The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets

Process Integration Approaches to Improve the Techno-Economic Feasibility of Torrefaction Process

Over the past few years, the torrefaction process has evolved into a promising pre-treatment process to improve the properties of biomass to a level at which it is competitive with coal. However, in order to make torrefied biomass pellets an economically viable alternative to coal and wood pellets, the techno-economic feasibility of the torrefaction process needs to be improved. Thus, new process configurations are required to produce torrefied biomass pellets and other high value products from the torrefaction process. This thesis presents new process configurations, which have been evaluated with laboratory experiments, process simulations and mathematical modeling.Two different biomass samples i.e. eucalyptus clone and pinewood were used in torrefaction experiments. Initially, the effect that torrefaction pretreatment has on the kinetics, reaction mechanisms and heat flow during biomass pyrolysis was studied using TGA and DSC analysis. The results showed that the pyrolysis reaction mechanism varied significantly with torrefaction treatment. The heat flow data from DSC showed that torrefied biomass pyrolysis requires more energy than dried biomass in order to initiate the pyrolysis reactions.In the second stage, the anaerobic digestion of torrefaction condensate for the efficient utilization of torrefaction volatiles was studied through batch anaerobic digestion assays. Torrefaction condensate produced at 225, 275 and 300 °C was used at various substrate to inoculum ratio i.e. 0.1, 0.2 and 0.5. The methane yield was in the range of 430 - 492 mL/g volatile solids (VS) and 430 - 460 mL/g VS under mesophilic and thermophilic conditions, respectively. With the higher loading, i.e. > 0.2 VSsubstrate:VSinoculum, the production of methane was inhibited because of the inhibitory compounds in the torrefaction condensate, such as furfural and guaiacol.Large quantities of binders are required to make the pelletization process effective and to improve the quality of the pellets. An innovative process configuration is hereby proposed for detoxifying the torrefaction condensate and to reduce the binders’ requirement. The removal of a major inhibitory compound, i.e. furfural, through adsorption using torrefied biomass as an adsorbent was also studied. The adsorption of furfural from the torrefaction condensate at 250 g/L dosage was around 54%. Finally, the influence of the detoxification of the torrefaction condensate on the AD process was studied through batch assays.Finally, the experimental results were used to simulate industrial scale operations to evaluate the feasibility of integrating the torrefaction process with anaerobic digestion. In addition, different process integration approaches were studied to identify possible heat energy recovery options in the torrefaction process, on its own, and also when integrated with AD. The standalone torrefaction process was compared with three different process configurations, which varied according to the intended application for the produced biogas. The mass balance showed that biomethane can be produced at 369 m3/h, at 10 t/h of torrefied biomass pellets production capacity. A sensitivity analysis showed that the cost of the feedstock has a significant effect on the economics of the overall process. The economic analysis showed that the price of torrefied biomass pellets could be significantly reduced if the torrefaction process is integrated with AD

4-[(4-Methyl­benz­yl)amino]-3-[(4-methyl­benz­yl)imino­meth­yl]-2H-chromen-2-one

The title compound, C26H24N2O2, was prepared from the reaction of 4-chloro-3-formyl­coumarin with p-methyl­benzyl­amine. Even though there are no strong and specific inter­actions in the crystal structure, the translationally related mol­ecules form chains along the b axis. The coumarin moieties are stacked through π–π inter­actions [centroid–centroid distance = 3.5275 (7) Å], forming layers perpendicular to the stacking direction

A Hybrid Cryptographic System for Secured Device to Device Communication

It is general fact that even after enormous expansion of wireless communication there are still dead regions that hampers the effective communication. With exponential rise in the smart phones, a new layer of communication has evolved that could address the concerns of dead regions and capacity barriers. D2D is the evolving communication technology which focuses on short distance hops between the public devices to reach the destination. The major drawback of this technology is that most of the devices are public hence trustworthiness of the entire channel needs to be addressed in order to make it a viable solution. In this paper, we introduce a novel hybrid cryptographic approach that could address multiple eavesdroppers’ scenario. This approach incorporates both Huffman coding and Binary coding to enhance the crypto benefits for the information transmitted over D2D channel that consists of several public devices. The dual-crypto nature of the proposed algorithm offers higher efficiency, better security and improved key transmission.  Thus, the proposed hybrid cryptographic approach is robust in nature while easy and simple to operate. In addition, the proposed approach could recover the original information without any distortion from the encrypted data making the approach lossless in nature. Further simulation results prove that the proposed offers confidentiality to the transmitted to data while addressing the network capacity crunch

Dynamic hip screw technique in the management of trochanteric fracture

Background: Study was conducted to find the results of dynamic hip screw in the management of trochanteric fracture by analyzing the factors which influence post-operative mobility.Methods: Study was conducted in the department of orthopedics, GSL Medical College. Individuals >18 years, both genders who were diagnosed having trochanteric type I and II Boyd and Griffin stable fractures were included in the study. All surgeries were performed under spinal anesthesia, internal fixation with dynamic hip screw and 135o angled blade plate. Injectable third generation cephalosporins were used 24 hours preoperatively, intra- operatively and 5 days post-operatively, and oral antibiotics till suture removal. Patients allowed to sit on bed on 2nd and 3rd day and static quadriceps exercises were started from 2nd day onwards, hip and knee flexion exercises from 6 or 7th day and weight bearing walking form 10th day.Results: The average age was of the participants was 61.53 years, ranged between 41 to 80 years; 65% were female participants and 35% were male patients. In the study, 20 (50%) patients had right side fracture and left sided affection of trochanteric fracture to the remaining 50%. Most of the patients (67.5%) in this study were classified as type II Boyd and Griffin criteria, and 32.5% were type I. The clinical and functional outcome was calculated using the Kyle's criteria; 25% (10) showed excellent response, followed by good (50%), fair (15%) and poor (10%) results.Conclusions: Dynamic hip screw is the operative treatment of choice for stable trochanteric fractures. However, studies on large sample for long time are recommended

Hybrid TTSV structure for heat mitigation and energy harvesting in 3D IC

Three Dimensional Integration seems to be one of the best candidates to overcome the various challenges and limitations faced by conventional planer integration. But, thermal issues related to this highly promising integration technique are the main bottleneck for wide scale application. This thermal issue threatens the further progress and development of the 3D IC. The best known possible way to reduce the heat generated within the integrated chip is cooling through the thermal through silicon via (TTSV). This work reports the utilization of time dependent fluctuation of temperature which is generated within the active layers of 3D IC. Pyroelectric effect of TTSV materials is used to convert the heat generated within 3D IC to electrical energy. 60K temperature fluctuation within the IC layer was used to convert as electrical energy and 9.89μW output power was observed. This paper reports the novelty of TTSV structure modification where TTSVs are used as simultaneous energy harvester and heat mitigator

Drug screening using shape-based virtual screening and in vitro experimental models of cutaneous Leishmaniasis

Cutaneous leishmaniasis (CL) is one of the most disregarded tropical neglected disease with the occurrence of self-limiting ulcers and triggering mucosal damage and stigmatizing scars, leading to huge public health problems and social negative impacts. Pentavalent antimonials are the first-line drug for CL treatment for over 70 years and present several drawbacks in terms of safety and efficacy. Thus, there is an urgent need to search for non-invasive, non-toxic and potent drug candidates for CL. In this sense, we have implemented a shape-based virtual screening approach and identified a set of 32 hit compounds. In vitro phenotypic screenings were conducted using these hit compounds to check their potential leishmanicidal effect towards Leishmania amazonensis (L. amazonensis). Two (Cp1 and Cp2) out of the 32 compounds revealed promising antiparasitic activities, exhibiting considerable potency against intracellular amastigotes present in peritoneal macrophages (IC₅₀ values of 9.35 and 7.25 μm, respectively). Also, a sterile cidality profile was reached at 20 μm after 48 h of incubation, besides a reasonable selectivity (≈8), quite similarly to pentamidine, a diamidine still in use clinically for leishmaniasis. Cp1 with an oxazolo[4,5-b]pyridine scaffold and Cp2 with benzimidazole scaffold could be developed by lead optimization studies to enhance their leishmanicidal potency

Action-vectors: Unsupervised movement modeling for action recognition

Representation and modelling of movements play a significant role in recognising actions in unconstrained videos. However, explicit segmentation and labelling of movements are non-trivial because of the variability associated with actors, camera viewpoints, duration etc. Therefore, we propose to train a GMM with a large number of components termed as a universal movement model (UMM). This UMM is trained using motion boundary histograms (MBH) which capture the motion trajectories associated with the movements across all possible actions. For a particular action video, the MAP adapted mean vectors of the UMM are concatenated to form a fixed dimensional representation referred to as 'super movement vector' (SMV). However, SMV is still high dimensional and hence, Baum-Welch statistics extracted from the UMM are used to arrive at a compact representation for each action video, which we refer to as an 'action-vector'. It is shown that even without the use of class labels, action-vectors provide a more discriminatory representation of action classes translating to a 8 % relative improvement in classification accuracy for action-vectors based on MBH features over naïve MBH features on the UCF101 dataset. Furthermore, action-vectors projected with LDA achieve 93% accuracy on the UCF101 dataset which rivals state-of-the-art deep learning techniques