Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the Usage of Invariant-Based App Behavioral Fingerprinting for the Detection of Obfuscated Versions of Known Malware

App fingerprints can be used to verify whether two apps are the same, and are useful tools for malware detection because they can allow to recognize obfuscated versions of known malware. Fingerprinting an app on the base of static features is known to fail against obfuscation, as it is successful in hiding the static characteristics that reveal the malicious nature of an app. In this paper we propose a novel way to compute app fingerprints, which is based on behavioral features. The aim is to capture the semantics of the app, so that obfuscation results ineffective. The technique we introduce exploits invariants, found among pairs of metrics, collected during app execution, and produces a fingerprint consisting of the list of the correlation values of these pairs. We present an experimental evaluation carried out on a real Android device, whose obtained results support the methodology we propose, and show it can be a viable research direction to investigate further.
Implementation of data abstraction in the relational database system INGRES

This paper discusses the design and implementation of an abstract data type (ADT) facility which was added to the INGRES database manager. Our implementation of ADTs allows a user to register ADTs and ADT operators with the run-time database manager, declare column values of relations to be instances of ADTs, and formulate queries containing references to ADTs and ADT operators. The user view, implementation, performance, and possible extensions to this new facility are described.
A Role for Atypical Cadherin Celsr3 in Hippocampal Maturation and Connectivity

Atypical cadherin Celsr3, a regulator of planar cell polarity, is critical for the development of the axonal blueprint. We previously showed that expression of Celsr3 is necessary to establish forebrain connections such as the anterior commissure and thalamocortical and corticospinal tracts. The requirement for Celsr3 during hippocampal wiring and its action in the hippocampus remain largely unexplored. Here, we compared the connectivity and maturation of the hippocampal formation in Celsr3|Foxg1 and Celsr3|Dlx mice. Celsr3 is inactivated in the whole telencephalon, including the hippocampal primordium, in Celsr3|Foxg1 mice, and in the early basal telencephalon, including ganglionic eminences and ventral diencephalon, in Celsr3|Dlx mice. Behavioral tests showed that both mutants were hyperactive and had impaired learning and memory. Abnormal cytoarchitecture of CA1, CA3, and dentate gyrus was found in the Celsr3|Foxg1 mutant, in which afferent and efferent hippocampal pathways, as well as intrinsic connections, were dramatically disrupted. In Celsr3|Dlx mutant mice, hippocampal cytoarchitecture was mildly affected and extrinsic and intrinsic connectivity moderately disturbed. In both mutants, pyramidal neurons in CA1 harbored atrophic dendritic trees, with decreased synapse density and increased proportion of symmetric versus asymmetric synapses, and long-term potentiation was altered. In contrast, mutant hippocampal neurons extended neurites that were normal, even longer than those of control neurons, indicating that anomalies in vivo are secondary to defective connections. Postnatal neurogenesis was preserved and mutant interneurons were able to migrate to the hippocampus. Thus, like in neocortex, Celsr3 is required for hippocampal development, connectivity and function, and for pyramidal cell maturation.
Differential Protein Synthesis in Candida albicans during Blastospore Formation at 24.5 °C and during Germ Tube Formation at 37 °C

SUMMARY: To identify proteins synthesized during blastospore to germ tube transformation in Candida albicans, membrane and cytoplasmic protein fractions labelled with 14C were analysed by SDS-PAGE and autoradiography. Four cytoplasmic proteins were detected in pulse-labelled lysates prepared from cells forming blastospores and germ tubes at 37 °C, but not in pulse-labelled lysates prepared from cells forming blastospores at 24.5 °C. Three proteins detectable in 24.5 °C lysates were diminished in 37 °C lysates.
Experimental validation of the generalized accurate modelling method for system-level bulk current injection setups up to 1 GHz

A small-signal model of an automotive system-level bulk current injection (BCI) setup developed with a generalized accurate method shown in the previous publication is verified on a case study with a demonstrator EUT module. The work utilizes an equivalent circuit modelling approach for the floating ungrounded EUT board and a macromodel for an active DUT IC. The simulation-based prediction of the BCI test results using an IC failure threshold and a small-signal simulation of RF levels at floating EUT module under BCI tests is shown. Due to high accuracy and detail of the BCI setup model, the prediction also shows very good correlation to real measurement data, both qualitatively and quantitatively, up to 1 GHz.
Establishing Style-Oriented Driver Models by Imitating Human Driving Behaviors

Driver modeling is important for both automobile industry and intelligent transportation. One of its key topics has been studied in this paper, i.e., the style-oriented driver modeling for speed control, and a modeling scheme based on distal learning control and real-world vehicle test data (VTD) is proposed to make this possible. The driver model adapts to be an inverse model of the vehicle at run, which is accomplished under the distal guidance of the discrepancy between the actual and the desired vehicle speed. To tackle the divergence and local mutability of real-world vehicle data, the partly connected multilayered perceptron (PCMLP), which is a locally designed neural network, is utilized to fulfill the imitating of human operations on gas or brake pedals during real-world driving. The FTP-75 driving cycles are borrowed to test the established driver models, and driving styles of the original human drivers are retained and reproduced while accomplishing the speed following task. Simulations are conducted to verify the effectiveness of the proposed scheme.
Problematizing Comparison in a Post-Exploration Age: Big Data, Educational Knowledge, and the Art of Criss-Crossing

This article discusses the problem of comparison within comparative and international education research. It pushes back against the algorithmic and computational creep in education, arguing that if Big Data analytics replace theory, reflexivity, and critique, we are left with sterilized and atrophied comparative education research. Following a review of several business-as-usual modalities of comparison and a consideration of the colonial and power-knowledge effects that educational comparisons have had both historically and at present, the article proposes a “criss-crossing” comparative education. The term “criss-crossing” references the traveling back and forth along intersecting lines. This approach puts relationality at the center, sees research as an active process of criss-crossing, and aims to surface the entangled complexity of sometime disparate educational actors, devices, discourses, and practices. Criss-crossing comparison recognizes that we construct knowledge from and through relationships,.
3D-2D Registration of Cerebral Angiograms: A Method and Evaluation on Clinical Images

Endovascular image-guided interventions (EIGI) involve navigation of a catheter through the vasculature followed by application of treatment at the site of anomaly using live 2D projection images for guidance. 3D images acquired prior to EIGI are used to quantify the vascular anomaly and plan the intervention. If fused with the information of live 2D images they can also facilitate navigation and treatment. For this purpose 3D-2D image registration is required. Although several 3D-2D registration methods for EIGI achieve registration accuracy below 1 mm, their clinical application is still limited by insufficient robustness or reliability. In this paper, we propose a 3D-2D registration method based on matching a 3D vasculature model to intensity gradients of live 2D images. To objectively validate 3D-2D registration methods, we acquired a clinical image database of 10 patients undergoing cerebral EIGI and established “gold standard” registrations by aligning fiducial markers in 3D and 2D images. The proposed method had mean registration accuracy below 0.65 mm, which was comparable to tested state-of-the-art methods, and execution time below 1 s. With the highest rate of successful registrations and the highest capture range the proposed method was the most robust and thus a good candidate for application in EIGI.
Multi-view based Approaches for Collective Learning and Retrieval Tasks

ence (by Research), is a bona fide record of the research work carried out by him under my supervision. The contents of this thesis, in full or in parts, have not been submitted to any other Institute or University for the award of any degree or diploma. ACKNOWLEDGEMENTS I would like to thank everyone with whom I have interacted and learned useful things of different magnitude. In this section, I will attempt to mention everyone (atleast most of them) who have played a constructive role in my Masters work. V Tendulkar, again for his support and guidance. I would like to specially thank Dr. BR and Dr. AVT for their help and care in all the issues. I am grateful to the course instructors who helped me improving my learning curve on various aspects of the subject. GTC members-Dr. Machine learning techniques can be used to build systems to perform various tasks such as classification, clustering, retrieval, etc. In traditional machine learning algorithms, the data points are represented in a d-dimensional space which defines a view of the data. But many real-world datasets possess rich additional information that can be leveraged to improve the system's performance, reduce labeling cost, etc. For example, in webpage classification the content of the web-page and text on hyperlink pointing to this page can be used as two different views. Multi-view learning is a technique which allows the use of multiple views where a model built on one of the views can aid learning the models built on other views. It has been shown that multi-view approaches are more effective than single-view approaches for various domains. In this work, we propose multi-view based approaches for the following two tasks: • Linked Data Classification: In general, all the machine learning paradigms assume the data points to be independent and identically distributed (i.i.d). But many real-world datasets such as webpages, images and protein interaction networks possess additional link information which provides local label smoothness. For example, in webpage classification, links between web-pages convey that there is a strong correlation between labels of the linked pages. This can be exploited to provide regularization and thereby improve the performance. Collective classification is one of the popular approaches that can handle this type of network data. It combines traditional machine learning and link based classification in an iterative procedure. This involves two important steps: 1) collective learning – training the …
Dependable Design for FPGA Based on Duplex System and Reconfiguration

A technique for highly reliable digital design in FPGAs is presented. Two FPGAs are used for duplex system design, but better dependability parameters are obtained by combination of totally self checking blocks based on parity predictor. Each FPGA can be reconfigured when a SEU fault is detected. Combinational circuit benchmarks have been considered in all our experiments and computations. All our experimental results are obtained by XILINX FPGA implementation by EDA tools. The dependability model and dependability calculations are presented
Layer Transfer from Chemically Etched 150 mm Porous Si Substrates

We demonstrate for the first time the successful layer transfer of an epitaxially grown monocrystalline Si film from a purely chemically etched porous Si substrate of 150 mm diameter to a glass carrier. The surface conditioning for all Si layer transfer processes based on porous Si has been, up to now without exception, carried out by electrochemical etching. In contrast, our chemical stain etching process uses an aqueous HF-rich HF/HNO3 solution. The porosity increases with increasing doping concentration of the Si substrate wafer and with increasing porous layer thickness. In contrast to the electrochemically etched double layers, the porosity profile of the stain etched substrates is highest at the original wafer surface and lowest at the interface between the porous layer and the Si bulk. The epitaxy process is adapted to the high porosity at the surface with regard to the reorganization of the porous layer.
Real time infrared image enhancement

This study evaluates the implementation of Balanced Contrast Limited Adaptive Histogram Equalization (BCLAHE) for infrared images (IR) on an embedded platform. The aim was to achieve real time performance for the operator display target application. The system configured for this aim is a dual processor media application device OMAP3530, which consists of an ARM and a DSP processor. System is configured so that hardware sources are used efficiently and various performance improvement techniques are investigated. Performance analysis is done over IR images with different dynamic range.
Antiproliferative and Apoptosis Induction Potential of the Methanolic Leaf Extract of Holarrhena floribunda (G. Don)

Natural plant products with potent growth inhibition and apoptosis induction properties are extensively being investigated for their cancer chemopreventive potential. Holarrhena floribunda (HF) is used in a wide range of traditional medicine practices. The present study investigated the antiproliferative and apoptosis induction potential of methanolic leaf extracts of HF against breast (MCF-7), colorectal (HT-29), and cervical (HeLa) cancer cells relative to normal KMST-6 fibroblasts. The MTT assay in conjunction with the trypan blue dye exclusion and clonogenic assays were used to determine the effects of the extracts on the cells. Caspase activities were assayed with Caspase-Glo 3/7 and Caspase-9 kits. Apoptosis induction was monitored by flow cytometry using the APOPercentage and Annexin V-FITC kits. Reactive oxygen species (ROS) was measured using the fluorogenic molecular probe 5-(and-6)-chloromethyl-2′,7′-dichlorofluorescein diacetate acetyl ester and cell cycle arrest was detected with propidium iodide. Dose-response analyses of the extract showed greater sensitivity in cancer cell lines than in fibroblast controls. Induction of apoptosis, ROS, and cell cycle arrest were time- and dose-dependent for the cancer cell lines studied. These findings provide a basis for further studies on the isolation, characterization, and mechanistic evaluation of the bioactive compounds responsible for the antiproliferative activity of the plant extract.
A multilayer control for multirotor UAVs equipped with a servo robot arm

A multilayer architecture to control multirotor UAVs equipped with a servo robot arm is proposed in this paper. The main purpose is to control the aerial platform taking into account the presence of the moving manipulator. Three layers are considered in this work. First, a novel mechanism is proposed considering a moving battery to counterweight the statics of the robotic arm. Then, in order to overcome the mechanical limitations of the previous layer, the residual of the arm static effects on the UAV is computed and compensated through the given control thrust and torques. Finally, an estimator of external forces and moments acting on the aerial vehicle is considered and the estimations are fed back to the controller to compensate neglected aerodynamic effects and the arm dynamics. The performance of the proposed architecture has been experimentally evaluated.
Regression testing in an industrial environment

his article discusses these problemsand presents both management andtechnical techniques to address them.Some of these solutions are alreadybeing used by software developers,even though they may not be widelyknown. This article proposes a multilevel regressiontesting framework that can be easily adapted into asoftware development and maintenance process inwhich regression testing can play a key role inimproving and ensuring consistent software quality.Our observations are based on our accumulatedexperience in software development and mainte-nance in the U.S. and Japan, including Hitachi Soft-ware Engineering (HSK). Regression testing has been extensively studiedby researchers [1, 3, 7, 10, 11] from a theoreticalpoint of view. However, there has been little discus-sion on applying regression testing in industrialenvironments. While researchers are mostly con-cerned with reducing the number of test cases forretesting, there are other important issues in usingregression testing in an industrial environment.Some of our observations include:• Regression testing is used extensively. In fact,
Rules of Thumb versus Dynamic Programming

This paper studies decisionmaking with rules of thumb in the context of dynamic decision problems and compares it to dynamic programming. A rule is a fixed mapping from a subset of states into actions. Rules are compared by averaging over past experiences. This can lead to favoring rules which are only applicable in good states. Correcting this good state bias requires solving the dynamic program. The authors provide a general framework and characterize the asymptotic properties. They apply it to provide a candidate explanation for the sensitivity of consumption to transitory income.
Efficient routing and scheduling algorithms for optical networks

This paper studies the problems of dedicating routes and scheduling transmissions in optical networks. In optical networks, the vast bandwidth available in an optical fiber is utilized by partitioning it into several channels, each at a different optical wavelength. A connection between two nodes is assigned a specific wavelength, with the constraint that no two connections sharing a link in the network can be assigned the same wavelength. This paper classifies several models related to optical networks and presents optimal or near-optimal algorithms for permutation routing and/or scheduling problems in many of these models. tally important because the entire network can be constructed out of passive (unpowered) optical components and hence made reliable as well as easy to operate, with all the control being done outside the network.
Dynamic Computation of Incentive Salience: “Wanting” What Was Never “Liked”

Pavlovian cues for rewards become endowed with incentive salience, guiding “wanting” to their learned reward. Usually, cues are “wanted” only if their rewards have ever been “liked,” but here we show that mesocorticolimbic systems can recompute “wanting” de novo by integrating novel physiological signals with a cue's preexisting associations to an outcome that lacked hedonic value. That is, a cue's incentive salience can be recomputed adaptively. We demonstrate that this recomputation is encoded in neural signals coursing through the ventral pallidum. Ventral pallidum neurons do not ordinarily fire vigorously to a cue that predicts the previously “disliked” taste of intense salt, although they do fire to a cue that predicts the taste of previously “liked” sucrose. Yet we show that neural firing rises dramatically to the salt cue immediately and selectively when that cue is encountered in a never-before-experienced state of physiological salt depletion. Crucially, robust neural firing to the salt cue occurred the first time it was encountered in the new depletion state (in cue-only extinction trials), even before its associated intense saltiness has ever been tasted as positively “liked” (salt taste had always been “disliked” before). The amplification of incentive salience did not require additional learning about the cue or the newly positive salt taste. Thus dynamic recomputation of cue-triggered “wanting” signals can occur in real time at the moment of cue re-encounter by combining previously learned Pavlovian associations with novel physiological information about a current state of specific appetite.
Cinobufotalin as an effective adjuvant therapy for advanced gastric cancer: a meta-analysis of randomized controlled trials

Purpose: This study aimed to investigate the efficacy and safety of combining cinobufotalin and chemotherapy for advanced gastric cancer (GC). Patients and methods: Literature retrieval was performed in Cochrane Library, Web of Science, PubMed, Embase, China National Knowledge Infrastructure (CNKI), Chinese Biological Medicine Database (CBM), Wanfang database and Chinese Scientific Journal Database (VIP) before September 2018. The primary reported outcomes including therapeutic efficacy, quality of life (QoL), and adverse events were systematically evaluated. Results: Data from 27 trials including 1,939 advanced GC patients were included. The results indicated that, compared with chemotherapy alone, the combination of chemotherapy and cinobufotalin significantly improved patients' overall response rate (odds ratio [OR] =1.88, 95% confidence interval [CI] =1.54–2.31, P<0.00001) and disease control rate (OR =2.05, 95% CI =1.63–2.58, P<0.00001). The QoL of patients also evidently improved after chemotherapy and cinobufotalin combined treatment, as indicated by increased QoL improved rate (OR =2.39, 95% CI =1.81–3.15, P<0.00001), Karnofsky Performance Score (OR =7.00, 95% CI =2.25–11.75, P=0.004) and pain relief rate (OR =7.00, 95% CI =2.25–11.75, P=0.004). Adverse events including nausea and vomiting, diarrhea, leukopenia, hand-foot syndrome, anemia, gastrointestinal side effects and peripheral neurotoxicity caused by chemotherapy were evidently alleviated (P<0.05) when cinobufotalin was administered to GC patients. Conclusion: Evidence from the meta-analysis suggested that the combination of chemotherapy and cinobufotalin is more effective in treating GC than chemotherapy alone. It alleviates the adverse effects associated with chemotherapy and improves the QoL of GC patients.
Regulatory change and structural effects in HSIF and HSI volatility

A systematic BEKK-GARCH model with multiple switch points in the variance equations is found to capture the structural changes that have taken place in the Hong Kong markets. Abolishment of the uptick rule in the Hong Kong stock market, increase of initial margins and electronic trading of Hang Seng Index Futures are found to have significant impacts These changes affect the volatility structure of the HSI and HSIF and hence their lead-lag relationship. The multivariate GARCH model with 3 specific switching points is found to be superior to any other combination of up to six separate switch points. These results provide invaluable insight for regulators and policy makers into impacts of regulatory change in these and similar markets.