolished intracellular oxidant generation. Pretreatment of cells with either chelerythrine (1 µM, protein kinase C inhibitor) or quinacrine (5 µM, phospholipase A2 inhibitor) before addition of glutamate also blocked oxidation of DCF. Generation of oxidants by glutamate was significantly reduced by incubating the cells in Ca2+‐free buffer. In cytotoxicity studies, a positive correlation was observed between glutamate‐induced death and oxidant generation. Glutamate‐induced cytotoxicity was blocked by MK‐801 and attenuated by treatment with l‐NAME, chelerythrine, SOD, or quinacrine. It is concluded that glutamate induces concurrent generation of NO and ROS by activation of both NMDA receptors and non‐NMDA receptors through a Ca2+‐mediated process. Activation of NO synthase and phospholipaseA2 contribute significantly to this response. It is proposed that simultaneous generation of NO and ROS results in formation of peroxynitrite, which initiates the cellular damage.
Toward Whole-Body Connectomics

Recent advances in neuro-technologies have revolutionized knowledge of brain structure and functions. Governments and private organizations worldwide have initiated several large-scale brain connectome projects, to further understand how the brain works at the systems levels. Most recent projects focus on only brain neurons, with the exception of an early effort to reconstruct the 302 neurons that comprise the whole body of the small worm, Caenorhabditis elegans. However, to fully elucidate the neural circuitry of complex behavior, it is crucial to understand brain interactions with the whole body, which can be achieved only by mapping the whole-body connectome. In this article, we discuss the current state of connectomics study, focusing on novel optical approaches and related imaging technologies. We also discuss the challenges encountered by scientists who endeavor to map these whole-body connectomes in large animals. This article is based on a Presidential Lecture presented at the 2016 Annual Meeting.
How Bad Can It Git? Characterizing Secret Leakage in Public GitHub Repositories

—GitHub and similar platforms have made public collaborative development of software commonplace. However, a problem arises when this public code must manage authentication secrets, such as API keys or cryptographic secrets. These secrets must be kept private for security, yet common development practices like adding these secrets to code make accidental leakage frequent. In this paper, we present the ﬁrst large-scale and longitudinal analysis of secret leakage on GitHub. We examine billions of ﬁles collected using two complementary approaches: a nearly six-month scan of real-time public GitHub commits and a public snapshot covering 13% of open-source repositories. We focus on private key ﬁles and 11 high-impact platforms with distinctive API key formats. This focus allows us to develop conservative detection techniques that we manually and automatically evaluate to ensure accurate results. We ﬁnd that not only is secret leakage pervasive — affecting over 100,000 repositories — but that thousands of new, unique secrets are leaked every day. We also use our data to explore possible root causes of leakage and to evaluate potential mitigation strategies. This work shows that secret leakage on public repository platforms is rampant and far from a solved problem, placing developers and services at persistent risk of compromise and abuse.
Resource-sharing optimization for multicast D2D communications underlaying LTE-A uplink cellular networks

In recent years, data traffic has effectively increased with the emergence of high data rate applications such as multimedia sharing services and online gaming. To accommodate this, an effective improvisation of the resource utilization is necessary. This motivated a new communication standard such as multicast Device-to-device (D2D) communications technique, which supports several receivers with a require level of signal-to-interference-plus-noise ratio (SINR) based on their applications. In this paper, we perform multicast D2D group formation based on the distance between D2D users and minimum SINR requirements. However, during D2D communications establishment, the system exhibits co-channel interference due to resource-sharing phenomenon between the conventional cellular users and D2D users. To mitigate the interference, we select the best users by considering a low-complexity metaheuristic-tabu search algorithm. The performance of the propose scheme is evaluated through extensive simulations using Monte-Carlo simulation. Simulation results demonstrate that the proposed scheme significantly improves the overall system data rate and increases the success rate of overall system1.
Deep brain stimulation of the subthalamic nucleus improves cognitive flexibility but impairs response inhibition in Parkinson disease.

BACKGROUND Deep brain stimulation of the subthalamic nucleus (STN) improves motor symptoms of Parkinson disease. Although several studies have assessed cognitive functions before surgery and after long-term STN stimulation, only a few have assessed patients while stimulation is on and off to more specifically address the short-term cognitive effects of STN deep brain stimulation. OBJECTIVE To examine the short-term effects of STN stimulation on several tests sensitive to executive function and the long-term effects of STN stimulation on a global cognitive scale. DESIGN Twenty-three patients with Parkinson disease were tested 6 to 12 months after surgery with STN stimulation switched on and off in a random order while taking their regular medication. The Unified Parkinson's Disease Rating Scale motor score was also rated in the on and off stimulation condition. The neuropsychological battery included digit span, verbal fluency, Stroop color test, and random number generation in a single- and dual-task condition. RESULTS Short-term stimulation improved the results on the Random Number Generation Task, requiring suppression of habitual responses, but induced more errors in the interference task of the Stroop color test. Digit span, verbal fluency, and dual-task performance results did not change. There was a significant correlation (r = 0.47, P =.02) between improved performance on the Random Number Generation Task and impaired response inhibition in the Stroop interference condition. A preoperative to postoperative comparison showed no changes in global cognitive function with long-term STN deep brain stimulation. CONCLUSIONS Short-term STN stimulation improves cognitive flexibility (giving up habitual responses) but impairs response inhibition. Long-term STN stimulation does not change global cognitive function.
Prolonged Severe Anemia and Transfusion Refusal Following Abdominal Surgery. A Case Report and Short Literature Review

Mura P1*, Deiana M1, D'Aloja E2, Paribello F2, Musu M1 and Finco G1 1Department of Medical Sciences “M. Aresu”, University of Cagliari, Cagliari, Policlinico “Duilio Casula”, SS554 Bivio per Sestu, 09042 Monserrato (CA), Italy 2Department of Public Health, Clinical and Molecular Medicine, Cagliari University, Italy *Corresponding author: Paolo Mura, Department of Medical Sciences “M. Aresu”, Anesthesia and Intensive Care Unit, University of Cagliari, Cagliari, Policlinico “Duilio Casula”, SS554 Bivio per Sestu, 09042-Monserrato (CA), Italy, Tel: +39 07051096543; E-mail: mura_paolo@hotmail.com